Understanding G × E Interaction of Elite Basmati Rice (Oryza Sativa L.) Genotypes Under North Indian Conditions Using Stability Models

Abstract

Information regarding the stability of genotypes is critical in expanding the adaptability of released genotypes. But, this information regarding the basmati (scented) rice genotypes cultivated under north Indian conditions are not well known. Therefore, here we have evaluated the twenty-two basmati rice genotypes for stability, based on important traits, and different production system. Genotypes were evaluated for two consecutive Kharif seasons under open field conditions in a randomized complete block design (RCBD). The genotypes were evaluated under four production systems namely, transplanted rice (TPR), system of rice intensification (SRI), direct seeded rice (DSR) in both settings, i.e. wet DSR (W) and dry DSR (D). The stability of genotypes was determined via Eberhart and Russell model, additive main effects and multiplicative interaction (AMMI), and genotype × environment interaction (GGE) biplot model. The stability and adaptability studied using Eberhart and Russell model, AMMI and GGE biplot identified Basmati-370 as the most stable genotype for biological weight; Pusa RH-10 for filled spikelet; CSR-30 for spikelet Number; and Traori Basmati for test grain weight. TPR was the most desirable test environment followed by SRI and DSR (W). Further, we have identified HKR 08-417 as the most suitable genotype for all of the production systems. Overall, this study provides information regarding stable basmati rice genotypes under the north Indian conditions.

Genetic gain and selection of stable genotypes in high zinc rice using AMMI and BLUP based stability methods

“…The AMMI stability model is commonly applied to decipher GEI patterns and select stable genotypes in target environments (Gauch, 2013). AMMI is a multivariate tool and it has been found to be a powerful tool for determining genotype-environment interactions and identifying stable and adaptive genotypes (Jain et al, 2019). There was sufficient significant G × E interaction among the genotypes and the two principal components (PCs) contributed more than 70% to the total variation of GEI, suggesting the AMMI bi-plot is effective in identifying the stable genotypes.…”

Multivariate analysis of GGE and AMMI biplot graph to investigate geno-type by environment interaction of rice hybrids at multiple locations in Malaysia

“…Overall, there were significant differences between PCV and GCV in YLD, NP, and FGP traits, indicating phenotypic performance under environmental influences. Large variation in the YLD trait may be due to meteorological factors such as rainfall and temperature, which invariably control the crop's yield potential (Shrestha et al, 2020;Kanfany et al, 2021). According to Xuan et al (2019), temperature factors have a significant impact on yield, particularly during the grain filling and flowering stages.…”

“…Although some hybrids may have wide adaptability across environments and some are highly influenced by the change in the environment which performs inconsistently in other locations (Akter et al, 2019). Environmental factors that contribute to hybrid rice yield include cool air temperature and high accumulative solar radiation during the grain filling stage (Jiang et al, 2016). According to Huang et al (2017), the yield of super hybrid rice Zhunliangyou 527 and Liangyou 293 could reach more than 12 t/ha in Hunan and Guizhou provinces of China but averaged 7.5 t/ha in Gazipur, Bangladesh.…”

Identification of Thermotolerant Rice Genotypes with Allele Coding at Seedling Stage

“…The yield improvement rate had fallen 10-20% at specific regions (Raza et al, 2019;Jain et al, 2018). The examination relied upon records from 227 farms in Thailand, Vietnam, Nepal, India, China, Bangladesh, and Pakistan (Jain et al, 2019). The instrument of this falling yield was not palatable, anyway may incorporate extended breath during warm nights, which devours energy without having the choice to photosynthesize (Fu et al, 2019).…”