Speaker
Description
Crop diversity and spatial distribution are key contributors to an agricultural landscapes’ potential to provide multiple ecosystem services (Beillouin et al., 2021). Next to biodiversity and other public benefits, diverse crop rotations can help reduce pest pressure, harness pre-crop benefits and mitigate climate- and price-related risks for farmers. Yet, in Germany today, 64% of arable land is covered by the same four crops, namely wheat, barley, rapeseed and maize (Statistisches Bundesamt, 2024). This dominance remains despite incentives and regulations concerning crop choice, crop change, maximum shares, minimum number of crops and so on. This situation points to tensions between different priorities concerning crop (rotation) planning between farmers and those interested in maximizing landscape multifunctionality which policies have not yet been able to mitigate.
Planning crop rotations for a field and yearly cropping plans for a farm is extremely complex, with nutrients, pests, risks, machinery use and marketing opportunities being only some of the many things that need to be considered (Gütschow et al., 2021; Pahmeyer et al., 2021). Given this complexity, a decision-making process considering all these factors plus an individual farmer’s preferences seems highly unlikely in practice, hinting to the use of simplified strategies and heuristics. These planning processes are, however, poorly understood. Understanding them better could help to identify leverage points for policy-making for a more diverse spatial and temporal distribution of crops.
This study aims to contribute to an improved understanding of crop (rotation) planning by identifying strategies that farmers employ in a mimicked planning process. For this purpose, we will design a game based on a simplified farm with information on soils, climate, etc., accompanied by context information on prices, regulations and subsidy schemes. The participants will be presented with a map of the farm’s fields and asked to do the crop planning by choosing crops for each field for the current year and one or more crop rotations for the farm. All information apart from the field location will be invisible at first but accessible via a chatbot. We will track the participants’ behaviour within the game to identify common planning strategies (e.g. order of information access) and analyse correlations between these strategies and the planning outcome parameters (such as spatial and temporal diversity) as well as farmer characteristics.
The study is still being planned, we wish to present the detailed design at the REECAP Meeting 2025.
| Status of your work | Experimental Design |
|---|---|
| Early Career Researcher Award | Yes, the paper is eligible |
