Page 1
What drives farmers to increase soil organic matter?
Insights from the Netherlands
R . H
I J B E E K
1
, A . A . P
R O N K
2
, M. K.
V A N
I
T T E R S U M
1
, H.F. M.
T E N
B
E R G E
2
, J. B
I J T T E B I E R
3
&
A . V
E R H A G E N
2
1
Plant Production Systems, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands,
2
Agrosystems Research, Wageningen University & Research, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands, and
3
Flanders Research Institute for Agriculture, Fisheries and Food, Burgemeester Van Gansberghelaan 92, 9820 Merelbeke, Belgium
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 used the theory of planned behaviour to analyse what prevents or
encourages Dutch farmers to increase the 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 SOM. Farmers’ answers were related to their intention to increase SOM content, use
of organic materials and perceived increase in SOM content. Our results showed that Dutch farmers
are well aware of the possible benefits of SOM content for crop productivity. Farmers’ attitude,
subjective norm and perceived decrease in SOM content were significantly related to their intention to
increase SOM content. In our farm survey, this intention was very strong: 90% of the farmers stated
a high or very high intention to increase the SOM content of their fields. A higher intention to
increase SOM content was correlated with the use of organic materials as expressed as total and
effective C (
P
=
0.003 and
P
=
0.002, respectively), but this did not lead to a perceived increase in
SOM content. From a farmer’s point of view, this indicates that increasing SOM content is to a large
degree beyond their direct influence. The Dutch Manure and Fertiliser Act, costs of organic inputs
and the need to cultivate profitable crops (such as potatoes or sugar beet) were indicated as important
impeding factors for increasing SOM content.
Keywords:
soil organic matter, soil management, organic materials, theory of planned behaviour,
farmers’ intentions, farmers’ behaviour, soil conservation
Introduction
Soil organic matter (SOM) content affects 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 the SOM
content of their fields. They can use more organic materials
(such as animal manures or compost) instead of mineral
fertilizers, include more cereals (rather than root crops) in
their crop rotation or cultivate green manures (Magdoff &
Weil, 2004). However, 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 challenging when
short-term profits outweigh long-term objectives (Ingram
et al.
, 2014; Mandryk, 2016).
In the Netherlands, organic materials 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 (Schr
€
oder & Neeteson, 2008), which has caused
some concern for farmers’ abilities to maintain or increase
SOM contents. Using approximately 2 million soil samples,
Correspondence: R. Hijbeek. E-mail: renske.hijbeek@wur.nl
Received March 2017; accepted after revision January 2018
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.
1
This is an open access article under the terms of the Creative Commons Attribution License, which permits use,
distribution and reproduction in any medium, provided the original work is properly cited.
Soil Use and Management
doi: 10.1111/sum.12401
SoilUse
and Management
Page 2
Reijneveld
et al.
(2009) showed that, between 1984 and 2004,
average SOM content remained stable in agricultural soils in
the Netherlands. Verloop
et al.
(2015) however found a
decrease in SOM content for each nutrient management
strategy assessed on a Dutch experimental farm. The latter
might be explained due to the relatively high initial SOM
content (4.8%) in combination with a sandy soil.
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.
An
important
component
in
understanding human behaviour is the effect of social
relationships (Coleman, 1990). Both the theory of reasoned
action (Fishbein, 1979) and the theory of planned behaviour
(Ajzen, 1991) include (i) people’s own attitude and (ii) their
subjective
(social)
norm
when
trying
to
understand
motivational influences on human behaviour. An attitude is
based on the degree to which a person expects a certain
impact or outcome. Subjective norm refers to the social
pressure people experience to perform a certain behaviour.
Unlike the theory of reasoned action, the theory of
planned behaviour also includes beliefs on the possession of
requisite resources and opportunities
–
(iii) the perceived
behavioural
control
(Madden
et al.
,
1992).
Perceived
behavioural
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; Figure 1).
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. In our study, we would like
to understand what prevents or encourages Dutch arable
farmers to increase SOM content. All three previously
mentioned components seem relevant for SOM management
(farmers’
attitudes,
subjective
norm
and
perceived
behavioural control), and therefore, we used the theory of
planned behaviour for obtaining quantitative measures of
their underlying beliefs, as done previously by Burton (2004)
and Wauters & Mathijs (2014).
When recently asked for their concerns on the future of
soil fertility, Dutch farmers placed SOM content at the top
of the list (Reijneveld, 2013). It is however yet unknown how
the different elements of the theory of planned behaviour
play a role in farmers’ intention to increase SOM content or
use of organic materials. In this study, we used the theory of
planned
behaviour
to
analyse
why
and
under
which
conditions Dutch arable farmers are willing to increase
the percentage of SOM content of their fields. We addressed
the following research questions:
1.
Which beliefs form farmers’ attitudes, subjective norms
and perceived behavioural controls regarding SOM and
its management?
2.
How do these beliefs influence the intention of farmers to
increase SOM content?
3.
How does actual behaviour (use of organic material
inputs) correspond to farmers’ intentions to increase
SOM content?
Materials and methods
A farm survey was conducted among 435 arable farmers in
the Netherlands. 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. Second, we related these beliefs to farmers’
intention to increase SOM content. Farmers’ intention to
Attitude
on outcomes
Subjective
norm
of social referents
Perceived
behavioural
control
on control factors
Intention
Behaviour
Figure 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.
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
2
R. Hijbeek
et al.
Page 3
increase SOM content was then related to actual (as stated
by respondent) use of organic materials. Finally, perceived
change in SOM content was related to perceived behavioural
control, intentions to increase SOM content and the use of
organic materials.
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 as follows: farmers on
sandy soils, farmers on loam soils and farmers on clay soils
(Figure S1). 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: (i) year of birth after 1947
and (ii) spending more than 20 h labour per week to
farming. In total, 4770 letters were sent to farmers with a
personal link and password to the online questionnaire.
Before data analysis, two criteria were used to select only
arable farmers from the respondents (following Andersen
et al.
(2007): (i) more than two-third of the monetary value of
agricultural outputs from arable crops; and (ii)
<
50% of
farmland was grassland (either temporary or permanent). In
addition, farmers with
<
10 ha of land or having peat soils
(either reporting peat soils 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 as 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 materials they
used and how much.
Finding underlying beliefs of attitude, subjective norm and
perceived behavioural control regarding SOM and its
management
Farmers’ attitude.
In the terminology of the theory of planned
behaviour (which this study used as a methodology), expected
or potential benefits are called ‘outcomes’. For brevity and
consistency, we therefore adopt the term ‘outcome’ in this text,
in spite of its potential ambiguity.
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), 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); see also Table S1.
For each farmer, an attitude on each outcome (i) was found
by multiplying belief strength with outcome valuation
(equation 1). Consequently, attitude values ranged between
10 and
+
10. Mean attitude values for each outcome were
found by taking the average across all farmers. 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 and
+
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).
attitude
i
¼
belief strength
i
ð
outcome valuation
i
3
Þ
ð
1
Þ
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 performed using a
Spearman correlation (Kendall, 1948).
Farmers’ subjective norm.
As part of the CATCH-C project
(CATCH-C, 2017), a series of open interviews revealed five
social referents to be most important for arable farmers in
the Netherlands, as follows: 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), 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 positive towards increasing SOM (1
–
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
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
Farmers’ beliefs on SOM
3
Page 4
example, farmers were asked how motivated they are to
comply with the opinion of fellow farmers (1
–
5). This is
called the farmers’ motivation to comply.
For each farmer, the subjective norm for each social
referent (k) was found by multiplying normative belief with
the motivation to comply (equation 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 the 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) were
taken as a proxy for overall subjective norm.
subjective norm
k
¼
motivation to comply
k
ð
normative belief
k
3
Þ
ð
2
Þ
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 material inputs, availability of organic materials,
cultivation of specialized crops and the Dutch Manure and
Fertiliser Act. For each control factor (
N
=
5), farmers were
asked how strongly they thought the factor was applicable to
them. For example, farmers were asked if availability of
organic material inputs was limited in their region on a scale
from 1 to 5. This is 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 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) were taken
as a proxy for overall perceived behavioural control.
perceived behavioural control
m
¼
control strength
m
ð
control power
m
3
Þ
ð
3
Þ
Dependency of farmers’ beliefs on farm characteristics.
Stat-
istical tests were performed to assess whether 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 farmland being
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 performed using a Kruskal
–
Wallis rank sum test (McDonald, 2009). If a significant
difference was found, pairwise comparisons were made 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).
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, correlation tests were performed between farmers’
intention to increase SOM content and calculated values of
attitude, subjective norm and perceived behavioural control
using Spearman’s rank correlation test (Kendall, 1948).
Testing correlations between farmers’ intention to increase
SOM content and (stated) use of organic materials
Farmers were asked to report their use of organic material
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 Table S2 for conversion factors). To
calculate effective C, humification coefficients were used
indicating the remaining fraction of C from organic material
inputs 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 materials expressed in total C and
effective C per ha, which we refer to as ‘actual organic
materials’. Consequently, farmers’ intentions to increase SOM
were correlated with their annual organic material inputs.
Testing correlations between perceived change in SOM,
perceived behavioural control, intentions to increase SOM
and use of organic materials
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 material input (as defined above), farmers’ intentions
to increase SOM and the use of organic materials. All
correlations were tested using Spearman’s rank correlation
test.
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
4
R. Hijbeek
et al.
Page 5
Results
Farm and farmers’ characteristics and actual use of organic
materials
Of the 4770 farmers who were sent an invitation, 542
farmers filled out the section of the survey that dealt with
SOM management. Of these farmers, 501 were confirmed to
be arable farmers, of which 10 were excluded from analysis
because their farm size was smaller than 10 ha and 52
farmers were excluded because they either farmed peat soils
or had an average SOM content above 12%. Four farmers
were removed because their stated application of organic
materials 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.
3
4
14
19
31
72
94
81
78
38
0
50
100
150
200
250
20
30
40
50
60
Age (yr)
Farmers in survey (
n
)
10
425
0
100
200
300
400
500
Male
Female
Gender
168 174
53
24
4 4 4 2
1
1
0
50
100
150
200
250
0
150
300
450
600
750
Farm size (ha)
Farmers in survey (
n
)
263
108
38
13 4 3 2 1
1
1
1
0
100
200
300
0
1000
2000
3000
Farm size (x1000 euro)
15
103
161
99
28 14 8 3 1 1
1
1
0
100
200
300
10
12
14
Farm intensity (x1000 euro/ha)
Farmers in survey (
n
)
89
33
313
0
100
200
300
400
500
Cereals
0
2
4
6
8
Mixed
Specialized
Land use
147
98
190
0
50
100
150
200
250
Sand
Loam
Clay
Dominant soil texture of farm
Farmers in survey (
n
)
4
98
126
83
38
35
7
8
8
8
10
3
0
50
100
150
200
250
0%
2%
4%
6%
8%
10%
12%
Average SOM content
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure 2
Characteristics of farmers in the farm survey (
N
=
435). Land use categories: cereals indicate
>
50% of farmland cultivated with cereals
such as wheat, barley, rye, oat and triticale. Specialized land use indicates more than 50% of farmland cultivated with specialized crops such as
potatoes, sugar beet, carrots and onions. Mixed land use means neither category is present on more than 50% of farmland.
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
Farmers’ beliefs on SOM
5
Page 6
The mean age of farmers included in analysis was 48.8 yrs
old, with the median age exactly 50 yrs (Figure 2a). Farmers
in our survey were some years younger compared to the
entire population of Dutch farmers, for which the mean age
was 55 yrs 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, using the monetary value of agricultural output at
farm-gate price (SO). The mean farm size of the farmers
included in the analysis was 79.4 ha (Figure 2b), which was
66
65
83
76
59
22
13
10
4
11
0
50
100
150
0%
20%
40%
60%
80%
100%
Percentage farmland with green manures
130
72 77
48
32
24
12 8
3
2
1
0
50
100
150
Application of slurry (t/ha)
31
23 21 14 8 4 6 2 3
2
1
0
100
200
300
10
15
Application of FYM (t/ha)
40
31
13 18 5 2 3
1 1
1 1
1
1
0
100
200
300
Application of compost (t/ha)
34
123
46
9
99
5
48
147
5
318
40
0
100
200
300
No
Sometimes
Always
Not present in crop rotation
Incorporation of straw (when crop present in rotation)
Maize
Grain
Rapeseed
27
84
138
117
28
9
2
1
2
1
0
50
100
150
Total C added (t/ha)
No. of farmers in
survey
No. of farmers in
survey
No. of farmers in
survey
No. of farmers in
survey
No. of farmers in
survey
38
101
135
64
31
18
9 7 1 2
1
1
1
0
50
100
150
0
20
40
60
0
5
0
10
20
30
0
1
2
3
4
5
0
1
2
3
Total effective organic C added (t/ha)
353
292
293
(a)
(b)
(c)
(d)
(e)
(f)
(g)
Figure 3
Use of organic inputs by farmers in the farm survey (
N
=
435).
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
6
R. Hijbeek
et al.
Page 7
−1
0
1
2
2.5
3.5
4.5
Outcome valuation
(negative [−2] to positive [+2])
All outcomes
Fungi
Fungi
Fungi
−1.2
−1.0
−0.8
2.2
2.6
Outcome strength (low [1] to high [5])
Outcome valuation
(negative [−2] to positive [+2])
Outcomes with negative outcome valuation
Sand (
N
= 147)
Loam (
N
= 98)
Clay (
N
= 190)
CEC
CEC
CEC
Nutrients
Nutrients
Nutrients
Productivity
Productivity Productivity
Rooting
Rooting
Rooting
Soil life
Soil life
Soil life
Structure & workability
Structure
Structure & water
Water
Water
Workability
Workability
1.2
1.4
1.6
1.8
2.0
4.2
4.6
3.0
4.0
5.0
2.4
2.8
4.4
4.8
Outcome valuation
(negative [−2] to positive [+2])
Outcomes with positive outcome valuation
Productiivity
Structure
Water
Workability
Rooting
Soil life
Nutrients
CEC
Fungi
(a)
(b)
(c)
Figure 4
Mean values of outcome strengths
related to mean outcome valuations. CEC
=
nutrient
binding
capacity;
nutrients
=
nutrient release. Symbol shapes indicate
dominant soil texture of the farms.
Table 1
Attitudes on SOM (calculated using equation 1). Farmers are grouped by the dominant soil texture of their farm
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*
4
8.30*
4
Increased water-holding capacity
8.78
ab,
*
2
7.99*
6
8.04
b,
*
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
*A significant difference between at least two outcome scores within one row (using the
post hoc
Conover-Iman test). Letters indicate a
significant difference within one column between outcome scores.
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
Farmers’ beliefs on SOM
7
Page 8
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 (Figure 2c), 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).
The mean reported SOM content of farms was 3.61% SOM
(Figure 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
grew specialized crops (such as potatoes, sugar beet and/or
onions) on more than half of their land (Figure 2h).
Most farmers (
N
=
409) also provided information on
their use of organic materials (Figure 3). The majority of the
farmers (around 87%) cultivated green manures on some
parts of their land, but the exact percentages of farmland
cultivated differed widely (Figure 3a). Slurry was the most
often used animal manure, with 81% of the farmers using
some amount of slurry (Figure 3b). Most farmers did not
use FYM (
N
=
271), while 21 farmers used
<
1 tonne FYM
per ha (together 292 farmers, Figure 3c). Most farmers did
not use compost (
N
=
258), and 35 farmers used
<
2 tonnes
compost per ha (together 293, Figure 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 (Figure 3e). When converting organic material
inputs into total C content, the mean application rate was
1.35 tonne C/ha/yr (
0.06, 95% confidence interval (CI),
Figure 3f). When converting organic material inputs into
effective C content, the mean application rate was 0.57 tonne
effective C/ha/yr (
0.04, 95% CI, Figure 3g).
Beliefs of Dutch farmers on SOM
Farmers’ attitude.
Dutch farmers had a strong positive
valuation of eight outcomes of SOM and a negative
valuation of one outcome (the last being soil fungi,
Figure 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 positively by Dutch arable farmers (Figure 4a). At the
other end of the spectrum, soil 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 outcome
strength and valuation.
Reported SOM contents of farmers showed a strong skew
to the right beyond 6% SOM (Figure 2h). Comparing
farmers
reporting
more
than
6%
SOM
and
farmers
reporting
<
6% SOM, no significant differences were found
in attitudes on SOM. Both the outcome strengths of SOM
on workability and on ease of rooting were however
significantly higher for farmers reporting
<
6% SOM than
those reporting more than 6% SOM (
P
=
0.03 and
P
=
0.04,
respectively; Wilcoxon
–
Mann
–
Whitney test).
Farmers evaluated the general term ‘soil life’ positively,
but when asked specifically about soil fungi, this was the
only outcome of SOM which was evaluated negatively
(Figure 4c). The perceived strength of SOM influencing 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 1).
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
varied non-significantly between 6.90 and 6.96). This shows
that farmers on different soil textures appreciated the effects
of SOM content equally, but for different reasons. On sandy
soils, farmers most valued the effect of SOM content on
productivity, water-holding capacity and soil structure. On
loam and clay soils, farmers most valued the effect of SOM
content on soil structure, ease of rooting and workability.
How are the specific effects of SOM related to perceived
effects of SOM on productivity? On sandy and clay soils,
correlations were highest overall (Figure 5). On sandy soils,
perceived effects of SOM on continuous nutrient release and
water-holding capacity were strongly correlated with how
farmers perceived the effects of SOM on productivity
(Spearman’s rho
>
0.5). On clay soils, perceived effects of
SOM on ease of rooting and soil life were strongly
correlated with perceived effects of SOM on productivity
(Spearman’s rho
>
0.5). On loam soils, perceived effects of
SOM on ease of rooting had the strongest correlation with
perceived effects of SOM on productivity (Spearman’s
rho
>
0.5).
Farmers’ subjective norm.
Considering the subjective norm of
Dutch arable farmers, there were significant differences
dependent on the age group. Younger farmers were less
motivated to comply with views from the given referents
(such as fellow farmers, research or advisors, Figure 6).
Overall, advisors were thought to be most positive about
increasing SOM content, while fellow farmers were thought
to be least positive about increasing SOM content.
On average, the 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
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
8
R. Hijbeek
et al.
Page 9
norm. Older farmers (55
–
65 yr) had a significantly more
positive overall subjective norm on increasing SOM content
than medium age (45
–
55 yr) or younger (18
–
45 yr) farmers
(6.29 vs. 5.96 and 5.28, respectively, Table 2).
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 evaluated most positively
(Figure 7). Availability and costs of organic materials were
considered more or less neutral in control power, while
Dutch law on manure and fertilizer 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 about
the long-term effect of SOM content. Farmers with medium-
intensive farms were most negative about the costs of
organic materials. Farmers with low-intensive farms were
0.40**
0.43
0.53**
0.49**
0.47**
0.51**
0.46**
0.04
0.34
0.44
0.28*
0.43**
0.38**
0.37**
0.53**
−0.07
0.52**
0.49
0.48**
0.48**
0.44**
0.45**
0.60**
0
Clay
Loam
Sand
Improved
soil structure
Easy rooting
Improved
workability
Increased water-
holding capacity
More soil life
Continuous
nutrient release
Increased binding
capacity of nutrients
Increase in soil fungi
Correlation with perceived effect of SOM on productivity
Figure 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). Larger circle sizes
indicate
a
higher
correlation.
Colours
indicate correlation direction (negative or
positive).
Advisors
Advisors
Advisors
Magazines
Magazines
Magazines
Fellow farmers
Fellow farmers
Fellow farmers
Research
Research
Research
Study clubs
Study clubs
Study clubs
3.6
3.8
4.0
4.2
1.3
1.5
1.7
Normative belief of referent on SOM
(negative [−2] to postive [+2] )
Farmers’ motivation to comply with referent
(none [1] to very much [5])
young (18−45 yr )
middle (45−55 yr )
old (55−65 yr )
Figure 6
Mean
values
of
normative
belief
related
to
mean
motivation to comply with referents. Symbol shapes indicate age
groups of farmers.
Table 2
Subjective norm on SOM (calculated using equation 2). Farmers are classified according to their age
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*
*A significant difference between at least two referent scores within one row (using the
post hoc
Conover-Iman test). Letters indicate a significant
difference within one column between referent scores.
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
Farmers’ beliefs on SOM
9
Page 10
least negative about the effects of specialized crops on the
management of SOM. Overall perceived behavioural control
was
not
significantly
different
across
farm
intensities
(Table 3).
For low, medium and high-intensity 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.
Correlations between beliefs and farmers’ intention to
increase SOM
Most Dutch arable 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 the SOM
content of their fields (indicating either 1 or 2 on a
scale of 5), and 7.4%
of the
farmers were 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 4).
For outcomes, (Table 4a) belief strengths and outcome
valuations were more or less equally related to farmers’
intention to increase SOM content (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 farmers’ intentions to increase
SOM content than between the motivations to comply with
those referents and farmers’ intentions to increase SOM
content
(Table 4b,
0.22
–
0.33
vs.
0.11
–
0.16).
Subjective
norms based on advisors and study clubs were slightly
more strongly related with intentions to increase SOM
content than the subjective norms of research, magazines
and fellow farmers.
Availability
Availability
Availability
Long-term effect
Long-term effect
Long-term effect
Law
Law
Law
Costs
Costs
Costs
Crop rotation
Crop rotation
Crop rotation
−1.0
−0.5
0.0
0.5
1.0
2.5
3.5
3.0
4.0
4.5
Control strength (low [1] to high [5])
Control power
(negative [−2] to positive [+2])
Low (
N
= 144)
Medium (
N
= 144)
High (
N
= 143)
Figure 7
Mean values of control strength related to control power.
Costs
=
costs of organic inputs. Availability
=
availability of organic
inputs. Crop rotation
=
crop rotation with specialized crops. Law
=
Dutch Manure and Fertiliser Act. Symbol shapes indicate farm
intensity.
Table 3
Perceived behavioural control on SOM content by farmers (calculated using equation 3). Farmers are divided into equally sized groups
according to their farm intensity. Low-intensity farms:
<
2273.40
€
/ha. Medium-intensity farms: 2 273.40
–
3062.40
€
/ha. High-intensity farms:
>
3062.40
€
/ha. A different uppercase letter indicates a significant difference within one column between control factors
Low-intensity farms
(
N
=
144)
Medium-intensity farms
(
N
=
144)
High-intensity farms
(
N
=
143)
Perceived
behavioural
control (
10 to
+
10)
Rank
Perceived
behavioural
control (
10 to
+
10)
Rank
Perceived
behavioural control
(
10 to
+
10)
Rank
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 materials
0.35
c
*
2
0.10
c
*
2
0.91
c
*
2
Availability of organic materials
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
*A significant difference between at least two values for perceived behavioural control within one row (using the
post hoc
Conover-Iman test).
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
10
R. Hijbeek
et al.
Page 11
Control factors were not or only weakly related to
farmers’ intention to increase SOM content (Table 4c,
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 materials, long-term effects of SOM and
crop rotations with specialized crops. This indicates that,
despite
low
availability
of
organic
materials
or
the
restrictions of the Dutch Manure and Fertiliser Act, farmers
still have the intention to increase SOM content. Whether
they succeed in doing so is a different question.
Correlations between farmers’ intention to increase SOM
and the actual use of organic materials
Comparing farmers’ intention to increase SOM content with
their use of organic materials, a positive correlation was
found with the use of slurry and compost (
P
=
0.044 and
P
=
0.035,
respectively,
Table 5).
When
converting
all
organic materials into total C added or total effective C
added, in both cases application amounts increased with
higher intentions to increase SOM content (
P
=
0.003 and
P =
0.002, respectively).
Correlations between perceived change in SOM, perceived
behavioural control, intentions to increase SOM and use of
organic materials
Farmers also indicated whether 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 in SOM content). In
addition, 11% of the farmers perceived some decrease (4).
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 fields was
perceived to be stable or increasing.
Perceived change in SOM content was significantly related
to the perceived behavioural control of farmers (Table 6,
significant numbers ranging between
0.22 and 0.25).
Table 4
Correlations between beliefs on expected outcomes, referents and control factors and farmers’ intentions to increase SOM content. Tests
of significance were performed 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. Referent
s
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 materials
0.10
0.043
0.15
0.002
0.13
0.007
Availability of organic materials
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
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
Farmers’ beliefs on SOM
11
Page 12
Among the different control factors, perceiving the Dutch
Manure and Fertiliser Act as a strong obstacle was most
closely related to perceived trends in SOM content.
No correlation was found between the use of organic
materials and perceived increase in SOM (Figure 8). This
might be due to the time it takes before organic materials
have an effect on SOM content and thus before any change
will be perceived by the farmers (for 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 materials (Figure 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.
Discussion
The 435 farmers who were included in analysis were
somewhat younger and had larger farms (both in total land
Table 5
Actual use of organic materials 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
Intention to increase SOM (1
=
low, 5
=
high)
P
1 (
N
=
2)
2 (
N
=
7)
3 (
N
=
31)
4 (
N
=
75)
5 (
N
=
290)
Green manures
Percentage farmland with green manures
24%
34%
20%
33%
33%
0.079
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
28/20/25
82/81/114
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
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
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
P
values below 0.05 (written in bold) 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).
Table 6
Correlations between farmers’ beliefs on control factors and perceived increase in SOM content. Tests of significance were performed
using Spearman’s rank correlation test. Numbers indicate Spearman’s rho
Control factors
Correlation
with perceived
increase in SOM
P
value
Correlation
with perceived
increase in SOM
P
value
Correlation
with perceived
increase in SOM
P
value
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 materials
0.11
0.021
0.11
0.024
0.11
0.024
Availability of organic materials
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
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
12
R. Hijbeek
et al.
Page 13
area cultivated and economic size) than average Dutch
arable farms. The differences were however small and the
SOM contents of farmers’ soils corresponded very well with
the average for the Netherlands (3.6 vs. 3.5% SOM). The
outcomes of this study could therefore be considered
reasonably
representative
for
the
larger
population
(
N
=
20 260 in 2013; CBS 2017) of Dutch arable farmers.
Farmers’ beliefs on SOM
In the Netherlands, farmers usually have sufficient access to
mineral fertilizers (Potter
et al.
, 2010; Eurostat, 2012). It is
therefore not surprising that Dutch farmers value nutrient
release and nutrient binding capacity of SOM the least
(Table 1 and Figure 4): they do not primarily rely on these
functions of SOM for productive capacity. Dutch 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
(Figure 4).
In
addition, farmers with lower SOM contents (
<
6%) have a
greater appreciation of the effect of SOM on workability,
which builds further confidence in the practical benefits of
this relationship. In this respect, farmers’ views also align
well with findings from field experiments (Soane, 1990; Watts
& Dexter, 1997; Barzegar
et al.
, 2002; Rawls
et al.
, 2003;
Hamza & Anderson, 2005).
One can question how far farmers’ attitudes on SOM are
primarily
based
on
their
own
practical
(empirical)
experience or on lessons taught in agricultural college, by
parents or on messages received from agricultural advisors.
Figure 8 shows a significant correlation between farmers’
attitudes on SOM and their subjective norm. Most likely,
farmers’ attitude is therefore based on a combination of the
two (i.e. on practical experience and information received
from social referents). Ideally, farmers are not only at the
receiving
end,
but
also
return
their
knowledge
and
experience to advisors and researchers, making this an
iterative process, something which we aimed to facilitate
with this study.
Younger farmers were less motivated to comply with
referents than older farmers, especially when it comes to
messages received from research, farmers’ magazines and
study clubs (Figure 6). The reason for this difference
between young and older farmers remains unclear. Younger
farmers in the Netherlands often have a higher education
level, which might make them more critical of statements
from social referents. At the same time, younger farmers
probably have access to a wider range of information
sources (through e.g. wider use of Internet, IT and social
media) than older farmers. Another relevant factor might be
the trend in privatization of both agricultural extension
services and research in the Netherlands during the last
decades (Roseboom & Rutten, 1998; Kierkels, 2006). This
trend might affect younger farmers’ confidence in the
objectivity of extension services and research (the latter was
specifically mentioned as an issue for Dutch farmers in a
series of semistructured interviews conducted by the authors;
Pronk
et al.
, 2014).
Farmers with high-intensity farms (with the highest
economic output per ha) value the long-term effect of SOM
more than medium or low-intensity farms and see the costs
of organic materials as less of a constraint (Table 3). At the
same time, farmers with more intensive farms cultivate more
specialized crops (mostly root and tuber crops), which makes
it more difficult for them to increase SOM content. This can
be an additional challenge as root and tuber crops depend
more on a good soil structure for successful crop growth
than cereals (Verheijen, 2005; Hijbeek
et al.
, 2017). 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.
Attitude on
SOM:
6.96
Subjective
norm:
5.88
Perceived
behavioural
control:
−0.36
Intention
to increase
SOM:
4.59
EOC added
(t/ha/yr):
0.57
Perceived
increase
in SOM:
3.68
0.31***
0.14**
0.33***
0.33***
0.15**
0.25***
−0.16**
0.10*
–10 to +10
1 to 5
1 to 5
Scale:
Figure 8
Correlations
between
farmers’
attitude,
subjective
norm,
perceived
behavioural control, intention to increase
SOM and perceived increase in SOM using
Spearman’s rank correlation test. Numbers
in circles indicate mean values. Numbers
besides arrows give correlation coefficients
(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;
***
means
P
<
0.001).
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
Farmers’ beliefs on SOM
13
Page 14
Farmers’ intentions to increase SOM and actual use of
organic materials
Farmers with a higher intention to increase SOM content
used significantly more compost and slurry (
P
=
0.035 and
P
=
0.044, respectively) and applied more total C and
effective
C
to
their
soils
(
P =
0.003
and
P
=
0.002
,
respectively). We, however, found that the use of organic
materials was not directly related to a perceived increase in
SOM content. Increasing SOM using organic materials
often takes many years or even decades (K
€
orschens
et al.
,
2013), which could be one explanation for this lack of
correlation.
Previously, it was observed that land ownership (or land
tenure arrangements) affects the degree of soil conservation
on farmland (Fraser, 2004). In our study, we did not find a
significant relation between land ownership and farmers’
intentions to increase SOM content (data not shown), but
this seems a subject worth exploring further.
Currently, Dutch and European manure laws mainly focus
on reducing nutrient leaching to ground and surface waters,
which could discourage organic matter applications. Within
this legal context, there is, however, potential to reuse
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 materials
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 in the Netherlands to cultivate crops
in a more balanced crop rotation by including a higher share
of cereals or other crops which are beneficial for SOM
content.
Broader relevance of the findings
This study looked specifically at farmers’ beliefs regarding
SOM content in the Netherlands. There is a large supply of
animal manure in the Netherlands due to the presence of a
relatively large livestock sector (Oenema & Berentsen, 2004),
making it a relatively cheap source of nutrients. In addition,
costs of mineral fertilizers are low relative to land prices in
the Netherlands, and Dutch farmers cultivate relatively a
high share of their land with root and tuber crops. In other
countries, farmers might consider the role of SOM less
important (due to lower share of root and tuber crops), but
nutrient supply and costs of organic materials might play a
larger role. The differences in attitude between farmers on
different soil textures, the differences in subjective norm with
farmers’ age and the differences in perceived behavioural
control between farm intensities might be more general
patterns. To find out whether and how farmers’ beliefs on
SOM exactly differ across regions will however require
further research.
Conclusions
Using the theory of planned behaviour, this study has gained
insight into the underlying beliefs of farmers on increasing
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, while farmers are most motivated
to comply with findings from research.
Farmers’ attitude, subjective norm and perceived decrease
in SOM content were significantly related to their intention
to increase SOM content (Figure 8). In our farm survey, this
intention was very strong: 90.1% of the farmers stated a
high or very high intention to increase the SOM content of
their fields.
Even though a higher intention of farmers to increase
SOM led to an increased use of organic materials, it did not
lead to a perceived increase in the SOM content of farmers’
fields. In contrast, perceived behavioural control did have a
significant effect on perceived increases in SOM content.
From a farmer’s point of view, this indicates that increasing
SOM content is to a large degree beyond their direct
influence. Despite their best intentions, costs of organic
materials, (economic) needs to cultivate specialized crops and
the Dutch Manure and Fertiliser Act have a larger effect on
perceived increases or decreases of SOM.
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. This publication was
financially supported by the Dutch Ministry of Economic
Affairs (KB14 and PPS Sustainable Soil Management TKI-
AF-12054A) and by the European Commission under the
CATCH-C project (Grant Agreement N
°
289782) within the
7th Framework Programme for Research, Technological
Development and Demonstration. Its content does not
represent the official position of the EC and is entirely the
responsibility of the authors.
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
14
R. Hijbeek
et al.
Page 15
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Figure S1.
Soil texture map of The Netherlands.
Table S1.
Expected outcomes of increasing SOM content,
farmers’ referents and control factors included in the farm
survey.
Table S2.
Conversion coefficients for calculating total and
effective C content of organic inputs.
©
2018 The Authors.
Soil Use and Management
published by John Wiley & Sons ltd on behalf of British Society of Soil Science.,
Soil Use and Management
16
R. Hijbeek
et al.