Article

# The former Iron Curtain still drives biodiversity–profit trade-offs in German agriculture

• Nature Ecology & Evolution 112791284 (2017)
• doi:10.1038/s41559-017-0272-x
Accepted:
Published online:

## Abstract

Agricultural intensification drives biodiversity loss and shapes farmers’ profit, but the role of legacy effects and detailed quantification of ecological–economic trade-offs are largely unknown. In Europe during the 1950s, the Eastern communist bloc switched to large-scale farming by forced collectivization of small farms, while the West kept small-scale private farming. Here we show that large-scale agriculture in East Germany reduced biodiversity, which has been maintained in West Germany due to >70% longer field edges than those in the East. In contrast, profit per farmland area in the East was 50% higher than that in the West, despite similar yield levels. In both regions, switching from conventional to organic farming increased biodiversity and halved yield levels, but doubled farmers’ profits. In conclusion, European Union policy should acknowledge the surprisingly high biodiversity benefits of small-scale agriculture, which are on a par with conversion to organic agriculture.

Agricultural intensification greatly gained momentum after World War II due to increasing use of agrochemicals and mechanization1,2,3 to mitigate starvation in almost the whole of Europe4. The vision was, at that time, to produce as much food as possible to overcome hunger and poverty in both the Eastern and the Western blocs (Supplementary Fig. 1). This led to increased yields, but was and still is coupled to biodiversity loss5,6. In the Eastern bloc, intensification was combined with a vast collectivization of farms, as farmers were forced to hand over their fields to state-owned cooperatives7. This practice aimed at increasing the efficiency of production through landscape-scale homogenization, including the removal of minor field roads, field margins, hedgerows and any semi-natural habitat inhibiting the ambitious production goals leading to large fields. This process was implemented in East Germany during 1953–1960 and resulted in a rapid change from small-scale agriculture, with more than 800,000 family farms, to large-scale agriculture, with fewer than 20,000 cooperatives. Meanwhile, such drastic change did not happen in the West8. After the German reunification in 1990, field sizes remained almost unchanged9, while ownership changed from cooperatives to private, often Western or foreign farmers. This marked field-size difference is still visible along the former ‘Iron Curtain’10 (Fig. 1). At the same time, European Union (EU) legislation under the Common Agricultural Policy started providing financial support through agri-environmental schemes (AES) with, for example, organic management11. Although some studies questioned the effectiveness of AES in terms of biodiversity gains12,13, both meta-analytical studies and large-scale field studies show that organic management supports threatened farmland biodiversity generally better than conventional farming14,15, while also producing healthier food and less contamination of soils and groundwater16. Biodiversity advantages of small-scale farming and landscape heterogeneity have been acknowledged widely in ecology17,18,19,20,21. However, to the best of our knowledge, the ecological and economic role of large-scale versus small-scale farming has never been studied together. Further, we compared ecological and economic consequences of small-scale agriculture with those of organic farming for the first time.

The historical East–West division enabled us to test the effectiveness of organic cereal management for biodiversity in large-scale versus small-scale agriculture. We measured the diversity of plants and arthropods (Methods) and hypothesized that (1) biodiversity is higher in small-scale cropland1 and (2) the effect of field size is more important for biodiversity than conversion to organic management. In 2013, we selected nine pairs of organic and conventional winter wheat fields in small-scale agricultural landscapes in former West Germany and in large-scale agricultural landscapes in former East Germany, respectively, all along the former inner German border (2 regions × 9 field pairs = 36 study fields; Supplementary Fig. 2). These two neighbouring study regions are representative of the farmland areas of the former East and West Germany22,23. We aimed to explore how biodiversity patterns change from field edges to field centres with the following within-field sampling design. We designated transects at field edges (directly next to narrow grassy field margins bordering dirt roads), field interiors (15 m from field edge) and field centres (120 m and 75 m from field edge in East and West, respectively). We performed our study in the agricultural matrix, minimizing the area and potential effect of non-agricultural habitats (Table 1)24. Landscape structure was very different between the two neighbouring regions, with fields more than six times larger in the East, and >70% longer field edges in the West. Conventional farmers in both regions used about five times the amount of nitrogen fertilizer compared with organic farmers, applied synthetic pesticides about five times per year (versus never) and had approximately two times higher yields than organic farmers25,26. This large difference in winter wheat yield between organic and conventional farmers is typical for the rich soils farmed in the study region27.

We also performed a detailed economic survey of our study farms based on farmer interviews (Methods). Total costs included expenses for mechanical field work, seeds, soil analyses, chemical plant protection, chemical growth regulators, synthetic and organic fertilizers, agricultural wage enterprises and working time. Total revenues included grain and straw revenues as well as subsidies for organic agriculture. Total profit was calculated by deducting total costs from total revenues per field per hectare. We hypothesized that (1) large-scale agriculture is more profitable due to lower variable costs28 and (2) organic agriculture is more profitable due to better marketing possibilities29,30.

## Results

We found that farmers’ profit from winter wheat was more than 100% higher per hectare under organic than conventional management (Fig. 2 and Supplementary Table 1). Subsidies for organic agriculture were €170 and €210 per ha in East and West (AES and subsidies vary among German federal states31), respectively, suggesting that these subsidies contributed to the difference in profit between the two management types. Although subsidies were a substantial part of profit for organic farmers, large differences between the two management regimes remain without these subsidies (mean values for West organic: €1,181 per ha; West conventional: €412 per ha; East organic: €1,663 per ha; East conventional: €874 per ha). We also found significantly higher profits per farmed area (~50–60%) in the large-scale than in the small-scale agricultural region. This is because of higher production costs in Western conventional farms due to current labour costs and higher revenues in Eastern organic farms32 probably associated with better marketing possibilities (Fig. 2 and Supplementary Table 1).

There was no effect of region on species richness of plants and arthropods (carabids, rove beetles, spiders), as well as no overall effect of region when all groups were considered together in a fixed-effect meta-analysis33 (Fig. 3, Supplementary Fig. 4 and Supplementary Tables 26) (Methods). The same was true when analysing arthropod abundances and plant cover (Supplementary Figs. 5 and 6). Organically managed fields harboured more species and individuals of all groups than conventionally managed fields. This effect was strongest for plants, which drove the overall summary effect resulting in 44% higher overall species richness in organically than conventionally managed fields. The statistical interaction of region and management was due to a higher effectiveness of organic management in the West for plant richness as well as spider abundances. Interestingly, both species richness and abundances were reduced by about 25% when comparing field edges with field interiors, but there was no further drop towards the field centres (except for spider richness). Hence, most farmland species and their populations were confined to the very edge of crop fields. This also implies that the higher biodiversity in the small-scale agricultural system in the West can be linked to the much higher amount of field edges1,17,19.

To further explore this pattern, we performed sample-based rarefaction curves34,35 on incidence data of all taxa in field edges combined by standardizing for field perimeter (field perimeters originate from the mean field size per region, Table 1). The rarefied species richness observed in different types of management (organic over conventional) and region (West over East) was significantly different (P < 0.05; Fig. 4). Small-scale conventional management in the West supported higher biodiversity than large-scale organic management in the East (Fig. 4). Although the species richness per field was similar in both regions (Fig. 3), having only nine small fields in the West gives a much higher species richness than four large fields with the same length of field perimeter in the East regardless of management type. This means that the species richness in the fields, that is, alpha diversity, of these two contrasting regions was similar, whereas the species turnover, that is, between-field beta diversity, was much higher in the West than in the East. In addition, richness was higher in organic than in conventional management.

## Discussion

Our study showed how the former Iron Curtain between East and West Germany and the associated divide in large-scale and small-scale agriculture are still shaping economic–ecological trade-offs in agriculture. We quantified the great contribution of small-scale agriculture to biodiversity, which was more important than organic management. Yield levels were the same across the East–West divide, but large-scale agriculture led to the highest profit (despite similar yield), and organic farming even doubled profit (despite halved yield). Although large-scale farms allow higher profits, which is in line with economies of scale28, future restructuring of agricultural landscapes towards small fields with field margins would probably be an economically viable option under an EU-subsidized policy on enhancing farmland biodiversity31. We emphasize the importance of quantifying ecological–economic trade-offs for a politically balanced view. Further, the long-term stability of former East–West contrasts in agricultural politics and farming practices suggests that evaluations of ecological and economic costs and benefits need to be regionally adapted, taking into account agricultural traditions and potential legacy effects36.

## Methods

### Biodiversity survey

In June 2013, we surveyed plants by estimating the relative cover per species in three plots (5 m × 1 m in size and 10 m distance between them) per transect (n = 324 plots). Arthropods (carabids, spiders and rove beetles) were collected with two funnel traps per transect in two one-week periods from May to June (n = 432 funnel traps; for the trapping method see Duelli et al.37).

### Economic comparison

The following cost factors were considered per study field: field preparation, including sowing and harvesting (for example, costs due to the use of cultivator, milling machine, plough, harrow, chipper, curry comb, seed drill, harvester and baler), seeds, soil analyses, chemical plant protection (for example, fungicides, insecticides, herbicides, rodenticides or molluscicides), chemical growth regulators, synthetic and organic fertilizers, agricultural wage enterprises and working time. If costs of preparation, sowing (including seed costs) and harvesting were not tractable by farmers, we noted working steps and machine data, and later calculated expenses by the use of the online plant process calculator of the agricultural advisory board for engineering and building38. In doing so, we considered field size, workability of soil (medium or heavy soil), mechanization (kW, machine type, working width of machines or sowing quantity), field-to-farm distance (set up to 1 km) and farming system (organic or conventional). In terms of other parameters (for example, machine costs such as fuel requirement, repair costs and depreciation), we used standardized settings of the online calculator. If farmers’ data did not fit exactly into the online calculator (for example, sometimes in the case of kW, field size or machine width), we used the next closest setting. In terms of farm-saved seed, we assumed €0.40 per kg of seed for conventional and €0.47 per kg of seed for organic farming systems (Association for Technology and Structures in Agriculture, personal communication) because statements of farmers showed a huge variation. Machine costs emerging through fertilization and chemical plant protection were calculated by using the default setting of the online calculator38 while considering the farming system (organic or conventional), field size, workability of soil (heavy or medium) and cultivation method (direct sowing method, non-plough tillage or conventional soil cultivation with plough). If farmers provided information only about the kind and quantity of product used without prices (four farmers), then costs for chemical plant protection products and growth regulators were derived from different price lists39,40,41,42. If farmers were unable to provide prices for synthetic fertilizers, cost calculation was based on individual average prices of the fertilizers in Germany for the marketing year 2013–2014 (Agrarmarkt Informations, personal communication). Because farmers used organic fertilizers originating from their own enterprises, they were able to tell us just the quantity and the type of organic fertilizer. Average prices were derived from our own survey of regional companies (Nährstoffverwertung Oldenburger Raum Münsterland, Naturdünger Verwertungs, Agrovermittlungsdienst Emsland-Bentheim, Bioenergiedorf Jühnde), which deal with or use natural fertilizers. Prices for liquid manure and digested residue were generally set with €4 per t or €4 per m³ (Lower Saxony) and €5 per t or €5 per m³ (Thuringia), and solid dung with €10 per t. To calculate the costs of working time, we recorded the estimated working hours of each farmer (with reference to the whole winter wheat season 2013–2014). Working time was related to hectares and multiplied by €15 (this amount was based on our own experiences as well as on a farmer’s estimate) to calculate costs per hectare.

In addition to the costs, we also considered the revenue side of the winter wheat season 2013–2014. Here, we recorded grain and straw yield as well as additional state grants for organic agriculture per study field. Grain yield was multiplied by actual proceeds stated by the farmers. Grain yield was sold or used as fodder, as seed or for baking purposes. If a crop was still not sold or used at the time of the survey, calculations were based on the estimated proceeds of each farmer. If straw was not left on the field, we also calculated the proceeds of straw (sold or used as fodder or litter). If not stated by the farmers (nine farmers), we used the average German sales price of straw (€73.8 per t) with reference to the marketing year 2013–2014 (Agricultural Market Information Company, 2015). Besides grain and straw proceeds, we also took into account state grants for organic agriculture as a source of revenue. Here, we considered federal state-specific subsidy rates of the business year 2013–2014 (cultural landscape programme of Thuringia: €170 per ha if organic farming was practised ≥six years; agri-environmental programme of Lower Saxony: €210 per ha if organic farming was practised ≥three years; Lower Saxony Ministry of Food, Agriculture and Consumer Protection and Thuringian Ministry of Infrastructure and Agriculture, personal communication).

All matters of costs and proceeds were calculated per hectare and year for each field. To obtain total revenue (€ per ha, field and business year), aggregated costs were subtracted from overall proceeds.

### Statistical analysis

Due to limited availability of organic farms in the East (fewer organic farms in the East, but with an order of magnitude larger size than in the West43), we applied a so-called partly cross-nested design by selecting from half of the farmers two fields and from the other half only one field: in both regions we had three villages with two organic–conventional pairs and three villages with one organic–conventional pair (see Supplementary Figs. 2 and 3). Therefore, we applied linear mixed-effects models by using the ‘lme4’44 package of the statistical software R45. All biodiversity data were pooled per sampling year and per transect before analysis by taking the mean cover for arable plants and the sum for arthropods. Response variables, if needed, were either log (carabid and rove beetle abundances) or logit (plant cover) transformed to achieve a normal error distribution and/or avoid heteroscedasticity and to get a better model fit. Additionally, all response data were standardized from zero to one46 to allow for direct comparisons of effects on the different dependent variables and to perform fixed-effect meta-analyses for getting the overall effects (see next paragraph). The partly cross-nested study design was taken into account in the random structure of the models. Accordingly, each model included the random effects: field (n = 36) nested in farm (n = 24), nested in village (n = 9); and field (n = 36) nested in pair (n = 18), nested in village (Supplementary Fig. 3). In addition, models contained the following fixed effects: region (East versus West), management (organic versus conventional), transect position (edge, interior or centre) and the interaction between region and management. This model formula in R-syntax is

$l m e r ( y ∼ ( R e g i o n + M a n a g e m e n t ) ∧ 2 + T r a n s e c t p o s i t i o n + ( 1 | V i l l a g e / F a r m / F i e l d ) + ( 1 | V i l l a g e / P a i r / F i e l d ) )$

Marginal and conditional R2 values for species richness and abundance models were calculated using the ‘r.squaredGLMM’ function of the ‘MuMIn’47 package of R. We did not simplify the models, to be able to directly compare their effect estimates among the different taxa and to summarize these estimates in a meta-analysis (see below).

One of the main interests was, besides investigating the environmental effects on each individual group, whether these environmental effects showed an overall effect. Therefore, we performed a series of unweighted fixed-effect meta-analyses for each effect type (region effect, management effect, effectiveness of organic management, edge versus interior effect, interior versus centre effect, edge versus centre effect) per measure type (species richness, abundance) with the ‘metafor’48 package of R. Weighting was not used, because data originate from the same experimental design with the same sample size per measure. This enabled us to get an effect estimate of all groups expressed as summary effect sizes with their corresponding 95% confidence intervals (presented in Fig. 3 and Supplementary Fig. 5).

We analysed the effects of region and management and their interaction on count data from economic surveys (profit, revenue and cost) with generalized linear mixed-effects models based on a negative binomial distribution for avoiding overdispersion. Random-effect terms correspond to the biodiversity analyses above without the random factor field, because that was the lowest level. This model formula in R-syntax is

$g l m e r ( y ∼ ( R e g i o n + M a n a g e m e n t ) ∧ 2 + ( 1 | V i l l a g e / F a r m ) + ( 1 | V i l l a g e / P a i r ) )$

We analysed the effects of region and management and their interaction on farm size with linear regression based on a normal distribution (no random effect). Finally, we analysed the effects of region and management and their interaction, presented in Table 1 with generalized linear mixed-effects models based on a normal distribution for all non-integer continuous data based on a normal distribution. One exception was the only count variable, namely, number of synthetic pesticide applications, which was analysed based on a negative binomial distribution for avoiding overdispersion. The structure of random effects was the same as in the case of economic survey data. In the case of number of synthetic pesticide applications, where effect of management could not be analysed (organic fields excluded because synthetic pesticides are not allowed), only village was used as a random factor.

### Code availability

A complete description of the main model is provided in the Methods, and all code is available on request from the authors.

### Data availability

Species presence data are available in the Supplementary Information (Supplementary Tables 36). The biodiversity and environmental data used in the analyses are archived at the research data repository Zenodo (https://doi.org/10.5281/zenodo.810513).

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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## Acknowledgements

This paper is the product of the project ‘Biodiversity and associated ecosystem services in small- vs. large-scale agriculture’ by the German Research Foundation (DFG BA 4438/1-1). We are grateful to L. Ádám for the identification of rove beetles, and to D.W. Crowder, A. Iverson and D. Kleijn for valuable comments on the manuscript. P.B. was supported by the Economic Development and Innovation Operational Programme of Hungary (GINOP–2.3.2–15–2016–00019).

## Author information

### Affiliations

1. #### Agroecology, University of Goettingen, Grisebachstraße 6, 37077, Göttingen, Germany

• Péter Batáry
• , Róbert Gallé
• , Friederike Riesch
• , Silvia Fusaro
• , Christoph Gayer
• , Anne-Kathrin Happe
• , Kornélia Kurucz
• , Dorottya Molnár
• , Verena Rösch
•  & Teja Tscharntke
2. #### GINOP Sustainable Ecosystems Group, MTA Centre for Ecological Research, Klebelsberg Kuno utca 3, 8237, Tihany, Hungary

• Péter Batáry
3. #### Department of Ecology, University of Szeged, Közép fasor 52, 6726, Szeged, Hungary

• Róbert Gallé
•  & Péter Császár
4. #### Institute of Grassland Science, University of Goettingen, von-Siebold-Straße 8, 37075, Göttingen, Germany

• Friederike Riesch
5. #### Restoration Ecology, Department of Ecology and Ecosystem Management, Technische Universität München, Emil-Ramann-Straße 6, 85354, Freising, Germany

• Christina Fischer
6. #### Biometry and Environmental System Analysis, University of Freiburg, Tennenbacher Straße 4, 79106, Freiburg, Germany

• Carsten F. Dormann
7. #### Department of Agricultural Economics and Rural Development, University of Goettingen, Platz der Göttinger Sieben 5, 37073, Göttingen, Germany

• Oliver Mußhoff
8. #### Department of Biology, Padua University, via Ugo Bassi 58, 35121, Padua, Italy

• Silvia Fusaro
9. #### Environment and Urban Planning, University of Applied Science Nürtingen-Geislingen, Schelmenwasen 4-8, 72622, Nürtingen, Germany

• Christoph Gayer
10. #### Ecological Networks, Technical University Darmstadt, Schnittspahnstraße 3, 64287, Darmstadt, Germany

• Anne-Kathrin Happe
11. #### Szentágothai Research Centre, University of Pécs, Ifjúság útja 20, 7624, Pécs, Hungary

• Kornélia Kurucz
12. #### Institute for Environmental Science, University of Koblenz-Landau, Fortstraße 7, 76829, Landau, Germany

• Verena Rösch
13. #### Plant Ecology and Ecosystem Research, University of Goettingen, Untere Karspüle 2, 37073, Göttingen, Germany

• Alexander Wietzke

### Contributions

P.B. and T.T. conceived the study; P.B., C.F., O.M. and T.T. developed the study; P.B., R.G., F.R., S.F., C.G., A.-K.H., K.K., D.M., V.R. and A.W. collected data; R.G. and P.C. identified arthropods; P.B. analysed data with substantial input from C.F.D.; and P.B. wrote the paper with substantial input from all authors.

### Competing interests

The authors declare no competing financial interests.

### Corresponding author

Correspondence to Péter Batáry.

## Electronic supplementary material

1. ### Supplementary Information

Supplementary Figures and Supplementary Tables