Variability, correlation and path analysis for yield and quality traits were studied on 27 rice genotypes. The higher estimates of PCV and GCV were observed for yield per plant (42.04) and filled seeds per panicle (33.9) indicate possibility of genetic improvement through direct selection. High heritability in broad sense coupled with high genetic advance as percent of mean exhibited by effective tillers, plant height, flag leaf length, filled grains per panicle, test weight, yield per plant, head rice recovery and length/breadth ratio indicating preponderance of additive gene action which provide good scope for further improvement by selection. Grain yield per plant had highest significant positive association with filled seeds per panicle, plant height, flag leaf length, effective tillers, flag leaf width and panicle length indicating importance of these characters for yield improvement, while head rice recovery was found to be significantly and positively correlated with milling percent and hulling percent. Path analysis reveals that test weight (3.48), effective tillers (1.57), and filled grains per panicle (1.41) had positive direct effect on grain yield per plant. Among the quality traits kernel length followed by milling percent and kernel elongation ratio had direct effect on head rice recovery.

: An experiment was conducted to estimate the genetic variability parameters for the quantitative characters in mid early group genotypes of rice cultivars. The analysis of variance revealed significant difference among the genotypes for the traits studied indicating that a large amount of variability was present in the material. The magnitude of phenotypic co-efficient of variation was higher to genotypic co-efficient of variation for all the traits. The highest value of phenotypic and genotypic co-efficient of variation was observed for yield followed by number of grains per panicle and test weight. High heritability associated with high genetic advance as per cent of mean was found for the characters number of grains per panicle, yield, test weight and plant height indicating the role of additive gene action in controlling these characters.HOW TO CITE THIS ARTICLE : Lingaiah, N. (2015). Genetic variability, heritability and genetic advance in rice (Oryza sativa L.). Asian J. Environ. Sci., 10(1): 110-112.

Genetic divergence and variability was assessed among 33 elite germplasm lines which were evaluated in RBD at Regional Agricultural Research Station, Warangal with an objective to classify  and understand the nature and magnitude of genetic diversity and variability  with regard to grain yield, yield components and quality traits using Mahalanobis D2 statistics. Genotypes were grouped into eight clusters and cluster I was the largest comprised of 23 genotypes followed by cluster II which consists of 4 genotypes and rest of the clusters from cluster III to cluster VIII contain one genotype each. The maximum cluster distance was found between cluster II and cluster IV (2178.98) followed by cluster III and cluster VIII (2160.16). The minimum inter cluster distance was observed between cluster I and cluster III (540.96). The intra cluster distance was maximum for cluster II (354.1) indicating existence of variability within the cluster. A perusal of results on cluster means revealed that cluster VIII recorded highest cluster mean for yield/plant, panicle length, test weight, plant height, volume expansion ratio, kernel length after cooking, length/breadth ratio, kernel length and kernel width, The most important trait causing maximum divergence was plant height (31.8) which ranked 168 times first followed by alkali spreading value, days to 50% flowering and test weight were responsible for differentiating the genotypes studied. Phenotypic coefficient of variation (PCV) was slightly higher than genotypic coefficient of variation (GCV) for all the traits. Moderate GCV and PCV estimates were observed for filled seeds/panicle, test weight, alkali spreading value, volume expansion ratio, yield/plant, water uptake and head rice recovery. High heritability coupled with moderate genetic advance as percent of mean was observed for test weight (98.5:46.62), filled seeds/panicle (92.6:46.26), alkali spreading value (90.9:43.15), yield/plant (78;35.30), head rice recovery (78.9:27.46) and volume expansion ratio (69.1:37.3). In this study the genotypes WGL 1143, WGL 1150, WGL 1149, Tellahamsa in cluster II and WGL 1062 in cluster IV and WGL 915 in cluster VIII were widely divergent and crosses may be effected among the genotypes of the clusters to get more heterosis and subsequently better recombinants in segregating generations.

The experiment was carried out under three seasons with 15 genotypes at Agricultural Research Station, Kunaram, Telangana state, India during rabi season (December to April) 2014–15 (E1), kharif season (July to November) 2015 (E2) and rabi season (December to April) 2015–16 (E3). The objective of the study was to assess the stability and adaptability of 15 rice genotypes of the various maturity groups over three seasons. The GGE biplot tool of these 15 rice genotypes of various maturity durations expressed a significant genotype, environment and G×E interaction for yield and days to 50% flowering. Genotype and environment interaction effect was responsible for the greatest part of the variation, followed by genotypes and environment effects for grain yield. Days to 50% flowering of genotypes was highly affected by environments followed by genotypes, and genotype and environment interaction. It also detected that rabi season 2014–15 (E1) was identified as the best suited season for the potential expression of the grain yield, while kharif season 2015 (E2) was the right season for the expression of reduced days to 50% flowering. Further, the what–won–where model indicated that short duration rice genotype G14 (KNM 1690) and medium duration genotype G9 (KNM 1632) in the environments rabi season 2014–15 (E1) and kharif season 2015 (E2), respectively and the early line G11 (KNM 1684) in the environment rabi season 2015–16 (E3) were the winning genotypes and suitable for their respective environments for grain yield. G7 (KNM 1616) was the vertex early genotype and closer to the ideal genotype expressed high yield and stability for all the environments. G13 (KNM 1689) and G14 (KNM 1690) were found to be stable for earliness across all the seasons and could be utilized for the development of early duration varieties. The rice genotype, G15 (BPT 5204) was found to be stable for lateness for all the seasons.

The aim of the study was to determine the genotype x environment interaction and stability performance of fifteen rice hybrids in three different production seasons during 2016, 2017 at Regional Agricultural Research Station, Warangal, Telangana.  Data was subjected to the additive mean effects and multiplicative interaction (AMMI) analysis, results indicated that significant genotype x environmental interaction (GEI) influenced the relative ranking of the hybrids across the seasons. It was evident from AMMI analysis that first two principal components accounted for 94.09%, which is enough to explain the variability among the hybrids. The hybrids, G9 (WGRH 18), G8 (WGRH-17) and G12 (WGRH-22) and G3 (WGRH-10), exhibited high grain yield. The AMMI 2 biplot revealed that the rice genotype, G15 (WGL-14), close to the origin indicated non sensitive nature of this genotype with the seasons and highly stable genotype across the environments with low yield potential when compared to hybrids. Whereas the rice hybrids, G9 (WGRH-18), G8 (WGRH-17), G3 (WGRH-10) and G5 (WGRH-14), were also close proximity to origin and have limited interaction with the seasons. The rice hybrid, G9 (WGRH-18), has high mean yield with stable performance over three environments being the overall best can be considered for the release after through conformation.

Background: The genotype × environment interaction greatly influences the success of breeding and in multi-location trials complicates the identification of superior genotypes for a single location, due to magnitude of genotype by location interaction are often greater than genotype by year interaction. This necessitates genotype evaluation in multi environments trials in the advanced stages of selection. Methods: Nine elite pigeonpea genotypes of mid-early duration were evaluated in six diverse locations in randomized complete block design with three replications during kharif, 2019 to ascertain the stable genotypes, environments discrimination and genotype by environment crossovers using AMMI and GGE biplot stability models. Result: The results in the present investigation revealed that first two principal components explained 73.4% of variation interaction, while, 80.50% in GGE biplot. Both the models identified WRGE-126 (G6) as stable performer with high yield (1733 kg ha-1) and among the locations Tandur (E1) measured as the ideal environment. Whereas, the environments, Adilabad (E3) and Warangal (E4) were observed representative with better discriminating ability.

Background: Jack bean is an under-exploited legume species, a source of food, medicine and cover crop. By virtue of its adaptive nature to low fertility soils, it is one of the few pulses that grow well on highly leached, nutrient depleted, lowland tropical soils. But, in India, crop improvement work is very little done. Stability of yield is a major criterion for farmer’s acceptability of any variety and there are several methods to estimate the stability and G x E interaction effects of a genotype across seasons. Among these, AMMI analysis is the most recent and widely exploited in different crops for the identification of stable genotypes. In this context, yield stability of 10 accessions of jack bean is studied to identify the stable genotypes.Methods: The experiment was conducted with 10 Jack bean genotypes in RCBD with two replications under rain fed conditions during 2017-2020 in Kharif for four seasons. The data was subjected to analysis of variance and then taken for AMMI and GGE analysis for identification of stable genotypes.Result: The combined analysis of variance revealed that there was highly significant variation (p less than 0.01) in grain yield and environments and genotype interaction among the genotypes. The average bean yield of the genotypes was 533.1 grams per plant. The highest and the lowest mean yield was recorded in PSR-12202 and CHMJB-02 respectively which was corroborated by the AMMI bi-plot as well. Similar to the AMMI bi-plot, the GGE bi-plot also confirmed that PSR-12202 was the stable genotype across the environments, whereas, G1, G2, G3, G4, G6, G7 and G8 were the other genotypes with low yields in some or all the environments. Kharif, 2018 and Kharif, 2020 are discriminating environments and are declared as the most representative than Kharif, 2017 and Kharif, 2019. Generally, PSR-12202 was the ideal genotype with higher mean yield and relatively good stability; G5 was the moderately good yielding genotype and the most unstable genotype; Whereas, G1, G2, G3, G4, G6, G7 and G8 were the poorly yielding and unstable genotypes. Both AMMI and GGE bi-plot are able to establish the genotypic stability and these models can be exploited for judging the genotypes for their GEI in other crops as well.

The objective of this study was to determine the genotype × environment interaction (GEI) and stability performance of eight promising cotton genotypes at four agro-ecologies in Telangana State. The experimental material consisting of eight genotypes were planted in randomized block design replicating thrice in four diverse environments of Telangana state during 2017, Kharif season. The present investigation was carried out in four diverse environments of Telangana state viz. RARS, Warangal, ARS, Adilabad, ARS, Modhole and RARS, Palem (Professor Jayashankar Telangana State Agricultural University) during 2017, Kharif season. The study was conducted at four diversified agro-ecologies of Telangana State. The experimental material comprised of eight genotypes viz., WGCV-109, ADB-638, WGCV-122, Narasimha, WGCV-119, WGCV-119, Srirama, WGCV-48 and ADB 645. First pooled analysis of variance was carried out to know the significance variation in genotype x environment interaction followed by AMMI analysis for genotype x environment interaction studies. Analysis of variance was significant for environments and (G x E) components indicating the use fullness of AMMI analysis in identifying the stable genotypes.  Among the eight cotton genotypes, WGCV-109, Narasimha and ADB-645 were found to be best yielders over environments whereas the genotypes G7 (WGCV-48) and G4 (Narasimha) found to be stable. Most of the genotypes showed environment specificity. As a result, almost all of the evaluated genotypes were affected by the genotype x environment interaction effects, hence no genotype had superior performance in all environments.