Amb motiu del tancament d'estiu, la validació de documents es reprendrà a partir del 28 d'agost de 2026. Disculpeu les molèsties.
Con motivo del cierre de verano, la validación de documentos se reanudará a partir del 28 de agosto de 2026. Disculpad las molestias
Due to the summer closure, document validation will resume starting August 28, 2026. We apologize for any inconvenience.

Files

695200.pdf (1.42 MB)

Document type

Article

Version

Published version

Publication date

2019-05-31

Publication license

cc-by (c) Yousfi, Salima et al., 2019
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/151855

Combined use of low-cost remote sensing techniques and C to assess bread wheat grain yield under different water and nitrogen conditions

Journal Title

Authors

Director/Tutor

Journal ISSN

Volume Title

Related resource

https://doi.org/10.3390/agronomy9060285

Abstract

Vegetation indices and canopy temperature are the most usual remote sensing approaches to assess cereal performance. Understanding the relationships of these parameters and yield may help design more efficient strategies to monitor crop performance. We present an evaluation of vegetation indices (derived from RGB images and multispectral data) and water status traits (through the canopy temperature, stomatal conductance and carbon isotopic composition) measured during the reproductive stage for genotype phenotyping in a study of four wheat genotypes growing under different water and nitrogen regimes in north Algeria. Differences among the cultivars were reported through the vegetation indices, but not with the water status traits. Both approximations correlated significantly with grain yield (GY), reporting stronger correlations under support irrigation and N-fertilization than the rainfed or the no N-fertilization conditions. For N-fertilized trials (irrigated or rainfed) water status parameters were the main factors predicting relative GY performance, while in the absence of N-fertilization, the green canopy area (assessed through GGA) was the main factor negatively correlated with GY. Regression models for GY estimation were generated using data from three consecutive growing seasons. The results highlighted the usefulness of vegetation indices derived from RGB images predicting GY.

Subject

Blat, Genètica vegetal

Subject (English)

Wheat, Plant genetics

Citation

Collections

Articles publicats en revistes (Biologia Evolutiva, Ecologia i Ciències Ambientals)

Citation

YOUSFI, Salima, et al. Combined use of low-cost remote sensing techniques and C to assess bread wheat grain yield under different water and nitrogen conditions. Agronomy. 2019. Vol. 9, num. 6, pags. 285. ISSN 2073-4395. [consulted: 11 of August of 2026]. Available at: https://hdl.handle.net/2445/151855

Export metadata

JSON - METS

Share record