Study on variability and genetic advance for yield and its attributing traits in rice genotypes (Oryza sativa L.) over the five environments of Chhattisgarh

Introduction

The global population and two-thirds of the population in India rice serves as a primary food source (Rout et al., 2020) [10] . India is one of the world's leading producers and exporters of rice and therefore, plays a vital role in ensuring global food security. During 2025-26, the India rice production was estimated at a record 154.02 million tonnes, and was reported to have surpassed China as the world's largest rice-producing nation (Press Information Bureau, 2026) [13] . Chhattisgarh is popularly known as Rice Bowl of India. Rice is grown on 68.8 percent (32.85 lakh hectares in Kharif and in addition to this, approx. 5 lakh hectares is sown during summer paddy during December to May) to thetotal of47.75 lakh hectares agricultural land of the state. The agricultural land is 34 percent of the state's total geographical area (Panda, 2025) [12] . The world population is projected to reach a peak of 10.4 billion by the 2080s, with approximately 9.7 billion people anticipated by 2050 (Norman et al., 2022) [9] . This possesses a significant challenge in feeding the world's population within the planet's ecological boundaries (Rockström et al., 2020) [15] . The national average yield of India isanticipated approximately to 2.7-3.0 t ha⁻¹, which remains substantially lower due to the continued cultivation of older inbred varieties, fragmented landholdings, limited quantity of quality seed access, and suboptimal agronomic practices (FAO, 2022). These problems can be managed to a great extent by screening and developing rice varieties having traits such as higher yields, shorter maturity period, better input efficiency, efficient land use and climate resilience varieties. In the state, Indira Gandhi Krishi Vishwavidyalaya, Raipur maintains the 23,250ricegermplasmwhich were collected and conserved from various locations of Chhattisgarh, Madhya Pradesh and adjoining state of Odissa.The existence of sufficient genetic variability is considered to beessential for any effective crop improvement initiative. In rice cultivation, relying solely on phenotypic selection based on performance can sometimes be misleading, as environmental factors may obscure the true genotypic potential, resulting in subpar performance in later generations. Thus, to attain consistent genetic enhancement, it is vital to select genotypes using genetic metrics such as heritability and genetic advance. The genotypic coefficient of variation (GCV) is a dependable measure for assessing the level of genetic variability and indicates the percentage of variation due to genetic factors (Maruti et al., 2025) [7] . Analysing genetic variability measures alongside heritability and genetic advance aids in forecasting the expected genetic gain from selection (Burton, 1952) [1] . In this contest, the present study was carried out using nine rice genotypes including two checks, subjected to multilocation trail in five environments for two consecutive seasons.

Materials and Methods

The field experiment was conducted in two consecutive Kharif (wet season) seasons during 2022 and 2023 at five different locations. The five different locations were College of Agriculture, IGKV, Raipur, KVK Dhamtari, KVK Bhatapara, KVK Balod and KVK Kanker. The experimental material comprises of nine genotypes of rice laid out in Randomized Complete Block Design (RCBD) with four replications. The plot size was 2 m x 5 m. A spacing of 20 cm between rows and 15 cm between plants was maintained throughout the experiment. The recommended agronomic and plant-protection practices were applied uniformly to all experimental plots during the crop-growth period to minimize non-experimental variation and ensure the establishment of a healthy and uniform crop stand. Observations were recorded on five randomly selected plants per replications for twelvemorphological traits, namely days to 50 per cent flowering, plant height (cm), total number of tillers per plant, number of effective tillers per plant, number of panicles per plant, panicle length (cm), total number of spikelets per panicle, number of fertile spikelets per panicle, spikelet fertility percent, grain yield per plant (g), grain yield per ha on plot basis (q)and harvest index and fifteen quality traits i.e.test weight, hulling percent, milling percent, head rice recovery, paddy length, paddy breadth, paddy length to breadth ratio, length of milled rice, breadth of milled rice, length to breadth ratio of milled rice, length of cooked kernel, kernel elongation ratio, Alkali spreading value, Gel consistency and Amylose content.The data were pooled over five locations in each Kharif season i.e.Kharif 2022 and Kharif 2023.The pooled data of individual season was then again pooled over the two seasons for the analysis of variation including genetic parameters like phenotypic coefficient of variation, genotypic coefficient of variance, heritability in broad sense and genetic advance as percent of mean. The genotypic (GCV) and phenotypic (PCV) coefficients of variation were calculated using the formula given by (Burton, 1952) [1] . The PCV and GCV values were categorized as low (0-10%), moderate (10-20%), and high (>20%) following (Sivasubramanian and Madhavamenon, 1973) [16] . Heritability in a broad sense (h²b) was estimated following the method of (Khush, 2013) [5] and (Hanson et al., 1956) [4] . The genetic advance as a percentage of the mean was calculated as per (Burton & De Vane, 1953) [1] . These genetic parameters provided insight into the variability present in the genotypes screened and provide the scope for improvement through selection (Murti et. al., 2025). GAM=(GA/GrandMean)×100

Estimation of Variability

Results and discussion

In the present investigation, analysis of variance pooled over five locations for two seasons were worked out which revealed significant mean squares for all the morphological characters and quality traits except for alkali spreading value, gel consistency and amylose content for genotypes. The highly significant mean square for environment was observed in all traits under study as revealed in Table 3 & Table 4. The mean sum of square for genotypes were also found highly significant for traits days to 50% flowering, plant height, total number of tillers per plant, number of effective tillers per plant, number of panicle per plant, panicle length, total number of spikelets per panicle, number of filled spikelets per panicle, spikelets fertility percent, grain yield per plant, grain yield per ha on plot basis, harvest index, test weight, hulling percent, milling percent, head rice recovery, paddy length, paddy breadth, paddy length to breadth ratio, breadth of milled rice, length of cooked rice, kernel elongation ratio (Table 3 & Table 4). The significant mean sum of square for genotypes was obtained in length of milled rice and length to breadth ratio of milled rice. Beside this the non-significant mean sum of square was obtained for the trait's alkali spreading value, gel consistency and amylose content. Further, for replication and replication with environment interaction analysis of variance revealed no significant mean sum of squares for all the morphological and quality traits in rice genotypes (Table 3& Table 4). Analysisofvariancein case of genotype with environment interaction reveals that mean sum of square was highly significant for the traits like total number of tillers per plant, number of effective tillers per plant, number of panicle per plant, panicle length, total number of spikelets per panicle, number of filled spikelets per panicle, spikelets fertility percent, grain yield per plant, grain yield per ha on plot basis, harvest index, test weight, hulling percent, milling percent, head rice recovery, paddy length, paddy breadth, paddy length to breadth ratio, breadth of milled rice, length of cooked rice (Table 3& Table 4). The mean sum of square was significant for the quality traits like alkali spreading value and gel consistency, while for the traits like days to 50% flowering, plant height, length of milled rice, length to breadth ratio of milled rice, kernel elongation ratio and amylose content were found non-significant for genotye with environment interaction. The above results clearly indicate that there was sufficient variability for almost all the characters among the nine genotypes. The traits like days to 50% flowering, plant height, length of milled rice, length to breadth ratio of milled rice, kernel elongation ratio and amylose content were found non-significant for genotype with environment interaction. It revealed that environment is not creating significant variation in the such traits. The genotypes for such traits supersheded the effects of environments for such traits.

In the combined analysis of variance over five environments and over two seasons, the values of genotypic and phenotypic coefficients of variation were moderate for morphological traits viz., total number of tillers per plant (15.63 and 17.32) followed by number of panicles per plant (11.96and 13.06),total number of spikelets per panicle (11.55and 13.47) and number of filled spikelets per panicle (11.38 and 14.10); quality traits viz., head rice recovery (11.96 and 13.65) (Table 5).Similar finding was reported for moderate GCV and PCV for characters by total number of tillers per plant and number of filled spikelets per panicle were reported by Gupta et al., 2021 [3] . Further, study revealed that the low genotypic and moderate phenotypic coefficient of variation was observed by number of effective tillers per plant (9.51 and 12.10), harvest Index (9.01 and 10.49) and spikelet fertility per cent (8.23 and 10.91) (Table 5).Similar observations of low GCV for low GCV for spikelet fertility was noted by Mohanty et al., 2020 [8] and Sreewongchai et al., 2010 [17] and Murti et al., 2025; for panicle length by Gupta et al., 2021 [3] . The fact that PCV values were greater than GCV values across all traits suggests that, there is significant genetic variability, the influence of environmental factors should be over looked in the expression of these traits. [11] and Sivasubramanian & Madhavamenon, 1973 and Murti et al., 2025 [16] . Considerable variability was observed in the estimates of broad-sense heritability and genetic advance as a percent of mean (GAM) among morphological yield traits and grain quality traits. Very high (>90%) heritability was observed for days to 50% flowering days {h 2 over environmental influences.Moderate heritability (30-60%) was noted for spikelet fertility percent {h 2 (b) = 56.92} indicating a higher influence of environmental factors.High GAM (>20%) was observed for total number of tillers per plant {GAM= 29.05 %}, number of panicles per plant {GAM= 22.56%}, grain yield per ha on plot basis {GAM= 21.06 %} and total number of spikelets per panicle {GAM= 20.41%} while, the quality traits head rice recovery {GAM= 21.61 %}.High magnitude of heritability was observed for plant height, grain yield per plant, harvestindex and 1000seedweight.is in accordance with Gupta et al. 2021 [3] and Kujur et al., 2023 [6] . High heritability was observed for effective tillers per plant, days to 50 percent flowering and number of filled grains per panicleis in accordance with Rashmi et al., 2017 and Gupta et al. 2021 [2,14] . The high heritability coupled with high genetic advance is the desirable as the traits features gets transfer to generation after generation in high frequency with high rate of genetic improvement as they are tightly linked with additive gene of action. In comparison, the noted moderate heritability and moderate genetic gain per generation for morphological traits such as spikelet fertility percent {h 2 (b) = 56.92, GA=12.79 %} indicate that simple selection processes are unlikely to yield significant genetic improvements.This challenge arises from the intricate genetic makeup of these traits, which are controlled by multipleminorgenesand are unresponsive to environmental variations. While yieldrelatedtraits can be effectively enhanced through straightforward phenotypic selection.

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