Genetic variability, heritability and genetic advance for yield and grain nutritional traits in F2 populations of rice (Oryza sativa L.)

Introduction

Rice (Oryza sativa L.) stands as the predominant staple food and is the primary source of nourishment for almost 70% of the people. It is the crucial for the dietary and food security source of many Asian countries (Kumar et al., 2020) [6] . India is the world's second largest producer of rice, accounting for 22% of global production. Although rice output and productivity have significantly improved. The green revolution has led to plateaus in yield levels, limiting production efforts to satisfy the demands of a growing population, particularly in developing and underdeveloped countries. According to reports, India's food security may be threatened by the country's projected population growth by 2050 (Fathima et al., 2021) [4] . Consequently, ensuring food security in the future is a very difficult issue, especially for Indian rice producers, since rice is essential to the nation's food and nutritional security. In order to break through yield barriers, rice varieties with high productivity levels and appropriate agronomic traits are needed. Hybridization has been identified as a key plant breeding strategy for achieving necessary diversity and high productivity levels. Additionally, selection in segregating generations must be directed toward producing superior yielding plants, for which yield variability and related qualities are crucial, and the development of efficient selection criteria for yield enhancement is necessary (Butardo et al., 2019) [1] . In such contexts, establishing sustainable breeding programs requires precise understanding of genetic divergence in yield components. Effective varietal improvement hinges on selecting parents with significant genetic differences for hybridization. Improvement for specific traits has been successfully achieved through the strategic use of F2 segregating populations, facilitating the fixation of desirable combinations. This study aims to assess variability, heritability, and genetic advance as a percentage of mean for grain yield and its components. Additionally, it explores the frequency distribution patterns based on skewness and kurtosis in the F2 segregating generation for three selected crosses. Such analyses are pivotal for identifying superior genotypes with potential for higher yield and better performance under varying environmental conditions.

Materials and Methods

The current investigation was carried out in the experimental field of Department of Plant Breeding and Genetics, Tamil Nadu Rice Research Institute, Aduthurai, Tamil Nadu. Based on documented results, parents having contracting grain iron and zinc were chosen and crossed and developed mapping populations during 2023. Three F2 generations resulting from the cross between ADT 56 x Kalanamak, CO 54 x Kalanamak, and CO 54 x Chinikinikar were grown. Twenty six days old seedlings were transplanted in to the main field with a spacing of 30 cm x 30 cm. A total of 222, 156 and 207 possible F2 segregants of each cross were identified and labeled for further assessment. The recommended agronomic practices and crop protection measures were followed during the crop growth period. Each F2 plant was harvested separately. Observations were recorded from randomly selected plants of each of the cross combinations and each of the randomly selected individual plant was tagged before anthesis. Data on all the seventeen characters namely DM-Days to Maturity, PH-Plant Height, NPT-number of productive tillers per plant, FL-Flag leaf length, FW-Flag leaf width, KL-kernel length; KB-kernel breadth; LBR-length-tobreadth ratio, PL-Panicle Length, PW-Panicle Weight, NB-Number of branches per panicle, FG-Filled Grains, UFG-Unfilled Grains, TNP-Total number of Grains per Panicle, TSW-Thousand Seed Weight, SPY-Single Plant Yield, FEB-Fe content in brown rice and ZNB-ZN content in brown rice and PCB-Protein content in brown rice were analyzed at TRRI, quality lab, with ED-XRF. The recorded data was subjected to statistical analysis as analysis of variance, genetic variability and correlation analysis using R studio software.

Results and Discussion

Mean Performance of F2 generation: The results of genetic variability and other genetic parameters are shown and represented in Table and Fig. 1 and2. A perusal of the results on mean performance and range of the yield contributing and nutritional traits were studied in the present investigation (Rao et al., 2014) [15] revealed maximum range for single plant yield followed by grain iron, zinc and protein content while minimum range was observed for flag leaf width. Single plant yield for ADT 56 x Kalanamak ranges from 9.20 gm to 53.40 gm with mean of 27.26 gm, and for CO 54 x Kalanamak 9.20 gm to 45.70 gm with a mean of 28.23 gm, and for CO 54 x Chinkinkar 7.60 gm to 52.60 gm with a mean of 25.26gm. Similarly, filled grains and total number of grains were recorded in the study ranged from 73.50 to 284.10 (ADT 56 x Kalanamak), 72.60 to 289.60 (CO54 x Kalanamak) and 51.20 to 283.30 (CO 54 x Chinkinkar). The maximum grain iron content noted as 16.90 kg/mg with a average mean of 12.32 kg/mg in ADT 56 x Kalanamak and incase of maximum grain zinc content observed as 39.55 mg/kg with a mean of 24.54 mg/kg in CO 54 × Chinkinkar. The average value for the grain protein content was 12.83 %. Similar results were reported by Bharath et al. 2018 [17] . The phenotypic variation for Days to maturity ranged from 102. 75 [3] .

Variability studies

Estimating genotypic and phenotypic coefficients of variation is crucial for understanding the impact of environmental factors on various traits. Environmental influence on a character is measured by the difference in coefficients of variation between genotype and phenotype (Akinwale et al., 2011). Genetic parameters were categorized as follows: GCV and PCV into low (0-10%), moderate (10-20%) and high (>20%); Heritability into low (0-30%), moderate (30-60%) and high (>60%); and genetic advance as a percentage of the mea into low (0-10%), moderate (10-20%) and high (>20). Table 1 displays the PCV and GCV values for characteristics in three crosses. The study on variability demonstrated for all the three crosses that the phenotypic and genotypic coefficients of variation were moderate for PH, FW, KB, L/B, FEB, ZNB and PCB. The high phenotypic and genotypic coefficients were recorded for NPT, FL, PW, FG, UFG, TGP and SPY. The phenotypic coefficient variation (PCV) and genotypic coefficient of variation (GCV) was highest for the UFG with 56.86 followed by SPY with 34.02 and lowest for KL 7.18 (CO 54 x Chinkinkar). Furthermore, the minimal difference between GCV and PCV for all traits indicated that environmental factors had a negligible effect on the expression of these characteristics. Both high heritability and high genetic advance were exhibited high by almost all traits except KL, which had moderate genetic advance mean and CO 54 x Kalanamak and CO 54 x Chinkinkar showed moderate genetic advance mean values for DM. The cross combination CO 54 x Chinkinkar had highest GAM value with 67.44 for single plant yield followed by NPT in ADT 56 x Kalanamak with 65.89. Traits showing high PCV, GCV, heritability and genetic advance per mean suggests that these traits are influenced significantly by additive gene action. Selective breeding efforts targeting these traits could yield beneficial improvements. Similar finding were reported by Bharath et al. 2018 [17] , Khandappagol, et al. 2019 [5] , Manojkumar et al. 2023 [9] , Piyari et al. 2023 [13] . Comparable results were observed by Prasanth et al. 2024 [18] who noted high heritability paired with substantial genetic advance for traits such as the number of productive tillers and filled grains per panicle. Heritability estimates of a trait reflects the reliability of predicting its phenotypic value. High heritability increases the effectiveness of selecting specific traits, thereby making their improvement particularly beneficial.

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