The present study was conducted to evaluate 30 rice genotypes at three different locations in eastern Uttar Pradesh during the Wet- 2020–21 and determine the impact of GEI on grain yield (tha^-1), days to 50% flowering, grain Fe content (PPM), and grain Zn content (PPM). The study also aimed to identify the genotypes that displayed the best performance according to the multi-trait stability index (MTSI), multi-trait genotype-ideotype distance index (MGIDI), and factor analysis and ideotype-design (FAI-BLUP) index. AMMI analysis demonstrated significant variation for environment (E), genotype (G), and genotype-by-environment interaction (GEI) (P < 0.01) for all the studied traits. The AMMI1 biplot showed that PC1 explained the majority of the variation for GY (77.6%), DTF (90.5%), Fe (73.5%), and Zn (86.8%), helping to identify stable and high-performing genotypes. AMMI2 biplot further resolved complex GEI patterns, highlighting genotypes with specific adaptability to individual environments. The GGE biplot revealed clear “which-won-where” patterns for GY, DTF, Fe, and Zn, explaining 94.37%, 99.71%, 83.49%, and 96.93% of GEI variation, respectively. BLUP analysis using a linear mixed model revealed significant GEI effects for GY, DTF, Fe, and Zn across 30 rice genotypes in three environments. Low heritability was observed for Fe (28.2%) and moderate for GY (54.4%) and Zn (56.4%), while DTF showed high heritability with strong genotypic accuracy. Genotype G7 was identified as stable, early, high-yielding, and rich in Fe based on HMGV, RPGV, and HMRPGV indices. The MTSI, MGIDI and FAI-BLUP analysis revealed that BHU-SKS-1 (G15) and IR105696 -1–2-3–1-1–1 -B (G9) were the most stable and best mean performer for high grain yield and high grain Fe & Zn content, while IR 108,195–3-1–1-2 (G7) was the most stable and best mean performer for high grain yield and high grain Fe content with early flowering.

Background: The research is based on the fertility status of the soil being affected year by year so here is a strategy to save the soil fertility level or soil health by the use of a leguminous cropping system. Methods: A study was conducted during 2018-2021 in the Meerut district of western Uttar Pradesh to evaluate the soil characterization of the rice - pea- sugarcane cropping system. The soil samples were analyzed for various parameters in the laboratory. The status of available NPK in soils and other soil properties like pH, electrical conductivity (EC) and organic carbon (OC) content were assessed. Result: Results reveal that the soils of the study area were neutral to strongly alkaline in reaction and non-saline in nature. Nutrient status regarding to the available nutrient in the surface (0-15 cm) and subsurface (15-30 cm) depth of soil indicate that soils are low in available N and medium in available P and K in surface and subsurface. A positive and significant correlation of NPK with organic matter content was found while significant and negative correlations exist between the soil pH.

Hundred advanced breeding lines of rice were studied under field conditions at the Agricultural farm of the Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi. The experiment was conducted following alpha lattice design with three replications to estimate interrelationship among yield, yield component and quality traits and to assess the direct and indirect effects of studied traits on grain yield for the selection of high-yielding rice lines with desired traits. Eighteen characters viz., days to 50% flowering (DFF), days to maturity (DTM), number of productive tillers per plant (NET), plant height (PH), panicle length (PL), number of filled grains per panicle(NFSPP), spikelet fertility % (SP), grain yield per plot (GYPP), harvest index (HI), 1000-grain weight (TW), kernel length (KL), kernel breadth (KB) and kernel L/B ratio (LBBC), Kernel length after cooking (KLAC), Kernel breath after cooking (KBAC), Elongation ratio (ER), Elongation index (EI) and Alkali digestion value (ADV) were studied. The correlation coefficient revealed that GYPP exhibited a significant positive correlation with several traits, including DFF (0.605, 0.485), DTM (0.605, 0.498), PH (0.264, 0.155), PL (0.524, 0.426), NET (0.203, 0.119), NFSPP (0.378, 0.307), SF (0.285, 0.233), and HI (0.448, 0.489). While significant negative corelation was observed with EI. Moreover, the traits DTM, PL, and HI had a significant and positive direct influence on GYPP, indicating their effectiveness in selection for increased grain yield.