Abstract

In most climate change research, agricultural yields are explained as a function of climatic and biophysical factors such as soil, rainfall and temperature. However, the increased use of integrated sensors, digital technologies and robotics within the agricultural sector has dramatically altered the way in which we produce food. Considering both the agriculture industry’s continuing widespread technological innovation and a rapidly changing biophysical environment, there is a need to explore how sociotechnical and climatic variables interact to determine yield. In this paper, we present a regression model derived from Agriculture and Agri-Food Canada (AAFC) yield data, Environment and Climate Change Canada (ECCC) climate and land capability data, and Statistics Canada Census of Agriculture databases that include sociotechnical variables such as farms that use GIS and GPS had access to high-speed internet alongside more traditional biophysical factors to predict canola (rape seed) yields in the southern prairies of Canada. We demonstrated that about 38% of canola yield variability could be explained by temperature and rainfall during the growing season (defined as 3 months of June, July and August) and access to high-speed internet, application of chemical fertilizer, fungicides and average age of farm operators. While of a preliminary nature, our results demonstrate that a better understanding of how climatic and sociotechnical factors interact is necessary to anticipate how climate change may affect the crop yield.

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