A model-based methodology to derive optimum nitrogen rates for rainfed crops – a case study for corn using STICS in Canada

Abstract

While the application of nitrogen (N) fertilizer increases crop productivity, the amount of N not taken up by the crop is released as reactive N causing adverse environmental effects, which are associated with short-term climate variations and consist of nitrous oxide emissions, ammonia volatilization, and nitrate leaching. Finding the optimum rate of N application accounting for both economic and environmental aspects is challenging because it varies according to soil texture and climatic conditions. In this paper, a model-based methodology is developed to identify the ecophysiological optimum N rates at which the applied N leads to minimum N excess with little loss in maximum achievable yield. A slope-based method is proposed to identify the optimum nitrogen use efficiency (NUEopt) and to determine the corresponding optimum N rate (Nopt) for each growing season in given agroclimatic regions according to soil properties. It uses the yield predicted by a process-based crop model, long time series of climate data, and a newly proposed Mitscherlich Baule-plateau (MB-P) function. The NUEopt is identified by evaluating the linearity of the relationship between yield and Nopt, and the reduction in yield compared to maximum achievable yield for a given soil and various growing seasons. We illustrate the methodology and its performance by presenting corn yield response to N rate applications predicted by the STICS crop model for dominant soils with contrasting properties with 48–61 years of daily climate data from five major regions located along an agroclimatic gradient of the Mixedwood Plains ecozone in Canada (42.3°N 83°W - 46.8°N 71°W), which is the main grain corn production ecozone of Canada. Our case study in this ecozone indicates that a newly proposed yield MB-P function outperforms the two commonly used functions, i.e., linear-plateau, and MB functions. The proposed methodology provides valuable information such as the likelihood of achieving a yield in a given region, the recommended N rate for given expected yield, the consequent N excess or deficit, and the reduction in yield compared to maximum achievable yield. The proposed methodology can be used in any region where a crop model has been adapted, and soil properties and time series of climate data are available. This approach sets the stage for an in-depth environmental assessment on reactive N within an integrated decision support system.