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In bio-economic farm models, crop choices are generally depicted as shares of land types which are aggregates of plots with similar characteristics. The ongoing process of digitalization allows access to highly detailed, spatially explicit farm data and facilitates to represent single plots instead. In our paper, we examine how different approaches to model crop choices influence the results of an arable farm in a bio-economic model. Three possible approaches are considered: ‘single plots’ with one crop per season, crop shares of land differentiated by soil type, called ‘categorized’, and crop shares on all arable land, termed ‘aggregate’. The analysis is conducted using a highly detailed, spatially explicit dataset of 8,509 arable farms located in the German federal state of North Rhine-Westphalia. Our analysis indicates that the ‘aggregate’ and ‘categorized’ land endowment approaches produce similar simulation results, which however diverge from the ‘single plot’ approach. We find that on average, crop choices per farm differ by 11% between the spatially explicit ‘single plot’ and the ‘aggregate’ land endowment approach in our case study region. Total work requirements are found to be on average 10% higher in the ‘aggregate’ approach compared to the ‘single plot’ approach, while energy requirements are relatively similar (average difference of 2.2%). Among other factors, we find the difference to be highly correlated with the number of plots a farm is endowed with. For instance, the average difference in crop choices increases from the sample average of 11% to 20.8% for those farms that are endowed with less than 10 plots (~ 50% of the case study population). Differences in simulated farm profits when comparing the ‘aggregate’ land endowment approach to the ‘single plot’ approach are found to range between -306 €/ha to 434 €/ha (mean: 4.57 €/ha, median: - 9.93 €/ha, S.D.: 71.47 €/ha). Our results suggest that for bio-economic farm analyses focusing on aggregate results over a larger sample of farms, both the ‘aggregate’ and ‘categorized’ land endowment approaches are sufficiently accurate in case of similar average numbers of plots per farm as in our study. If single farm results or variability in the population are targeted, we propose to incorporate the ‘single plot’ approach in bio-economic farm analyses. The same holds for decision support systems focusing on individual farm responses to policy changes or technology adoption.

Deciding on which crop to plant on a field and how to fertilize it has become increasingly complex as volatile markets, location factors as well as policy restrictions need to be considered simultaneously. To assist farmers in this process, we develop the web-based, open source decision support system ‘Fruchtfolge’ (German for ‘crop rotation’). It provides decision makers with a crop and management recommendation for each field based on the solution of a single farm optimization model. The optimization model accounts for field specific location factors, labor endowments, field-to-farm distances and policy restrictions such as measures linked to the EU Nitrates Directives and the Greening of the EU Common Agricultural Policy. ‘Fruchtfolge’ is user-friendly by automatically including big data related to farm, location and management characteristics and providing instant feedback on alternative management choices. This way, creating a first optimal cropping plan generally requires less than five minutes. We apply the decision support system to a German case study farm which manages fields outside and inside a nitrate sensitive area. In the year 2021, revised fertilization regulations come in force in Germany, which amongst others lowers maximal allowed nitrogen applications relative to crop nutrient needs in nitrate sensitive areas. The regulations provoke profit losses of up to 15% for the former optimal crop rotation. The optimal adaptation strategy proposed by ‘Fruchfolge’ diminishes this loss to 10%. The reduction in profit loss clearly underlines the benefits of our support tool to take optimal cropping decisions in a complex environment. Future research should identify barriers of farmers to apply decision support systems and upon availability, integrate more detailed crop and field specific sensor data.

Legumes can limit the impact of agricultural systems on the environment by limiting N fertilization, diversifying crop rotation and substituting imported protein-rich feed. However, their production remains low in the European Union, which led to specific policies. France established Voluntary Coupled Support scheme for legumes. Germany did not introduce a coupled support, but provides more favorable implementation of the Nitrates Directive for legumes by allowing spreading manure on these crops. Our study assesses economic and environmental impacts of the coupled support and measures of the Nitrates Directive affecting legume production in France and Germany. We employ the bio-economic model FarmDyn, parameterized for a typical dairy farm in France and Germany. Legumes are introduced as cash crops and on-farm feed, highlighting interactions between crop and animal productions. Different levels of coupled support per hectare were analyzed and the French versus the German implementation of the Nitrates Directive were compared. Results suggest that voluntary coupled support leads to an increase in legume production but to a lesser extend in the German farm than in the French farm, due to higher opportunity costs of legumes. In both farms, the increase in legume production leads to limited environmental benefits: nitrogen leaching and global warming potential slightly decrease. In the French farm, the German implementation of the Nitrates Directive fosters legume production. Thus, this study shows that allowing manure spreading on legumes can help reaching high legume production in livestock farms. However, this further increase in legume production does not lead to environmental benefits. Thus, allowing manure spreading on legumes to increase their production should be justified by other goals such as improving the protein self-sufficiency of the farm.