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Open AccessArticle

Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions

1
Section of Plant Physiology, University of Barcelona, 08028 Barcelona, Spain
2
AGROTECNIO Center, University of Lleida, 25198 Lleida, Spain
3
Institut Technique des Grandes Cultures, Alger 16016, Algeria
4
International Center for Agricultural Research in the Dry Areas (ICARDA), Avenue Hafiane Cherkaoui, Rabat 10112, Morocco
*
Author to whom correspondence should be addressed.
Agronomy 2019, 9(6), 285; https://doi.org/10.3390/agronomy9060285
Received: 11 April 2019 / Revised: 21 May 2019 / Accepted: 30 May 2019 / Published: 31 May 2019
(This article belongs to the Special Issue Remote Sensing Applications for Agriculture and Crop Modelling)
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. View Full-Text
Keywords: wheat; canopy temperature depression; NDVI; RGB images; grain yield; δ13C wheat; canopy temperature depression; NDVI; RGB images; grain yield; δ13C
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Yousfi, S.; Gracia-Romero, A.; Kellas, N.; Kaddour, M.; Chadouli, A.; Karrou, M.; Araus, J.L.; Serret, M.D. Combined Use of Low-Cost Remote Sensing Techniques and δ13C to Assess Bread Wheat Grain Yield under Different Water and Nitrogen Conditions. Agronomy 2019, 9, 285.

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