Yield Stability and Farmer Preference of Cowpea (Vigna Uncuilata) Lines in semi-arid Eastern Kenya

Kuruma, Rose Wangari; Patrick, Samalang; Kahwaga, Charles Muriuki

doi:10.21825/af.v32i2.15768

Cited by 2 publications

(3 citation statements)

References 20 publications

(24 reference statements)

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“…The main challenge in intensifying sustainable rice production is the availability of varieties that are resistant to pests and diseases [16]. The genetic resistance of promising lines is expected to solve problems of pests and disease in the field [17,18]. Rice varieties are commonly prone to brown planthopper (Nilaparvata lugens), leaf blast (Pyricularia oryzae), and bacterial leaf blight (Xanthomonas oryzae PV.…”

Section: Introductionmentioning

confidence: 99%

Participatory Varietal Selection for Promising Rice Lines

et al. 2021

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The purpose of rice breeding is to create varieties that are well adapted, highly productive, and acceptable to farmers. However, rice productivity is limited as a result of combined biotic stresses (pests/diseases). This study combines assessment by farmers with the evaluation by breeders with respect to promising rice lines within a range of environments. The aim is to investigate farmers’ preferences and to characterize the yield of promising rice lines, as well as their resistance to pests/diseases by consulting 120 farmers and breeders. This study used an oversite design replicated three times with thirteen promising lines and two varieties, which were all evaluated at farmers’ fields between December 2019 and May 2020. The Importance Performance Analysis was used to compare line performance and farmers’ expectations. Lines Gamapadi-2 and Gamapadi-4 had the highest acceptability scores based on the farmers’ preferences. The yield performances were evaluated using the Finlay–Wilkinson test and the genotypes were evaluated using environmental models (GGE biplot) to determine the most stable lines to be recommended for large-scale planting. The Finlay–Wilkinson and GGE biplot conclusion analyses also showed that the Gamapadi-2 and Gamapadi-4 lines exhibited high potential yield and stability, as well as indications of specific advantages. The results for both lines in all locations indicated no symptoms of damage caused by brown planthoppers or bacterial leaf blight. These lines in all age ranges at two sites showed no symptoms of leaf blast.

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Section: Introductionmentioning

confidence: 99%

Participatory Varietal Selection for Promising Rice Lines

et al. 2021

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“…This is because AMMI analysis is very efficient in identifying both high yielding and stable genotypes (Asio et al, 2005). AMMI analysis has been extensively used to determine stable cowpea genotypes (Asio et al, 2005;Ddamulira et al, 2015;Horn et al, 2018;Tariku et al, 2018;Kuruma et al, 2019;Abiriga et al, 2020;Haisirikul et al 2020). The AMMI method has also been used to assess the causes of G × E interactions in crops like rye grass (van Eeuwijk and Elgersma, 1993) and wheat (Mohammadi et al, 2017) but not cowpea.…”

Section: Introductionmentioning

confidence: 99%

“…et al (2018),Kuruma et al (2019) andAbiriga et al (2020) who reported only two significant PCAs (PCA1 and PCA2) in their assessment of G × E interactions among cowpea genotypes.…”

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Yield stability among cowpea genotypes evaluated in different environments in Uganda

Mbeyagala

Ariko

Atimango

et al. 2021

Cogent Food & Agriculture

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Cowpea (Vigna unguiculata) is one of the important legume crops in Uganda especially in the eastern and northern regions. Its productivity in the country is, however, still low. Environmental constraints and occurrence of genotype × environment interactions (G × E) contribute greatly to the low production. In this study, an additive main effects and multiplicative interaction analysis (AMMI) model was used to assess the yield stability of 30 cowpea genotypes planted in five locations for three consecutive seasons. Also, the relationship of G × E interaction with genotypic/weather variables was analysed. AMMI analysis of variance indicated that environment had the highest contribution to the variation in grain yield. Postdictive success method indicated AMM1-3 as the best model since the first three interaction principal component axes (PCA1, PCA2, PCA3) were significant (p = 0.0000). Together, the first three PCAs explained 75.1% of the G × E interaction sum of squares. However, according to the predictive success method, AMMI-1 was the best model. The AMMI stability value (ASV) identified five genotypes; UCR 5279, Ayiyi, IT07K-257-33, ACC122*WC66 and IT06K-281-1 as being more stable. These ABOUT THE AUTHORS Mbeyagala, E. K (PhD) is a Senior Scientist (Legume breeding) working for the National Agricultural Research Organization (NARO) based at the National Semi-Arid Resources Research Institute (NaSARRI). He is currently the leader of the National Dryland Legume Research Program and also heads cowpea, mungbean and pigeonpea breeding activities in Uganda. His research focuses on developing varieties with tolerance/resistance to biotic/abiotic stresses and with acceptable farmer/consumer traits. Ariko, J.

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Yield Stability and Farmer Preference of Cowpea (Vigna Uncuilata) Lines in semi-arid Eastern Kenya

Cited by 2 publications

References 20 publications

Participatory Varietal Selection for Promising Rice Lines

Participatory Varietal Selection for Promising Rice Lines

Yield stability among cowpea genotypes evaluated in different environments in Uganda

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