Evaluation of soybean (Glycine max L. Merr.) lines for grain yield and drought-tolerance.
Mathonsi, Thubelihle Lungelo.
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Soybean (Glycine max L. Merr.) is ranked as the fourth-highest commercial agronomic seed crop in South Africa. An increase in animal feed demand has mainly driven the significant growth in demand for soybean oilcake and oil. This demand is also contributed by increasing demand for protein-rich foods, especially among the middle class. However, soybean production has always been variable in different seasons in South Africa mainly due to the occurrence of droughts in some provinces causing the yields to decline. Therefore, enhancing grain yield and drought tolerance would preserve farmers’ profits at large and smallscale farms. The present study was undertaken during 2019 and 2020 summer growing seasons in the field and greenhouse trials to: i) screen 36 soybean genotypes for drought-tolerance using morphological and physiological traits, ii) assess drought-tolerance in soybean genotypes using drought-tolerance indices and iii) estimate the variance components and heritability of yield and yield components of soybean under wellwatered and water-stressed. Thirty-six soybean lines obtained from the International Institute of Tropical Agriculture (IITA) were screened for drought-tolerance in the field and greenhouse under water-stressed and well-watered regimes using morphological, physiological traits and yield-based selection indices. The targeted traits were; plant height (PH), stem diameter (STD), leaf width (LW), leaf length (LL), seed moisture content (SMOI), stomatal conductance (STC), chlorophyll content (CC), 100 seed weight (SW) and biomass Yield (BMS). High genetic variation was observed in grain yield and morpho-physiological traits under both well-watered and water-stressed regimes. Genotype effect was significant for PH, LL, LW, STD, BM, SW and GY. The water regime indicated a significant effect for PH, LL, LW, STD, SMOI and GY. The environment effect was significant for all morphological traits PH, LL, LW, STD, FLW, SMOI, SW and GY. The environment by water regime interaction showed a significant effect for PH, FLW, SMOI, SW. A significant reduction in agronomic traits was observed for G10, G12, G22 and G29, which were the best potential genotypes for improving drought-tolerance. The PH, LL, LW, STD; GY, SMOI, BM and SW could effectively be used for selection in the yield improvement of soybeans under water stress conditions, since they were positively correlated with GY. The Principal components analysis (PCA) and cluster plot analysis approach was very helpful in identifying high-yielding, drought-tolerant genotypes, discriminating and grouping genotypes based on their responses to water stress. The principal components indicated that first dimension (Dim1) was consistently correlated with PH, LL, LW and STD. The SW, CC, FLW, STC, SMOI, BM and GY were either associated with second dimension (Dim2) or third dimension (Dim3). The cluster plot showed that G1, G10, G12, G20, G22, G25 and G29 under WS in the field experiment had high means values and were consistency associated with STD, LL, STC, FLW, SMOI, BM, SW and GY based on principal components and cluster plot, represented as cluster II. Whistle, G5, G7, G10, G12, G13, G14, G17, G21, G22, G23, G27, G29 and G31, showed significantly high mean values and association with PH, STD, LW, LL, STC, CC, BM, SW and GY in the greenhouse environment. The selection for drought-tolerance among 36 soybean lines under well-watered and water-stressed regime was performed using yield based selection indices, including Drought intensity index (DII), Stress susceptibility index (SSI), yield index (YI), Stress tolerance index (STI), Mean relative performance (MRP), GMP-Geometric mean of productivity (GMP), Yield stability index (YSI), Mean productivity(MP), TOL-Stress tolerance (TOL), Harmonic mean (HM) and Relative efficiency index (REI). The ANOVA indicated that the main effects due to the environment, genotype and water regime were significant for GY at the level of significance of (P≤0.05), (P≤0.001) and (P≤0.001). The drought-tolerant indices with significantly positive correlation with the grain yield under well-watered and water-stressed regimes were MRP, GMP, MP, MRP, HM and REI (P<0.001-P<0.05). These indices were comparably effective than SSI, YI, STI, YSI and TOL in selecting and predicting better grain-yielding soybean genotypes under a well-watered and water-stressed regime. Most of the soybean genotypes studied resembled water stress tolerance, including G22, G4, G8, G1, G23, G5, G20, G24, G27, G25, G16, G14, G7, G2, G28, G11, G6, G34, G10, G30, G3, G15, G19, G36, G17, G21, G31, G18, G33, G35, G13, based yield reduction rankings. Among these genotypes, G1, G19, G13, G33, G31 showed high mean performance, tolerance and association with SSI, STI, MRP, GMP, MP, TOL, HM and REI. However, G26, G32, G9, G29, G12 were considered moderately susceptible to water stress and G7, G8, G14, G22, G34 had low mean performance values and low association with indexes. The 36 imported lines from the International Institute of Tropical Agriculture (IITA) were assessed in the field and greenhouse environments, using a 6×6 alpha-lattice design with two replications. Water stress was applied up two weeks after 50% flowering for each genotype and a well-watered regime was used as a control treatment. The genotypes were screened using morphological and physiological traits including; PH, STD, LW, SMOI, STC, CC, SW, BM and GY for estimating variance components and broad-sense heritability. The present study showed the existence of genetic variability among 36 soybean genotypes examined. Hence, one can examine the presence of variability in these soybean lines for crop improvement programs through indirect selection. According to the results, a higher genotypic coefficient was observed for grain yield under both water regimes, consistent with wide-ranging heritability. The PCV was higher than the GCV for all traits across environments and water regimes, thereby suggesting the significance of the environmental expression for all traits. The PCV was higher than the GCV for all traits in all environments and water regimes, suggesting the importance of the environmental effect for all traits. The PCV values ranged from 7.03 to 92.84, while the GCV values ranged from 0.07 to 60.77. GY and BM showed high PCV and GCV values in each environment and under the respective water regimes, which signifies a considerable genotypic variation in these traits. Additionally, the phenotypic expression of these traits would help identify genotypic potential and are efficient further to be used to improve breeding plants. Because of the reduced effects of environmental stress, there was no clear trend in the traits examined for heritability in both environments and water households. Heritability estimates under WW ranged from -0.34 to 55%, while under WS, they ranged from -0.29 to 43%. Overall, most traits had low heritability in both water regimes. Consequently, one should be careful in selecting for droughttolerance using the traits examined.