Joachim Aurbacher

Simulating regional climate-adaptive field cropping with fuzzy logic management rules and genetic advance

Agriculture is a largely technical endeavour involving complicated managerial decision-making that affects crop performance. Farm-level modelling integrates crop models with agent behaviour to account for farmer decision-making and complete the representation of agricultural systems. To replicate an important part of agriculture in Central Europe a crop model was calibrated for a unique regions predominant crops: winter wheat, winter and spring barley, silage maize and winter rapeseed. Their cultivation was then simulated over multiple decades at daily resolution to test validity and stability, while adding the dimension of agent behaviour in relation to environmental and economic conditions. After validation against regional statistics, simulated future weather scenarios were used to forecast crop management and performance under anticipated global change. Farm management and crop genetics were treated as adaptive variables in the milieu of shifting climatic conditions to allow projections of agriculture in the study region into the coming decades.

Integrating GIS-based field data and farm modeling in a watershed to assess the cost of erosion control measures: An example from southwest Germany

Policy measures regulating agricultural production are becoming increasingly important in the control of erosion and water runoff. In order to enable planners and other authorities to implement such measures efficiently, detailed information regarding the relevant costs and benefits is necessary. However, economic models to date have tended to utilize a spatial resolution that is insufficient to reveal the effects of such measures on a micro scale. Farming practices, like cross-slope cultivation, filter strips, or field divisions, exert varying impacts on small spatial structures. The benefits and costs of such measures depend to a large degree on local conditions, such as field size and slope. Environmental models as well as economic modeling must take these factors into account. This paper presents a novel approach called CULTIVASIM that directly incorporates field-level topographic and geometric data into a farm economic model. This allows researchers to gain insight into the cost structure of these tasks on a field level as well as a farm level when considering whole-farm adaptation possibilities. Results show that while filter strips lead to relatively uniform costs in relation to area, the costs of field division and cross-slope cultivation vary greatly depending on the field geometry. This information should be included when planning control measures and designing compensation schemes. This new model approach can be used to calculate the economic costs and benefits of using precision conservation practices across the landscape.

Modeling perceptions of climatic risk in crop production

In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of “still-good yield” (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in the analysis.