This study also found that genetic improvement of ASR resistance and selected agronomic traits in soybean is possible through the use of repeated selection breeding procedures that result in the accumulation of additive genetic effects. Muthamia of the National Gene Bank of Kenya (NGBK) for providing soybean germplasm for this study.
Soybean production and uses
Soybean production constraints
This demand is projected to increase to more than 150,000 tons per year in the next 10 years indicating the need for increased domestic production. Soybean production in Kenya is low due to a number of challenges, including low-yielding varieties, lack of markets, poor agronomic practices, lack of awareness of its potential, competition with other pulses, drought, logging water and pest and disease attacks (Hartman et al., 2011).
Soybean rust
However, significant yield losses of between 10 and 80% have been reported in unprotected fields in several parts of the world, including some African countries (Yorinori et al., 2005). Furthermore, most commercial varieties (e.g. Nyala, Gazelle and EAI 3600) grown in Kenya are highly susceptible to ASR (Mahasi et al., 2009).
Breeding for ASR resistance
Previous genetic studies on the inheritance of ASR resistance have reported variable findings on the mode of gene action and mode of inheritance among different sources (Garcia et al., 2008). However, farmers have not adopted most of these “improved” soybean varieties but continue to grow their local varieties (Mahasi et al., 2009).
Problem statement
5 The International Institute of Tropical Agriculture (IITA) has developed high-yielding and rust-resistant soybean varieties with the aim of increasing soybean production in Africa (Vandeplas et al., 2010). 6 constraints on soybean production and marketing, and farmers' preferences with the aim of developing soybean varieties that will meet farmers' demands and flourish in their specific agro-ecological zone.
Objective of the study
Research hypotheses
Thesis outline
Introduction
This chapter provides a brief overview necessary to guide research into breeding soybeans for rust resistance and other agronomic traits in Kenya. Efforts made to control ASR are also discussed, with an emphasis on host resistance; identification of rust resistance mechanisms and their mode of inheritance, and proposed breeding approaches.
Soybean taxonomy, cytology, floral and pollination system
Soybean origin and distribution
Importance of soybeans
Soybean production constraints in Kenya
Asian soybean rust
- Geographical distribution of ASR
- Symptoms of ASR
- Diagnosis of ASR
- Epidemiology of ASR
Initial symptoms of ASR are small water-soaked lesions that develop into either gray, black, or reddish-brown lesions primarily on the underside of leaves; but sometimes they can appear in leaves, pods, cotyledons and stems (Li, 2009b) (Fig 1.1). Urediniospores may be released continuously for several weeks, depending on the initial inoculation and the volume of spores produced within the first three weeks.
Factors affecting ASR development
- Host range of ASR fungus
- Environmental factors affecting ASR development
- Effect of ASR on soybean development stages and maturity
- Effect of ASR on yield and seed quality
Soybean phenological stages and duration of maturation play an important role in the development of ASR (Tschanz et al., 1985). According to Kawuki et al. 2004), soybean plants are susceptible to rust at all stages of development.
Control strategies for ASR
- Cultural practices and nutrient management strategies
- Biopesticidal and biological control
- Chemical control
- Host plant resistance
Partial resistance involving reduction in the rate of disease progression has also been reported in soybean (Hartman et al., 2005). However, accurate identification and assessment of partial resistance in breeding programs is challenging and time-consuming (Hartman et al., 2005).
Other breeding and selection procedures for ASR
- Introgression of rust resistance from perennial Glycine spp. to cultivated soybeans
- Mutation breeding
- Use of molecular markers and marker assisted selection
- Pyramiding ASR resistant genes
Preliminary evaluations conducted in Uganda showed significant tolerance differences among soybean genotypes that could be exploited by breeders in soybean breeding programs (Kawuki et al., 2004). Hyuuga) from a Japanese cultivar was also mapped to the same MLG C2 region as Rpp3 using SSR markers (Monteros et al., 2007).
Participatory breeding approaches
Genetic analysis for inheritance of ASR resistance
Diallel mating design
Genotype x environment interactions (GEI)
28 The AMMI model combines both the main effects of genotype (G) and environment (E) as additive effects, and the G x E interaction as a multiplicative component of principal component analysis (Asrat et al., 2009). In addition, it is effective in identifying superior cultivars (“who won where”) and potential mega-environments (Kaya et al., 2006).
Summary
Response of soybean rust resistant line identified in Paraguay to Mississippi isolates of Phakopsora pachyrhizi. Evaluation of USDA soybean germplasm accessions for resistance to soybean rust in the Southeastern United States.
Introduction
42 Participatory approaches help researchers understand farmers' awareness, knowledge and disease management, thereby providing a basis for further development of integrated disease management strategies (Hoffmann et al., 2007). This study was therefore designed to: (i) identify preferred soybean cultivars and selection criteria; (ii) understand farmers' perception, knowledge and management of ASR;.
Materials and methods
- Study area and sampling
- Survey methodology, data collection and analysis
Information on farmers' demographic characteristics, general soybean production, land size, land allocated to soybean production, soybean varieties grown, farmer preferences for different varieties; comparison of the local and improved varieties and their desired characteristics were collected. Data on the farmers' perception and knowledge about ASR, source of planting material and soybean rust control methods were also collected.
Results
- Respondents characteristics
- Soybean production in the different counties
- Purpose of soybean cultivation
- Soybean cropping systems
- Soybean varieties commonly grown by farmers in Kenya
- Adoption of improved soybean varieties
- Farmers preferences for soybean varieties
- Sources of seed
- Farmers’ desired traits in an ideal soybean variety
- Farmers desired traits based on gender
- Farmers’ perceptions of ASR
- Farmers’ perception on ASR - predisposing factors and symptoms
- Management of ASR
- Other soybean production and marketing constraints
About 38% of the farmers from Kirinyaga, Embu and Meru counties were satisfied with the local varieties and they were not aware of the improved varieties. Lack of market was mentioned by 16.5% of the farmers as a major constraint across all regions.
Discussion
It also improves nutritional status, soil fertility and reduces pest and disease attacks (Yu et al., 2009). Preferences for the local rust-susceptible cultivars (Nyala and Gazelle) due to their desired traits have significantly contributed to the wide spread of ASR (Mahasi et al., 2009).
Conclusion
Changes in susceptibility to soybean rust caused by Phakopsora pachyrhizi associated with plant age and leaf node position [Online] http: //www.plantmanagementnetwork.org/infocenter/topic/. Reducing labor and input costs in soybean production by smallholder farmers in southwestern Kenya.
Introduction
74 Although potential sources of ASR-resistant and tolerant lines have been identified, this resistance is often separated from one geographic region to another. For example, only Rpp1 and Rpp4 resistance genes had resistance reactions to ASR in Nigeria (Twizeyimana et al., 2009), Rpp2 and Rpp4 genes were resistant in Brazil (Silva et al., 2008) and only Rpp2 gene was resistant (in Uganda ). Oloka et al., 2008).
Materials and methods
- Soybean genotypes
- Screen house experiment
- Field evaluation and experimental sites
- Treatments, experimental design and planting
- Data collection
- Data analysis
Using a hemocytometer, the concentration of urediniospores was adjusted to 1x106 urediniospores per milliliter before inoculation (Twizeyimana et al., 2007). Data were subjected to analysis of variance using the statistical package Genstat (12th edition) ( Payne et al., 2009 ) for all traits.
Results
- Screen house evaluations
- Field evaluations
Plant introductions G10428 (Rpp4) and G8586 (Rpp2) exhibited moderate levels of resistance with rust scores of 4.3 and 4.5, respectively. Rust score was assessed at different growth stages of R1 (early flower), R2 (full flower), R4 (full pod) and R6 (full seed) among soybean genotypes at KARI-Embu research station in the cold season of 2011.
Discussion
101 Soybean growth stage plays an important role in the development of ASR (Tschanz et al., 1985). In this study, rust severity was positively correlated with AUDPC values, sporulation score, 100 seed weight and oil content.
Conclusion
Molecular mapping of soybean rust (Phakopsora pachyrhizi) resistance genes: discovery of a new locus and alleles. Evaluation of soybean germplasm accessions for soybean rust in the southeastern United States and efforts to.
Introduction
These findings raised more questions than answers, and more genetic studies are needed to understand the type of gene action that controls rust resistance in different soybean germplasms. Therefore, the objective of this study was to determine the type of gene action controlling rust resistance and selected agronomic traits.
Materials and methods
- Site characteristics
- Soybean germplasm and diallel crosses
- Field evaluation of the genotypes
- Data collection
- Data analysis
Sij = SCA effects for cross between ith parent and jth parent εijk = experimental error associated with ijth genotype in kth environment. A ratio for GCA/SCA close to 1 indicates the importance of additive effects in trait inheritance.
Results
- Analysis of variance
- Mean performance of the parents and the F 2 populations
- GCA and SCA estimates for ASR and selected agronomic traits
- GCA estimates of individual parents for ASR and other agronomic traits
In all environments, neither parent had significant GCA effects on days to flowering, days to maturity, and grain yield. 122 Table 4.9: SCA estimates for flowering, days to maturity, plant height and grain yield from KARI-Embu and KARI-Mwea Research.
Discussion
Effects can be considered during breeding for rust resistance and other quantitative soybean traits. In addition, pyramidal parents that have resistance genes are likely to increase rust resistance (Maphosa et al., 2012a).
Conclusion
New gene conferring resistance to Asian soybean rust: allelic testing for the Rpp2 and Rpp4 loci. Interference of genotype × environments interaction in the genetic control of Asian soybean rust resistance.
Introduction
In recent years, application of both the AMMI model and GGE biplot has become common among plant breeders for the interpretation of GEI. However, in Kenya, the application of the AMMI model and the GGE-biplot statistical tools in soybean breeding has not been documented for the analysis of multi-site trials and the identification of the best test environments.
Materials and methods
- Soybean germplasm
- Site characteristics, experimental design and planting
- Data collection
- Data analysis
Where Yij = Average grain yield (kg /ha-1) of the ith line in the jth environment;. 136 Yi1 and Yi2= points of IPCA1 and IPCA2 respectively for the ith cultivar;.
Results
- Analysis of variance
- AMMI analysis of variance
- AMMI genotype ranking
- AMMI biplot analysis
- GGE bipot analysis
- Best performing soybean genotypes
- Soybean yield performance and stability
- Relationship among the environments
Abbreviations for the names of the environments and genotypes are presented as respectively in Tables 5.1 and 5.3. Abbreviations for the names of the environments and genotypes are presented as respectively in Tables 5.1 and 5.3.
Discussion
This result illustrates the value of selecting genotypes with good yield and stability efficiently (Dehghani et al., 2009). Mega-environments help plant breeders select high-performing genotypes for a specific environment, making better use of GEI (Jandong et al., 2011).
Conclusion
Analysis of additive main effects and multiplicative interactions (AMMI) of dry leaf yield in tobacco hybrids across environments. Additive main effects and multiplicative interaction analysis of wheat yield performance in rice genotypes across environments.
Introduction
Summary of the major findings
- Identification of farmers’ preferred varieties, perceptions on soybean rust disease
- Evaluation of soybean genotypes for ASR resistance and its correlation with
- Combining ability for resistance to ASR and selected agronomic traits in
- Genotype x environment interaction (GEI) and stability for soybean grain yield in
Of the four known genotypes carrying single resistant genes, accessions G10428 (Rpp4) and G8586 (Rpp2) exhibited the highest level of resistance across environments. Environment EM1 (KARI-Embu, short rain) was good at distinguishing genotypes, but a poor representative of the test environments, therefore it is only suitable for developing specifically adapted genotypes.
Breeding implications and the way forward
This study identified resistant and moderately resistant genotypes in the exotic germplasm and one advanced line. This study determined that both additive and non-additive gene action were important for all but that one trait studied.
