Page 1
O R I G I N A L A R T I C L E
European survey shows poor association between soil
organic matter and crop yields
Wytse J. Vonk
.
Martin K. van Ittersum
.
Pytrik Reidsma
.
Laura Zavattaro
.
Luca Bechini
.
Gema Guzma´n
.
Annette Pronk
.
Heide Spiegel
.
Horst H. Steinmann
.
Greet Ruysschaert
.
Renske Hijbeek
Received: 26 March 2020 / Accepted: 20 October 2020 / Published online: 4 November 2020
Ó
The Author(s) 2020
Abstract
A number of policies proposed to increase
soil organic matter (SOM) content in agricultural land
as a carbon sink and to enhance soil fertility. Relations
between SOM content and crop yields however remain
uncertain. In a recent farm survey across six European
countries, farmers reported both their crop yields and
their SOM content. For four widely grown crops
(wheat, grain maize, sugar beet and potato), correla-
tions were explored between reported crop yields and
SOM content (N = 1264). To explain observed
variability, climate, soil texture, slope, tillage inten-
sity, fertilisation and irrigation were added as co-
variables in a linear regression model. No consistent
correlations were observed for any of the crop types.
For wheat, a significant positive correlation (
p
\
0.05)
was observed between SOM and crop yields in the
Continental climate, with yields being on average
263
±
4 (95% CI) kg ha
-
1
higher on soils with one
percentage point more SOM. In the Atlantic climate, a
significant negative correlation was observed for
wheat,
with
yields
being
on
average
75
±
2
(95%CI) kg ha
-
1
lower on soils with one percentage
point more SOM (
p
\
0.05). For sugar beet, a
significant positive correlation (
p
\
0.05) between
Electronic supplementary material
The online version of
this article (
https://doi.org/10.1007/s10705-020-10098-2
) con-
tains supplementary material, which is available to authorized
users.
W. J. Vonk (
&
)
M. K. van Ittersum
P. Reidsma
R. Hijbeek
Plant Production Systems, Wageningen University and
Research, Wageningen, The Netherlands
e-mail: wytsevonk@gmail.com
L. Zavattaro
Department of Agricultural Forest and Food Sciences,
Universita` Degli Studi Di Torino, Turin, Italy
L. Bechini
Department of Agricultural and Environmental Sciences,
Universita` Degli Studi Di Milano, Milan, Italy
G. Guzma´n
Institute for Sustainable Agriculture-CSIC, Cordoba,
Spain
A. Pronk
Agrosystems Research, Wageningen University and
Research, Wageningen, The Netherlands
H. Spiegel
Institute for Sustainable Plant Production, Austrian
Agency for Health and Food Safety, Vienna, Austria
H. H. Steinmann
Centre for Biodiversity and Sustainable Land Use, Georg-
August-Universita¨t Go¨ttingen, Go¨ttingen, Germany
G. Ruysschaert
Flanders Research Institute for Agriculture, Fisheries and
Food Research (ILVO), Merelbeke, Belgium
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Nutr Cycl Agroecosyst (2020) 118:325–334
https://doi.org/10.1007/s10705-020-10098-2
(0123456789().,-volV)
( 01234567
89().,-volV)
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SOM and crop yields was suggested for all climate
zones, but this depended on a number of relatively low
yield observations. For potatoes and maize, no signif-
icant correlations were observed between SOM con-
tent and crop yields. These findings indicate the need
for a diversified strategy across soil types, crops and
climates when seeking farmers’ support to increase
SOM.
Keywords
Soil organic matter
Crop yield
Europe
Arable farming
Survey
Introduction
Agricultural science has a long history of searching for
correlations between soil organic matter (SOM)
content and soil fertility (Russell
1977
). SOM is found
to affect soil water retention (Nyamangara et al.
2001
;
Zebarth et al.
1999
), nutrient availability and the
suppression of pests and soil borne diseases (Asirifi
et al.
1994
; Darby et al.
2006
). While all these
processes are beneficial for crop yields, the size of
these benefits remains uncertain.
More recently, a number of studies attempted to
quantify the direct benefits of SOM on crop yields.
Several studies found a significant positive correlation
(de Moraes Sa et al.
2014
; Lucas and Weil
2012
;
Oldfield et al.
2019
,
2020
). However, others indicate
that no significant effect of SOM on crop productivity
could be found (Hijbeek et al.
2017a
; Loveland and
Webb
2003
; Schjønning et al.
2018
). These diverging
findings call for a deeper search to understand the
conditions under which SOM may contribute to
improved soil fertility and crop yields.
Most of the mentioned studies used field or pot
experiments. Findings in controlled experiments may
however deviate from farmers’ experience in the field
with more varying circumstances and less controlled
management. The inclusion of farmers’ experiences
would add a valuable dimension to the available data
on SOM and crop yields. Even more so, because the
benefits of SOM depend on farm management, as
more intensive management and reliance on technical
means reduce dependence of crop yield on SOM
functions (van Noordwijk et al.
1997
).
Next to management, the beneficial effect of SOM
on crop yields depends on climates and soil types.
Increase in SOM content may also have potentially
negative effects: slow nutrient mineralisation by
organic matter, for example, might not supply nutri-
ents at the precise moments when the crop needs those
nutrients leading to potentially larger nutrient losses
(Chen
2006
).
In a recent large-scale farm survey across Europe,
farmers were asked to report their average SOM
content and crop yields. Analysis of these data could
give further insight in the relationship between SOM
and crop yields under actual farming conditions,
taking into account the variation in climates, soil types
and cultivated crops. Using these data, we aim to
answer the following three research questions:
1.
Can correlations be found between SOM content
and crop yields under current European farming
practices, based on farmers’ observations?
2.
What is the influence of climate, slope, soil texture
and crop type on the correlation between SOM and
crop yields?
3.
How is the correlation between SOM and crop
yields affected by farm management such as
irrigation, tillage intensity and fertiliser use?
Material and methods
Study area
The relationship between SOM content and crop
yields was analysed based on a farm survey conducted
in 2013 as part of the European Catch-C project. Farm
survey data for the following six countries was used:
Austria, Belgium, Germany, Spain, Italy and the
Netherlands. For each country, details on climates and
respondents are listed in Table
1
. Methodology related
to the findings presented here is described below; more
details about the Catch-C farm survey are described by
Bijttebier et al. (
2015
).
Analysis of survey data
Main variables
The two main variables used for our analysis were
observed yield (tonnes ha
-
1
) from the crops of interest
and the reported average SOM content (%) across the
whole farm. Representative crop yields for the last
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326
Nutr Cycl Agroecosyst (2020) 118:325–334
Page 3
years were asked. To obtain a substantial sample size,
the analysis was done for four widely cultivated crops:
grain maize, wheat, sugar beet and potato. Reported
SOM content was often based on soil tests. If farmers
were uncertain about their SOM level, they were given
the option to leave the question open. The latter was
much more apparent in countries where less knowl-
edge was available to farmers about their soil status
(e.g. Spain).
Biophysical co-variables
To explain correlations between SOM and crop yields,
soil texture, slope and climate were included as
biophysical co-variables. For soil texture, farmers
indicated the farm area consisting of sand, loam or
clay soils (1, 2 and 3 respectively). Following, in our
analysis we calculated an average value for soil texture
per farm. So for example, a farm with 50.0% sandy
soils and 50.0% loamy soils would get an average
texture value of 1.5. For slope, a similar procedure was
followed with 1 indicating level land (0%) to 5 a steep
slope (
[
15%).
Climate type was based on the climate zones
described by Metzger et al. (
2005
). To create a larger
sample size per climate zone, they were aggregated
into an Atlantic (Atlantic north; Atlantic central),
Continental (Continental; Pannonian) and Mediter-
ranean (Mediterranean north; Mediterranean south)
zone.
Farm management co-variables
Irrigation, tillage practice and fertiliser use were
included as management co-variables. A weighted
tillage intensity value was calculated similar to the
other weighted values resulting in a value between 1
and 3; 1 meaning solely no tillage and 3 meaning
solely ploughing. Irrigation was indicated by yes or
no: a farm with or without irrigation use.
Fertiliser use was reported in amounts of slurry,
farm yard manure, compost and mineral fertiliser. All
fertiliser types were converted into kg N ha
-
1
yr
-
1
,
using N coefficients (nitrogen fertiliser replacement
value) based on literature (Supplementary Table 1).
Mineral fertiliser was reported in kg N ha
-
1
yr
-
1
.
Data organization and outlier removal
To increase the accuracy of reported SOM contents,
farmers could indicate if they were uncertain about the
average SOM content of their farm. Farmers who did
Table 1
Climate zones, number of survey respondents, average SOM content and reported crop yields per country
Country
Climate zone
# Farmers
(response
rate)
Average
SOM
content (%)
Average wheat
yield (tonnes
FM ha
-
1
yr
-
1
)
Average maize
yield (tonnes
FM ha
-
1
yr
-
1
)
Average sugar
beet yield
(tonnes FM
ha
-
1
yr
-
1
)
Average Potato
yield (tonnes
FM ha
-
1
yr
-
1
)
Austria
Continental
48 (*)
3.01
(N = 30)
6.29 (N = 25)
10.38 (N = 16)
72.75 (N = 15)
45.71 (N = 7)
Belgium
Atlantic
983
(26%)
2.73
(N = 322)
9.00 (N = 111)
11.44
(N = 159)
71.25 (N = 64)
44.01
(N = 170)
Germany
Atlantic
Continental
439(16%)
3.35
(N = 260)
7.26 (N = 190)
9.83 (N = 31)
66.75 (N = 83)
40.87 (N = 49)
Spain
Mediterranean
208 (*)
1.70
(N = 34)
3.00 (N = 3)
13.00 (N = 2)
–
–
Italy
Mediterranean
310 (*)
2.68
(N = 100)
5.53 (N = 40)
11.08 (N = 47)
55.75 (N = 4)
–
Netherlands
Atlantic
621
(10%)
3.77
(N = 516)
9.20 (N = 340)
12.30 (N = 33)
80.39 (N = 367)
51.70
(N = 246)
Total
2609
3.26
(N = 1262)
8.31 (N = 709)
11.26
(N = 288)
76.77 (N = 533)
47.71
(N = 470)
N indicates the sample size
*Response rate is not known in these areas
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Nutr Cycl Agroecosyst (2020) 118:325–334
327
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not indicate their average SOM content, or were
uncertain about it, were excluded from analysis. In
addition, SOM values above 12.0% were removed (as
this might refer to a peat soil; N = 96). Soil organic
carbon, which is used in Germany and Belgium, was
converted into SOM using the conventional factor of
1.724 (Pribyl
2010
). Soils containing less than 0.1%
SOM were considered outliers and also removed from
analysis (N = 9). Because we were interested in the
shape of the point cloud, we only removed extreme
crop yield outliers per country [Q3
?
3*inter quartile
range (IQR)], which were considered biophysically
impossible (N = 28). Outliers of total effective fer-
tiliser application were removed using 3.0*IQR
(759 kg N ha
-
1
yr
-
1
; N = 47).
Statistical analyses
Multiple linear regression and model selection
First, QQ-plots were made to assess if the data was
normally distributed (Supplementary Figure S1). To
assess the influence of SOM and different co-variables
on crop yields, multivariate analyses were performed
for each crop using the car package for R (Fox
2018
),
according to the following equation:
Yield
SOM
þ
climate
þ
texture
þ
slope
þ
tillageintensity
þ
effectivefertiliser
þ
irrigation
þ
e
:
ð
1
Þ
Initially a full model (Eq.
1
, including all co-
variables) was run. Following, the explanatory power
of the full model was compared with a reduced model
(excluding SOM as an explanatory variable) to assess
the added value of SOM to explain observed crop
yields. Using this approach however bears a risk of
overfitting the data. To account for overfitting,
corrected Akaike information criterion (AICc) can
be used to evaluate the explanatory power of statistical
models and the relevance of co-variables.
Subsequently, a model selection was performed
using AICc for each crop to assess variable impor-
tance, and specifically the importance of SOM in
explaining crop yields. To this end, the dredge
function from the MuMIn package for R-studio
(Barton
2019
) was used. Based on the AICc values
(Giraud
2014
) a model ranking was made. For all crop
types, multiple top ranking models could be consid-
ered as the ‘best’ model, as their explanatory value
was similar (
D
AICc
\
2). The mean effect size of
SOM on crop yield and corresponding 95% confidence
intervals were calculated across these top ranking
models. The first best model for every crop was further
checked for significance using an Anova type 3
table [for models with interactions, from the car
package (Fox
2018
)].
Quantile regression
Linear regression does not investigate the shape of the
point cloud from the data. To investigate if SOM
influences minimum or attainable crop yields (the
shape of the point cloud), a quantile regression was
performed using the Quantreg package from Koenker
et al. (
2019
). Quantile regression is similar to linear
regression, but only investigates a certain part of the
data. Lower and upper quantiles of the data were
investigated: tau = 0.1 and 0.9. In this study, the 0.1
quantile gives an indication for minimum yield of a
crop for a certain SOM content. The 0.9 quantile gives
an indication for the attainable yield at a certain SOM
content. Whether slopes of the lower and upper
quantiles were significantly different from zero was
assessed using the ‘NID’ method to calculate standard
errors (Koenker et al.
2019
).
Results
Reported SOM contents and crop yields
Shares of respondents that reported SOM content
differed per country: in the Netherlands, the response
proportion was the largest (83.1%) and in Spain it was
lowest (16.3%). The average SOM content varied
from 1.7% in Spain to 3.8% in the Netherlands. For all
countries combined, the average SOM content was
3.26% (Table
1
), with a median of 2.59% SOM.
The highest average crop yields were observed in
the more northern European countries, the lowest in
Spain and Italy. This was not the case for maize, with
maize yields in Italy and Spain being similar to the
more northern European countries. Among the respon-
dents in Spain, none of the farmers cultivated potato or
sugar beet (Table
1
). The average reported crop yields
in this survey differed less than 15.0%, both positive
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328
Nutr Cycl Agroecosyst (2020) 118:325–334
Page 5
and negative, from national statistics averages
(EUFADN
2019
) for all countries, except for wheat
yield in Austria (22.7% higher reported average yield).
SOM and crop yields
Wheat yields were significantly correlated with SOM
content using simple linear regression (
p
\
0.05), but
the size and the direction differed per climate zone: the
correlation being negative in the Atlantic climate, and
positive in the Continental climate (Fig.
1
). For the
other crops, no correlation was observed using simple
linear regression.
To assess the influence of SOM on crop yields when
including effects of all co-variables (such as soil
texture), a multivariate regression was done. Descrip-
tive information about the co-variables can be found in
supplementary Table S2. When comparing a full
model (Eq.
1
, with SOM as an explanatory variable)
with a reduced model (without SOM as an explanatory
variable), no differences were found in the total
explanatory value. R
2
adjusted was the largest for
wheat with (0.5497) and without SOM (0.5503). In all
cases, R
2
adjusted were similar between the full model
and the reduced model, with the largest difference for
maize (0.0236).
Based on AICc values, a model ranking was made
(Supplementary Table 3). The number of top ranking
models having a similar AICc value differed per crop
type (wheat N = 8; maize N = 3; sugar beet N = 5 and
potato N = 21). For wheat, maize and sugar beet,
SOM was included as an explanatory variable for crop
yields in all the top ranking models. For potato, SOM
was included as an explanatory variable in more than
Fig. 1
Influence of climate on the relation between SOM and
wheat yield (
a
), maize yield (
b
), sugar beet yield (
c
) and potato
yield (
d
). The data points for yield are given different colours
and shapes per climate: Green square = Mediterranean, Blue
circle = Continental and Red triangle = Atlantic. The coloured
lines indicate significant simple linear regression for each
climate zone. The black dotted lines indicate two quantiles of the
data (tau = 0.1 and 0.9)
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Nutr Cycl Agroecosyst (2020) 118:325–334
329
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half of the top ranking models. However, only for
wheat and sugar beet the effect size of SOM on crop
yield was significant. For these two crops, the effect
sizes within the top ranking models with a similar
AICc value is provided in Supplementary Tables 4 and
5.
Correcting for the influence of other co-variables,
across the different multiple regression models, wheat
yields were on average 263
±
4 (95% confidence
interval—CI) kg ha
-
1
higher on soils with one
percentage point more SOM in the Continental
climate. In the Atlantic climate, wheat yields were
on average 75
±
2 (95%CI) kg ha
-
1
lower on soils
with one percentage point more SOM (
p
\
0.046).
Sugar beet yield was positively correlated with SOM
in all climate zones (
p
= 0.016), with on average
1007
±
157 (95% CI) kg yield increase per percent-
age point SOM. However, this value depended on a
small number of low yield observations. Excluding
these points (N = 9) resulted in more similar top
ranking models. SOM was included in 11 of the 20 top
ranking models only, with yields being on average
75
±
279 (95% CI) kg higher per percentage point
SOM in all climate zones. For potato and maize, there
was no significant effect of SOM on crop yields when
correcting for the influence of other co-variables.
The models with highest AICc value (Table
2
;
Supplementary Table 3) were selected to further
analyse the role of SOM, as they are similar
(
D
AICc
\
2) to the other best models. For wheat,
interactions between SOM and climate, and SOM and
tillage intensity were included in the selected best
model. For maize, interactions between SOM and
climate, and SOM and effective fertiliser were
included (Table
2
). For all crop types, crop yields
differed significantly between climate zones.
Another notable result is the effect of soil texture
and tillage on sugar beet yield, i.e. a finer soil texture
and more intensive tillage was correlated with higher
sugar beet yield (Table
2
). The effect of soil texture did
not depend on the low yield observations, but tillage
did (
p
= 0.076 without low yield observations). For
potato, more intensive tillage was also correlated with
higher yields.
Effect of SOM on minimum and attainable yields
For wheat, the lower quantile of yields had a
significant positive slope (
p
\
0.001, dotted line in
Table
2
ANOVA
tables
from
the
first
best
selected
model
based
on
their
AICc
value
for
wheat,
maize,
sugar
beet
and
potato
yield.
Information
of
other
top
ranking
models
can
be
found
in
Supplementary
Table
3
Wheat
Maize
Sugar
beet
Potato
Estimate
p
value
Estimate
p
value
Estimate
p
value
Estimate
Pr(
\
F)
SOM
-
0.7150
0.00905**
-
0.5410
0.16025
0.8839
0.01649*
–
–
Climate
X
\
2.2e
-
16
***
X
\
2e
-
16***
X
\
2.2e
-
16***
X
6.301e
-
12***
Slope
–
–
–
–
–
–
–
–
Soil
texture
0.6542
1.992e
-
14***
–
–
4.1037
6.238e
-
06***
–
Tillage
intensity
-
0.5280
0.14628
–
–
4.0510
0.01324*
4.0033
0.03539*
Effective
fertiliser
–
–
-
0.0100
0.68413
–
–
–
–
Irrigation
-
0.1639
0.14320
0.7508
0.04728*
–
–
1.4709
0.13170
SOM
9
Climate
X
0.00573**
X
0.00254**
–
–
–
–
SOM
9
Tillage
intensity
0.2425
0.01710*
–
–
–
–
–
–
SOM
9
Effective
fertiliser
–
–
0.0040
0.01848*
–
–
–
–
Signif.
codes:
***
=
0.001,
**
=0.01,
*
=0.05,.
=0.1,’’
=1
123
330
Nutr Cycl Agroecosyst (2020) 118:325–334
Page 7
Fig.
1
). This shows that for wheat, higher SOM is
correlated with a higher minimum yield. There was no
significant effect on attainable or minimum yield for
the other crops investigated. The lower quantile of the
sugar beet data had a close to significant positive slope
(
p
= 0.067).
Discussion
Correlations between SOM and yields differ
per crop type and climate zone
Our results show that, when present, correlations
between reported SOM and crop yields differ per crop
type and climate zone. In other cases, such correlations
were absent. The negative correlation between SOM
content and observed wheat yields in the Atlantic
climate correspond well to recent findings for Den-
mark of Schjønning et al. (
2018
) and Oelofse et al.
(
2015
) who found a negative effect of SOM content on
potentials yields in the same climate. The positive
correlations found between SOM and sugar beet yields
in all climate zones correspond well to other studies,
such as those by Oldfield et al. (
2019
), Hijbeek et al.
(
2017a
), and Verheijen (
2005
), who suggested that
there is more evidence of a positive effect of SOM on
yields of root and tuber crops than on cereal yields.
This was partly supported by this study but needs more
investigation as the finding relied on a small number of
data points and potato yield was not correlated with
SOM content.
There was no consistent correlation between the
other co-variables (such as soil texture, tillage inten-
sity or irrigation) and crop yield, except for the
influence of climate as discussed above. For example,
sugar beet and wheat yields were found to be higher
with finer soils, perhaps related to an improved water
or nutrient holding capacity of these soils. However, it
is surprising that a similar correlation was not found
for potato and maize. SOM content was found to differ
across climate zones, with highest SOM values being
reported in the Atlantic climate, followed by the
Continental and Mediterranean climate. This corre-
sponds well with observations that increasing SOM is
more difficult in a warmer climate (Leiro´s et al.
1999
;
Miller et al.
2004
). At the same time, less variation in
the reported SOM contents in the Mediterranean
climate might also explain why it was more
challenging to find correlations with crop yields. In
addition, as thresholds for SOM content are currently
uncertain (Hijbeek et al.
2017b
), it is unknown if the
ranges of SOM content in the different climate zones
were sufficient to observe yield effects.
Similarity in findings between field experiments
and farmers’ observations
Whilst many studies have investigated the relation
between SOM content and crop yields using experi-
mental data (e.g. Dawe et al.
2003
; Han et al.
2018
;
Oelofse et al.
2015
; Pan et al.
2009
), we were not
aware of published studies which rely on farmers’
perceptions. The similarities between our results and
previous studies are therefore the more striking as
these previous studies were based on experimental
conditions, while this study was based on farmers
observations, often in more heterogeneous conditions.
Using minimum yield as an indicator the effect
of SOM on crop yields
In our study, the upper quantile of crop yields was
assumed to be representative for attainable yields
(highest farmers’ yields) at a given SOM content,
while the lower quantile was assumed to be represen-
tative for minimum crop yields at a certain SOM
content. Unlike previous studies who also used the
concept of attainable yield (i.e. Hijbeek et al.
2017a
;
Oelofse et al.
2015
; Schjønning et al.
2018
), in our
study no significant effect of SOM on attainable yield
was found. This deviation might be caused by the
difference in measurement of attainable yield: previ-
ous studies based estimates of attainable yield on
nitrogen response curves in controlled experimental
settings, while we relied on the upper quantile of the
yield cloud of farmers’ observations, inherently con-
taining more variation. Our study did, however, find
that a higher SOM content was significantly positively
correlated with higher minimum wheat and slightly
positively correlated with minimum sugar beet yields.
This study therefore suggests that besides attainable
yield, minimum yield might also be a useful indicator
to assess yield effects of SOM.
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Limitations of this study
Confounding factors
Finding effects of SOM on crop yield across different
climates and soil types is inherently problematic as
soil types and climates not only influence the SOM
content but also have a direct impact on crop yields
and crop management, giving rise to potential con-
founding factors (Hijbeek et al.
2018
). In our study,
where possible, we have assessed the effect of SOM on
crop yields within climate zones. In addition, we have
included soil texture as an explanatory variable in our
model and thereby assessed the added value of
including SOM, next to soil texture, in explaining
variation in observed crop yields.
Using farm survey data
With this study, we did not aim to give a definitive
answer to the question what the role of SOM is for crop
yields. Rather, we hope that by sharing farmers’
observations (a relatively underexploited data source
on this topic) and comparing these with findings from
agronomic field experiments, we have added one more
building block to the puzzle. Benefits of farm survey
data include gaining insight in actual on-farm situa-
tions and perceptions, and an improved ability to
explain effects to farmers. The use of farm survey data,
however, also has several limitations, of which we will
discuss the main ones below.
In this study, we relied on reported values for
average SOM content and representative crop yields at
farm level by farmers. First, this will have caused more
variation in the data than if one researcher would have
made all the measurements using a more standardized
approach. For example, the reported values on SOM
might have been based on different sampling depths.
The effect of this on reported values is unknown and
could therefore not be included in the analysis. On the
other hand, our approach probably positively affected
the sample size of the dataset. To diminish the impact,
farmers were given the option to report if they were
aware or not of their average SOM content, which will
have led to more precise answers, but might also have
led to a bias in the data set towards farmers who
measure their SOM content more often. Moreover, as
representative crop yields of recent years were
requested, yield extremes might not always have been
included. It is not known to which extent this affects
the correlation between SOM and crop yield.
A second limitation of the approach is that average
SOM, crop yield and management (such as fertiliser
use, tillage and irrigation) information was requested
for the entire farm, rather than for individual fields. In
reality, farmers most likely differentiate crop man-
agement across fields and crops, and SOM and crop
yields may differ between fields within a farm. If field
and crop-level data would have been available,
possibly more of the variation in yield could have
been explained by management factors (e.g. Silva
et al.
2017
), and better correlations might have been
found between SOM and crop yield. Also, we cannot
exclude that other attributes such as tillage depth and
date of application would affect the data. To account
for this variation in the farm survey data, quantile
regressions were performed. The conducted quantile
regression could have been improved when conducted
per climate zone or soil texture, however sample sizes
were too small to allow for this.
Finally, the farm survey could not account for
differences in soil life or fractions of SOM, whilst this
could be of importance when determining the rela-
tionship between SOM and crop yield (Six and Jastrow
2002
).
Model selection
The effect of SOM on crop yields was analysed based
on a model selection. In literature, much uncertainty
exists about model selection strategies. Thoughtless
model selection is repeatedly mentioned as leading to
model overfitting, which results in finding effects that
are false in reality (Burnham and Anderson
2002
). To
avoid this, we considered both the first selected model
and lower ranked models with a similar AICc value.
Additionally, we used a full model, which includes all
parameters, to compare with the selected models.
Conclusions and recommendations
SOM is often mentioned as a soil quality indicator, but
this study, based on farmers’ perceptions, only partly
supports the claim that SOM has a positive effect on
crop yield. Based on the presented results, increasing
SOM seems to be most relevant in Europe when
cultivating root or tuber crops (such as sugar beet) or
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Nutr Cycl Agroecosyst (2020) 118:325–334
Page 9
wheat in a Continental climate. This study does not
intend to suggest that SOM should be ignored as a soil
quality indicator or does not provide other important
ecosystem services in current European farm prac-
tices, but our findings do provide little evidence for a
consistent causal relation between SOM and crop
yields across climates and crop types.
Our study was based on farmers’ reporting of the
SOM content of their soils. If research funds would
allow for more operational costs and logistics, future
research could improve our analysis by including soil
samples and in-field measurements of an array of soil
properties, potentially improving insights into under-
lying mechanisms. The study also relied on farmers’
reports on crop yields being representative for the last
few years. If possible, future research could investi-
gate the role of SOM on annual variation in crop
yields, especially focussing on more extreme weather
events (such as very dry or wet years), potentially
enhancing insights into the function of SOM for soil
resilience.
To increase SOM content in agricultural soils,
farmers’ support is essential. Our findings suggest that
only in specific conditions an effect of SOM on crop
yield is observed, either positive or negative. In the
positive cases, support from farmers to increase SOM
can be gained by highlighting these benefits. In other
cases, support has to be gained through other incen-
tives. Our findings therefore indicate the need for a
diversified strategy across soil types, crops and
climates when seeking farmers’ support to increase
SOM.
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. First, we thank Hein ten Berge (WUR) for
coordinating the Catch-C project. In Austria, main contacts were
the provincial and regional officials of the chamber of
agriculture in Lower Austria. We thank Norman Schlatter and
Taru Sanden (AGES) for helping out with the survey. In
Belgium, data was collected with the help of Erwin Wauters and
Jo Bijttebier (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 (WUR) helped with extracting farmers’
addresses from the database and Bert Rijk (WUR) supported the
survey logistics. In Germany, data was collected with the help of
Janine Mallast (IGZ), Isabell Raschke and Magdalena Werner
(University of Go¨ttingen). We thank all colleagues for their
efforts and support.
Authors’ contributions
Set up of the research: W.J.V., R.H.,
M.K.I., P.R. Data collection: R.H., L.Z., L.B., G.G., A.P., H.S.,
H.H.S., G.R. Data analysis: W.J.V., R.H., M.I., P.R. Writing of
the manuscript: W.J.V., R.H., M.I., P.R., L.Z., L.B., G.G., A.P.,
H.S., H.H.S., G.R
Funding
Data collection for this publication was financially
supported 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.
Compliance with ethical standards
Conflicts of interest
The authors declare that they have no
conflict of interest.
Availability of data and material
Data will be published on a
data portal upon acceptance.
Open Access
This article is licensed under a Creative Com-
mons Attribution 4.0 International License, which permits use,
sharing, adaptation, distribution and reproduction in any med-
ium or format, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The
images or other third party material in this article are included in
the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not
included in the article’s Creative Commons licence and your
intended use is not permitted by statutory regulation or exceeds
the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit
http://creativecommons.org/licenses/by/4.0/
.
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