On the role of soil organic matter for crop
production in European arable farming
Renske Hijbeek
On the role of soil organic matter for crop
production in European arable farming
Renske Hijbeek
Thesis committee
Promotor
Prof. Dr. Martin K. van Ittersum
Personal chair at the Plant Production Systems Group
Wageningen University & Research
Co-promotor
Dr. Hein F. M. ten Berge
Senior researcher at Agrosystems Research
Wageningen University & Research
Other members
Prof. Dr. Oene Oenema, Wageningen University & Research
Dr. Julie Ingram, University of Gloucestershire, UK
Dr. Frank Verheijen, University of Aveiro, Portugal
Dr. Hans J. M. van Grinsven, Netherlands Environmental Assessment Agency (PBL), the
Hague, the Netherlands
This research was conducted under the auspices of the C.T. de Wit Graduate School
Production Ecology and Resource Conservation - PE&RC.
On the role of soil organic matter for crop
production in European arable farming
Renske Hijbeek
Thesis
submitted in fulfilment of the requirements for the degree of doctor
at Wageningen University
by the authority of the Rector Magnificus
Prof. Dr. A.P.J. Mol,
in the presence of the
Thesis Committee appointed by the Academic Board
to be defended in public
on Wednesday 4 October 2017
at 1.30 p.m. in the Aula.
Renske Hijbeek
On the role of soil organic matter for crop production in European arable farming
211 pages
PhD thesis, Wageningen University, Wageningen, the Netherlands (2017)
With references, with summary in English
ISBN 978-94-6343-663-2
DOI https://doi.org/10.18174/421022
“
The management of organic matter for nutrient supply and soil improvement is as old
as the history of arable agriculture itself. Yet science has been slow to provide the
predictive understanding that will assist farmers to move beyond their own traditional
knowledge based on centuries of empirical trial and error
”
(Palm et al. 2001)
Abstract
The aim of this thesis was to improve understanding of the role of organic inputs and soil organic
matter (SOM) for crop production in contemporary arable farming in Europe. For this purpose,
long-term experiments were analysed on the additional yield effect of organic inputs and savings in
mineral fertiliser. In addition, a farm survey was conducted to find drivers and barriers for the use
of organic inputs and to assess if arable farmers in Europe perceive a deficiency of SOM.
The findings in this thesis suggest that at least on the shorter term, on average, there seems to be no
immediate threat from a deficiency of SOM to crop production in arable farming in Europe. The
long-term experiments showed that with sufficient use of only mineral fertilisers, on average,
similar yields could be attained over multiple years as with the combined use of organic inputs and
mineral fertiliser. This was reflected in the farm survey, in which a large majority of farmers
indicated not to perceive a deficiency of SOM. Analysis of long-term experiments also showed that
more mineral fertiliser N was saved when using farmyard manure at high N rates (with mineral
fertiliser application) than at low N rates (without mineral fertiliser application), based on
comparisons at equal yield.
Specific crops and environments did benefit from organic inputs and more SOM in terms of crop
production. Long-term experiments showed that organic inputs give benefit to crop production in
wet climates and on sandy soils. In addition, farmers perceived a higher deficiency of SOM on steep
slopes, sandy soils, wet and very dry climates. The additional yield effect of organic inputs was
significant for potatoes. More in general, farmers who cultivated larger shares of their land with
specialized crops (including potatoes, sugar beets, onions and other vegetables) than cereals
perceived a higher deficiency of SOM. It seems that while the functions of SOM can be replaced
with technical means to a large extent (
e.g.
tillage, use of mineral fertilisers), there are limits to this
technical potential when environmental conditions are more extreme and crops are more
demanding.
The farm survey revealed that farmers perceive a trade-off between improved soil quality on the
one hand and increased pressures from weeds, pests and diseases and financial consequences on the
other hand when using organic inputs. If policies aim to stimulate the maintenance or increase of
SOM, more insight is needed into the conditions that regulate the pressures of weeds, pests and
diseases in response to organic inputs. Financial consequences (at least on the short term) should
also be accounted for. More importantly however, benefits from SOM for crop production cannot
be taken for granted. Only in specific situations such benefits will exist. If European policies on
SOM aim to include benefits for crop production, focus should be on areas with more extreme
environmental conditions (very dry or wet climates, steep slopes, sandy soils), or cropping systems
with more specialized or horticultural crops rather than cereals.
Table of contents
Contents
Chapter 1
General introduction
1
Chapter 2
Do organic inputs matter
–
a meta-analysis of additional yield
effects for arable crops in Europe
13
Chapter 3
Nitrogen fertiliser replacement values for organic inputs
appear to increase with N application rates
41
Chapter 4
Drivers and barriers for arable farmers to use organic inputs
in six agro-ecological zones across Europe
57
Chapter 5
How are farmers’ beli
efs on soil organic matter related to
their use of organic inputs? A case study in the Netherlands
95
Chapter 6
Do farmers perceive a deficiency of soil organic matter? A
European and farm level analysis
123
Chapter 7
General discussion
159
List of references
171
Summary/ Samenvatting
193
Acknowledgements
204
About the author
206
List of publications
207
PE&RC Education certificate
209
Financial support
211
General introduction
1
Chapter 1. General introduction
Soil organic matter (SOM) is known to improve many soil properties such as soil
structure, water holding capacity and nutrient supply (Johnston et al. 2009). For this
reason, SOM content is commonly seen as the main indicator for soil fertility (Reeves
1997). Yet there is surprisingly little scientific consensus on the exact relation between
SOM and crop production. For such a generally accepted indicator this might seem
rather strange at first. However, when the many pathways through which SOM affects
soil productivity are considered, and how these may vary, depending on the prevailing
environment and cultivation technologies, it becomes more understandable.
European agriculture benefits from a mostly temperate climate and has achieved
relatively high yields through intensive use of external inputs and technologies.
Nonetheless, concerns have been raised that SOM is declining in European soils (Morari
et al. 2016; Toth et al. 2008). Therefore, proposals have been made to maintain SOM
above 3.5% SOM (EC 2011a) or increase SOM annually with 4
‰
(UNFCCC 2015) to
protect productive capacity of soils. It is currently unclear whether these concerns are
justified. The aim of this thesis is to improve understanding of the role of organic inputs
and SOM for crop production in contemporary arable farming in Europe. Effects of
SOM on crop productivity are analysed in European soils and climates and with current
degrees of intensification. In addition, perceptions of European arable farmers on the
use of organic inputs and their SOM content are assessed using a farm survey.
In this chapter, I introduce my thesis by providing a short overview on SOM
composition and the carbon cycle (Section 1.1). This should be considered background
information and is not intended to be comprehensive. For further details, readers are
advised to follow the references. Subsequently, an overview is given on the different
functions SOM has in arable farming and how these relate to environmental conditions
and farm management (Sections 1.2 to 1.5). This leads us to the research questions and
outline of this thesis (Sections 1.6 and 1.7).
1.1.
SOM and the carbon cycle
SOM is composed of plant or animal debris (including faeces) in various stages of
decomposition to humus and includes the living organisms in soils (Oades 1988). Based
on physical fractionation, SOM can be divided into a number of fractions based on either
particle size, density class or aggregate size (Stockmann et al. 2013). Based on chemical
Chapter 1
2
fractionation, SOM is often divided into humic acids, fulvic acids, and humin
(Stevenson 1994; Stockmann et al. 2013).
In contrast, for modelling purposes SOM is often divided into a number of arbitrary
pools, based on decomposition rates rather than chemical or physical characteristics
(Shibu et al. 2006). When these model pools were conceived, they were conceptual in
nature and could not be directly related to measurable fractions (Zimmermann et al.
2007). Some authors have tried to relate SOM fractions to SOM pools used for
modelling purposes (Dungait et al. 2012; Wander 2004; Zimmermann et al. 2007). In
these cases, often three broad categories are used which can be denoted active, slow or
passive SOM (for an example see Table 1.1). Besides chemical stabilization, SOM can
also be physically stabilized through association with silt and clay particles (Feller &
Beare 1997; Six et al. 2002) or by forming micro and macro aggregates (Tisdall & Oades
1982).
Table 1. 1
Mean residence time of SOM pools used in the Century model linked to chemical compounds (based on
Dungait et al. 2012; Stockmann et al. 2013).
Mean residence time (yr)
Chemical compounds
Active SOM
1-2
Living biomass
Particulate organic matter
Polysaccharides
Slow SOM
15-100
Lignified tissues
Waxes
Polyphenols
Passive SOM
500-5000
Humic substances
Clay-OM complexes
Biochar
Rice and MacCarthy (1991) have shown that elemental composition of SOM (carbon
[C], hydrogen [H], oxygen [O], nitrogen [N] and sulphur [S]) is very similar around the
world, regardless of the origin. This suggests that a relatively narrow range of
compositions exists for SOM, especially for C content. Conventionally the factor 1.72
is used between SOM and SOC (Pribyl 2010; Waksman & Stevens 1930).
Despite the similarity in elemental contents, it is not possible to construct a molecular
structure that fully describes a humic substance (MacCarthy 2001). In addition, no
scientific consensus exists on how formation of SOM molecules takes place. Lehmann
and Kleber (2015) identify four theoretical models on SOM formation: 1) classic
humification; 2) selective preservation; 3) progressive decomposition and 4) soil
continuum model. In classic humification, synthesis of decomposed and smaller
molecules results in large macromolecules resistant to decomposition (Schulten et al.
General introduction
3
1991; Stevenson 1994). Selective preservation is similar to classic humification, with
the difference that compounds can be labile or more recalcitrant and that the latter do
not necessarily have to be large macromolecules (Baldock et al. 1992). In progressive
decomposition, organic materials are degraded on an energetic downhill trajectory, as
opposed to the classic humification model (Hedges et al. 2000; Lehmann & Kleber
2015). Finally, in the soil continuum model, a continuum of organic fragments exists
that are continuously processed towards smaller molecular sizes while at the same time
opportunities for incorporation into aggregates increase (Lehmann & Kleber 2015).
Carbon is an essential component of SOM and part of the global carbon cycle. In the
global carbon cycle, three large reservoirs of carbon exist: sedimentary carbonates and
kerogens (together > 60,000,000 Pg C, Falkowski et al. 2000), ocean reservoirs (38,400
Pg C, Falkowski et al. 2000) and reservoirs of fossil fuels (mean estimation 1,972 Pg C,
Ciais et al. 2013). Sedimentary carbonates and large parts of the ocean reservoirs are
inorganic carbon. In addition to these reservoirs, smaller pools of carbon exist, of which
size estimations differ. Generally, the soil carbon pool (around 1,500 Pg C) is thought
to be roughly three times larger than the terrestrial vegetation carbon pool (500 Pg C)
and two times larger than the carbon present in the atmosphere (currently around 829
Pg C, Ciais et al. 2013; Janzen 2004; Scharlemann et al. 2014).
Since the start of the industrial revolution (middle of the 18
th
century) concentrations of
atmospheric carbon dioxide (CO
2
) have increased from around 280 ppm to 400 ppm in
2016 (Dlugokencky & Tans 2017; Eggleton 2013). This increase in CO
2
concentration
is mainly caused by the burning of fossil fuels and to a lesser extent cement production
and land use change (Le Quéré et al. 2012).
1.2.
SOM content of arable fields
Between 1765 and 2005, 6.6-6.8 million km2 of forest land has been converted into crop
land (Meiyappan & Jain 2012). Conversion from natural vegetation
–
especially forests
- to agriculture almost always causes a decrease in SOM content (Poeplau & Don 2015)
due to non-permanent vegetation, export of biomass and consequently reduced amounts
of organic inputs. It has been estimated that the global soil carbon pool has decreased
78 + 12 Pg between 1850 and 1998 due to land use change and soil cultivation (Lal
2004a). If SOM content could be increased in arable lands, this would be a potential
global carbon sink (Lal 2004b). Therefore, a number of initiatives exist to promote soil
carbon sequestration, such as the recent 4/1000 initiative (UNFCCC 2015).
Chapter 1
4
Increasing SOM content in arable fields is however strongly limited by environmental
conditions and inherent SOM dynamics (Ingram & Fernandes 2001). SOM content is a
balance between rates of inputs (composition) and rates of outputs (decomposition).
This balance is affected by three environmental factors (soil texture, temperature and
precipitation) and management (Burke et al. 1989; Haynes & Naidu 1998; Kätterer et
al. 1998; Krull et al. 2001; Leiros et al. 1999; Miller et al. 2004; Motavalli et al. 1995).
Which crops a farmer cultivates and the achieved yields determine together the amounts
of available crop residues that can be returned to the soil. Amounts of crop residues can
be increased by cultivating green manures within a crop rotation, including more cereals
or returning crop residues such as straw which otherwise would have been sold. If
available, a farmer can also import organic inputs from other fields or livestock farms.
In addition, a number of crop management choices such as drainage or tillage systems
and crop protection or erosion prevention measures influence the actual SOM content
of a field.
On a given arable field, a farmer can thus increase SOM content by: 1) increasing
organic inputs, ii) reducing tillage or iii) reducing drainage (Verheijen 2005b). The
effect of reducing tillage on SOM depends on soil texture, with more effects shown on
clay soil than on sandy soils (Chivenge et al. 2007). Reducing tillage might only increase
stratification of SOM in the surface soil horizons, while reducing SOM content below,
with no net change as a result (Baker et al. 2007; Luo et al. 2010).
1.3.
Benefits of SOM for soil fertility
SOM content is known to improve soil fertility by stabilizing soil structure, increasing
nutrient availability and improving water holding capacity (Johnston et al. 2009; Watts
& Dexter 1997). Despite these positive attributes, crop yields do not always increase
with higher SOM contents (Loveland & Webb 2003). This discrepancy between
improvements in soil quality and lack in yield response has been explained by De Haan
(1977) and Janssen (2002). They assert that apparently sometimes factors improved by
adding organic matter are not the yield limiting ones.
Crop yield factors can be divided into three categories: water, nutrients and pests and
diseases (Fig. 1.1, arrows 8 to 10; van Ittersum & Rabbinge 1997). SOM contents affects
yield factors in a number of ways such as by increasing the cation exchange capacity,
improving aggregate stability or changing soil microbial mass (Fig. 1.1, arrows 1 to 6).
Farmers might profit from SOM content if yields are increased (Fig. 1.1, arrow 11) or if
General introduction
5
improved workability of the soil leads to more flexibility in their working schedule (Fig.
1.1, arrows 7 and 12).
During the last century, different technical replacements have been developed which
replace some of the functions of SOM (Fig. 1.2, van Noordwijk et al. 1997). Mineral
fertilisers can complement nutrient supply, irrigation can complement water holding
capacity and an array of pest control methods have been developed (chemical or
biological) which can replace or complement these functions of SOM. The degree of
intensification often determines the extent to which a cropping system relies on SOM or
on technical replacements to provide the functions mentioned.
Fig 1. 1: Schematic overview of the different functions of SOM and relations with crop yields and farm level
benefits.
Chapter 1
6
In traditional agricultural systems, SOM content is maintained either by importing
organic inputs from other fields or by rotating cultivated land (
e.g.
slash-and-burn
agriculture or using a ley-arable cropping system). Slash-and-burn agriculture is hereby
a typical example of a cropping system that primarily relies on SOM for all the
functions. Hydroponic horticulture is a typical example in which SOM has been
completely replaced by technical means. Most other cropping systems fall somewhere
in between (horizontal axis Fig. 1.2).
For nutrient supply, using a combination of both mineral fertilisers and organic inputs
has been recommended, in what is framed as integrated soil fertility management
(Richards et al. 2016; Vanlauwe et al. 2010). N in organic inputs is released more slowly
than N in mineral fertilisers, mainly depending on the C/N ratio (Flavel & Murphy
2006). Therefore, N in organic inputs must be carefully matched with mineral fertiliser
N application to make sure that sufficient N is available for crop growth while avoiding
leaching. One manner in which this can be done is to characterize organic inputs by their
Nitrogen Fertiliser Replacement Value (NFRV, Jensen 2013). NFRV is defined as the
amount of mineral fertiliser N saved when using organic input-N (kg/kg), while attaining
the same crop yield (Herron & Erhart 1965).
It is often difficult to disentangle the different functions of SOM as they are
interdependent. Janssen (2002) made a division between effects of SOM through the
Fig 1. 2: Schematic overview of the existing technical replacements of SOM (van Noordwijk et al. 1997).
General introduction
7
provision of macro-nutrients (N, P and K) and other effects. Any effect of organic inputs
or SOM on crop yield not related to macro-nutrients is called
“the additional yield effect
of SOM”
(Janssen 2002). De Haan (1977) made an investigation into the additional yield
effect of farmyard manure (FYM) in two experiments in the Netherlands. After around
20 years he found a positive additional yield effect for potatoes and sugar beets, but no
effects on cereals. As his analysis was only based on two experimental sites with FYM,
it’s not clear
to which extent these results are valid for different types of organic inputs
across different climates and soil types.
1.4.
Threshold values for SOM
With a decrease in SOM content, most soil properties change along a continuum (Karlen
et al. 2001). This continuous change is one of the factors which makes it difficult to
define a critical or desirable SOM content for farmers (Sparling et al. 2003). Even so,
for the percentage of SOM to be a useful indicator for productive capacity, target values
need to be specified. Sparling et al. (2003) argue in favour of a minimum or threshold
soil C value:
“below which there would be loss of desirable soil characteristics,
productive capacity and ecological functions that were not readily restored within an
acceptable timeframe”.
A number of authors have tried to specify minimum or threshold values for SOM for
crop production (also called critical levels), using a range of approaches such as
information from soil surveys, literature reviews, soil organic carbon modelling, expert
opinions or a combination of these (Aune & Lal 1997; Körschens et al. 1998; Loveland
& Webb 2003; Sparling et al. 2003). Proposed threshold values range between 1 to 5.1%
SOM (0.6 to 3% SOC). None of the authors of these studies asked farmers for their
views whilst farmers are the group of stakeholders with the longest and most practical
exposure to SOM of their fields. Farmers would therefore be well suited to asses SOM
in view of their local context.
1.5.
Management of SOM by farmers
When managing their farm, farmers need to balance SOM management with different
objectives such as profit maximization or labour use efficiency (Mandryk 2016). In a
recent farm survey in Denmark, Case et al. (2017) found low costs an important driver
for the use of organic inputs. Glenk et al. (2017) however found that organic inputs have
mixed effects on farm gross margin. In two different areas in Europe (Scotland and
Chapter 1
8
Spain), incorporation of straw was found to have a negative effect on farm gross margin,
cultivation of green manure a small negative or neutral effect and the application of
animal manure had a positive effect on farm gross margin. This study relied on an
economic model which only included effects on crop yields and input costs. Farmers
might have a larger number of drivers and barriers for using certain measures, beyond
effects on yields and input costs, such as seasonal labour or machinery requirements.
Glenk et al. (2017)
note that farmers’ behaviour may be motivated by factors which are
not directly economic such as perceived workability of the soil, or soil health for future
generations.
Similarly, Van den Putte et al. (2010) argue that farmers often have too few incentives
to use soil conservation measures because
productive capacities of farms are not affected
by soil degradation in the short term (and thus not included in economic models). Simple
cost-
benefit models do not capture the complexity of farmers’ behaviour and attitudes
(Lynne et al. 1988). Therefore, Burton (2004) argues that using the theory of planned
behaviour (Ajzen 1991)
might improve our understanding of farmers’ willingness to
adopt certain management practices.
According to the theory of planned behaviour, farmers will base their SOM management
on three main constructs: 1) their attitude, 2) their subjective (social) norm and 3) the
degree of perceived behavioural control. The attitude refers to the expected impact or
outcome of SOM management. The subjective norm is the social pressure to manage
SOM in a certain way. Perceived behaviour control reflects past experiences and
anticipated impediments and obstacles for SOM management. This theory can be used
to find drivers and barriers for farmers to use specific measures (such as specific types
of organic inputs) or to understand farmers’ intention to i
ncrease SOM content of their
soils.
1.6.
Research questions
My aim in this thesis is to improve understanding of the role of organic inputs and SOM
for crop production in contemporary arable farming in Europe. The adjective term
contemporary relates to the current level of agricultural intensification in Europe
(relatively high yields and intensive use of external inputs and technologies), placed
more to the right on the horizontal axis of Fig. 1.2. The following research questions are
addressed:
General introduction
9
What is the additional yield effect of organic inputs for arable crops in Europe?
(
Chapter 2
)
Do mineral fertiliser savings from organic inputs depend on total N supply?
(
Chapter 3
)
What are currently the main drivers and barriers for arable farmers to use organic
inputs? (
Chapter 4
)
How are farmers’ beliefs on SOM related to their use of organic inputs
? (
Chapter 5
)
Do farmers perceive a deficiency of SOM? (
Chapter 6
)
This thesis uses data form field and farm level to answer the research questions. To
answer the first two research questions, data from long-term experiments were analysed.
To answer the last three research questions, a large farm survey was conducted in six
European countries (the Netherlands, Belgium, Austria, Spain, Italy and Germany).
1.1.
Outline of the thesis
This thesis consists of this introductory chapter, five research chapters and a discussion
chapter. Each of the research chapters answers a research question. In Fig. 1.3 an
overview is given of the research questions, outcomes and the relationships between the
different components of this thesis.
The effect of SOM on crop production will depend on the measure used to maintain or
increase SOM content. My research focuses on the use of organic inputs as a measure
to maintain or increase SOM content. As mentioned, organic inputs can be crop residues,
green manures, compost or animal manures (also called organic fertilisers, organic
amendments, organic manures or organic resources). In Chapters 2 to 5, relationships
between the use of different types of organic inputs and crop production are analysed.
In Chapters 2, 5 and 6, the relationships between SOM and crop production are analysed
(Table 1.2).
Chapter 1
10
General introduction
11
Table 1. 2: Focus on either SOM or on the use of organic inputs in each research chapter
Subject of investigation:
Organic inputs
SOM
Chapter 2. Additional yield effects of organic inputs
X
X
Chapter 3. Mineral fertiliser savings from organic inputs
X
Chapter 4. Drivers and barriers for organic inputs
X
Chapter 5.
Farmers’ beliefs on SOM
(case study in the Netherlands)
X
X
Chapter 6. Perceived deficiency of SOM by farmers
X
I hypothesize that the contribution of SOM to crop production on arable farms depends
on environmental conditions and crop types cultivated. Where possible, analyses
therefore include effects of environmental factors (slope, soil texture and climate) and
types of crops cultivated on the relation between SOM and crop production (Table 1.3).
Table 1. 3: Overview of environmental factors and land use included as co-variables in each chapter
Climate
Slope
Soil
texture
Crop
type
Chapter 2. Additional yield effects of organic inputs
X
X
X
Chapter 3. Mineral fertiliser savings from organic inputs
Chapter 4. Drivers and barriers for organic inputs
X
X
X
Chapter 5.
Farmers’ beliefs on SOM
(case study in the Netherlands)
X
Chapter 6. Perceived deficiency of SOM by farmers
X
X
X
X
Chapter 2 differentiates the additional yield effect for climate, soil texture and crop type.
Chapter 4 differentiates drivers and barriers for organic inputs by agro-ecological zones,
which are based on climate, slope and soil texture. In a case study in the Netherlands,
Chapter 5 differentiates farmers’ intention to increase SOM content by soil texture.
Chapter 6 differentiates perceived deficiency of SOM by farmers by climate, slope, soil
texture and crop types cultivated.
In
Chapter 2
, a meta-analysis was performed to find the additional yield effect of organic
inputs and SOM. Using data from 20 long-term experiments, yield response curves to
mineral fertiliser-N were calculated, with and without organic inputs (and with sufficient
P and K supply). The additional yield effect of organic inputs was calculated by taking
the difference between the maxima of the yield response curves. The size of the
additional yield effect was related to clay content, aridity, crop types and SOM content
of the fields.
In
Chapter 3
, yield response curves to mineral fertiliser-N were calculated, with and
without organic inputs (and with sufficient P and K supply). Using data from eight long-
Chapter 1
12
term experiments, savings in mineral fertiliser N from organic inputs were compared at
low and high total N supply.
In
Chapter 4
, the theory of planned behaviour was used to analyse drivers and barriers
for the use of organic inputs (incorporation of straw, cultivation of green manures or
cover crops, FYM and compost). A farm survey was held among 1180 arable farmers
in six agro-ecological zones in four European countries (the Netherlands, Belgium,
Austria and Italy). Drivers and barriers were based on underlying beliefs of farmers and
classified into nine main categories (soil type & climate, soil quality, crop protection,
land use, technical, financial, environmental impact, legal and social). Drivers and
barriers were compared per type of organic input and agro-ecological zone.
To assess
how farmers’
underlying beliefs on SOM are related to their intention to
increase SOM content and use of organic inputs, a case study was done in the
Netherlands (
Chapter 5
). In an online survey, 435 arable farmers were asked questions
to understand their attitude (perceived benefits), subjective norm (social pressure) and
perceived behavioural control (anticipated impediments and obstacles) related to
management of SOM. Farmers’ answers were
compared to their intention to increase
SOM content, reported use of organic inputs and perceived increase in SOM content.
A risk indicator for SOM deficiency is proposed in
Chapter 6
based on environmental
factors (climate, slope and soil texture) and agricultural land use. The proposed risk
indicator was tested using a farm survey among 1452 farmers in five European countries
(Belgium, Germany, Austria, Spain and Italy). Threshold values for SOM content were
developed by comparing perceived deficiency of SOM to reported SOM contents by
farmers.
Chapter 7
summarizes and integrates the various chapters. First, an answer is given to
each research question. Then, the influence of environmental factors and crop types on
the relation between SOM and crop production on arable farms in Europe is discussed.
Following, attention is given to synergies and trade-offs with other ecosystems services
and limitations of the study. Finally, implications of the findings are presented.
Additional yield effect of organic inputs
13
Chapter 2.
Do organic inputs matter
–
a meta-analysis of additional yield effects
for arable crops in Europe
Renske Hijbeek
1
, Martin K. van Ittersum
1
, Hein F.M. ten Berge
2
, Gerrit Gort
3
, Heide
Spiegel
4
, and Andy P. Whitmore
5
1
Plant Production Systems, Wageningen University and Research, Wageningen, the
Netherlands.
2
Plant Research International, Wageningen University and Research, Wageningen, the
Netherlands.
3
Biometris, Wageningen University and Research, Wageningen, the Netherlands.
4
Institute for Sustainable Plant Production, Austrian Agency for Health and Food Safety,
Vienna, Austria.
5
Sustainable Soils and Grassland Systems, Rothamsted Research, Harpenden, UK
Plant and Soil (2017) 411: 293-303
Chapter 2
14
Abstract
Organic inputs have a positive effect on the soil organic matter balance. They are
therefore an important asset for soil fertility and crop growth. This study quantifies the
additional yield effect due to organic inputs for arable crops in Europe when macro-
nutrients are not a limiting factor.
A meta-analysis was performed using data from 20 long-term experiments in Europe.
Maxima of yield response curves to mineral fertiliser nitrogen were compared, with and
without organic inputs, under abundant P and K supply.
We were surprised to find that, across all experiments, the mean additional yield effect
of organic inputs was not significant (+ 1.4 percent + 1.6 - 95 % Confidence Interval).
In specific cases however, especially for root and tuber crops, spring sown cereals, or
for very sandy soils or wet climates, organic inputs did increase attainable yields. A
significant correlation was found between increase in attainable yields and increase in
soil organic matter content.
Aggregating data from 20 long-term experiments in Europe, this study shows that
organic inputs and/or soil organic matter do not necessarily increase yields, given
sufficient nutrients are supplied by mineral fertilisers. Results show the relevance of
some environmental factors for additional yield effect of organic inputs, but no simple
relation between organic inputs and crop growth.
Keywords:
soil fertility, soil organic matter, organic inputs, crop yield, food security,
soil carbon sequestration
Abbreviations
C
carbon
K
potassium
N
nitrogen
P
phosphorus
SOC soil organic carbon
SOM soil organic matter
Additional yield effect of organic inputs
15
2.1.
Introduction
Soil organic matter (SOM) is often considered the most important indicator of soil
fertility (Johnston et al. 2009; Reeves 1997)
. It contributes to each of fertility’s three
dimensions: the physical (structure, aeration, water retention), the biological (biomass,
biodiversity, nutrient mineralisation, disease suppression) and the chemical (nutrient
supply) dimension. On this basis, maintaining SOM is an important strategy to maintain
crop productivity (Lal 2004a). SOM contains about 50% organic carbon (Pribyl 2010),
making it’s increase a potential means to mitigate greenhouse gas emissions
(Smith
2016). Because of this positive contribution to climate change mitigation and food
security, a voluntary action plan has been proposed at COP21 to increase SOM in all
soils, called “
the 4/1000 initiative: Soils for Food security and Climate
”
(UNFCCC
2015).
In some cases however, yield effects of SOM seem smaller than expected. Reviewing
the literature, Loveland and Webb (2003) found it difficult to establish a critical level of
SOM for temperate regions. They also did not find evidence for an adverse effect on
crop yields where SOM contents in the soils of England and Wales were reduced.
Similarly, comparing potential yields of winter wheat and spring barley across a large
range of SOM contents in Denmark, Oelofse et al. (2015) found no significant effect on
yields of winter wheat and only a small effect on yields of spring barley.
The mentioned studies compared the effect of actual SOM content, they did not assess
specific management practices used to increase SOM. In arable soils, SOM can be
increased by increasing organic inputs or reducing organic outputs (Freibauer et al.
2004). Increasing organic inputs can be done by increasing returned biomass (roots,
litter) via higher yields or adding additional organic inputs such as compost, animal
manure or crop residues. Decreasing organic outputs can be done by changing the
moisture content of the soil or by using reduced or no tillage, although the effect of the
latter two remain disputed (Govaerts
∗
et al. 2009). Actual increase in SOM depends on
a number of factors, such as the current amount of SOM, type of organic input, and
environmental factors such as temperature, soil texture, and humidity (Smith et al.
1997).
Studies assessing the effects of organic inputs on crop yields show mixed results. A
recent meta-analysis of 32 long-term experiments in China compared the combined use
of organic inputs and fertilizers with either only organic inputs or only fertilisers (Wei
et al. 2016). The average yield increase of combining organics and fertilizers on wheat,
Chapter 2
16
maize and rice was found to be 8% compared to using only fertilizers. In a different case
however, Dawe et al. (2003) found no improvement in grain yield trends with
application of either manure or straw in intensive rice systems.
How do these contrasting insights compare? Although previous research has found a
positive effect of either organic inputs or SOM on crop yields (Monreal et al. 1997; Wei
et al. 2016), it has been argued that in these studies the effect of nutrients is seldom
separated from other effects (Oelofse et al. 2015). In fact, Wei et al. (2016) mention this
as the largest limitation of their study.
To circumvent this limitation, we have assessed the effect of organic inputs in a system
without macro-nutrient limitation. In such a system, any effect of organic inputs on yield
can be attributed to improved soil structure or soil life (the other two components of soil
fertility). In our study, effects of organic inputs (also called organic fertilisers, organic
manures or organic inputs) on attainable crop yields were examined in 20 long term
experiments across a variety of soils and climates in Europe. To exclude the effects of
macro-nutrients, the yield effect was analysed under abundant phosphorus (P) and
potassium (K) supply and varying rates of nitrogen (N). Using this approach, we answer
the following research question: Do organic inputs increase attainable yields?
Previously, any effect of organic inputs or SOM on crop yield which are not related to
macro-nutrients has been called the
“additional yield effect”
(Janssen 2002). Our
objective is: to find the additional yield effect of organic inputs, beyond the nutrients
supplied.
2.2.
Materials and methods
2.2.1.
Literature search
To find data on long term experiments in Europe, two databases with metadata were
used: the EuroSOMNET metadata on 110 long-term experiments and a database
compiled in a recent European research project called CATCH-C, containing 377 long-
term experiments. Promising experiments were selected and publications were searched
using online search engines (Google scholar, ISI Web of knowledge). When more
publications were available for one treatment, only yield data from the most recent
publication was included.
The following selection criteria were used to select experiments: (1) at least 4 increasing
levels of N applications without organic inputs; (2) at least 4 increasing N application
Additional yield effect of organic inputs
17
levels with organic inputs; (3) P and K applied in ample amounts on all fields; (4) at
least 5 years of yield data; (5) if crops are cultivated in rotation, yield data available for
at least 2 rotations; (6) yield data reported for individual crop types (mean yield values
averaged over rotation were excluded).
Data from 20 experiments was found adhering to these selection criteria (Fig. 2.1 and
Table 2.1). Following, 107 distinct data sets were created, each representing a single
combination of experiment location, crop type and organic input type, covering a
number of years of yield observations. All data were processed in R 3.0.0.
Fig 2. 1: Overview of locations of long term experiments included in the meta-analysis (N=20).
Chapter 2
18
Additional yield effect of organic inputs
19
2.2.2.
Calculating additional yield effect of organic inputs for each set of
data
Crop yields are known to steeply increase at lower levels of N application while
levelling off or slightly decrease at high levels of N application. When yields are known
at different levels of N application, response curves can be fitted (Cerrato & Blackmer
1990). For each set of data in our meta-analysis, two yield response curves were drawn:
one with and one without organic inputs (Fig. 2.2a). To fit the curves, the following
equation was used (George 1984):
𝑦𝑖𝑒𝑙𝑑 = 𝑎 + 𝐵 ∗ 0.99
𝑁
+ 𝑐 ∗ 𝑁 + 𝜀 (2.1)
In Equation 2.1, N is nitrogen added as mineral fertiliser (kg N/ha), a, b and c are
parameters to be fitted and
𝝴
is the error term. The maximum of each curve was
calculated by setting the first-order derivative equal to zero and inserting the optimal N
rate in Equation 2.1. As P and K were applied in ample amounts, at the maximum of
each curve N, P and K (the macro-nutrients necessary for crop growth) are not a limiting
factor for crop yields. Accordingly, the maximum of each curve was regarded as the
attainable yield for local environmental conditions and management. The additional
yield effect of organic inputs was calculated by taking the difference between the
attainable yield with and without organic inputs (Fig. 2.2b).
Fig 2. 2: Example of yield response curves to mineral fertiliser-N under sufficient P and K supply with and without
organic inputs. a) Black line is the response curve without organic inputs. Green line is the response curve with
organic inputs. Squares indicate the maximum of each curve. The vertical difference between the two maxima is
due to the additional yield effect of organic inputs. b) Green circle is the relative difference between the two
maxima. Green line indicates the 95% confidence interval due to the goodness of fit of the two curves. Yield data
is from maize grown in Novi Sad between 1996 and 2003, with and without farmyard manure.
Chapter 2
20
For each data set, response curves might fit the data points better or worse, creating an
error in the estimation of the additional yield effect. To correct for the goodness of fit of
each curve, the delta method (Oehlert 1992) was used, giving a variance for each data
set. The inverse of the variance was used as a weighting factor for the calculated
additional yield effect of each data set. To enable comparisons among crops, the relative
difference was chosen as the response variable in the meta-analysis, expressing the
additional yield effect of organic inputs as percentage of attainable yield with only
mineral fertiliser.
Supplementary Fig. 2.1 to 2.4 show the individual response curves, while
Supplementary Fig.2.5 gives the additional yield effect and related 95% confidence
interval for each data set (pages 30 to 35).
2.2.3.
Removing of outliers
Sizes of the additional yield effects were checked for outliers by assessing the point
cloud across different variables and constructing a funnel plot. If a data point was located
outside the point cloud and P and K could not be excluded as yield limiting factors in
the treatment without organic inputs, the data was removed from the meta-analysis (this
was only necessary in one case).
2.2.4.
Assessing influence of co-variates
To assess the influence of environmental conditions, crop characteristics or type of
organic input, a number of factors and co-variables were identified. Two grouping
factors were used: type of organic input and crop type. In some cases, a combination of
organic inputs was used, for example straw and slurry, where one of year straw was
applied and the next year slurry. Each combination of organic inputs was included as a
separate category.
In addition, for each dataset the following information was obtained from the literature:
clay content, percentage of SOM content at the beginning of each experiment, amount
of carbon (C) in organic input, SOM change during each experiment and duration of
each experiment. When numbers were given in percentage of soil organic carbon (SOC),
they were multiplied with the conventional factor 1.724 ((Pribyl 2010; Waksman &
Stevens 1930). Duration of each experiment was multiplied with yearly C applied to
give the total C added over the years. Geographical coordinates of each experiment were
used to find the CGIAR-CSI Global Aridity Index (Trabucco & Zomer 2009).
Additional yield effect of organic inputs
21
To assess the effect of the grouping factors and co-variates, a mixed effects model with
a hierarchical structure (Konstantopoulos 2011) was used. Mixed effect models allow
for incorporation of random effects, which is important when observations are not from
a stratified or random sampling design as is typical in meta-analyses (Gurevitch &
Hedges 1999). The following two random effects were incorporated in the analysis: (1)
Experiment: As a single experiment may produce multiple data sets, experiment was
used as a random factor. (2) Treatment without organic inputs: Within a single
experiment, multiple treatments with organic inputs can exist (for example one
treatment with farmyard manure and one with crop residues) which are all compared to
the same treatment without organic inputs (with only mineral fertiliser).
Group means for crop type and type of organic input were estimated with R-package
lsmeans (Lenth 2015). To find the marginal effect of each co-variate on the additional
yield effect of organic inputs, a separate model was made for each co-variate using the
function lme (linear mixed-effects model) of package nlme (Pinheiro et al. 2015).
Within these models, log-likelihood was maximized and yield effects were weighted by
the inverse of the variance. Interaction between crop type and co-variate were checked
on significance. Only SOM change had a significant interaction with crop type.
2.2.5.
Model selection
To assess which combination of co-variates and factors could best explain the difference
in the additional yield effect of organic inputs, multi-model dredging was performed
using the dredge function in the R-package MuMin (Barton 2015). This function
constructs a list of models by combining the given co-variates and then gives a ranking
according to the corrected Akaike Information Criteria (AICc), an indicator commonly
used to assess model fit (Bozdogan 1987).
Two model selections were run. In the first model selection, only data from experiments
was included for which information on both percentage of clay and SOM content was
available (15 out of 20
)
. For the second model selection, only experiments were included
for which data on SOM change was available (8 out of 20).
2.2.6.
Sensitivity analysis
For some sets of data, maximum yield was not reached within the N applications of the
experimental set-up. These maxima had to be estimated beyond the experimental set-
up, resulting in a higher uncertainty. When analysing the data, these points could be
Chapter 2
22
either included or excluded, with each choice having its own advantage. Excluding these
data points gives a dataset with more certainty on each individual estimate, but including
them increases the size of the total dataset. Because a greater uncertainty results in a
larger variance, meaning a smaller weight is given to a yield effect which is calculated
with a maximum yield outside the experimental setup, we chose to include these data
sets in the meta-analysis. To see the effect of including or excluding the maxima outside
the experimental set-up, a sensitivity analysis was done on the main results.
2.3.
Results
The mean additional yield effect of organic inputs across all 107 data sets is not
significant in this meta-analysis (1.4 percent + 1.6 - 95% CI). When excluding maxima
estimated outside the experimental set-up, the mean yield effect is slightly higher: 1.9
percent + 2.0 (95% CI.), yet still not significant.
2.3.1.
Additional yield effect across type of organic inputs, crop types and
time of sowing
Comparing different types of organic inputs, the yield effect is roughly similar, but only
the mean additional yield effect of farmyard manure is significantly positive (2.2 percent
+ 1.8 - 95% CI, Fig. 2.3a). Yet we did find effects on specific crops. For potatoes the
mean yield increase is 7.0 percent + 4.9 (95% CI). In addition, our results show that
maize, a crop with a less developed root system than wheat or barley, also benefits
significantly from organic inputs (mean yield effect of 4.0 percent + 3.7 - 95% CI, Fig.
2.3b).
Across the 20 experiments, cereals sown in winter do not benefit form organic inputs in
our meta-analysis (Fig. 2.3c). On the other hand, spring sown cereals do benefit (3.4
percent + 2.6 - 95% CI). Spring sown crops have a shorter time frame to develop their
root system which is needed to acquire sufficient nutrients and water (Johnston et al.
2009). Organic inputs, by improving soil structure, might facilitate this process,
resulting in larger yields.
Additional yield effect of organic inputs
23
Fig 2. 3
Influence of type of organic input (a), crop type (b) and time of sowing (c) on the additional yield effect
of organic inputs. Circles are mean additional yield effects, lines the 95% confidence interval. Numbers in brackets
are the number of data sets in each group. Only groups with at least 8 data sets are shown. Green residues are
either green manures or beet leaves. Groups with less than 8 data sets and results of the sensitivity analysis are
shown in Supplementary Fig. 2.6.
2.3.2.
Influence of soil, climate and amount of C added
Crops grown on more sandy soils show a positive yield effect of organic inputs, while
more clayey soils show neutral or negative yield effects (Fig. 2.4a). Relatively sandy
soils normally have a poorer soil structure, which can be improved by adding organic
inputs. Soils with low SOM content would also be expected to benefit more from organic
inputs, but this is not apparent in our results (Fig. 2.4b).
For each experiment, climate was expressed in terms of aridity using the CGIAR-CSI
Global Aridity Index (Trabucco & Zomer 2009). Lower values indicate lower
temperatures with more rainfall while higher numbers indicate higher temperatures with
Chapter 2
24
less rainfall. In our study, crops grown in wetter climates benefit more from organic
inputs (Fig. 2.4c).
Experiments differ in the type and the amounts of organic inputs applied annually, and
in their duration. After converting all organic inputs to total C (tonnes C/ha, cumulated
over the years), no significant relation was found between total C input and the yield
effect (Fig. 2.4d).
2.3.3.
Relative increase in SOM
For a subset of experiments, percentage increase in SOM during the experiment could
be calculated. When running a model selection, combining the relative increase in SOM
content with crop type gives the largest explanation of variance in the additional yield
effect of organic inputs (Supplementary Tables 2.2 and 2.3, pages 37-38). The
magnitude of the effect is shown in Fig. 2.5a.
Fig 2. 4: Influence of soil texture (a), SOM content at the start of the experiment (b) climate (c) and amount of C
applied over the years (d) on the additional yield effect of organic inputs. Clay content is expressed as the
percentage of particles < 2µm in the soil. Climate is expressed as the CGIAR-CSI Global Aridity Index. Larger
points have a smaller variance and therefore a higher weight. P (Δ intercepts) is the
probability for the intercepts
to be equal. P (slope) is the probability the common slope is equal to zero.
Additional yield effect of organic inputs
25
Fig 2. 5
Relation between increase in SOM and yield increase. a) Increase in yield related to increase in SOM. x-
axis: increase in SOM between the treatment with only mineral fertiliser and the treatment with organic inputs
added. y-axis: difference in maximum yield between the treatment with only mineral fertiliser and the treatment
with organic inputs added. b) axes vice versa from a. Larger points have a smaller variance and therefore a higher
weight. P (Δ intercepts) is the probability for the intercepts to be equal. P (Δ slope) is the probability for the slopes
to be equal.
2.4.
Discussion
When discussing possible benefits of organic inputs and SOM beyond nutrients
supplied, it has been suggested that root or tuber crops might benefit more than cereals
(De Haan 1977; Verheijen 2005a). The reason being that root and tuber crops depend
more on soil structure for their successful cultivation and harvesting. Our study confirms
this suggestion with a mean yield increase for potatoes of 7 percent.
Crops grown in both very dry or very wet conditions could potentially benefit from
organic inputs as SOM increases water holding capacity in dry climates (Díaz-Zorita et
al. 1999) and prevents soil compaction in wet climates (Soane 1990). In our study, crops
grown in wetter climates do benefit more from organic inputs. As expected for a meta-
analysis over Europe, most of our experiments (16) however have a humid climate
(index > 0.65), with three experiments having a dry sub-humid climate (index 0.5-0.65),
one a semi-arid climate (0.2-0.5) and none arid or hyper arid climates (index <0.2). As
very dry climate were not included, this could be the reason why we could not confirm
whether organic inputs have additional yield effects in dry climates.
Very weathered soils, mostly occurring in tropical regions, were also not included in our
meta-analysis. Weathered soils often have very low cation exchange capacity (Palm et
al. 1997) and lack a number of micro nutrients necessary for crop growth (Gupta et al.
2008). On weathered soils therefore, yield effect of organic inputs could be larger when
related to treatments with only N, P and K supplied. A recent global database suggests
Chapter 2
26
experimental set-ups as used in our meta-analysis do not exist outside temperate regions
(ISCN 2015), establishment of such long term experiments would therefore be
recommended.
Before analysis, percentage of SOM at the start of each experiment was expected to be
the largest influencing factor. Yet, no significant difference was found comparing
experiments with different SOM contents (Fig. 2.4b). There is however uncertainty
associated with comparing SOM contents across 20 experiments. When available,
measurements of the upper soil layer or plough layer (often 24-30 cm) were included in
the analysis, yet depth of measurement was not always explicitly stated. In addition,
measurements of SOM are known to deviate, depending on methods used (Hoogsteen
et al. 2015). Even though some error in SOM measurements might be involved, our
finding does correspond well with a recent study in Denmark comparing yields of winter
wheat across a large range of SOM contents (Oelofse et al. 2015), with similarly no
effects found.
Fig. 2.5a seems to indicate that more so than the total SOM at start (Fig. 2.4b) or the
total C added (Fig. 2.4d), it is the percentage of fresh SOM in the soil which makes a
difference. If so, this finding corresponds well with suggestions of (Loveland & Webb
2003) that the proportions of fresh SOM is more important than the total pool of SOM.
On the other hand, higher yields also have an effect on SOM by returning more crop,
root and stubble residues (Glendining & Powlson 1995). One could therefore question
if larger yields in our analysis are the result of the increased SOM content (Fig.2.5a),
or vice versa (Fig. 2.5b)? In practice, both possibilities might be true and
–
if so
–
can
be mutually reinforcing: in some cases more SOM gives somewhat higher yields, which
adds more organic matter to the soil which in turn gives higher yields, which then again
gives more SOM.
2.4.1.
Limitations of study and broader contextualization
This meta-analysis did not find a significant mean additional yield effect of organic
inputs. When assessing the use of organic inputs on a farm or regional level however,
other aspects might also be relevant. Organic inputs can promote the buffering function
of soil in years with less favourable conditions, thereby reducing yield variability (Pan
et al. 2009). In our experiments, variability in attainable yields was not lessened with
organic inputs (data not shown), but this could be tested further under more extreme
climates.
Additional yield effect of organic inputs
27
Using organic inputs can also have environmental effects. Soils with higher SOM
contents for example might create a more flourishing habitat for soil biota (Chang et al.
2007). Maintaining SOM contents can therefore contribute to biodiversity conservation.
Combining organic inputs with mineral fertilisers can decrease the demand for mineral
fertilisers which can have positive effects such as a decrease in demand for fossil fuels
(Wood & Cowie 2004). In our meta-analysis, the savings of mineral fertiliser N with
organic inputs are substantial (Supplementary Fig. 2.7 and Table 2.4, page 39). The
savings in mineral fertiliser N however do not outweigh the total N in the organic inputs
and the occasionally added mineral fertiliser N for growth of green manures or
decomposition of straw. Consequently, organic inputs might affect the extend of nitrate
leaching, nitrous oxide or ammonia emissions. For nitrate leaching, both positive
(Leclerc et al. 1995) and negative cases (Basso & Ritchie 2005; Oelofse et al. 2015) are
known. It has been suggested that the number of years of application is crucial and that
over the long-term, if nutrients are applied attuned to crop requirements, organic inputs
have no significant effect on nitrate leaching (Maeda et al. 2003).
Even though the mean additional yield effect across all data sets was not significant, a
large variance exists between data sets (additional yield effects ranged between -10 to
+18%; supplement Fig. 2.5). Using grouping factors (crop type, type of organic input)
and co-variates (clay content, aridity), some variance was explained, but large parts
remained unknown. In some individual cases, organic inputs did increase attainable
yield significantly. In others, organic inputs might have had little effects on soil
structure, either because soil structure was already very good or because it was beyond
simple repair. These type of nuances can be tackled in-depth in single experiments, but
are difficult to disentangle when aggregating larger data sets. Combining meta-analysis
with more in-depth studies is therefore vital for more thorough understanding of
processes and mechanisms involved.
2.5.
Conclusions
Using organic inputs to increase SOM is often seen as a win-win situation for food
security and climate change mitigation, such as in the recently proposed “
4/1000
initiative”
at COP21 (UNFCCC 2015). Using organic inputs to sequester carbon might
be a viable option to buy time for developing technologies for reducing industrial
emissions (IGBP 1998), this meta-analysis however shows that benefits for crop yields
cannot be assumed to follow directly. On sandy soils, in wet climates and for certain
crops (some root or tuber crops and spring sown cereals) organic inputs can increase
Chapter 2
28
yields beyond the nutrients they supply. In those cases, increases in attainable yields
vary mostly between 3 to 7%. In the majority of cases however, supplying only mineral
fertiliser gives similar yields.
2.6.
Acknowledgments
We thank all who set up, maintained and shared data from long-term experiments in
Europe through their publications. We thank Dietmar Barkusky (ZALF), Matthias
Wendland (Bayern LFL), Guido Baldoni (University of Bologna) and Dorota Pikuła
(IUNG) for providing additional data. We acknowledge Margaret Glendining for
providing an overview on long term experiments in Europe and sharing her network.
We thank Jan Verhagen and Bert Janssen for suggestions on methodology and Steve
McGrath and Ken Giller for reviewing the text.
Additional yield effect of organic inputs
29
Supplement Chapter 2
Supplementary Fig. 2.1
Response curves of root and tuber crops
30
Supplementary Fig. 2.2
Response curves of spring sown cereals
31
Supplementary Fig. 2.3
Response curves of winter sown cereals
32
Supplementary Fig. 2.4
Response curves of winter sown cereals
continued
33
Supplementary Fig. 2.5
Additional yield effect and 95% confidence
interval for each data set.
34
Supplementary Table 2.1
Legend for Supplementary Fig. 2.4
35
Supplementary Fig. 2.6
Sensitivity analysis for including or excluding
estimations of maximum yield outside the
experimental set-up
36
Supplementary Table 2.2
Results of model selection including all
experiments with data available on both
percentage of clay and SOM content
37
Supplementary Table 2.3
Results of model selection including all
experiments with data available on on SOM
change
38
Supplementary Fig. 2.7
Illustration of difference in mineral fertiliser N
application rate needed to reach equal crop
yield with and without organic inputs.
39
Supplementary Table 2.4
Comparing mineral fertiliser N needed to
achieve equal yields with and without organic
inputs
39
Supplement Chapter 2
30
Response curves of root and tuber crops
Supplementary Fig. 2. 1: Yield response curves of root and tuber crops to mineral fertiliser with and without
organic inputs. X-axes are mineral fertiliser applied in kg N/ha. Y-axes are crop yield in tonnes/ha. Colours
indicate types of organic input. Squares indicate the maximum of each curve. Shaded areas lie outside the
experimental set-up.
Additional yield effect of organic inputs
31
Response curves of spring sown cereals
Supplementary Fig. 2. 2: Yield response curves of spring sown cereals to mineral fertiliser with and without
organic inputs. X-axes are mineral fertiliser applied in kg N/ha. Y-axes are crop yield in tonnes/ha. Colours
indicate types of organic input. Squares indicate the maximum of each curve. Shaded areas lie outside the
experimental set-up.
Supplement Chapter 2
32
Response curves of winter sown cereals
Supplementary Fig. 2. 3: Yield response curves of winter sown cereals to mineral fertiliser with and without
organic inputs. X-axes are mineral fertiliser applied in kg N/ha. Y-axes are crop yield in tonnes/ha. Colours
indicate types of organic input. Squares indicate the maximum of each curve. Shaded areas lie outside the
experimental set-up.
Additional yield effect of organic inputs
33
Supplementary Fig. 2. 4: Yield response curves of winter sown cereals to mineral fertiliser with and without
organic inputs. X-axes are mineral fertiliser applied in kg N/ha. Y-axes are crop yield in tonnes/ha. Colours
indicate types of organic input. Squares indicate the maximum of each curve. Shaded areas lie outside the
experimental set-up.
Supplement Chapter 2
34
Additional yield effect for all data sets
Supplementary Fig. 2. 5: Additional yield effect of organic inputs across experiments, crops and types of organic
input. Lines indicate 95% confidence interval.* indicates maximum yield was estimated outside the experimental
set-up. Numbers of data sets are explained in Supplementary Table 2.1
Additional yield effect of organic inputs
35
Supplementary Table 2. 1: Explanation of dataset numbers of Supplementary Fig. 2.4. m = maize; p= potatoes; s
= sugar beet; sb = spring barley; wb =winter barley; wr = winter rye; ww = winter wheat; sw = spring wheat.
FYM = farm yard manure, GM = green manure, BL = beat leaves.
1
Grabow: p with FYM
55
Puch: s with straw & GM & BL
2
Methau: p with straw
56
Bologna 1: ww with slurry
3
Muncheberg: sb with FYM
57
Vienna: ww with straw & GM & BL
4
Vienna: s with straw & GM & BL
58
Ivanovice: ww with FYM
5
Muncheberg: m with FYM
59
Methau: ww with straw
6
Sproda: s with FYM
60
Speyer: wb with straw & GM & BL
7
Grabow: sb with FYM
61
Prah Ruzyne: ww with FYM
8
Tartu: p with FYM
62
Muencheberg: wr with FYM
9
Bad Lauchstadt: p with FYM
63
Vienna: ww with slurry
10
Grabow: m with FYM
64
Sproda: s with straw
11
Puch: m with straw & GM & slurry
65
Prah Ruzyne: s with straw & GM & BL
12
Vienna: wb with straw & GM & BL
66
Limburgerhof: m with straw & GM & slurry
13
Bad Lauchstadt: p with straw
67
Limburgerhof: m with straw & GM
14
Muencheberg: m with straw
68
Lukavec: wb with straw & GM
15
Muencheberg: sb with straw
69
Novi Sad: ww with FYM
16
Puch: m with slurry
70
Speyer: s with straw & GM & BL
17
Lukavec: p with FYM
71
Iasi: s with straw & BL
18
Tartu: p with straw & BL
72
Speyer: ww with FYM
19
Iasi: s with FYM
73
Bad Lauchstadt: s with straw
20
Vienna: s with slurry
74
Ivanovice: ww with straw & BL
21
Puch: s with straw & BL
75
Muencheberg: ww with FYM
22
Puch: m with straw & slurry
76
Keszthely: wb with straw & GM
23
Tartu: sb with straw & BL
77
Rau: ww with FYM
24
Rau: s with FYM
78
Novi Sad: s with straw & BL & slurry
25
Vienna: wb with slurry
79
Limburgerhof: ww with straw & GM & slurry
26
Oldenburg: s with straw & GM & BL
80
Sproda: ww with FYM
27
Keszthely: wb with FYM
81
Methau: s with straw
28
Vienna: wb with FYM
82
Muencheberg: s with straw
29
Grabow: ww with FYM
83
Madrid: ww with straw
30
Rau: s with straw & GM & BL
84
Sproda: sb with FYM
31
Oldenburg: wb with straw & GM & BL
85
Bad Lauchstadt: s with GM
32
Methau: sb with straw
86
Rau: ww with straw & GM & BL
33
Oldenburg: s with slurry
87
Novi Sad: ww with straw & BL & slurry
34
Muencheberg: s with FYM
88
Madrid: sb with straw
35
Tartu: sw with FYM
89
Novi Sad: ww with straw & BL
36
Novi Sad: m with straw & BL & slurry
90
Prah Ruzyne: ww with straw & GM & BL
37
Vienna: ww with FYM
91
Bologna 1: m with straw
38
Novi Sad: m with straw & BL
92
Muencheberg: p with FYM
39
Muencheberg: ww with straw
93
Speyer: ww with straw & GM & BL
40
Iasi: m with FYM
94
Sproda: ww with straw
41
Lukavec: p with straw & GM
95
Madrid: sb with FYM
42
Bologna 1: ww with FYM
96
Ivanovice: wb with FYM
43
Oldenburg: ww with straw & GM & BL
97
Lukavec: wb with FYM
44
Novi Sad: m with FYM
98
Muencheberg: p with straw
45
Puch: m with straw
99
Sproda: sb with straw
46
Iasi: m with straw & BL
100
Prah Ruzyne: wb with FYM
47
Oldenburg: wb with slurry
101
Tartu: sw with straw & BL
48
Limburgerhof: ww with straw & GM
102
Rau: wb with straw & GM & BL
49
Oldenburg: ww with slurry
103
Rau: wb with FYM
50
Bologna 1: ww with straw
104
Ivanovice: wb with straw & BL
51
Bad Lauchstadt: p with GM
105
Tartu: sb with FYM
52
Novi Sad: s with straw & BL
106
Madrid: ww with FYM
53
Muencheberg: wr with straw
107
Prah Ruzyne: wb with straw & GM & BL
54
Speyer: wb with FYM
Supplement Chapter 2
36
Sensitivity analysis
Supplementary Fig. 2. 6: Results of sensitivity analysis for including or excluding estimations of maximum yield
outside the experimental set-up. Circles are mean effect size, lines show the 95% confidence interval. The number
in brackets is the number of data sets in each group. GM = green manure or cover crop.
Additional yield effect of organic inputs
37
Model selection
Supplement Chapter 2
38
Additional yield effect of organic inputs
39
Savings of mineral fertilisers when using organic inputs (at equal yields)
Supplementary Table 2.4: Comparing mineral fertiliser N needed to achieve equal yields with and without organic
inputs (as illustrated in supplementary Fig. 6). Mineral fertiliser N requirements are compared at the highest
yield which is achieved both with and without organic inputs. Analysis and results only for data sets with N
contents of organic inputs measured/reported (38 data sets across 8 experiments).
Organic input
#
data
sets
Mean fertiliser
N savings with
organic inputs
(kg N/ha/year)
Mean mineral
fertiliser N
added to straw
or green
manures
(kg N/ha/year)
Mean total N
in organic
inputs
(kg N/ha/year)
Δ mineral
fertiliser N +
added N +
total N organic
inputs
(kg N/ha/year)
FYM
17
-76.92
0.00
120.22
43.30
Slurry
1
-109.25
0.00
189.68
80.42
Straw
15
-7.42
0.00
22.18
14.76
Straw & beet
leaves
2
-81.16
16.67
79.42
14.93
Straw & GM
& beet leaves
3
-64.44
16.67
49.30
1.53
Supplementary Fig. 2.7: Illustration of difference in mineral fertiliser N application rate needed to reach equal
crop yield with and without organic inputs. Squares indicate the maximum of each curve. The blue arrow shows
the difference in mineral fertiliser N needed to achieve a similar level of high yield (N savings). Data is from
maize grown in Novi Sad between 1996 and 2003, with and without farmyard manure.
Chapter 3
40
Mineral fertiliser savings from organic inputs
41
Chapter 3.
Nitrogen fertiliser replacement values for organic inputs appear to
increase with N application rates
Renske Hijbeek
1
, Hein F.M. ten Berge
2
, Andy P. Whitmore
3
, Dietmar Barkusky
4
, Jaap
J. Schröder
2
, and Martin K. van Ittersum
1
1
Plant Production Systems, Wageningen University and Research, Wageningen, the
Netherlands.
2
Plant Research International, Wageningen University and Research, Wageningen, the
Netherlands.
3
Sustainable Soils and Grassland Systems, Rothamsted Research, Harpenden, UK
4
Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Straße 84,
15374 Muencheberg, Germany
Nutrient Cycling in Agroecosystems (DOI 10.1007/s10705-017-9875-5)
Chapter 3
42
Abstract
Nitrogen (N) supply from organic inputs (such as farmyard manure (FYM), slurries or
crop residues) to crops is commonly expressed in the input
’s Nitrogen Fertiliser
Replacement Value (
NFRV
). Values for
NFRV
can be determined by comparison of crop
yield or N uptake in amended plots against mineral fertiliser-only plots. Most
commonly,
NFRV
is defined as the amount of mineral fertiliser N saved when using
organic input-N (kg/kg), while attaining the same crop yield. Factors known to affect
NFRV are crop type cultivated, soil type, manuring history and method or time of
application.
We investigated whether long-term
NFRV
depends on N application rates. Using data
from eight long term experiments in Europe, values of
NFRV
at low total N supply were
compared with values of
NFRV
at high total N supply. Our findings show that FYM has
a significant higher
NFRV
value at high total N supply than at low total N supply (1.12
vs 0.53,
p
= 0.04). For the other input types investigated,
NFRV
was also higher at high
total N supply than at low total N supply, but sample sizes were too small or variations
too large to find significant differences.
Farmers in Europe usually operate at high rates of total N applied. If fertiliser
supplements are based on
NFRV
of the manure estimated at low total N supply, N
fertiliser requirements might be overestimated. This might lead to overuse of N, lower
N use efficiency and larger losses of N to the environment.
Keywords:
Nitrogen Fertiliser Replacement Value, Mineral Fertiliser Equivalent,
organic inputs, mineral fertiliser, Nitrogen, soil fertility, crop yield
Abbreviations
DM
dry matter weight
FM
fresh matter weight
FYM
farmyard manure
K
potassium
LS
least squares
N
nitrogen
Mineral fertiliser savings from organic inputs
43
NFRV
Nitrogen Fertiliser Replacement Value
NFRV
high
NFRV at high total N supply
NFRV
low
NFRV at low total N supply
N
saved high
mineral fertiliser N saved from organic inputs at high total N supply
N
saved low
mineral fertiliser N saved from organic inputs at low total N supply
N yield
N content crop yield
P
phosphorus
3.1
Introduction
Efficient use of nitrogen (N) requires careful matching of N supply to crop demand.
Often application of mineral fertilisers is combined with application of organic inputs
such as farmyard manure (FYM), slurries, and crop residues (also called organic
manures, organic fertilisers or organic inputs). N in organic inputs generally has a lower
availability to crops than N in mineral fertilisers, mainly depending on the C:N ratio of
the input (Flavel & Murphy 2006). Therefore, N in organic inputs must be carefully
matched with mineral fertiliser N application to avoid leaching while making sure
sufficient N is available for crop growth. This requires the characterization of the
organic inputs by their Nitrogen Fertiliser Replacement Value (
NFRV
), also called the
Mineral Fertiliser Equivalent (Jensen 2013).
NFRV
can be based on the amount of mineral fertiliser N which is substituted by an
amount of organic input-N (kg/kg) while attaining the same crop yield (Herron & Erhart
1965; Schilling 1987; Schröder 2005b). Crop yields can be expressed as fresh matter
weights (FM), dry matter weights (DM) or N contents (N yields, Jensen 2013). These
are all valid procedures, with the difference that values of
NFRV
based on N yields are
often slightly lower than those based on DM or FM weights (Jensen 2013). N in organic
inputs is always accompanied by other nutrients, such as phosphorus (P), potassium (K)
or sulphur (S) which also affect crop yields. It is therefore important to exclude these
effects when estimating values of
NFRV
based on yields (either DM, FM or N yields,
Schröder 2005b)
Lory et al. (1995) have suggested to calculate
NFRV
using economic N rates (mineral
fertiliser N application rates at which marginal crop yields offset marginal fertiliser
costs) with and without organic inputs. Yields at economic N rates with and without
Chapter 3
44
organic inputs might however differ, which hampers comparison at equal yield levels.
In addition, economic N rates are dependent on prices of fertilisers and harvested
products, which makes estimations less robust through time and space.
Another manner to determine
NFRV
of organic inputs is by using isotope dilution
techniques. Using
15
N labelled materials, the fate of N from either organic inputs or
mineral fertilisers is measured among plant and soil fractions and compared (Diekmann
et al. 1993; Janzen et al. 1990). Additional methods to calculate
NFRV
(such as the
analysis of near infrared reflectance spectra of organic inputs) have been proposed but
need further development (Delin et al. 2012).
Values for
NFRV
differ when estimated in the first year of application of organic inputs
(short-term
NFRV
) or after repeated applications and several years (long-term NFRV,
Gutser et al. 2005; Schröder 2005a), with higher values found for long-term
NFRV
. For
FYM,
NFRV
ranges between 0.10 and 0.70 (Birkmose 2009; Jensen 2013; Pikula et al.
2016; Webb et al. 2013). For slurry,
NFRV
ranges between 0.20 and 0.90 (Birkmose
2009; Delin et al. 2012; Jensen 2013; Kundler et al. 1989; Langmeier et al. 2002; Webb
et al. 2013). For straw,
NFRV
has been estimated to be around zero (Dhillon and Dev
(1984) as cited by Katyal 1993). For green manures,
NFRV
has been estimated ca. 0.4
(Janzen et al. 1990), but this will probably depend on the species of green manure
cultivated.
Factors known to affect
NFRV
(at a given dose of the input) include the form of N in the
input, crop type cultivated, soil type, method of application, time of application and the
manuring history which may govern N retention and losses (Birkmose 2009; Jensen
2013; Katyal 1993; Kundler et al. 1989; Webb et al. 2013). Here we evaluate the effect
of an additional factor on
NFRV
which is currently not taken into account: the total N
supply. We address the following research question: Does
NFRV
of organic inputs
depend on total N supply?
3.2
Materials and methods
To answer the research question, values for long-term
NFRV
were calculated based on
yield response curves (either DM or FM weights of marketable products) at low and
high total N supply. This required data from field trials where different N rates were
applied with and without organic inputs. In such a set-up, mineral fertiliser N saved with
an addition of organic input at low mineral fertiliser rates (
N
saved low
) can be compared
with mineral fertiliser N saved by adding the same amount of organic input at high
Mineral fertiliser savings from organic inputs
45
mineral fertiliser rates (
N
saved
high
)
.
In these cases, sufficient P and K has to be applied to
ensure these macro-nutrients are not a yield limiting factor and affect the calculation of
NFRV
of organic inputs. Organic inputs however do not only supply N, P and K but also
other nutrients, such as sulphur (S). Over the past decades, S was often not yield limiting
due to the ample supply from the atmosphere (Eriksen 2009). More recently this has
changed but not always adjusted for in field experiments.
In Chapter 2, a database was compiled with data from 20 long term experiments. All
these 20 experiments fulfilled the following six criteria: (1) at least four increasing levels
of mineral fertiliser N rates without organic inputs; (2) at least four increasing mineral
fertiliser N rates with organic inputs; (3) P and K applied in ample amounts on all fields;
(4) at least five years of yield data; (5) if crops are cultivated in rotation, yield data
available for at least two rotation cycles; (6) yield data reported for individual crop types
(no aggregated data of whole-rotation yield output were used).
Assessing
NFRV
called for an additional requirement: (7) N contents of organic inputs
must be known. Using these seven criteria, 38 data sets (with multiple crops and organic
input types) were assembled from eight experiments in five countries across Europe
(Table 3.1).
The experiments ranged in duration between 15 to 44 years. These data, therefore, can
be used to consider long-term
NFRV
, including the greater part of the residual effect of
manure (Hernández et al. 2013; Jensen 2013; Schröder 2005a). The setup of these
experiments implies that mineral fertiliser N rates are always confounded with total N
supply. We therefore use the term ‘total N supply’.
For each data set, two yield response curves to mineral fertiliser-N were fitted, one
without (Y
0
) and one with organic inputs (Y
A
), following George (1984), see Fig. 3.1.
𝑌 = 𝑎 + 𝑏 ∗ 0.99
𝑁
+ 𝑐 ∗ 𝑁 + 𝜀 (3.1)
In Equation 3.1,
Y
is crop yield (t/ha) expressed in either fresh or dry matter weight of
the marketable product,
N
is the mineral fertiliser N rate (kg N/ha),
a
,
b
and
c
are
parameters to be fitted and
𝝴
is the error term.
Chapter 3
46
Mineral fertiliser savings from organic inputs
47
For each pair of curves, the amount of mineral fertiliser saved with an organic input was
calculated at low and high total N supply (
N
saved low
and
N
saved
high
).
N
saved low
was defined
as the amount of mineral fertiliser N required without organic inputs to match the yield
obtained with the input alone (Equation 3.2; N = 0):
𝑌
0
(𝑁
saved low
) = 𝑌
𝐴
(𝑁 = 0) (3.2)
To calculate
N
saved
high
, the highest yield level which was reached by both response
curves within the experimental set-up was determined for each pair of response curves
and called
𝑌
max
0,𝐴
. This could be the maximum of one of the two response curves (as in
Fig. 3.1a) or the highest yield reached within the experimental set-up (as in Fig. 3.1b).
Accordingly,
𝑁
max
0
was defined as the mineral fertiliser N rate needed to reach
𝑌
max
0,𝐴
without organic inputs. Following,
N
saved high
is the reduction in mineral N fertiliser when
reaching
𝑌
max
0,𝐴
with organic inputs (Equation 3.3):
𝑌
𝐴
(𝑁
𝑚𝑎𝑥
0
− 𝑁
𝑠𝑎𝑣𝑒𝑑 ℎ𝑖𝑔ℎ
) = 𝑌
𝑚𝑎𝑥
0,𝐴
(3.3)
Thus,
N
saved low
represents the reduction of mineral fertiliser when using organic inputs
at low total N supply.
N
saved high
represents the reduction of mineral fertiliser when using
organic inputs at high total N supply matching the highest yield possible. For each
dataset, a pair of response curves with
N
saved low
and
N
saved high
indicated is presented in
the supplementary information (Supplementary Fig. 3.1 to 3.4, pages 54 to 56).
Fig 3. 1: Illustration of calculating savings of N with organic inputs at low total N supply (N
saved low
) and at high
total N supply (N
saved high
). Y
0
is the yield response curve without organic inputs. Y
A
is the yield response curve
with organic inputs. At the left yields are shown from maize grown in Muencheberg between 1995-2003, with and
without straw. At the right yields are shown from winter barley grown in Keszthely between 1999-2010, with and
without farmyard manure.
Chapter 3
48
To find
NFRV
low
and
NFRV
high
, both
N
saved low
and
N
saved high
were divided by the total N
content of the organic inputs (Equations 3.4 and 3.5).
𝑁𝐹𝑅𝑉
𝑙𝑜𝑤
=
𝑁
𝑠𝑎𝑣𝑒𝑑 𝑙𝑜𝑤
𝑇𝑜𝑡𝑎𝑙 𝑁 𝑜𝑟𝑔𝑎𝑛𝑖𝑐 𝑖𝑛𝑝𝑢𝑡
(3.4)
𝑁𝐹𝑅𝑉
ℎ𝑖𝑔ℎ
=
𝑁
𝑠𝑎𝑣𝑒𝑑 ℎ𝑖𝑔ℎ
𝑇𝑜𝑡𝑎𝑙 𝑁 𝑜𝑟𝑔𝑎𝑛𝑖𝑐 𝑖𝑛𝑝𝑢𝑡
(3.5)
Following, for each pair of response curves, the difference between
NFRV
low
and
NFRV
high
was calculated. Statistical models based on the function lme (linear mixed-
effects model) of package nlme (Pinheiro et al. 2015) were used to find the least squares
(LS) means for
NFRV
low
and
NFRV
high
per type of organic input. These models can
account for random effects. In this analysis, the eight experiment locations were
included as random effects. All data were processed in R version 3.2.5.
3.3
Results
For FYM, mean
NFRV
low
was 0.53 (+ 0.26 - 95% Confidence Interval [CI]) and mean
NFRV
high
was 1.12 (+ 0.71 - 95% CI; N=17, Table 3.2).
NFRV
high
was 2.13 times larger
than
NFRV
low
(
p
= 0.04, Table 3.2). For slurry, only one dataset was available. For this
set, the ratio between
NFRV
high
and
NFRV
low
was 1.65 (N=1, Table 3.2).
For straw, one third of the
NFRV
values were negative at low total N supply, while
almost half (47%) of the
NFRV
values for straw were negative at high total N supply.
Mean
NFRV
low
for straw was 0.12 (+ 0.36
–
95% CI) and mean
NFRV
high
was 0.35 (+
1.67
–
95% CI).
NFRV
high
for straw was 3.07 times larger than mean
NFRV
low
, but not
significantly so due to the large variation in the effects of straw on mineral fertiliser
requirements (
p
=0.79).
In five data sets, a combination of straw and green residues (green manures and/or beet
leaves) was used as an organic input. At low total N supply, this combination had a
negative
NFRV
in two cases and no negative
NFRV
at high total N supply. Mean
NFRV
low
was 0.14 (+ 0.39
–
95% CI) and mean
NFRV
high
was 0.91 (+ 1.21
–
95% CI).
For the combination of straw and green residues,
NFRV
high
was 6.6 times larger than
NFRV
low,
but not significantly so, due to the small sample size (Table 3.2).
Mineral fertiliser savings from organic inputs
49
Chapter 3
50
3.1
Discussion
For all types of organic inputs, mean
NFRV
was higher at high total N supply than at
low total N supply. Only for FYM the difference was significant (
p
=0.04). For the other
types of organic input either the sample sizes were too small or the variation too large.
Currently, values for
NFRV
are often estimated by applying organic inputs without
mineral fertilisers. In Europe, farmers often apply a mixture of organic inputs and
mineral fertilisers, thus having a higher total N supply (Potter et al. 2010). We compared
values for
NFRV
at high total N supply (either the highest yield within the experimental
set-up or the maximum of a response curve) with values of
NFRV
for organic inputs at
low total N supply (at the start of the response curves). In practice, farmers will (1)
operate within environmental restrictions on N applications and (2) try to achieve an
economic optimum. Therefore, most often farmers will target yields somewhat below
the maximum of the N response curves, which will generally be closer to the values we
calculated for
NFRV
high
than for
NFRV
low
.
Crop residues are often not used by farmers to replace mineral fertilisers as they have a
high C:N ratio and might require N for decomposition (Knapp et al. 1983). In these
cases, crop residues have a negative
NFRV
. Straw had a negative NFRV in one third
(
NFRV
low
) to half (
NFRV
high
) of the cases in our study. A combination of green manures
and straw had a negative NFRV in two fifth (
NFRV
l
ow
) to none (
NFRV
high
) of the cases.
Our findings are based on 38 datasets from eight long term experiments. Besides straw
and FYM, however, there were only a limited number of observations per organic input
type (Table 3.2). Further work is needed to assess to what extent
NFRV
differs between
low and high total N supply for the other input types, for different soil and climate
conditions and in combination with different crop types. Such an exercise would require
a much larger dataset, preferably with N contents of organic inputs available.
A number of mechanisms might change the shape of the yield response curve and
therefore cause a difference between the values of
NFRV
low
and
NFRV
high
, on which we
will now elaborate. A first mechanism could be that organic inputs provide other
benefits than N to the crops (other nutrients or soil improvements), which has not been
adjusted for in the mineral fertiliser only plots. In this case, the calculated values of
NFRV
cannot be solely attributed to N but include additional yield effects (Janssen 2002;
Wadman et al. 1987). Elimination of the limitation(s) that caused such additional yield
effect (
e.g
. by supplying a mineral S fertilizer) would result in a steeper mineral fertilizer
Mineral fertiliser savings from organic inputs
51
response (
i.e
. a better conversion of applied mineral fertiliser N into DM or FM yield).
Consequently,
NFRV
high
would decrease and approach
NFRV
low
. A second alternative
explanation is that addition of mineral fertiliser might increase available N from the
input (or vice versa). Third, addition of mineral fertiliser could increase uptake
efficiency of N from organic input (or vice versa). Finally, a combination of above
mechanisms might take place at the same time.
In our study, savings of mineral fertiliser when using organic inputs were compared at
low and high total N supply. At low N supply, using only organic inputs was compared
with using only mineral fertiliser. At high total N supply, using a combination of organic
inputs and mineral fertiliser was compared with using only mineral fertiliser. The larger
value of NFRV at high total N supply could suggest that the NFRV of organic inputs is
higher when combined with mineral fertiliser than without. This suggestion could be
further investigated by calculating NFRV (at equal yield) of organic inputs when applied
in different amounts. If validated, this would open a new perspective on the advantage
of distributing available organic inputs among many farmers in a given region: the
region would require less fertiliser to produce the same yield output, than when organic
inputs were concentrated in few farms.
In this study, we have used yield response curves expressed in either dry or fresh matter
to calculate values for
NFRV
low
and
NFRV
high
. Other methods exist to calculate
NFRV
,
such as based on N content (Jensen 2013) or using isotope dilution techniques
(Diekmann et al. 1993; Janzen et al. 1990). An exploration of different methods into
NFRV
low
and NFRV
high
might give further insights into possible mechanisms.
3.2
Conclusions
Currently, values for
NFRV
are often based on experiments where crop yields in plots
with only organic inputs (no mineral fertilisers) are compared with crop yields in plots
with only mineral fertiliser, at relatively low total N supply. In many European
countries, however, farmers operate at high total N supply.
NFRV
coefficients play a
key role in fertiliser recommendation systems and tools (e.g. MANNER-NPK,
Nicholson et al. 2013) and various national Action Programmes in response to the EU
Nitrates Directive (EEC 1991). For example, current Action Programmes in the UK,
Denmark and the Netherlands use
NFRV
values of 0.10, 0.45 and 0.30-0.60 kg/kg,
respectively for total N in FYM, and values of 0.45, 0.75 and 0.60-0.80 kg/kg for pig
Chapter 3
52
slurry (Dalgaard et al. 2014; RVO 2014; UK 2015)
1
. These values are often a political
compromise, but based on short or long-term estimations of
NFRV
. These statutory
values define, in combination with N application standards, the maximum amounts of
N fertiliser that farmers in those countries may apply, depending on crop and soil type.
This meta-analysis of eight long term experiments on different types of organic inputs
shows that
NFRV
of FYM was roughly up to two times larger at high than at low total
N supply rate. Currently,
NFRV
is usually assessed at the ‘lower end’ of the N response
curve. Considering that farmers in Europe normally operate at relatively high mineral
fertilisation rates, the use of
NFRV
determined at the ‘lower’ end of the N response curve
may underestimate
NFRV
of organic inputs. When using these lower values to estimate
the N fertilizer replacement value of organic inputs, complementary mineral N fertiliser
requirements are estimated to be larger than actually needed for achieving certain yields.
This might lead to overuse, lower N use efficiency and more losses to the environment.
If our findings can be further generalized, the observed contrast in
NFRV
between the
respective N ranges may have practical implications for fertiliser recommendations,
depending on the mechanisms underlying the contrast. Similarly, the contrast might
justify adjustment of statutory values for fertiliser equivalency coefficients as used to
regulate N use in the Action Programmes - under the Nitrates Directive - of various EU
member states.
3.3
Acknowledgments
We thank all who set up, maintained and shared data from long-term experiments in
Europe through their publications. We thank Guido Baldoni (University of Bologna) for
providing additional data, and Margaret Glendining (Rothamsted Research) for
providing an overview on long term experiments in Europe and sharing her network.
We are grateful to Gerrit Gort (Wageningen University and Research) for his support
on statistics.
1
Values for the Netherlands refer to arable crops on sandy soils
Mineral fertiliser savings from organic inputs
53
Supplement Chapter 3
Supplementary Fig.3.1
Mineral fertiliser N savings from FYM at low
and high total N supply
54
Supplementary Fig. 3.2
Mineral fertiliser N savings from slurry at low
and high total N supply
55
Supplementary Fig. 3.3
Mineral fertiliser N savings from a combination
of straw and green residues at low and high total
N supply
55
Supplementary Fig. 3.4
Mineral fertiliser N savings from straw at low
and high total N supply
56
Supplement Chapter 3
54
N savings from FYM at low and high total N supply
Supplementary Fig. 3. 1: Yield response curves to mineral fertiliser, with and without FYM. Blue arrows pointing
to the left indicate a saving of mineral fertiliser N when applying FYM. Blue arrows pointing to the right indicate
additional mineral fertiliser N is needed when applying FYM. Years of yield data: A) 1990-2000; B) 1986-2006;
C) 1984-1992; D) 1999-2010; E) 2002-2007; F) 2002-2007; G) 1985-1993; H) 1983-1999; I) 1996-2004; J) 1984-
2002: K) 1995-2003; L) 1978-1982; M) 1994-1999; N) 1994-1999; O) 1999-2010; P) 1999-2010; Q) 1999-2010
Mineral fertiliser savings from organic inputs
55
N savings from slurry at low and high total N supply
Supplementary Fig. 3. 2: Yield response curves of winter wheat to mineral fertiliser, with and without slurry
applied in Bologna (1999-2010). Blue arrows pointing to the left indicate saving of mineral fertiliser N when
applying slurry at low and high total N supply.
N savings from a combination of straw and green residues at low and high total N
supply
Supplementary Fig. 3. 3:
Yield response curves to mineral fertiliser, with and without a combination of straw and
green residues. X-axes indicate mineral fertiliser N, including additional mineral fertiliser N added for the
cultivation of green manures or decomposition of straw (at each site on average 16.7 kg N/ha/year). Blue arrows
pointing to the left indicate a saving of mineral fertiliser N for a combination of straw and green residues. Blue
arrows pointing to the right indicate additional mineral fertiliser N is needed for a combination of straw and green
residues. Years of yield data: A) 1984-1992; B) 1986-2006; C) 1994-1999; D) 1994-1999; E) 1994-1999.
Supplement Chapter 3
56
N savings from straw at low and high total N supply
Supplementary Fig. 3. 4: Yield response curves to mineral fertiliser, with and without straw application. Blue
arrows pointing to the left indicate a saving of mineral fertiliser N when applying straw. Blue arrows pointing to
the right indicate additional mineral fertiliser N is needed when applying straw. Years of yield data: A) 1991-
2001; B) 1990-2000; C) 1999-2010; D) 1999-2010; E) 1999-2010; F) 1999-2010; G) 1995-2003; H) 1983-1999;
I) 1978-1982; J) 1985-1993; K) 1996-2004; L) 1984-2002; M) 1999-2010; N) 1999-2010; O) 1999-2010
Drivers and barriers for organic inputs
57
Chapter 4.
Drivers and barriers for arable farmers to use organic inputs in six
agro-ecological zones across Europe
Renske Hijbeek
1
, Anette A. Pronk
2
, Martin K. van Ittersum
1
, Jan Verhagen
2
, Greet
Ruysschaert
3
, Jo Bijttebier
3
, Laura Zavattaro
4
, Luca Bechini
5
, Norman Schlatter
6
, Hein
F.M. ten Berge
2
1
Plant Production Systems, Wageningen Plant Research, Wageningen University and
Research, the Netherlands
2
Agrosystems Research, Wageningen University and Research, the Netherlands
3
Flanders research institute for agriculture, fisheries and food, Belgium
4
Department of Agricultural Forest and Food Sciences, Università degli Studi di Torino,
Italy
5
Department of Agricultural and Environmental Sciences, Università degli Studi di
Milano, Italy
6
Department for Soil Health and Plant Nutrition, Austrian Agency for Health and Food
Safety, Austria.
To be submitted
Chapter 4
58
Abstract
Soil organic matter (SOM) can be built up by using organic inputs (such as straw,
compost, farmyard manure or the cultivation of green manures or cover crops). SOM is
known to have benefits for long-term soil fertility and can provide certain ecosystem
services. This could be an incentive to stimulate farmers to use more organic inputs.
Farmers’ behaviour is known to be motivated by a large number of factors. Using th
e
theory of planned behaviour, we aim to disentangle these factors. We address the
following research question: What are currently the main drivers and barriers for arable
farmers in Europe to use organic inputs?
This study focuses on six agro-ecological zones in four European countries (Austria,
Belgium, Italy and the Netherlands) and four practices (straw incorporation, cultivation
of green manure or cover crops, application of compost and application of farmyard
manure). In a first step, relevant factors were identified for each practice using five to
ten semi-structured interviews with farmers per agro-ecological zone. In a second step,
these factors were quantified as either drivers or barriers in a large scale farm survey
among 1180 farmers.
In the semi-structured interviews, 91 relevant factors were identified influencing
farmers’ decisions to use an organic input. In the larger farm survey, 60% of the factors
included were evaluated as drivers, while 40% were evaluated as barriers for the use of
organic inputs. Major drivers to use organic inputs were related to perceived effects on
soil quality (such as improved soil structure or reduced erosion) and the positive
influence from social referents (such as fellow farmers or agricultural advisors). Major
barriers to use organic inputs were financial (increased costs or foregone income) and
perceived effects on crop protection (such as increased pressure from weeds, pests and
diseases, or increased pesticide use).
Our study shows that farmers perceive a trade-off between improved soil quality on the
one hand and increased pressures from weeds, pests and diseases and financial
consequences on the other hand when using organic inputs. Motivating farmers to use
organic inputs therefore requires specific guidance on how to adapt cultivation practices
in order to reduce the pressure of weeds, pests and diseases for specific soil types,
weather conditions, and crops cultivated. In addition, more research is needed on the
long-term financial effects of using organic inputs.
Key-words:
drivers; barriers; straw; farmyard manure; green manures; cover crops;
compost; soil organic matter; Europe; agro-ecological zones
Drivers and barriers for organic inputs
59
Abbreviations
AEZ
agro-ecological zone
CT
conservation tillage
FYM
farmyard manure
SOM
soil organic matter
4.1.
Introduction
Using organic inputs (such as straw, green manures, compost or animal manure)
contributes to the soil organic matter (SOM) content of a soil (Panagos et al. 2015).
SOM is generally considered an important indicator for soil fertility (Christensen &
Johnston 1997; Reeves 1997) as it is known to stabilize soil structure, increase nutrient
availability and improve water holding capacity (Johnston et al. 2009; Watts & Dexter
1997). In addition, SOM content can have environmental benefits such as supporting
soil biodiversity or sequestering carbon (Chang et al. 2007; Freibauer et al. 2004).
Recently, a decline in SOM content has been identified as a threat to soil quality at
European scale (Stolte et al. 2015; Toth et al. 2008). Precise magnitudes of decline are
unknown as monitoring of SOM is currently sparse (ten Berge et al. 2017) or give
contrasting results. For Great Britain, monitoring studies suggest no changes in SOM
content over the last decades (Chapman et al. 2013; Reynolds et al. 2013). For Austria
and the Netherlands, positive changes in SOM content have been reported (Dersch 2015;
Reijneveld et al. 2009). For Germany, Italy, Belgium, Finland and Denmark, a negative
change in SOM content of arable soils over the last decades has been reported (Capriel
2013; Fantappiè et al. 2011; Goidts & van Wesemael 2007; Heikkinen et al. 2013;
Taghizadeh-Toosi et al. 2014).
Currently, maintenance of SOM content is included in the cross-compliance standards
of Good Agricultural and Environmental Conditions (GAEC, EC 2013). Most member
states have specified only a ban on burning of stubble, but a small number of countries
has added additional measures including the use of organic inputs (such as solid manures
or soil organic manures; Frelih-Larsen et al. 2016). Frelih-Larsen et al. (2016)
recommend a strengthening of GAEC 6 by adding a requirement to incorporate crop
residues, either directly or following mulching, after composting or after use as animal
bedding. Before enforcing such a measure, it is important to understand the current
drivers and barriers for farmers in Europe to use organic inputs.
Chapter 4
60
Van den Putte et al. (2010) argue that farmers often have few incentives to use soil
conservation measures because
productive capacities of farms are not affected by soil
degradation on the short term. Glenk et al. (2017) made an economic farm level analysis
of SOM management in two areas in Scotland and Spain and found varied effects on
farm gross margin, depending on input type and region. They did however note that
farmers’ behaviour may be motivated by factors which are not directly economic such
as perceived workability of the soil, or soil health for future generations. Simple cost-
benefit models might therefore not capture the complexity
of farmers’ behaviour and
attitudes (Lynne et al. 1988). Burton (2004)
argues that understanding farmers’
willingness to adopt certain management practices improves when the theory of planned
behaviour (Ajzen 1991) is used as a conceptual framework.
Fig 4. 1: Illustration of the framework of the Theory of planned behaviour (Ajzen 1991).
According to the theory of planned behaviour (Fig. 4.1), people base their behaviour on
three main constructs: 1) their attitude, 2) their subjective (social) norm and 3) the degree
of perceived behavioural control. In this framework, an attitude refers to the degree to
which a person expects a certain outcome, together with the desirability of that outcome.
An outcome is an expected result or impact of the practice, for example increased weed
pressure or improved soil fertility. Subjective norm refers to the influence from social
referents to perform a behaviour. Social referents in this case are for example fellow
farmers or an agricultural advisor. Perceived behavioural control refers to perceived
support or hindrance from control factors. A control factor is a specific, often local,
condition that governs the impact of the practice, or facilitates or hampers its adoption.
These three constructs together lead to an intention, which might lead to a certain
behaviour (Ajzen 1991).
Drivers and barriers for organic inputs
61
The theory of planned behaviour has been successfully applied previously in an
agricultural context. For example, Werner et al. (2017) found that in Germany, attitudes
and perceived behavioural control were linked to intentions of farmers to cultivate cover
crops.
In this study, the theory of planned behaviour is used to identify drivers and barriers for
the use of different organic inputs. We presume that specific drivers and barriers will be
context-specific, depending among others on soil types and climates. Therefore, we
assess drivers and barriers in six regions with specific combinations of slope, soil texture
and climate, hereafter called agro-ecological zones (AEZ). We ask the following
research question: What are currently the main drivers and barriers for arable farmers to
use organic inputs?
4.2.
Materials and methods
4.2.1.
Study approach
The study approach consisted of two steps: 1) a qualitative step to identify relevant
outcomes, referents and control factors for each practice; 2) a quantitative step to
quantify each relevant outcome, referent or control factor (Fig. 4.2). For the first step,
semi-structured interviews were held with a small number of farmers, whilst for the
second step, a much larger farm survey was conducted. In this manner, a bottom-up
approach is used relying on
farmers’ knowledge o
f their local farming conditions in each
AEZ.
Fig 4. 2: Overview of the study approach.
4.2.2.
Study area
This study formed part of the European project CATCH-C in which AEZs in Europe
were identified based on three environmental factors: climate, slope and soil texture.
Each AEZ has more or less homogeneous conditions for each factor (Hijbeek et al.
2013). Six AEZs were studied in four countries (Austria, Belgium, Italy and the
Chapter 4
62
Netherlands). Selection of AEZs in each country was mainly based on the area of the
AEZ and the economic importance of agriculture in the AEZ. Six AEZs were selected
(Fig. 4.3 and Table 4.1): Austria (AT1), Belgium (BE1), Italy (IT1, IT2) and the
Netherlands (NL1, NL2). Within each AEZ, data collection was focused on arable
farmers.
Fig 4. 3: Map of study area indicating the six AEZs in four European countries (Austria, Belgium, Italy and the
Netherlands).
In Austria, AT1 has a relatively dry climate (mean aridity index of 0.51), level or gentle
slopes and a medium soil texture (meaning > 15% sand and 18-35% clay or 15-65%
sand and < 18% clay). In Belgium, BE1 has a relatively wet climate (mean aridity index
0.73), a medium fine soil texture (meaning <15% sand, < 35% clay) and level to gently
sloping lands.
In Italy, IT1 and IT2 both have a very dry climate. IT1 is mainly present in the Po plain
and has more level land. IT2 is mostly located in the hills of centre and southern Italy
and has steeper slopes. To differentiate this difference, in figures and tables IT1 is
labelled IT1-level and IT2 is labelled IT2-sloping. To keep the text flowing, this
adjective is not added in the text.
Drivers and barriers for organic inputs
63
In the Netherlands, NL1 and NL2 both have a wet climate and level land. NL1 however
has medium or medium fine soil textures and NL2 has only coarse soil textures (meaning
> 65% sand and < 18% clay). To differentiate this difference in soil texture between the
two AEZs in the Netherlands, NL1 is labelled NL1-clay and NL2 is labelled NL2-sand
in figures and tables.
Table 4. 1
Characteristics of each AEZ. Climate zones follow Metzger et al. (2005). Aridity index of each climate
zone is calculated by dividing annual precipitation by potential evapotranspiration, using spatial climatic data for
the period 1975-2009 (Janssen et al. 2009). A lower aridity index indicates a drier climate. Soil texture classes
follow classes used in the European soil database (EC & ESBN 2004).
Climate zone
Average aridity
index of the climate
zone
Slope
Soil texture
AT1
Pannonian
0.51 (dry)
level (0°) or gentle (2-3°) medium
BE1
Atlantic Central
0.73 (wet)
nearly level (1°)
medium fine
IT1-level
Mediterranean North 0.38 (very dry)
level (0°) or gentle (2-3°) coarse to medium fine
IT2-sloping
Mediterranean North 0.38 (very dry)
gentle to moderate (2-7°)
medium or medium
fine
NL1-clay
Atlantic North and
Central
0.76 (wet)
level (0°)
medium or medium
fine
NL2-sand
Atlantic Central
0.76 (wet)
level (0°)
coarse
Farmers were questioned on four types of organic inputs: incorporation of small grain
cereal straw or grain maize straw; cultivation of green manures or cover crops;
application of compost; and application of farmyard manure (FYM). Not all practices
were included in each AEZ (Table 4.2). Incorporation of straw was included in five
AEZs. Cultivation of green manures or cover crops was included in six AEZs.
Application of compost was included in two AEZs. Application of FYM was included
in only one AEZ.
Table 4. 2: Overview of AEZs and types of organic inputs included in the farm survey.
Incorporation of
straw
Cultivation of green
manures or cover crops
Compost
FYM
AT1
X
BE1
X
X
X
X
IT1-level
X
X
IT2-sloping
X
X
NL1-clay
X
X
NL2-sand
X
X
X
4.2.3.
Step 1: semi-structured interviews
In the first step, five to ten farmers were interviewed in each AEZ to identify outcomes,
referents and control factors for each practice. The objective of these semi-structured
interviews was to use farmers’ knowledge to identify relevant factors for each practice,
Chapter 4
64
based on the theory of planned behaviour. Farmers were asked five questions to identify
outcomes of each practice, four questions to identify social referents for each practice
and three questions to identify control factors for each practice (specific questions asked
are listed in Supplementary Table 4.1, page 84).
The semi-structured interviews in the six AEZs resulted in 285 identified outcomes,
referents and control factors. Each of these was related to a type of organic input and an
AEZ. As could be expected, a substantial number of these items were similar, or almost
similar, across different types of organic inputs and AEZs. Therefore, mentioned
outcomes, referents and control factors were clustered in 101 classes (40 outcome
classes, 12 referent classes and 49 control factor classes). Following, they were given
shorthand labels (such as soil structure, or contract workers), which had overlap between
outcomes, referents and control factors (
e.g.
the label contract worker could refer to a
referent or a control factor). This resulted in 91 labels. The 91 labels were classified into
nine main categories (Table 4.3). The nine main categories were: soil type & climate,
soil quality, crop protection, land use, technical, financial, environmental impact, legal
and social (Fig. 4.4).
Fig 4. 4: Overview of the nine main categories used for clustering outcomes, referents and control factors as
identified in the semi-structured interviews.
Drivers and barriers for organic inputs
65
Chapter 4
66
4.2.4.
Step 2: farm survey
The outcomes, referents and control factors for the use of organic inputs identified in
the semi-structured interviews were quantified in a large farm survey. For each outcome,
referent and control factor, two questions were asked, based on the theory of planned
behaviour. In this manner, the attitude, subjective norm and perceived behavioural
control on the use of organic inputs could be calculated.
The sampling for the questionnaires depended on the availability of a valid sampling
frame of arable farmers within each country (
i.e.
contact details of farmers). The most
ideal sampling frame to obtain a completely random sample is a nation-wide database
of farmers’ addresses. Such a database was available for Flanders (Belgium) and the
Netherlands. In these countries farmers were invited by letter to participate in the farm
survey. In Austria and Italy researchers depended on farmers’ associations, farmers’
extension services or other contacts to distribute the questionnaires. Questionnaires were
filled online (Austria and the Netherlands) or as a paper questionnaire (Belgium and
Italy). Response rates varied between 12 to 59% (Table 4.4).
To reduce the length of the questionnaires and increase the response rate, in Belgium
and the Netherlands questionnaires were split into two or three parts allocated to
different groups of farmers. As such, each group of farmers received a questionnaire
with a common introduction section combined with in-depth questions only on a specific
type of organic input. Questions on the incorporation of straw were then for example
only sent to a subset of farmers. To reduce errors in farmers’ answers, respondents were
not obliged to fill out every question. In the case of partly completed questionnaires,
only the filled parts were used in the analysis. For these two reasons, numbers of farmers
can differ between analyses. Questionnaires were returned during the summer and
autumn of 2013. Filled questionnaires were checked on irregularities (typing mistakes
or extreme numbers) and if any, removed from analysis.
Table 4. 4: Numbers of farmers included in the semi-structured interviews (step 1) and the larger farm survey
(step 2) NA = not applicable.
Step 1
Step 2
Semi-structured
interviews
Questionnaires
sent
Response
Response rate
AT1
8
open online survey
34
NA
BE1
7
1600
430
27%
IT1-level
8
211
124
59%
IT2-sloping
9
165
98
59%
NL1-clay
10
2700
336
12%
NL2-sand
5
2000
241
12%
Drivers and barriers for organic inputs
67
In the farm survey, farmers were also asked if they used each type of organic input.
Hence, farmers were asked whether they cultivate green manures or cover crops,
incorporate straw, use FYM or compost, on at least one of their fields.
Quantification of attitudes
For each identified outcome (i), two types of questions were asked in the farm survey:
1) To which degree a farmer expects a certain outcome (
i.e.,
result, effect) from the
given practice (‘belief strength’) and 2) how the outcome is valued on a scale from
‘bad’ to ‘good’ (‘outcome valuation’). For example: a possible outcome from the
application of compost is improved soil structure. In the farm survey, farmers were first
asked to which degree they expect compost to improve soil structure (1 = little, 5 =
much) and second how they evaluate an improvement in soil structure (1 = bad, 5 =
good). Outcome valuations were lowered by three points to give a negative to positive
scaling (-2 to +2). In the data analysis, beliefs strengths and outcome valuations were
multiplied to obtain an attitude score for each outcome (Equation 4.1, values for attitude
ranging from -10 to +10).
𝑎𝑡𝑡𝑖𝑡𝑢𝑑𝑒
𝑖
= 𝑏𝑒𝑙𝑖𝑒𝑓 𝑠𝑡𝑟𝑒𝑛𝑔𝑡ℎ
𝑖
∗ (𝑜𝑢𝑡𝑐𝑜𝑚𝑒 𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛
𝑖
− 3) (4.1)
Quantification of subjective norm
For each social referent (k) identified in the semi-structured interviews, two types of
questions were asked in the farm survey: 1) how motivated they are to comply with the
referents’ view (‘motivation to comply’) and 2) t
o which degree the referent is positive
or negative towards a practice
(‘normative belief’). For example: A
social referent for
the incorporation of straw are fellow farmers. In the farm survey, farmers were first
asked if they thought fellow farmers are positive on the incorporation of straw (1 =
negative, 5 = positive). Second, farmers were asked how motivated they are to comply
with the opinion of fellow farmers (1 = little, 5 = much). Normative beliefs were lowered
by three points to give a negative to positive scaling (-2 to +2). In the data analysis,
motivations to comply and normative beliefs were multiplied to obtain a subjective norm
score for each referent (Equation 4.2, values for subjective norm ranging from -10 to
+10).
𝑠𝑢𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒 𝑛𝑜𝑟𝑚
𝑘
= 𝑚𝑜𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 𝑡𝑜 𝑐𝑜𝑚𝑝𝑙𝑦
𝑘
∗ (𝑛𝑜𝑟𝑚𝑎𝑡𝑖𝑣𝑒 𝑏𝑒𝑙𝑖𝑒𝑓
𝑘
− 3) (4.2)
Quantification of perceived behavioural control
For each identified control factor (m), also two types of questions were asked in the farm
survey: 1) to which degree that control factor is valid for the farm
(‘control strength’)
and 2) to which degree the control factor makes the practice attractive or difficult
Chapter 4
68
(‘control power’).
For example: a control factor for using compost is the availability of
compost in the farmers’ region. In the f
arm survey, farmers were first asked if there is
compost availability in the region (1 = not agree, 5 = agree) and second how much they
thought availability of compost will affect utilization (1 = negative, 5 = positive).
Control powers were lowered by three points to give a negative to positive scaling (-2
to +2). In the data analysis, control strengths and control powers were multiplied to
obtain a value for perceived behavioural control for each control factor (Equation 4.3,
values for perceived behavioural control ranging from -10 to +10).
𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑏𝑒ℎ𝑎𝑣𝑖𝑜𝑢𝑟𝑎𝑙 𝑐𝑜𝑛𝑡𝑟𝑜𝑙
𝑚
= 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑠𝑡𝑟𝑒𝑛𝑔𝑡ℎ
𝑚
∗ (𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑝𝑜𝑤𝑒𝑟
𝑚
− 3) (4.3)
Classification as drivers or barriers
For each attitude, subjective norm and perceived behavioural control, the mean value
across all respondents in an AEZ was calculated, together with the 95% confidence
interval. The 95% confidence interval was calculated as
1.96 ∗
𝑠𝑑
√𝑁
in which sd is the
standard deviation and N is the number of farmers who answered the question.
Significantly positive values (when zero was not included in the 95% confidence
interval) were considered drivers. Significantly negative values were considered
barriers.
Each driver or barrier belonged to one of the 91 sub categories (Table 4.3) and nine main
categories (Fig. 4.4). If two identified factors for a type of organic input and AEZ fell
into the same subcategory (
e.g.
soil fertility) and they were both positive (or both
negative), the average was taken. When two identified factors for a practice and AEZ
fell into the same sub category but the signs differed (the one being negative and the
other positive) they were kept separate in the analysis (meaning the sub category
represents both a driver and a barrier for a given practice). The latter was done because
apparently sometimes an outcome or control factor (
e.g.
soil fertility) can be both a
driver and a barrier, and we wanted to keep this information visible in the results.
4.3.
Results
4.3.1.
Characteristics of farmers in the survey
When conducting the farm survey, we aimed to target arable farmers with specific
climates, slopes and soil textures (Table 4.1), based on European scale datasets.
Targeting farmers with similar conditions for slope and soil texture however proved to
be difficult as these characteristics differ at smaller geographical scales. Overall, soil
Drivers and barriers for organic inputs
69
textures and slopes of the respondents in each AEZ showed correspondence with the
targeted AEZs but there was also considerable variation within each AEZ (Table 4.5).
Table 4. 5: Characteristics (slope, soil texture) of the AEZ and their farms in the survey, averaged per AEZ. N
indicates number of farmers on which the analysis is based, if a range is specified, this means that the number of
farmers varied by practice.
AT1
(N=34)
BE1
(N=371-395)
IT1-level
(N=102-114)
IT2-sloping
(N=82-85)
NL1-clay
(N=331-333)
NL2-sand
(N=217-219)
Slope (mean percentage
farm area)
Level (0°)
18%
39%
80%
8%
100%
100%
Nearly level (1°)
22%
31%
10%
8%
0%
0%
Gently sloping (2-3°)
32%
22%
5%
31%
0%
0%
Sloping (4-7°)
16%
7%
4%
39%
0%
0%
Steep (>8°)
12%
2%
1%
14%
0%
0%
Soil
texture
(mean
percentage farm area)
Sand
26%
8%
12%
1%
1%
93%
Loam
40%
91%
72%
62%
7%
1%
Clay
32%
1%
16%
38%
91%
1%
Peat
0%
0%
0%
0%
0%
4%
In Austria, respondents to the farm survey had a very diverse range of slopes and soil
textures. In Belgium, farmers had mainly loamy soils (91%) and mostly level to gently
sloping lands. In Italy, IT1 farmers had mainly level land and in IT2 farmers had mainly
gentle to steep sloping land. In the Netherlands, farmers in NL1 had mainly clay soils
and farmers in NL2 had mainly sandy soils.
4.3.2.
Current use of organic inputs
In all AEZs, farmers used organic inputs to some degree (Table 4.6). When cultivating
cereals, the majority of farmers incorporated straw sometimes to always, although some
differences between AEZs were observed. In BE1, fewer farmers incorporated straw
(26%), probably at least partly due to the high use of FYM (in which case straw is
exported to livestock farms and then again imported as FYM). When cultivating grain
maize, the majority of farmers also incorporated straw sometimes to always, only in
NL1 this was not the case.
A large difference in the cultivation of green manures or cover crops is observed
between the six AEZs (Table 4.6). While in AT1, BE1, NL1 and NL2 most farmers
cultivated green manures or cover crops on at least one of their fields (> 80%), only a
small minority cultivated green manures or cover crops in IT1 and IT2 (10%).
Chapter 4
70
Table 4. 6: Use of organic inputs in each AEZ. Bold numbers indicate combinations of AEZ and input type for
which questions on outcomes, referents and control factors are included in the farm survey. For straw
incorporation, percentages are calculated as share of the farmers who cultivate the specified crop.
AT1
BE1
IT1-level
IT2-sloping
NL1-clay
NL2-sand
Straw incorporation
(% farmers incorporating straw
sometimes to always)
1
small grain cereal straw
71%
26%
-
-
68%
60%
maize straw
88%
94%
-
-
38%
65%
cereals, maize or sunflower
-
-
93%
69%
-
-
Cultivation of green manure or
cover crops on at least one field
(% farms)
Green manure or cover crop
93%
87%
10%
10%
84%
83%
Application FYM or compost
(% farmers applying compost or
FYM on at least some part of
the land)
Compost
17%
8%
42%
55%
35%
29%
FYM
46%
71%
57%
67%
39%
16%
A higher percentage of farmers in IT1 and IT2 (42 and 55%) used compost compared to
farmers in AT1 and BE1 (17% and 8%), with NL1 and NL2 in between (35% and 29%).
The use of FYM varies widely, with the highest percentage of farmers using FYM in
BE1 and IT2 (71% and 67%) and the lowest in NL2 (16%). In the Netherlands, farmers
on sandy soils are often located near pig farms, which create a large availability of slurry,
possibly explaining the low use of FYM.
4.3.3.
Drivers and barriers for the incorporation of straw
Incorporation of straw was included in the farm survey of five AEZs (BE1, IT1, IT2,
NL1 and NL2). The total number of outcomes, referents and control factors identified
for the incorporation of straw
was 93 (summed over the five AEZ’s, or on average 15.5
per AEZ). Out of these, 48 were evaluated significantly positive as drivers and 33 were
evaluated significantly negative as barriers by the farmers (Fig. 4.5). Of the drivers,
around 40% were related to soil quality (N=19).
1
Only farmers are included when a specific crop (maize, cereal) was present in the a crop rotation of farm. In
Italy, sunflower was also included in the question on straw incorporation.
Drivers and barriers for organic inputs
71
Fig 4. 5: Drivers and barriers for incorporation of straw per AEZ
. ■ = mean attitude score on outcome; ● = mean
subjective norm of referent ▲= mean perceived behavioural control on control factor. Lines indicate 95%
confidence interval. N = numbers of farmers i
ncluded in analysis. Numbers in [] link to farmers’ descriptions in
Supplementary Table 4.2. GM = green manure. Colours indicate main categories.
Chapter 4
72
Of the barriers, Almost half was financial (N=15), yet there were also some financial
drivers (N=6). All outcomes and control factors related to crop protection were
evaluated as barriers (4 out of 4).
In all AEZs, effects on soil structure and SOM content were identified as outcomes and
evaluated as drivers for straw incorporation (Fig. 4.5). In almost all AEZs - besides BE1
- weeds, pests and diseases in general or specifically fungal diseases were evaluated as
a barrier for straw incorporation. In addition, costs of straw incorporation and loss of
income from selling straw were also evaluated as barriers for straw incorporation in all
AEZs. In most AEZs, subjective norms of referents were positive on straw
incorporation. Only in BE1, a negative subjective norm exists from fellow farmers and
contract workers.
In BE1, legal nutrient limits were seen as a driver as well as a barrier. Like the
Netherlands, the region of Flanders in Belgium (where the study was conducted), is a
Nitrate Vulnerable Zone, which gives stricter limitations on nutrient inputs for farmers.
Straw is however not counted within the legal nutrient limits, therefore it is seen by
some farmers as an easy manner to increase SOM content. At the same time, other
farmers prefer to add N to straw for decomposition, in which case the legal nutrient
limits are a barrier. The relatively high application of FYM in BE1 (Table 4.6) also
makes incorporation of straw less attractive.
In Italy (both IT1 and IT2), legal prohibition of burning straw was considered a driver
for straw incorporation. In IT1 specifically, effects of straw incorporation on product
quality (protein content) was a driver, while in IT2 availability of adequate machinery
for chopping and incorporating residue was a driver.
In the Netherlands (NL1 and NL2), reduced labour or time requirements for straw
incorporation (compared to removing it from the land) and positive effects on
workability of the soil were considered important drivers. Fertiliser use was evaluated
by farmers as a strong barrier, because farmers believe additional fertiliser is needed for
straw decomposition.
4.3.4.
Drivers and barriers for the cultivation of green manures or cover
crops
Cultivation of green manures or cover crops was evaluated in the farm survey of all six
AEZs (Fig. 4.6).
Drivers and barriers for organic inputs
73
Fig 4. 6: Drivers and barriers for cultivation of green manures or cover crops per AEZ
. ■ = mean attitude score
on outcome; ● = mean subjective norm of referent ▲= mean perceived behavioural control on control factor.
Lines indicate 95% confidence intervals. N = numbers of farmers included in analysis. Numbers in [] link to
farmers’ descriptions in
Supplementary Table 4.3. GM = green manure. CT = conservation tillage. For legend of
colours see Fig. 4.5 or 4.
7
Chapter 4
74
The total number of outcomes, referents and control factors identified for the cultivation
of green manure or cover crops was 138 (summed over the six
AEZ’s, or on average
23
per AEZ). The majority (N=80) was evaluated significantly positive as drivers, a
minority (N=42) was evaluated significantly negative as barriers. A small minority
(N=16) was not significantly different from zero (Fig. 4.6). Of the drivers, 35% was
related to soil quality (N=28). One third of the barriers was financial (N=14), yet there
were also some financial drivers (N=9). All outcomes and control factors related to
environmental impact were evaluated as drivers (5 out of 5).
Like for straw incorporation, effects on soil structure and SOM content were identified
and evaluated as drivers in each AEZ for the cultivation of green manures or cover crops
(Fig. 4.6). The effect of green manures or cover crops on soil erosion was also identified
and evaluated as a driver in almost each AEZ, except in IT1 (not included in survey
because it was not identified as an outcome or control factor in the semi-structured
interviews). In the Netherlands (NL1 and NL2) subjective norms of all referents were
positive, unlike for example Italy (IT1 and IT2) where especially fellow farmers and
family were perceived to have a negative view on the cultivation of green manures or
cover crops.
In four AEZs (AT1, BE1, NL1 and NL2) weeds, pests and diseases (as a general term)
or more specifically increases in weeds, nematodes, herbicide use or pesticide use (as
specific terms) were evaluated negatively as barriers for the cultivation of green manures
or cover crops. In BE1 however, positive effects of green manures or cover crops on
weeds were also a driver. In IT1 and IT2 issues with crop protection were not mentioned
in the semi-structured interviews with the farmers and therefore not included in the farm
survey. In all AEZs, costs and effects on income were important barriers for the
cultivation of green manures or cover crops.
In AT1, effects of cultivating green manures or cover crops on the water holding
capacity of the soil was evaluated as well a driver as a barrier (Fig. 4.6a). Previous
studies have shown
–
on average - no significant effect on water holding capacity from
the cultivation of green manures or cover crops in this area (Bodner 2013; Bodner et al.
2011). Austrian farmers can receive a subsidy for the cultivation of green manures or
cover crops, which is evaluated as a driver. To receive a subsidy, farmers have to follow
specific trainings.
In Flanders (BE1), farmers can also receive a subsidy for cultivating green manures or
cover crops at the time the survey was conducted. To be eligible for subsidy, farmers in
Flanders have to sow the green manure before a certain date (before the 1
st
of September
Drivers and barriers for organic inputs
75
or the 15
th
of October, depending on their location). As the time period between harvest
of some crops and legal sowing date is perceived as too short, this preconditions was
considered a barrier.
In Italy (IT1 and IT2), costs were a major barrier for the cultivation of green manures or
cover crops. Additional costs include production factors such as seeds, but also
mechanical operations such as tillage or sowing. Interestingly, farmers perceived a
positive effect of green manure or cover crops on fertiliser use in IT2, with savings of
fertiliser in the following cash crop. In IT1, having clay soils was seen as a barrier for
the cultivation of green manure or cover crops. On clay soils, seedbed preparation for
green manures might be more complicated due to excessive soil water content.
In the Netherlands (NL1 and NL2), improved workability of the soil was evaluated as
an important driver. This relates to perceived reductions in fuel costs and perceived
reductions in the loss of soil during harvest. In the Netherlands (NL1 and NL2),
requirements in labour and time to cultivate green manures or cover crops was
considered a barrier. This relates specifically to the small time frame available at the end
of summer after harvesting the main crop for the sowing of the green manure.
4.3.5.
Drivers and barriers for the application of compost or FYM
Application of compost was included in the farm survey of two AEZs (BE1 and NL2-
sand), while the use of FYM was included only in BE1. As both types of input are often
imported on arable farms, the results of these two input types are presented together.
The total number of outcomes, referents and control factors identified for the application
of compost or FYM was 54 (summed over three AEZs, or on average 18 per AEZ). Out
of these, 23 were evaluated significantly positive as drivers and 25 were evaluated
significantly negative as barriers (Fig. 4.7). Of the drivers, more than half were related
to soil quality (N=13). Forty percent of the barriers was financial (N=10) and almost a
quarter of the barriers was technical (N=6). A number of outcomes and control factors
related to crop protection, environmental impact and legal aspects were identified
(respectively 2, 2 and 1). These were all evaluated as barriers.
For compost, the effect on SOM content was evaluated positively by farmers as a driver
in both AEZs (Fig. 4.7). In BE1, effects of compost on various aspects related to soil
quality were also evaluated as drivers (
e.g.
soil life, soil health, soil fertility and soil
structure). In the Netherlands compost may be applied throughout the year - in contrast
to animal manures - making it a more attractive option compared to other organic inputs.
Therefore, the legally allowed timing of application was also evaluated as a driver for
Chapter 4
76
compost in NL2. Availability of alternative organic inputs such as animal manure,
pollution of compost and required labour and time for spreading were considered
barriers for using compost in both BE1 and NL2. In addition a range of other issues were
evaluated as barriers in BE1, such as availability, required knowledge, nitrogen
leaching, increases in weeds, pests and diseases, legal nutrient restrictions, availability
of appropriate machinery and compatibility with residue incorporation.
Fig 4. 7: Drivers and barriers for application of compost or FYM per AEZ
. ■ = mean attitude score on outcome;
● = mean subjective norm of referent ▲= mean perceived behavioural control on control factor. Lines indicate
95% confidence intervals. N = numbers of farmers included in analysis. Numbers in [] link to farmers’ descrip
tions
in Supplementary Tables 4.4 to 4.5. FYM = farmyard manure. N = nitrogen. Colours indicate main categories.
Drivers and barriers for organic inputs
77
For the application of FYM, a similar range of positive effects were found on soil
quality as from compost (
e.g.
soil life, soil health, soil fertility and soil structure). An
important barrier for FYM in Flanders was the need for appropriate storage facilities
during winter. Many farmers state that they do not have such a storage place and that
this makes the use of FYM unattractive.
4.3.6.
Drivers and barriers aggregated per category
Of all the 285 identified outcomes, referents and control factors in the semi-structured
interviews, 151 were evaluated significantly positive as a driver and 100 were evaluated
significantly negative as a barrier (Fig. 4.8).
Fig 4. 8: Counts and percentages of drivers and barriers per practice and category. Red colours indicate barriers
and blue colour indicates drivers.
Chapter 4
78
Aggregating drivers across AEZs, soil quality stands out as the most important driver
for using organic inputs. For all types of organic input, soil quality consistently has the
highest count and percentages of cases in which it is evaluated as a driver (except
environmental impact of green manures or cover crops, Fig. 4.8b).
For each type of organic input, financial consequences are most often mentioned as a
barrier, but crop protection has the highest percentage of cases in which it is evaluated
as a barrier compared to the total sum of barriers and driver in that category. Both
financial consequences and crop protection are therefore important barriers for the types
of organic input considered in this study.
4.4.
Discussion
4.4.1.
Drivers and barriers identified
In this study, a large number of outcomes, referents and control factors (285) were
identified being relevant to farmers for using organic inputs. To support data analysis, a
clustering was made consisting of 91 labels falling into nine main categories (Table 4.3).
Using a bottom-up approach, the categories were based on insights provided by farmers.
For all types of organic inputs, important drivers were perceived effects on soil quality
and a positive influence from social referents. In contradiction to the assumption that
farmers have too few incentives to use soil conservation measures because productive
capacities are not affected by soil degradation on the short term (Van den Putte et al.
2010), farmers in our survey show a great interest in maintaining long term soil fertility
and SOM contents. Effects on SOM content and soil fertility are consistently amongst
the top drivers for each type of organic input (Fig.4.5, 4.6 and 4.7). We confirmed the
notion that farmers’ behaviour
is motivated by a wide array of factors which are not all
directly economic (Glenk et al. 2017) as financial aspects were only a selected part of
the total listed drivers and barriers. If mentioned, financial consequences of using
organic inputs were more often evaluated negative than positive.
In our farm survey, farmers consistently evaluated the effects of organic inputs on crop
protection negatively. From field experiments, cereal straw is known to spread an
important fungal disease causing grain contamination (Fusarium head blight, FHB) to
subsequent cereals in crop rotations (Maiorano et al. 2008), although some measure to
relieve this pressure are also known. For example, the spread of FHB can be reduced
when crop residues are incorporated into the soil (Blandino et al. 2012). Organic inputs
have been observed to reduce soil-borne diseases, but only if they are rich in nitrogen
Drivers and barriers for organic inputs
79
(Bailey and Lazarovits 2003). In a recent literature review on the role of compost on
soil-borne plant diseases, Mehta et al. (2014) found a large variation in effects, but
mainly positive effects on disease suppression when composts were enriched with
specific soil microbes. In a more general observation, Bailey and Lazarovits (2003) note
that it takes time to develop disease suppressive soils when using organic inputs and that
benefits manifest only after a number of successive years. If farmers see negative effects
on crop protection in the first year of using a specific type of organic input, they might
however not be willing
–
without convincing evidence - to wait and see if this changes
in the future.
4.4.2.
Relating drivers and barriers to the use of organic inputs
When considering the use of organic inputs in each AEZ (Table 4.6), the most striking
was the difference in cultivation of green manures or cover crops. In both IT1 and IT2,
only 10% of farmers cultivated green manures or cover crops, whilst in the other AEZs
this percentage was above 83%. Looking at the drivers and barriers for the cultivation
of green manures or cover crops (Fig. 4.6), the number of identified outcomes, referents
and control factors is smaller in IT1 and IT2 (12 and 15 respectively) compared to the
other AEZs (ranging from 23 in BE1 to 38 in AT1). This situation, however, cannot
satisfactorily explain the low adoption in Italy, because a similar situation (small number
of outcomes, referents and control factors) was also found for straw incorporation, for
which the adoption was higher. Relatively more outcomes, referents and control factors
were evaluated as barriers compared to drivers in IT1 and IT2 (50% and 47%) than in
the other AEZs (ranging from 16% in NL1 and NL2 to 43% in BE1).
As in the other AEZs, costs and foregone income were also mentioned in IT1 and IT2
as barriers for the cultivation of green manures or cover crops. In IT1 and IT2 however,
fellow farmers and family were also perceived to have a negative view on the cultivation
of green manures or cover crops whilst suppliers and advisors were somewhat neutral.
In Italy, suppliers have a more positive view regarding straw incorporation (Fig. 4.5b,c).
Thus, it appears that the community around farmers does not help in promoting the
adoption of green manures or cover crops. From the personal experience of the Italian
co-authors it is known that, because green manure is still a relatively new practice for
Italian farmers, communication and learning from peers are very important factors to
increase the adoption. From the experience of farmers who are already successful with
green manures or cover crops in Italy, other farmers could learn what are the best species
or mixes, seed rate, seeding date, seeding technique, and termination technique in a
Chapter 4
80
given soil and climate. Currently the adoption of green manures or cover crops is
however low so that farmers have little exposure to these practices.
4.4.3.
Straw incorporation and N requirements
For the incorporation of straw, legal nutrient limits are mentioned to be relevant in BE1,
NL1 and NL2. Flanders (Belgium) and the Netherlands are designated Nitrate
Vulnerable Zones (EEC 1991). In these cases, farmers are restricted by legal nutrient
limits when using mineral fertilisers and/or organic inputs. In BE1, legal nutrient limits
are as well a driver and barrier for the incorporation of straw. This can be explained by
the variability in N demands of straw decomposition. In certain cases, incorporation of
straw can supply N to crops, whilst in other circumstances, it can induce temporary N
immobilization and needs N for decomposition (Chapter 3 of this thesis; Silgram &
Chambers 2002). A similar phenomenon is seen in IT1, where fertiliser use is evaluated
as a driver and a barrier for the incorporation of straw by farmers.
4.4.4.
Limitations of the study
Agro-ecological zones
For this study, farmers were clustered in AEZs to assess drivers and barriers for the use
of organic inputs of large groups of farmers in certain areas. The aim was to assess these
drivers and barriers for farmers with similar environmental conditions (soil textures,
slopes, climates) After data collection, it became obvious that slope and soil texture
varied greatly within each AEZ. For the purpose of this study, it was however not
possible to divide farmers on their separate farm characteristics, because numbers per
group would become too small to make meaningful comparisons. In further chapters of
this thesis which are based on the farm survey (Chapters 5 and 6), environmental
conditions for each individual farm will be calculated and used as such in the data
analysis.
Underlying beliefs
In this study, we assessed means of the combined scores for attitude, subjective norm
and perceived behavioural control. These concepts are based on underlying farmers’
beliefs (
e.g.
attitudes are formed by belief strengths and outcome valuations, Equation
4.1). In future work, these two separate components might be explored in more detail,
as done for example by Bechini et al. (2015) for straw incorporation in Italy and by
Viaene et al. (2016) for compost in Flanders.
Drivers and barriers for organic inputs
81
Assessing the different types of organic inputs separately
Drivers and barriers were analysed separately for the use of each type of organic input.
Farmers indicated that the use of one type of organic input depends on the use of another
type of organic input (for example, if a farmer already uses slurry, that might be a reason
not to use FYM). Both the Netherlands and Belgium have a large supply of animal
manure due to the presence of a relatively large livestock sectors (Oenema and
Berentsen 2004; Viaene et al. 2016). Sometimes, animal slurry is even offered for free
to arable farmers. In these cases, this cheap offer of slurry is a barrier for the use of for
example compost (BE1 and NL2, Fig. 4.7). In a case study in the Netherlands (Chapter
5 of this thesis), an aggregated measure of all organic inputs used by a farmer will be
used, expressed in total C or effective C. In addition, drivers and barriers for increasing
SOM content (as a general concept) will be analysed, including underlying beliefs.
4.5.
Conclusion
Major drivers for the use of organic inputs across six AEZs in this study were perceived
effects on soil quality (
e.g.
soil structure, soil fertility, soil health) and in most cases a
positive influence from social referents. Major barriers for the use of organic inputs were
perceived negative effects on (or the need for) crop protection (
e.g.
effects on the
incidence of weeds, pests and diseases or effects on herbicide or pesticide requirements)
and financial consequences (higher costs; income forgone).
Our findings shows that farmers perceive a trade-off between improved soil quality on
the one hand and increased pressures from weeds, pests and diseases and financial
consequences on the other hand when using organic inputs. If agricultural and
environmental policies aim to include the use of organic inputs as a measure to maintain
or increase SOM contents (as proposed by Frelih-Larsen et al. 2016), then farmers need
specific guidance on how reduce the pressure of weeds, pests and diseases. Ideally, this
advice would be differentiated for specific soil types, weather conditions, and crops
cultivated.
On the moment, financial consequences of using organic inputs cannot be assumed to
be neutral on the short term, at least not from a farmers’ perspective. Therefore, more
research is needed on the long-term financial effects of using organic inputs. If the use
of organic inputs has societal benefits beyond the farm-gate (
e.g.
biodiversity
conservation or soil carbon sequestration), these financial consequences could be
accounted for.
Chapter 4
82
4.6.
Acknowledgements
Our gratitude goes to all European farmers who spared some of their time to fill in the
questionnaire. Setting up a large scale farm survey would not have been possible without
numerous colleagues across different institutes. In Austria, main contacts were the
provincial and regional officials of the chamber of agriculture in Lower Austria. In
Belgium, data was collected with the help of Erwin Wauters (ILVO). In Italy, we
received support from universities of Ancona, Bari, Milano, Napoli Udine, Pisa,
Teramo, Tuscia, Ancona and Perugia; provincial and regional officials; farmers unions
(Coldiretti, Confagricoltura, Confederazione Italiana Agricoltori); CadirLab
(Quargnento, AL); Consorzio Agrario di Siena; advisors of the Italian Farm
Accountancy Data Network; Chiara Costamagna; students of the University of Torino
and University of Milano. In the Netherlands, Rob Smidt helped with extracting
farmers’ addresses from the database and Bert Rijk
supported the survey logistics. We
thank all colleagues for their efforts and support.
Drivers and barriers for organic inputs
83
Supplement chapter 4
Supplementary Table 4.1
Questions asked in semi-structured interviews
84
Supplementary Table 4.2
Farmer
s’ descriptions of outcomes, referents
and control factors for the incorporation of
straw
85
Supplementary Table 4.3
Farmer
s’ descriptions of outcomes, referents
and control factors for the cultivation of
green manures or cover crops
88
Supplementary Table 4.4
Farmer
s’ descriptions of outcomes, referents
and control factors for the application of
compost
92
Supplementary Table 4.5
Farmer
s’ descriptions of outcomes, referents
and control factors for the application of
FYM
93
Supplement Chapter 4
84
Questions asked in semi-structured interviews
Supplementary Table 4. 1 Questions asked in the semi-structured interviews. For each type of organic input in
each AEZ, all questions were asked with each farmer, which resulted in a list of outcomes, referents and control
factors. Between brackets [] the specific type of organic input was mentioned by the interviewer.
A. General questions on the BMP
Do you use [this practice]?
B. Questions to elicit behavioural outcomes (with the example of low pressure tires):
What do you believe are the advantages of [this practice]?
What do you believe are the disadvantages of [this practice]?
Is there anything else you associate with [this practice]?
Would [this practice] lead to any good or bad things?
Can you think of other consequences of [this practice]?
C. Questions to elicit normative referents:
Are there any individuals or groups who would approve of [this practice]?
Are there any individuals or groups who would disapprove of [this practice]?
Are there any other individuals or groups who come to mind when you think about [this practice]?
Are there people or groups of people who try to influence your decision to use [this practice]?
D. Questions to elicit control factors:
What factors or circumstances would enable (make it easy) you to use [this practice]?
What factors or circumstances would make it difficult or impossible for you to use [this practice]?
Are there any other issues that come to mind when you think about the difficulty of using [this practice]?
Drivers and barriers for organic inputs
85
Incorporation of straw
Supplementary Table 4. 2: Labels (as used in the figures)
and related farmers ‘descriptions of outcomes, referents
and control factors for the incorporation of straw
AEZ
[#]
Label
Category TPB
Farmers' description
BE1
[1]
Soil structure
outcome
Improved soil structure
BE1
[2]
Long term quality
Outcome
Good investment for my soil
BE1
[3]
Soil fertility
outcome
Increased soil fertility
BE1
[4]
Soil organic matter
outcome
More soil humus
BE1
[5]
Legal nutrient limits
control factor
Straw is not calculated as source of N
and P in legislation
BE1
[6]
Market
control factor
Not easy to find a buyer for straw
BE1
[7]
Livestock farmers
referent
Animal farmers
BE1
[8]
Wet climate
control factor
Straw is often too wet and of bad
quality
BE1
[9]
Fertiliser use
control factor
Additional nitrogen is needed to
digest straw
BE1
[10] High biomass straw
control factor
Dry matter yield of straw is high on
my field
BE1
[11] Straw decomposition
outcome
Straw is hard to digest
BE1
[12] # field operations
control factor
Two operations are needed instead of
one
BE1
[13] Sowing cover crops
outcome
Sowing cover crops is difficult
BE1
[14] Cooperation livestock
control factor
Agreement with animal farmer (straw
against manure)
BE1
[15] Night harvests
control factor
I prefer to harvest cereals at night
BE1
[16] Costs chopping straw
control factor
Increased cost for chopping straw
BE1
[17] Legal nutrient limits
control factor
Not allowed to give enough nitrogen
to digest straw
BE1
[18] Other organic inputs
control factor
I often use manure
BE1
[19] Fellow farmers
referent
Other arable farmers
BE1
[20] Contract workers
referent
Contract worker
BE1
[21] Fuel use
outcome
Additional fuel is needed
BE1
[22] Straw prices
control factor
Good prices for straw
IT1-level
[23] Soil structure
outcome
Improved soil structure
IT1-level
[24] Soil organic matter
outcome
Higher soil organic matter
IT1-level
[25] Fertiliser use
outcome
Reduced use of mineral fertilisers
IT1-level
[26] Burning not allowed
control factor
Crop residues burn is forbidden
IT1-level
[27] Suppliers
referent
Advisors of companies selling
production factors
IT1-level
[28] Fellow farmers
referent
Other farmers
IT1-level
[29] Protein content
outcome
Increased protein content in wheat
grain
IT1-level
[30] Straw decomposition
outcome
Slow decomposition of crop residues
in soil
IT1-level
[31] Fellow farmers
referent
Farm that collect crop residues
IT1-level
[32] Straw prices
control factor
Residues selling at a high price
IT1-level
[33] Fertiliser use
outcome
Increased nitrogen fertiliser use
Supplement Chapter 4
86
IT1-level
[34] Weather
control factor
Adverse environmental conditions
that hinder residues degradation
IT1-level
[35] Fungal diseases
outcome
Increased risk of fungal diseases
IT2-sloping [36] Soil fertility
outcome
Increased soil fertility
IT2-sloping [37] Soil structure
outcome
Improved soil structure
IT2-sloping [38] Soil organic matter
outcome
Higher soil organic matter
IT2-sloping [39] Machinery
control factor
I have adequate machineries
IT2-sloping [40] Burning not allowed
control factor
Legislation forbids crop residues
burning
IT2-sloping [41] Suppliers
referent
Advisors of producer association
IT2-sloping [42] Family
referent
Family members
IT2-sloping [43] Fellow farmers
referent
Other farmers
IT2-sloping [44] Availability of straw
control factor
Crop residues given for free
IT2-sloping [45] Costs chopping straw
control factor
Residues chopping and distribution is
expensive
IT2-sloping [46] Straw prices
control factor
Crop residue sale is possible, high
price residues & else loss of income
IT2-sloping [47] Weeds, pests, diseases
outcome
More weeds, pests and diseases
IT2-sloping [48] Fertiliser use
outcome
Increased nitrogen fertiliser use
IT2-sloping [49] Sowing cover crops
outcome
Following crop sowing hindered by
residues
NL1-clay
[50] Soil structure
outcome
Improves soil structure
NL1-clay
[51] Soil organic matter
outcome
Provides organic matter to the soil
NL1-clay
[52] Soil fauna
outcome
Improves soil fauna
NL1-clay
[53] Soil workability
outcome
Improves soil cultivation
NL1-clay
[54] Soil nutrients
outcome
When straw is not removed nutrients
stay in the field
NL1-clay
[55] Labour/time
outcome
Easier to incorporate straw than to
remove it
NL1-clay
[56] Compatibility GM
control factor
I sow a green manure after my wheat
NL1-clay
[57] Magazines
referent
Magazines are positive
NL1-clay
[58] Advisors
referent
Extension agents recommend the
incorporation of straw
NL1-clay
[59] Study club
referent
Study club is positive
NL1-clay
[60] Fellow farmers
referent
Other arable farmers are positive
NL1-clay
[61] Wet climate
control factor
The weather is often too wet to
remove the straw
NL1-clay
[62] Other organic inputs
control factor
There are enough other ways to apply
organic matter
NL1-clay
[63] Cooperation livestock
control factor
I have a corporation with a husbandry
farm for the straw
NL1-clay
[64] Straw as crop cover
control factor
I use the straw to cover beats and
potatoes
NL1-clay
[65] Straw prices
control factor
Price is often too good to incorporate
it
NL1-clay
[66] Legal nutrient limits
control factor
The Manure and Fertiliser Act makes
it impossible to apply the necessary N
to the straw to decompose
Drivers and barriers for organic inputs
87
NL1-clay
[67] Cultivation silage corn
control factor
I have silage corn: to incorporate
straw of corn I need to change to corn
cop mix
NL1-clay
[68] Machinery
outcome
Incorporation does not need heavy
machinery
NL1-clay
[69] Costs
outcome
Costs extra money
NL1-clay
[70] Fungal diseases
outcome
Increases fungal diseases
NL1-clay
[71] Fertiliser use
outcome
Decomposition of straw needs extra N
NL2-sand
[72] Soil structure
outcome
Improves soil structure
NL2-sand
[73] Soil organic matter
outcome
Provides organic matter to the soil
NL2-sand
[74] Soil fauna
outcome
Improves soil fauna
NL2-sand
[75] Soil workability
outcome
Improves soil cultivation
NL2-sand
[76] Labour/time
outcome
Easier to incorporate straw than to
remove it
NL2-sand
[77] Magazines
referent
Magazines are positive
NL2-sand
[78] Compatibility GM
control factor
I sow a green manure after my wheat
NL2-sand
[79] Soil nutrients
outcome
When straw is not removed nutrients
stay in the field
NL2-sand
[80] Study club
referent
Study club is positive
NL2-sand
[81] Fellow farmers
referent
Other arable farmers are positive
NL2-sand
[82] Advisors
referent
Extension agents recommend the
incorporation of straw
NL2-sand
[83] Wet climate
control factor
The weather is often too wet to
remove the straw
NL2-sand
[84] Straw as crop cover
control factor
I use the straw to cover beats and
potatoes
NL2-sand
[85] Other organic inputs
control factor
There are enough other ways to apply
organic matter
NL2-sand
[86] Straw prices
control factor
Price is often too good to incorporate
it
NL2-sand
[87] Legal nutrient limits
control factor
The Manure and Fertiliser Act makes
it impossible to apply the necessary N
to the straw to decompose
NL2-sand
[88] Cooperation livestock
control factor
I have a corporation with a husbandry
farm for the straw
NL2-sand
[89] Cultivation silage corn
control factor
I have silage corn: to incorporate
straw of corn I need to change to corn
cop mix
NL2-sand
[90] Costs
outcome
Costs extra money
NL2-sand
[91] Fungal diseases
outcome
Increases fungal diseases
NL2-sand
[92] Machinery
outcome
Incorporation does not need heavy
machinery
NL2-sand
[93] Fertiliser use
outcome
Decomposition of straw needs extra N
Supplement Chapter 4
88
Cultivation of green manures or cover crops
Supplementary Table 4. 3: Labels (as used in the figures)
and related farmers ‘descriptions of outcomes, referents
and control factors for the cultivation of green manures or cover crops
AEZ
[#]
Label
Category TPB
Farmers' description
AT1
[94]
Reduced erosion
outcome
Reduced erosion
AT1
[95]
Soil structure
outcome
Soil is rooted and loosened
AT1
[96]
Soil fauna
outcome
Enhanced soil life
AT1
[97]
Soil organic matter
outcome
Increased humus content
AT1
[98]
Soil water
outcome
Improved water storage over the
winter
AT1
[99]
Soil nutrients
outcome
Fixation of nitrogen & enrichment
soil other nutrients
AT1
[100] Attracting insects
outcome
Attractive & food for (beneficial)
insects
AT1
[101] Extensification
outcome
Relaxing of the crop rotation
AT1
[102] Landscape view
outcome
More beautiful landscapes
AT1
[103] Weather
control factor
Sufficient precipitation
AT1
[104] Seed costs
control factor
Cheap seeds
AT1
[105] Machinery
control factor
Available technical equipment & use
of cultivator
AT1
[106] Subsidy
control factor
Support by ÖPUL
AT1
[107] Adjacent agricultural
land
control factor
Contiguous agricultural area
AT1
[108] Agricultural education
referent
Agricultural school
AT1
[109] Magazines
referent
Literature
AT1
[110] Compatibility CT
control factor
Combination with mulch or non-
inversion tillage
AT1
[111] Advisors
referent
Advisor of the Chamber of
Agriculture & "Bioforschung Austria"
AT1
[112] Fodder need
control factor
Fodder for the animals
AT1
[113] Crop rotation
control factor
Gaps in the crop rotation are needed
AT1
[114] Land availability
control factor
Higher availability of agricultural area
AT1
[115] Fellow farmers
referent
Other farmers
AT1
[116] Government
referent
Politicians
AT1
[117] Study club
referent
Society "Distelverein" (Association
for Agriculture and Nature
Conservation)
AT1
[118] Suppliers
referent
Advisors of seed companies
AT1
[119] Livestock
control factor
Reduced livestock
AT1
[120] Weeds, pests, diseases
outcome
"Green bridges" cause a higher
disease pressure
AT1
[121] Knowledge
control factor
No sufficient know-how
AT1
[122] Crop rotation
outcome
Not possible to use the field for cash
crops
AT1
[123] Residue incorporation
outcome
Difficult incorporation of crop
residues
AT1
[124] Soil water
outcome
Loss of water that is no longer
available for the main crop
Drivers and barriers for organic inputs
89
AT1
[125] Risk low biomass GM
outcome
High risk of failure
AT1
[126] Weeds
outcome
Higher weed pressure
AT1
[127] Labour/time
outcome
Time consuming
AT1
[128] Income
outcome
Reduction of the income
AT1
[129] Pesticide use
outcome
Higher application of plant protection
AT1
[130] Fuel use
outcome
Higher use of fuel
AT1
[131] Costs
outcome
Higher costs
BE1
[132] Soil structure
outcome
Improved soil structure
BE1
[133] Soil health
outcome
Increased soil health
BE1
[134] Reduced erosion
outcome
Lower erosion risk
BE1
[135] Soil carbon
sequestration
outcome
More carbon in soil
BE1
[136] Weeds
outcome
Prevents development of weeds
BE1
[137] Soil workability
outcome
Can be tilled earlier to till in spring
BE1
[138] Nitrogen leaching
outcome
Prevents nitrogen leaching
BE1
[139] Legal nutrient limits
control factor
I fertilize as much as is allowed on
my parcels
BE1
[140] Fellow farmers
referent
Other arable farmers
BE1
[141] Subsidy
control factor
Subsidy compensates cost of cover
crops
BE1
[142] Government
referent
Flemish government stimulates cover
crops by providing subsidy
BE1
[143] Fertiliser use
control factor
Additional fertilization is needed for
white mustard
BE1
[144] Labour/time
control factor
Additional labour for sowing and
incorporation
BE1
[145] Seeding
control factor
I grow seed for cover crop myself
BE1
[146] Costs
control factor
Increase of total cost
BE1
[147] Wet climate
control factor
Weather conditions are often bad in
autumn
BE1
[148] Administrative
demands
control factor
Too much administration to get
subsidy
BE1
[149] Crop rotation
control factor
Crops are harvested late in autumn
BE1
[150] Legal sowing
restrictions
control factor
sowing before Sept 1 to get subsidy
BE1
[151] Machinery
control factor
No appropriate machinery for sowing
& incorporation
BE1
[152] Weeds
outcome
Might result in more weeds
BE1
[153] Herbicide use
outcome
Increased use of herbicides
BE1
[154] Land owners
referent
Owner of land
IT1-level
[155] Soil organic matter
outcome
Higher soil organic matter
IT1-level
[156] Soil structure
outcome
Improved soil structure
IT1-level
[157] Soil nutrients
outcome
Higher soil nitrogen content
IT1-level
[158] Crop yields
outcome
Higher crop yield
IT1-level
[159] Advisors
referent
Advisors of professional organisation
IT1-level
[160] Suppliers
referent
Advisors of companies selling
production factors & producer
associations
Supplement Chapter 4
90
IT1-level
[161] Sod seeding
control factor
I do sod seeding
IT1-level
[162] Clay soil
control factor
Clay soils
IT1-level
[163] Fellow farmers
referent
Other farmers
IT1-level
[164] Knowledge
control factor
I know green manure benefits
IT1-level
[165] Income
control factor
No incentives for green manure
IT1-level
[166] Costs
control factor
Additional costs for green manure
IT2-sloping [167] Soil structure
outcome
Improved soil structure
IT2-sloping [168] Soil organic matter
outcome
Higher soil organic matter
IT2-sloping [169] Fertiliser use
outcome
Reduced use of mineral fertilisers
IT2-sloping [170] Protein content
outcome
Increased protein content in following
crop
IT2-sloping [171] Reduced erosion
outcome
Reduced erosion
IT2-sloping [172] Income
control factor
I have incentives for green manure
IT2-sloping [173] Protein content
contracts
control factor
Cultivation contracts that remunerate
high protein content
IT2-sloping [174] Clay soil
control factor
Clay soils
IT2-sloping [175] Suppliers
referent
Advisors of companies selling
production factors & producer
associations
IT2-sloping [176] Costs fertilisers
control factor
Low prices of mineral fertilisers
IT2-sloping [177] Soil water
outcome
Green manure depletes the soil water
content
IT2-sloping [178] Family
referent
Family members
IT2-sloping [179] Fellow farmers
referent
Other farmers
IT2-sloping [180] Machinery
control factor
Lack of adequate machineries
IT2-sloping [181] Costs cultivation
outcome
Higher cultivation costs
NL1-clay
[182] Soil structure
outcome
Better soil structure
NL1-clay
[183] Soil workability
outcome
Improve soil handling
NL1-clay
[184] Soil organic matter
outcome
More organic matter
NL1-clay
[185] Soil nutrients
outcome
Support long term soil fertility & N
mineralisation
NL1-clay
[186] Soil fauna
outcome
Increase soil fauna
NL1-clay
[187] Reduced erosion
outcome
Less wind and soil erosion
NL1-clay
[188] Nitrogen leaching
outcome
Less nitrogen leaching
NL1-clay
[189] Advisors
referent
Extension agents recommend green
manures
NL1-clay
[190] Magazines
referent
Magazines are positive
NL1-clay
[191] Study club
referent
Study club is positive
NL1-clay
[192] Fellow farmers
referent
Other arable farmers are positive
NL1-clay
[193] Suppliers
referent
Green manure seed salesmen are
positive
NL1-clay
[194] Ploughing straw
control factor
I like to plough down my straw
NL1-clay
[195] Other organic inputs
control factor
Enough other ways to apply organic
matter
NL1-clay
[196] Timing application
control factor
It is not always possible to apply
liquid manure in time
Drivers and barriers for organic inputs
91
NL1-clay
[197] Cultivation winter
wheat
control factor
I mainly grow winter wheat
NL1-clay
[198] Cultivation potatoes
control factor
I grow a lot of early potatoes
NL1-clay
[199] Crop rotation
control factor
sometimes growing season is too
short for good crop
NL1-clay
[200] Land exchange
control factor
I exchange land with husbandry
farmers
NL1-clay
[201] Wet climate
control factor
In the fall there are not enough dry
days to sow green manures
NL1-clay
[202] Legal nutrient limits
control factor
Nitrogen quotum too low to grow
green manures
NL1-clay
[203] Weeds
outcome
More weeds in following crop
NL1-clay
[204] Nematodes
outcome
More nematodes
NL1-clay
[205] Labour/time
outcome
Requires extra time
NL1-clay
[206] Costs
outcome
Increases costs
NL2-sand
[207] Soil structure
outcome
Better soil structure
NL2-sand
[208] Soil organic matter
outcome
More organic matter
NL2-sand
[209] Reduced erosion
outcome
Less wind and soil erosion
NL2-sand
[210] Soil nutrients
outcome
Support long term soil fertility & N
mineralisation
NL2-sand
[211] Soil fauna
outcome
Increase soil fauna
NL2-sand
[212] Soil workability
outcome
Improve soil handling
NL2-sand
[213] Nitrogen leaching
outcome
Less nitrogen leaching
NL2-sand
[214] Advisors
referent
Extension agents recommend green
manures
NL2-sand
[215] Magazines
referent
Magazines are positive
NL2-sand
[216] Study club
referent
Study club is positive
NL2-sand
[217] Fellow farmers
referent
Other arable farmers are positive
NL2-sand
[218] Suppliers
referent
Green manure seed salesmen are
positive
NL2-sand
[219] Ploughing straw
control factor
I like to plough down my straw
NL2-sand
[220] Other organic inputs
control factor
Enough other ways to apply organic
matter
NL2-sand
[221] Crop rotation
control factor
sometimes growing season is too
short for good crop
NL2-sand
[222] Cultivation potatoes
control factor
I grow a lot of early potatoes
NL2-sand
[223] Timing application
control factor
It is not always possible to apply
liquid manure in time
NL2-sand
[224] Cultivation winter
wheat
control factor
I mainly grow winter wheat
NL2-sand
[225] Land exchange
control factor
I exchange land with husbandry
farmers
NL2-sand
[226] Wet climate
control factor
Not enough dry days to sow green
manures
NL2-sand
[227] Legal nutrient limits
control factor
Nitrogen quota too low to grow green
manures
NL2-sand
[228] Weeds
outcome
More weeds in following crop
NL2-sand
[229] Labour/time
outcome
Requires extra time
NL2-sand
[230] Nematodes
outcome
More nematodes
Supplement Chapter 4
92
NL2-sand
[231] Costs
outcome
Increases costs
Application of compost
Supplementary Table 4. 4: Labels (as used in the figures)
and related farmers ‘descriptions
of outcomes, referents
and control factors for the use of compost
AEZ
[#]
Label
Category TPB
Farmers' description
BE1
[232] Soil fauna
outcome
Improved soil life
BE1
[233] Soil health
outcome
Improved soil health
BE1
[234] Reduced erosion
outcome
Lower erosion risk
BE1
[235] Soil organic matter
outcome
Increased humus content of soil
BE1
[236] Soil nutrients
outcome
Improved long term N release by
the soil
BE1
[237] Soil structure
outcome
Obtain less heavy soils
BE1
[238] Fellow farmers
referent
Other arable farmers make little use
of compost
BE1
[239] Livestock farmers
referent
I can do animal farmers in area a
favour by using their
slurry/farmyard manure
BE1
[240] Magazines
referent
agricultural magazines
BE1
[241] Other organic inputs
control factor
I prefer organic fertilizer of animal
origin & solid/liquid
BE1
[242] Residue incorporation
control factor
I incorporate straw
BE1
[243] Contract workers
control factor
Dependent on contractor to spread
compost
BE1
[244] Fertiliser use
outcome
Supply of nitrogen needed to digest
compost
BE1
[245] Heterogeneous spread
outcome
no homogenous spread
BE1
[246] Other organic inputs
control factor
More than enough slurry & manure
available
BE1
[247] Uncertainty N release
outcome
Unsure on timing of N release for
crop
BE1
[248] Legal nutrient limits
control factor
Legislation for fertilization is too
strict
BE1
[249] Variation in quality
control factor
Much variation in quality
BE1
[250] Labour/time
control factor
Slurry is spread for me. compost
not
BE1
[251] Diseases
outcome
Higher risk on diseases
BE1
[252] Weeds
outcome
More weeds
BE1
[253] Nitrogen leaching
outcome
Higher risk on too high N residue in
autumn
BE1
[254] Costs
control factor
Expensive or difficult
transport/compared to
alternatives/variable/
BE1
[255] Knowledge
control factor
Not enough knowledge on
composition/ no experience
BE1
[256] Availability of compost
control factor
Not sure on availability when
needed & low offer
BE1
[257] Polluted compost
outcome
Contains waste products
NL2-sand
[258] Soil organic matter
outcome
Compost provides organic matter
Drivers and barriers for organic inputs
93
NL2-sand
[259] Timing application
outcome
Can be applied in the fall/winter
NL2-sand
[260] Advisors
referent
Extension agents are positive
NL2-sand
[261] Fellow farmers
referent
Other arable farmers are positive
NL2-sand
[262] Study club
referent
Study club is positive
NL2-sand
[263] Availability of compost
control factor
It is not available in my region
NL2-sand
[264] Costs
control factor
Compost application increases costs
NL2-sand
[265] Other organic inputs
control factor
Plenty of other possibilities to apply
organic matter
NL2-sand
[266] Legal nutrient limits
control factor
The levy free Phosphate level is too
low
NL2-sand
[267] Other organic inputs
control factor
Slurry is largely available
NL2-sand
[268] Labour/time
outcome
Cost more labour to apply
NL2-sand
[269] Polluted compost
outcome
It can contain unwanted waste
Application of FYM
Supplementary Table 4. 5: Labels (as used in the figures)
and related farmers ‘descriptions of outcomes, referents
and control factors for the use of FYM
AEZ
[#]
Label
Category TPB
Farmers' description
BE1
[270] Soil structure
outcome
Better soil structure compared to
slurry
BE1
[271] Soil fauna
outcome
More soil life
BE1
[272] Reduced erosion
outcome
Lower erosion risk
BE1
[273] Soil organic matter
outcome
More organic matter compared to
slurry
BE1
[274] Soil water
outcome
Improved water holding capacity of
the soil
BE1
[275] Soil nutrients
outcome
Higher N supplying capacity of the
soil & soil fertility
BE1
[276] Contract workers
control factor
Depending on the contractor
BE1
[277] Livestock farmers
referent
Animal farmers offer more slurry
BE1
[278] Legislation on effective
N
control factor
Working with system of effective
nitrogen
BE1
[279] Fellow farmers
referent
Other arable farmers apply it a lot
BE1
[280] Contract workers
control factor
Contractor not available when
farmyard manure has to be spread
BE1
[281] Machinery
control factor
Appropriate machinery not available
& homogeneous spread
BE1
[282] Uncertain N release
outcome
Less sure on timing and quantity of N
release by the soil compared to
mineral fertilizer and slurry
BE1
[283] Costs
control factor
Slurry is less expensive for me/
transport costs FYM
BE1
[284] Availability of FYM
control factor
Limited supply of farmyard manure
in my area/ supply varies/region
BE1
[285] Storage facilities
control factor
Farmyard manure has to be stored on
the farm/ appropriate storage
capacity
Supplement Chapter 4
94
Farmers’ beliefs on SOM
95
Chapter 5
How are farmers’ beliefs on
soil organic matter related to their use of organic
inputs? A case study in the Netherlands
Renske Hijbeek
1
, Annette A. Pronk
2
, Martin K. van Ittersum
1
, Hein F.M. ten Berge
2
, Jo
Bijttebier
3
and Jan Verhagen
2
1
Plant Production Systems, Wageningen University and Research, Wageningen, the
Netherlands.
2
Agrosystems Research, Wageningen University and Research, the Netherlands
3
Flanders Research Institute for Agriculture, Fisheries and Food, Belgium
Accepted for publication in Soil Use and Management under the title:
“What drives
farmers to increase soil organic matter? A case study in the Netherlands”
Chapter 5
96
Abstract
Soil organic matter (SOM) is an important resource base for arable farming. For policies
on SOM to be effective, insight is needed on why and under which conditions farmers
are willing to increase SOM content.
This study uses the theory of planned behaviour to analyse what prevents or encourages
Dutch farmers to increase the percentage of SOM content of their fields. In an online
survey, 435 arable farmers were asked questions to understand their attitude (perceived
benefits), subjective norm (social pressure) and perceived behavioural control
(anticipated impediments and obstacles) related to management of S
OM. Farmers’
answers were related to their intention to increase SOM content, use of organic inputs
and perceived increase of SOM content.
Our results show that Dutch farmers are well aware of the possible benefits of SOM
content for crop productivity. Most Dutch farmers also have a positive subjective norm
on SOM (rating 5.84 on a scale from -10 to +10, based on
e.g.
scientific research or
agricultural advisors). Consequently, most farmers in our survey have a high or very
high intention to increase SOM content (90.1%). A higher intention to increase SOM
content was correlated with the use of organic inputs as expressed in total and effective
C (
P=0.003 and P=0.002
respectively), but this did not lead to a perceived increase in
SOM content. The Dutch Manure and Fertiliser Act and the need to cultivate profitable
crops (such as potatoes or sugar beets) were indicated as important impeding factors for
increasing SOM content.
Keywords
: soil organic matter; soil management; organic inputs; theory of planned
behaviour; farmers’ intentions; farmers’ behaviour; soil conservation
Abbreviations
C
carbon
FYM
farmyard manure
SO
standard output
SOM
soil organic matter
Farmers’ beliefs on SOM
97
5.1.
Introduction
Soil organic matter (SOM) content co-determines many soil properties including soil
structure, nutrient availability and soil health (Johnston et al. 2009). Increasing SOM
content can therefore be seen as a strategic means to safeguard long term farm
productivity. As such, SOM management is an important farm objective for many Dutch
arable farmers (Mandryk et al. 2014).
Farmers can use different practices to increase SOM content of their fields. They can
use more organic inputs (such as animal manures or compost) instead of mineral
fertilisers, include more cereals in their crop rotation or cultivate green manures
(Magdoff & Weil 2004). These practices might conflict with other farm objectives such
as profit maximization, labour use efficiency or minimization of gross margin variation.
Farmers need to balance these objectives, which can prevent implementation of
practices to increase SOM content of their topsoil. This can become especially more
challenging when short-term profits outweigh long-term objectives (Ingram et al. 2014;
Mandryk 2016).
In the Netherlands, organic inputs are widely available for arable farmers due to the
large livestock sector and related production of animal manures. Since the 1980s
however, restrictions on the use of organic manure have been implemented (Schröder &
Neeteson 2008)
, which has caused some concern for farmers’ abilities to maintain or
increase SOM contents. Using approximately two million soil samples, Reijneveld et al.
(2009) showed that between 1984 and 2004, average SOM content remained stable in
agricultural soils in the Netherlands. Some increases in SOM content were found in
regions with lower initial SOM contents and decreases in SOM content in regions with
higher initial SOM content.
Maintenance of SOM is a policy target, as documented in European policy documents
(EC 2011a, b) and international food security and climate objectives (UNFCCC 2015).
Through maintenance of SOM, soil is being protected as a resource base for food
production, soil life is conserved (Chang et al. 2007) and carbon sequestered (Smith
2016). For policies on SOM to be effective, insight is needed on why and under which
conditions farmers are willing to increase SOM content.
Behavioural research approaches aim to identify what prevents or encourages
individuals from displaying a certain type of behaviour. In this case, we would like to
understand what prevents or encourages Dutch arable farmers to increase SOM content.
Chapter 5
98
Several models have been developed to try to understand and predict human behaviour.
The most widely used of these models is the theory of planned behaviour (Ajzen 1991),
which will be used in this study.
According to the theory of planned behaviour, people base their behaviour on three main
constructs: 1) their attitude, 2) their subjective (social) norm and 3) the degree of
perceived behavioural control. In this framework, an attitude refers to the degree to
which a person expects a certain impact or outcome. The subjective norm refers to the
social pressure to perform a behaviour. Perceived behaviour control refers to the
perceived ease of performing a behaviour, and reflects both past experiences and
anticipated impediments and obstacles. These three constructs together lead to an
intention, which might lead to a certain behaviour (Ajzen 1991, Fig. 5.1, other relations
between component might also be possible).
According to the theory of planned behaviour, attitudes, subjective norm and perceived
behavioural control are formed by underlying beliefs. These can be beliefs on the
outcomes of a certain behaviour, beliefs on the views of social referents or beliefs on
the strength of control factors restricting a certain behaviour.
When recently asked for their concerns on the future of soil fertility, Dutch farmers place
SOM content at the top of the list (Reijneveld 2013). It is however yet unknown how
Fig 5. 1: Illustration of the framework of the Theory of planned behaviour (Ajzen, 1991). When experiences are
clear and risks are low, perceived behaviour control is directly linked to actual behaviour.
Farmers’ beliefs on SOM
99
the different elements of the theory of planned behaviour play a role in farmers’
intention to increase SOM content or use of organic inputs. In this study the theory of
planned behaviour is used to analyse why and under which conditions Dutch arable
farmers are willing to increase the percentage of SOM content of their fields. We address
the following research questions for arable farmers in the Netherlands:
1.
Which beliefs form farmers’ attitudes, subjectiv
e norms and perceived
behavioural controls regarding SOM and its management?
2.
How do these beliefs (Q. 1) influence the intention of farmers to increase SOM
content?
3.
How does actual behaviour (use of organic inputs) correspond to farmers’
intention to increase SOM content?
5.2.
Materials and Methods
A farm survey was conducted among 435 arable farmers in the Netherlands (Section
5.2.1). Using this survey, we first studied the underlying beliefs of attitude, subjective
norm and perceived behavioural control of Dutch farmers regarding SOM and its
management (Section 5.
2.2). Second, we related these beliefs to farmers’ intention to
increase SOM content (Section 5.2.3). F
armers’ intention to increase SOM content
was
then related to actual (as stated by respondent) use of organic inputs (Section 5.2.4).
Finally, perceived change in SOM was related to perceived behavioural control,
intentions to increase SOM content and the use of organic inputs (Section 5.2.5).
5.2.1.
Online farm survey
An online survey was held among arable farmers in the Netherlands. We focused only
on arable farmers because (compared to livestock farmers) arable farmers cultivate less
grass and feedstocks and are therefore assumed to be more dependent on high soil
fertility and related SOM content. Three groups of farmers were targeted: farmers on
sandy soils, farmers on loam soils and farmers on clay soils (Supplementary Fig. 5.1,
page 121). Addresses were obtained from the national agricultural census 2012 (CBS
2012). While selecting addresses the following two criteria were used to exclude
pensioners and hobby farmers: 1) year of birth after 1947 and 2) spending more than 20
hours labour per week to farming. In total, 4770 letters were sent to farmers with a
personal link and password to the online questionnaire.
Chapter 5
100
Before data analysis, two criteria were used to select only arable farmers from the
respondents (following Andersen et al. 2007): 1) more than 2/3 of the monetary value
of agricultural outputs from arable crops; and 2) less than 50% of farm land was
grassland (either temporary or permanent). In addition, farmers with less than 10 ha of
land or having peat soils (either reporting peat soils and/ or more than 12% SOM on
average across all fields) were also excluded from data analysis.
Each farmer was asked to provide information on age, gender, farm size (ha), crop
rotation, soil texture and average SOM content. To reduce errors, farmers could leave
certain questions unanswered (for example due to confidentiality of crop rotation or
manure application). In those cases, only filled sections were used in the data analysis.
Economic farm sizes (expressed in the monetary value of agricultural output at farm-
gate price) were calculated using farm activities (
e.g.
crops cultivated, ha) and Standard
Output (SO) coefficients for the Netherlands (Eurostat 2013). Economic intensity of
each farm was calculated by dividing economic farm size by farm ha (and thus expressed
in €/ha). In addition, farmers were asked to indicate which type of organic inputs they
used and how much.
5.2.2.
Finding underlying beliefs of attitude, subjective norm and perceived
behavioural control regarding SOM and its management
Farmers’ attitude
A literature review revealed nine outcomes that SOM may have for soil properties,
processes and functions in arable farming: improved soil structure, rooting, workability,
water holding capacity, soil life, nutrient release, nutrient binding capacity, soil fungi
and productivity (Allison 1973; Gregorich et al. 1994; Johnston et al. 2009; Murphy
2014).
For each outcome (N = 9, Table 5.1), farmers were asked to rate its probability of
occurrence on a Likert scale from not very likely (1) to very likely (5). For instance,
farmers were asked to rate the likelihood that SOM improves workability of their soil.
This is called the belief strength. Farmers were also asked to evaluate each outcome. In
the same example, farmers were asked to evaluate workability from negative (1) to
positive (5). This is called the outcome valuation. Outcome valuations were lowered by
three points to give a negative to positive scaling (-2 to +2).
For each farmer, an attitude on each outcome (i) was found by multiplying belief
strength with outcome valuation (Equation 5.1). Consequently, attitude values ranged
Farmers’ beliefs on SOM
101
between -10 and +10. Mean attitude values for each outcome were found by taking the
average across all farmers.
𝑎𝑡𝑡𝑖𝑡𝑢𝑑𝑒
𝑖
= 𝑏𝑒𝑙𝑖𝑒𝑓 𝑠𝑡𝑟𝑒𝑛𝑔𝑡ℎ
𝑖
∗ (𝑜𝑢𝑡𝑐𝑜𝑚𝑒 𝑣𝑎𝑙𝑢𝑎𝑡𝑖𝑜𝑛
𝑖
− 3) (5.1)
Table 5. 1:
Expected outcomes of increasing SOM content, farmers’ referents and control factors included in the
farm survey. For each factor, two types of scales were used, scale 1 and scale 2. Scores on these respective scales
wer
e multiplied to find farmers’ attitudes, subjective norm and perceived behavioural control on increasing SOM
content, following Equations 5.1 to5. 3.
Scale 1
Scale 2
A. Outcomes
Belief strength
Outcome valuation
Improved soil structure
not likely (1) - likely (5)
bad (-2) to good (+2)
Easy rooting
not likely (1) - likely (5)
bad (-2) to good (+2)
Increased productivity
not likely (1) - likely (5)
bad (-2) to good (+2)
Improved workability
not likely (1) - likely (5)
bad (-2) to good (+2)
Increased water holding capacity
not likely (1) - likely (5)
bad (-2) to good (+2)
More soil life
not likely (1) - likely (5)
bad (-2) to good (+2)
Continuous nutrient release
not likely (1) - likely (5)
bad (-2) to good (+2)
Increased binding capacity of
nutrients
not likely (1) - likely (5)
bad (-2) to good (+2)
Increase in fungi
not likely (1) - likely (5)
bad (-2) to good (+2)
B. Referents
Normative belief
Motivation to comply
Advisors
negative (-2) to positive (+2)
none (1) to very much (5)
Research
negative (-2) to positive (+2)
none (1) to very much (5)
Magazines
negative (-2) to positive (+2)
none (1) to very much (5)
Study clubs
negative (-2) to positive (+2)
none (1) to very much (5)
Fellow farmers
negative (-2) to positive (+2)
none (1) to very much (5)
C. Control factors
Control strength
Control power
Effects of SOM are long term instead
of short term
none (1) to very much (5)
negative (-2) to positive (+2)
Costs of organic inputs
none (1) to very much (5)
negative (-2) to positive (+2)
Availability of organic inputs
none (1) to very much (5)
negative (-2) to positive (+2)
Crop rotation with specialized crops
1
none (1) to very much (5)
negative (-2) to positive (+2)
Dutch Manure and Fertiliser Act
none (1) to very much (5)
negative (-2) to positive (+2)
Usually, overall attitude of farmers is calculated as the sum of the attitude values for the
separate outcomes. In this study however, we aimed for consistency in scales between
categories by keeping all scales between -10 to +10. Therefore, the mean attitude value
of all outcomes was taken as a proxy for overall attitude (which had no further
consequences in data analysis except for improving readability of tables).
1
In the Dutch questionnaire the term ‘hakvruchten’ was used. This c
ategory of crops includes potatoes, sugar
beets, carrots, onions and other vegetables
Chapter 5
102
Farmers were not only asked to rate specific outcomes of SOM (such as improved water
holding capacity or nutrient binding capacity), but also to rate the overall effect of SOM
on crop productivity as a more general term (thus bypassing any presumed mechanisms
of contribution to crop yields). To assess how the perceived specific outcomes of SOM
are related to perceived effect of SOM on productivity as a general term, a correlation
analysis was done using a Spearman correlation (Kendall 1948).
Farmers’ subjective norm
As part of the CATCH-C project, a series of open interviews revealed five social
referents to be most important for arable farmers in the Netherlands, being: advisors,
research, magazines, study clubs and fellow farmers (Pronk et al. 2014). These five
referents were included in our questionnaire. For each referent (N = 5, Table 5.1),
farmers were asked if they thought the referent had a negative or positive view on
increasing SOM. For example, farmers were asked if they thought agricultural advisors
are pos
itive on increasing SOM (1 to 5). This is called the normative belief of farmers’
referents on SOM. For each social referent, farmers were also asked how motivated they
are to comply with the referents’ view. For example, farmers were asked how motivated
t
hey are to comply with the opinion of fellow farmers (1 to 5). This is called the farmers’
motivation to comply.
For each farmer, subjective norm for each social referent (k) was found by multiplying
normative belief with the motivation to comply (Equation 5.2). Normative beliefs were
lowered by three points to give a negative to positive scaling (-2 to +2). This resulted in
a subjective norm value for each referent for each farmer between -10 and +10. Mean
values of subjective norm for each referent were found by taking the average value of
the farmers in the survey. Means of all subjective norm scores (N=5, see Table 5.1) were
taken as a proxy for overall subjective norm.
𝑠𝑢𝑏𝑗𝑒𝑐𝑡𝑖𝑣𝑒 𝑛𝑜𝑟𝑚
𝑘
= 𝑚𝑜𝑡𝑖𝑣𝑎𝑡𝑖𝑜𝑛 𝑡𝑜 𝑐𝑜𝑚𝑝𝑙𝑦
𝑘
∗ (𝑛𝑜𝑟𝑚𝑎𝑡𝑖𝑣𝑒 𝑏𝑒𝑙𝑖𝑒𝑓
𝑘
− 3) (5.2)
Farmers’ perceived behavioural control
Perceived behavioural control of farmers on increasing SOM content was assessed using
five control factors: the long-term effect of SOM, costs of organic inputs, availability of
organic inputs, cultivation of specialized crops and the Dutch
Manure and Fertiliser Act.
For each control factor (N = 5, Table 5.1), farmers were asked how strongly they
thought the factor was applicable to them. For example, farmers were asked if
availability of organic inputs was limited in their region on a scale from 1 to 5. This is
Farmers’ beliefs on SOM
103
called the control strength. For each control factor, farmers were also asked to what
extent they thought the control factor hampers increasing SOM content. For example,
farmers were asked if they thought limited availability prevents increasing SOM on a
scale from 1 to 5. This is called the control power.
For each farmer, perceived behavioural control of each control factor (m) was found by
multiplying control strength with control power (Equation 5.3). Values for control
power were lowered by three points to obtain a negative to positive scaling (-2 to +2).
This resulted in a value for perceived behavioural control for each control factor for each
farmer between -10 and +10. Mean values were found by taking the average score of all
farmers in the survey. Means of all values for perceived behavioural control (N=5, see
Table 5.1) were taken as a proxy for overall perceived behavioural control.
𝑝𝑒𝑟𝑐𝑒𝑖𝑣𝑒𝑑 𝑏𝑒ℎ𝑎𝑣𝑖𝑜𝑢𝑟𝑎𝑙 𝑐𝑜𝑛𝑡𝑟𝑜𝑙
𝑚
= 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑠𝑡𝑟𝑒𝑛𝑔𝑡ℎ
𝑚
∗ (𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑝𝑜𝑤𝑒𝑟
𝑚
− 3) (5.3)
Dependency of farmers’ beliefs on farm characteristics
Statistical tests were done to assess if beliefs of attitude, subjective norm and perceived
behavioural control were dependent on certain farm characteristics such as soil texture,
age, farm size or farm intensity. Farms and farmers were classified according to their
age, soil texture, farm size and farm intensity. For soil texture, the dominant soil texture
present on a farm (largest share of farm land being either sand, loam or clay) was chosen
as an indicator. When constructing classes based on continuous variables (such as age
or farm intensity), farmers were divided into three equally sized groups (N = 145) where
possible.
For each class, means of beliefs were calculated and a test of significant difference was
done using a Kruskal-Wallis rank sum test (McDonald 2009). If a significant difference
was found, pairwise comparisons were done using the Conover-Iman test (Holm, 1979)
to find which group means actually differed (
e.g.
whether attitudes of farmers on the
effect of SOM on water holding capacity on clay soils differed from those farmers on
loam soils or farmers on sandy soils or both).
5.2.3.
Testing correlations between underlying beliefs and farmers’
intention to increase SOM
Farmers were asked to indicate their intention to increase SOM content on a Likert scale
from low (1) to high (5). Correlations were tested between intention to increase SOM
content and stated beliefs on outcomes, referents and control factors. In addition,
Chapter 5
104
correlation tests were done between farmers’ intention to increase SOM conten
t and
calculated values of attitude, subjective norm and perceived behavioural control using
Spearman’s rank correlation test
(Kendall 1948).
5.2.4.
Testing correlations between farmers
’
intention to increase SOM
content and (stated) use of organic inputs
Farmers were asked to report their use of organic inputs, such as compost, slurry,
farmyard manure (FYM) and the incorporation of straw. Fresh weight quantities as
reported by farmers were converted to total carbon (C) and effective C content (see for
conversion coefficients Supplementary Table 5.1, page 122). To calculate effective C,
humification coefficients were used indicating the remaining fraction of residues which
is still present in the soil after one year (Wolf & Janssen 1991). This conversion allowed
for the calculation of an annual input of organic inputs expressed in total C and effective
C per ha, which we refer to as ‘actual organic input’. Following, farmers’ intentions to
increase SOM were correlated with their annual organic input.
5.2.5.
Testing correlations between perceived change in SOM, perceived
behavioural control, intentions to increase SOM and use of organic
inputs
Farmers were asked to state whether SOM content of their fields showed an increasing
or decreasing trend, on a scale from 1 to 5. This perceived trend was correlated with
actual organic input (as defined above),
farmers’ intention
s to increase SOM and the use
of organic inputs. All correlations were tested using Spearman’s rank correlation test
.
5.3.
Results
5.3.1.
Farm and Farmers’ characterist
ics and actual use of organic inputs
Of the 4770 farmers who were sent an invitation, 542 farmers filled out the section of
the survey that dealt with SOM. Of these farmers, 501 were confirmed to be arable
farmers (more than 2/3 of the Standard Output (SO) comes from arable crops and less
than 50% of farm land is grassland). Of these 501 farmers, 10 were excluded from
analysis because their farm size was smaller than 10 ha. Another 52 farmers were
excluded because they either reported to farm peat soils or have an average SOM content
above 12%. Four farmers were removed because their stated application of organic
Farmers’ beliefs on SOM
105
inputs seemed unreasonably high (more than two times the legally permitted N
application), which we interpreted as typographical error. As a result, 435 farmers were
included in the analysis.
Fig 5. 2: Characteristics of farmers in the farm survey (N = 435). Land use categories: Cereals indicates more
than 50% of farm land is cultivated with cereals such as wheat, barley, rye, oat, triticale and/or grass. Specialized
land use indicates more than 50% of farm land is cultivated with specialized crops such as potatoes, sugar beets,
carrots, onions. Mixed land use means none of both categories is present on more than 50% of farm land.
Chapter 5
106
Mean age of farmers included in the analysis was 48.8 years old, with median age
exactly 50 years (Fig. 5.2a). Farmers in our survey were some years younger compared
to the entire population of Dutch farmers, for which mean age was 55 years in 2012
(Voskuilen et al. 2013). The difference in age can at least partly be attributed to our
criteria used for address selection (only farmers born after 1947 were included).
Farm size can be expressed in ha or monetary values, for example by using the monetary
value of agricultural output at farm-gate price (SO). Mean farm size of the farmers
included in the analysis was 79.4 ha (Fig. 5.2c), which was larger than the mean farm
size of Dutch arable farmers in the same year (57.2 ha according to CBS (2017),
excluding farms SO < € 25,000). Mean economic farm size was € 238,924 (
Fig. 5.2d),
which was also larger than mean economic farm size of Dutch arable farmers in 2013
(€ 187,700 according to CBS (2017), excluding farms SO < € 25,000).
Mean reported SOM contents of farms was 3.61% SOM (Fig.5.2e), which is very similar
to mean SOM content of Dutch arable farms on mineral soils in the last decades (around
20 g C/kg soil in the upper 25 cm, or approximately 3.5% SOM, as reported by
Reijneveld (2013). Most farmers cultivated specialized crops (such as potatoes, sugar
beets or onions) on more than half of their land (Fig.5.2h).
Most farmers (N = 409) also provided information on their use of organic inputs (Fig.
5.3). The majority of the farmers (around 87%) cultivated green manures on some parts
of their land, but exact percentages of farm land cultivated with green manures differed
widely (Fig. 5.3a). Slurry was the most often used animal manure, with 81% of the
farmers using some amount of slurry (Fig. 5.3b). Most farmers did not use FYM (N =
271), while 21 farmers used less than 1 tonne FYM per ha (together 292 farmers, Fig.
5.3c). Most farmers did not use compost (N = 258), and 35 farmers used less than 2
tonnes compost per ha (together 293, Fig. 5.3d).
When grain maize or other cereals were cultivated, more than half of the farmers
incorporated the straw, from at least sometimes up to always (Fig. 5.3e). When
converting organic inputs into total C content, mean application was 1.35 tonne
C/ha/year (+ 0.06 - 95% Confidence Interval [CI], Fig. 5.3f). When converting organic
inputs into effective C content, mean application was 0.57 tonne effective C/ha/year (+
0.04, 95% CI, Fig. 5.3g).
Farmers’ beliefs on SOM
107
Fig 5. 3: Use of organic inputs by farmers in the farm survey (N = 435).
Chapter 5
108
5.3.2.
Beliefs of Dutch farmers on SOM
Farmers’ attitude
Dutch farmers had a strong positive valuation on eight outcomes of SOM and a negative
valuation of one outcome (the last being soil fungi, Fig 5.4b).
Among the positive outcomes, nutrient release and nutrient binding capacity were
considered the least strong outcomes of SOM content and were also evaluated least
positive by Dutch arable farmers (Fig. 5.4a). At the other end of the spectrum, soil
Fig 5. 4: Mean values of outcome strengths related to mean outcome valuations. CEC = nutrient binding
capacity; nutrients = nutrient release.
Farmers’ beliefs on SOM
109
structure was considered one of the strongest outcomes of SOM and was also evaluated
most positively by Dutch arable farmers. For farmers on sandy soils, water holding
capacity takes exactly the same position as soil structure for both outcome strength and
valuation. For farmers on clay soil, workability takes almost the exact same position as
soil structure for outcomes strength and valuation.
Farmers evaluated the general term ‘soil life’ positive, but when asked specifically for
soil fungi, this was the only outcome of SOM which was evaluated negatively (Fig.
5.4c). The perceived strength of SOM causing soil fungi was however much weaker
than the strength of the other perceived outcomes (2.6-2.8 and.4.3-4.9 respectively).
Combining outcome strengths and valuations into attitude scores, farmers with different
soil textures had a significantly different attitude on a number of outcomes from SOM.
These outcomes were: workability, productivity, water holding capacity, nutrient
binding capacity and soil fungi (Table 5.2).
Table 5. 2: Attitudes on SOM (calculated using Equation 5.1). Farmers are grouped by the dominant soil texture
of their farm. Letters indicate a significant difference within one column between outcome scores. * indicates a
significant difference between at least two outcome scores within one row (using the post hoc Conover-Iman test).
Sand (N = 147)
Loam (N = 98)
Clay (N = 190)
Attitude
(-10 to +10)
Rank
Attitude
(-10 to +10)
Rank
Attitude
(-10 to +10) Rank
Improved soil structure
8.76
ab
3
9.17
a
1
9.12
a
1
Easy rooting
8.54
bc
4
8.92
ab
2
8.61
b
3
Improved workability
7.99
cd
*
6
8.70
ab
*
3
9.08
a
*
2
Increased productivity
8.83
a
*
1
8.62
c
*
4
8.30
c
*
4
Increased water holding capacity
8.78
ab
*
2
7.99
c
*
6
8.04
bc
*
6
More soil life
8.44
abc
5
8.10
bc
5
8.07
bc
5
Continuous nutrient release
6.64
e
8
7.36
d
7
6.87
d
7
Increased binding capacity of
nutrients
7.70
d
*
7
5.87
e
*
8
6.68
d
*
8
Increase in soil fungi
-3.19
f
*
9
-2.50
f
*
9
-2.44
e
*
9
MEAN
6.94
6.91
6.93
Despite these differences in attitude for specific benefits of SOM, mean attitudes on the
overall benefit of SOM did not vary significantly between soil textures (mean attitude
scores vary non-significantly between 6.90 and 6.96). This shows that farmers across
different soil textures appreciated the effects of SOM content equally, but for different
reasons. On sandy soils, farmers valued the effect of SOM content on productivity, water
holding capacity and soil structure the most. On loam and clay soils, farmers valued the
effect of SOM content on soil structure, ease of rooting and workability the most.
Chapter 5
110
How are the perceived effects of SOM on specific soil characteristics related to
perceived effects of SOM on crop productivity? On sandy and clay soils correlations
were overall highest (Fig. 5.5). On sandy soils, perceived effects of SOM on crop
productivity was strongest correlated with perceived effects of SOM on continuous
nutrient release and water holding capacity
(Spearman’s rho > 0.5). On clay soils,
perceived effects of SOM on crop productivity was strongest correlated with perceived
effects of SOM on ease of rooting and soil life.
(Spearman’s rho > 0.5). On loam soils,
perceived effects of SOM crop productivity was strongest correlated with perceived
effect of SOM on ease of rooting
(Spearman’s rho > 0.5).
Fig 5. 5: Correlations between perceived effect of SOM on specific soil characteristics and perceived effects of
SOM on productivity, using Spearman’s rank correlation test. Numbers indicate Spearman’s rho. Stars show
confidence level of correlation coefficients (* means P<0.05. ** means P<0.01).
Farmers’ subjective norm
Considering the subjective norm of Dutch arable farmers, there were significant
differences dependent on the age group. The results indicate that younger farmers are
less motivated to comply with views from the given referents (such as fellow farmers,
research or advisors, Fig. 5.6). Overall, advisors were thought to be most positive about
increasing SOM content, whilst fellow farmers were thought to be least positive on
increasing SOM content.
Farmers’ beliefs on SOM
111
On average, subjective norm of farmers to increase SOM content was positive (5.84 on
a scale from -10 to +10). Age group had a significant effect on the subjective norm.
Older farmers (55-65 year) had a significantly more positive overall subjective norm on
increasing SOM content than medium age (45-55 year) or younger (18-45 year) farmers
(6.29 vs 5.96 and 5.28 respectively, Table 5.3).
Table 5. 3: Subjective norm on SOM (calculated using Equation 5.2). Farmers are classified according to their
age. Letters indicate a significant difference within one column between referent scores. * indicate a significant
difference between at least two referent scores within one row (using the post hoc Conover-Iman test).
Young 18-45 yr
(N = 124)
Middle 45-55 yr
(N = 176)
Old 55-65 yr
(N = 131)
Subjective norm
(-10 to +10)
Rank
Subjective norm
(-10 to +10)
Rank
Subjective norm
(-10 to +10)
Rank
Advisors
6.25
a
1
6.80
a
1
6.75
ab
2
Research
5.06
b
*
3
6.27
ab
*
2
6.83
a
*
1
Study club
5.41
b
*
2
5.94
b
*
4
6.23
b
*
4
Magazines
4.85
b
*
4
6.03
b
*
3
6.55
ab
*
3
Fellow farmers
4.81
b
5
4.74
c
5
5.07
c
5
MEAN
5.28*
5.96*
6.29*
Fig 5. 6: Mean values of normative belief related to mean motivation to comply with referents. Shapes indicate
age groups of farmers.
Chapter 5
112
Farmers’ perceived behavioural control
Considering the perceived behavioural control of Dutch arable farmers, the long-term
effect of SOM was evaluated as the factor with the highest control strength and valuated
most positively (Fig. 5.7). Availability and costs of organic inputs were considered more
or less neutral in control power, while Dutch law on manure and fertiliser use (The
Dutch Manure and Fertiliser Act) and crop rotations were considered to have a negative
influence on SOM management.
There were some significant differences in perceived behavioural control, depending on
farm intensity (
€
/ha). Farmers with high intensive farms were most positive on the long-
term effect of SOM content. Farmers with medium intensive farms were most negative
on the costs of organic inputs. Farmers with low intensive farms were least negative on
the effects of specialized crops on the management of SOM. Overall perceived
behavioural control was not significantly different across farm intensities (Table 5.4).
For low, medium and high intensive farms, control factors had exactly the same ranking
in order of relevance. For all farm intensities, the Dutch Manure and Fertiliser Act was
considered the most hampering factor, followed by crop rotations with specialized
crops.
Fig 5. 7: Mean values of control strength related to control power. Crop rotation = crop rotation with specialized
crops. Law = Dutch Manure and Fertiliser Act. Shapes indicate farm intensity.
Farmers’ beliefs on SOM
113
Table 5. 4: Perceived behavioural control on SOM content by farmers (calculated using Equation 5.3). Farmers
were divided into equally size
d groups according to their farm intensity. Low intensive farms: < 2273.4 €/ha.
Medium intensive farms: 2273.4 -
3062.4 €/ha. High intensive farms: > 3062.4 4 €/ha. A different uppercase letter
indicates a significant difference within one column between control factors. * indicates a significant difference
between at least two values for perceived behavioural control within one row (using the post hoc Conover-Iman
test).
Low intensive farms
(N = 144)
Medium intensive farms
(N = 144)
High intensive
farms
(N = 143)
Perceived
behavioural
Control
(-10 to +10) Rank
Perceived
behavioural
control
(-10 to +10) Rank
Perceived
behavioural
Control
(-10 to +10)
R
a
n
k
Effects of SOM are long term
instead of short term
4.00
d
*
1
4.13
d
*
1
5.00
d
*
1
Costs of organic inputs
0.35
c
*
2
-0.10
c
*
2
0.91
c
*
2
Availability of organic inputs
-0.19
c
3
-0.24
c
3
0.16
c
3
Crop rotations with specialized
crops
-
1.24
b
*
4
-
1.90
b
*
4
-
2.84
b
*
4
Dutch Manure and Fertiliser Act
-4.61
a
5
-4.80
a
5
-4.81
a
5
MEAN
-0.34
-0.58
-0.31
5.3.3.
Correlations between
farmers’ beliefs and farmers’ intention to
increase SOM
Most Dutch farmers who participated in the survey stated an intention to increase SOM
content (90.1%). On a scale from 1 to 5, 71.9% gave a 5 and 18.2% a 4. Only 2.5% of
the farmers did not want to increase SOM content of their fields (indicating either 1 or
2 on a scale of 5) and 7.4% of the farmers was neutral (indicating a 3).
In the previous sections, underlying beliefs of Dutch arable farmers on SOM were
assessed. How are these underlying beliefs related to farmers’ intention to increase SOM
content? Almost all beliefs on outcomes and social referents were significantly but
weakly
related to farmers’
intentions to increase SOM content. Beliefs on control factors
were much less related to farmers’ intentions to increase SOM content (Table 5.5)
For outcomes, (Table 5.5a), belief strengths and outcome valuations were more or less
equally related to farmers’ intention to increase SOM conten
t (significant spearman
Rho’s between 0.15 and 0.30).
The strongest relation was found between perceived
increase in crop productivity and intention to increase SOM content.
There was a stronger correlation between normative beliefs of referents and farme
rs’
intentions to increase SOM content than between the motivations to comply with those
referents and farmers’ intentions to increase SOM content
(Table 5.5b, 0.22-0.33 vs
Chapter 5
114
0.11-0.16). Subjective norms based on advisors and study clubs were slightly stronger
related with intentions to increase SOM content than the subjective norms of research,
magazines and fellow farmers.
Table 5. 5:
Correlations between farmers’ intentions to increase SOM content and beliefs on expected outcome
s,
referents and control factors on increasing SOM content. Tests of significance were done using Spearman’s rank
correlation test. Numbers indicate Spearman’s rho.
Correlation
with
intention to
increase
SOM
P
value
Correlation
with
intention to
increase
SOM
P
value
Correlation
with
intention to
increase
SOM
P
value
A. Outcomes
Belief strength
Outcome valuation
Attitude
Improved soil structure
0.21
0.000
0.29
0.000
0.28
0.000
Easy rooting
0.16
0.001
0.26
0.000
0.24
0.000
Increased productivity
0.30
0.000
0.26
0.000
0.32
0.000
Improved workability
0.22
0.000
0.15
0.002
0.18
0.000
Increased water holding
capacity
0.18
0.000
0.29
0.000
0.27
0.000
More soil life
0.18
0.000
0.26
0.000
0.24
0.000
Continuous nutrient release
0.17
0.000
0.16
0.001
0.18
0.000
Increased binding capacity of
nutrients
0.21
0.000
0.27
0.000
0.26
0.000
Increase in soil fungi
-0.07
0.127
0.07
0.143
0.12
0.013
B. Referents
Normative belief
Motivation to comply
Subjective norm
Advisors
0.33
0.000
0.13
0.006
0.29
0.000
Research
0.25
0.000
0.08
0.084
0.21
0.000
Magazines
0.25
0.000
0.16
0.001
0.24
0.000
Study clubs
0.33
0.000
0.09
0.053
0.29
0.000
Fellow farmers
0.22
0.000
0.11
0.027
0.21
0.000
C. Control factors
Control strength
Control power
Perceived behavioural
control
Effects of SOM are long term
instead of short term
0.05
0.291
0.12
0.010
0.11
0.018
Costs of organic inputs
0.10
0.043
0.15
0.002
0.13
0.007
Availability of organic inputs
0.07
0.177
-0.03
0.591
-0.05
0.267
Crop rotation with specialized
crops
0.15
0.002
-0.11
0.021
-0.13
0.008
Dutch Manure and Fertiliser Act
0.19
0.000
-0.08
0.091
-0.06
0.212
Farmers’ beliefs on SOM
115
Control factors
were not or only weakly related with farmers’ intention to increase SOM
content (Table 5.5c, significant numbers ranging between -0.11 to 0.19). Significant
relations between perceived behavioural control and intentions to increase SOM content
were only found for costs of organic inputs, long-term effects of SOM and crop rotations
with specialized crops. This indicates that despite low availability of organic inputs or
the restrictions of the Dutch Manure and Fertiliser Act, farmers still have the intention
to increase SOM content. If they succeed in doing so is a different question.
5.3.4.
Correlations between farmers’ intention to increase SOM
and actual
use of organic inputs
Comparing farmers’ intention to increase SOM content with their use of organic inputs,
a positive correlation was found with the use of slurry and compost (
p
=0.044 and
p
=0.035 respectively, Table 5.6c).
Table 5. 6: Actual use of organic inputs by farmers with different intentions to increase SOM content. For
incorporation of straw, only farmers for which the mentioned cereal is included in their crop rotation are shown.
P values below 0.05 indicate a significant positive trend between a intention to increase SOM and the use of an
organic input (straw - spearman rank correlation test, all others - Jonckheere-Terpstra trend test).
Intention to increase SOM (1 = low, 5 = high)
1
(N=2)
2
(N=7)
3
(N=31)
4
(N=75)
5
(N=290)
p
A.
Green manures
Percentage farm land with green manures
24%
34%
20%
33%
33%
0.079
B.
Straw
Grain maize (never/sometimes/always) N = 99
0/1/0
0/1/0
4/6/0
6/9/3
40/23/6
0.11
Other cereals (no/sometimes/yes) N = 390
0/1/1
3/0/5
17/5/8
28/20/2
5
82/81/11
4
0.06
Rapeseed(no/sometimes/yes) N = 63
0/1/0
0/1/0
1/10/1
0/9/2
4/32/2
0.81
C.
Off-farm
Slurry application (t/ha)
3.00
6.70
10.71
11.86
12.56
0.044
FYM application (t/ha)
3.41
0.73
1.52
1.06
1.15
0.555
Compost application (t/ha)
0.00
0.00
1.39
1.80
2.09
0.035
D.
Total
Total C added (t/ha)
1.16
1.09
1.04
1.33
1.40
0.003
Effective C added (t/ha)
0.43
0.35
0.45
0.55
0.60
0.002
Chapter 5
116
When converting all organic inputs into total C added or total effective C added (Table
5.6d), in both cases application amounts increased with higher intentions to increase
SOM content (
p
=0.003 and
p=
0.002 respectively).
5.3.5.
Correlations between perceived change in SOM, perceived
behavioural control, intentions to increase SOM and use of organic
inputs
Farmers also indicated if SOM contents on their farm increased or decreased. Asked if
SOM content was decreasing on their farms, on a scale from 1 to 5, only 1% indicated
a 5 (meaning large decrease of SOM content). Following, 11% of the farmers indicated
to perceive some decrease. Around one third (33.6%) of the farmers gave a neutral
indication, while the majority (55% of the farmers) indicated a 1 or a 2, meaning that
the SOM content of their farms was stable or increasing.
Perceived change in SOM content was significantly related to the perceived behavioural
control of farmers (Table 5.7, significant numbers ranging between -0.22 and 0.25).
Among the different control factors, perceiving the Dutch Manure and Fertiliser Act as
a strong obstacle was strongest related with perceived trends in SOM content.
Table 5. 7:
Correlations between farmers’ beliefs on control factors and perceived increase in SOM content. Tests
of significance are done using Spearman’s rank correlation test. Numbers indicate Spearman’s rho.
Correlation
with
perceived
increase in
SOM
P
value
Correlation
with
perceived
increase in
SOM
P
value
Correlatio
n with
perceived
increase in
SOM
P
value
Control factors
Control strength
Control power
Perceived behavioural
control
Effects of SOM are long term
instead of short term
0.04
0.392
0.21
0.000
0.17
0.000
Costs of organic inputs
-0.11
0.021
0.11
0.024
0.11
0.024
Availability of organic inputs
-0.22
0.000
0.09
0.059
0.05
0.310
Crop rotation with specialized
crops
-0.16
0.001
0.12
0.013
0.13
0.008
Dutch Manure and Fertiliser
Act
-0.19
0.000
0.25
0.000
0.24
0.000
No correlation was found between the use of organic inputs and perceived increase in
SOM (Fig. 5.8). This might be due to the time it takes before organic inputs have an
effect on SOM content and thus before any change will be perceived by the farmers (for
Farmers’ beliefs on SOM
117
example through soil analyses) - this can take decades. At the same time, the lack of
correlation also illustrates how difficult it can be for farmers to increase SOM content.
There was a weak but significant negative correlation between perceived increase in
SOM and the intention to increase SOM content
(Spearman’s Rho
-0.16). The more
SOM content was perceived to
decrease, the higher was farmers’ intention to increase
SOM content. Thus, a perceived decrease in SOM content can lead to a higher intention
to increase SOM content, which again can lead to an increase in the use of organic inputs
(Fig. 5.8). In addition, a significant correlation was found between perceived
behavioural control and perceived increase (or decrease) of SOM content, highlighting
the apparent importance of control factors on the long term.
5.4.
Discussion
The 435 Dutch arable farmers included in the analysis of this study were somewhat
younger and had larger farms (both in total land area cultivated and economic size) than
average Dutch arable farmers. The differences were however small. In addition, SOM
contents o
f farmers’ soils corresponded very well with the average for the Netherlands
Fig 5. 8: Correlations bet
ween farmers’ attitude, subjective norm, perceived behavioural control, intention to
increase SOM and perceived increase of SOM using Spearman’s rank correlation test. Numbers in circles
indicate mean values. Numbers besides arrows give correlation coeffic
ients (Spearman’s rho). Numbers at top of
figure indicate potential ranges. Dotted lines indicate relations with no significant correlation. Stars show
confidence level of correlation coefficients (* means P<0.05. ** means P<0.01
Chapter 5
118
(3.6 vs 3.5% SOM). The outcomes of this study could therefore be considered
representative for the larger population of Dutch arable farmers.
In the Netherlands, farmers usually have sufficient access to mineral fertilisers (Eurostat
2012; Potter et al. 2010). It is therefore not surprising that Dutch farmers value nutrient
release and nutrient binding capacity of SOM the least (Table 5.2 and Fig. 5.4): they do
not primarily rely on these functions of SOM for crop production. Farmers value the
effect of SOM content on soil structure more, especially where it improves workability
on clay soils and water holding capacity on sandy soils. In this respect, farmers’ views
align well with findings from field experiments (Barzegar et al. 2002; Hamza &
Anderson 2005; Rawls et al. 2003; Soane 1990; Watts & Dexter 1997).
Farmers with a higher intention to increase SOM content used significantly more
compost and slurry (
P
=
0.035
and
0.044
respectively) and applied significantly more
total C and effective C to their soils (
P=0.003
and
0.002
respectively). We however
found that the use of organic inputs was not directly related to a perceived increase in
SOM content. Increasing SOM content by using organic inputs often takes many years
or even decades (Körschens et al. 2013), which could be one explanation for this lack
of correlation.
Comparing farm intensities, farmers with high intensive farms (with the highest
economic output per ha), value the long-term effect of SOM more than medium or low
intensive farms and see the costs of organic inputs as less of a constraint (Table 5.4). At
the same time, farmers with more intensive farms cultivate more specialized crops
(including root and tuber crops), which makes it more difficult for them to increase SOM
content. This can be an additional challenge considering root and tuber crops depend
more on a good soil structure for successful crop growth than cereals (Verheijen 2005b).
Over the past decades, the ratio of gross margins between specialized crops and cereals
has gone up (KWIN 2015), thereby making crop rotations with higher shares of cereals
economically challenging.
Comparing age groups, younger farmers were less motivated to comply with social
referents than older farmers, especially when it comes to messages received from
research, farmers’ magazines and study clubs (
Fig. 5.6). In general, advisors were
perceived to be most positive about increasing SOM content, especially compared with
fellow farmers, who were perceived to be the least positive.
Farmers’ beliefs on SOM
119
This study looked specifically at farmers’ beliefs in increasing SOM content in
the
Netherlands. In the Netherlands there is a large supply of animal manure due to the
presence of a relatively large livestock sector (Oenema & Berentsen 2004), making this
a relatively cheap source of nutrients. In addition, costs of fertilizers are low relative to
land prices in the Netherlands. Therefore, in other countries nutrient supply of SOM and
costs of organic inputs might play a larger role.
5.5.
Conclusion
Using the theory of planned behaviour, this study has gained insight into the underlying
beliefs of farmers to increase SOM content. We found that Dutch arable farmers are well
aware of the benefits of SOM. Most Dutch farmers also have a positive subjective norm
on SOM (rating 5.84 on a scale from -10 to +10). Advisors seem most positive on
increasing SOM content, whilst Dutch arable farmers are most motivated to comply with
findings from research.
Farmers’ at
titude, subjective norm and perceived decrease in SOM content were
significantly related to their intention to increase SOM content (Fig. 5.8). In our farm
survey, this intention was very strong: 90.1% of the farmers stated a high or very high
intention to increase SOM content of their fields.
In contrast, perceived behavioural control had no significant correlation with farmers’
intention to increase SOM content. Perceived behavioural control did have a significant
positive correlation with perceived increase in SOM content. From a farmers point of
view, this indicates that increase of SOM content is to a large degree beyond their direct
influence. Important impeding factors on SOM management were found to be the Dutch
Manure and Fertiliser Act and need for crop rotations with specialized crops.
Currently, Dutch and European laws on manure and fertiliser use mainly focus on
reducing nutrient leaching to surface waters, not necessarily enhancing organic matter
applications. There is however potential to re-use organic wastes with low nutrient
contents to arable land (van der Kolk & Zwart 2013). Meyer-Kohlstock et al. (2015)
recently found that only one third of biowaste is used as compost in Europe.
International policies to increase SOM content could therefore start by investigating
how the availability of organic inputs can be increased by facilitating the use of organic
wastes. Meyer-Kohlstock et al. (2015) recommend setting recycling targets and
implementation of collection systems. In addition, incentives are needed for farmers to
Chapter 5
120
cultivate crops in a more balanced crop rotation,
i.e.
a higher share of cereals or other
crops which are beneficial for SOM content.
5.6.
Acknowledgements
We thank all Dutch farmers who participated in the online farm survey. Gratitude goes
to Rob Smidt for extracting farmers’ addresses from the database and to Bert Rijk for
supporting survey logistics. We thank Greet Ruysschaert, Marian Vlaswinkel, Joske
Houtkamp, Mark Kronen, Jan Reimer and Zwier van der Vegte for testing our
questionnaire and giving useful suggestions for improvement. Finally, thanks to all our
colleagues in the CATCH-C project for the pleasant collaboration.
Farmers’ beliefs on SOM
121
Supplement Chapter 5
Supplementary Fig. 5.1
Soil texture map of the Netherlands
121
Supplementary Table 5.1 Conversion coefficients for C and effective C
contents of organic inputs
122
Soil texture map of the Netherlands
Supplementary Fig. 5. 1: Soil texture map of the Netherlands. Sampling area for this study includes farmers on
sand, loam and clay soils. Figure is adapted from the Dutch soil map (WUR-Alterra, 2006).
Supplement Chapter 5
122
Conversion coefficients for C and effective C contents of organic inputs
Supplementary Table 5. 1: Conversion coefficients for calculating total and effective C content of organic inputs.
If estimates of biomass or C content are based on multiple literature sources, averages are taken. Where
information was absent, carbon content of dry organic plant material is assumed to be 0.425 tonne C per tonne
dry organic matter (Janssen, 2002). FM = Fresh matter.
Conversion coefficients
Comments
References
On-farm
Total C
(tonnes
C/ha)
Effective C
(tonnes effective
C/ha)
Green manures
1.28
0.34 For each ha
cultivated with
green manures
(De Jonge 1981 as cited by
Dekker et al. 2004; Florentín
2011; Timmer et al. 2004)
Straw from maize
1.60
0.57 For each ha
cultivated with
maize, when
incorporated
On biomass estimates:
(LNE 2009)
On straw to grain ratios:
(De
Jonge 1981 as cited by Dekker et
al. 2004; Fleischer et al. 1989; Lal
1995)
Straw from other
cereals (such as
wheat, barley, rye,
oat, triticale)
1.63
0.54 For each ha
cultivated with
cereals, when
incorporated
On biomass estimates:
(KWIN 2015; LNE 2009)
On straw to grain ratios:
(Bauder 2000; De Jonge 1981 as
cited by Dekker et al. 2004; Lal
1995)
Straw from
rapeseed
1.15
0.40 For each ha
cultivated with
rapeseed,
when
incorporated
On biomass estimates:
(KWIN 2015)
On straw to grain ratios:
(De Jonge 1981 as cited by
Dekker et al. 2004; Lal 1995)
Off-farm
(tonne
C/tonne FM
organic
input)
(tonne effective
C /tonne FM
organic input)
Slurry
0.03
0.011
(CBAV 2017; de Vries 2014;
LNE 2009; Pronk & Korevaar
2008)
FYM
0.08
0.044
(De Jonge 1981 as cited by CBAV
2017; De Vries et al. 2012;
Dekker et al. 2004; LNE 2009;
Pronk & Korevaar 2008)
Compost
0.10
0.093
(De Jong 1981 as cited by CBAV
2017; Dekker et al. 2004; LNE
2009; Pronk & Korevaar 2008)
Perceived deficiency of SOM
123
Chapter 6
Do farmers perceive a deficiency of soil organic matter? A European and farm
level analysis
Renske Hijbeek
1
, Anouk Cormont
2
, Gerard Hazeu
3
, Luca Bechini
4
, Laura Zavattaro
5
,
Bert Janssen
1
, Magdalena Werner
6
, Norman Schlatter
7
, Gema Guzmán
8
, Jo Bijttebier
9
,
Annette A. Pronk
10
, Michiel van Eupen
2
, Martin K. van Ittersum
1
1
Plant Production Systems, Wageningen Plant Research, Wageningen University and
Research, the Netherlands
2
Earth informatics, Wageningen Environmental Research, Wageningen University and
Research, the Netherlands
3
Spatial Knowledge Systems, Wageningen Environmental Research, Wageningen
University and Research, the Netherlands
4
Department of Agricultural and Environmental Sciences, Università degli Studi di
Milano, Italy
5
Department of Agricultural Forest and Food Sciences, Università degli Studi di Torino,
Italy
6
Centre of Biodiversity and Sustainable Land Use, University of Göttingen, Germany
7
Department for Soil Health and Plant Nutrition, Austrian Agency for Health and Food
Safety, Austria.
8
Department of Agronomy, University of Cordoba, Spain
9
Flanders research institute for agriculture, fisheries and food, Belgium
10
Agrosystems Research, Wageningen University and Research, the Netherlands
Ecological Indicators (2017) 83: 390
–
403
Chapter 6
124
Abstract
Agricultural soils with too little soil organic matter (SOM) content are characterized by
fertility problems. A number of authors have tried to specify threshold values for SOM
content to indicate what is ‘too little’, ranging from 1 to 5%, below which yields may
be affected. How much SOM content is sufficient, however, depends on a number of
environmental factors. In addition
, up to date farmers’ perceptions were not included
when developing thresholds. Therefore, this study focuses on the following three
objectives: (1) to put forward a risk indicator for SOM deficiency based on
environmental factors and agricultural land use; (2) to test the risk indicator using
farmers’ perceptions and (3) to establish threshold values for SOM content based on
farmers’ perceptions.
For objective 1, literature was reviewed on effects of environmental factors and land use
on SOM deficiency. Findings were combined into nine options for a risk indicator on
SOM deficiency, mapped at European scale. For objective 2, a farm survey was done
among 1452 arable farmers in five European countries (Belgium, Germany, Austria,
Spain and Italy). Associations between perceived deficiency of SOM by farmers and
environmental factors, land use and the risk indicator were investigated. For objective
3, farmers’ perceptions on SOM
deficiency were related to the average SOM content of
their fields.
Mapping the risk indicator at European scale gives a high to very high risk of SOM
deficiency for 7 to 37% of European agricultural land, mainly located in Southern and
Eastern Europe. Of the farmers in our survey, 18% perceived a high to very high SOM
deficiency.
A weak correlation was found between the risk indicator and farmers’
perceptions of SOM deficiency (0.15-
0.18, Spearman’s rank correlation).
Stronger
relations were found between separate environmental factors and perceived SOM
deficiency. Apparently, having a more extreme environmental condition for one factor
gives a higher chance of perceiving a deficiency of SOM than a combination of
moderate environmental conditions. Based on farmers’ perceptions
threshold intervals
for SOM content were established (sand: 1.2
–
4.7%, loam: 0.6
–
2.6% and clay: 1.0
–
2.4%).
If policies on SOM management want to include benefits for crop production, targeting
areas with a relatively high risk of SOM deficiency, more extreme environmental
conditions or with very low SOM contents (below the given threshold intervals) seems
most promising.
Perceived deficiency of SOM
125
Keywords:
soil organic matter; soil organic carbon; productive capacity; risk indicator;
farmers' perceptions; SOM deficiency; threshold value; critical level; land use; arable
farming; Europe; crops; agriculture
Abbreviations
IQR
inter-quartile range
L
lower extreme: lowest point observed within Q1
–
(1.5*IQR) and Q3 +
(1.5*IQR)
L
L
lower extreme for the range of SOM contents of farmers with a low
perceived deficiency of SOM
L
VL
lower extreme for the range of SOM contents of farmers with a very low
perceived deficiency of SOM
Q1
first quartile
Q3
third quartile
SOM
soil organic matter
U
upper extreme: highest point observed within Q1
–
(1.5*IQR) and Q3 +
(1.5*IQR)
U
H
upper extreme of the range of SOM contents of farmers with a high
perceived deficiency of SOM
U
VH
upper extremes of the range of SOM content of farmers with a very high
perceived deficiency of SOM
6.1.
Introduction
Percentages of soil organic matter (SOM) in soils vary widely, from below 1% for some
sandy soils, to almost 100% for certain peat soils (Loveland & Webb 2003). When
pastures or forests are converted to arable land, SOM content decreases often to less
than 10% SOM, depending on soil texture, climate, land use and management
(Verheijen 2005b). This decrease in SOM also reduces the global carbon stock (Smith
2004), which could be an incentive to maintain or increase SOM content in arable soils
above certain levels, especially if this improves productive capacity.
Chapter 6
126
SOM improves a number of soil properties relevant for productive capacity such as soil
structure, water holding capacity and buffering of nutrients (Hudson 1994; Johnston et
al. 2009; Oades 1984). Farming on soils with a loose soil structure, low water holding
capacity or low availability of nutrients can generate less profit, which would be an
incentive for farmers to maintain SOM content above certain threshold values (Gardner
& Barrows 1985; Kimetu et al. 2008; Scrimgeour & Shepherd 1998). At the same time,
demands for organic inputs (such as straw) are increasing with new markets for bio-
energy and renewable materials emerging (Nicholson et al. 2014).
With a decrease in SOM content, most soil properties change along a continuum (Karlen
et al. 2001), making it difficult to define a critical or desirable C content for farmers
(Sparling et al. 2003). Even so, for the percentage of SOM to be a useful indicator for
productive capacity, target values need to be specified. Sparling et al. (2003) argue in
favour of a minimum or threshold soil C value:
“below which there would be loss of
desirable soil characteristics, productive capacity and ecological functions that were
not readily restored within an acceptable timeframe”.
This study follows this definition,
focussing mainly on the relevance for productive capacity. With sufficient supply of
external inputs or when target yields are low, threshold values will mostly depend on
the contribution of SOM to stability of soil structure and related ease of cultivation
(Janssen & De Willigen 2006).
Setting threshold values can be an important guide for farmers to improve their
management and for policy makers when providing farm subsidies. A number of authors
have tried to specify minimum or threshold values for SOM for crop production (also
called critical levels), using a range of approaches such as information from soil surveys,
literature reviews, soil organic carbon modelling, expert opinions or a combination of
these. Table 6.1 shows a summary of these threshold values. Often, values mentioned
by authors are very tentative. When based on experiments, threshold values are often
related to observed losses in aggregate stability or direct losses in yield.
When threshold values depend on soil texture, typically higher values are given for soils
with more clay or fine silt particles. How much SOM is needed however will probably
not only depend on soil texture, but also on other environmental factors (such as slope
or climate) and land use (types of crops cultivated).
Perceived deficiency of SOM
127
Chapter 6
128
Furthermore, none of the studies asked farmers for their views whilst farmers are the
group of stakeholders with the longest and most practical exposure to SOM of their
fields.
For targeted and effective policies on SOM management, more insight is needed under
which circumstances increases in SOM content benefit the productive capacity of soils.
It is hereby imperative to base this insight not only on results from experimental fields,
but also on farmers’ knowledge an
d perceptions. Currently, it is unclear which areas
have a high risk of SOM deficiency
for productive capacity and farmers’ knowledge has
not been included when proposing thresholds for SOM content in agricultural soils.
Therefore, this study focuses on the following three research objectives:
1.
To put forward a risk indicator for SOM deficiency based on environmental
factors and land use.
2.
To test the risk indicator using farmers’ perceptions.
3.
To establish threshold values for SOM content based on farmers’ perceptions.
Objectives 1 and 3 give insight where policies on SOM management can increase
productive capacities of soils, whilst objective 2 brings together farmers’ knowledge
and existing scientific evidence.
6.2.
Methodology
For objective 1, literature was reviewed on the influence of different environmental
factors and land use on the relationship between SOM content and productive capacity.
Effects of environmental factors and land use were combined into an aggregated risk
indicator on SOM deficiency, mapped at European scale (Section 6.2.1).
For objectives 2 and 3, a farm survey was conducted to estimate farmers’ perceptions
on SOM deficiency
of their fields. Following, farmers’ perceptions w
ere related to
environmental factors, land use, the combined risk indicator and the average SOM
content of farmers’ fields (Section
6.2.2).
6.2.1
Developing a risk indicator on SOM deficiency
In this study, a higher risk of SOM deficiency indicates that with similar soil
management, a farmer has an increased chance of perceiving a reduction in productive
capacity due to low SOM contents compared to a farmer with a lower risk of SOM
Perceived deficiency of SOM
129
deficiency. A reduction in productive capacity might be observed directly (
e.g.
lower
yields) or indirectly (reduced workability of the soil). From this definition it follows that
specific problems perceived by the farmer due to SOM deficiency (
e.g.
concerning soil
structure) can be solved by increasing SOM content. The risk of SOM deficiency for the
productive capacity of agricultural soils depends on environmental conditions and land
use. In this paper, we aim to define a risk indicator on SOM deficiency with a scale from
1 to 5.
To develop a risk indicator on SOM deficiency (objective 1), a number of consecutive
steps were followed. First scientific literature was reviewed to find effects of
environmental factors and land use on the risk of SOM deficiency. Following, findings
were used to define a risk indicator on SOM deficiency. Finally, the risk indicator on
SOM deficiency was mapped at European scale. Each step is explained below.
Literature review on effects of environmental factors and land use on SOM and risk of
SOM deficiency
Relevant environmental factors for SOM deficiency were identified based on three
criteria: 1) The factor is not changed by human management (therefore shaping the
context in which farmers have to operate); 2) Datasets have to be available at European
scale; 3) Literature has to be available on how the environmental factor influences the
relation between SOM content and productive capacity. Following these criteria, the
following three environmental factors were selected: land slope, soil texture, and
climate.
Soil texture was chosen to describe soil types instead of soil taxonomy classes for a
number of reasons: 1) Soil classifications schemes such as the World Reference Base
for Soil Resources (ISSS Working Group, WRB 1998) have many taxonomy classes
(WRB has 32 main groups) which makes statistical analyses difficult. 2) We wanted to
compare the risk indicator with farmers’ perceptions in which case we relied on farmers’
descriptions of soils. Farmers are more aware of their soil texture than of the scientific
names given to their soil profiles. 3) Soil textures have more easily understandable
relations with aggregate stability, water holding capacity and productive capacity than
do soil classifications.
Scientific literature was searched using online search engines (Google scholar, ISI Web
of knowledge) to find how these different factors plus land use (types of crops
cultivated) influence the effect of SOM content on productive capacity. Based on the
Chapter 6
130
literature, two types of effects on the relation between SOM and productive capacity
were identified for each environmental factor and land use: 1) a direct effect on actual
SOM content (
e.g.
wetter climates slow down decomposition rates) and 2) an indirect
effect on how likely it will be that SOM affects productive capacity (
e.g.
wetter climates
give a higher chance on soil compaction which SOM might alleviate). The second effect
illustrates that in some conditions, SOM will have a larger contribution to productive
capacity than others. The difference between the two types of effects is schematically
shown in Fig 6.1.
Fig 6. 1: Schematic overview of two types of effects of environmental factors and land use on the relation between
SOM and productive capacity: (1) a direct effect on actual SOM content and (2) an indirect effect on the possible
contribution of SOM content to productive capacity. Corn figure adapted from Thomas (2010).
In the following four paragraphs, the specific effects of each environmental factor and
land use are described. A summary of the literature findings is given per effect type in
Table 6.2. Often, findings were described in trends rather than precise magnitudes.
Therefore, qualitative diagrams were drawn for each factor, showing an increase,
decrease or parabolic curve along an environmental spectrum (Fig 6.2).
Perceived deficiency of SOM
131
Slope effect
Soils on steeper slopes often have less SOM while they would benefit more from the
soil structural effects of SOM content and steeper slopes are more prone to erosion (Fox
& Bryan 2000; Gregorich et al. 1998). Erosion causes a loss of soil and thus SOM, while
SOM itself can reduce runoff by increasing porosity and enhancing aggregate stability,
both diminishing surface sealing and promoting infiltration rates (Benito & Diaz-Fierros
1992; Malinda 1995; Rhoton et al. 2002). Therefore, soils on steeper slopes are expected
to have a higher risk of SOM deficiency to maintain productive capacity (Fig 6.2a)
Soil texture effect
SOM content improves structures of both very heavy and very light soils. Light sandy
soils normally have a loose soil structure, which can be improved by adding organic
inputs (Chaney & Swift 1986; Johnston et al. 2009; Tisdall & Oades 1982). SOM binds
more easily to clay particles and therefore, on average, clay soils have a higher SOM
content (Körschens et al. 1998; Reeves 1997; Verheijen et al. 2005). When clay soils
are very heavy, however, they are
–
despite the higher SOM contents - more prone to
soil compaction. Soil compaction causes mechanical impedance for root growth and
inadequate aeration (Janssen & van der Weert 1977; Wiersum 1957). Increasing SOM
contents can reduce compaction of soils (Soane 1990). Therefore, soils at both ends of
the soil texture spectrum are expected to have a higher risk of SOM deficiency than in
the middle (Fig 6.2b).
Climate effect
Soil productivity benefits from SOM in both very dry and very wet climates. For crops
cultivated in hot and dry areas, SOM increases water holding capacity (Díaz-Zorita et
al. 1999). In wet climates, SOM prevents soil compaction and water logging (Soane
1990). At the same time, soils in cold and wet climates are expected to have more SOM
content due to slower decomposition (Gonçalves & Carlyle 1994; Verheijen 2005b).
Yet apparently, effects of reduced decomposition are often not sufficient to prevent
compaction. Therefore, a parabolic relationship is proposed between aridity and risk of
SOM deficiency (Fig 6.2c).
Land use effect (types of crops cultivated)
The type of crops a farmer cultivates (
i.e.
land use) has an effect on the SOM balance,
whilst also determining the dependence on soil structure for crop growth.
Chapter 6
132
Perceived deficiency of SOM
133
In general, cereals and grass provide more crop residues (root, stubble, leaves) which
can be returned to the field as organic inputs than specialized or horticultural crops (such
as root or tuber crops, legumes or flowers (Zwart et al. 2013)).In addition, most
specialized and horticultural crops have a less developed root system and therefore
depend more on a better soil structure for their successful cultivation and harvesting (De
Haan 1977; Hijbeek et al. 2017; Verheijen 2005b). Specialized and horticultural crops
thus contribute less to the amount of SOM content whilst benefiting more from it.
Therefore, soils with more specialized and/or horticultural crops than cereals are
expected to have a higher risk of SOM deficiency (Fig 6.2d).
Combining effects of environmental factors and land use into a risk indicator
Environmental effects and land use do not affect SOM deficiency in solitude, but
interact with each other. For example, a farmer will probably have a higher risk of SOM
deficiency on a sandy soil when the farm is on a steep slope than on level land. This risk
will again be different, depending on land use or climate.
Fig 6. 2: Proposed relationships between environmental factors and land use and risk of SOM deficiency, based
on literature. X-axes show ranges of different environmental factors and land use. Y-axes show risk of SOM
deficiency
Chapter 6
134
Effects of all four factors (Fig. 6.2) interact with each other. Therefore, effects of
different environmental factors and land use were combined into a risk indicator on
SOM deficiency. This risk indicator gives a risk score of SOM deficiency for each
combination of slope, soil texture, climate and land use on a scale of 1 to 5.
To calculate the risk indicator, effects of environmental factors and land use were added
one by one using expert views. First, effects of soil texture and land slope were
combined into a soil risk score. Each combination of soil texture and slope was given a
score between 1 and 5, where 1 indicates a very low soil risk of SOM deficiency (
e.g.
a
loamy soil with no slope) and 5 a very high soil risk of SOM deficiency (
e.g.
a sandy
soil on a steep slope). Assigning the numbers 1 to 5 included some arbitrary choices. To
reduce the effect of this arbitrariness, three types of combination options were proposed,
resulting in a low, medium and high estimate for soil risk (Supplementary Tables 6.1-
6.3, page 155).
Second, soil risk scores were combined with aridity into an environmental risk score.
Each combination of a soil risk and a degree of aridity was given a score between 1 and
5. Again, 1 indicates a very low environmental risk of SOM deficiency (
e.g.
a low soil
risk with an intermediate climate) and 5 a very high environmental risk of SOM
deficiency (
e.g.
high soil risk with a very dry climate). Again, three types of combination
options were proposed, resulting in a low, medium and high estimate for environmental
risk (Supplementary Tables 6.4-6.6, page 156).
Finally, environmental risk scores were combined with type of land use into a total risk
score on SOM deficiency. Each combination of environmental risk and land use was
given a score between 1 and 5, where 1 indicates a very low risk of SOM deficiency
(
e.g.
low environmental risk with cereals or grass) and 5 a very high risk of SOM
deficiency (
e.g.
high environmental risk with specialized crops). One combination
option was proposed, in which cereals and grass reduce the risk and specialized and
horticultural crops increase the risk of SOM deficiency (Supplementary Table 6.7, page
157).
A flow diagram of the procedure to combine the different factors into a risk indicator is
shown in Fig. 6.3. The procedure resulted in nine different versions of the risk indicator
(3 options for soil risk x 3 options for environmental risk x 1 option for the effect of land
use).
Perceived deficiency of SOM
135
Creating a spatial overview of the risk of SOM deficiency in Europe
To create a spatial overview on risk of SOM deficiency in Europe, a dataset was needed
for each environmental factor and land use. For slope, the GTOPO30 (a global elevation
dataset; USGS 1996) was used. For soil texture, the European Soil Database (EC &
ESBN 2004) was selected. For climate, the degree of aridity was calculated by dividing
annual precipitation with potential evapotranspiration, using spatialized climatic data
for the period 1975-2009 (Janssen et al. 2009). For land use, the land use dimension of
the European farm typology as developed by Andersen et al. (2007) was used.
Datasets were combined spatially using QUICKScan (Verweij et al. 2016). For each
data pixel (km
2
), QUICKScan used the rules from Supplementary Tables 6.1-6.7 and
calculated a combined risk score on SOM deficiency. This resulted in nine maps
showing estimated risk of SOM deficiency with European coverage.
6.2.2
Perceived deficiency of SOM by farmers
To test the risk indicator (objective 2) and to establish threshold values for SOM content
(objective 3), a farm survey was held in five European countries. Respondents came
from a wide range of soil types, climate zones and land uses across Europe. By doing
so, we capitalized on many years of practical farming experience across different soils
and SOM contents.
Fig 6. 3: Flow diagram of the procedure followed to combine effects of different factors into a risk indicator on
SOM deficiency. SI = Supplementary Information.
Chapter 6
136
All farmers were asked if they perceived a deficiency of SOM on a Likert scale from 1
to 5 (very low to very high SOM deficiency). This number is a holistic measurement of
SOM deficiency
perceived by farmers. Farmers’ perceptions
might be based on many
aspects (experience, observations, information, instinct, tacit knowledge), which they
translate into one number. Underlying mechanisms and disadvantages of insufficient
SOM (such as little water holding capacity, poor soil structure, little nutrient supply) all
play a role, but in this study are aggregated together into one term (SOM deficiency).
As seen in Fig 6.1, environmental factors and land use affect the relation between SOM
and productivity in two ways: (1) a direct effect on actual SOM content (e.g. wetter
climates slow down decomposition rates) and (2) an indirect effect on how likely it will
be that SOM affects productive capacity (e.g. wetter climates give a higher chance on
soil compaction which SOM might alleviate). To account for both mechanisms, first,
average SOM contents of farms were related to environmental factors and land use.
Then, perceived deficiency of SOM was related to environmental factors and land use
separately and to the risk indicator. In this manner, we compared scientific findings on
the influence of environmental factors and land use on SOM deficiency
with farmers’
practical experience. Next, perceived deficiency of SOM was related to the average
SOM content of each farm to find threshold values. Finally, farmers with a very high or
very low perceived deficiency of SOM were clustered around gradients of
environmental factors and land use. Each of these steps is explained in more detail
below. All data was processed in R (version 3.2.5).
Farm survey
A large-scale farm survey was held in five European countries (Belgium, Germany,
Austria, Spain and Italy). This survey was sent out as part of a European research project
CATCH-C. The sampling for the questionnaires depended on the availability of a valid
sampling frame within each country (
i.e.
contact details of farmers). The most ideal
sampling frame to obtain a completely random sample is a nation-wide database of
farmers’ addresses. Such a database was available for Flanders in Belgium and some
states i
n Germany. In the other countries, researchers had to depend on farmers’
associations, farmers’ extension services or other contacts to distribute the
questionnaire. Questionnaires were distributed online (Austria), as paper questionnaire
(Belgium and Italy), or both (Germany and Spain). In Spain questionnaires were only
distributed in Andalusia.
Perceived deficiency of SOM
137
Questionnaires were returned during the summer and autumn of 2013. Filled
questionnaires were checked on irregularities (typing mistakes or extreme numbers) and
if any, removed from analysis. Because of the specific nature of peat soils (Lucas, 1982),
farmers reporting peat soils or reporting an average SOM content above 12% were
excluded from analysis. To reduce errors in farmers’ answers, farmers were not obliged
to fill out every question. If a farmer was uncertain of the SOM content of their soils,
they could tick the option: “
I do not know
”. Questions on slope or land use information
could also be left unanswered. In those cases, only the filled parts of the questionnaire
were used in the analysis.
Relating SOM contents to environmental factors and land use
Environmental factors and land use are known to affect SOM content (Fox & Bryan
2000; Gonçalves & Carlyle 1994; Gregorich et al. 1998; Körschens et al. 1998;
Verheijen et al. 2005)
. SOM contents of farmers’ fields in our survey
were related to the
factors identified above (slope, soil texture, climate and land use) to assess if patterns
are similar as found in the literature.
Farmers were asked to declare the average SOM content of their fields and to report
different soil textures present on their farm, slopes of their land and types of crops
cultivated. Farms were then classified according to their dominant soil textural class
(largest percentage of farm land in a certain soil texture class), dominant slope (largest
percentage farm land with a certain slope) and land use type (> 50% farm land with
cereals and/or grass; > 50% with specialized and/or horticultural crops; a mix of these
two categories and forage crops). Supplementary Table 6.8 (page 157) gives an
overview of the crops cultivated by the farmers in our survey and how they were
classified. Farm locations were used to calculate an aridity index.
For each environmental factor, similar classes were used as in the risk indicator
(Supplementary Tables 6.1-6.7). Following, ranges of SOM contents were calculated
for each class. Because SOM contents among farms did not have a normal distribution
(distribution was slightly skewed to the right), group medians were compared using the
non-parametric Kruskal-Wallis test (Conover 1971; Pohlert 2014). If applicable, a post-
hoc Nemenyi test was used to see which group medians differed (Sachs 1997).
Relating perceived deficiency of SOM to environmental factors and land use
Perceived deficiency of SOM by farmers was compared with patterns found in the
literature. Frequency distributions of SOM deficiency (expressed on a 1 to 5 Likert
Chapter 6
138
scale) were made, classifying farmers by their soil texture, slope, climate or land use.
For each environmental factor and land use, similar classes were used as in the risk
indicator (Supplementary Tables 6.1-6.7). Following, a non-parametric test using
ordinal data, the Jonckheere-Terpstra test (Jonckheere 1954; Seshan 2012) was done to
assess if trends in perceived SOM deficiency by farmers followed similar patterns as
found in the literature (Fig 6.2).
Relating perceived deficiency of SOM to the risk indicator
Using the indicator developed in Section 2.1.2, a risk of SOM deficiency was calculated
for each farmer. To assess if a high risk indication also corresponds with a high
perceived deficiency
of SOM by farmers, a Spearman’s rank correlation test
(Kendall
1948) was performed.
Establishing threshold intervals of SOM based on farmers’ pe
rceptions
Can threshold values for SOM be determined, based on farmers’ perceptions? To find
the answer to this question, SOM contents of farms were related to perceived deficiency
of SOM. First, farmers were classified according to their perceived deficiency of SOM:
very low, low, neutral, high, or very high perceived deficiency of SOM. A low or very
low perceived deficiency of SOM means farmers have a positive perception on the SOM
content of their fields, while a high or very high perceived deficiency of SOM means
farmers have a negative perception on the SOM content of their fields. Ranges of SOM
contents were calculated for each level of SOM deficiency (from very low to very high).
Because SOM contents did not have a normal distribution, inter-quartile ranges were
used to calculate lower and upper extremes (Frigge et al. 1989; McGill et al. 1978). With
this non-parametric method, the distance between the first quartile (Q1) and the third
quartile (Q3) is the inter-quartile range (IQR). Values lower than Q1
–
(1.5*IQR) and
higher than Q3 + (1.5*IQR) were considered outliers. Causes for numbers to be an
outlier can be either extreme soil conditions or mistakes in the recordings. The upper
extreme is the highest SOM value not being an outlier. The lower extreme is the lowest
SOM value not being an outlier.
Based on the lower extremes of farmers with a low perceived deficiency of SOM and
the upper extremes of farmers with a high perceived deficiency of SOM, threshold
intervals for SOM were calculated for each soil textural class. Below each interval,
farmers perceive a neutral, high or very high deficiency of SOM. Above each interval,
farmers perceive a neutral, low or very low deficiency of SOM.
Perceived deficiency of SOM
139
Clustering farmers along gradients of environmental factors and land use
Below a certain SOM content, farmers could be found with any perceived degree of
SOM deficiency (from very low to very high SOM deficiency). At these low SOM
contents, different farmers might have the same SOM content, one perceiving a very
low deficiency of SOM and the other a very high deficiency of SOM. Using only farmers
with a low SOM content and with either a very high or very low perceived deficiency
of SOM, a principal component analysis was done to find possible causes. Instead of
using classes for each environmental factor or land use type, for the principal component
analysis continuous variables were used. For soil texture, the percentage of farm land
with sandy soils was used as a variable. For slope, the percentage of land with moderate
or steep slopes was used. For land use, the percentage of farm land cultivated with
specialized or horticultural crops was used. As climate could not be converted into a
continuous variable at farm level, it was left out of the principal component analysis.
6.3.
Results
6.3.1
A spatial overview of the risk of SOM deficiency in Europe
Fig 6.4 shows the spatial distribution of estimated risk of SOM deficiency in Europe,
using nine different manners of combining environmental factors and land use. This
figure is based on four datasets (Section 6.1.1) that are combined following the scheme
of Fig 6.3.
Depending on the manner in which environmental factors and land use are combined
(Supplementary Tables 6.1 to 6.7), 7 to 37% of agricultural land in Europe has a high or
very high risk of SOM deficiency (Fig. 6.4). It is self-evident that combining high
estimates for soil risks and high estimates for environmental risks gives the largest share
of a high total risk of SOM deficiency.
Higher estimates for soil or environmental risk might give a larger share of agricultural
land with a higher risk of SOM deficiency, the distribution between areas is very similar
(Fig 6.4). On all maps, soils with a high or very high risk of SOM deficiency are mainly
located in southern and eastern European countries (in areas with drier climates, steeper
slopes, coarse soils and/or more cultivation of specialized or horticultural crops). Soils
with a lower risk of SOM deficiency are mainly located northern and western European
countries.
Chapter 6
140
Fig 6. 4
Percentage of European agricultural land area with a low to very high risk of SOM deficiency within
each of the nine risk indicator options. Maps are obtained following the procedure of Fig. 6.3.
Perceived deficiency of SOM
141
6.3.2
Perceived deficiency of SOM by farmers
Respondents to farm survey
In total 1642 arable farmers (all cultivating more than 50% of their farm land with arable
crops) responded to the farm survey. Seventeen respondents were removed from
analysis because of irregularities in answers. An additional 83 farmers reported to have
peat soils and/or had an average SOM content above 12% and were also excluded from
analysis. A summary of farm characteristics per country is provided in Supplementary
Table 6.9 (page 158).
Not all farmers provided information on slope, soil texture and land use (Table 6.3). In
total 1447 farmers provided information on slope, 1481 on soil texture and 1466 on land
use. For all farmers included in the analysis (1542) an aridity index was calculated.
Using the information on environmental factors and land use, a risk indication on SOM
deficiency was calculated for 1371 farmers. In addition, 683 farmers reported an average
SOM content, while 1469 farmers gave an indication of perceived deficiency of SOM.
Relatively more farmers from Austria and Germany reported SOM contents than did
farmers from Belgium, Italy and Spain (respectively, 68%, 62%, 42%, 34% and 22%),
probably because in Austria and Germany soil analyses are more common.
Table 6.3: Numbers of farmers providing specific information in the farm survey. In total 1542 farmers were
included in the analysis. To reduce errors, farmers could leave questions unanswered, therefore numbers differ
per category.
Total
(n)
Farmers
providing
average SOM
content (n)
Farmers
providing
perceived
deficiency of
SOM (n)
Farmers
providing
average SOM
content and
perceived
deficiency of
SOM (n)
Farmers with information on slope
1447
665
1402
657
Farmers with information on soil texture
1481
680
1433
672
Farmers with an aridity index calculated
1542
683
1469
675
Farmers with information on land use
1466
668
1411
660
Farmers for which risk indicator can be
calculated
1371
648
1331
640
SOM contents related to environmental factors and land use
SOM contents reported by farmers in the survey were very variable, ranging from 0.5
to 11.6 % SOM (mean 2.6%, median 2.2%, standard deviation 1.3%). This indicates that
our survey covered a wide range of conditions. In Section 2.1.1, the effects of
environmental factors and land use on the risk of SOM deficiency were discussed,
Chapter 6
142
including effects on actual SOM content. How do SOM contents reported by our farmers
vary with environmental factors and land use? In Fig 6.5,
SOM contents of farmers’
fields are related to their slope, soil texture, aridity and land use.
On farmers’ fields, the effect of slope (Fig. 6.5a) was as expected: soils with steeper
slopes have lower SOM content. The effect of soil texture however is less clear: in our
dataset (Fig. 6.5b) farmers with loamy soils have the lowest SOM content, while this
was expected for farms with sandy soils. This might be due to some unbalance in the
sampling design as slightly more farms with loamy soils are located in drier climates
with steeper slopes. Soils on steeper slopes and in drier climates are expected to have
lower SOM content due to faster decomposition and/or increased erosion risk
(Gregorich et al., 1998), so this might be interfering with the effect of soil texture.
Fig 6. 5
Range of SOM content per environmental factor and land use type as reported by the farmers. X-axes
are classes of different environmental factors and land use. Y-axes are the SOM contents reported by farmers.
Violin shapes show the density distribution of farmers reporting a certain SOM content. Inside the violins,
boxplots give medians, quartiles, lower extremes and upper extremes. If the letters above the violins differ, there
is a significant difference between the medians (using a Nemenyi test).
Perceived deficiency of SOM
143
As expected, farms in very dry climates and farms with more specialized and/or
horticultural crops have less SOM content (Fig. 6.5c,d). However, SOM contents on
farms in wet climates do not differ significantly from SOM contents on farms with dry
climates. The lack of a significant difference might be due to the low number of farms
in dry climates (19). Interestingly, farms with mixed cropping systems reported the
highest average SOM content, higher than those with mainly cereals and/or grass.
Perceived deficiency of SOM related to environmental factors and land use
Comparing patterns based on the literature (6.2a) and perceived deficiency of SOM by
farmers (6.6a), some similarities can be observed. From findings in the literature, risk
of SOM deficiency is expected to increase with increasing slopes (Fig 6.2a). A similar
pattern is observed in our farm data on mean perceived SOM deficiency per class (p =
0.004, Jonckheere-Terpstra trend test, Fig 6.6a).
Fig 6. 6
Frequency distributions of perceived deficiency of SOM related to environmental factors and land use.
X-axes are classes of each environmental factor and land use. Colours indicate percentage of farmers perceiving
a certain deficiency of SOM (left y-axis). Circles indicate mean perceived deficiency per class (right y-axis). P-
values give significance of trends using the Jonckheere-Terpstra trend test. Lines between means are fitted using
the function lm (least squares).
Chapter 6
144
From the literature, a parabolic curve was expected in which risk of SOM deficiency
first decreases with increasing clay content, and then starts to increase for very heavy
clay soils (Fig. 6.2b). Based on our farm survey, perceived deficiency of SOM by
farmers does decrease with increasing clay content (p = 0.015, Fig. 6.6b), but our farm
data does not show a parabolic curve. The latter could be explained because our farm
survey did not include many farms with very heavy clay soils (fine or very fine soils
with more than 35% clay particles).
From findings in the literature, soil in very dry and very wet climates are expected to
have a higher risk of SOM deficiency than soils in intermediate climates (Fig. 6.2c). In
our farm data, a significant tendency for a parabolic curve can be observed (p = 0.001,
Fig. 6.6c).
Finally, based on findings from literature, farms cultivating more specialized or
horticultural crops are expected to have a higher risk of SOM deficiency (Fig 6.2d). A
significant trend (p = 0.001, Fig. 6.6d) was found with a higher perceived deficiency of
SOM by farmers cultivating more specialized or horticultural crops than those with
mixed crops and those with more cereals and/or grass.
Perceived deficiency of SOM related to the risk indicator
Farmers’ perceptions confirm the p
roposed relationships between environmental factors
and land use and risk of SOM deficiency on which the risk indicator is based (as
discussed in the previous section). How does the risk indicator itself correlate with
perceived deficiency of SOM by farmers? Do farmers with a higher risk indication for
SOM deficiency also perceive a higher deficiency of SOM on their fields? Among the
farmers of our survey up to a quarter had a high to very high risk of SOM deficiency (3-
25%, depending on the risk indicator option). Among the same farmers, 18%
experienced a high to very high SOM deficiency, which is within the same range. Using
Spearman’s rank correlation test however, there was only a weak positive correlation
between the risk indicator and farmers’ percept
ions (both Likert scale 1-5). Depending
on which of the nine risk indicator options, the Spearman's rank correlation coefficient
differed between 0.15 and 0.18, albeit with high significance (p<0.0001). The low
correlations indicate that none of the nine risk indicator options gives a very accurate
prediction of SOM deficiency as perceived by farmers.
Perceived deficiency of SOM
145
Threshold values of SOM based on farmers’ perceptions
Ranges of SOM content for each degree of perceived SOM deficiency (very low, low,
neutral, high or very high) were dependent on the dominant soil texture of the farms
(Fig. 6.7).
Fig 6. 7: SOM contents reported by farmers, classified by perceived deficiency of SOM (672 farmers) in (a) sandy,
(b) loamy and (c) clay soils. X-axes are SOM contents reported by farmers. Y-axes are perceived SOM deficiency.
Violin shapes show the density distribution of farmers reporting a certain SOM content. Inside the violin shapes,
boxplots show medians, first and third quartiles and lower and upper extremes. L indicates lower extreme of
farmers with a low (L
L
) or very low (L
VL
) perceived SOM deficiency. U indicates upper extreme of farmers with a
high (U
H
) or very high (U
VH
) perceived SOM deficiency. Points are considered outliers.
Chapter 6
146
Within each violin shape of Fig. 6.7, boxplots show the lower (L) and upper (U)
extremes of SOM content for each group. Points below the lower extremes and points
above the upper extreme are considered outliers.
Lower extremes of SOM for farmers with a low or very low perceived deficiency of
SOM (L
L
or L
VL
), tend to be higher than lower extremes of SOM for farmers with a high
or very high perceived deficiency of SOM. This is the case for both sandy and clay soils
(Fig. 6.7a,c). This indicates a certain SOM content is needed before farmers start
perceiving a (very) low SOM deficiency. For farmers on sandy soils, L
L
and
L
VL
were
1.2%, 1.9% SOM respectively. For farmers on loamy soils, L
L
and
L
VL
are 0.6% and
0.7% SOM respectively. For farmers on clay soils, L
L
and
L
VL
were 1.0% and 2.0% SOM
respectively.
Upper extremes of SOM content for farmers with a high or very high perceived
deficiency of SOM (U
H
or U
VH
), are lower than upper extremes of SOM content for
farmers with a low or very low perceived deficiency of SOM. This is the case for all
three soil textures (Fig. 6.7). This indicates that farmers only perceive a high or very
high SOM deficiency up to a certain percentage of SOM content. Above this SOM
content (U
H
), no farmer perceives a high or very high SOM deficiency. For farmers on
sandy soils, U
H
and U
VH
were 4.7% and 3.4% SOM respectively. For farmers on loamy
soils, U
H
and U
VH
were both 2.6% SOM. For farmers on clay soils, U
H
and U
VH
were
2.4% and 1.5% SOM respectively.
For each soil texture, L
L
and U
H
values are combined into a threshold interval (yellow
areas Fig 6.8, sand: 1.2
–
4.7%, loam: 0.6
–
2.6% and clay: 1.0-2.4%). Below each interval,
farmers perceive a neutral, high or very high deficiency of SOM (red areas, Fig. 6.8).
Fig 6. 8:
Threshold intervals (in yellow) of SOM based on farmers’ perceptions, dependent on dominant soil
texture of a farm. Yellow areas indicate the threshold interval. Below each interval, farmers perceive a neutral,
high or very high deficiency of SOM (red areas). Above each interval, farmers perceive a neutral, low or very
low deficiency of SOM (green areas). Numbers of farmers are indicated between brackets.
Perceived deficiency of SOM
147
Above each interval, farmers perceive a neutral, low or very low deficiency of SOM
(green areas, Fig. 6.8). Within each threshold interval, farmers can perceive any degree
of SOM deficiency (from very low to very high), indicating that other factors (such as
crop types cultivated, tillage management or irrigation) are more important.
Clusters of farmers along gradients of environmental factors and land use
The maximum SOM content of farmers with a very high perceived deficiency of SOM
is 3.4% SOM (U
vh
for sandy soils, see Fig. 6.7a). No farmers with more than 3.4% SOM
perceived a very high SOM deficiency, in any texture class. Below this value, SOM
content might be similar among farmers, but farmers can have a very different
perception of SOM deficiency, ranging from very low to very high (Fig. 6.7).
Soil texture seems to explain some but not all of the differences in perceived deficiency
of SOM. To gain more insight on the difference in perceived SOM deficiency of farmers
with a low SOM content, only farmers with less than 3.4% SOM were selected for a
principal component analysis.
The principal component analysis (Fig. 6.9) shows that farmers with a very low
perceived deficiency of SOM on their farm (blue dots) can be found pretty much
anywhere along the axes of soils, slopes and crops. Farmers with very high perceived
deficiency of SOM (red dots) however, are mainly found at the extreme ends of the axes.
Fig 6. 9: Clusters of farmers (with < 3.4% SOM) perceiving a very high or a very low SOM deficiency. Axes
(principal component 1 and 2) are combinations of percentage of farm land with sandy soil, moderate or steep
slopes and shares of specialized and/or horticultural crops.
Chapter 6
148
At the extreme end of an axis, farmers score very strongly on only one environmental
factor (slope, texture, land use). They either have a high percentage of steep slopes, a
high percentage of sandy soil or a high percentage of specialized and/or horticultural
crops. This would indicate that not so much the combination of environmental factors
and land use determines a very high perceived deficiency of SOM, but rather an extreme
value for one of these factors.
6.4.
Discussion
6.4.1
Putting forward a risk indicator on SOM deficiency
Up to date, a number of European risk maps have been developed on different soil
degradation themes, such as soil erosion (Kirkby et al. 2004), soil compaction
(Houšková & Montanarella 2008)
and landslides (Günther et al. 2014). Until now no
European risk map on SOM deficiency has been made, although three European maps
show actual soil organic carbon, minimum soil organic carbon and maximum soil
organic carbon (Stolbovoy & Montanarella 2008). Our maps represent a first attempt to
highlight areas where farmers have a higher risk of SOM deficiency to maintain
productive capacity. Fig. 6.4 shows a high to very high risk of SOM deficiency for 7 to
37% of European agricultural land (depending on the option to combine factors), mainly
located in Southern and Eastern Europe. The maps also show that most of the European
agricultural land falls within the class “very low”, “low” or “middle” ris
k of SOM
deficiency. The farm survey gave similar results with 18% of the interviewees
perceiving a high to very high SOM deficiency. Ca. 53% of the interviewees perceived
a “very low” or “low” SOM
deficiency and of the remaining farmers 29% giving an
indication in the middle. This could be regarded an encouraging result, showing that for
most of the European farmers we have contacted, covering a wide range of cropping,
climatic and soil types, a too low SOM content is not an issue.
6.4.2
Testing the risk indicat
or using farmers’ perceptions
Relating the risk indicator on SOM deficiency to perceived SOM deficiency by farmers,
there is a significant positive correlation (
p
< 0.001). The correlation is however very
weak (Spearman’s rank correlation coefficient of 0.
15 to 0.18) and with large samples
significance of a correlation can be misleading (Lantz 2013). Nonetheless, the
correlation indicates that farmers in areas with a higher risk indication are more likely
to experience a deficiency of SOM, but not unambiguously so. Stronger relations seem
Perceived deficiency of SOM
149
to exist between the individual factors and perceived SOM deficiency (Fig. 6.6). These
relations resemble the findings from the literature (Fig 6.2) on which the risk indicator
is based.
Why does combining the factors into one indicator not increase predictive capacity? A
preliminary answer can be found in the results of the Principal Component Analysis (Fig
6.9). Apparently, having a more extreme environmental condition for only one factor
(for example, a very sandy soil or land with a very steep slope) gives a higher chance of
perceiving a deficiency of SOM, rather than a certain combination of moderate factors
(in this case, a slightly sandy soil combined with a gentle slope). One explanation could
be that it might be easier for farmers to adapt their management in a situation with a
combination of moderate environmental factors than to adapt to one extreme
environmental condition.
We now consider which is the best measure
: the risk indicator or farmers’ perceptions?
The answer is probably that both have their value. While the risk indicator is more
objective as it uses a standardized approach, it is also more reductionist, disregarding
specific management practices (tillage type, use of organic inputs or irrigation), land use
history, farmers’ expertise and the actual SOM content that influence farmers’
perceptions. Perceptions of farmers are more holistic, but also subjective, depending on
personal preferences and factors other than those included in the risk indicator, which
probably explains the large variation. In addition, defining the risk indicator included a
number of arbitrary choices, not unseen in soil quality targeting, as working within a
continuum sometimes requires human value judgement when grouping variables
(Sparling et al. 2003).
6.4.3
Using farmers’ perceptions to define threshold values for SOM
content
Based on farmers’ perceptions, threshold intervals for SOM were determined for soil
texture (sand: 1.2
–
4.7%, loam: 0.6
–
2.6% and clay: 1.0-2.4%). When SOM content falls
below a threshold interval, a farmer will perceive benefit from more SOM, irrespective
of other factors. When SOM content is above a threshold interval, farmers will not
perceive benefit from more SOM. Within each threshold interval, farmers can perceive
any level of SOM deficiency (from very low to very high), indicating that other factors
(such as management) are more relevant.
Chapter 6
150
Setting threshold intervals instead of specific threshold values is a better representation
of both the fluidity of the data, the variation in contextual factors, and the large variation
in farmers’ perceptions at equal SOM contents, revealed by our farm survey. These
intervals should therefore be taken as indications of where low soil productive capacity
might occur because of low SOM contents. Compared to previously suggested threshold
values for SOM content (Table 6.1, values ranging between 1 and 5.1% SOM), our
intervals fall in the middle of the range for sandy soils and are at the lower end for loam
and clay soils.
6.4.4
Limitations of the study and suggestions for further research
Correctly combining environmental factors and land use seems to be challenging,
especially for more extreme conditions. As a consequence, this indicator could be
improved when more precise knowledge becomes available on how SOM content
affects productive capacity under different environmental conditions. If more spatial
data on farm management (such as tillage practices) comes available in the future, the
effect on risk of SOM deficiency can also be included, thereby improving the risk
indicator. Alternatively, a similar approach could be used at regional scale with country
specific datasets at a finer spatial resolution (soil maps, digital elevation models and
land use databases). In that case, a higher spatial accuracy could be achieved.
Part of this study depended on information provided by farmers. This was considered
an excellent method to capitalize on practical know-how and tacit knowledge (Corbeels
et al. 2000; Musinguzi et al. 2015). There are however uncertainties associated with
analysing data from a farm survey, of which we discuss the most important ones. First,
farmers were asked which percentage of their farmland had a certain class of slope and
soil texture. Wherever possible we used common terms and indicated percentages, but
national and cultural values might have had some influence. Following, farms were
classified according to their dominant slope or soil texture, which meant loss of
information, especially in areas with very heterogeneous environments. Finally, SOM
contents were reported by farmers and not measured in the fields, which would have
given a more accurate comparison. It is likely that these uncertainties increased the
variation in our data. Further studies could expand our approach by including on-farm
soil analyses.
Our study shows that farmers can perceive a certain level of SOM deficiency (very low,
low, neutral, high or very high) along a wide range of SOM contents. In other words,
Perceived deficiency of SOM
151
farmers with a similar SOM content do not necessarily perceive the same degree of SOM
deficiency, even under the same soil texture. It would be interesting to differentiate these
intervals further for different slopes, climates and land uses. Unfortunately, the sample
size of our farm survey was too small to allow for such an exercise.
Another explanation for the wide interval ranges of SOM content could perhaps be
found in the roles different fractions of SOM play. There is recent evidence that specific
SOM fractions play specific roles in improving aggregate stability of soils (Dexter et al.
2008). Organic inputs from straw, stubble or roots might have a proportionally large
impact on stable aggregates compared to organic inputs from animal manures (Powlson
et al. 2011; Watts et al. 2006). Evaluating total SOM content
–
the only information
farmers have from ordinary soil analyses - lacks this type of detail. Future studies could
explore the contributions from different SOM fractions further.
Last, in this study we only looked at benefits of SOM for productive capacity. Economic
benefits are expected to follow from a higher productive capacity either direct through
increased yields or indirect through improved workability of the soil which gives more
flexibility in field operations. Besides economic benefits, SOM content also has
environmental benefits such as supporting soil biodiversity or sequestering carbon
(Chang et al. 2007; Freibauer et al. 2004). Integrating these different aspects fell
however outside the scope of the current study objectives. Our findings could be used
in a more broader analysis in which economic and environmental benefits are integrated.
Besides a spatial, this could also include a temporal dimension.
6.5.
Conclusion
This study used two very different approaches (developing a risk indicator and a farm
survey) to assess SOM deficiency and has tried to link them. The risk indicator was
based on increasing or decreasing risk of SOM deficiency along certain environmental
(slope, soil texture, aridity) and land use gradients, based on a literature review.
Remarkably similar patterns were observed between the findings from the literature
review and perceived deficiency of SOM by farmers in the survey. In addition, a
correlation was found between the risk indicator and farmer
s’ perceptions of SOM
deficiency
. Large part of the variance in farmers’ perceptions, however, could not be
explained by the environmental factors or land use as investigated in this study. This is
not surprising when one realizes the variation in other factors (nutrient management,
irrigation, tillage), farmers’ experience and land use history. Nonetheless, our findings
Chapter 6
152
provide some relevant insights (
e.g.
threshold intervals based on soil texture) and call
for further investigations in which practical and expert knowledge are used
complementary.
Currently, worldwide and in Europe specifically, policies are proposed to maintain or
increase SOM content in agricultural fields (EC 2006, 2011b, 2013; Toth et al. 2008;
UNFCCC 2012, 2015). Maintaining and in particular increasing SOM content can be a
very costly effort, requiring many years of additional inputs. Based on differences in
carbon sequestration potential, van Groenigen et al. (2017) argue for a spatially
differentiated strategy for soil carbon sequestration. Our study argues likewise, yet
based on spatial differences in contributions from SOM to productive capacity. If
policies on SOM management want to include benefits for productive capacity, efforts
should be focussed on areas with a higher risk of SOM deficiency (mainly Southern and
Eastern Europe), more extreme environmental conditions (very dry or very wet climates,
steep slopes, very sandy soils), cropping systems with larger shares of specialized or
horticultural crops or with very low SOM contents (below the given threshold intervals).
6.6.
Acknowledgements
First and foremost, our gratitude goes to all European farmers who spared some of their
time to fill in the questionnaire. Setting up a large scale farm survey would have been
impossible without numerous colleagues across different institutes. In Italy, we received
support from universities of Ancona, Bari, Milano, Napoli Udine, Pisa, Teramo, Tuscia,
Ancona and Perugia; provincial and regional officials; farmers unions (Coldiretti,
Confagricoltura, Confederazione Italiana Agricoltori); CadirLab (Quargnento, AL);
Consorzio Agrario di Siena; advisors of the Italian Farm Accountancy Data Network;
Chiara Costamagna; students of the University of Torino and University of Milano. In
Austria, main contacts were the provincial and regional officials of the chamber of
agriculture in Lower Austria, Upper Austria and Tyrol. In Spain, data was collected and
compiled through colleagues of the University of Córdoba, the Andalusian Institute of
Agricultural and Fisheries Research and Training, the Institute for Sustainable
Agriculture and farmers associations (ASAJA, AEAC-SV), with support from Ángela
Portero (UCO). In Belgium, data was collected with the help of Greet Ruysschaert and
Erwin Wauters (ILVO). In Germany, data was collected with the help of Janine Mallast
(IGZ) and Isabell Raschke (UGOE). Joost Wolf, Hein ten Berge, Jan Verhagen and Igor
Staritsky (WUR) helped with developing the initial farm typology on which the
environmental and land use classifications are based, while Peter Verweij (WUR)
Perceived deficiency of SOM
153
assisted in constructing the European risk map. We thank all colleagues for their efforts
and support.
Supplement Chapter 6
154
Supplement Chapter 6
Supplementary Tables
6.1 to 6.3
Three options for combining slope and soil texture
into a soil risk estimate of SOM deficiency
155
Supplementary Tables
6.4 to 6.6
Three options for combining soil risk and aridity
into an environmental risk estimate of SOM
deficiency
156
Supplementary Table
6.7
Combining environmental risk and land use into a
(total) risk estimate of SOM deficiency
157
Supplementary Table
6.8
Classification of crops cultivated by farmers
participating in the survey
157
Supplementary Table
6.9
Characteristics of farmers included in the analysis
158
Perceived deficiency of SOM
155
Three options for combining slope and soil texture into a soil risk estimate of SOM
deficiency
Supplementary Table 6.1: Low soil risk estimate (soil texture x slope).
Level
(0°)
Nearly
level (1°)
Gentle
(2-3°)
Moderate
(4-7°)
Strong to
steep
(>8°)
Coarse (> 65% sand , < 18% clay)
2
2
3
4
5
Medium (> 15% sand, 18-35% clay or 15-65%
sand, < 18% clay)
1
1
2
3
4
Medium fine (<15% sand, < 35% clay)
1
2
3
3
4
Fine (35-60% clay)
2
2
3
4
5
Very fine (>60% clay)
2
2
3
4
5
Supplementary Table 6.2: Medium soil risk estimate (soil texture x slope).
Level
(0°)
Nearly
level (1°)
Gentle
(2-3°)
Moderate
(4-7°)
Strong to
steep
(>8°)
Coarse (> 65% sand , < 18% clay)
2
3
4
5
5
Medium (> 15% sand, 18-35% clay or 15-
65% sand, < 18% clay)
1
1
2
3
4
Medium fine (<15% sand, < 35% clay)
1
2
3
3
4
Fine (35-60% clay)
2
2
3
4
5
Very fine (>60% clay)
2
3
4
5
5
Supplementary Table 6.3: High soil risk estimate (soil texture x slope).
Level
(0°)
Nearly
level (1°)
Gentle
(2-3°)
Moderate
(4-7°)
Strong to
steep
(>8°)
Coarse (> 65% sand , < 18% clay)
2
3
4
5
5
Medium (> 15% sand, 18-35% clay or 15-
65% sand, < 18% clay)
1
2
3
4
5
Medium fine (<15% sand, < 35% clay)
1
2
3
4
5
Fine (35-60% clay)
2
3
4
5
5
Very fine (>60% clay)
3
4
5
5
5
Supplement Chapter 6
156
Three options for combining soil risk and aridity into an environmental risk
estimate of SOM deficiency
Supplementary Table 6.4: Low environmental risk estimate (soil risk x aridity).
Very low
soil risk
(1)
Low soil
risk (2)
Average
soil risk
(3)
High soil
risk (4)
Very
high soil
risk (5)
Very dry (0-0.4)
2
3
4
5
5
Dry (0.4-0.6)
1
2
3
4
5
Intermediate (0.6-0.7)
1
1
2
3
4
Wet (0.7-0.8)
1
2
3
4
5
Very wet (>0.8)
2
3
4
5
5
Supplementary Table 6.5: Medium environmental risk estimate (soil risk x aridity).
Very low
soil risk
(1)
Low soil
risk (2)
Average
soil risk
(3)
High soil
risk (4)
Very
high soil
risk (5)
Very dry (0-0.4)
2
3
4
5
5
Dry (0.4-0.6)
1
2
3
4
5
Intermediate (0.6-0.7)
1
2
3
4
5
Wet (0.7-0.8)
1
2
3
4
5
Very wet (>0.8)
2
3
4
5
5
Supplementary Table 6.6: High environmental risk estimate (soil risk x aridity).
Very low
soil risk
(1)
Low soil
risk (2)
Average
soil risk
(3)
High soil
risk (4)
Very
high soil
risk (5)
Very dry (0-0.4)
3
4
5
5
5
Dry (0.4-0.6)
2
3
4
5
5
Intermediate (0.6-0.7)
1
2
3
4
5
Wet (0.7-0.8)
2
3
4
5
5
Very wet (>0.8)
3
4
5
5
5
Perceived deficiency of SOM
157
Combining environmental risk and land use into (total) risk of SOM deficiency
Supplementary Table 6.7: (total) Risk of SOM deficiency estimate (environmental risk x land use).
Very
low
environment
risk (1)
Low
environment
risk (2)
Average
environment
risk (3)
High
environment
risk(4)
Very
high
environment
risk (5)
Permanent crops, grass and
cereals
1
1
2
3
4
Mixed crops: any other
crops or land independent
farms
1
2
3
4
5
horticulture and specialized
crops
2
3
4
5
5
Fallow land
2
3
4
5
5
Classification of crops cultivated by farmers participating in the survey
Supplementary Table 6.8: Classification of crops cultivated by farmers in the survey.
Cereals and grass
Horticultural and specialized crops
Other
barley
durum
grain maize
oat
rice
rye
sorghum
spelt
triticale
wheat
other cereals or grass
aloe vera
asparagus
beans
beetroot
bitter vetch
cabbage
carrots
Cherries
chickpeas
Chicory
Cichorei
Cotton
cucumber
fave beans
Flax
flower seeds
Flowers
Garlics
grass seeds
green peppers
Lentils
melons
mint
mustard
oil flax
oil seeds
onion
parsley
peas
potatoes
pumpkin
rhubarb
soy bean
strawberries
sugar beet
tobacco
tomatoes
turnip
vegetable seeds
other vegetables open air
other beets
other legumes
buckwheat
energy maize
forage barley
forage ley
forage or silage maize
Italian rye grass
mint
silage rye
other forage or fodder
crops
Supplement Chapter 6
158
Farmers’ characteristics
General discussion
159
Chapter 7. General discussion
The aim of this thesis was to improve understanding of the role of organic inputs and
SOM for crop production in contemporary arable farming in Europe. For this purpose,
long-term experiments were analysed on the additional yield effect of organic inputs
and savings in mineral fertiliser. In addition, a farm survey was conducted to find drivers
and barriers for the use of organic inputs and to assess if farmers perceive a deficiency
of SOM. In this chapter, the findings of this thesis are discussed along the following
lines: general findings (Section 7.1); influence of environmental factors (Section 7.2);
influence of crop types cultivated (Section 7.3); types of organic inputs (Section 7.4);
synergies and trade-offs with other ecosystem services (Section 7.5); limitations of the
study (Section 7.6) and implications of the findings (Section 7.7). A summary of the
main findings is presented in Fig. 7.1.
7.1.
General findings
This thesis set out to answer five main research questions (Section 1.6). Here the
questions are listed together with a short answer based on the findings in each chapter.
Q 1: What is the additional yield effect of organic inputs for arable crops in Europe?
Based on 20 long-term experiments, the mean additional yield effect of organic inputs
for arable crops in Europe is not significantly different from zero (1.4% + 1.6 - 95%
Confidence Interval [CI]). In specific cases however, especially for root and tuber crops,
spring sown cereals, or for very sandy soils or wet climates, organic inputs did increase
attainable yields (between 3 and 7%). Initial SOM contents of the experiments were not
related to the additional yield effect of organic inputs, but a correlation was found
between increases in additional yield and increases in SOM content.
Q 2: Do mineral fertiliser savings from organic inputs depend on total N supply?
Based on eight long-term experiments, the use of FYM saves more mineral fertiliser N
at high total N supply than at low total N supply. The nitrogen fertiliser replacement
value (NFRV, based on equal yields) of FYM was 2.1 times larger at high than at low
total N supply (
p
= 0.04). For the other types of organic inputs investigated (slurry, straw
or a combination of straw and green manures), NFRV was also higher at high total N
supply than at low total N supply. In these cases however, sample sizes were too small
or variations too large to find significant differences.
Chapter 7
160
Q 3: What are currently the main drivers and barriers for arable farmers to use organic
inputs?
Using a farm survey in six agro-ecological zones in four countries, major drivers to use
organic inputs (incorporation of straw, cultivation of green manures, FYM and compost)
were perceived effects on soil quality (such as improved soil structure or reduced
erosion) and the positive influence from social referents (such as fellow farmers or
agricultural advisors). Major barriers to use organic inputs were financial (increased
costs or foregone income) and perceived increases in pressures from weeds, pest and
diseases.
Q 4:
How are farmers’ beliefs on SOM related to their use of organic inputs
?
A case study in the Netherlands shows that arable farmers specifically value the effect
of SOM content on soil structure, especially where it improves workability on clay soils
and water holding capacity on sandy soils. Soil fungi were evaluated as a negative
outcome of SOM. Among the different control factors, the long-term effect of SOM
content was perceived as very strong and evaluated very positively. Advisors were
considered to have the most positive view on SOM and fellow farmers were considered
to be least positive. Farmers’ beliefs were correlated weakly but significant
ly with their
intention to increase SOM content. The large majority of Dutch farmers in the survey
(91%) had a high or very high intention to increase SOM content. A higher intention to
increase SOM content was significantly correlated with the use of organic inputs as
expressed in total and effective C inputs (C remaining in the soil after one year).
Q 5: Do farmers perceive a deficiency of SOM?
Based on a farm survey in five countries (Austria, Belgium, Germany, Spain and Italy),
18% of 1452 farmers perceived a high to very high SOM deficiency. Perceived
deficiency of SOM content was related to climate (aridity), slope, soil texture and land
use. Based on farmers’ perceptions threshold intervals for SOM content were
established (sand: 1.2
–
4.7%, loam: 0.6
–
2.6% and clay: 1.0
–
2.4%). Below a threshold
interval, a farmer will benefit from more SOM content, irrespective of other factors such
as land use or management. Above a threshold interval, farmers do not expect
production benefits from additional SOM. Some indication was found that being at the
extreme end for one environmental factor gives a higher chance of perceiving a
deficiency of SOM than having a combination of moderate environmental conditions.
General discussion
161
Chapter 7
162
7.2.
Influence of environmental factors on the role of SOM for
crop production
In Chapters 2, 4, 5 and 6, environmental factors were included in data collection and
analysis to assess their effects on the role of SOM for crop production (Table 1.3). Here
findings are discussed per environmental factor.
Climate
The meta-analysis of 20 long-term experiments showed that in wet climates, the
additional yield effect of organic inputs was larger than in more moderate or dry climates
(Fig. 2.4c). This was probably related to an improved drainage and aeration and the
prevention of soil compaction in wet climates (Soane 1990). This finding was reflected
in the farm survey, as farmers in wet climates perceived a higher deficiency of SOM
than those in dry climates.
Beforehand, it was rather expected that crops in dry climates would benefit more from
organic inputs as SOM increases the water holding capacity of soils (Díaz-Zorita et al.
1999), but this was not confirmed in the meta-analysis. Possibly, crops were well
irrigated in the experiments and therefore the water holding effect of SOM did not add
any benefit to crop yields. If and how often experimental fields were irrigated was
however not mentioned in the publications on which this analysis was based.
An alternative explanation is that the increase in SOM content did not increase water
holding capacity sufficiently to affect crop yields. It has to be borne in mind however
that even though some experiments were included with a dry sub-humid semi-arid
climates, lower aridity indices are more common outside Europe. One indication that
crop production benefits more from organic inputs or SOM in drier climates was found
in the farm survey as farmers in very dry climates perceived a higher deficiency of SOM
than those in between (Fig. 6.6c). Possibly, farmers operate in less controlled or more
water limited settings than those found at the experimental sites.
Land slope
Land slope was only included as a co-variable in the farm survey. As expected, farmers
on steeper slopes perceived a higher deficiency of SOM than those on less steep slopes
(Fig. 6.6a). On steep slopes, SOM content was lower than on less steep slopes (probably
due to soil erosion). In addition, SOM can promote infiltration rates (Rhoton et al. 2002),
which is more appreciated on steeper slopes.
General discussion
163
When assessing drivers and barriers to use organic inputs, two agro-ecological zones
were included with steeper slopes (AT1 and IT2-sloping; Table 4.5). In both agro-
ecological zones, reduced erosion was evaluated as a driver for cultivating green
manures or cover crops (Fig. 4.6a,d). It is however unclear if this is due to the increased
SOM content or due to the reduction of bare soils when cultivating green manures or
cover crops.
Soil texture
The meta-analysis of 20 long-term experiments showed that on soils with less clay
content (
i.e.
more sandy soils), the additional yield effect of organic inputs was larger
than on soils containing more clay content (Fig 2.4a). Probably, the organic inputs and
increased SOM content improved the loose soil structure of the sandy soils (Tisdall &
Oades 1982).
The additional yield effect of organic inputs on sandy soils was reflected in the farm
survey as perceived deficiency of SOM was higher on sandy soils (Fig. 6.6b). A priori,
it was expected that farmers on very heavy clay soils would also perceive a higher SOM
deficiency (Table 6.2). This could not be verified in the farm survey as no farmers were
included with more than 35% clay content in their soils (and these very heavy clay soils
are rare in Europe, EC & ESBN 2004).
In the Dutch case study, farmers on sandy soils valued effects of SOM on productivity
and water holding capacity the most, farmers on loam soils valued effects of SOM on
soil structure and rooting the most and farmers and clay soils valued effects of SOM on
soil structure and workability the most (Table 5.2).
7.3.
Influence of crop types cultivated on the role of SOM for
crop production
A previous analysis of two field experiments in the Netherlands (De Haan 1977)
suggested that root and tuber crops will benefit more from organic inputs or SOM than
cereals, even beyond the macro-nutrients supplied. This suggestion is supported for
potatoes in the meta-analysis of 20 long-term experiments (Fig. 2.3b). The additional
yield effect for potatoes was on average 7.0% (+ 4.9 -95% CI). This might be because
root and tuber crops depend more on soil structure for their successful cultivation and
harvesting. Spring sown cereals (including maize) also benefit (3.4 percent + 2.6 - 95%
CI.). Spring sown crops have a shorter time frame to develop their root system which is
Chapter 7
164
needed to acquire sufficient nutrients and water (Johnston et al. 2009). In addition, maize
has a less developed root system than wheat or barley, which might be another
explaining factor.
In the farm survey, land use classes were made comparing farmers cultivating more
specialized or horticultural crops with farmers cultivating more grass and cereals or a
mixture of crops. Among the specialized and horticultural crops cultivated by the
farmers were root and tuber crops such as potatoes or sugar beets, but also beans and
other vegetables (Supplementary Table 6.8). Farmers who cultivated more specialized
or horticultural crops perceived a higher deficiency of SOM than farmers who cultivated
more cereals (Fig. 6.6d). As crop rotations differed widely between farmers, it was not
evident if there was a specific selection of specialized or horticultural crops causing this
increase in perceived SOM deficiency. Further research would therefore be
recommended looking into more details between specific crop types.
7.4.
Types of organic inputs
It was found that the type of organic input used (
e.g.
FYM, compost, slurry) affects the
relation between SOM and crop production. The additional yield effect of FYM is larger
than that of slurry, straw or a combination of straw and green manures (even when
compensating for the N immobilization by applying additional mineral fertiliser N, Fig.
2.3a).
When assessing the mineral fertiliser N savings from organic inputs, straw has a varying
effect on N supply, from negative to positive (Table 3.2). This was confirmed in the
farm survey, in which farmers indicated that incorporation of straw sometimes saves the
use of mineral fertiliser N whilst in other cases it needs mineral fertiliser N for
decomposition (Fig. 4.5).
Based on data from eight long-term experiments, NFRV of FYM increases with total N
supply (Table 3.2). This could suggest that NFRV of organic inputs is higher when
combined with mineral fertiliser than without. If validated, this would open a new
perspective on the advantage of distributing available organic inputs among many
farmers in a given region: the region would require less fertiliser to produce the same
yield output, than when organic inputs were concentrated in few farms.
The main drivers to use organic inputs were perceived effects on soil quality and
influence from social referents (for all types of organic inputs). In addition, green
General discussion
165
manure was also perceived to have a positive effect on the environment. For all input
types, perceived pressures from weeds, pests and diseases were evaluated as important
barriers (Fig. 4.8). In addition, farmers perceived more negative financial consequences
from FYM and compost than from the cultivation of green manures or incorporation of
straw.
7.5.
Synergies and trade-offs with other ecosystem services
SOM content in arable farms does not only affect crop production, but can also
contribute to other ecosystem services. Ecosystem services which have been suggested
positively or negatively affected by the use of organic inputs or SOM content in arable
soils include climate change mitigation (soil carbon sequestration, greenhouse gas
emissions), positive effects on crop protection, nutrient leaching and soil biodiversity
conservation. Here effects of SOM on these ecosystem services will be discussed.
Climate change mitigation
Currently, much emphasis is placed on the carbon sequestration potential of SOM in
agriculture. As SOM is known to improve soil quality, this is argued a win-win situation
for climate change mitigation and food security (see for example Lal 2004a or the
4/1000 initiative by the UNFCCC 2015). This thesis has shown that SOM can positively
affect crop production, but only in specific situations, namely depending on
environmental conditions, practices used to maintain or increase SOM content and crop
types cultivated. In many other cases, no significant additional yield effect of SOM
could be shown.
For soil carbon sequestration, there are a number of limitations. First, after some time
under a specific management, SOM will reach an equilibrium state in which inputs
balance outputs (Janssen 2002). In such a state, adding organic inputs does not sequester
more carbon. This means by definition there is only a limited potential for soil carbon
sequestration. This limited potential also exists due to the limited availability of organic
resources and competition with other uses (such as feed for livestock, fibre or as fuel).
Some studies have shown that bioenergy produced from straw might be a more effective
climate change mitigation measure than storage in soil, especially when assessed over
longer periods of time (Poeplau et al. 2015; Powlson et al. 2008). In addition, built up
of organic carbon in soils must always accompanied by other elements such as N. This
means that increasing SOM might need additional mineral fertiliser production (Kirkby
et al. 2016; Richardson et al. 2014), with associated costs and environmental impacts.
Chapter 7
166
While the use of organic inputs can temporarily sequester carbon, there is a risk for
increased greenhouse gas emissions, potentially offsetting any gains for climate change
mitigation (Bos et al. 2017).
While this thesis shows that increasing SOM does not always increase crop production,
the reverse relationship has a more general validity (
i.e.
higher crop production
increases SOM). If, through any means, higher crop yields can be reached, this will lead
to production of more aboveground biomass and roots and potentially a higher
availability of crop residues which can be returned to the field (Fig. 2.5b, Yang &
Janssen 1997).
Nutrient leaching
In some cases, the use of organic inputs instead of mineral fertilisers has been suggested
to reduce nutrient leaching (Leclerc et al. 1995). For example, during the 1980s, the
Synchrony Hypothesis was formulated on nutrient supply from organic inputs (Swift
(1985) as cited by Palm et al. 2001). The Synchrony Hypothesis states that the release
of nutrients (N,P) from organic inputs can be synchronized with plant growth demands.
In this manner, synchrony reduces leaching of nutrients while enhancing crop growth.
Based on a multitude of studies, Palm et al. (2001) however conclude that there is no
single or combination of organic material that releases N in perfect synchrony to crop
demand. This means that some percentage of nutrients applied with organic inputs will
always be prone to losses to the environment. Following, a number of studies have tried
to compare nutrient losses applied with mineral fertilisers versus application with
organic inputs. It has been suggested that over the long-term, if nutrients are applied
attuned to crop requirements, there is no difference in nitrate leaching between using
either organic inputs or mineral fertilisers (Maeda et al. 2003).
Crop protection measures
There is some evidence from field experiments that in certain cases the use of organic
inputs might reduce the need for chemical crop protection. This is especially the case
when they are rich in nitrogen (Bailey & Lazarovits 2003) or enriched with specific soil
microbes (Mehta et al. 2014). There are also findings pointing to the contrary.
Incorporation of straw is for example known to spread diseases to subsequent cereals in
crop rotations (Maiorano et al. 2008). In our farm survey, farmers consistently evaluated
the effects of organic inputs on crop protection negatively (Fig. 4.8). There is therefore
a need for more research on the relation between the use of various organic inputs and
General discussion
167
pressures from weeds, pests and diseases. Farmers need specific guidance on which
combination of organic inputs and crops can increase or reduce the pressure of weeds,
pests and diseases in a given environment.
Soil biodiversity
Soil is one of the most species-rich habitats of terrestrial ecosystems (Decaëns et al.
2006; Wolters 2001). In agricultural systems, most of the biodiversity resides in soils
(Brussaard et al. 2007). In part due to these large number of soil species, most of the
relationships between soil biodiversity and crop production remain unknown. There are
close relations between SOM and soil life, but the precise mechanisms are not fully
understood (Six et al. 2006). Losses of SOM might however lead to losses in soil
biodiversity (Tibbet 2016) and agricultural intensification is known to affect
biodiversity in agricultural soils (Tsiafouli et al. 2015). In this case, the
‘precautionary’
principle (Decaëns et al. 2006) could apply as we are not aware of the risks of losing
soil biodiversity on the long term.
7.6.
Limitations of the study
Analysis of total SOM content instead of different SOM fractions
In this thesis, the role of SOM for crop production was based on total C content in the
soil (Fig. 2.4b, Fig. 2.5, Fig. 6.7 and Fig. 6.8) or total or effective C content of organic
inputs (Fig. 2.4d, Table 5.6 and Fig. 5.8). These numbers were either reported by farmers
(Chapter 6), calculated using technical coefficients (Chapters 2 and 5) or based on soil
analyses in experimental fields (Chapter 2). All these three methods (reports by farmers,
using technical coefficients or soil analyses in experimental fields) might have caused
some degree of error or variation in the data. This was however inevitable when relying
on published data and farm surveys. Nonetheless some interesting relationships and
patterns could be observed (as described in the previous sections).
There is recent evidence that specific SOM fractions play specific roles in improving
aggregate stability of soils (Dexter et al. 2008). Organic inputs from straw, stubble or
roots might have a proportionally larger impact on aggregate stability compared to
organic inputs from animal manures (Powlson et al. 2011; Watts et al. 2006). In contrast,
comparing effects from animal manure and crop residues on soil structure, Bhogal et al.
(2009) found that animal manures increased topsoil porosity, plant available water
capacity and decreased bulk density while crop residues did not. Evaluating total SOM
Chapter 7
168
content lacks this type of detail. Ordinary soil analyses (for field experiments or farmers)
report only total SOC or SOM content. Taking into account specific fractions would
therefore require introduction and acceptance of new soil analysis methods.
Which fractions of SOM play a larger role in aggregate stability is also important when
considering possible trade-offs or synergies with soil carbon sequestration. If the
contribution from SOM to soil fertility is mainly from fresh or active fractions, there is
less synergy with soil carbon sequestration, which by default relies on very stable and
inactive pools of carbon. If the correlation in Fig. 2.5a is a causal relation (meaning a
relative increase in SOM content causes a relative increase in additional yield), then this
thesis would support the first option. In that case the proportion of fresh or active SOM
is more important for soil structural benefits to crop production than older fractions.
This would explain the observation in Fig. 2.4b, in which there is no relation between
SOM content at the start of each experiment and the additional yield effect of organic
inputs. If large parts of these different SOM percentages are older SOM fractions (and
if older SOM fractions are less beneficial for soil structural benefits to crop growth),
then fields with both lower and higher SOM contents benefit equally from fresh organic
inputs.
Study area
The objective of this thesis was to cover large parts of Europe. Per chapter however,
study areas differed. The meta-analysis of Chapter 2 was based on long-term
experiments in nine countries (Germany, Italy, Romania, Czech Republic, Hungary,
Spain, Serbia, Estonia and Austria), while a subset of these was taken in Chapter 3 (Italy,
Romania, Hungary, Czech Republic and Germany), depending on data availability.
Chapter 4 was based on a farm survey in four countries (the Netherlands, Belgium,
Austria and Italy), whilst Chapter 5 was a case study in the Netherlands. Finally, Chapter
6 was based on a farm survey in five countries, again differing in composition
(Germany, Italy, Belgium, Austria and Spain).
The data used in each chapter depended on available publications (Chapters 2 and 3) or
inclusion of arable farmers and incorporation of questions in the farm survey of the
CATCH-C project (Chapters 4, 5 and 6). Therefore, study areas differed between
chapters and datasets were sometimes unbalanced with regard to spread of farmers
across climates and soil textures and practices included. In addition, response rates to
questionnaires were sometimes low, which might have given a bias in the type of
General discussion
169
farmers filling in the questionnaire. Nonetheless, ranges of environmental conditions
and spread of locations does seem to justify more general conclusions.
As this study only focussed on Europe, very weathered soils, mostly occurring in
tropical regions, were not included. Weathered soils often have very low cation
exchange capacity (Palm et al. 1997) and lack a number of micro nutrients necessary for
crop growth (Gupta et al. 2008). On weathered soils therefore, additional yield effects
of organic inputs and perceived deficiency of SOM could be larger. In tropical regions
however, soil analyses by farmers are often less common. In addition, a recent global
database suggests experimental set-ups as used in our meta-analysis do not exist outside
temperate regions (ISCN 2015), establishment of such long term experiments would
therefore be recommended.
Focus on mineral soils only
This study is limited to mineral soils, organic (peat) soils were excluded (besides a small
percentage of Dutch farmers in Chapter 4). Peat soils have very high contents of SOM,
but because of their organic nature, their structure and response to organic inputs is very
different from mineral soils (Lucas 1982). Peat soils are especially important when
considering climate change mitigation as, globally, peat soils contain approximately 455
Pg of carbon, which is twice the amount found in the world’s forest biomass
(Dunn &
Freeman 2011). Often, peat soils are drained to make them suitable for cultivation,
which causes substantial amounts of greenhouse gas emissions (Erkens et al. 2016).
These dynamics were however outside the scope of this study.
7.7.
Implications of the findings
The findings in this thesis indicate that at least on the shorter term, on average, there
seems to be no immediate threat from a deficiency of SOM to crop production in arable
farming in Europe. The long-term experiments showed that with sufficient use of only
mineral fertilisers, similar yields could be attained over multiple years as with the
combined use of organic inputs and mineral fertiliser. This result was independent from
SOM content in the experimental fields (Chapter 2). Similarly, the large majority of the
farmers interviewed (82%) did not perceive a high or very high deficiency of SOM for
productive capacity (Chapter 6). Likewise, a major barrier identified for the use of
organic inputs are the financial consequences at farm level (either increased costs or
reduced income, Chapters 4,5). This suggests that at least on the short term, on average,
European arable farmers do not benefit financially from increasing SOM content.
Chapter 7
170
These findings differ when looking in more detail at specific environmental conditions,
types of crops cultivated and types of organic inputs used (Fig 7.1). SOM and organic
inputs give more benefits to crop production in wet climates, on very sandy soils and on
steep slopes. It seems that while the functions of SOM can be replaced with technical
means to a large extent (
e.g.
tillage, use of mineral fertilisers), there are limits to this
technical potential when environmental conditions are more extreme or crops are more
demanding.
Specialized crops (such as root and tuber crops) benefit more from organic inputs than
cereals. Even though additional yield effects of organic inputs were small, they do exist
for potatoes (Fig. 2.3b). More in general, farmers who cultivate a larger share of their
land with specialized or horticultural crops (such as sugar beets, onions, carrots or other
vegetables) perceived a higher deficiency of SOM (Fig. 6.6d). Often, such crops are the
cash crops on which the income of a farmer relies, more than on cereals. Small changes
in yields of these crops might therefore have a relatively large impact on the long-term
viability of a farm.
This thesis has found that - for the current degree of intensification in European arable
farming - the contribution of SOM to crop production in European arable farming
depends on a number of environmental factors, types of organic inputs used and crop
types cultivated. If policies on SOM management aim to include benefits for crop
production, it would therefore be advisable to focus on areas with more extreme
environmental conditions.
In his thesis on the on-farm benefits of SOM for England and Wales, Verheijen (2005b)
calls for a paradigm change in which SOM’s importance for crop production shifts fr
om
a primary function to a secondary, or buffer, function. In this proposed new paradigm:
“
SOM still contributes to most agro-production sub-functions, but its magnitude is
relatively low and in many cases only (measurably) occurring in ‘extreme’ climatic
years”
(Verheijen 2005b). The findings in this thesis support his suggestion and give
reason to in
include besides ‘more extreme climatic years’ also ‘more extreme
environmental conditions’
and possibly ‘more demanding crops’
.
List of references
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Summary
193
Summary
Soil organic matter (SOM) is often considered the most important indicator of soil
fertility. It
contributes to each of fertility’s three dimensions: the physical (structure,
aeration, water retention), the biological (biomass, biodiversity, nutrient mineralisation,
disease suppression) and the chemical (nutrient supply) dimension. Yet there is currently
little scientific consensus on the precise relation between SOM and crop production.
Recently, concerns have been raised that SOM is declining in European soils and that
SOM content should be maintained above a certain threshold to (among others) protect
productive capacity of soils. It is currently unclear whether these concerns are justified.
The aim of this thesis was to improve understanding of the role of organic inputs and
SOM for crop production in contemporary arable farming in Europe. On a given arable
field, a farmer can increase SOM content by increasing organic inputs, reducing tillage
or reducing drainage. In this thesis, the research focus was on using organic inputs to
increase SOM contents. Types of organic inputs investigated were application of
farmyard manure (FYM), slurry or compost, incorporation of straw and the cultivation
of green manures. The hypothesis was that the contribution of SOM to crop production
on arable farms depends on environmental conditions and crop types cultivated.
The following research questions were addressed: (1) What is the additional yield effect
of organic inputs for arable crops in Europe? (2) Do mineral fertiliser savings from
organic inputs depend on total N supply? (3) What are currently the main drivers and
barriers for arable farmers to use organic inputs? (4)
How are farmers’ beliefs on SOM
related to their use of organic inputs? (5) Do farmers perceive a deficiency of SOM?
Data from field and farm level were used to answer the research questions. To answer
the first two research questions, data from long-term experiments were analysed. To
answer the last three research questions, a large farm survey was conducted in six
European countries (the Netherlands, Belgium, Austria, Spain, Italy and Germany).
Previous studies assessing the effects of organic inputs and SOM on crop yields have
shown mixed results. Some studies have found positive effects while others found none.
One cause for this variation may be found in mixing the effects of nutrient supply from
organic inputs or SOM and other effects such as improved soil structure or soil life. To
circumvent this limitation, in this thesis effects of organic inputs on crop yields were
assessed in a system without macro-nutrient limitation. Any effect found on crop yields
was called the ‘additional yield effect’.
Summary
194
To find the additional yield effect of organic inputs, a meta-analysis was performed of
20 long-term experiments (
Chapter 2
).Yield response curves to mineral fertiliser-N
were calculated, with and without organic inputs (with sufficient P and K supply). The
additional yield effect of organic inputs was calculated by taking the difference between
the maxima
(‘attainable yield’)
of the yield response curves. Across all experiments, the
mean additional yield effect of organic inputs was not significant (+ 1.4 percent + 1.6 -
95% Confidence Interval). In specific cases however (
i.e.
for root and tuber crops, spring
sown cereals, or for very sandy soils or wet climates), organic inputs did increase
attainable yields (depending on the co-variable, additional yield effects varied mostly
between 3-7%). A significant correlation was found between increase in attainable
yields and increase in SOM content.
The use of organic inputs can save mineral fertiliser N. Factors known to affect savings
of mineral fertiliser include the form of N in the organic input, crop type cultivated, soil
type, method of application, time of application and the manuring history which may
govern N retention and losses. In this thesis, an additional factor on mineral fertiliser N
savings was assessed which is currently not taken into account: the total N supply itself.
In
Chapter 3
,
a subset of eight experiments was taken from Chapter 2 in which N
contents of organic inputs were reported. Mineral fertiliser savings were compared at
low and high total N supply. Results show that FYM saves significantly more mineral
fertiliser N at high total N supply than at low total N supply (Nitrogen Fertiliser
Replacement Value (NFRV) of 1.12 vs 0.53,
p
= 0.04). The other organic input types
investigated also saved more mineral fertiliser N at high total N supply than at low total
N supply, but sample sizes were too small or variations too large to find significant
differences.
Besides effects on yield or savings of mineral fertilisers, farmers can have a wide array
of drivers and barriers to use organic inputs or increase SOM content on their farm.
According to the theory of planned behaviour, people base their behaviour on three main
constructs: (1) their attitude (based on outcomes), 2) their subjective norm (based on
social referents) and 3) the degree of perceived behavioural control (based on control
factors). These three constructs are based on underlying beliefs of farmers and together
lead to an intention, which might lead to a certain behaviour.
To find drivers and barriers for the use of organic inputs (
Chapter 4
), semi-structured
interviews were held with arable farmers in six agro-ecological zones in four European
countries (the Netherlands, Belgium, Austria and Italy). These semi-structured
Summary
195
interviews were used to identify outcomes, referents and control factors relevant to the
use of four types of organic inputs (incorporation of straw, cultivation of green manures,
application of FYM and application of compost). Following, a large farm survey was
held among 1180 arable farmers to quantify the outcomes, referents and control factors
as either drivers or barriers. Drivers and barriers were classified into nine main
categories (soil type & climate, soil quality, crop protection, land use, technical,
financial, environmental impact, legal and social).
Major drivers to use organic inputs were perceived effects on soil quality (such as
improved soil structure or reduced erosion) and the positive influence from social
referents (such as fellow farmers or agricultural advisors). Major barriers to use organic
inputs were financial (increased costs or foregone income) and perceived effects on crop
protection (such as increased weeds, pest and diseases or increased pesticide use). These
findings show that farmers perceive a trade-off between improved soil quality on the
one hand and increased pressures from weeds, pests and diseases and financial
consequences on the other hand when using organic inputs.
A case study among 435 arable farmers in the Netherlands (
Chapter 5
), showed that
farmers specifically value the effect of SOM content on soil structure, especially where
it improves workability on clay soils and water holding capacity on sandy soils. Soil
fungi were evaluated as a negative outcome of SOM. Among the different control
factors, the long-term effect of SOM content was perceived as very strong and evaluated
very positively. Advisors were considered to have the most positive view on SOM and
fellow farmers were considered to be least positive. Farmers’ beliefs were correlated
weakly but significantly with their intention to increase SOM content. The large
majority (91%) of the farmers in the survey had a high or very high intention to increase
SOM content. A higher intention to increase SOM content was significantly correlated
with the use of organic inputs as expressed in total and effective C inputs.
In total 1452 farmers in five European countries (Belgium, Germany, Austria, Spain and
Italy) were asked if they perceive a deficiency of SOM (
Chapter 6
). Results show that
18% perceived a high or very high SOM deficiency, 53% perceived a very low or low
SOM deficiency and 29% giving an indication in the middle. Perceived deficiency of
SOM content was related to climate (aridity), slope, soil texture and land use. Based on
farmers’ perceptions threshold intervals for SOM con
tent were established (sand: 1.2
–
4.7%, loam: 0.6
–
2.6% and clay: 1.0
–
2.4%). Below a threshold interval, a farmer will
benefit from more SOM content, irrespective of other factors such as land use or
Summary
196
management. Above a threshold interval, more SOM content will not be beneficial for
productive capacity from a farmers’ point of view.
Some indication was found that being
at the extreme end for one environmental factor gives a higher chance of perceiving a
deficiency of SOM than having a combination of moderate environmental conditions.
The findings in this thesis indicate that at least on the shorter term, on average, there
seems to be no immediate threat from a deficiency of SOM to crop production in arable
farming in Europe. The long-term experiments showed that with sufficient use of only
mineral fertilisers, on average, similar yields could be attained over multiple years as
with the combined use of organic inputs and mineral fertiliser. This result was
independent from SOM content in the experimental fields.
Effect of organic inputs and SOM differ when taking into account specific
environmental conditions, types of crops cultivated and types of organic inputs used.
Long-term experiments show that organic inputs give more benefits to crop production
in wet climates and on sandy soils. In addition, farmers perceive more benefits from
SOM on steep slopes, sandy soils, wet or very dry climates. The additional yield effect
of organic inputs is larger for potatoes than for cereals and more in general, farmers who
cultivate larger shares of their land with specialized crops (including potatoes, sugar
beets, onions and other vegetables) perceive more benefit from SOM. Using FYM has
more benefits to crop production than using straw (even when compensating for the N
immobilization by applying additional mineral fertiliser N). It seems that while the
functions of SOM can be replaced with technical means to a large extent (tillage, use of
mineral fertilisers), there are limits to this technical potential when environmental
conditions are more extreme and crops are more demanding.
Indications were found that more mineral fertiliser N can be saved when using farmyard
manure at high N rates (with mineral fertiliser application) than at low N rates (without
mineral fertiliser
application), based on comparisons at ‘equal yield’. If further
validated, this could imply an advantage of distributing available farmyard manure (and
possibly other types of organic input) among many farmers in a given region: the region
would require less mineral fertiliser to produce the same yield output, than when organic
inputs were concentrated in few farms. Main drivers for farmers to use organic inputs
are the perceived effects on soil quality, while the main barriers are the perceived
financial consequences and increased pressures from weeds, pests and diseases.
If policies aim to stimulate the maintenance or increase of SOM, more insight is needed
under which conditions the use of organic inputs increases or reduces pressures of
Summary
197
weeds, pests and diseases. Financial consequences (at least on the short term) should
also be accounted for. More importantly however, benefits from SOM for crop
production cannot be taken for granted. Only in specific situations does this assumption
hold. If European policies on SOM want to align benefits for the larger society (such as
soil carbon sequestration, biodiversity conservation) with benefits for crop production,
focus should be on areas with more extreme environmental conditions (very dry or very
wet climates, steep slopes, very sandy soils), or cropping systems with more specialized
or horticultural crops and less cereals.
Samenvatting
198
Samenvatting
Organische stof (OS) in de bodem wordt vaak gezien als de belangrijkste indicator voor
bodemvruchtbaarheid. OS draagt namelijk bij aan elk van de drie dimensies van
bodemvruchtbaarheid: de fysische (structuur, beluchting, infiltratie, watervasthoudend
vermogen), de biologische (biomassa, biodiversiteit, mineralisatie van nutriënten,
ziektewering) en de chemische (retentie en levering van nutriënten) dimensie.
Desalniettemin is er momenteel weinig wetenschappelijke consensus over de precieze
relatie tussen OS en gewasproductie.
De afgelopen jaren zijn meerdere zorgen geuit over een mogelijke daling van OS in
Europese bodems. Dit heeft geleid tot verschillende voorstellen om te streven naar een
bepaald percentage OS om (onder andere) de productiecapaciteit van bodems te
waarborgen. Het is momenteel onduidelijk of deze zorgen gerechtvaardigd zijn. Het doel
van dit proefschrift was om de rol van organische meststoffen en OS voor
gewasproductie in hedendaagse Europese akkerbouw beter te begrijpen. Op een bepaald
perceel akkerbouwgrond kan een boer het percentage OS verhogen door meer
organische meststoffen aan te voeren, het ploegen te verminderen of door drainage te
verminderen. In dit proefschrift ligt de nadruk op het toedienen van meer organische
meststoffen om OS te verhogen of te behouden. De volgende soorten organische
meststoffen
1
zijn daarbij in acht genomen: vaste mest, drijfmest, compost, het inwerken
van stro en het telen van groenbemesters. De hypothese van dit onderzoek was dat de
bijdrage van OS aan gewasproductie in de akkerbouw afhangt van milieufactoren en de
gewassen die geteeld worden.
De volgende onderzoeksvragen zijn gesteld: (1) Wat is het specifieke effect van
organische meststoffen op de gewasopbrengst van akkerbouwgewassen in Europa? (2)
Is de besparing van kunstmest door gebruik te maken van organische meststoffen
afhankelijk van de totale stikstof (N) gift? (3) Wat zijn momenteel de belangrijkste
motivaties en belemmeringen voor boeren om organische meststoffen te gebruiken? (4)
Hoe zijn overtuigingen van boeren over OS gerelateerd aan hun daadwerkelijk gebruik
van organische meststoffen? (5) Ervaren boeren een tekort aan OS? Analyse van data
vond plaats op zowel veld- als bedrijfsniveau. De eerste twee onderzoeksvragen zijn
beantwoord met behulp van lange-termijn experimenten. De laatste drie
1
Bij gebrek aan een eenduidige alomvattende Nederlandse term wordt het woord organische meststof hier gebruikt
voor zowel dierlijke mest als plantaardig materiaal (zoals compost of gewasresten).
Samenvatting
199
onderzoeksvragen zijn beantwoord met behulp van een grootschalige enquête, gehouden
in zes Europese landen (Nederland, België, Oostenrijk, Spanje, Italië en Duitsland).
Eerdere studies naar het effect van organische meststoffen en OS op gewasopbrengsten
lieten wisselende resultaten zien. Sommige studies vonden positieve effecten op de
gewasopbrengst, terwijl andere studies geen effect aantoonden. Het verschil in
bevindingen kan mogelijk verklaard worden doordat in sommige studies bepaalde
effecten van OS, zoals een verbeterde bodemstructuur of een gewijzigd bodemleven,
verward worden met een verhoogde beschikbaarheid van nutriënten. Om deze koppeling
uit te sluiten, is in dit proefschrift het effect van organische meststoffen en OS op de
gewasopbrengst geanalyseerd in een situatie waarbij macronutriënten niet limiterend
zijn voor de gewasgroei. In deze situatie wordt een effect van OS op gewasopbrengst
het ‘specifieke effect’ van OS genoemd, ook wel eerder het ‘rest
-
effect’ genoemd, maar
niet te verwarren met de Engelse term
‘
residual effect
’
wat in deze context een andere
betekenis heeft.
Om het specifieke effect van organische meststoffen te achterhalen is een meta-analyse
gedaan van 20 lange termijn experimenten (
Hoofdstuk.2
). Curves van gewasopbrengst
in relatie tot de toediening van kunstmest-N zijn berekend, met en zonder toevoeging
van organische meststoffen (en met toediening van voldoende fosfor (P) en kalium (K)).
Het specifieke effect van organische meststoffen is uitgedrukt als het verschil tussen de
maxima (de haalbare gewasopbrengsten) van de beide curves. Gemiddeld over de 20
lange termijn experimenten was het specifieke effect van organische meststoffen niet
significant verschillend van nul (+ 1.4 procent + 1.6 - 95% betrouwbaarheidsinterval).
Echter, onder bepaalde omstandigheden (bij het telen van wortel- en knolgewassen of
zomergranen, of op erg zandige gronden of in een nat klimaat), verhoogde het gebruik
van organische meststoffen de haalbare opbrengst wel. Het positieve effect varieerde
hierbij, afhankelijk van de situatie, tussen de 3 en 7%. Daarnaast werd er een
significante correlatie vastgesteld tussen een verhoging van de haalbare gewasopbrengst
en de toename van het percentage OS in de bodem.
Het gebruik van organische meststoffen kan een besparing opleveren in het gebruik van
kunstmest-N. Hoeveel kunstmest er precies bespaard wordt hangt af van de
samenstelling van de organische meststof, het gewas dat geteeld wordt, bodemtype,
moment en manier van toediening en de voorgeschiedenis van een perceel dat zorgt voor
behoud of verlies van N in de bodem. In dit proefschrift is een bijkomende factor
Samenvatting
200
onderzocht, die tot heden niet is meegenomen in eerdere studies: de totale hoeveelheid
N die toegediend wordt.
In
Hoofdstuk 3
is een deelverzameling genomen van de experimenten uit Hoofdstuk 2.
In deze deelverzameling van acht experimenten was bekend hoeveel N toegediend is
met organische meststoffen. Besparingen van kunstmest (door gebruik te maken van
organische meststoffen) zijn vergeleken bij een lage en hoge N gift. Resultaten laten
zien dat om een bepaalde gewasopbrengst te halen, het gebruik van vaste mest
significant meer kunstmest bespaart bij een hoge dan bij een lage N gift
(stikstofwerkingscoefficiënt van 1.12 ipv 0.53,
p
=0.04). Ook de andere soorten
organische meststoffen bespaarden meer kunstmest-N bij een hoge dan een lage N gift,
maar de steekproefomvang was daar te klein of de variatie in de uitkomsten te groot om
een significant verschil te kunnen aantonen.
Naast effecten op gewasopbrengst of het besparen van kunstmest kunnen boeren een
breed scala hebben van motivaties en belemmeringen om organische meststoffen te
gebruiken of OS te verhogen in hun bodem. Volgens de ‘Theorie van Gepland Gedrag’
baseren mensen hun gedrag op drie belangrijke factoren: (1) hun attitude (gebaseerd op
overtuigingen over
‘gevolgen’
van gedrag), (2) hun sociale norm (gebaseerd op
overtuigingen met betrekking tot
‘
sociale referenten
’
) en (3) ervaren gedragscontrole
(gebaseerd op overtuig
ingen over ‘controle factoren’
). Gezamenlijk leiden ze tot een
intentie, die kan leiden tot een bepaald gedrag of keuzes in management.
Om de motivaties en belemmeringen te vinden voor het gebruiken van organische
meststoffen (
Hoofdstuk 4
), zijn semi-gestructureerde interviews gehouden met
akkerbouwers in zes agro-ecologische zones in vier Europese landen (Nederland,
België, Oostenrijk en Italië). Door middel van deze interviews zijn relevante gevolgen,
sociale referenten en controle factoren voor het gebruik van vier soorten organische
meststoffen (stro, groenbemesters, vaste mest en compost) in kaart gebracht. Vervolgens
is een grootschalige enquête gehouden onder 1180 boeren om de attitudes, sociale
normen en gedragscontrole te kwantificeren als motiverende factoren of
belemmeringen. Motiverende factoren en belemmeringen zijn vervolgens
geclassificeerd in negen categorieën (bodemtype & klimaat, bodemkwaliteit,
gewasbescherming, landgebruik, technisch, financieel, milieu-impact, juridisch en
sociaal).
Belangrijke motiverende factoren voor boeren om organische meststoffen te gebruiken
bleken waargenomen effecten op bodemkwaliteit (zoals een verbeterde bodemstructuur
Samenvatting
201
of verminderde erosie) en de positieve invloed van sociale referenten (zoals andere
boeren of adviseurs). Belangrijke belemmeringen waren financieel gerelateerd (zoals
hogere kosten of lagere inkomsten) en te verwachten effecten op gewasbescherming
(zoals verhoogde onkruid- of ziektedruk, meer plagen of meer noodzaak om pesticiden
te gebruiken). Deze bevindingen laten zien dat boeren een dilemma ervaren tussen een
verbeterde bodemkwaliteit aan de ene kant en een hogere druk van onkruid, ziekten
en/of plagen en financiële consequenties aan de andere kant bij het gebruiken van
organische meststoffen.
Meer gedetailleerd onderzoek onder 435 akkerbouwers in Nederland (
Hoofdstuk 5)
laat
zien dat boeren OS vooral waarderen vanwege het positieve effect op de
bodemstructuur. Op kleigronden heeft dit voornamelijk te maken met een verbetering
in de bewerkbaarheid van de bodem, terwijl op zandgronden de bijdrage aan het
watervasthoudend vermogen gewaardeerd wordt. De Nederlandse boeren evalueerden
het effect van OS op bodemschimmels als een negatieve uitkomst.
Het lange-termijn effect van OS werd erg sterk geacht en positief gewaardeerd door de
Nederlandse akkerbouwers. Van de verschillende sociale referenten werden adviseurs
gezien als degenen met de meest waardering voor OS en andere boeren als degenen met
de minst positieve waardering van OS. Overtuigingen van boeren waren zwak maar
significant gerelateerd met hun intentie om OS te verhogen. Het merendeel van de
Nederlandse boeren (91%) had een sterke of erg sterke intentie om OS te verhogen. Een
sterkere intentie om OS te verhogen was significant gerelateerd met een hoger
daadwerkelijk gebruik van organische meststoffen (uitgedrukt in totale of effectieve C
die jaarlijks werd toegediend) door de respondenten.
In
Hoofdstuk 6
is ingegaan op de vraag of akkerbouwers in vijf Europese landen (België,
Duitsland, Oostenrijk, Spanje en Italië) een tekort ervaren aan OS. Van de 1452
geïnterviewden ervaarde 18% een groot of erg groot tekort aan OS, 53% een klein of
erg klein tekort en 29% van de akkerbouwers gaf een neutrale indicatie. Ervaren tekort
aan OS was gerelateerd aan klimaat (droog of nat), helling, bodemtextuur en
landgebruik. Gebaseerd op de percepties van de boeren zijn ranges van grenswaarden
voor OS vastgesteld (zand: 1.2-4.7%, zavel 0.6-2.6% en klei: 1.0-2.4%). Beneden de
gegeven ranges ervoeren de boeren een voordeel van OS, ongeacht andere factoren zoals
landgebruik of management. Boven de gegeven ranges ervoeren boeren een toename in
OS niet als een voordeel voor de productie capaciteit van de bodem. Er was enige
indicatie dat een extreme waarde van één factor (zoals helling, bodemtextuur, of
Samenvatting
202
landgebruik) een grotere kans geeft op een tekort aan OS dan een combinatie van
gemiddelde factoren.
De resultaten in dit proefschrift laten zien dat op de korte termijn, gemiddeld genomen,
er geen sprake is van een onmiddellijk risico van een tekort aan OS voor gewasproductie
op akkerbouwgrond in Europa. De lange-termijn experimenten laten zien dat met
voldoende gebruik van alleen kunstmest er, gemiddeld genomen over meerdere jaren,
dezelfde gewasopbrengsten kunnen worden behaald als met een combinatie van
kunstmest en organische meststoffen. Dit resultaat was onafhankelijk van het OS-
gehalte in de betreffende experimenten.
Effecten van organische meststoffen op gewasopbrengsten verschillen echter
afhankelijk van omgevingsfactoren, type gewassen die geteeld worden en het soort
organisch meststof dat gebruikt wordt. Lange-termijn experimenten laten zien dat
organische meststoffen meer voordeel opleveren voor gewasproductie in nattere
klimaten en op zandige gronden. Daarnaast ervaren boeren meer voordeel van OS op
steilere hellingen, op zandige gronden en in natte of erg droge klimaten. Het specifieke
effect van organische meststoffen is groter voor aardappels en meer in het algemeen
ervaren boeren die meer gespecialiseerde gewassen telen (zoals aardappels,
suikerbieten, uien en andere groentegewassen) meer voordeel van OS. Het gebruik van
vaste mest heeft meer voordelen voor de gewasproductie dan het inwerken van stro
(zelfs als er gecompenseerd wordt voor N-immobilisatie door meer kunstmest-N toe te
dienen). Het lijkt erop dat alhoewel de functies van OS merendeels vervangen kunnen
worden door technische middelen (zoals door te ploegen of door gebruik te maken van
kunstmest), dit vervangingspotentieel beperkter is wanneer milieufactoren extremer zijn
en er meer veeleisende gewassen geteeld worden.
Het gebruik van vaste mest bespaart (per eenheid vaste mest) meer kunstmest als het
toegediend wordt in combinatie met een relatief hoge kunstmest-N gift dan in
combinatie met een relatief lage kunstmest-N gift of zonder kunstmest-N. Dit zou
kunnen impliceren dat met de beschikbare hoeveelheid vaste mest in een regio meer
kunstmest bespaard wordt indien deze verdeeld wordt over meerdere bedrijven, in plaats
van geconcentreerd wordt toegediend op enkele bedrijven. Dit zou mogelijk ook kunnen
gelden voor andere soorten organische meststoffen.
Belangrijke motivaties voor boeren om organische meststoffen te gebruiken zijn
positieve effecten op bodemkwaliteit, terwijl de belangrijkste belemmeringen liggen in
de financiële consequenties en een verhoogde druk van onkruiden, ziekten en plagen.
Samenvatting
203
Als beleidsmakers een stimulans wil geven om OS in akkerbouwgronden te behouden
of te verhogen, dan zal er meer inzicht nodig zijn waarom en onder welke
omstandigheden het gebruik van organische meststoffen leidt tot een verhoogde druk
van onkruiden, ziekten en plagen. Financiële consequenties (ten minste die op de korte
termijn spelen) zouden hierbij ook betrokken kunnen worden. Belangrijker nog is het
om te beseffen dat voordelen van OS voor de gewasopbrengst zich niet altijd voordoen.
Alleen in specifieke situaties treedt dit voordeel op. Als Europees beleid omtrent OS in
landbouwgronden maatschappelijke voordelen (zoals koolstofopslag of behoud van
biodiversiteit) wil afstemmen met voordelen voor gewasproductie, dan zal de nadruk
moeten liggen op gebieden met extremere milieus (erg droge of natte klimaten, steilere
hellingen, zandige gronden), of op regio’s met meer
gespecialiseerde gewassen en een
laag aandeel granen.
Acknowledgements
204
Acknowledgements
Like any other scientific work, this work would not have been possible without support
of numerous colleagues, friends and family. First and foremost, I would like to thank
my promotor Martin van Ittersum, whose patience almost seemed endless. Thanks,
Martin!! I also want to thank Hein ten Berge, project leader of CATCH-C and my co-
promotor. Thanks for your enthusiasm, Hein and - when in Wageningen
–
for creating
time for discussions and the occasional coffee.
One of the most cherished parts of my PhD journey was spending time at Rothamsted
Research. I still vividly remember the lunch sessions and Friday tea with cookies. Right
in a very difficult time of my PhD I found a great inspiration in my supervisor abroad
Andy Whitmore. Thanks for hosting me, Andy. I wish I could have stayed longer.
In Wageningen, on the very first day of my PhD journey, I met Anouk Cormont whom
proved to be a very strong ally in sharing both work and the latest baby pictures :-). Not
much later I met her colleague Gerard Hazeu who brought up the idea of soil organic
matter deficiency as a concept. Thanks to both of you for the pleasant collaboration. I
also cherish the collaboration with Annette Pronk who shared her wealth of practical
knowledge and
–
together with Bert Rijk - kept the Dutch farm survey running when I
was on leave. In addition, both Jan Verhagen and Peter Leffelaar were very helpful in
sharing their old boxes of literature on soil organic matter with me. This was an old-
fashioned but handy manner to get started. At the end of my PhD, Jaap Schröder proved
to be a useful sparring partner.
Within the CATCH-C project, I found many many nice collaborations with Carlo
Grignani, Laura Zavattaro, Luca Bechini, Chiara Costamagna, Magdalena Werner,
Janine Krüger, Juan Vicente Giráldez, Gema Guzmán, Tom Vanwalleghem, Karl
Vanderlinden, Greet Ruysschaert, Jo Bijttebier, Tommy D’Hose, Heide Spiegel,
Norman Schlatter, Taru Lehtinen, Nadine Turpin, Alina Syp and many others. Thanks
to all of you for making the project not only successful but also fun!
The fun part in my PhD also benefitted from numerous office mates at the Plant
Production Systems group to share lunch, coffee or a Friday afternoon drink with.
Charles Bagura, Guillaume Ezui, Edouard Rurangwa, Jairos Rurinda, Godfrey Taulya,
Wilson Leonardo, Gatien Falconnier, Lotte Klapwijk, Esther Ronner, Greta van den
Brand, Ilse de Jager, Wytze Marinus, Lenny van Bussel, Argyris Kanellopoulos, Sheida
Sattari, Jiska van Vliet, Jannicke Wichern, Pytrick Reidsma, Tom Schut, Katrien
Acknowledgements
205
Deschaemaker, Juliana Gil, Gerrie van de Ven, Maja Slingerland, Beyene Melisse, Anne
de Valen
ҫ
a, Marieke Sassen, Madeleine Florin, Sander de Vries, Christiaan Biemond,
Bob Douma, Joost van Heerwaarden, Petra Rietberg and many many others: thanks a
lot! With a special thanks to Ria van Dijk, Linda Kaster, Charlotte Schilt and Alex-Jan
de Leeuw for keeping the office running! When times became less fun and more difficult
I also found support from many colleagues, especially Esther was often willing to share
some PhD-sorrow, thanks for the friendship offered.
At Rothamsted I was lucky to find Shibu Muhammed, Lindsay Todman, Gordon Dailey,
Kevin Coleman, Margaret Glendining and Tom Sizmur as my temporary colleagues:
Thanks for making me feel welcome. The last few months of my PhD I spent
–
together
with colleagues in crime Joao da Silva and Lotte Woittiez - writing at the temporal
housing of Agrosystems Research. Thanks for hosting us, Raymond! And thanks to all
the other AGRO-colleagues for sharing much-appreciated coffee breaks in the B-Bar or
outside in the sun.
Almost coming to the end, I would like show my gratitude to Ken Giller for introducing
me to Martin and sharing his views on soils and farming systems. Another major
influence has been Bert Janssen. Thanks, Bert, for sharing your piles of knowledge and
critical mind.
Finally, in retrospect, my life choices benefitted much from my (grand) parental
influences. I want to thank my mother Ineke Ginjaar, who taught me the essential lesson
that there is no better man for a job than a woman. Thanks to my father Gerrit Hijbeek
for sharing his love for nature with his children. I want to thank my grandmother Nell
Ginjaar-Maas for motivating young Dutch girls to choose a technical study and take the
future in their own hands, I surely benefitted from her efforts. I thank my grandfather
Leendert Ginjaar for teaching me that solid arguments matter more than authority does.
I thank my grandparents Roel and Maartje Hijbeek for brightening up the day with jokes
and laughter. I want to thank my brother and sister for being there. I thank Eric for
sharing the journey with me. Finally, I want to thank Jason for being such a beautiful
shining light. May all your dreams be bright and passionate.
About the author
206
About the author
Renske Hijbeek was born on the 14
th
of July 1983 in Rotterdam, the Netherlands. After
secondary school she travelled for some time through North America and studied for
one year Physics at the
Rijksuniversiteit Groningen
before landing in Wageningen. In
Wageningen she obtained a BSc degree in Environmental Science. She conducted an
internship in Sierra Leone with a local youth organisation and wrote her BSc thesis on
the relation between environmental degradation and violent conflict. During this time
she was board member of the study association for environmental students and student
assistant at the department for Environmental Science.
Following her BSc degree, she completed an MSc degree in Human Ecology at the
Vrije
Universiteit Brussel
. She conducted her MSc thesis at the Laboratory of Plant Biology
and Nature Management. The subject of her thesis was the use of plotless vegetation
sampling methods in mangrove forests. This included a period of field work in Kenya
and resulted in a scientific publication. After graduation she worked at the Dutch
ministry of Agriculture and Economic Affairs on topics related to sustainable
agriculture. During this time, she was seconded for half a year to the taskforce on
resource use efficiency of DG Environment at the European Commission in Brussels.
Between 2012 and 2017, Renske worked at the Plant Production Systems Group of
Wageningen University, mainly in the European FP7 project CATCH-C. In this project
she collaborated with researchers from different institutes across eight European
countries, developed a farm typology and helped to set up a farm survey among more
than 3000 farmers. This PhD thesis is in part based on data collected in this project.
During her PhD, she went for an exchange period of six months to Rothamsted Research
in the UK.
Publications
207
Publications
Refereed scientific journals
Hijbeek, R., van Ittersum, M. K., ten Berge, H. F. M., Gort, G., Spiegel, H. & Whitmore, A. P.
2017. Do organic inputs matter
–
a meta-analysis of additional yield effects for arable
crops in Europe.
Plant and Soil
411: 293-303
Hijbeek R., Cormont A., Hazeu G.W., Bechini L., Zavattaro L., Janssen B.H., Werner M.,
Schlatter N., Guzmán G., Bijttebier J., Pronk A.A., van Eupen M. & van Ittersum, M.K.
2017. Do farmers perceive a deficiency of soil organic matter? A European and farm
level analysis
. Ecological Indicators
83: 390
–
403
Hijbeek R., ten Berge H.F.M., Whitmore A.P., Barkusky D., Schr
ӧ
der, J.J. & van Ittersum,
M.K. 2017. Apparent Nitrogen fertiliser replacement values for organic amendments
appear to increase with N application rates.
Nutrient Cycling in Agroecosystems
DOI
10.1007/s10705-017-9875-5
Hijbeek, R.,Pronk, A.A., van Ittersum, M.K., ten Berge, H.F.M., Bijttebier, J. & Verhagen, A.
2017 (in press) What drives farmers to increase soil organic matter? A case study in The
Netherlands.
Soil Use and Management
Khan, M. N. I., Hijbeek, R., Berger, U., Koedam, N., Grueters, U., Islam, S. Z., Hasan, M. A.
& Dahdouh-Guebas, F. 2016. An Evaluation of the Plant Density Estimator the Point-
Centred Quarter Method (PCQM) Using Monte Carlo Simulation.
PLoS ONE
11(6):
e0157985.
Hijbeek, R., Koedam, N., Khan, M. N. I., Kairo, J. G., Schoukens, J. & Dahdouh-Guebas, F.
2013. An Evaluation of Plotless Sampling Using Vegetation Simulations and Field Data
from a Mangrove Forest.
PLoS ONE,
8 (6): e67201
.
Conference proceedings
Ruysschaert, G., Bijttebier, J. Bechini, L., Zavattaro, L., Werner, M., Hijbeek, R, Pronk, A.,
Ten Berge, H. 2017. Barriers and drivers for adoption of non-inversion tillage in four
European countries. 1st World conference Soil and Water Conservation under Global
change. Lleida, Spain 12-16 June 2017
ten Berge, H., Hijbeek, R., van Ittersum, M., Gort, G. & Whitmore, A. 2016. Apparent long-
term fertilizer replacement value of arganic amendments depends on mineral fertilizer
N range itself.
Efficient use of different sources of nitrogen in agriculture
–
from theory
to practice Skara, Sweden 27 June
–
29 June 2016
, 438.
Hijbeek, R., Ten Berge, H., Van Ittersum, M. & Whitmore, A. 2015. Effects of organic matter
on attainable yields across Europe. In:
Book of Abstracts Wageningen Soil Conference
2015 ‘Soil Science in a Changing World’.
Wageningen.
Publications
208
Bijttebier, J., Ruysschaert, G., Marchand, F., Hijbeek, R., Pronk, A., Schlatter, N., Guzmán, G.,
Syp, A., Werner, M., Bechini, L., Guiffant, N. & Wauters, E. 2014. Assessing farmers'
intention to adopt soil conservation practices across Europe. In:
11th European IFSA
Symposium, Farming Systems Facing Global Challenges: Capacities and Strategies,
Proceedings, Berlin, Germany, 1-4 April 2014.
International Farming Systems
Association (IFSA) Europe, pp. 1903-1910.
Hijbeek, R., Pronk, A., ten Berge, H., van Ittersum, M., Ruysschaert, G. & Verhagen, A. 2014.
Barriers to adopting best management practices aiming at soil fertility and GHG
mitigation across dairy farmers in The Netherlands. In:
11th European IFSA
Symposium, Farming Systems Facing Global Challenges: Capacities and Strategies,
Proceedings, Berlin, Germany, 1-4 April 2014.
International Farming Systems
Association (IFSA) Europe, pp. 1903-1910.
Scientific reports
Bijttebier, J., Ruysschaert, G., Hijbeek, R., Rijk, B., Werner, M., Raschke, I., Steinmann, H.,
Zylowska, K., Pronk, A., Schlatter, N., Guzmán, G., Syp, A., Bechini, L., Turpin, N.,
Guiffant, N., Perret, E., Mauhe, N., Toque, C., Zavattaro, L., Costamagna, C., Grignani,
C., Lehninen, A. B., A., Spiegel, H., Portero, A., Van Walleghem, T., Pedrera, A.,
Lahguna, A., Vanderlinden, K. & Giraldez, V. 2015. Farmers review of Best
Management Practices: drivers and barriers as seen by adopters and non-adopters.
D4.422. CATCH-C report. p. 171
Pronk, A., Bijttebier, J., ten Berge, H., Ruysschaert, G., Hijbeek, R., Rijk, B., Werner, J.,
Raschke, I., Steinmann, H. & Zylowska, K. 2015. List of drivers and barriers governing
soil management by farmers, including cost aspects: D4.434. CATCH-C report. p. 180
Pronk A.A., Rijk B. and Hijbeek R. 2014. Task 4.2 Farm survey country report: The
Netherlands., CATCH-C report. Wageningen University and Research Centre,
Wageningen, p. 262.
Hijbeek R., Wolf J. and Van ittersum M.K. 2013. A typology of farming systems, related soil
management and soil degradation in eight European countries. D2.242. CATCH-C
report. Wageningen University and Research Centre, Wageningen, p. 226.
Other
Hijbeek, R. 2017. Relatie organische stof en gewasopbrengsten is niet zo simpel.
Boerderij,
http://www.boerderij.nl/Home/Blogs/2017/2/Relatie-organische-stof-en-
gewasopbrengsten-is-niet-zo-simpel-96207E/
Hijbeek, R. 2016 Organic matter in European soils: Solution for food security and climate
change mitigation?
Website of the Global Research Alliance on agricultural
greenhouse gas emissions.
Pronk, A. & Hijbeek, R. 2015. Bodemverbetering: vooral traditioneel.
Boerderij,
100, A14-
A15.
PE&RC Training and Education Statement
209
PE&RC Training and Education Statement
With the training and education activities listed below the
PhD candidate has complied with the requirements set by
the C.T. de Wit Graduate School for Production Ecology and
Resource Conservation (PE&RC) which comprises of a
minimum total of 32 ECTS (= 22 weeks of activities)
Review of literature (4.5 ECTS)
The role of soil organic matter for arable farming in Europe
Writing of project proposal (4.5 ECTS)
Raising soil organic matter content in European arable lands: a viable option at field,
farm or regional level?
Post-graduate courses (4.2 ECTS)
Theoretical ecology; Rijksuniversiteit Groningen (2012)
Linear models, generalized linear models and mixed linear models; PE&RC (2014)
Introduction to R for statistical analysis; PE&RC (2014)
Laboratory training and working visits (0.6 ECTS)
Workshop on the theory of planned behaviour and on how to conduct semi-structured
interviews with farmers; ILVO, BE (2013)
Invited review of (unpublished) journal manuscript (5 ECTS)
European journal of agronomy: crop residue management and wheat productivity (2016)
Agronomy journal: straw management, tillage options and maize yields (2016)
Nutrient cycling in agroecosystems: comparison of nitrogen flow indicators for dairy
farms (2017)
Ecological Indicators: effects of organic and mineral fertilisers on soil indicators and
crop production (2017)
Soil Use and Management: effect of different organic inputs in conventional and organic
cropping systems (2017)
PE&RC Training and Education Statement
210
Deficiency, refresh, brush-up courses (4.5 ECTS)
Geo-information tools; WUR (2012)
Basic statistics; PE&RC (2014)
Competence strengthening / skills courses (2.7 ECTS)
Project and time management; WGS (2013)
Techniques for writing and presenting a scientific paper; WGS (2013)
PE&RC Annual meetings, seminars and the PE&RC weekend (2.1 ECTS)
PE&RC Weekend (2012, 2016)
PE&RC Carousel (2016)
Wageningen PhD symposium (2017)
Discussion groups / local seminars / other scientific meetings (8 ECTS)
PhD Discussion group Sustainable Intensification of Agricultural Systems (SIAS)
(2013-2016)
Annual meetings of the European research project CATCH-C; in the Netherlands, Italy
and Poland (2012-2015)
Presentation of PhD research at a lunch meeting of the Conservation Science group of
Cambridge University, UK (2016)
Presentation of PhD research at a foodFirst Vijverberg session on the precarious balance
between science and policy making (2016)
International symposia, workshops and conferences (6.6 ECTS)
International conference Soil Carbon Sequestration; Reykjavik, Iceland (2013)
Wageningen Soil Conference; Wageningen, the Netherlands (2015)
Ecosummit; Montpellier, France (2016)
Lecturing / supervision of practicals / tutorials (4.2 ECTS)
Modelling Biological Systems
Analysing Sustainability of Farming Systems
Supervision of a MSc student (3 ECT)
Organic matter inputs and nitrate leaching
Acknowledgement of financial support
211
Renske Hijbeek was financially supported by the European Commission, Directorate
General for Research & Innovation, within the 7th Framework Programme of RTD,
Theme 2
–
Biotechnologies, Agriculture & Food (Grant Agreement N° 289782). The
views and opinions expressed in this thesis are purely those of the writers and may not
in any circumstances be regarded as stating an official position of the European
Commission.
Financial support from Wageningen university for printing this thesis is gratefully
acknowledged
Cover design by Arjan Hijbeek (
www.arjanhijbeek.com
)
Printed by Gildeprint Drukkerijen
Propositions
1.
More soil organic matter does not necessarily imply more soil fertility.
(this thesis)
2.
The potential to replace functions of soil organic matter for arable crop
production by technical means is more limited under extreme environmental
conditions.
(this thesis)
3.
Sequestering carbon in agricultural soils is at best a short-term, not a
sustainable solution to mitigate climate change.
4.
The need for economic quantification of ecosystem services is a defeat of the
rule of law by the rule of the market.
5.
The large volume of literature available on any given topic makes it easy to
transform opinions into facts and facts into opinions.
6.
It is better to evaluate research proposals on questions asked and
methodologies proposed, rather than on promised impacts and innovations.
7.
Societal valorisation of life science research does not only need clear policy
recommendations from scientists, it also requires expert policy makers to
capture underlying scientific principles.
8.
Without an obligatory and state-paid paternity leave equal in length to the
current obligatory and state paid maternity leave, all attempts to achieve
professional gender equality will be futile.
Propositions belonging to the thesis, entitled
On the role of soil organic matter for crop production in European arable farming
Renske Hijbeek
Wageningen, 4
October 2017
