Agroecosystem modeling and precision agriculture for sustainable nitrogen management

Abstract

Context: As a key nutrient for crop production, nitrogen (N) has long been used as fertilizer in agriculture, particularly after the advent of industrial N fixation in the first decade of the 20th century. However, increasing N-based fertilizers in combination with low N use efficiency leads to environmental N losses. Reactive nitrogen (Nr) losses in the form of NH3, NO3- and N2O are a major threat to the environment and in need of mitigation. Due to the high spatial and temporal variability of Nr losses, quantifying the environmental impacts of agriculture is difficult. Objective: To reflect on methods used for quantifying and reducing N losses from agricultural systems that hold promise for large-scale application. Methods: This paper reviews precision agriculture (PA) practices and the representation of the N cycle in major crop system models (CSMs) to identify beneficial linkages between PA and CSMs to identify sustainable N management options. Results and conclusions: A combination of geographic information system (GIS), Remote Sensing (RS) and CSMs offers a promising approach to provide data at different spatial scale to predict environmental losses of N from agroecosystems. In this process, Artificial Intelligence (AI) can be used to process large datasets, improve and supplement the parameterization of CSMs, integrate sensor data, and support CSMs. Significance: Using a combination of GIS, RS and AI to empower CSMs to guide PA practices will allow sustainable N management to be implemented at scale.