CropSuite – A comprehensive open-source crop suitability model considering climate variability for climate impact assessment

Abstract. Increasing demand for agricultural land resources and changing climate conditions require for strategic land-use planning and the development of adaptation strategies. Therefore, information about the suitability of agricultural land is a necessary prerequisite. Current suitability approaches often focus on single crops, can only be applied regionally and usually neglect the impact of climate variability on crop suitability. Here, we introduce CropSuite, a new comprehensive and easy-to-use open-source crop suitability model that makes it possible to overcome these shortcomings. CropSuite uses a fuzzy logic approach and is based on the assumption of Liebig’s law of the minimum. It includes a spatial downscaling approach for climate data, which allows for performing crop suitability analysis at very high spatial resolution. Several factors that impact on crop suitability can flexibly be integrated into CropSuite by determining membership functions. CropSuite allows for the consideration of irrigated and rainfed agricultural systems, vernalization requirements for winter crops, lethal temperature thresholds, photoperiodic sensitivity and several other limitations. The model calculates and outputs climate-, soil-, and crop suitability, the optimal sowing date, the potential for multiple cropping, the (most) limiting factor(s), as well as the recurrence rate of potential crop failures.

In this study, we apply CropSuite for 48 crops at a spatial resolution of 30 arc seconds (1 km at the equator) for Africa. Thereby, we consider regionally important staple and cash crops, such as coffee, cassava, banana, oil palm, cocoa, cowpea, groundnuts, mango, millet, papaya, rubber, sesame, sorghum, sugar cane, tobacco, and yams. We find that the consideration of climate variability for calculating crop suitability makes a significant difference on suitable areas, but also affects optimal sowing dates, and multiple cropping potentials. The most vulnerable regions for climate variability are identified in Somalia, Kenya, Ethiopia, South Africa, and the Maghreb countries. The results provide valuable crop-specific information that can be further used for climate impact assessments, adaptation and land-use planning.