Context: Climate change will alter the global distribution of climatically suitable space for many species, including agricultural crops. In some locations, warmer temperatures may offer opportunities to grow novel, high value crops, but non-climatic factors also inform agricultural decision-making. These non-climatic factors can be difficult to quantify and incorporate into suitability assessments, particularly for uncertain futures. Objective: to demonstrate how qualitative and quantitative techniques can be combined to assess crop suitability with consideration for climatic and non-climatic factors. Methods: We carried out a horizon scanning exercise that used Delphi methodology to identify possible novel crops for a region in south-west England. We show how the results of the expert panel assessment could be combined with a crop suitability model that only considered climate to identify the best crops to grow in the region. Results and conclusions: Whilst improving climate and crop models will enhance the ability to identify environmental constraints to growing novel crops, we propose horizon scanning as a useful tool to understand constraints on crop suitability that are beyond the parameterisation of these models and that may affect agricultural decisions. Significance: a similar combination of qualitative and quantitative approaches to assessing crop suitability could be used to identify potential novel crops in other regions and to support more holistic assessments of crop suitability in a changing world.

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.