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Climate change research identifies risks to agriculture that will impact agricultural land suitability. To mitigate these impacts, agricultural growing regions will need to adapt, diversify, or shift in location. Various machine learning algorithms have successfully modelled agricultural land suitability globally, predominantly using climate and soil features. Topography controls many of the environmental processes that impact agriculture, including soils, hydrology, and nutrient availability. This research evaluated the relationship between specialty crops and topography using land-surface parameters extracted from a 30 m DEM, soil features, and specialty crop presence/absence data derived from eight years of previous land classifications in southern Ontario, Canada. Using random forest, a model was developed for each specialty crop where feature permutation importance, Matthew’s correlation coefficient, and the area under the precision-recall curve was calculated. Elevation relative to watershed minimum and maximum, direct radiation on Day 172, and spherical standard deviation of normals were identified as the mean most important topographic features across all models and beet crops were found to have the highest association with topographic features. These results identify locations of agricultural expansion opportunities if climate becomes more favourable. The importance of topography in addition to climate and soils when identifying suitable areas for specialty crops is also highlighted.
Lisso et al. (Thu,) studied this question.