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

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.

Comparative Performance of High-Yielding European Wheat Cultivars Under Contrasting Mediterranean Conditions

“…In this regard, the formulation of vegetation indices, measured through multispectral sensors and, more recently, with conventional digital cameras, has proven to be a very useful option for rapid and nondestructive screening of the responses of cereal to a water regimen, heat ( Araus and Kefauver, 2018 ; Gracia-Romero et al, 2019 ), and N fertilization ( Cabrera-Bosquet et al, 2011 ; Kefauver et al, 2017 ; Fernandez-Gallego et al, 2019 ). The normalized difference vegetation index (NDVI) is one of the most well-established multispectral vegetation indices ( Marti et al, 2007 ; Cabrera-Bosquet et al, 2011 ; Duan et al, 2017 ), while, among the estimates derived from RGB images, the greener area (GGA) has proven its performance in assessing crop growth, photosynthetic area, and canopy senescence ( Yousfi et al, 2016 , 2019 ; Fernandez-Gallego et al, 2019 ).…”

Comparison of Selection Traits for Effective Popcorn (Zea mays L. var. Everta) Breeding Under Water Limiting Conditions

“…A portable chlorophyll meter (SPAD) can be an interesting tool for plant stress diagnosis ( Romano et al., 2011 ; Araus et al., 2012 ; Zia et al., 2013 ; De Castro et al., 2014 ). In fact, spectrometric methods can be used to efficiently evaluate different WCs and assess the agronomic performance of cereals ( Yousfi et al., 2019 ).…”

Screening of Popcorn Genotypes for Drought Tolerance Using Canonical Correlations

“…Thus, SPAD index is an essential tool for diagnosing plant stress [13,[23][24][25]. Actually, spectrometric methods are efficient to evaluate the different water conditions and the performance of cereals [45].…”