An insight into variability and association among yield and its components under alkalinity and inland salinity stress in rice (Oryza sativa L.)

INTRODUCTION

Salinity is the major abiotic stress after drought in rice-growing areas of the world and nearly 6.73 million hectares of land in India are saline-affected soils [10]. The area under salinization is increasing due to low precipitation, high surface evaporation, weathering of native rocks, irrigation with saline water, and poor cultural practices [17]. Though irrigated lands are further expected to suffer from secondary inland salinization, the situation is likely to be aggravated by an increase in sea level due to climate change [23]. It has been estimated that more than 50% of the arable land will be affected by salinity by the end of 2050 [8]. Effects of salinity and alkalinity vary on plants and even though, both the soils produce ionic and osmotic stress, alkalinity leads to a high pH environment. High pH affects both plants and soil hence, alkaline stress has a more detrimental effect on plants than salt stress. In general, salt affects the growth of crop plants by limiting the absorption of water through the roots affecting the metabolic processes of the plant. It has an immediate effect on cell growth and enlargement, as a high concentration of salt results in toxicity. Ion toxicity is the primary cause while osmotic stress and oxidative damage are secondary causes of salt damage. The saline stress has adverse affects on rice crop during the entire crop duration resulting in low seed germination rate, retarding the seedling growth, seedling mortality, low biomass accumulation, reduction in the main root length and several lateral roots, reduction in tillering ability, a lesser number of spikelets per panicle, greater spikelet sterility percentage, reduced panicle weight thereby causing a significant reduction in grain yield.

            The presence of sufficient genetic variability, knowledge of the nature of association among different characters, and the relative contribution of different characters to yield are prerequisites for a successful breeding program.  Heritability and genetic advance are important selection parameters and are more helpful in predicting genetic gain under effective selection [2]. Therefore, the present investigation was undertaken to estimate the genetic parameters, character association, and path coefficients in a set of 36 genotypes to be used as genetic stock for developing high-yielding alkalinity and inland salinity-tolerant paddy varieties.

MATERIALS AND METHODS

Experimental material

In the present study, 36 genotypes (Table 1) were evaluated in randomized block design with three replications at Agricultural Research Station, Kampasagar, Nalgonda during rabi, 2021-22.  The field is an in-situ alkaline stress block with a pH of 9.30, E.C of 4.68 dSm^-1, and ESP value of 88.0. Seeds were sown in the nursery and 30 days old seedlings were transplanted in the main field i.e., the stress block. For saline resistance FL478, for alkaline resistance, CSR23 and CSR36, and for susceptibility Pusa44 were used as checks. All the recommended package of practices and need-based plant protection measures were followed to ensure a normal crop. Data on 10 traits viz. seedling mortality (SM), days to 50% flowering (DFF), plant height (PH), panicle length (PL), number of productive tillers/hill (NPT), number of grains per panicle (NGP), number of filled grains per panicle (NFG), sterility percentage (SP), 1000 grain weight (TW), grain yield (kg/ha) were taken on five randomly selected plants in each genotype in each replication. Data on the days to 50 % flowering and yield (kg/ha) were recorded on a whole plot basis.

            The analysis of variance and its significance for all the traits were worked out [12] and the phenotypic coefficient of variation (PCV) and genotypic coefficient of variation were also calculated [3]. Heritability in the broad sense (h² (b.s.)) and genetic advance as a percent of the mean was estimated [6 and 9]. Path and correlation coefficient analysis was done to find the association between yield and its components [4].

RESULTS AND DISCUSSION

Analysis of variance revealed significant differences among the tested genotypes for the majority of the traits indicating the existence of variability (Table 2). The genetic variability depicted in the form of box plots (Fig 1) showed the frequency distribution for ten traits among the genotypes. The traits, namely, DFF, PH, PL, NPT, NGP, NFG, SP, TW, and GY exhibited greater genetic variability under alkali and inland saline situations. Concerning the normal probability plot, under saline conditions, treatments depicted normal distribution for seedling mortality while the remaining traits, viz., DFF, PH, PL, NPT, NGP, NFG, SP, TW, and GY had few accessions slightly deviating from the normal distribution indicating variability among the genotypes.

Thirty-six genotypes along with checks were evaluated for grain yield and yield attributing traits and the means of the entries in three replications were also analyzed for the estimation of components of genetic variance. Seedling mortality had a general mean (Table 3) of 27.4% ranging from 10.3% (FL478) to 60.3% (Pusa44). The genotypic and phenotypic coefficients of variation estimates (Table 4) for SM were high i.e., 33.6% and 35.5%, respectively. The observed heritability estimate for seedling mortality was high (89.9%) with high genetic advance (65.7%) indicating the preponderance of additive gene action in governing the trait so, this trait can be further improved by simple selection.

Days to 50 percent flowering had a general mean value of 100.9 days with a range of 93.3 (IRRI154) to 108 days (KPS10651). The genotypic and phenotypic coefficients of variation estimates were found to be low i.e., 2.2% and 4.9%, respectively suggesting a narrow range of genetic variability. Further, a low heritability estimate (20.3%) coupled with a low genetic advance (2.0%) indicated the preponderance of non-additive gene action and selection is highly ineffective in improving this trait. These results conform with the findings of [7] and [13].

In the present study, the plant height exhibited a range of 78.33 cm (Sahel177) to 111.33 cm (IR13F167) with an average of 94.2 cm. Low GCV (8.0 %) and PCV (9.0 %) estimates were observed for this trait suggesting a narrow range of genetic variability. High heritability (78.1%) with moderate genetic advance (14.5), revealed the predominance of additive gene effects and simple selection would be rewarding for improvement of this trait. These results are similar to the findings of [15] and [18].

The panicle length ranged from 18.2 (KPS10651) to 24.3 cm (Sahel177) with an average value of 21.3 cm. The genotype Sahel177 had the highest panicle length compared to other genotypes and checks and therefore can be used as a donor parent in hybridization programs. The GCV and PCV for this trait were low i.e., 3.0% and 7.9%, respectively indicating the narrow range of genetic variability. Low heritability (17.3%) and genetic advance (3.0%) were observed for this trait indicating selection is ineffective in improving the trait and [5] and [19] mentioned similar findings.

The average value for productive tillers was 5.4 with a range from 3.9 (KPS10661 and KPS10667) to 6.9 (Sahel177). The genotype Sahel177 had the highest number of productive tillers suggesting its utilization in yield improvement programs. Moderate values of GCV and PCV were found (12.9% and 17.0%, respectively) revealing moderate genetic variability. Heritability was moderate (56.5%), and low genetic advance (1.1%) limits further improvement through direct selection due to the favorable influence of the environment on the trait. These results were following that of [13] and [20].

The number of grains ranged from 33.3 to 80.0 with an average value of 61.6. The genotypes IR69726 and KPS10321 had the lowest and the highest number of grains per panicle, respectively. Moderate GCV and high PCV were observed for this trait (17.8 and 22.3, respectively) indicating the existence of an adequate amount of genetic variation. The heritability estimates for this trait were high (63.8%) coupled with high genetic advance (29.3%). Hence, direct selection could be effective for desired genetic improvement for this trait. Similar kind of results was reported by [15] and [18].

The number of filled grains per panicle had shown a very wide distinctness (21.3-29.3 g) among the genotypes as very low in three genotypes i.e., Pusa44, IR69726 and IRRI154, and highest in KPS2874. High GCV (20.7%) and PCV (26.6%) were observed with a high heritability estimate of 60.2% coupled with high genetic advance (33.1%) inferring the preponderance of additive gene action in governing the trait.

The sterility percentage was in the range of 12.2% (M202) to 56.1% (Pusa44) with a mean value of 29.9%. The magnitude of GCV (40.8%) and PCV (47.1%) was high suggesting the existence of an ample genetic variation. High heritability (74.9%) coupled with high genetic advance (72.7%) were found indicating the presence of additive gene action and [7] obtained similar results.

Low 1000 grain weight was observed in eight genotypes viz. KPS10316, KPS10319, KPS10628, KPS10654, KPS10656, KPS10658, KPS10672, and KPS10683 ranging from 15.6-17.0 g, and grain weight was medium (17.1-24.5g) in the majority of the genotypes and only two genotypes NSICRC240 and FL478 had highest grain weight (25.7g and 25.9g, respectively). Moderate GCV (17.2%) and PCV (17.6%) were recorded with a high heritability of 96.0% coupled with high genetic advance (34.8%) showing the presence of additive gene action in the inheritance of the trait. These findings were following that of [14] and [19].

In the present study, grain yield ranged from 1825 kg/ha (Pusa44) to 4096 kg/ha (KPS10654) with an average value of 3075 kg/ha. The genotypes showed much lesser yield as compared to the checks due to high seedling mortality and sterility percentage. The genotypes KPS10633, KPS10642, KPS10651, KPS10654, M202, Sahel177, and CT11891 had comparable yields with the checks and can be used as donor parents in hybridization programs. Moderate GCV (14.5%) and PCV (16.0%) were observed for this trait suggesting a moderate range of genetic variability. A high heritability estimate (82.4%) coupled with high genetic advance (27.2%) indicated the control of additive genetic effects and selection was very effective through direct selection for the improvement of this trait and [1] and [21] mentioned similar findings.

Character association is driven by the correlation coefficient which aids in the evaluation of the relative influence of various yield-attributing traits on grain yield. In the present study, the correlation between yield and its attributing traits (Table 5) revealed that grain yield was positively significant with the number of productive tillers (0.5171^**) and the number of filled grains per panicle (0.5413^**). Traits i.e., number of productive tillers and number of filled grains per panicle could be considered as a selection criteria for higher grain yield as these traits were mutually and directly associated with grain yield [11]. However, seedling mortality (-0.6391^**) and sterility percentage (-0.506^**) had a significant and negative association with grain yield and would hinder the expression of the grain yield [22].  Hence, the selection of the genotypes with a low percentage of seedling mortality results in a low sterility percentage.

An estimate of simple correlation would not provide the true contribution of the characters towards the yield and path analysis allows separating into the direct effect and indirect effects through other attributes by apportioning the correlations for better interpretation of cause and effect (Table 6, Figure 2). Path coefficient analysis revealed a positive direct effect of several productive tillers/hill (0.2749), number of grains per panicle (0.5647), and 1000 grain weight (0.0032) on yield (kg/ha). These findings are in line with the findings of [11] and [16]. The number of filled grains per panicle had positive indirect effects on yield through the number of productive tillers/hill (0.0643) and the number of grains per panicle (0.3396). Seedling mortality and sterility percentage had a negative direct effect (-0.3715 and -0.4821, respectively) as well as a significant negative correlation with yield (kg/ha) (-0.6391^** and -0.506^**, respectively). Therefore, it is logical to select genotypes with low seedling mortality and sterility percentage for improvement of yield in paddy.

In the present study, an adequate amount of genetic variation, high heritability coupled with high genetic advance was observed for seedling mortality, number of filled grains per panicle, and sterility percentage. Hence, a simple and early-generation selection of promising lines by involving present genotypes in future hybridization programs would be very effective. The yield had a positive and significant association with the number of productive tillers/hill and the number of filled grains per panicle, and the latter exhibited high positive indirect effects on yield via the number of productive tillers/hill and the number of grains per panicle. Therefore, these traits would be most suitable for direct and indirect selection of yield in rice improvement programs. Lines CT118911, Sahel177, M202, and KPS10654 were found promising with early flowering and tolerant to salinity stress with low yield reduction and can be utilized as donor parents in future breeding programs.

REFERENCES

[1.] Bhati M, Babu GS, Aishwarya SR. Genetic variability, correlation and path coefficient for grain yield and quantitative traits of elite rice (Oryza sativa L.) genotypes at Uttar Pradesh. Electronic Journal of Plant Breeding. 2015; 6(2): 586- 591.

[2.] Bisne R, Sarawgi AK, Verulkar SB. Study of heritability, genetic advance and variability for yield contributing characters in rice. Bangladesh Journal of Agricultural Research. 2009; 34(2): 175-179.

[3.] Burton GW, Devane EH. Estimating heritability in tall fescue (Festuca arundinacea) from replicated clonal material. Agronomy Journal. 1953; 45(10): 478-481.

[4.] Dewey JR, Lu KH. Correlation and path coefficient analysis of components of crested wheatgrass seed production. Agronomy Journal. 1959; 51: 515-518.

[5.] Divya J, Raju ChD, Subba Rao LV, Aparna K. Genetic variability studies in genetically diverse rice genotypes. International Journal of Current Microbiology and Applied Sciences. 2018; 7(4): 1169–1172.

[6.] Hanson G, Robinson HF, Comstock RE. Biometrical studies on yield in           segregating population of Korean Lespedeza. Agronomy Journal. 1956; 48: 268-274.

[7.] Hasan-Ud-Daula H, Sarker U. Variability, heritability, character association, and         path coefficient analysis in advanced breeding lines of rice (Oryza sativa L.).             Genetika. 2020; 52(2): 711-726.

[8.] Jamil A, Riaz S, Ashraf M, Majid R Foolad. Gene expression profiling of plants under        salt stress. Critical Reviews in Plant Sciences. 2011; 30(5): 435-458.

[9.] Johnson HW, Robinson, HF, Comstock RE. Estimates of genetic and              environmental variability in soybean. Agronomy Journal. 1955; 47(7): 314-318.

[10.] Krishnamurthy SL, Sharma SK, Gautam RK, Kumar V. Path and association            analysis and stress indices for salinity tolerance traits in promising rice (Oryza sativa    L.) genotypes. Cereal research communications. 2014; 42(3): 474-483.

[11.] Muthuvijayaragavan R, Murugan E. Association Analysis of F₂ Generation in Rice (Oryza sativa L. ) under Salinity Condition. Electronic Journal of Plant Breeding. 2020; 11(1): 1–14.

[12.] Panse VG, Sukhatme PV. Statistical Methods for Agricultural Workers. 1985; ICAR, New Delhi. 236-246.

[13.] Parimala K, Devi KR. Estimation of variability and genetic parameters in indica and japonica genotypes of rice (Oryza sativa L.). International Journal of Current Microbiology and Applied Sciences. 2019; 8(3): 1138–1142.

[14.] Rashid K, Kahliq I, Farooq O, Ahsan MZ. Correlation and cluster analysis of some yield and yield related traits in rice (Oryza sativa L.). Journal of Recent Advances in Agriculture. 2014; 2(6): 271–276.

[15.] Saha S, Hassan L, Haque M, Islam M, Rasel M. Genetic variability,    heritability,         correlation and path analyses of yield components in traditional rice (Oryza sativa L.)         landraces. Journal of the Bangladesh Agricultural University. 2019; 17(1): 26-32.

[16.] Sameera S, Srinivas T, Rajesh A, Jayalakshmi V, Nirmala P. Variability and   path co-efficient for yield and yield components in rice. Bangladesh Journal of            Agricultural Research. 2016; 41(2): 259-271.

[17.] Shrivastava P, Kumar R. Soil salinity: a serious environmental issue and plant growth          promoting bacteria as one of the tools for its alleviation. Saudi journal of biological         sciences. 2015; 22(2): 123-131.

[18.] Singh N, Verma, OP. Genetic variability, heritability and genetic advance in rice (Oryza sativa L.) under salt stressed soil. Journal of Pharmacognosy and Phytochemistry. 2018; 7(3): 3114-3117.

[19.] Sumanth V, Suresh BG, Ram BJ, Srujana G. Estimation of Genetic Variability , Heritability and Genetic Advance for Grain Yield Components in Rice ( Oryza sativa L .). Journal of Pharmacology and Phytochemistry. 2017;  6(4): 1437–39.

[20.] Swapnil KP, Chakraborty M, Singh D, Kumari P, Ekka J. Genetic variability, correlation and path coefficient studies in F₂ generation of rice (Oryza sativa L.). International Journal of Chemical Studies. 2020; 8 (4): 3116-3120.

[21.] Tiwari JK. Association analysis and selection strategies for various yield contributing traits in rice genotypes. Applied Biological Research. 2017; 19(1): 35-40.

[22.] Touhiduzzaman SRK, Asif M, Mehraj H, Jamal Uddin AFM. Correlation and Genetic Distance on Sixteen Rice Varieties Grown Under SRI. Advances in Plants & Agriculture Research. 2016; 3(3): 00100.

[23.] Wassman R, Hien NX, Hoanh CT and Tuong TP. Sea level rise affecting Vietnamese Mekong delta: water elevation in flood season and implications for rice production. Climate Change. 2004; 66(1): 89-107.