Evaluation of the EPIC model for predicting wheat, barley, and canola yield on a 19-year alternative cropping system study in a Canadian Prairies

Farmers in Canadian Prairies diversify their crop production and reduce their reliance on fertilizers and pesticides to improve economic and environmental sustainability. The Alternative Cropping System (ACS) field experiment was conducted in 1994 – 2013 at Scott, SK with the goal to assess inputs and cropping diversity in sustaining crop production and soil quality. Modeling is a useful tool that allows researchers and scientists to make important short- and long-term management decisions concerning crop production and environmental quality. The Environmental Policy Integrated Climate (EPIC) model was updated with relevant weather, tillage, and crop management operations from the ACS study at Scott. The model was validated using annual and long-term yield data for wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.). Regression results based on EPIC simulations indicated that model captured long-term yield trends for all three crops, but was weak at predicting annual yield variations. This was due to the soil variability and sloping gradient at the research site, significant variation in precipitation rates and temperatures compared to region normals, occurrence of extreme weather events like hail which caused excessive damage to the crops, and model’s overestimation of mineralized N under low-nitrogen input systems. The R2 values were generally > 0.60 for long-term yield predictions, and < 0.50 for year-to-year variability. The EPIC model should be adjusted with respect to parameters that control soil hydrology and water use by plants, as well as N cycling. Overall, EPIC replicated main and simple effects of agricultural input systems and rotations on long-term crop yield. This shows that the model may be used as a long-term decision tool on agricultural productivity and sustainability in the region