Water Use Efficiency, Spectral Phenotyping and Protein Composition of Two Chickpea Genotypes Grown in Mediterranean Environments under Different Water and Nitrogen Supply

Abstract

Chickpea is a drought-tolerant crop and an important source of protein, relevant to its beneficial effects. The aim of this study was to assess the response to agronomic management, including water and nitrogen supply, of crop physiological and agronomic traits in relation to water use efficiency and grain protein composition. Two varieties, Pascià and Sultano, were grown at two different sites in South Italy under rainfed and irrigated conditions, with and without starter nitrogen fertilization. Crop physiological assessment was carried out by hyperspectral phenotyping at flowering and during grain filling. Increases in grain yield and grain size in relation to water supply were observed for water use up to about 400 mm. Water use efficiency increased under starter nitrogen fertilization, and Pascià showed the highest values (4.8 kg mm−1). The highest correlations of the vegetation indexes with the agronomic traits were observed in the later growth stage, especially for the optimized soil-adjusted vegetation index (OSAVI); furthermore, grain filling rate showed a strong relationship with photochemical reflectance index (PRI). Experimental factors mainly influenced protein composition rather than protein content. In particular, the 7s vicilin protein fraction showed a negative correlation with grain yield and water use, while lectin showed an opposite response. Both fractions are of interest for consumer’s health because of their allergenic and antinutritional properties, respectively. Data from spectral phenotyping will be useful for digital farming applications, in order to assess crop physiological status in modern agricultural systems.

Plant phenotyping relevance

ハイパースペクトル表現型計測を用いて作物の生理状態を評価し、スペクトル指標と収量・成長特性の関係を検証しており、測定手法の応用が中心的に扱われている。

Code and data availability

The paper's phenotype/trait data (Table S1: agronomic performance, vegetation indexes, protein quality) are not publicly deposited; the Data Availability Statement says they are available only on request. No public code, images, or models are mentioned.

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