| چکیده انگلیسی مقاله |
Extended Abstract
Introduction and Objective: Lentil is a popular legume crop in the Mediterranean region, widely grown for its nutritious seeds and improving soil fertility. Interest in legumes is increasing as a protein source to replace meat in the future. Identification of high-yield genotypes with adaptation to a wide range of environments is one of the major goals in crop as well as lentil breeding programs. Combining the best linear unbiased predictions (BLUP), additive main effects, and multiplicative interaction (AMMI) methods in multi-environment experiments and multi-trait stability selection (MTSI), helps to better evaluate plant genotypes and achieve more accurate results. Additive main effect and multiplicative interaction (AMMI) and best linear unbiased prediction (BLUP) are two methods for analyzing multi-environment trials. The linear mixed effects model (LMM) and the restricted maximum likelihood (REML) estimator methods are among the important methods that have been proposed to analyze the data of multi-environmental experiments. In this regard, with principal component analysis or single value analysis on the matrix, the best-unbiased linear predictions (BLUPs) obtained from the interaction of genotype and environment are performed. In this method, the stability index of the weighted average of absolute scores of the best unbiased linear forecasts (WAASB), the weighted average of the stability index of WAASB and the dependent variable (WAASBY) are used. Researchers have also proposed a multi-trait stability index (MTSI) based on factor analysis, in which grain yield and other traits and the stability of each of them are simultaneously used to identify stable genotypes. This research was done to identify stable and high-yielding lentil genotypes in autumn cultivation.
Material and Methods: To evaluate the stability of seed yield of 12 lentil genotypes along with three check genotypes including Kimia, Bileh Sawar and local landrace, an experiment was conducted as a randomized complete block design in three replications at Agricultural Research Stations of Khorramabad (Lorestan), Zanjireh (Ilam) and Sararoud (Kermanshah) in three cropping years (2019-2022). Each plot consisted of four lines with a length of four meters and a distance of 25 cm from each other. During the growing season, in addition to the usual crop care such as weeding and pest control, the desired traits and characteristics such as the number of days to 50% flowering, plant height and number of days to maturity were measured. After the maturity and harvesting of experiment, 100 seed weights and the yield of each plot were measured. Combined analysis of variance was performed using SAS software and the average traits of the treatments were compared using the LSD test. For statistical analyses, the Metan Ver.1.9.0 (Multi environment trial analysis) package was used in the R software environment. To estimate stability quantities, singular value decomposition (SVD) was applied to the matrix of unbiased best linear predictions (BLUPs) obtained from genotype-by-environment interactions with a linear mixed effect model (LMM). Variance components were estimated by the Restricted Maximum Likelihood (REML) method. After analyzing the variance of the data, to estimate the stability parameters of WAASB and WAASBY (for simultaneous selection based on average performance and stability), the eigenvalues obtained from AMMI analysis on BLUP were used and the best genotypes were selected with these two indicators. From the Harmonic Average of the Genotypic Values (HMGV) index, genotypic stability values were obtained. The compatibility of genotypes was evaluated based on the relative performance index of genotypic values (RPGV). The harmonic mean index and relative performance of genotypic value (HMRPGV) were used to simultaneously evaluate stability, compatibility and seed yield.
Results: the effect of environment, genotype and genotype × environment interaction were significant on seed yield, plant height, days to flowering, days to maturity, seed filling period, seed filling ratio, seed yield formation rate, rainfall efficiency and single seed weight. The genotype effect was significant on all traits except the seed-filling period. Based on biplot analysis, genotypes 4, 6, 7, 9 and 10 had higher yield stability in addition to the highest seed yield. The Scree test showed that the first three principal components explained 45.41, 19.13, and 14.34% of the genotype × environment interaction variation obtained from BLUP for grain yield, respectively, in total, they justified 78.87% of the variation. Based on a weighted average of absolute scores of best linear unbiased predictions (WAASB), genotypes 6, 10 and 12 were high-yielding and stable. Genotypes 1 and 10 were superior based on the multi-trait selection index (MTSI). The harmonic mean and relative performance of genotypic values (HMRPGV) introduced genotypes 10, 9, 4 and 12 as the genotypes that had high stability and compatibility in addition to high seed yield.
Conclusion: In total and based on all the analyses, genotype 10 was the most stable genotype, which, in addition to seed yield, was superior to other genotypes in terms of other measured traits and can be a candidate for introduction as a new cultivar. |