Optimization of 4R nitrogen fertilization practices in response to production system uncertainties

IPNI-2014-CAN-4RC03

22 May 2017

2016 Annual Interpretive Summary


Nitrogen (N) fertilization provides essential benefits for food production, but its optimal management is subject to a high level of complexity. The fertilizer industry, agronomists, consultants, and farmers recognize the 4Rs as the basis for optimum fertilization, but their implementation is knowledge-intensive and site-specific. For a full implementation of the 4R strategy for N, it is necessary to address the risks and opportunities at the field scale, with respect to weather and its interactions with soils and other management factors.

The goal of this project is to quantify the influence of soil and weather conditions (i.e., temperature, precipitation): 1) experienced prior to the growing season; 2) from sowing to topdressing application (if applicable); and 3) after the last N application on the potential for crop yield response and N losses. For this matter, it will be necessary to study the accuracy of site-specific weather forecasts and the opportunity for their inclusion in a probabilistic strategy to optimize N use efficiency, while safeguarding crop yield potential, and to explore system sensitivity to possible interactions with other soil amendments, cultivar specification, tillage systems and different influential management-induced factors.

In 2015, a decision support tool called webSCAN was developed based on a dataset of 322 site-years of corn N response data from Ontario and Quebec. The webSCAN tool adjusts N rates for rainfall prior to and around sidedress application time. This tool, combining the "right time" and "right rate" approaches was found to enhance profitability of N use on corn. Work in 2016 expanded the crop response database to include 302 site-years for potatoes.

A paper published in the 2017 Proceedings of the European Conference on Precision Agriculture showed how a probabilistic modeling approach could be derived from the corn N response database. The paper demonstrated that owing to the typical uncertainties faced by growers, higher application rates shift the maximum probability toward greater net return to use of N fertilizer. Following submission of this paper, the research group had a breakthrough, and is currently developing a full probabilistic model approach to quantify the increased profit, and avoidance of surplus N application, associated with use of a weather-based decision support tool. This tool will help farmers and crop advisers not only to select the most profitable application rate, but also to manage risks associated with over and under application.