The genotype by environment interaction (GEI) reduces the success of genotype selection and recommendations by breeders, thus slowing down the progress of plant breeding. The understanding of genotype by environment interaction (GEI) multi-locational yield trials (MLYT) enables researchers to identify locations which are efficient in distinguishing tested genotypes, which are ideal across the testlocations as well as environments which are good representatives of the target regions of interest. The main objective of the study was to assess the genotype by environment interaction on grain yield stability of promising sorghum genotypes across five diverse environments of Zimbabwe. Sorghum yield data of twenty-seven cultivars was obtained from the replicated trials. After performing a pooled analysis of variance for grain yield across five diverse environments during the 2013/14 rainy season, the GxE interaction was significant (P<0.001), and this justified need for testing for GEI components using the GGE biplot analysis to enhance the understanding the effects of components. The results revealed that three mega-environments were identifiable which are Matopos, Save-Valley and Kadoma falling in one mega-environment, then Makoholi was identified as a second mega-environment and then Gwebi was identified as the third mega-environment. Gwebi had the most discriminating ability and good representativeness whereby Save Valley had a poor discriminating ability as well as least representativeness.

Cotton is a very important crop that consists of traits with different associationship due to genetic and environmental factors. In order to determine the degree of association between seed cotton yield and important traits, a study was done using an RCBD experiment with ten genotypes. Seed cotton yield, GOT, lint yield, boll weight, bolls per plant, seed weight, plant height, fibre length, elongation, fineness and strength data were collected and analysed. Genotypic and phenotypic correlation analysis was done in Meta R. Estimation of direct and indirect effects was done using path analysis in Microsoft Excel. Analysis of variance revealed significant differences for boll weight, seed weight, GOT and plant height. Seed cotton yield was correlated with lint yield (r = 0.71***), fibre elongation (r = 0.54***), bolls per plant (0.27***), seed weight (r = 0.22***), strength (r = 0.21***) and fineness (r = 0.13*) at genotypic level. Ginning outturn was correlated with lint yield (r = 0.70***), elongation (r = 0.60***) and strength (r = 0.50***). Boll weight was correlated with seed weight (r = 0.56***) whilst plant height was highly associated with fibre strength (r = 0.58***). The adjusted R Square (0.98), low standard error (0.12) and low residual effect (R = 0.01) in regression analysis indicated that ABOUT THE AUTHOR Chapepa Blessing is a plant breeder working at Cotton Research Institute of Zimbabwe as a cotton breeder. His research areas are developing, implementation analyzing data and information dissemination of the national cotton breeding program in the Department of Research and Specialist Services of the Ministry of Agriculture in the country. Other research interests include crop modelling using remote sensing technology and molecular tool applications. Marco Mare is also plant breeder at Cotton Research Institute and is involved in developing, implementation analyzing data and information dissemination of the national cotton breeding program. Washington Mubvekeri is the head of Cotton Research Institute and is responsible for all cotton research programs in the country.

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BackgroundThe Zimbabwean national cotton breeding programme has the mandate to develop superior cotton (Gossypium Hirsutum) varieties with good field performance and high fibre properties. Cotton productivity in Zimbabwe has remained very low, with national average seed cotton yield record of 650kg ha-1 (AMA Report, 2019) compared to the potential 2000kg ha-1. Since this is a result of many biotic and abiotic factors, field experiments laid in a Randomized Complete Block Design were conducted on ten genotypes (seven test genotypes and three check varieties) from 2012 to 2019 across 13 diverse locations in Zimbabwe to evaluate cotton yield performance, stability and adaptability by Analysis of Variance (ANOVA) and Genotype and Genotype by Environment Interaction (GGE) Biplot methods. ResultsThe Analysis of Variance indicated significant (P< 0.001) effects of Genotype (G), Environment (E) and their Interaction (GE). The highest percentage of variation was explained by E/G/GE (60.34%) while G/E+GE together explained the rest of the variation (<40%). Joint effects of G and GE were partitioned using the GGE biplot analysis explaining total of 59.08% (PC1 = 36.96% and PC2 =22.12%) of the GGE sum of squares. The biplot analysis revealed that candidates 917-05-7, TN96-05-9, 912-05-1 and GN 96 (b)-05-8 were the ideal and stable genotypes. The candidate variety 917-05-7 significantly (P< 0.001) showed superior yield performance over checks CRI-MS1 and CRI-MS2 recording 5% and 5.5% yield increase respectively. Candidate 917-05-7 recorded a higher earliness index (78.11%) over checks CRI-MS1 and CRI-MS2 (77 and 76% respectively) thus indicating potential attributes for good cotton production with more pick-able bolls earlier than the current commercial varieties.ConclusionCandidate 917-05-7 has been identified as the ideal genotype in terms of high yielding potential, and stability hence recommended for commercial release and use as breeding parent for future breeding programs.

Background The Zimbabwe national cotton breeding programme has the mandate to develop superior cotton (Gossypium Hirsutum) varieties with good field performance and high fibre properties. Cotton productivity in Zimbabwe has remained very low, with national average seed cotton yield record of 650 kg/ha (AMA Report, 2019) compared to the potential 2000 kg/ha. Though this is a result of many biotic and abiotic factors, field experiments laid in a Randomized Complete Block Design were conducted on ten genotypes (seven test genotypes and three check varieties) from 2012 to 2019 across 13 diverse locations in Zimbabwe to evaluate cotton yield performance, stability and adaptability by Analysis of Variance (ANOVA) and Genotype and Genotype by Environment Interaction (GGE) Biplot methods.Results The Analysis of Variance indicated significant (P < .001) effects of Genotype (G), Environment (E) and their Interaction (GE). The highest percentage of variation was explained by E/G/GE (60.34%) while G/E + GE together explained the rest of the variation (< 40%). Joint effects of G and GE were partitioned using the GGE biplot analysis explaining total of 59.08% (PC1 = 36.96% and PC2 = 22.12%) of the GGE sum of squares. The biplot analysis revealed that candidates 917-05-7, TN96-05-9, 912-05-1 and GN 96 (b)-05-8 were the ideal and stable genotypes. The candidate variety 917-05-7 significantly (P < .001) showed superior yield performance over checks CRI-MS1 and CRI-MS2 recording 5% and 5.5% yield increase respectively. Candidate 917-05-7 recorded a higher earliness index (78.11%) over checks CRI-MS1 and CRI-MS2 (77 and 76% respectively) thus indicating potential attributes for good cotton production with more pick-able bolls earlier than the current commercial varieties.Conclusion Candidate 917-05-7 has been identified as the ideal genotype in terms of high yielding potential, and stability hence recommended for commercial release and use as breeding parent for future breeding programs.

The success of any breeding program rests upon the active involvement and participation of key stakeholders or technology recipients. Cotton (Gossypium hirsutum L.) is a versatile crop that is grown in most parts of the world, hence the need to involve different players in the process. Zimbabwe’s national variety development program includes a “Client-oriented” research approach called “Participatory Variety Selection” (PVS) in the process. The process that involved the evaluation of different advanced cotton genotypes by farmers in different cotton growing areas included five advanced genotypes and one commercial variety popularly grown by farmers. These were grown in a Mother-Baby Trial arrangement. Through the integration of farmers’ and researchers’ selection criteria, the study sought to enhance the identification and selection of best-performing cotton genotypes under diverse growing conditions. The study established that farmers’ preferred cotton attributes included large bolls (> 5g), uniform boll split (to avoid many picks), short interboll distance (many bolls per fruiting branch), uniform short height (1.0-1.2m), more bolls per plant (>30) and low pest damage (bollworms and Jassid). Through the use of these attributes, the farmers identified SN-96-5, 830-01-3, and 645-98-11 as their best performing genotypes through voting and Focus Group Discussions that were conducted where they recorded 206 votes, 130 votes, and 129 votes respectively. Total Seed Cotton Yield data from the farmer-managed plots (Baby Trial) and Researcher-managed (Mother Trial) were recorded and subjected to statistical analysis. The study results which revealed significant differences in the genotypic, environmental variance, and interaction (Table 5) (P<0.04, P<0.001, and P<0.035 respectively) identified genotype SN-96-5 as the best performing genotype. AMMI and GGE biplots also indicated that SN-96-5 was the most ideal, high-yielding, and fairly stable genotype. Therefore, SN-96-5 is recommended for release and commercial production in Zimbabwe.