Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R² = 0.74–0.90 for prediction) or random forest regression models (R² = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R² < 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions.

Soil tests can help optimize nitrogen (N) fertilizer rates, thereby improving farmer profitability and environmental performance. In US Midwest maize (Zea mays) production, however, most soil N tests have limited accuracy to predict N fertilizer requirements. Here we tested the individual and combined ability of 30 soil tests (12 rapid N extractions, seven biological carbon or N tests, six long‐term incubation kinetic parameters, and five other routine soil tests), as well as environmental and management data, to predict maize response to N fertilizer across 56 site‐years in the US Midwest. Out of 30 soil tests, and across all site‐years, a 14‐d aerobic incubation best predicted whether maize responded to N fertilizer, and a 5‐min tetraphenyl borate extraction best predicted agronomic optimum N rate. We combined these two tests to evaluate their ability to predict N fertilizer response against the most commonly used soil N test in the US Midwest, the pre‐sidedress or late‐spring nitrate test (PSNT or LSNT). The combination of soil tests nearly doubled the ability to predict nonresponsive sites compared to PSNT, and on average resulted in a 40% reduction in over‐application and 37% reduction in under‐application of N fertilizer. Weather and management variables marginally improved the prediction of maize N response. Our results indicate that a simple combination of biological N mineralization (14‐d aerobic incubation) and chemical extraction (5‐min tetraphenyl borate) assays could improve current N fertilizer recommendations.