Integrating BLUP, AMMI, and GGE Models to Explore GE Interactions for Adaptability and Stability of Winter Lentils (Lens culinaris Medik.)

Abstract

Lentil yield is a complicated quantitative trait; it is significantly influenced by the environment. It is crucial for improving human health and nutritional security in the country as well as for a sustainable agricultural system. The study was laid out to determine the stable genotype through the collaboration of G × E by AMMI and GGE biplot and to identify the superior genotypes using 33 parametric and non-parametric stability statistics of 10 genotypes across four different conditions. The total G × E effect was divided into two primary components by the AMMI model. For days to flowering, days to maturity, plant height, pods per plant, and hundred seed weight, IPCA1 was significant and accounted for 83%, 75%, 100%, and 62%, respectively. Both IPCA1 and IPCA2 were non-significant for yield per plant and accounted for 62% of the overall G × E interaction. An estimated set of eight stability parameters showed strong positive correlations with mean seed yield, and these measurements can be utilized to choose stable genotypes. The productivity of lentils has varied greatly in the environment, ranging from 786 kg per ha in the MYM environment to 1658 kg per ha in the ISD environment, according to the AMMI biplot. Three genotypes (G8, G7, and G2) were shown to be the most stable based on non-parametric stability scores for grain yield. G8, G7, G2, and G5 were determined as the top lentil genotypes based on grain production using numerical stability metrics such as Francis’s coefficient of variation, Shukla stability value (σi2), and Wrick’s ecovalence (Wi). Genotypes G7, G10, and G4 were the most stable with the highest yield, according to BLUP-based simultaneous selection stability characteristics. The findings of graphic stability methods such as AMMI and GGE for identifying the high-yielding and stable lentil genotypes were very similar. While the GGE biplot indicated G2, G10, and G7 as the most stable and high-producing genotypes, AMMI analysis identified G2, G9, G10, and G7. These selected genotypes would be used to release a new variety. Considering all the stability models, such as Eberhart and Russell’s regression and deviation from regression, additive main effects, multiplicative interactions (AMMI) analysis, and GGE, the genotypes G2, G9, and G7 could be used as well-adapted genotypes with moderate grain yield in all tested environments.

Nutritional and bioactive properties and antioxidant potential of Amaranthus tricolor, A. lividus, A viridis, and A. spinosus leafy vegetables

“…ANOVA analysis indicated a noteworthy difference for our examined characters. A significant variation in the analysis of variance was found also in agronomic traits of maize [ [58] , [59] , [60] , [61] ], rice [ [62] , [63] , [64] , [65] , [66] , [67] , [68] , [69] , [70] , [71] , [72] , [73] , [74] , [75] , [76] , [77] , [78] , [79] , [80] ], okra [ [81] , [82] , [83] ], broccoli [ 84 ], pulses [ [85] , [86] , [87] ], and coconut [ 88 , 89 ] which confirmed our current findings.…”

Assessment of yield performances for grain sorghum varieties by AMMI and GGE biplot analyses

“…Unlike the AMMI1 biplot, AMMI2 biplot projects the interrelationships of genotypes and environments by using IPCA1 and IPCA2 scores. As described by Hossain et al. (2023) and Ljubicic et al.…”

Evaluation of yield attributes and bioactive phytochemicals of twenty amaranth genotypes of Bengal floodplain

“…Replication-wise means data of a trait was obtained by averaging the sample mean [ 43 , 44 ]. The average data of various traits were analyzed statistically [ [45] , [46] , [47] ] and biometrically [ 48 , 49 ]. All mean data obtained for each character were analyzed using Statistix 8 software [ [50] , [51] , [52] , [53] ].…”