Identification of stable chickpeas under dryland conditions by mixed models

Abstract

Abstract Chickpea (Cicer arietinum L.) is one of the most important legume crops, mainly grown in tropical and subtropical climates. Evaluation of yield performance in crops under multienvironments is applied to verify the stability of cultivars. The aim of this study is to apply the analytical and experimental models to identify the high‐yielding and stable genotypes of chickpea under dryland conditions. Sixteen chickpea lines and two control cultivars were cultivated in randomized complete block design with three replications in four regions at three cropping seasons (2016–2019). Third type of biplot showed that G4, G15, G10, G9, and G18 were highly productive and widely stable. A selection index based on different weights of seed yield and WAASB stability indicated genotypes G7, G9, G15, G4, G16, G18, G12, and G5 were high yielding and stable. Data mining showed that high rainfall in winter can lead to high yield. Partial least squares regression (PLSR) analysis indicated that rainfall in autumn and spring and low temperature in all of the three seasons involved in genotype by environment interaction (GEI). Factorial regression (FR) also indicated that temperature during spring and winter plays an important role in GEI. In conclusion, based on all experimental approaches, G15, G16, and G5 were stable and high‐yielding genotypes. The PLSR biplot indicated G15 was the genotype that less affected by high temperature in three seasons and lack of rainfall in spring and autumn, it can be used in cultivar introduction processes for dryland cultivation.

Keywords

• AMMI
• analytical approaches
• BLUP
• Cicer arietinum
• data mining
• FR