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Plant and Soil

, Volume 411, Issue 1–2, pp 293–303 | Cite as

Do organic inputs matter – a meta-analysis of additional yield effects for arable crops in Europe

  • R. Hijbeek
  • M.K. van Ittersum
  • H.F.M. ten Berge
  • G. Gort
  • H. Spiegel
  • A.P. Whitmore
Open Access
Regular Article

Abstract

Background and aims

Organic inputs have a positive effect on the soil organic matter balance. They are therefore an important asset for soil fertility and crop growth. This study quantifies the additional yield effect due to organic inputs for arable crops in Europe when macro-nutrients are not a limiting factor.

Methods

A meta-analysis was performed using data from 20 long-term experiments in Europe. Maxima of yield response curves to nitrogen were compared, with and without organic inputs, under abundant P and K supply.

Results

We were surprised to find that, across all experiments, the mean additional yield effect of organic inputs was not significant (+ 1.4 % ± 1.6 (95 % confidence interval)). In specific cases however, especially for root and tuber crops, spring sown cereals, or for very sandy soils or wet climates, organic inputs did increase attainable yields. A significant correlation was found between increase in attainable yields and increase in soil organic matter content.

Conclusions

Aggregating data from 20 long-term experiments in Europe, this study shows that organic inputs and/or soil organic matter do not necessarily increase yields, given sufficient nutrients are supplied by mineral fertilisers. Results show the relevance of some environmental factors for additional yield effect of organic inputs, but no simple relation between organic inputs and crop growth.

Keywords

Soil fertility Soil organic matter Organic inputs Crop yield Food security Soil carbon sequestration 

Abbreviations

SOM

Soil organic matter

SOC

Soil organic carbon

Introduction

Soil organic matter (SOM) is often considered the most important indicator of soil fertility (Johnston et al. 2009; Reeves 1997). It contributes to each of fertility’s three dimensions: the physical (structure, aeration, water retention), the biological (biomass, biodiversity, nutrient mineralisation, disease suppression) and the chemical (nutrient supply) dimension. On this basis, maintaining SOM is an important strategy to maintain crop productivity (Lal 2004). SOM contains about 50 % organic carbon (Pribyl 2010), making it’s increase a potential means to mitigate greenhouse gas emissions (Smith 2016). Because of this positive contribution to climate change mitigation and food security, a voluntary action plan has been proposed at COP21 to increase SOM in all soils, called “the 4/1000 initiative: Soils for Food security and Climate” (UNFCCC 2015).

In some cases however, yield effects of SOM seem smaller than expected. Reviewing the literature, Loveland and Webb (2003) found it difficult to establish a critical level of SOM for temperate regions. They also did not find evidence for an adverse effect on crop yields where SOM contents in the soils of England and Wales were reduced. Similarly, comparing potential yields of winter wheat and spring barley across a large range of SOM contents in Denmark, Oelofse et al. (2015) found no significant effect on yields of winter wheat and only a small effect on yields of spring barley.

The mentioned studies compared the effect of actual SOM content, they did not assess specific management practices used to increase SOM. In arable soils, SOM can be increased by increasing organic inputs or reducing organic outputs (Freibauer et al. 2004). Increasing organic inputs can be done by increasing returned biomass (roots, litter) via higher yields or adding additional organic inputs such as compost, animal manure or crop residues. Decreasing organic outputs can be done by changing the moisture content of the soil or by using reduced or no tillage, although the effect of the latter two remain disputed (Govaerts∗ et al. 2009). Actual increase in SOM depends on a number of factors, such as the current amount of SOM, type of organic input, and environmental factors such as temperature, soil texture, and humidity (Smith et al. 1997).

Studies assessing the effects organic inputs on crop yields show mixed results. A recent meta-analysis of 32 long-term experiments in China compared the combined use of organic inputs and fertilizers with either only organic inputs or only fertilizers (Wei et al. 2016). The average yield increase of combining organics and fertilizers on wheat, maize and rice was found to be 8 % compared to using only fertilizers. In a different case however, (Dawe et al. 2003) found no improvement in grain yield trends with application of either manure or straw in intensive rice systems.

How do these contrasting insights compare? Although previous research has found a positive effect of either organic inputs or SOM on crop yields (Monreal et al. 1997; Wei et al. 2016), Oelofse et al. (2015) argue that in these studies the effect of nutrients is seldom separated from other effects. In fact, Wei et al. (2016) also mention this as the largest limitation of their study.

To circumvent this limitation, we have assessed the effect of organic inputs in a system without macro-nutrient limitation. In such a system, any effect of organic inputs on yield can be attributed to improved soil structure or soil life (the other two components of soil fertility). In our study, effects of organic inputs (also called organic fertilisers, organic manures or organic amendments) on attainable crop yields were examined in 20 long term experiments across a variety of soils and climates in Europe. To exclude the effects of macro-nutrients, the yield effect was analysed under abundant phosphorus (P) and potassium (K) supply and varying rates of nitrogen (N). Using this approach, we answer the following research question: Do organic inputs increase attainable yields? Previously, any effect of organic inputs or SOM on crop yield which are not related to macro-nutrients has been called the “additional yield effect” (Janssen 2002). Our objective is: to find the additional yield effect of organic inputs, beyond the macro-nutrients supplied.

Materials and methods

Literature search

To find data on long term experiments in Europe, two databases with metadata were used: the EuroSOMNET metadata on 110 long-term experiments and a database compiled in a recent European research project (CATCH-C (2015)) containing 377 long-term experiments. Promising experiments were selected and publications were searched using online search engines (Google scholar, ISI Web of knowledge). When more publications were available for one treatment, only yield data from the most recent publication was included.

The following selection criteria were used to select experiments: (1) at least 4 increasing levels of N applications without organic inputs; (2) at least 4 increasing N application levels with organic inputs; (3) P and K applied in ample amounts on all fields; (4) at least 5 years of yield data; (5) if crops are grown in rotation, yield data available for at least 2 rotations; (6) yield data reported for individual crop types (mean yield values averaged over rotation were excluded).

Data from 20 experiments was found adhering to these selection criteria (Fig. 1 and Table 1). Following, 107 distinct data sets were created, each representing a single combination of experiment location, crop type and organic input type, covering a number of years of yield observations. All data was processed in R 3.0.0 (R Core Team 2015).
Fig. 1

Overview of locations of long term experiments included in the meta-analysis (20)

Table 1

Details of experiments (20) included in the meta-analysis. For each experiment, clay content, percentage SOM at start, CGIAR-CSI Global Aridity Index, starting time, crops included in analysis and references used are given. For Vienna, additional data was provided by Heide Speigel (AGES). For Muencheberg, data was provided by Dietmar Barkusky (ZALF). For Grabow, data was provided by Dorota Pikuła (IUNG). For Bologna, additional data was provided by Guido Baldoni (University of Bologna). For Puch, additional data was provided by Matthias Wendland (Bayern LFL)

Experiment

Clay content (% < 2 μm)

SOM at start (%)

CGIAR-CSI Aridity index

Starting time

Crops

Types of organic inputs

References used

Bad Lauchstadt

21

3.56

0.68

1978

p,s

FYM, GM, straw

Eich et al. 2013; Pfefferkorn and Körschens 1995

Bologna 1

-

1.3

0.75

1966

m,ww

FYM, slurry, straw

Giordani et al. 2010; Triberti et al. 2008

Grabow

2

1.29

0.73

1980

m,p sb,ww

FYM

-

Iasi

39

-

0.61

1984

m,s

FYM, straw & BL

Hideborn Alm and Dahlin 2007; Mogârzan et al. 2007; Vasilica et al. 1997

Ivanovice

33

-

0.7

1984

wb,ww,

FYM, straw & BL

Hideborn Alm and Dahlin 2007; Vrkoc et al. 1996

Keszthely

21.3

1.4

0.72

1984

wb

FYM, straw & GM

Hoffmann et al. 1997; Kismányoky and Tóth 2012

Limburgerhof

10

1.29

0.72

1987

m, ww

straw & GM, straw & GM & slurry

Lang et al. 1995

Lukavec

15

3.3

0.84

1984

p,wb

FYM, straw & GM

Káš et al. 2010; Vrkoc et al. 1996; Vrkoč et al. 2002

Madrid

27

1.19

0.31

1985

sb,ww

FYM, straw

López-Fando and Pardo Fernández 2008; López-Fando et al. 1999

Methau

14.8

3.3

0.81

1966

p,sb,s,ww

straw

Albert and Grunert 2013; Körschens et al. 2014

Muencheberg

4.05

0.99

0.75

1962

sb,s,wr,ww

FYM, straw

-

Novi Sad

-

2.62

0.64

1984

m,s,ww

FYM, straw & BL, straw & BL & slurry

Starčević et al. 2005; Starčević et al. 1997

Oldenburg

6.19

2.84

1.09

1984

s,wb,ww

slurry, straw & GM & BL

Klasink and Steffens 1995

Prah Ruzyne

33

-

0.62

1984

s,wb,ww

FYM, straw & GM & BL

Vrkoc et al. 1996

Puch

23

1.86

1.22

1984

m,s,

slurry, straw, FYM, straw & BL, straw & GM & BL, straw & GM & slurry, straw & slurry

Hege and Offenberger 2006

Rauischholz-hausen

17

2.24

1.06

1984

s,wb,ww

straw & GM & BL

Von Boguslawski 1995

Speyer

8.9

1.24

0.8

1984

s,wb,ww

FYM, straw & GM & BL

Bischoff 1995

Sproda

6.3

2.2

0.68

1966

sb,s,ww

FYM, straw

Albert and Grunert 2013; Körschens et al. 2014

Tartu

7.7

1.71

1.05

1989

p,sb,sw

FYM, straw & BL

Kanal et al. 2003; Kuldkepp et al. 1996

Vienna

25.2

2.55

0.71

1986

s,wb,ww

FYM, slurry, straw & GM & BL

Hösch and Dersch 2002; Spiegel et al. 2010

m maize, p potatoes, s sugar beet, sb spring barley, wb winter barley, wr winter rye, ww winter wheat, FYM farm yard manure, GM green manure, BL beat leaves

Calculating additional yield effect of organic inputs for each set of data

Crop yields are known to steeply increase at lower levels of N application while levelling off or slightly decrease at high levels of N application. When yields are known at different levels of N application, response curves can be fitted (Cerrato and Blackmer 1990). For each set of data in our meta-analysis, two yield response curves were drawn: one with and one without organic inputs (Fig. 2). To fit the curves, the following formula was used (George 1984):
$$ \mathrm{yield}=\mathrm{a}+\mathrm{b}*{0.99}^{\mathrm{N}}+\mathrm{c}*\mathrm{N}+\boldsymbol{\upvarepsilon} $$
(1)
Fig. 2

Example of yield response curve to mineral fertiliser-N under sufficient P and K supply with and without organic inputs. a Black line is the response curve without organic inputs. The green line is the response curve with organic inputs. Squares indicate the maximum of each curve. The difference between the two maxima is due to the additional yield effect of organic inputs. b Green circle is the relative difference between the two maxima. Green line indicates the 95 % confidence interval due to the goodness of fit of the two curves. Yield data is from maize grown in Novi Sad between 1996 and 2003, with and without farmyard manure

In formula 1, N is nitrogen added as mineral fertiliser (kg N/ha), a, b and c are parameters to be fitted and ε is the error term. The maximum of each curve was calculated by setting the first-order derivative equal to zero and inserting the optimal N rate in Eq. 1. As P and K were applied in ample amounts, at the maximum of each curve N, P and K (the macro-nutrients necessary for crop growth) are not a limiting factor for crop yields. Accordingly, the maximum of each curve was regarded as the attainable yield for local environmental conditions and management. The additional yield effect of organic inputs was calculated by taking the difference between the attainable yield with and without organic inputs.

For each data set, response curves might fit the data points better or worse, creating an error in the estimation of the additional yield effect. To correct for the goodness of fit of each curve, the delta method (Oehlert 1992) was used, giving a variance for each data set. The inverse of the variance was used as a weighting factor for the calculated additional yield effect of each data set. To enable comparisons among crops, the relative difference was chosen as the response variable in the meta-analysis, expressing the additional yield effect of organic inputs as percentage of attainable yield with only mineral fertiliser. Fig. S1- S3 in Online Resource 1 show the individual response curves, while Fig. S4 in Online Resource 1 gives the additional yield effect and related 95 % confidence interval for each data set.

Removing of outliers

Yield effects were checked for outliers by assessing the point cloud across different variables and constructing a funnel plot. If a data point was located outside the point cloud and P and K could not be excluded as yield limiting factors in the treatment without organic inputs, the data was removed from the meta-analysis (This was only necessary in one case).

Assessing influence of co-variates

To assess the influence of environmental factors, crop characteristics or type of organic input, factors and co-variables were identified. Two grouping factors were used: type of organic input and crop type. In some cases, a combination of organic inputs was used, for example straw and slurry, where one of year straw was applied and the next year slurry. Each combination of organic inputs was included as a separate category.

In addition, for each dataset the following information was obtained from the literature: clay content, percentage of SOM content at the beginning of each experiment, amount of carbon in organic input, SOM change during each experiment and duration of each experiment. When numbers were given in percentage of soil organic carbon (SOC), they were multiplied with the conventional factor 1.724 (Pribyl 2010; Waksman and KR 1930). Duration of each experiment was multiplied with yearly carbon applied to give the total C added over the years. Geographical coordinates of each experiment were used to find the CGIAR-CSI Global Aridity Index (Trabucco and Zomer 2009).

To assess the effect of the grouping factors and co-variates, a mixed effects model with a hierarchical structure (Konstantopoulos 2011) was used. Mixed effect models allow for incorporation of random effects, which is important when observations are not from a stratified or random sampling design as is typical in meta-analyses (Gurevitch and Hedges 1999). The following two random effects were incorporated in the analysis: (1) Experiment: As a single experiment may produce multiple data sets, experiment was used as a random factor. (2) Treatment without organic inputs: Within a single experiment, multiple treatments with organic inputs can exist (for example one treatment with farmyard manure and one with crop residues) which are all compared to the same treatment without organic inputs (with only mineral fertiliser).

Group means for crop type and type of organic input were estimated with R-package lsmeans (Lenth 2015). To find the marginal effect of each co-variate on the additional yield effect of organic inputs, a separate model was made for each co-variate using the function lme (linear mixed-effects model) of package nlme (Pinheiro et al. 2015). Within these models, log-likelihood was maximized and yield effects were weighted by the inverse of the variance. Interaction between crop type and co-variate were checked on significance. Only SOM change had a significant interaction with crop type.

Model selection

To assess which combination of co-variates and factors could best explain the difference in the additional yield effect of organic inputs, multi-model dredging was performed using the dredge function in the R-package MuMin (Barton and Barton 2015). This function constructs a list of models by combining the given co-variates and then gives a ranking according to the corrected Akaike Information Criteria (AICc), an indicator commonly used to assess model fit (Bozdogan 1987).

Two model selections were run. In the first model selection, only data from experiments was included for which information on both percentage of clay and SOM content was available (15 out of 20). For the second model selection, only experiments were included for which data on SOM change was available (8 out of 20).

Sensitivity analysis

For some sets of data, maximum yield was not reached within the N applications of the experimental set-up. These maxima had to be estimated beyond the experimental set-up resulting in a higher uncertainty. When analysing the data, these points could be either included or excluded, with each choice having its own advantage. Excluding these data points gives a dataset with more certainty on each individual estimate, but including them increases the size of the total dataset. Because a greater uncertainty results in a larger variance, meaning a smaller weight is given to a yield effect which is calculated with a maximum yield outside the experimental setup, we chose to include these data sets in the meta-analysis. To see the effect of including or excluding the maxima outside the experimental set-up, a sensitivity analysis was done on the main results.

Results

The mean additional yield effect of organic inputs across all 107 data sets is not significant in our meta-analysis (1.4 % ± 1.6 (95 % c. i.)). When excluding maxima estimated outside the experimental set-up, the mean yield effect is slightly higher: 1.9 % ± 2.0 (95 % c.i.), yet still not significant.

Additional yield effect across type of organic inputs, crop types and time of sowing

Comparing different types of organic inputs, the yield effect is roughly similar, but only the mean additional yield effect of farmyard manure is significantly positive (2.2 % ± 1.8 (95 % c.i.) – Fig. 3a). Yet we did find effects on specific crops. For potatoes the mean yield increase is 7.0 % ± 4.9 (95 % c.i.). In addition, our results show that maize, a crop with a less developed root system than wheat or barley, also benefits significantly from organic inputs (mean yield effect of 4.0 % ± 3.7 (95 % confidence interval) – Fig. 3b).
Fig. 3

Influence of type of organic input (a), crop type (b) and time of sowing (c) on additional yield effect of organic inputs. Circles are mean additional yield effects, lines the 95 % confidence interval. Numbers in brackets are the number of data sets in each group. Only groups with at least 8 data sets are shown. Green residues are either green manures or beet leaves. Groups with less than 8 data sets and results of the sensitivity analysis are shown in Fig. S5 of Online Resource 1

Across the 20 experiments, cereals sown in winter do not benefit form organic inputs in our meta-analysis (Fig. 3c). On the other hand, spring sown cereals do benefit (3.4 % ± 2.6 (95 % c.i.)). Spring sown crops have a shorter time frame to develop their root system which is needed to acquire sufficient nutrients and water (Johnston et al. 2009). Organic inputs, by improving soil structure, might facilitate this process, resulting in larger yields.

Influence of soil, climate and amount of C added

Crops grown on more sandy soils show a positive yield effect of organic inputs, while more clayey soils show neutral or negative yield effects (Fig. 4a). Relatively sandy soils normally have a poorer soil structure, which can be improved by adding organic inputs. Soils with low SOM content would also be expected to benefit more from organic inputs, but this is not apparent in our results (Fig. 4b).
Fig. 4

Influence of soil texture (a), SOM content at the start of the experiment (b) climate (c) and amount of C applied over the years (d) on the additional yield effect of organic inputs. Clay content is expressed as the percentage of particles <2 μm in the soil. Climate is expressed as the CGIAR-CSI Global Aridity Index. Larger points have a smaller variance and therefore a higher weight. P (Δ intercepts) is the probability for the intercepts to be equal. P (slope) is the probability the common slope is equal to zero

For each experiment, we expressed climate in terms of aridity using the CGIAR-CSI Global Aridity Index (Trabucco and Zomer 2009). Lower values indicate lower temperatures with more rainfall while higher numbers indicate higher temperatures with less rainfall. In our study, crops grown in wetter climates benefit more from organic inputs (Fig. 4c).

Experiments differ in the type and the amounts of organic inputs applied annually, and in their duration. After converting all organic inputs to total C (ton C/ha, cumulated over the years), no significant relation was found between total C input and the yield effect (Fig. 4d).

Relative increase in SOM

For a subset of experiments, percentage increase in SOM during the experiment could be calculated. When running a model selection, combining the relative increase in SOM content with crop type gives the largest explanation of variance in the additional yield effect of organic inputs (Tables S1 and S2 in Online Resource 1). The magnitude of the effect is shown in Fig. 5a.
Fig. 5

Relation between increase in SOM and yield increase. a Increase in yield related to increase in SOM. x-axis: increase in SOM between the treatment with only mineral fertiliser and the treatment with organic inputs added. y-axis: difference in maximum yield between the treatment with only mineral fertiliser and the treatment with organic inputs added. b axes vice versa from a. Larger points have a smaller variance and therefore a higher weight. P (Δ intercepts) is the probability for the intercepts to be equal. P (Δ slope) is the probability for the slopes to be equal

Discussion

When discussing possible benefits of organic inputs and soil organic matter beyond nutrients supplied, it has been suggested that root or tuber crops might benefit more than cereals (Haan 1977; Verheijen 2005). The reason being that root and tuber crops depend more on soil structure for their successful cultivation and harvesting. Our study confirms this suggestion with a mean yield increase for potatoes of 7 %.

Crops grown in both very dry or very wet conditions could potentially benefit from organic inputs as SOM increases water holding capacity in dry climates (Díaz-Zorita et al. 1999) and prevents soil compaction in wet climates (Soane 1990). In our study, crops grown in wetter climates do benefit more from organic inputs. As expected for a meta-analysis over Europe, most of our experiments (16) however have a humid climate (index >0.65), with three experiments having a dry sub-humid climate (index 0.5–0.65), one a semi-arid climate (0.2–0.5) and none arid or hyper arid climates (index <0.2). As very dry climate were not included, this could be the reason why we could not confirm whether organic inputs have additional yield effects in dry climates.

Very weathered soils, mostly occurring in tropical regions, were also not included in our meta-analysis. Weathered soils often have very low cation exchange capacity (Palm et al. 1997) and lack a number of micro nutrients necessary for crop growth (Gupta et al. 2008). On weathered soils therefore, yield effect of organic inputs could be larger when related to treatments with only N, P and K supplied. A recent global database suggests experimental set-ups as used in our meta-analysis do not exist outside temperate regions (ISCN 2015), establishment of such long term experiments would therefore be recommended.

Before analysis, percentage of SOM at the start of each experiment was expected to be the largest influencing factor. Yet, no significant difference was found comparing experiments with different SOM contents (Fig. 4b). There is however uncertainty associated with comparing SOM contents across 20 experiments. When available, measurements of the upper soil layer or plough layer (often 24–30 cm) were included in the analysis, yet depth of measurement was not always explicitly stated. In addition, measurements of SOM are known to deviate, depending on methods used (Hoogsteen et al. 2015). Even though some error in SOM measurements might be involved, our finding does correspond well with a recent study in Denmark comparing yields of winter wheat across a large range of SOM contents (Oelofse et al. 2015), with similarly no effects found.

Figure 5a seems to indicate that more so than the total SOM at start (Fig. 4b) or the total C added (Fig. 4d), it is the percentage of fresh SOM in the soil which makes a difference. If so, this finding corresponds well with suggestions of Loveland and Webb (2003) that the proportions of fresh SOM is more important than the total pool of SOM. On the other hand, higher yields also have an effect on SOM by returning more crop, root and stubble residues (Glendining and Powlson 1995). One could therefore question if larger yields in our analysis are the result of the increased SOM content (Fig.5a), or vice versa (Fig. 5b)? In practice, both possibilities might be true and – if so – can be mutually reinforcing: in some cases more SOM gives somewhat higher yields, which adds more organic matter to the soil which in turn gives higher yields, which then again gives more SOM.

Limitations of study and broader contextualization

This meta-analysis did not find a significant mean additional yield effect of organic inputs. When assessing the use of organic inputs on a farm or regional level however, other aspects might also be relevant. Organic inputs can promote the buffering function of soil in years with less favourable conditions, thereby reducing yield variability (Pan et al. 2009). In our experiments, variability in attainable yields was not lessened with organic inputs (data not shown), but this could be tested further under more extreme climates.

Using organic inputs can also have environmental effects. Soils with higher SOM contents for example might create a more flourishing habitat for soil biota (Chang et al. 2007). Maintaining SOM contents can therefore contribute to biodiversity conservation.

Combining organic inputs with mineral fertilisers can decrease the demand for mineral fertilisers which can have positive effects such as a decrease in demand for fossil fuels (Wood and Cowie 2004). In our meta-analysis, the savings of mineral fertiliser N with organic inputs are substantial (Fig. S6 and Table S3 in Online Resource 1). The savings in mineral fertiliser N however do not outweigh the total N in the organic inputs and mineral N added for growth of green manures or decomposition of straw. Consequently, organic inputs might affect the extend of nitrate leaching, nitrous oxide or ammonia emission. For nitrate leaching, both positive (Leclerc et al. 1995) and negative cases (Basso and Ritchie 2005; Oelofse et al. 2015) are known. It has been suggested that the number of years of application is crucial and that over the long-term, if nutrients are applied attuned to crop requirements, organic inputs have no significant effect on nitate leaching (Maeda et al. 2003).

Even though the mean additional yield effect across all data sets was not significant, a large variance exists between data sets. Using grouping factors (crop type, type of organic input) and co-variates (clay content, aridity), some variance was explained, but large parts remained unknown. In some individual cases, organic inputs did increase attainable yield significantly (Fig. S4 in Online Resource 1). In others, organic inputs might have had little effects on soil structure, either because soil structure was already very good or because it was beyond simple repair. These type of nuances can be tackled in-depth in single experiments, but are difficult to disentangle when aggregating larger data sets. Combining meta-analysis with more in-depth studies is therefore vital for more thorough understanding of processes and mechanisms involved.

Conclusions

Using organic inputs to increase soil organic matter is often seen as a win-win situation for food security and climate change mitigation, such as in the recently proposed “4/1000 initiative” at COP21 (UNFCCC 2015). Using organic inputs to sequester carbon might be a viable option to buy time for developing technologies for reducing industrial emissions (IGBP 1998), this meta-analysis however shows that benefits for crop yields cannot be assumed to follow directly. On sandy soils, in wet climates and for certain crops (some root or tuber crops and spring sown cereals) organic inputs can increase yields beyond the nutrients they supply. In those cases, increases in attainable yields vary mostly between 3 to 7 %. In the majority of cases however, supplying only mineral fertiliser gives similar yields.

Notes

Acknowledgments

We thank all who set up, maintained and shared data from long-term experiments in Europe through their publications. We thank Dietmar Barkusky (ZALF), Matthias Wendland (Bayern LFL), Guido Baldoni (University of Bologna) and Dorota Pikuła (IUNG) for providing additional data. We acknowledge Margaret Glendining for providing an overview on long term experiments in Europe and sharing her network. We thank Jan Verhagen and Bert Janssen for suggestions on methodology and Steve McGrath and Ken Giller for reviewing the text.

Supplementary material

11104_2016_3031_MOESM1_ESM.docx (2.3 mb)
ESM 1 (DOCX 2307 kb)

References

  1. Albert E, Grunert M (2013) Wirkung einer langjährig differenzierten mineralisch-organischen Düngung auf Ertrag, Humusgehalt, N-Bilanz und Nährstoffgehalte des Bodens. Arch Agron Soil Sci 59:1073–1098CrossRefGoogle Scholar
  2. Barton K, Barton MK (2015) Package ‘MuMIn’. 1.15.6 edn, The Comprehensive R Archive Network.Google Scholar
  3. Basso B, Ritchie JT (2005) Impact of compost, manure and inorganic fertilizer on nitrate leaching and yield for a 6-year maize–alfalfa rotation in Michigan. Agriculture, Ecosystems & Environment 108:329–341. doi: 10.1016/j.agee.2005.01.011 CrossRefGoogle Scholar
  4. Bischoff R (1995) Der Internationale Organische Stickstoffdauerdüngungsversuch (IOSDV) Speyer. Arch Agron Soil Sci 39:461–471CrossRefGoogle Scholar
  5. Bozdogan H (1987) Model selection and Akaike's information criterion (AIC): the general theory and its analytical extensions. Psychometrika 52:345–370CrossRefGoogle Scholar
  6. CATCH-C (2015) European research project CATCH-C. www.catch-c.eu
  7. Cerrato M, Blackmer A (1990) Comparison of models for describing; corn yield response to nitrogen fertilizer. Agron J 82:138–143CrossRefGoogle Scholar
  8. Chang E-H, Chung R-S, Tsai Y-H (2007) Effect of different application rates of organic fertilizer on soil enzyme activity and microbial population. Soil Science and Plant Nutrition 53:132–140CrossRefGoogle Scholar
  9. Dawe D, Dobermann A, Ladha J, Yadav R, Bao L, Gupta R, Lal P, Panaullah G, Sariam O, Singh Y (2003) Do organic amendments improve yield trends and profitability in intensive rice systems? Field Crop Res 83:191–213CrossRefGoogle Scholar
  10. Díaz-Zorita M, Buschiazzo DE, Peinemann N (1999) Soil organic matter and wheat productivity in the semiarid argentine pampas. Agron J 91:276–279CrossRefGoogle Scholar
  11. Eich D, Körschens M, Pfefferkorn A (2013) 100 Jahre Agrar-und Umweltforschung Bad Lauchstädt: Geschichte der Forschungsstätte von 1895 bis 1995. Springer-VerlagGoogle Scholar
  12. Freibauer A, Rounsevell MD, Smith P, Verhagen J (2004) Carbon sequestration in the agricultural soils of Europe. Geoderma 122:1–23CrossRefGoogle Scholar
  13. George B (1984) Design and interpretation of nitrogen response experiments. Nitrogen requirement of cereals: proceedings of a conference organised by the Agricultural Development and Advisory Service, September 1982. London: HMSO, 1984.Google Scholar
  14. Giordani G, Comellini F, Triberti L, Nastri A (2010) Dopo 15 anni di residui interrati al grano non serve più l’azoto. l'informatore agrario. Edizioni l'informatore agrario S.p.A.Google Scholar
  15. Glendining M, Powlson D (1995) The effects of long continued applications of inorganic nitrogen fertilizer on soil organic nitrogen—a review. Lewis Publishers, Boca Raton-London-Tokyo, Soil Management Experimental Basis for Sustainability and Environmental Quality, pp. 385–446Google Scholar
  16. Govaerts∗ B, Verhulst∗ N, Castellanos-Navarrete A, Sayre K, Dixon J, Dendooven L (2009) Conservation agriculture and soil carbon sequestration: between myth and farmer reality. Critical Reviews in Plant Science 28:97–122CrossRefGoogle Scholar
  17. Gupta UC, Wu K, Liang S (2008) Micronutrients in soils, crops, and livestock. Earth Science Frontiers 15:110–125. doi: 10.1016/S1872-5791(09)60003-8 CrossRefGoogle Scholar
  18. Gurevitch J, Hedges LV (1999) Statistical issues in ecological meta-analyses. Ecology 80:1142–1149CrossRefGoogle Scholar
  19. Haan SD (1977) Humus, its formation, its relation with the mineral part of the soil, and its significance for soil productivity. Soil Organic Matter Studies; Proceedings of a Symposium.Google Scholar
  20. Hege U, Offenberger K (2006) Effect of differentiated mineral fertilization and organic manuring on yield, product quality and N balances in the international permanent organic nitrogen experiment (IOSDV) Puch. Arch Agron Soil Sci 52:535–550CrossRefGoogle Scholar
  21. Hideborn Alm K, Dahlin S (2007) Success Stories of Agricultural Long-term Experiments. Success Stories of Agricultural Long-term Experiments, 9 edn. Ake Barklund, KSLA, Royal Swedish Academy of Agriculture and ForestryGoogle Scholar
  22. Hoffmann S, Kismányoky T, Balázs J (1997) Der Internationale Organische Stickstoffdauerdüngungsversuch (IOSDV) Keszthely nach 12 Versuchsjahren. Arch Agron Soil Sci 41:123–132CrossRefGoogle Scholar
  23. Hoogsteen MJJ, Lantinga EA, Bakker EJ, Groot JCJ, Tittonell PA (2015) Estimating soil organic carbon through loss on ignition: effects of ignition conditions and structural water loss. Eur J Soil Sci 66:320–328. doi: 10.1111/ejss.12224 CrossRefGoogle Scholar
  24. Hösch J, Dersch G (2002) The international organic nitrogen long-term fertilization experiment (IOSDV) at Vienna. Arch Agron Soil Sci 48:471–484CrossRefGoogle Scholar
  25. IGBP Terrestrial Carbon Working Group (1998) The Terrestrial Carbon Cycle: Implications for the Kyoto Protocol. Science 280:1393–1394. doi: 10.1126/science.280.5368.1393 CrossRefGoogle Scholar
  26. ISCN (2015) ISCN Database. In: ISC Network (ed), available at http://iscn.fluxdata.org/Data/Pages/AccessData.aspx.
  27. Janssen BH (2002) Organic matter and soil fertility. Wageningen Agricultural UniversityGoogle Scholar
  28. Johnston AE, Poulton PR, Coleman K (2009) Soil organic matter: its importance in sustainable agriculture and carbon dioxide fluxes. Advances in agronomy 101:1–57CrossRefGoogle Scholar
  29. Kanal A, Kautz T, Ellmer F, Rühlmann J (2003) Einfluss langjährig differenzierter Düngungsmassnahmen auf die Schwefel-und Stickstoffversorgung von Sommergerste in Berlin-Dahlem (D) und Tartu (Est. Arch Agron Soil Sci 49:543–553CrossRefGoogle Scholar
  30. Káš M, Haberle J, Matějková S (2010) Crop productivity under increasing nitrogen rates and different organic fertilization systems in a long-term IOSDV experiment in the Czech Republic. Arch Agron Soil Sci 56:451–461CrossRefGoogle Scholar
  31. Kismányoky T, Tóth Z (2012) Effect of mineral and organic fertilization on soil organic carbon content as well as on grain production of cereals in the IOSDV (ILTE) long-term field experiment, Keszthely, Hungary. Arch Agron Soil Sci 59:1121–1131CrossRefGoogle Scholar
  32. Klasink A, Steffens G (1995) Der Internationale Organische Stickstoffdauerdüngungsversuch (IOSDV) Oldenburg nach Neun Versuchs-jahren. Arch Agron Soil Sci 39:449–460CrossRefGoogle Scholar
  33. Konstantopoulos S (2011) Fixed effects and variance components estimation in three-level meta-analysis. Res Syn Meth 2:61–76CrossRefGoogle Scholar
  34. Körschens M, Albert E, Baumecker M, Ellmer F, Grunert M, Hoffmann S, Kismanyoky T, Kubat J, Kunzova E, Marx M, Rogasik J, Rinklebe J, Rühlmann J, Schilli C, Schröter H, Schroetter S, Schweizer K, Toth Z, Zimmer J, Zorn W (2014) Humus and climate change - results of 15 long-term experiments. Arch Agron Soil Sci 60:1485–1517. doi: 10.1080/03650340.2014.892204 CrossRefGoogle Scholar
  35. Kuldkepp P, Teesalu T, Liiva I (1996) Einfluss mineralischer und organischer N-düngung auf Ertrag, Qualitätsmerkmale und auf die N-Bilanz im IOSDV Tartu/Estland. Arch Agron Soil Sci 40:97–105CrossRefGoogle Scholar
  36. Lal R (2004) Soil carbon sequestration impacts on global climate change and food security. Science 304:1623–1627. doi: 10.1126/science.1097396 CrossRefPubMedGoogle Scholar
  37. Lang H, Dressel J, Bleiholder H (1995) Langzeitwirkung der Stickstoffdüngung IOSDV—Standort Limburgerhof (Deutschland) in der Reihe ‘Internationale Organische Stickstoffdauerdüngungsversuche. Arch Agron Soil Sci 39:429–448CrossRefGoogle Scholar
  38. Leclerc B, Georges P, Cauwel B, Lairon D (1995) A Five Year Study on Nitrate Leaching under Crops Fertilised with Mineral and Organic Fertilisers in Lysimeters. Biological Agriculture & Horticulture 11:301–308. doi: 10.1080/01448765.1995.9754714 CrossRefGoogle Scholar
  39. Lenth RV (2015) Using lsmeans. In: Uo Iowa (ed), The Comprehensive R Archive Network.Google Scholar
  40. López-Fando C, Pardo Fernández MT (2008) Long-term effect of organic and inorganic nitrogen Fertilizers on soil N balance and crop productivity.Google Scholar
  41. López-Fando C, Fernandez MTP, Wegener HR (1999) Erträge und N-Bilanzen im IOSDV Madrid im Laufe von vier Rotationen. Arch Agron Soil Sci 44:489–505CrossRefGoogle Scholar
  42. Loveland P, Webb J (2003) Is there a critical level of organic matter in the agricultural soils of temperate regions: a review. Soil Tillage Res 70:1–18CrossRefGoogle Scholar
  43. Maeda M, Zhao B, Ozaki Y, Yoneyama T (2003) Nitrate leaching in an Andisol treated with different types of fertilizers. Environ Pollut 121:477–487. doi: 10.1016/S0269-7491(02)00233-6 CrossRefPubMedGoogle Scholar
  44. Mogârzan A, Vasilica C, Axinte M, Zaharia M, Slabu C, Robu T (2007) The effect of organic-mineral fertilization on the yield and quality of sugar beet in a long term experiment at Ezăreni–Iasi. Lucrări Ştiinţifice 50Google Scholar
  45. Monreal CM, Zentner RP, Robertson JA (1997) An analysis of soil organic matter dynamics in relation to management, erosion and yield of wheat in long-term crop rotation plots. Can J Soil Sci 77:553–563. doi: 10.4141/s95-076 CrossRefGoogle Scholar
  46. Oehlert GW (1992) A note on the delta method. Am Stat 46:27–29Google Scholar
  47. Oelofse M, Markussen B, Knudsen L, Schelde K, Olesen JE, Jensen LS, Bruun S (2015) Do soil organic carbon levels affect potential yields and nitrogen use efficiency? An analysis of winter wheat and spring barley field trials. Eur J Agron 66:62–73CrossRefGoogle Scholar
  48. Palm CA, Myers RJ, Nandwa SM (1997) Combined use of organic and inorganic nutrient sources for soil fertility maintenance and replenishment. Replenishing soil fertility in. Africa:193–217Google Scholar
  49. Pan G, Smith P, Pan W (2009) The role of soil organic matter in maintaining the productivity and yield stability of cereals in China. Agriculture, Ecosystems &amp. Environment 129:344–348Google Scholar
  50. Pfefferkorn A, Körschens M (1995) Der Internationale Organische Stickstoffdauerdüngungsversuch (IOSDV) Bad Lauchstädt nach 16 Jahren. Arch Agron Soil Sci 39:413–427CrossRefGoogle Scholar
  51. Pinheiro J, Bates D, DebRoy S, Sarkar D, Team RC (2015) Nlme: Linear and Nonlinear Mixed Effects Models. The Comprehensive R Archive Network.Google Scholar
  52. Pribyl DW (2010) A critical review of the conventional SOC to SOM conversion factor. Geoderma 156:75–83CrossRefGoogle Scholar
  53. R Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing.Google Scholar
  54. Reeves DW (1997) The role of soil organic matter in maintaining soil quality in continuous cropping systems. Soil Tillage Res 43:131–167. doi: 10.1016/S0167-1987(97)00038-X CrossRefGoogle Scholar
  55. Smith P (2016) Soil carbon sequestration and biochar as negative emission technologies. Global Change Biology: n/a-n/a. doi: 10.1111/gcb.13178 Google Scholar
  56. Smith P, Smith JU, Powlson DS, McGill WB, Arah JRM, Chertov OG, Coleman K, Franko U, Frolking S, Jenkinson DS, Jensen LS, Kelly RH, Klein-Gunnewiek H, Komarov AS, Li C, Molina JAE, Mueller T, Parton WJ, Thornley JHM, AP W (1997) A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments. Geoderma 81:153–225CrossRefGoogle Scholar
  57. Soane BD (1990) The role of organic matter in soil compactibility: a review of some practical aspects. Soil Tillage Res 16:179–201CrossRefGoogle Scholar
  58. Spiegel H, Dersch G, Baumgarten A, Hösch J (2010) The international organic nitrogen long-term fertilisation experiment (IOSDV) at Vienna after 21 years. Arch Agron Soil Sci 56:405–420CrossRefGoogle Scholar
  59. Starčević L, Malesević M, Marinković B, Crnobarać J (1997) Der Internationale Organische Stickstoffdauerdüngungsversuch (IOSDV) Novi Sad nach 12 Jahren. Arch Agron Soil Sci 41:155–166CrossRefGoogle Scholar
  60. Starčević L, Latković D, Malešević M (2005) Dependence of corn yield on weather conditions and nitrogen fertilization in IOSDV Novi Sad. Arch Agron Soil Sci 51:513–522CrossRefGoogle Scholar
  61. Trabucco A, Zomer RJ (2009) Global Potential Evapo-Transpiration (Global-PET) and Global Aridity Index (Global-Aridity). available from the CGIAR-CSI GeoPortal at: http://www.csi.cgiar.org.
  62. Triberti L, Nastri A, Giordani G, Comellini F, Baldoni G, Toderi G (2008) Can mineral and organic fertilization help sequestrate carbon dioxide in cropland? Eur J Agron 29:13–20CrossRefGoogle Scholar
  63. UNFCCC (2015) Join the 4/1000 Initiative. Soils for Food Security and Climate. Lima- Paris Action AgendaGoogle Scholar
  64. Vasilica C, Mogârzan A, Axinte M, Chetrone M (1997) Einfluss veschiedener Formen der organischen Düngung in Kombination mit mineralischem Stickstoff auf die Ertragsleistung von Zuckerrüben, Winterweizen und Mais und auf die Nährstoffbilanzen im Boden. Arch Agron Soil Sci 41:133–142CrossRefGoogle Scholar
  65. Verheijen FGA (2005) On-farm benefits from soil organic matter in England and Wales. Cranfield University, Faculty of EnvironmentGoogle Scholar
  66. Von Boguslawski E (1995) Das Zusammenwirken der mineralischen Düngung mit verschiedenen Formen der organischen Düngung im IOSDV Rauischholzhausen. Arch Agron Soil Sci 39:403–411CrossRefGoogle Scholar
  67. Vrkoc F, Skala J, Suskevic M (1996) Neunjährige Ertragsergebnisse der Internationalen Organischen Stickstoffdauerdüngungsversuche in der Tschechischen Republik. Arch Agron Soil Sci 40:115–132CrossRefGoogle Scholar
  68. Vrkoč F, Vach M, Veleta V, Košner J (2002) Influence of different organic mineral fertilization on the yield structure and on changes of soil properties. Rostlinná Výroba 48:216–221Google Scholar
  69. Waksman SA, KR STEVENS (1930) A critical study of the methods for determining the nature and abundance of soil organic matter. Soil Sci 30:97–116CrossRefGoogle Scholar
  70. Wei W, Yan Y, Cao J, Christie P, Zhang F, Fan M (2016) Effects of combined application of organic amendments and fertilizers on crop yield and soil organic matter: An integrated analysis of long-term experiments. Agriculture, Ecosystems & Environment 225:86–92. doi: 10.1016/j.agee.2016.04.004 CrossRefGoogle Scholar
  71. Wood S, Cowie A (2004) A review of greenhouse gas emission factors for fertiliser production. IEA bioenergy task.Google Scholar

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Plant Production SystemsWageningen University and ResearchWageningenThe Netherlands
  2. 2.Plant Research InternationalWageningen University and ResearchWageningenThe Netherlands
  3. 3.BiometrisWageningen University and ResearchWageningenThe Netherlands
  4. 4.Austrian Agency for Health and Food SafetyInstitute for Sustainable Plant ProductionViennaAustria
  5. 5.Sustainable Soils and Grassland SystemsRothamsted ResearchHarpendenUK

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