Aims: To find out genetic variation of pigeon pea Germplasms population on Chhattisgarh, with H. armigera, M. vitrata Larvae Population. Study Design: Augmented RBD Design in 4 block with 3 check varieties. Place and Duration of Study: College of Agriculture Raipur, IGKVV, Chhattisgarh. During Kharif 2019-2020. Methodology: The experimental materials were used 100 local landraces of Pigeonpea and three popular standard checks. The Morphological observations on various agro-morphological characters including qualitative and quantitative characters and Incidence of major insects of Pigeonpea were recorded. Results: outcome of the study reviled that Incidence of major insect of Pigeonpea 10 genotypes of pigeonpea are resistant and 10 are susceptible occurred. Analysis of variance indicated that the mean sum of squares due to genotypes were highly significant for all the characters with p-value of 0.001 and some traits check varieties 0.001. Results of genetic variability analysis state that’s highest genetic advance as percent of the mean for traits are days to fifty percent flowering, plant height, seed protein content, and days to maturity. Conclusion: The Pigeonpea accession used in the study revealed significant variability for most of the morphological traits. Amongst the genotypes studied, high coefficients of variation were observed for most of the characters studied indicating the existence of sufficient variability.

The present investigation was carried out at S.G. College of Agriculture and Research Station, Jagdalpur, Chhattisgarh. The experimental materials used were 94 local landraces of rice and three popular standard checks. The experiment was conducted during kharif 2016 in RBD. Observations were recorded for 16 qualitative and 20 qualitative characters and analysis of variance for characters showed differences for various characters. Correlation analysis revealed positive and highly significant correlation of total number of filled grains per panicle, total number of grains per panicle, plant height and number of effective tiller per plant, harvest index, test weight, flag leaf length and days to maturity had positive highly significant correlation with grain yield per plant. According to the direct effects on grain yield, the order of yield components was direct selection for characters viz. flag leaf length, days to maturity, plant height, number of effective tillers per plant, harvest index, and total number of grains per panicle will be very effective for increasing grain yield.

The glycemic index (GI) of rice is a complex trait, affected by amylose content (AC), size, and packaging of starch granules (SGs). In this study, the electron microscopy results of starch morphology of nine rice genotypes showed varying AC (6.93–36.9%), and the predicted GI (pGI: 41.07–82.46) in relation to genetic factors revealed that smaller SG surface area (20.06 µm²) and irregular morphology (Hap 3-3 P-11, pGI = 41.07) were associated with a lower pGI, while larger SG surface area (47.68 µm²) and spherical structure were associated with a higher pGI (NON-HAI, pGI = 82.46). The expression of starch biosynthesis and packaging-related genes (OsSSIIb, OsSSIIc, OsSBEIIa, OsISA1, OsISA3, OsGBP, OsFLO6, and OsBT1) revealed downregulation of OsGBP and OsISA3 genes in low pGI lines IRRI-147 (pGI = 56.2) and Hap 3-1-p-18 (pGI = 41.79), respectively, while higher levels of expression of the OsBT1 gene in Makro (pGI = 59.06) and OsSSIIb in Swarna (pGI = 58.06) were observed. All these genotypes had similar AC (~30%), but the difference in expression pattern was correlated with starch granule morphology, suggesting its role in influencing pGI. Further, analysis of allelic variation in eight starch-related genes across 20 rice genotypes showed that allelic variants of only OsGBP were correlated with AC, where allele group 2 showed lower AC (9.62%), while all other allele groups showed consistently high AC (22–24%). These findings underscore the critical role of starch granule morphology and OsGBP allelic variation in determining AC and GI, providing actionable insights for developing low GI rice varieties using tools like CRISPR.