Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors.

ABSTRACT: The development of accurate grain yield (GY) multivariate models using normalized difference vegetation index (NDVI) assessments obtained from aerial vehicles and additional agronomic traits is a promising option to assist, or even substitute, laborious agronomic in-field evaluations for wheat variety trials. This study proposed improved GY prediction models for wheat experimental trials. Calibration models were developed using all possible combinations of aerial NDVI, plant height, phenology, and ear density from experimental trials of three crop seasons. First, models were developed using 20, 50 and 100 plots in training sets and GY predictions were only moderately improved by increasing the size of the training set. Then, the best models predicting GY were defined in terms of the lowest

SUBMITTER: Gracia-Romero A 

PROVIDER: S-EPMC10106603 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature