Recently, researchers from Institute of Tobacco Research, Chinese Academy of Agricultural Sciences (TRI, CAAS), Precision Agriculture Center at the College of Food, Agricultural and Natural Resource Sciences, University of Minnesota (PAC, CFANS, UMN), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (ICS, CAAS), and some other cooperation units have developed a strategy for in-season dynamic diagnosis of maize nitrogen (N) status across the growing season by integrating proximal sensing and crop growth modeling.

Efficient and accurate in-season diagnosis of crop N status is crucially important for precision N management. Crop sensing technology has been increasingly applied for crop N status estimation and diagnosis with the advantages of rapid, non-invasive, and real-time detection. While mechanistic crop growth models are valuable tools for simulating crop growth and development from sowing to harvest, allowing for the evaluation of crop responses to different weather and soil conditions, management practices, and genetic characteristics.

This study integrated plant N concentration derived from leaf fluorescence sensor data and aboveground biomass based on the best-performing spectral index calculated from active canopy reflectance sensor data with simulated plant N concentration and aboveground biomass using a crop growth model, DSSAT-CERES-Maize, for dynamic in-season maize N status diagnosis across the growing season. The data integration method using both proximal sensing and crop growth modeling produced accurate predictions of a reliable N status indicator, N nutrition index (NNI), and N status diagnostic outcomes for key growth stages in this study and could be used to simulate maize N status across the growing season, showing the potential for in-season dynamic N status diagnosis and management decision support.

The study entitled “In-season dynamic diagnosis of maize nitrogen status across the growing season by integrating proximal sensing and crop growth modeling” has been published online in Computers and Electronics in Agriculture and can be accessed through the following link: https://doi.org/10.1016/j.compag.2024.109240.