Abstract

Process-based models are effective tools for assessing the sustainability of agricultural productivity and environmental health under various management practices and rotation systems. The objectives of this study were to (1) calibrate and evaluate the DeNitrification-DeComposition (DNDC) model using measurements of yields, nitrogen (N) uptake, soil inorganic N, soil temperature, soil moisture and nitrous oxide (N2O) emissions under long-term fertilized continuous corn (CC) and corn-oats-alfalfa-alfalfa (COAA) rotation systems in southwest Ontario from 1959 to 2015, Canada, and (2) explore the impacts of four diverse rotation systems (CC, COAA, corn-soybean-corn-soybean (CSCS) and corn-soybean-winter wheat (CSW)) on corn yields and annual N2O emissions under long-term climate variability. DNDC demonstrated “good” performance in simulating corn, oats and alfalfa yield (normalized root mean square error (nRMSE) < 20%, Nash-Sutcliffe efficiency (NSE) > 0.5 and index of agreement (d) > 0.8). The model provided “fair” to “good” simulations for corn N uptake and soil inorganic N (NSE > 0.2 and d > 0.8), and also daily soil temperature and soil moisture (nRMSE <30% and d > 0.7) for both calibration and validation periods. The model demonstrated “good” performance in estimating daily and cumulative N2O emissions from both the continuous and rotational corn, whereas it produced “poor” to “good” predictions for N2O emissions from the rotational oats and alfalfa crops, however, the emissions from these crops were very low and the relative magnitude of these emissions between all crops investigated were well predicted. The lowest N2O emissions were from COAA followed by CSCS, CSW then CC. The highest corn yields were from COAA, followed by CSW, CSCS, then CC. This study highlights how modelling approaches can help improve the understanding of the impacts of diversified rotations on crop production and greenhouse gas emissions and contribute towards developing policies aimed at improving the sustainability and resiliency of cropping systems.

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