Abstract Context Against a background of unprecedented climate change, humanity faces the challenge of how to increase global food production without compromising the natural environment. Crop suitability models can indicate the best locations to grow different crops and, in doing so, support efficient use of land to leave space for, or share space with, nature. However, challenges in downscaling the climate data needed to drive these models to make predictions for the future has meant that they are often run using national or regional climate projections. At finer spatial scales, variation in climate conditions can have a substantial influence on yield and so the continued use of coarse resolution climate data risks maladaptive agricultural decisions. Opportunities to grow novel crops, for which knowledge of local variation in microclimate may be critical, may be missed. Objective We demonstrate how microclimate information can be acquired for a region and used to run a mechanistic crop suitability model under present day and possible future climate scenarios. Methods We use microclimate modelling techniques to generate 100 m spatial resolution climate datasets for the south-west of the UK for present day (2012–2017) and predicted future (2042–2047) time periods. We use these data to run the mechanistic crop model WOrld FOod STudies (WOFOST) for 56 crop varieties, which returns information on maximum crop yields for each planting month. Results and conclusions Over short distances, we find that the highest attainable yields vary substantially and discuss how these differences mean that field-level assessments of climate suitability could support land-use decisions, enabling food production whilst protecting biodiversity. Significance We provide code for running WOFOST in the WofostR R package, thus enabling integration with microclimate models and meaning that our methodology could be applied anywhere in the world. As such, we make available to anyone the tools to predict climate suitability for crops at high spatial resolution for both present day and possible future climate scenarios.


  • Producing enough food to feed our growing population will be a major challenge faced by humanity over the course of this century

  • We demonstrate the application of microclimate modelling techniques across a region to generate the data to run the mechanistic crop model WOrld FOod STudies (WOFOST) under current and possible future climate conditions

  • We show that estimating productivity at fine spatial scales can be important as, at 100 m spatial resolution, maximum yields for all crops varied substantially across short distances in both time periods, and the impacts of climate change on crop yields were spatially heterogeneous

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Producing enough food to feed our growing population will be a major challenge faced by humanity over the course of this century. Over the same period that food production must increase to meet demand, unprecedented climate change is expected, and this may have significant consequences for agricultural output. Many studies that have projected yields under future climate scenarios predict significant losses in production for major crops If we cannot increase agricultural productivity on the land already under cultivation, natural habitat, and the biodiversity it sup­ ports, will be lost (Kehoe et al, 2017; Laurance et al, 2014). The ability accurately to predict crop suitability under present and future climates is an important and timely goal, which could lead to agricultural decisions that support global food security and aid nature conservation. Crop suitability can be computed using a correlative approach


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