8. Thesis outline
6.3 Breeding implications and the way forward
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• Soybean genotypes were ranked differently in different environments, suggesting a cross-over type of GEI.
• Genotypes Sable and BRS MG46 recorded the highest yields in some environments, but Using AMMI and GGE analysis showed that they were highly unstable in terms of a high GEI effect.
• The best genotypes were 916/5/19 and G7955 with high yields and yield stability across all the test environments.
• Environment EM2 (KARI-Embu, long rains) was the most representative of all the test environments, as well as the most effective in terms of discriminating between genotypes.
• Environment IG1 (KARI-Igoji, short rains) and MW1 (KARI-Mwea, short rains) were poor at discriminating between soybean genotypes.
• Environment EM1 (KARI-Embu, short rains) was good for discriminating genotypes but a poor representative of the test environments, therefore it is only suitable for developing specifically adapted genotypes.
157 possess good quality traits (protein and oil content) that meet industrial requirements, and the stay-green trait for green pod sales. If Kenyan plant breeders can incorporate these traits into new soybean varieties, then some of challenges faced during soybean production, marketing and consumption would be solved. Efforts should also be made to address other socio- economic constraints by involving extension agents, microfinance institutions, and policy makers. There is also a need to link farmers to the markets offered by the processing industries, and to train farmers in the technologies of processing and utilization of soybean.
The majority of farmers were not aware of ASR, indicating a clear need for extension services to improve farmers’ knowledge, particularly on disease identification and management strategies.
The control measures employed by farmers were minimal and sometimes non-existent. This is partly because small-scale farmers cannot afford to buy fungicides and spraying equipment to control ASR, which is what the commercial farmers use to control it. In addition, cultural practices alone are not effective for controlling ASR. Furthermore, there are no available commercial rust resistant varieties. Therefore, breeding for rust resistant soybean cultivars would be the best option for managing ASR for small scale farmers in Kenya.
Soybean germplasm has been screened all over the world for ASR resistance. However, no good resistance has been found yet that is at a high level and is stable. Several factors are behind this problem, including the limited genetic diversity of soybean globally, the large number of hosts of P. pachyrhizi, the rapid evolution of P. pachyrhizi races, and the interaction between genotype, environment and pathogen. This study identified resistant and moderately resistant genotypes in the exotic germplasm and one advanced line. However, this resistance may be temporary because these varieties have not been grown widely, which would select for new races of P. pachyrhizi with virulence to match the resistance. This theory is confirmed by the fact that all the commercial varieties, advanced lines, genebank accessions and collections from the farmers’ fields were susceptible to Kenyan rust races. Therefore, the resistant genotypes identified in this study could be used as sources of resistant genes or donor parents to improve the commercial varieties and advanced lines, using a backcrossing programme. But this approach is risky due to the rapid development of new virulent races of P. pachyrhizi.
The genetic studies indicated presence of sufficient genetic variability among the parental lines for improving rust resistance and other agronomic traits in soybeans. This study established that both additive and non-additive gene action were important for all the traits studied but that
158 additive gene action was predominant over non-additive gene action, as confirmed by GCA/SCA ratio. This indicated the possibility of high genetic gains due to additive gene effects of the genotypes used in this study because a strong GCA effect is a desirable predictor of segregants’ performance. This shows that rust resistance, early flowering, early maturity and reduced plant height can be improved effectively through simple selection in early generations.
A recurrent selection program using parents with excellent agronomic and quality traits would accumulate the additive genes governing these traits. In only 2-3 generations of screening for multiple traits concurrently plant breeders could create highly resistant soybean with stable quantitative resistance, combined with excellent agronomic and quality traits. In order to undertake this, the problem of increasing male sterility and the numbers of crosses that a breeder can successfully make needs to be solved, probably using male gametocides.
Non-additive gene action played a major role in controlling soybean grain yield. Therefore selection at advanced generations would be effective for substantial genetic gains in grain yield.
In addition, breeding procedures such as bulk breeding and single seed descent methods would be suitable for improving soybean grain yields. Among the parental genotypes, G10428, G8586 and Namsoy 4M expressed good general combining ability for resistance to both rust severity and sporulation, indicating that they could contribute towards rust resistance. In addition, inclusion of G7955, Maksoy 1N, G58 and Nyala as parents in the breeding programme would incorporate the important traits of early maturity and lodging resistance.
The ultimate goal in any breeding programme is to develop high yielding, stable genotypes that are economically profitable. Using the AMMI and GGE biplot models, this study identified genotypes G7955 and 916/5/19 as high performing genotypes that were stable in all six test environments. These genotypes are therefore recommended for commercial production in Central and Eastern Kenya. Genotypes BRS MG46 and Sable were the highest yielding genotypes but also highly responsive to the environments. Therefore, these genotypes can only be recommended for specific environments or be utilized to improve yields in the breeding programmes. Genotype G7955 was moderately resistant to rust while 916/5/19, BRS MG46 and Sable were highly susceptible; hence they require further improvements on rust resistant.
Environment EM2 was identified as the most suitable for testing soybean genotypes because it is a long rainy season, with temperatures ranging from 14-250C and fertile soils (humic nitosols) suitable for soybean production.