Methods using remote sensing associated with artificial intelligence to forecast corn yield at the management zone level can help farmers understand the spatial variability of yield before harvesting. Here, spectral bands, topographic wetness index, and topographic position index were integrated to predict corn yield at the management zone using machine learning approaches (e.g., extremely randomized trees, gradient boosting machine, XGBoost algorithms, and stacked ensemble models). We tested four approaches: only spectral bands, spectral bands + topographic position index, spectral bands + topographic wetness index, and spectral bands + topographic position index + topographic wetness index. We also explored two approaches for model calibration: the whole-field approach and the site-specific model at the management zone level. The model’s performance was evaluated in terms of accuracy (mean absolute error) and tendency (estimated mean error). The results showed that it is possible to predict corn yield with reasonable accuracy using spectral crop information associated with the topographic wetness index and topographic position index during the flowering growth stage. Site-specific models increase the accuracy and reduce the tendency of corn yield forecasting on management zones with high, low, and intermediate yields.

This publication aims to guide cotton producers, Extension agents, crop advisors, and consultants on the correct method of petiole sampling for nitrate-nitrogen analysis and interpretation of petiole nitrate-nitrogen test results, and also discusses ways in which monitoring petiole nitrate-nitrogen concentration can be used as a tool for effective in-season N management in cotton. Written by Akash Shah, Hardeep Singh, Lakesh Sharma, Ethan Carter, and Guilherme Morata, and published by the UF/IFAS Department of Agronomy, June 2025.