Genotype environment interaction and stability performance were investigated on grain yield per plot in eight environments during Rabi (here, rabi means that a crop has been grown in Rabi season: crops that are sown in winter and harvested in spring in the Indian subcontinent) 2019–2020 and 2020–2021 using 100 diverse wheat genotypes. Research was conducted at Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana in India. The analysis of variance revealed that genotype, environment and their interaction had a highly significant effect on the yield as reflected in Eberhart and Russel model and The Eberhart and Russell model indicated the suitability of the genotypes WH 1142, PBW 661, PBW 475 and DBW 17 with high mean, bi > 1 and non-significant deviation from regression to favorable environment, whereas the genotypes UP 2660 and DBW 88 with high mean, bi < 1 and non-significant deviation from regression were found suitable for poor environment. The Additive Main Effects and Multipicative Interaction (AMMI) analysis of variance for grain yield per plot across the environments showed that 26.41% of the total variation was attributed to genotypic effects, 70.22% to environmental effects and 3.37% to genotype × environment interaction effects. AMMI biplot study indicated the genotypes PBW 750, DPW 621-50, WH 542, PBW 486, PBW 661 and WH 1192 stable across the environments as they did not exert strong interactive forces; hence, they were selected as potential candidates for possible release in the study areas. Furthermore, the which-won–where model indicated the adaptation of genotypes PBW 706, PBW 769, DBW 116, WH 1157, WH 789 and WH1186 to first mega-environment and genotypes DBW 16, WH 1152, WH 1105 and PBW 503 in the second. These genotypes could be utilized in breeding programs to improve grain yield in bread wheat and may be used as stable breeding material for commercial cultivation.

Knowledge of genetic diversity in lentil is imperative for selection of parental genotypes that could yield heterotic combinations. The aim of the present study was to investigate the genetic diversity among 43 diverse lentil genotypes to identify complementary and unique genotypes for breeding programmes. Field experimentation was carried out in two winter seasons (2019–2020 and 2020–2021) in Hisar (29°10′ N, 75°46′ E) using randomized block design (RBD) with three replications. The chi-square test analysis showed significant genotypic variation for qualitative traits. There was substantial genetic variation among the genotypes for most quantitative traits, connoting the need to exploit a high degree of genetic variation through selection. Multiple-trait selection would also be beneficial, as seed yield was positively associated with most quantitative traits. The principal component analysis recognized seed yield (SY), days to 50% flowering (DTF), days to maturity (DTM), number of pods per plant (NPP), number of primary branches (NPB), plant height (PH) and biological yield (BY) as target traits that prominently described variation within lentil genotypes. The cluster analysis discriminated the lentil genotypes into five discrete clusters. Cluster III and V were the most distant groups, implying wider diversity among the genotypes of these groups. Furthermore, cluster analysis identified genotypes IPL 316, LH 17-19, LH 18-04, LH 17-17, IPL 81 and Pant L-8 as high-yielding genotypes, while L 4717 was identified as an early-maturing genotype. Therefore, to obtain a broad spectrum of early-maturing high-yielding segregants, the selected genotypes may serve as superior parental lines for structuring breeding strategies.

Background: Lentil (Lens culinaris Medik. ssp. culinaris) is the oldest domesticated grain legume. It is cultivated for its nutritious lens-shaped seed in marginal and rainfed areas of temperate countries and most tropical highlands. The present study was carried out to get better insights into genetic variability, trait association and to assess direct and indirect effects of various attributing components on seed yield. Methods: The experimental material consisted of 43 lentil genotypes, grown in randomized block design with three replications during Rabi 2019-20 and 2020-21. The data on 11 quantitative traits was pooled across two seasons and statistical analyses were done using R studio and INDOSTAT software. Result: High PCV and GCV coupled with high heritability and genetic advance were observed for 100-seed weight, seed yield and biological yield suggesting that expression of these traits is controlled by additive gene action. Most of the quantitative traits were positively correlated with seed yield. Biological yield, harvest index, number of primary branches and number of pods per plant had highest positive direct effect on seed yield. Therefore, genotypes LL 931, L 4717, IPL 316, LH 18-04, LH 17-19 and DPL 15 should be used in crossing programmes to obtain high yielding transgressive segregants.

Hexaploid wheat is the main cereal food crop for most people but it is highly influenced by climatic variations. The influence of these climatic variations was studies in wheat genotype WH -1184 in field conditions under two different environments (normal and late sown) and it was found that the genotype is less yielding under late sown conditions. To study the effects of heat stress at transcript level, it was grown under two different conditions (WH-1184 control and heat treated) in pots and transcriptome analysis based on Illumina Novoseq 6000 was carried out for the identification of the differentially expressed genes (DEGs) and metabolic processes or gene regulations influenced by heat stress which lead to a reduction in both quality and quantity of wheat production. These DEGs were utilized to set up a subsequent unigene assembly and GO analysis was performed using unigenes to analyze functions of DEGs which were classified into three main domains, i.e., biological process, cellular component, and molecular function. KEGG (Kyoto Encyclopedia of Genes and Genomes) ontology was used to visualize the physiological processes or to identify KEGG pathways that provide plants their ability to shield in adverse conditions of heat stress. From KEGG ontology, it was reported that genes which encoded protein detoxification and ABC1 domain-containing protein were upregulated while genes thatencoded glutathione transferase (GST), peroxidase, and chitinase enzymes were downregulated. Downregulation of these enzymes during heat stress causes oxidative damages in plants while upregulated proteins play a main role in detoxification to protect plants from heat stress. It was hypothesized that the yield of WH-1184 decreased 44% under heat stress due to the downregulation of genes that encoded GST, peroxidase, and chitinase enzymes which can protect plants from oxidative damage. Hence, upregulation of these genes might be helpful for the adaptation of this genotype under heat stress condition.