Genotype x Environment Interactions on Seed Yield of Inter-racial Common Bean Lines in Kenya

Abstract

Determination of yield stability is critical in identifying new common bean cultivars with either specific or broad adaptation in target environments. This study aimed to assess genotype by environment (G x E) effects on agronomic performance of 78 F 1.7 lines selected with molecular markers for multiple disease resistance from 16 inter-racial bean populations. Field trials were conducted in low-, medium-and high altitude conditions in Kenya. Data collected on seed yield were subjected to additive main-effects and multiplicative interaction (AMMI) model to separate additive variance from the G x E interaction and to determine the stability of genotypes across locations. Results showed that G x E effects were highly significant (P<0.001), implying that tested lines behaved differently across the three locations. Better yields were recorded from high altitude Tigoni site while the lowest were from low altitude Mwea site. Yield across sites ranged from 1,518 to 2,748; 1,324 to 3,860; 1,537 to 3,722 and 1,010 to 3,718 kg ha-1 for pinto, red mottled, red kidney and mixed color bean lines, respectively. Number of pods plant-1 was the most strongly correlated to seed yield and could be, therefore, used as an indirect selection criterion for seed yield. The environment was responsible for the largest part of yield variability (86.4%, 84.8%, 82.3% and 49.5% for pinto, red kidney, red mottled and mixed color bean lines, respectively). KMA13-22-21 and KMA13-29-21 were the most stable high yielding lines across locations. Higher yielding lines were the most unstable across sites. Two pinto, four red kidney, 15 red mottled, and two mixed color lines did better than their corresponding checks with yield advantages of 7.6, 14.3, 71.5, and 34.9%, respectively. These lines should, therefore, be selected for further testing and release.

Trichoderma parareesei Favors the Tolerance of Rapeseed (Brassica napus L.) to Salinity and Drought Due to a Chorismate Mutase

“…The combined effects of Trichoderma strain T34 and abiotic stress were analyzed by two-way ANOVA followed by Sidak's multiple comparison test, indicating significant differences as follows: * p < 0.05; ** p < 0.01; *** p < 0.001; **** p < 0.0001; ***** p < 0.00001. The GxE interaction was performed following the methodology described by Mondo et al in 2019 [36]. Using the interactions between rapeseed, different stress conditions, and different inoculations (using both parameters as different environments), we determined the absence of GxE interaction in our study.…”

Yield stability in small red inter-racial common bean lines in Kenya

“…RWR719 is a breeding line developed in Rwanda, resistant to Pythium root rot (Otsyula et al, 2003), and G10909 is a CIAT accession with broad resistance to angular leaf spot (Mahuku et al, 2011). The 16 populations were developed from crosses among donor of resistance to major common bean diseases and commercial cultivars grown in Eastern Africa (Njuguna, 2014;Mondo et al, 2019). Among parental genotypes, Mex54 and G10909 were used as sources of resistance to angular leaf spot; G2333 to anthracnose; RWR719 and AND1062 to Pythium root rots; while BRB191 was for bean common mosaic virus.…”

“…This paper reports on small red bean lines generated by the abovedescribed breeding programme. Data on the other market classes were previously reported by Mondo et al (2019). Crosses that produced these small red bean genotypes are presented in Table 2.…”

Stability and phenotypic adaptability by AMMI analysis in cowpea beans (Vigna unguiculata (L.) Walp.)

“…Va r ious methods have been carried out in cowpea in order to release cultivars, among them, the AMMI (additive main effects and multiplicative interaction) model, which is multivariate and appropriate for analyzing two-dimensional matrices, such as those containing genotype and environmental data (EL-SHAIENY et al, 2015;MONDO;KIMANI;NARLA, 2019;TOSQUY-VA L LE et al, 2019). The AMMI statistics presented in biplots can be used to provide insightful interpretation of data from large and complex experiments (EBDON; GAUCH JR, 2002).…”