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Current data and modeling bottlenecks for predicting crop yields in the United Kingdom

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Corcoran, E 
Afshar, M 
Curceac, S 
Lashkari, A 
Raza, MM 


jats:pIdentifying and implementing management actions that can mitigate the impacts of climate change on domestically grown crops is crucial to maintaining future food security for the United Kingdom (UK). Crop models serve as critical tools for assessing the potential impacts of climate change and making decisions regarding crop management. However, there is often a gap between yields predicted by current modeling methods and observed yields. This has been linked to a sparsity of models that investigate crop yield beyond field scale or that include data on crop management or crop protection factors. It remains unclear whether the lack of available data imposes these limitations or if the currently available data presents untapped opportunities to extend models to better capture the complex ecosystem of factors affecting crop yield. In this paper, we synthesize available data on plant physiology, management, and protection practices for agricultural crops in the UK, as well as associated data on climate and soil conditions. We then compare the available data to the variables used to predict crop yield using current modeling methods. We find there is a lack of openly accessible crop management and crop plant physiology data, particularly for crops other than wheat, which could limit improvements in current crop models. Conversely, data that was found to be available at large scales on climate and soil conditions could be used to explore upscaling of current approaches beyond the field level, and available data on crop protection factors could be integrated into existing models to better account for how disease, insect pest and weed pressures may impact crop yield under different climate scenarios. We conclude that while a lack of available data on crop management, protection, physiology, at scales other than field level, and for species other than wheat currently hampers advancement of modeling methods for UK crops, future investment into data collection and management across a broader range of factors affecting crops, at larger scales and for a broader range of crop species could improve predictions of crop plant development and yield.</jats:p>


Peer reviewed: True

Acknowledgements: The authors thank the Research Compute Platforms group at The Alan Turing Institute for providing computational resources which aided in conducting the literature review, and the Rothamsted Research Electronic Research Archive (e-RA) Project Team for facilitating access to relevant datasets.


3002 Agriculture, Land and Farm Management, 30 Agricultural, Veterinary and Food Sciences, 3004 Crop and Pasture Production, 13 Climate Action, 2 Zero Hunger

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Frontiers in Sustainable Food Systems

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Frontiers Media SA