Modelling crop land use change derived from influencing factors selected and ranked by farmers in North temperate agricultural regions.

To develop meaningful land use scenarios, drivers that affect changes in the landscape are required. In this study, driving factors that influence farmers to change crops on their farm were determined. A questionnaire was administered to four independent groups of farmers who identified and ranked influencing factors pertaining to their choices of crops. The farmers were located in two mid-latitude agricultural watersheds (in Germany and Canada). The ranked influencing factors were used to develop a "farmer driven" scenario to 2040 in both watersheds. Results showed that the most important influencing factors for farmers to change crops were the "economic return of the crop" and "market factors". Yet, when the drivers of crop land use change were grouped into two categories of "financial" and "indirectly-related financial" factors, the "financial" factors made up approximately half of the influencing factors. For some responses, the "indirectly-related financial" factors (i.e. "access to farm equipment", the "farm experience", and "climate") ranked higher than or just as high as the financial factors. Overall, in the four farmer groups the differences between the rankings of the influencing factors were minor, indicating that drivers may be transferable between farms if the farmers are full-time and the farming regions have comparable growing seasons, access to markets, similar technology, and government programs for farm income. In addition to the "farmer driven" scenario, a "policy driven" scenario was derived for each watershed based only on available information on the financial incentives provided to farmers (i.e. agricultural subsidies, income support, crop insurance). The influencing factors ranked by the farmers provided in-depth information that was not captured by the "policy driven" scenario and contributed to improving predictions for crop land use development. This straight-forward method to rank qualitative data provided by farmers can easily be replicated in other watersheds to improve environmental impact modelling.
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