Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors
Author
Yannam, Venkata Rami Reddy
Gulino, Davide
Publication date
2023-04-03ISSN
1664-462X
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 Bayesian information criterion (BIC) and the inclusion of days to heading, ear density or plant height together with NDVI in most cases were better (lower BIC) than NDVI alone. This was particularly evident when NDVI saturates (with yields above 8 t ha-1) with models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error. These results showed an improvement of NDVI prediction models by the addition of other agronomic traits. Moreover, NDVI and additional agronomic traits were unreliable predictors of grain yield in wheat landraces and conventional yield quantification methods must be used in this case. Saturation and underestimation of productivity may be explained by differences in other yield components that NDVI alone cannot detect (e.g. differences in grain size and number).
Document Type
Article
Document version
Published version
Language
English
Subject (CDU)
633 - Field crops and their production
Pages
10
Publisher
Frontiers Media
Is part of
Frontiers in Plant Science
Citation
Gracia-Romero, Adrian, Rubén Rufo, David Gómez-Candón, José Miguel Soriano, Joaquim Bellvert, Venkata Rami Reddy Yannam, Davide Gulino, and Marta S. Lopes. 2023. "Improving In-Season Wheat Yield Prediction Using Remote Sensing And Additional Agronomic Traits As Predictors". Frontiers In Plant Science 14. doi:10.3389/fpls.2023.1063983.
Grant agreement number
MINECO/Programa Estatal de I+D+I orientada a los retos de la sociedad/AGL2015-65351-R/ES/HERRAMIENTAS PARA LA SELECCION ASISTIDA POR MARCADORES EN PROGRAMAS DE MEJORA DE TRIGO A ESCALA NACIONAL E INTERNACIONAL: ADAPTACION AL CAMBIO CLIMATICO Y CALIDAD INDUSTRIAL/
MICIU/Programa Estatal de generación del conocimiento y fortalecimiento científico y tecnológico del sistema I+D+I/PID2019-109089RB-C31/ES/Mejora de la precisión y eficiencia en la selección de caracteres complejos en la mejora del trigo en ambientes mediterráneos mediante selección asistida y selección genómica/TRENDING_Wheat
MICINN/Programa Estatal para impulsar la investigación científico-técnica y su transferencia/TED2021-131606B-C21/ES/ /
Program
Cultius Extensius Sostenibles
Ús Eficient de l'Aigua en Agricultura
This item appears in the following Collection(s)
- ARTICLES CIENTÍFICS [2555]
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/