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Systematic review article, organic farming provides reliable environmental benefits but increases variability in crop yields: a global meta-analysis.

organic farming research paper

  • 1 School of Biological Sciences, Washington State University, Pullman, WA, United States
  • 2 Department of Entomology, Washington State University, Pullman, WA, United States
  • 3 Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
  • 4 Tree Fruit Research and Extensions Center, Washington State University, Wenatchee, WA, United States
  • 5 Department of Entomology, University of Georgia, Athens, GA, United States
  • 6 Centre for Tropical Environmental Sustainability Science, James Cook University, Cairns, QLD, Australia

To promote food security and sustainability, ecologically intensive farming systems should reliably produce adequate yields of high-quality food, enhance the environment, be profitable, and promote social wellbeing. Yet, while many studies address the mean effects of ecologically intensive farming systems on sustainability metrics, few have considered variability. This represents a knowledge gap because producers depend on reliable provisioning of yields, profits, and environmental services to enhance the sustainability of their production systems over time. Further, stable crop yields are necessary to ensure reliable access to nutritious foods. Here we address this by conducting a global meta-analysis to assess the average magnitude and variability of seven sustainability metrics in organic compared to conventional systems. Specifically, we explored the effects of these systems on (i) biotic abundance, (ii) biotic richness, (iii) soil organic carbon, (iv) soil carbon stocks, (v) crop yield, (vi) total production costs, and (vii) profitability. Organic farms promoted biotic abundance, biotic richness, soil carbon, and profitability, but conventional farms produced higher yields. Compared to conventional farms, organic farms had lower variability in abundance and richness but greater yield variability. Organic farms thus provided a “win-win” (high means and low variability) for environmental sustainability, while conventional farms provided a “win-win” for production by promoting high crop yields with low variability. Despite lower yields, and greater yield variability, organic systems had similar costs to conventional systems and were more profitable due to organic premiums. Our results suggest certification guidelines for organic farms successfully promote reliable environmental benefits, but greater reliance on ecological processes may reduce predictability of crop production.

Introduction

Organic agriculture is an ecologically intensive production system expanding worldwide as demand for sustainability increases ( Eyhorn et al., 2019 ; Willer et al., 2019 ). Although organic farms produce lower yields than comparable conventional farms ( Seufert et al., 2012 ; Ponisio et al., 2015 ), they are more profitable, more friendly to pollinators and the environment, and deliver equally or more nutritious foods with fewer pesticide residues ( Kennedy et al., 2013 ; Tuck et al., 2014 ; Reganold and Wachter, 2016 ; Kovács-Hostyánszki et al., 2017 ; Seufert and Ramankutty, 2017 ). These findings are based on comparison studies ( Mäeder et al., 2002 ; Crowder et al., 2010 ; Reganold et al., 2010 ; Forrest et al., 2015 ) and meta-analyses ( Crowder et al., 2010 , 2012 ; Gattinger et al., 2012 ; Seufert et al., 2012 ; Tuck et al., 2014 ; Crowder and Reganold, 2015 ; Ponisio et al., 2015 ; Lichtenberg et al., 2017 ; Lori et al., 2017 ) of the mean effects of organic and conventional farming systems on sustainability metrics such as biodiversity, yield, soil quality, and profitability; they did not consider variability. In contrast, few studies have considered variability of any sustainability metric over time ( Pimentel et al., 2005 ; Smith et al., 2007 ).

Not taking variability of sustainability metrics into account when comparing production systems is a key knowledge gap. Producers depend on reliable provisioning of yields, profits, and environmental services to ensure production system sustainability over time. Moreover, reduced yield variability is necessary to ensure reliable food access for consumers ( Schmidhuber and Tubiello, 2007 ; Müller et al., 2018 ; Mehrabi and Ramankutty, 2019 ). Low variability allows producers to achieve consistent production and avoid unprofitable years, while also ensuring that consumers have reliable access to nutritious and sufficient food. When producers are able to generate consistent crop yields, food prices are also less volatile and global trade markets are more stable ( Müller et al., 2018 ). Decreased variability in ecosystem services can also prevent food shortages ( Mehrabi and Ramankutty, 2019 ). Systems that promote low variability in ecosystem services may also be more resilient to changing climatic conditions, including an increased frequency of drought and extreme weather events ( Schmidhuber and Tubiello, 2007 ).

We hypothesize that organic farms will have lower yields as well as greater variability in yield over time compared to conventional farms ( Knapp and van der Heijden, 2018 ). This is because organic farms are more reliant on ecosystem services for production of high crop yields, whereas conventional farms can rely more heavily on external inputs ( Reganold and Wachter, 2016 ). Specifically, conventional producers may be more readily able to react to pest outbreaks or low soil nutrient availability by applying a broad range of pesticides and synthetic fertilizers. In contrast, organic producers may be more strongly affected by changing environmental conditions that affect ecosystem service providers, such as soil microbes, pollinators, and natural enemy species ( Mäeder et al., 2002 ; Crowder et al., 2010 ; Kennedy et al., 2013 ; Karp et al., 2018 ). If organic farms experience greater variability in yields, organic farmers may experience greater variability in profits. At the same time, we hypothesize that organic farms, when compared to their conventional counterparts, will have lower variability in environmental sustainability because they have a narrower range of control methods and management options ( Reganold and Wachter, 2016 ). However, these hypotheses have never been tested. There also have been few attempts to identify “win-win” scenarios where organic or conventional farming systems optimize high productivity and low variability of various sustainability metrics.

We addressed these knowledge gaps by conducting a global meta-analysis assessing the mean and variability of seven sustainability metrics from paired comparison studies of organic and conventional systems. Our meta-analysis spanned 61 crop types on 6 continents ( Figure 1 ; Figure S1 ) to assess seven sustainability metrics: (i) biotic abundance, (ii) biotic richness, (iii) soil organic carbon, (iv) soil carbon stocks, (v) crop yield, (vi) total production costs, and (vii) profitability. We also assessed how each metric varied for annual vs. perennial crops, study type (experimental station vs. farm), certification body (United States vs. European Union), and crop type. Overall, our study shows that the variability of ecosystem health metrics and farm production differs between organic and conventional systems and reveals areas where ecological intensive production systems need to improve to better address United Nations sustainable development goals (UN SDG; FAO et al., 2018 ).

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Figure 1 . Study locations for seven sustainability metrics: biodiversity (abundance and richness), soil carbon (organic carbon and carbon stock), crop yield, and profitability (benefit/cost ratios and production costs).

Data Generation

We leveraged data from prior meta-analyses that reported means and standard deviations for paired organic and conventional systems related to the seven metrics of sustainability (see Supplementary Methods and Supplementary Data 1 ). We gathered estimates from the meta-data, which included one metric of mean and standard deviation averaged across replicates and years for each crop and/or organism measured in each study. Our variation metric thus represents a combination of spatial-temporal variability, hereafter variability. We gathered additional data from each study on country, continent, annual or perennial production system, certification body (United States or European Union), study type (experimental station or farm), and crop type ( Tables S1–S5 ; Supplementary Data 2 ). For biotic abundance and biotic richness, we report estimates by organismal (arthropod, microbe, plant, vertebrate) and functional group (decomposer, natural enemy, herbivore, pollinator, producer) when available ( Table S2 ). Organismal groups represent distinct taxonomic groups without regard to function, while functional group classifications represent the function of organisms on ecosystem services in agroecosystems. The final dataset had between 30 and 290 data points for each of the seven sustainability metrics ( Figure 1 ; Figure S1 ; Tables S6, S7 ).

Meta-Analysis

For each sustainability metric, we used two variability measures: (1) standard deviation, which indicates absolute variability and (2) coefficient of variation, which indicates relative variability (i.e., standardized by means) ( Knapp and van der Heijden, 2018 ) ( Figure S2 ). Because the coefficient of variation is unitless and is scaled by the mean, it does a better job than standard deviation of accounting for differences in the means of the sustainability metrics between organic and conventional systems. We thus focus our results and discussion on the coefficient of variation, but we present the standard deviation in the Supplemental Materials for clarity and transparency. We calculated the coefficient of variation for all services by dividing the system (organic or conventional) standard deviation by the system mean.

To compare effects of farming system on means and variability for each sustainability metric, we used the log-response ratio as an effect size metric ( Hedges et al., 1999 ). We used the log-response ratio rather than a weighted effect size for three reasons following Cardinale et al. (2006) and Crowder et al. (2010 , 2012 ). First, weighted effect sizes could not be calculated for studies that did not report variability. Second, our biotic abundance and biotic richness studies classified organisms at varying levels of biotic resolution. Studies classified at a courser resolution had less variability, and a weighted metric would give these studies greater weight. Finally, studies conducted on experimental stations often relied on small-plots with extensive replication, while studies conducted on farms often were conducted at larger spatial scales but had decreased replication. Using weighted metrics would thus give small-plot studies on experimental stations considerably greater weight than on-farm studies which often are more representative of real-world farming situations ( Crowder and Reganold, 2015 ).

Once log response-ratio effect sizes were calculated, we used one-sample t -tests ( Crowder and Reganold, 2015 ; Lichtenberg et al., 2017 ) to determine whether the mean effect sizes for each sustainability metric, and associated variability, differed between conventional and organic sites. In parallel with the t -tests, we calculated 90% confidence intervals for each metric examined by adding and subtracting from the mean the standard error multiplied by the critical t -value at an α of 0.10 for a two-tailed test. We described effect sizes that were significant using an α of 0.10 to capture effects that may be ecologically important in accordance with a recent policy statement by the American Statistical Association, which notes that reliance on an arbitrary alpha value of 0.05 can lead to erroneous conclusions ( Wasserstein and Lazar, 2016 ).

Evaluating Effects of Covariates on Mean and Variability of Sustainability Metrics

Our meta-analysis included a wide range of studies, and the context of these studies might affect the mean and variability of each of the seven sustainability metrics. To evaluate context-dependency, we considered effects of three key factors on each response variable. First, we assessed perenniality of crops given that perennial crops may have lower temporal variability than annual crops due to turnover of fields from year to year. Second, we considered whether studies were conducted on experimental stations or farms. Studies on experimental stations may not have sufficient time for organic treatments to manifest and may show greater differences than farm studies. Moreover, studies from experimental stations may not be as affected by economic considerations compared to farms where producer's livelihoods are at stake. Third, organic certification standards differ between the United States (US) and the European Union (EU), so we also evaluated the effects of certification scheme to determine if variation in these standards has affected sustainability of organic farming in the unique regions. Each comparison used a different subset of the data ( Tables S8–S12 ). For example, when comparing US and EU certification schemes we excluded studies conducted in other regions.

For comparisons of how these covariates affected the means and variability for each of the seven sustainability metrics, we calculated 90% confidence intervals for each metric and used a linear mixed model with study as a random effect. In some cases the random effect described sufficiently small data that reliable model fits could not be made, and therefore we used Welch's t -tests for these comparisons. Comparisons focused on the mean, standard deviation, and coefficient of variation of each sustainability metric for each covariates. To further evaluate context dependency, we calculated 90% confidence intervals from a t -distribution for each crop type (cereals, fruits, oil crops, pulses, root crops, vegetables, or forage) ( Table S13 ), for means and coefficient of variation of each of the seven focal metrics.

Results and Discussion

Effects of organic agriculture on mean sustainability metrics.

Compared to conventional sites, organic sites had greater (i.e., log response ratio > 0) mean biotic abundance, biotic richness, soil organic carbon, carbon stocks, and profitability ( Figure 2A ; Table 1 ), with stronger effects (i.e., higher log mean response ratios) observed for abundance, richness, and profitability compared to soil metrics ( Table 1 ). Organic sites had lower yield than conventional sites but had similar total production costs ( Figure 2A ; Table 1 ); the reduced yield effect was similar in magnitude to the positive effects of organic farming on abundance, richness, and profitability ( Table 1 ). For biotic abundance and richness, organic sites had higher mean effect sizes for all organismal and functional groups, except microbial richness, herbivore abundance, and decomposer richness, which were similar for both management systems ( Figures 3A,B ; Figure S3 ; Tables S14, S15 ). Overall, these results are in line with previous research showing that sustainability metrics other than crop yield, including greater profitability, are promoted by organic farming systems compared to conventional systems (e.g., Crowder et al., 2010 , 2012 ; Gattinger et al., 2012 ; Kennedy et al., 2013 ; Tuck et al., 2014 ; Crowder and Reganold, 2015 ; Reganold and Wachter, 2016 ; Lichtenberg et al., 2017 ).

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Figure 2. (A) Mean response ratio, (B) standard deviation response ratio, and (C) coefficient of variation response ratio of organic compared to conventional farming systems for seven sustainability metrics. All values shown are means ± 90% CI (black) and ± 95% CI (gray). **Indicates 90% and 95% CIs did not overlap 0.

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Table 1 . Mean response ratio, standard deviation response ratio, and coefficient of variation response ratio mean and 90% confidence interval (CI) for 7 sustainability metrics.

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Figure 3 . Functional group (decomp = decomposer, enemy = natural enemy, herbivore, pollinator, producer) (A) abundance mean response ratio, (B) abundance standard deviation response ratio, (C) abundance coefficient of variation response ratio, (D) richness mean response ratio, (E) richness standard deviation response ratio, and (F) richness coefficient of variation response ratio. All values shown are means ± 90% CI (black) and ± 95% CI (gray). *Indicates 90% CI did not overlap 0; **indicates 95% CI did not overlap 0.

The benefits of organic farming for mean biotic abundance, mean biotic richness, and mean profitability were robust to variation in each of the covariates we evaluated (annual vs. perennial crops, experimental station vs. farm, and US vs. EU certification guidelines), while mean yield was lower for organic systems across all these comparisons ( Figure 4 ; Tables S12–S21 ). Alternatively, mean soil organic carbon and mean carbon stocks each benefited more from organic farming in the US than in the EU and carbon stock benefited more from organic farming in annual compared to perennial crops as demonstrated by a significant effect of each covariate on the response ratio ( Figure 4 ; Tables S12–S21 ).

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Figure 4 . Response ratio for mean sustainability (± 90% CI black; 95% CI gray) for (A) annual crop types, (B) perennial crop types, (C) on experimental stations, (D) on farms, (E) studies in the United States, and (F) studies in the European Union. *Indicates 90% CI did not overlap 0; **indicates 95% CI did not overlap 0.

Effects of Organic Agriculture on Variability of Sustainability Metrics

Compared to conventional sites, organic sites had lower variability (i.e., CV response ratio < 0) in biotic abundance and richness ( Figure 2C ; Table 1 ) but higher yield variability ( Figure 2C ; Table 1 ), although the magnitude of effects on yield variability (i.e., log variability response ratios) were more than twice as large as the magnitude of effects on biotic communities ( Table 1 ). In contrast, organic and conventional sites exhibited similar variability in soil organic carbon, carbon stocks, total production costs, and profitability ( Figure 2C ; Table 1 ). Standard deviations in biotic abundance and richness were generally greater on organic farms, but the magnitude of this difference was small in comparison to the large difference in mean biotic abundance and richness in the two farming systems ( Figure 2B ; Figure S4 ). Similarly, organic farming significantly reduced mean yield, but not standard deviation, leading to a higher coefficient of variation ( Figure 2 ; Figure S4 ).

Our evaluation of covariates suggests our findings are quite robust. Of the 19 comparisons we made for effects of covariates (annual vs. perennial crops, experimental station vs. farm, and US vs. EU certification guidelines) on coefficient of variation response ratios for the sustainability metrics, only two were significant at α = 0.10 ( Figure 5 ; Tables S17, S19, S21 ). Further breaking down variability of abundance and richness by organismal type or functional groups indicated that organic farming had significantly lower variability in plant and producer richness, whereas other organismal groups had similar variability between systems ( Figures 3C–F ; Figure S3 ; Tables S14, S15 ).

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Figure 5 . Response ratio for coefficient of variation of sustainability (± 90% CI black; 95% CI gray) for (A) annual crop types, (B) perennial crop types, (C) on experimental stations, (D) on farms, (E) studies in the United States, and (F) studies in the European Union. *Indicates 90% CI did not overlap 0; **indicates 95% CI did not overlap 0.

Organic certification restricts organic farms to using a narrower set of agrochemical inputs than conventional farms ( Reganold and Wachter, 2016 ). This may discourage disturbance and may result in lower variability in environmental health and impacts ( Schrama et al., 2018 ). Our results reflect this, as organic sites had lower variability in biotic abundance and richness. This suggests that organic certification schemes effectively limit negative environmental effects of agriculture on communities, which benefits beneficial natural enemy populations ( Crowder et al., 2010 ; Porcel et al., 2018 ) and pollinators ( Kennedy et al., 2013 ). Because organic certification standards exclude most chemical fertilizers and pesticides, organic farmers have to plan ahead, not having instant solutions to the problems that come up. Organic systems are also more information intensive, requiring farmers to learn how to manage longer rotations, grow different crops together, and come up with cultural and mechanical strategies for battling weeds and disease ( Reganold and Wachter, 2016 ; Seufert and Ramankutty, 2017 ). This in turn forces heavier reliance on ecosystem service providers, such as biological control of pests, to produce adequate yields ( Karp et al., 2018 ). While organic fields have a greater richness of these ecosystem service providers ( Crowder et al., 2010 ; Kennedy et al., 2013 ; Tuck et al., 2014 ; Lichtenberg et al., 2017 ), the effects of organic management do not persist at larger scales ( Schneider et al., 2014 ), potentially denying organic farmers any spillover benefits.

Conventional farms, in contrast, may be capable of more readily adapting to changing environmental pressures by having a greater variety of pesticide and fertilizer options, leading to a decrease in yield variability. This is similar to studies showing that greater dependency of a crop on ecological services, like pollination or biological control, can be positively correlated with greater yield variability ( Garibaldi et al., 2011 ; Martin et al., 2013 ). Recent theoretical work also suggests that increased yield of crops dependent on ecosystem service providers may often be correlated with higher variability ( Montoya et al., 2019 ). These factors are reflected in the lower yield variability on conventional farms in our dataset. However, although yields were more variable on organic compared with conventional farms, we observed similar variability in costs and profitability for the two systems ( Figure 2 ; Table 1 ). This suggests that geographical or temporal fluctuations in crop prices or organic premiums may allow organic farmers to achieve relatively consistent profits regardless of differences in crop yield. While organic farms often have higher labor costs, they have reduced variable costs, which likely contributes to the overall lack of differences in cost variability across the two systems ( Crowder and Reganold, 2015 ). Moreover, price premiums on organic goods are likely the primary reason that organic farms produce greater overall profits than conventional farms despite lower overall yields and greater yield variability ( Crowder and Reganold, 2015 ).

Soil organic carbon had higher means on organic farms but similar variability across the two systems. In addition to land use, climate, elevation, soil type, and soil thickness are the most influential factors affecting soil carbon in agroecosystems ( Teng et al., 2017 ). This is important because these factors are independent of cropping system, which may contribute to the similar variability we observed between organic and conventional sites. Additionally, practices such as tillage and biomass harvest affect variability of soil carbon within both organic and conventional systems ( West and Post, 2002 ), and alter communities of beneficial soil arthropods ( Patterson et al., 2019 ). We did observe greater effects of farming system (organic vs. conventional) on soil organic carbon in annual vs. perennial crops, such that differences in tillage for organic and conventional systems may have important effects on soil health ( Figure 5 ).

Relationships Between Mean and Variability of Sustainability Metrics

We assessed whether the mean and variability of each sustainability metric were related. If particular systems produce high means and low variability, this reflects a “win-win” scenario that would benefit producers. Importantly, a plot of each mean sustainability metric response ratio by its coefficient of variation response ratio showed a similar pattern across crop types: for each metric examined, the higher the mean, the lower the coefficient of variation ( Figure 6 ; Figure S5 ; Tables S22–S27 ). Thus, cropping systems with the highest yields tended to have the least variable yields ( Figure 6C ), and systems with high levels of richness had the least variable richness ( Figure 6B ). These results suggest that practices which lead to greater mean sustainability are also associated with less variation in the sustainability metric, and vice versa. Organic agriculture produced a “win-win” for biological communities by promoting high diversity with low variability, while conventional agriculture produced a “win-win” for production by promoting high yields with low variability. Organic and conventional agriculture generally had similar costs and profitability for crop types examined ( Figures 6D,E ).

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Figure 6 . Mean response ratio (± 90% CI black; 95% CI gray) vs. coefficient of variation response ratio (± 90% CI black; 95% CI gray) for seven FAO crop types (cereals = red, fruits = orange, oil crops = yellow, pulses = green, root crops = teal, vegetables = blue, and forage crops = pink) for (A) biotic abundance, (B) biotic richness, (C) crop yield, (D) total production costs, and (E) profitability.

Within crop types, there appeared to be a trade-off between yield and biotic richness ( Figure 6 ; Figure S5 ), which conforms with previous studies ( Gabriel et al., 2013 ). Crop types with high mean and low coefficient of variation for organic system yields ( Figure 6C ) tended to have low mean and high coefficient of variation in organic system species richness compared to conventional systems ( Figure 6B ). Cereals had the greatest increase in richness from organic methods and the greatest decrease in yield, for example. In contrast, fruit crops had the smallest increase in richness but also the lowest decrease in yield; vegetable and forage crops had an intermediate biotic richness and yield tradeoff ( Figure 6 ; Figure S5 ). This may be related to the varying degrees of pollinator dependence by crop type- fruits tend to be highly reliant on pollination while cereals crops are not ( Andersson et al., 2012 ). Different specific management methods used in different crop systems may explain why the magnitude of differences in the ecological, production, and economic outcomes of organic vs. conventional farming depended on crop types. Identifying which specific practices are associated with these outcomes would likely lead to improvements in the magnitude and stability of sustainability metrics across the spectrum of crop types.

Conclusion and Future Directions

Our results identify an apparent trade-off between variability in environmental impacts and agricultural production, such that efforts to reliably curb environmental impacts through organic certification may come at a cost of lower and more variable yields. However, despite the lower yields and greater yield variability on organic farms, organic farms were more profitable, and had similar costs compared than conventional farms. This is likely due to the organic premiums received, which can vary with market conditions and mitigate effects of lower yields ( Crowder and Reganold, 2015 ). Despite the relatively high profits of organic farming systems, a critical hurdle for organic farming, and other types of ecologically intensive farming systems, is to identify methods to improve the stability of ecosystem service provisioning that enhances and supports consistently high yields. This effort will benefit from experimental research on agricultural systems that focuses on not just means but also variation in yields and biological conservation in agroecosystems. If it is not possible to identify methods to reduce variability in food production by stabilizing ecosystem services, we will need to strike a balance between the stability of food production and the ecological functions provided by agroecosystems.

An important caveat associated with our results is that the vast majority of research to date has been conducted in developed countries, mainly in the European Union and the United States ( Figure 1 ; Figure S1 ). While variability in agricultural yields in these countries may impact global market volatility and commodity prices, agricultural policies in these developed countries can help buffer the effects of yield volatility on greater market impacts ( Thompson et al., 2018 ). In contrast, food security is at greatest risk in less-developed countries, where we have the least data. Commercial management practices in developed countries may be able to tolerate higher variability of services because they have a wider array of tools to manage and control changing field conditions, but the negative effects of inputs like synthetic fertilizer and pesticides suggest this management may come at a cost of ecosystem stability. This relationship between yield and the stability of other sustainability metrics may play out differently in less-developed countries where low-income farmers cannot keep up the inputs necessary to maintain yields in conventional or organic systems and fall into a poverty gap ( Tittonell and Giller, 2013 ).

A key to accomplishing the United Nations Food and Agricultural Organization's sustainable development goals is to both reduce the environmental impacts of agriculture, as well as to improve food security by reducing yield variability in food-insecure regions ( FAO et al., 2018 ). Our results highlight a need to better understand (i) the ways in which these goals are linked and (ii) trade-offs between variability in environmental impacts and yields that may exist. If the trade-offs we have identified are ubiquitous in both developed and less-developed regions, new methods of improving the long-term sustainability of ecosystem service provisioning are needed to avoid trade-offs between food security and ecosystem stability.

Data Availability Statement

All datasets generated for this study are included in the manuscript/ Supplementary Files .

Author Contributions

All authors contributed conception and design of the study. OS, AC, and JT organized the database. OS and TN performed the statistical analyses. OS, AC, CR, AD, RO, TN, and DC wrote sections of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

This work was supported by the USDA National Institute of Food and Agriculture, Hatch project 1014754.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank W. Ford for critical edits on the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2019.00082/full#supplementary-material

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Keywords: agroecosystems, biodiversity, conventional agriculture, meta-analysis, profitability, soil carbon, stability, sustainability

Citation: Smith OM, Cohen AL, Rieser CJ, Davis AG, Taylor JM, Adesanya AW, Jones MS, Meier AR, Reganold JP, Orpet RJ, Northfield TD and Crowder DW (2019) Organic Farming Provides Reliable Environmental Benefits but Increases Variability in Crop Yields: A Global Meta-Analysis. Front. Sustain. Food Syst. 3:82. doi: 10.3389/fsufs.2019.00082

Received: 31 May 2019; Accepted: 16 September 2019; Published: 27 September 2019.

Reviewed by:

Copyright © 2019 Smith, Cohen, Rieser, Davis, Taylor, Adesanya, Jones, Meier, Reganold, Orpet, Northfield and Crowder. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Olivia M. Smith, olivia.m.smith@wsu.edu

This article is part of the Research Topic

Ecological Intensification and Diversification as Mechanisms for Sustainable Agriculture

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  • Published: 07 June 2021

Multi-community effects of organic and conventional farming practices in vineyards

  • Noémie Ostandie 1 ,
  • Brice Giffard 1 ,
  • Olivier Bonnard 1 ,
  • Benjamin Joubard 1 ,
  • Sylvie Richart-Cervera 1 ,
  • Denis Thiéry 1 &
  • Adrien Rusch 1  

Scientific Reports volume  11 , Article number:  11979 ( 2021 ) Cite this article

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  • Agroecology
  • Biodiversity

Understanding the response of biodiversity to organic farming is crucial to design more sustainable agriculture. While it is known that organic farming benefits biodiversity on average, large variability in the effects of this farming system exists. Moreover, it is not clear how different practices modulate the performance of organic farming for biodiversity conservation. In this study, we investigated how the abundance and taxonomic richness of multiple species groups responds to certified organic farming and conventional farming in vineyards. Our analyses revealed that farming practices at the field scale are more important drivers of community abundance than landscape context. Organic farming enhanced the abundances of springtails (+ 31.6%) and spiders (+ 84%), had detrimental effects on pollinator abundance (− 11.6%) and soil microbial biomass (− 9.1%), and did not affect the abundance of ground beetles, mites or microarthropods. Farming practices like tillage regime, insecticide use and soil copper content drove most of the detected effects of farming system on biodiversity. Our study revealed varying effects of organic farming on biodiversity and clearly indicates the need to consider farming practices to understand the effects of farming systems on farmland biodiversity.

Introduction

Agriculture, which dominates more than one third of the world’s terrestrial surface, is recognized as one of the main drivers of biodiversity loss 1 . The growing demand for agricultural commodities is expected to strengthen the expansion and intensification of agricultural land, with strong impacts on biodiversity 2 , 3 . To overcome this challenge, the land-sharing approach suggests promoting ecosystem services delivered by biodiversity through the development of more environmentally friendly agriculture supporting both production and biodiversity conservation on the same land 4 , 5 . Organic farming, which is often seen as a prototype of such agriculture, is expanding, and approximately 71 million hectares of farmland are currently under certified organic farming at the global scale 6 , 7 . Promoting organic farming is one of the main agro-environmental policies around the world 8 . For instance, in its recent Green Deal, Europe Union officially targets to reach 25% of its total farmland under organic farming by 2030. However, the benefits of certified organic farming in reducing the environmental footprint of agriculture are widely debated, and large uncertainty exists around the performance of such farming 9 , 10 .

Overall, biodiversity is known to benefit from organic farming 11 , 12 , 13 . Several meta-analysis have found that organic farming increases the abundance of organisms by 50% and species richness by 30% 12 , 13 . Organic farming is particularly significant and beneficial to soil microbes, plants, pollinators or predators 14 , 15 . However, the claim that agri-environment measure such as organic farming contribute to halting the biodiversity decline has been recently challenged 16 , 17 . Several studies have reported that the effects of organic farming are highly variable, and recent evidence even pinpointed that organic farming could have negative effects on some biodiversity components 13 , 17 , 18 , 19 . Birkhofer et al. (2014) reported that there are both winners and losers of organic farming across a large range of organisms including bird, ground beetle, spider, butterfly and moth communities. Examining how multiple species or functional groups respond to organic farming is of major importance to understand the actual effects of this popular agri-environment measure on biodiversity.

The beneficial effects of certified organic farming on biodiversity are usually attributed to the ban of synthetic pesticides and fertilizers as well as to higher levels of soil organic matter or longer and more diversified crop rotations compared to those of conventional farming 20 , 21 . However, several practices allowed by organic farming certification standards such as copper or sulphur-based fungicides, microbial insecticide or intensive soil tillage have negative impacts on biodiversity 22 , 23 . Moreover, studies explicitly considering the different farming practices actually applied in fields when comparing the relative effects of organic and non-organic management are rare. It is likely that this poorly-explored source of variability might partly explain some inconsistent results reported in the literature. In addition, it has been recently argued that simplifying technical choices of farmers into broad comparisons of conventional versus alternative systems could limit our understanding of the socioecological impacts of agriculture and could precludes the development of novel systems that can potentially deliver multiple beneficial outcomes 24 . Explicitly analyzing the impacts of farming practices on multitrophic biodiversity is therefore a necessary step to understand the variable performance of organic farming and to set the scene for the ecological intensification of farming systems.

A considerable number of species have large home ranges and exploit multiple resources in different habitats, leading to a major effect of landscape context on species assemblages in agricultural fields 25 , 26 . Despite its local effects on biodiversity, organic farming at larger spatial scales may therefore affect biodiversity dynamics in agricultural landscapes 27 , 28 . Recent evidence have demonstrated that the effects of organic farming on biodiversity and ecosystem services are scale dependent 28 , 29 . However, only few studies have considered how the spatial expansion of organic farming affect biodiversity.

The aim of this study was to quantify the impacts of organic farming and underlying farming practices on vineyard biodiversity considering seven different taxonomic groups, from microbes to pollinators. In addition, we investigated the scale of the effects of organic farming on these different groups, from the field to the landscape scale. We decided to focus on vineyard-dominated landscapes of southwestern France as vineyards of this region can be intensively managed (on average 17 pesticide treatments are applied per unit area each year, 30 and because organic farming area is rapidly increasing (area under organic farming increased by 300% in ten years 31 . We expected an overall positive effect of organic farming on the abundance and taxonomic richness of belowground and aboveground communities. Moreover, we hypothesized that the scale of organic farming effects would depend on the community considered, with a priori expectations of a stronger effect of local management on decomposer communities (with limited dispersal abilities) and greater importance of organic farming proportion in the landscape for pollinator and predator communities (with higher dispersal ability).

Effect of organic farming at the field and landscape levels

Organic farming at the field scale was retained in all best models (models with a ΔAICc < 2 in the model averaging procedure) explaining the abundances of above- and belowground communities but had contrasting effects on these communities (Fig.  1 , all plots representing the effect of significant explanatory variables on each response variables are provided as supplementary material Figure S1-S8 , see also Table S1 for coefficient estimates in all best models). Organic farming, in comparison to conventional farming, increased the abundance of spiders (+ 84%) and springtails (+ 31.6%) but decreased the abundance of pollinators (− 11.6%) as well as soil microbial biomass (− 9.1%) (Suppl. Mat. Figure S1 ). Organic farming at the field scale was the most important variable explaining spider abundance, as it accounted for 73% of the variance explained by the model (variance explained by the fixed effects, R 2 m, was 27%), while it explained 18% of the explained variance for the abundance of pollinators (R 2 m = 38%), 17% of the explained variance for soil microbial biomass (R 2 m = 57%) and 10% of the explained variance for the abundance of springtails (R 2 m = 64%) (Fig.  1 ). Organic farming had no effects on the abundances of ground beetles, mites and soil microarthropods (Fig.  1 ). Organic farming was less important in explaining changes in taxonomic richness of the different communities, as it only had a strong and positive effect on spider species richness (76% of explained variance; R 2 m = 26%; see Figure S9). At the landscape scale, increasing the proportion of organic farming decreased the abundance (accounting for 20% of explained variance, R 2 m = 49%) and taxonomic richness of ground beetles (accounting for 27% of explained variance, R 2 m = 55%) as well as the abundance of soil microarthropods (accounting for 37% of explained variance, R 2 m = 16%) (Fig.  1 , Suppl. Mat. Figures S3 and S9 ).

figure 1

Results of the best models explaining the abundances of (a) pollinators, (b) ground beetles, (c) spiders, (d) springtails, (e) mites, (f) soil microarthropods and (g) microbial biomass according to the type of farming system (organic or conventional), landscape context, farming practices independent of farming systems and soil characteristics. Stacked bars show the relative effects of estimates (%R 2 ) for each explanatory variable calculated as the ratios between the parameter estimates and the sum of all parameter estimates based on a model averaging approach applied to model 1. Points are estimates of the model coefficients, and lines represent confidence intervals. All continuous predictors were scaled to interpret parameter estimates at comparable scales. All individual plots representing the effects of significant explanatory variables of Fig.  1 are provided as supplementary material (Fig S1-S8 ). Note that results of best models for models 2 explaining abundances of the different groups by specific farming practices, landscape context and soil characteristics are provided in Figure S10. This figure was made using R version 4.0.3 ( https://www.R-project.org/ ) and Inkscape 1.0 ( www.inkscape.org ).

Our analyses revealed that tillage intensity mainly mediated the observed field-scale effects of organic farming on the above- and belowground communities (model 2 outputs in Suppl. Mat. Figure S10 ). Tillage intensity decreased the abundances of pollinators and ground beetles as well as microbial biomass, while it increased the abundance of springtails (Suppl. Mat. Figure S2 ). No significant effect of tillage intensity was found on the taxonomic richness of the above- and belowground communities (model 2 outputs in Suppl. Mat. Figure S11 ). Our analyses also revealed a significant interaction effect of tillage intensity at the field scale and the proportion of organic farming in the landscape on the abundance of soil microarthropods (model 2 outputs in Suppl. Mat. Figure S10 ). Fields with higher tillage intensity had a higher abundance of soil microarthropods than fields with lower tillage intensity in landscapes with a high proportion of organic farming, while the opposite was true in landscapes with a low proportion of organic farming.

Effect of semi-natural habitats at the landscape scale and interaction with the local farming system

The proportion of semi-natural habitats was selected in all the best models but had opposite effects on the abundance or biomass of the above- and belowground communities (Fig.  1 ). Increasing the proportion of semi-natural habitats enhanced the abundance of pollinators (38% of explained variance, R 2 m = 38%) and ground beetles (17% of explained variance, R 2 m = 49%), while it decreased the abundance of springtails (26% of explained variance, R 2 m = 64%) and mites (37% of explained variance, R 2 m = 49%) (Suppl. Mat. Figure S4 ). The proportion of semi-natural habitats never affected the taxonomic richness of the above- and belowground communities (model 1 and 2 outputs in Suppl. Mat. and Figures S9 and S11 ).

The interaction between the local farming system and the proportion of semi-natural habitats was selected in all best models explaining the abundance or biomass of the above- and belowground communities, except for the pollinator community. Fields under organic farming had a higher abundance of ground beetles than conventional fields when located in landscapes with a low proportion of semi-natural habitats, while the opposite was true in landscapes with a high proportion of semi-natural habitats (Suppl. Mat. Figure S5 ). In contrast, fields under organic farming had lower abundances of springtails, mites, and microarthropods as well as microbial soil biomass than conventional fields in landscapes with a low proportion of semi-natural habitats, while the opposite was true in more complex landscapes with a high proportion of semi-natural habitats (Suppl. Mat. Figure S5 ).

Insecticide use

Independently of the type of farming system, insecticide use intensity decreased the abundances of pollinators (20% of explained variance, R 2 m = 38%), ground beetles (23% of explained variance, R 2 m = 49%) and springtails (17% of explained variance, R 2 m = 64%) as well as the taxonomic richness of pollinators (67% of explained variance, R 2 m = 19%) (Fig.  1 , Suppl. Mat. Figures S6 and S9 ). No significant effects of insecticide use intensity were found on the taxonomic richness of the other above- or belowground communities.

Soil copper and soil texture

The amount of copper in the soil was found to affect belowground communities as it increased the abundance of mites (10% of explained variance, R 2 m = 49%) but decreased the abundances of springtails (22% of explained variance, R 2 m = 64%) and other microarthropods (19% of explained variance, R 2 m = 16%) (Fig.  1 , Suppl. Mat. Figure S7 ). The proportion of clay in the soil was an important covariable for belowground communities, as its increase was associated with increases in the abundance of mites and soil microbial biomass but a decrease in the abundance of microarthropods (Fig.  1 , Suppl. Mat. Figure S8 ). The taxonomic richness of ground beetles was negatively affected by the proportion of clay in the soil, while the taxonomic richness of microarthropods benefited from an increase in clay content (Suppl. Mat. Figures S8 and S9 ).

Our study reveals contrasting effects of organic and conventional farming on biodiversity across multiple trophic groups, with strong effects on abundance and limited effects on taxonomic richness of the different groups considered. Explanatory power of models indicate that the effects of farming practices were better captured on community abundances than on taxonomic richness as explanatory power of models fitted on taxonomic richness were low, except for carabids (Figure S9 and see below). Among the seven groups studied, organic farming at the field scale enhanced the abundances of springtails (+ 31.6%) and spiders (+ 84%), had detrimental effects on pollinator abundance (- 11.6%) and soil microbial biomass (- 9.1%), and did not affect the abundance of ground beetles, mites or microarthropods. Using a multiscale design that made it possible to evaluate the scale of the effects of organic farming on the seven taxonomic groups considered, we found that organic farming at the field scale is a more important driver of above- and belowground community characteristics than the proportion of organic farming at the landscape scale. At the field scale, we show that beyond the type of farming system, features such as tillage intensity, insecticide use and soil copper content are important variables that affect biodiversity, with a predominance of negative impacts on the abundances of these communities.

Previous studies have demonstrated that, on average, organic farming increases taxonomic richness by approximately 30% and abundance by 50% but have also reported highly variable responses among taxa 11 , 12 , 13 . Our study based on large sampling of multiple communities operating at different trophic levels highlights the strong variability in the response of different trophic groups to organic farming. We therefore did not validate our initial hypothesis about an overall positive effect of organic farming on the abundance and taxonomic richness of multiple communities. However, our analyses demonstrate the importance of considering farming practices beyond organic and conventional systems to understand apparent idiosyncratic responses. Tillage intensity was one of the main differences that discriminated between organic and conventional systems, as organic farmers cannot use synthetic herbicides to control weeds 32 . Our analyses show that organic farming benefits springtail abundance in the topsoil through higher tillage intensity, which may increase food availability and limit soil compaction 19 , 33 , 34 . The positive effect of organic farming on spider abundance confirms results from other studies 20 , 28 , 35 . Our results indicate that this positive effect is not driven by tillage intensity or any other covariable related to farming practices or soil conditions (see Suppl. Mat. Figure S10 ) but suggest that spiders may have benefited from the higher prey availability resulting from organic farming (as found for springtails) 33 , 36 . However, not all organisms benefited from higher trophic resource availability under organic farming, as pollinator abundance and soil microbial biomass decreased in organic fields compared to conventional fields. This may appear contradictory to what has been demonstrated in the literature as, on average, both pollinators and soil microbial biomass have been found to benefit from organic farming compared to conventional farming 13 , 15 . Again, our analyses provide insights into the key role of farming practices in explaining these effects, as both pollinator abundance and soil microbial biomass were negatively affected by the higher tillage intensity found in organic fields. Higher tillage intensity strongly limits flower availability and the emergence of ground-nesting wild bees 37 , 38 . Similarly, higher tillage intensity is known to create less favorable environmental conditions that reduce soil microbial biomass compared to that under reduced or no-tillage systems 39 , 40 . Finally, tillage intensity and clay content in the soil strongly limited the abundance and richness of ground beetles independently of the type of farming system, showing that the practices composing farming systems are an important aspect to consider to understand the overall effect of the type of farming system. Such effects occur because the life cycle of ground beetles is strongly related to soil conditions 41 , and a reduction of the abundance of ground beetles by half due to tillage has already been recorded 42 .

We expected that the proportion of organic farming in the landscape would be a strong driver of above- and belowground communities. However, we found that organic farming in the landscape had little effect on the abundance or taxonomic richness of the above- and belowground communities, indicating that the scale of effect of organic farming was mainly the field scale for the taxonomic groups we considered. We therefore did not validate our hypothesis stating that the scale of effect of organic farming would be driven by the trophic level considered and its average dispersal ability. For instance, we could have expected that spiders would benefit from a higher proportion of organic farming in the landscape as they strongly benefit from organic farming at the field scale and they have relatively high dispersal abilities 28 , 26 . The fact that organic farming in the landscape had little effect on multitrophic biodiversity might have result from very limited spillover between fields resulting from negative impacts of other farming practices in the landscape, such as pesticide use 43 , the spatial arrangement of organic farming in interaction with other aspects of landscape structure 44 or a gradient of organic farming proportions (i.e., 0–24% in a 1 km radius) that is not sufficient to detect effects on above- and belowground communities 29 , 45 . Only ground beetles responded to organic farming in the landscape in terms of both abundance and taxonomic richness. Ground beetles were negatively affected by an increase in the area of organic farming. This suggests upscaling effects of organic farming at the landscape mediated by higher tillage intensities that limit ground beetle spillover 46 , 47 . Landscape complexity characterized by the amount of semi-natural habitats in the landscape was a major driver of changes in the abundances of four of the seven taxonomic groups studied: pollinators, ground beetles, mites and springtails. However, the directions of the effects differed between the above- and belowground communities, as the abundances of pollinators and ground beetles benefited from a higher proportion of semi-natural habitats while the abundances of springtails and mites were reduced by an increasing proportion of semi-natural habitats. Semi-natural habitats play a key role in pollinator and ground beetle communities because they provide food sources, overwintering sites and refuges from disturbance 17 , 48 . Maintaining such habitats in vineyard landscapes is therefore essential for these communities. In contrast, negative effects of the proportion of semi-natural habitats on springtail and mite abundances have already been reported 49 , 50 , suggesting physical barriers to passive dispersal 51 or greater top-down control by their predators in more complex landscapes 33 . Moreover, a significant effect of the interaction between local farming system and the proportion of semi-natural habitats appeared in five of seven models explaining the abundance of above- and belowground communities, indicating that landscape context modulates the local effect of farming systems, as demonstrated in other studies 52 , 53 . A very interesting result of our study is that, independently of the type of farming system, the use of insecticides (either organic or synthetic) decreased the abundances of pollinators, ground beetles and springtails as well as the taxonomic richness of pollinators. Insecticides are known to have both lethal and sublethal effects on bees and natural enemies 54 , by affecting reproductive success, immunity, mobility or foraging ability 54 . Our study clearly indicates that such negative effects are detectable in the field, as we found decreases of 32.9% and 20.3% in pollinator abundance and taxonomic richness as well as decreases of 80.7% and 73.3% in the abundances of ground beetles and springtails, respectively, for a treatment intensity ranging from 0 (no insecticide) to 4 (4 full doses of insecticide applied per field). We were not able to examine the effect of fertilizers use on above- and belowground communities as very few farmers used fertilizers. However, investigating how the type and the amount of fertilizers affect multiple trophic groups through bottom-up effects would be of major interest. Our analyses also suggest that farming practices may impact communities on much longer temporal scales than the field season. Indeed, copper accumulates in the topsoil and is strongly affected by the historical use of copper-based treatments 18 . Our results show negative effects of copper content in the soil on the abundances of springtails and soil microarthropods, indicating that soils with higher concentrations of copper had detrimental effects on belowground communities independent of the type of farming system 55 . Copper is used in both organic and conventional systems because of its fungicidal properties and therefore affects microbial communities in soil as well as other organisms involved in nutrient cycling 56 . Considering how temporal dynamics in farming practices affect multiple communities is therefore of crucial importance for designing agricultural landscapes that buffer against biodiversity declines 57 .

It has been recently argued that simple comparisons between broad categories of farming systems could hinder a mechanistic understanding of the socioecological impacts of different forms of agriculture 24 . To our knowledge, our study provides the first empirical test of this idea and demonstrate the added value of analyzing both aspects to stimulate the development of innovative cropping systems. Our modelling approach considering two different sets of explanatory variables, one at the farming system scale (model 1) and one considering individual farming practices (model 2) for each community, provides important and complementary information to understand the relationships between agriculture and farmland biodiversity. Interestingly, and despite the fact that both models were always informative for community-abundance models (Table S1), the relative quality of model 1 and 2 provides information about the responses of each group to farming practices. Models considering the type of farming systems (model 1) were always more informative than models considering individual farming practices (model 2) for the abundances of pollinators, ground beetles, spiders, springtails and microbial biomass (Table S1). This indicates that there might be other key aspects discriminating organic and conventional farming systems that are important for these communities that are not captured by individual farming practices considered in our study. However, models considering individual farming practices were better than models considering the type of farming system for abundances of mites and soil microarthropods, suggesting that specific farming practices independently of the certification scheme are more important for these communities (Table S1).

Our study carefully analyzing how farming practices at different spatial scales affect multiple components of biodiversity represents a major step forward in understanding the relationships between farming practices and biodiversity in agricultural landscapes. The field scale was found to be the most important scale of effect of farming practices across the seven taxonomic groups considered, and organic farming at this scale mainly affected abundance, not taxonomic richness. The low explanatory power of models explaining taxonomic richness of multiple communities by farming practices is not surprising as a previous study in the same study region reported that taxonomic richness or arthropods do not differ significantly between organic farming and conventional fields 58 . Of course, other environmental variables such as abiotic factors not included in our analyses may have impacted taxonomic richness or community composition of the different communities. However, our study is largely focused on arthropods and it would be interesting to include other species with different life-cycles and habitat domains such as plants, birds or bats in such analyses. Our results suggest that independently of the type farming system, specific features of agricultural practices, such as tillage intensity and pesticide use, drive the contrasted responses of the abundance of the different taxonomic groups. Future research could explore how trophic interactions across functional groups contribute to explain the effects of agricultural management on biodiversity.

Moreover, while our results are based on highly replicated sampling points in space, we acknowledge that investigating legacy effects between farming practices and biodiversity dynamics using repeated measures over time would be of major interest. Our study provides key information for further designing farming systems that minimize negative impacts on multitrophic biodiversity and highlight key agronomic issues related to organic and non-organic farming. Decreasing tillage intensity, copper and insecticide use should be of major concern in vineyard landscapes if we are to conciliate grape production and biodiversity conservation. Such agroecological pathway implies finding technical solutions for farmers to reduce tillage and pesticide use while limiting competition for nutrients and pest pressure. Future research aimed at quantifying the consequences of the detected changes in community abundances on agroecosystem functioning is now of major importance to fully anticipate the effect of expanding agroecological practices at the global scale.

Material and methods

Experimental design and field characteristics.

Our study sites were located in a vineyard-dominated region in southwestern France (44°48’N, 0°14’W). Our study design consisted of 20 pairs of organic and conventional vineyards (40 fields). The vineyards were selected in order to obtain pairs distributed along two uncorrelated (Pearson correlation = -0.33, p-value > 0.05) landscape gradients: a gradient of proportion of organic farming (ranging from 0.1% to 24.2%) and a gradient of proportion of semi-natural habitats (ranging from 0.4% to 75.1% and composed of semi-natural forests (65%) and open habitats (35%) such as meadows and shrublands) in a 1 km radius (Figure S12 and S13). Such an experimental design makes it possible to disentangle the relative effects of local farming practices from the proportion of semi-natural habitats and farming practices at the landscape scale. Landscape variables were calculated using QGIS 2.18.1 (QGIS Development Team 2016).

Information about the farming practices of the 40 fields was collected from farmers using a structured interview. We collected data on pesticide use (type of molecule, quantity applied and area treated), which could be organic or not, and soil tillage intensity. To quantify the level of pesticide use, we calculated the treatment frequency index (TFI), which corresponds to the number of recommended doses used per hectare, for the different groups of pesticides (insecticide, fungicide and herbicide) used in organic and conventional farming systems. The TFI includes all type of synthetic or non-synthetic (eg, copper) products applied by farmers. The TFI was respectively calculated for insecticides, fungicides and herbicides as the sum of the ratios between the applied and recommended doses for each application 59 (the TFI was not weighted by area treated as the total area of each field was treated for each applications in all fields). This indicator therefore quantifies the number of recommended doses applied per field for each pesticide groups. Soil tillage intensity (Tillage) was calculated as the ratio between the surface under tillage and the total surface of the fields, considering both row and interrow management. We used this approach because the proportion of soil area under tillage varies between fields and because winegrowers use different types of soil tillage under the vine row and between vine rows (i.e., interrows), which can include tilling or mowing every other row.

In addition, we characterized soil texture using the proportion of clay in the topsoil (0–15 cm) and measured the amount of copper in the soil (EDTA Copper, ISO 22066 norm) by mixing 9 subsamples extracted using soil cores (5 cm in diameter) at 15 m intervals in all rows and interrows (tilled or not) from the topsoil (0–15 cm).

Aboveground communities

Pollinators were sampled between April and August 2018 using colored pan traps and sweep netting. From April to May, pollinators were collected using pan traps on three sampling dates, with two sampling points per field and per date. The two sampling points were located in grassy interrows 15 m apart and were active for 48 h. Each sampling point was composed of two sets of three colored pan traps (blue, yellow and white), one set localized at ground level and the other set localized at vegetation level (60 cm from the ground level) 60 , 61 . Each colored trap was made of 500 mL plastic bowls, filled with soapy water. From July to August, two sweep netting sessions, with a sweep net of 35 cm in diameter, were performed in all fields. For each session, sweep netting was conducted along two 30 m transects, one in a grassy interrow and one in a tilled interrow, spaced by 15 m and starting at 15 m from the edge. Each transect was sampled using one sweep per footstep. For each field and session, we repeated this operation twice during the same day: in the morning (before 12 p.m.) and in the afternoon (after 2 p.m.). Samplings occurred on dry and sunny days with low wind speeds. All collected individuals were stored in 70% ethanol, and individuals were identified to the lowest possible taxonomic resolution. Only wild pollinators (bees, bumble bees and hoverflies) were considered for the analyses, while honey bees ( Apis mellifera ) were removed from the analyses to avoid bias due to the presence of beehives around plots. To calculate taxonomic richness, we used taxonomic units based on the lowest level of identification bees, bumblebees and hoverflies. For bees of the genus Lasioglossum , due to the difficulty of identification at the species level, we considered subgenera, and based on the strength of the distal veins of the forewing, we divided the bees into two groups under the same subgenus.

Predators were sampled on three different dates in June, July and October 2018 with three pitfall traps for each field and date. Pitfall traps were made of 750 mL plastic cups with 11.5 cm diameter. On each date, the pitfall traps were placed along a transect under a vine row starting 15 m from the field border and were located 15 m from each other. The transect was located towards the middle of the field. The pitfall traps were half filled with soapy water and were left open for 10 days. Spiders and ground beetles were collected and stored in 70% ethanol. Individuals were identified to the lowest possible taxonomic resolution: the species level for all carabids; for spiders, 11% to the family level (e.g., Lycosidae), 12% to the genus level (e.g., Pardosa sp . ) and the rest to the species level.

Belowground communities

Soil arthropods were collected from the topsoil (0–15 cm) in October 2018. In each of the 40 fields, 500 mL of soil was constituted by mixing 9 subsamples extracted using soil cores (5 cm in diameter) spaced at 15 m intervals in all rows and interrows (tilled or not). Soil arthropods were then directly extracted using a Berlèse-Tullgren extractor for five days 62 (ISO 23611–2:2006 norm), with a light and associated temperature gradient over the soil core (48 h without light and 72 h days with light), which was crumbled into a 2 mm plastic sieve suspended over a collecting vessel containing 70% alcohol. All arthropods collected were counted and identified to the order or family level, and springtails were identified to the species level. We divided soil arthropods into three groups: springtails, mites and other microarthropods including small spiders, ants, ground beetle larvae, symphylans, pauropods, chilopods, isopods, diplopods, diplurans and proturans. Soil microbial biomass was collected from the topsoil (0–15 cm) in November 2018. In each of the 40 fields, 500 mL of soil was constituted by mixing 9 subsamples extracted using soil cores (5 cm in diameter) spaced at 15 m intervals in all rows and interrows (tilled or not). We quantified the microbial biomass by fumigation following the ISO 14240–2 norm.

Statistical analyses

In order to select relevant and uncorrelated explanatory variables describing differences between organic and conventional systems, we first performed a principal component analysis (PCA) of farming practices based on the collected information. Tillage intensity as well as levels of insecticide, herbicide and fungicide use were included in the PCA (scaled variables). We did not include the amount and type of fertilizers as very few farmers used fertilizers. The PCA identified two main independent axes of farming practices that jointly explained more than 75% of the total variance, with axes 1 and 2 explaining 46.4% and 29.3% of the total variance, respectively (Fig.  2 ). The first axis discriminated organic from conventional farming systems, with organic farming systems associated with higher tillage intensity and conventional farming systems associated with higher levels of herbicide and fungicide use (Fig.  2 and Table S1 ). The second axis was independent of organic and conventional systems and was associated with the level of insecticide use (Fig.  2 ). To avoid redundant information and collinearity in subsequent analyses, we selected soil tillage intensity and insecticide use as two independent variables describing differences in farming practices between fields.

figure 2

Principal component analysis (PCA) of the four variables used to characterize profiles of agricultural practices in our study. Purple triangles represent plots under organic systems, and grey circles, systems under conventional farming. The largest triangles and circles represent centroids of the ellipses characterizing organic and conventional farming systems, respectively. Pearson correlation matrix between variables are provided in Suppl. Mat. Fig S13 . This figure was made using R version 4.0.3 ( https://www.R-project.org/ ).

Second, we examined how local farming practices and landscape context affect above- and belowground communities by using generalized linear mixed models (GLMMs) and a multimodel inference approach 63 . We constructed two models (model 1 and model 2) with different sets of explanatory variables for abundance and taxonomic richness of each community considered (e.g., abundance and taxonomic richness of pollinators). Model 1 included a set of fixed effects, namely, local farming system (organic or conventional) and proportions of semi-natural habitats and organic farming in the surrounding landscape, as well as 3 independent variables, namely, insecticide use intensity and soil copper and clay contents. These three local covariables were included in the full model as they were not associated with the type of farming system. We also added interaction terms between the local farming system and the proportion of semi-natural habitats and between the local farming system and the proportion of organic farming to test for potential modulation of the local effect of farming practices by landscape context. Model 2 had the same structure except that the local farming system was replaced by tillage intensity to examine if potential differences between organic and conventional farming systems were indeed related to differences in soil tillage intensity between these two systems. We included tillage intensity in the model based on the outputs of the PCA of farming practices, which showed that tillage intensity as well as fungicide use intensity was highly correlated with the first axis (axis 1) and nicely discriminated organic from conventional systems.

We fitted generalized linear mixed models (GLMMs) with a Gaussian distribution to explain the taxonomic richness of pollinators, ground beetles, springtails and soil microarthropods as they were normally distributed. We used the Poisson distribution to explain the abundances of pollinators, ground beetles, springtails, mites, and soil microarthropods, microbial biomass and the taxonomic richness of spiders. Finally, we used a negative binomial distribution to explain the abundance of spiders. All the models were fitted with “site” as a random term (one organic and one conventional plot in the same site) to account for the experimental design.

For each set of models, all the possible candidate models were ranked using the Akaike information criterion with correction for small sample sizes (AICc) and models with a ΔAICc < 2 were retained among the set of best models for inference 63 , 64 (see outputs of models in Suppl. Mat. Table S1 ). Such set of best models was then used to estimate the mean effects and confidence intervals of each explanatory variable using model averaging and the full averaging procedure of models. For models fitted with a Gaussian distribution, multimodel inference was performed with the maximum likelihood (ML) ratio, and selected models were refitted using restricted maximum likelihood (REML) to obtain standardized estimates and p-values 63 . For models with Poisson and negative binomial distributions, multimodel inference was performed under REML. Multimodel inference was performed using confidence intervals on the full average model using the MuMIn package 65 . All analyses were performed using R (R Core Team) 66 . Correlation matrix between all explanatory variables are provided in the Supplementary Material (Figure S13 ). The residuals of the models were checked for normality and homoscedasticity using the “DHARMa” pacvkage 67 . Collinearity between explanatory variables was assessed using the variance inflation factor (all VIFs were lower than 2). All continuous explanatory variables were scaled by the mean and standard deviation. GLMMs were fitted using the “lme4” package 68 .

We evaluated the relative importance of the predictors in explaining the abundance and richness of each community by calculating the percentage of variance they explained based on the ratio between the absolute values of the standardized regression coefficient and the sum of all standardized regression coefficients of predictors for each model 57 .

Ethical approval and informed consent

Informed consent was obtained from all farmers. Information about their farming practices were carried out in accordance with relevant guidelines and regulations including the General Data Protection Regulation 2016/679 (GDPR). All experimental protocols were approved by INRAE.

Data availability

The datasets used in the study will be available from DRYAD repository after acceptance of the paper.

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Acknowledgements

This research was funded by the Conseil Interprofessionnel des Vins de Bordeaux (ALAMBIC project). We thank the 40 grapevine growers for allowing us access to their vineyards.

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N.O., B.G., D.T. and A.R. conceived the ideas and designed methodology; N.O., O.B., S.R.C., B.J. collected the data; N.O. analyzed the data; N.O. led the writing of the manuscript; All authors contributed critically to the drafts and gave final approval for publication.

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Ostandie, N., Giffard, B., Bonnard, O. et al. Multi-community effects of organic and conventional farming practices in vineyards. Sci Rep 11 , 11979 (2021). https://doi.org/10.1038/s41598-021-91095-5

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