Journal of Crop Breeding

Extended Abstract
Background: Lentil (Lens culinaris Medikus) is an important staple food crop. It is a rich source of protein (20– 36%) and contains essential micronutrients and vitamins in a highly bioavailable form to human body. In addition to its nutritional value, lentils generally have a fast cooking time compared to other legumes due to their smaller seed size and thin seed coat. Lentil is grown on 5675756 ha with a production over 7068621 tonnes worldwide. Lentil is a highly adaptable plant that grows under varying climate and soil conditions. It is commonly cultivated in the Mediterranean and subtropical dryland regions, where synthetic fertilizers are typically not needed for cultivation due to their ability to fix atmospheric nitrogen (N2) by the lentil crop. However, global lentil productivity has been reported to decline over the last decades due to fluctuations in the climate, such as extreme temperatures, flooding, droughts, and a rise in the prevalence of pests and diseases. Multiple stresses, such as high temperatures and drought during the seed filling stage, adversely affect lentil productivity. On the other hand, increased heat intensity and water deficits have a substantial impact on lentil nutritional quality. Several studies have reported that iron and zinc concentrations in lentil seed were reduced due to high temperature and drought conditions. Protein content in the seeds was also affected. To attain global food security under changing climate, lentil breeders face a significant challenge of simultaneously enhancing both yield and nutritional quality. Genotype × environment interactions (GEIs) play an important role in the selection of suitable germplasm in breeding programs. In multi-environment experiments, lentil yield is influenced by the genetic structure, environment, and genotype × environment interaction. To better interpret the genotype × environment interaction, the additive main effects and the multiplicative interaction (AMMI) model is one of the most common methods in the study of multi-environment experiments. The current study aimed to investigate the genotype and environment interaction on lentil genotypes and to identify stable, high-yielding genotypes compatible with the climatic conditions of temperate rainfed regions of Iran.
Methods: In this study, 10 promising lentil genotypes, along with “local”, “Kimia”, and “Bilesavar” cultivars, were cultivated in a randomized complete block design for three consecutive cropping years (2019-2022) in Lorestan/Khoramabad, Ilam/chardavel, and Kermanshah/sararood. In the field, each plot consisted of 4-meter planting rows with a distance of 25 cm and a density of 200 seeds per square meter. Stability analysis was performed using the AMMI multivariate method and the superiority index. Statistical analyses were performed using Metan and GGE packages of multi-environment experiments in R software.
Results: The AMMI analysis of variance showed that the effects of environment, genotype, the genotype × environment, and the first three main components were significant. Therefore, due to the significance of the genotype × environment interaction, it was possible to perform stability analysis on these data. According to AMMI analysis, the first and second main components of the genotype-environment interaction accounted for 33.3 and 27.3% of genotype × environment interaction variations, respectively. The effect of the first seven main components was significant and in total explained 99.5% of the genotype × environment interaction variations. The shares of the environment, genotype, and genotype × environment interaction in the sum of total squares were 64.04, 4.286, and 31.67 percent, respectively. Among the studied genotypes, Genotype G12 with 715 kg/ha, followed by genotypes G6 and G4 produced the highest grain yield. Based on the ASV stability index, genotypes G2, G3 and G5, based on the SIPC index, genotypes G13, G2, G3, and G5, based on the EV index, genotypes G2, G3, and G5, based on the Za index, genotypes G3, G6, G5, and G2, and based on the WAAS index, genotypes G6, G5, G7, and G9 were the most stable genotypes. Based on the simultaneous selection index of SSIASV, genotypes G3, G6, G5, and G2, based on the SSISIPC index, genotypes G3, G6, G5, and G2, based on the SSIEV index, genotypes G3, G6, G5, and G2, based on the SSIZA index, genotypes G3, G6, G5, and G2, and based on the SSIWAAS index, genotypes G6, G5, G7, and G9 were the best genotypes in terms of yield and stability. Based on the AMMI1 biplot, genotypes G2, G7, G3, and G6 with mean grain yield higher than the overall average and lowest values of IPCA1 were identified as stable genotypes with high general compatibility. In the AMMI2 biplot, genotypes G3, G5, and G7, in addition to high general stability, produced higher grain yield than the overall average. In addition to the AMMI indices, Lin and Binn's superiority index was also used to identify the best genotypes, and based on this, genotypes G12, G4, G6, and G3 were the most stable genotypes in the studied environments. Using AMMI distance parameter, genotypes G2, G3, G5, and G7 were recognized as genotypes with stable yields.
Conclusion: In general, genotypes 6 (FLIP2012-172L), 4 (FLIP2012-86L), 3 (FLIP2012-48L), 5 (FLIP2012-149L), and 7 (FLIP2012-176L) produced high yields in most of the environments based on different indices and showed good stability in most methods. Therefore, they could be candidates for the introduction of new cultivars.