CHAPTER 6 GENERAL OVERVIEW OF THE STUDY
6.2 Summary of major findings
6.2.1 Genotype by environment interaction and stability of 25 soybean genotypes The data collected from multi-location trials conducted in the 2017/18 rainy season using six sites in four countries viz. Zambia, Malawi, Zimbabwe and Mozambique were used to evaluate the stability of 20 elite soybean lines and five commercial checks for seed yield. The data were subjected to GGE biplot and AMMI analyses. The following were the findings from the two methods:
The AMMI analysis revealed that the contribution of environment, genotypes and GEI effects to the total variation were 21%, 32% and 47%, respectively. Five IPCAs were
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found to be highly significant (P<0.001). The GEI was the highest contributor to the total variation and the GEI present was of the crossover type.
Both AMMI and GGE biplot analyses indicated Lusaka West as the highest yielding and most informative environment.
According to the GGE biplot analysis, Rattray Arnold Research Station was the most ideal environment for testing genotypes.
In the GGE biplot analysis, three mega-environments were identified. Mega- environment one was made up of IITA-SARAH, Lusaka West, RARS and Nampula, while the Mega-environments two and three were composed of Chipata and Chitedze, respectively.
AMMI analysis showed that the lines TGx2002-17DM, TGx2001-10DM, TGx2001- 18DM, TGx2014-24FM, TGx2001-6FM and TGx2002-3DM were specifically adapted to Chitedze, Nampula, IITA-SARAH, Lusaka West and Chipata, respectively.
Both analyses revealed that the line TGx2014-5GM was the widely adapted and the second highest yielding (4143 kg/ha) genotype.
6.2.2 Genotype by trait associations, correlations and path analysis for seed yield The data collected on the number of days to 50% flowering, pod clearance, plant height, days to maturity, pod number per plant, hundred seed weight and grain yield from the six locations were subjected to correlation and path coefficient analyses for seed yield and genotype by trait analysis. The findings were as follows:
Both GT biplot and correlation coefficient analysis revealed that pod number per plant and hundred seed weight were the most positively correlated traits with grain yield, while days to 50% flowering had a negative association with grain yield.
In sequential path analysis, the number of pods per plant and hundred seed weight recorded the highest positive and significant direct effects on seed yield, while plant height had a high indirect effect on seed yield.
The GT biplots revealed that lines TGx2014-5GM and TGx2002-23DM had good combinations of high yields with large seed size and high pod number.
6.2.3 Genetic variability and diversity among elite lines of soybean
The combined data from the six locations on the seven morphological traits were subjected to cluster and principal component analyses in order to study genetic diversity among the 25 genotypes. The variance components estimated from the data (Vg, Vgl and Ve) were used to calculate Vp, broad sense heritability, PCV, GCV, GA and GAM. The findings were as follows:
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The GCV values were lower than the PCV values, which meant that all the traits were influenced by the environment. However, small differences were observed between the two coefficients of variation for all the traits except for pod clearance, which was highly affected by the environment.
The genetic advance values for seed yield, 100 seed weight and pod number per plant were 731 kg/ha, 2.77 g and 12, respectively. This meant that the yield of the progeny would be increased by 731 kg if the best genotypes were selected as parents for the next cycle (selection intensity of 5%). The mean yield of the offspring would change from 3146 kg/ha to 3877 kg/ha. Similarly, hundred seed weight and number of pods per plant would be increased from 14.95 g to 17.72 g and 51 to 63, respectively.
Moderate GCV values of 13.45% and 13.49%, high heritability values of 70% and 69%
and GAM values of 23.24% and 23.04% were recorded for grain yield and number of pods per plant, respectively. This suggested that the two traits were highly influenced by additive gene effects, therefore phenotypic selection based on these traits would be highly effective and high genetic gains would be achieved.
Hundred seed weight also showed that it could be improved through selection because it recorded the highest heritability (84%), moderate to high GAM (18.5%) and its GCV value was closer to moderate (9.8%).
Although the traits plant height and days to maturity recorded high heritability values of 62% and 78%, they had low GCV values of 3.55% and 5.75% and GAM values of 6.46% and 5.75%, respectively. These were indications of likely influence of non- additive gene effects on these traits, thus direct selection may not be effective.
The low GCV (4.92%), heritability (27%) and GAM (5.3%) values were recorded for pod clearance, suggesting that this trait was largely influenced by environmental effects and direct selection to improve it would be ineffective.
The 25 genotypes were grouped into eight sub-clusters.
The most dissimilar genotypes were the check MRI Dina and TGx2001-11DM in sub- clusters 1 and 8, respectively.
The sub-cluster 6 had the highest means of the most desirable traits (large seed size, high pod number per plant and seed yield). The three genotypes in this sub-cluster i.e.
line TGx2014-5GM, checks SC Safari and SC Squire had an average yield of 4045 Kg/ha, seed size of 17.8 g per 100 seeds and 62 pods per plant.
Only two principal components, PC1 and PC2 were found to explained the variation and both of them were significant because they had eigenvalues greater than 1.0 (PC1=3.11, PC=1.67). Together they accounted for 68.25% of the total variation.
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The genotypes TGx2014-5GM (G4), SC Safari (CH4), TGx2002-23DM (G9), SC Squire (CH5) and TGx2002-5FM (G14) recorded the highest yields and number of pods and had the largest seeds.