Assessing and Predicting Effects of Input and Crop Diversity on Soil Available Nitrogen and Extractable Phosphorus, a Case Study

Available soil nitrogen and phosphorus are important nutrients used in crop production and their loss from agricultural land greatly influences environmental quality. The Alternative Cropping System (ACS) field experiment (1994-2013) was conducted at Scott, Saskatchewan, Canada, to assess the interaction of agricultural inputs and cropping diversity in sustaining environmental quality and crop production in the Canadian Prairies. Agricultural inputs were organic, minimum, and conventional tillage. Cropping diversities were wheat-fallow, diversified annual grains, and diversified annual perennials. Available soil nitrogen was highest in conventional tillage under wheat-fallow and lowest in the organic tillage under diversified annual perennials. Soil phosphorus was highest in minimum and conventional tillage under diverse annual perennials, minimum tillage under wheat-fallow, and lowest in organic tillage under wheat-fallow. Modeling is a useful tool that allows researchers and scientists make important short- and long-term management decisions concerning crop production and environmental quality. The Environmental Policy Integrated Climate model was updated with relevant weather, tillage, and crop management operations from the ACS study to predict nitrogen and phosphorus content in the soil and compare them with experimental values. The model reproduced well long-term nitrogen and phosphorus dynamics. Models’ predicting accuracy for soil nitrogen was highest when simulated nitrogen under wheat, barley, and canola were compared with experimental values. Model’s predicting accuracy under wheat, barley, and canola for soil phosphorus was comparable to soil phosphorus under all crops. We concluded that soil nitrogen and phosphorus were affected by tillage and crop diversity in this long-term study. We also concluded that long-term nitrogen and phosphorus dynamics may be equally or better predicted by analyzing simulated model output from under wheat, barley, and canola compared to analyzing model output from every crop in the rotation. This approach will allow researchers to reduce time and resources required to set up the simulations and analyze model output.