Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant ( ) influence on cassava fresh storage root yield (t·ha⁻¹), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava.

Cassava is a significant contributor to food security and an income source for smallholder farmers in southern Ethiopia. However, little research effort has been done so far based on designing field experiment samples for the biochemical composition of cassava accession at the country level. The study was conducted to assess the biochemical composition of cassava accessions in southwest Ethiopia. Flour samples from the storage roots of 64 cassava accessions were collected and were run in duplicates. Data on 13 biochemical characters were collected and analyzed using standard methods. The analysis of variance showed significant to very highly significant differences among the tested accessions for biochemical composition. The flour moisture ranged from 4.83–10.11%, dry matter (89.89-95.17%), organic matter (86.71–92.65%), ash (2.1–3.96%), fiber (1.14–3.00%), fat (0.26-1.4%), crude protein (1.28-2.86%), starch (65.1–74.2%), carbohydrate (81.29–87.94%), energy (341.44–367.61 kcal/100g DM), and cyanide (1.67–3.14). The highest GCV = 29.54% was shown for crude fat, followed by GCV = 16.94% for crude fiber, and GCV = 16.11% for tannin, whereas, among the characters, dry matter was observed to be the lowest (GCV = 0.84%). The GAM ranged from protein 0.30% to 54.94% for fat, while heritability ranged from flour moisture and dry matter (17.29%) to 84.88% for cyanide. The first five principal components explained 80.1% of the total variation, with PC I accounting for 37%, PC II 15.4%, PC III 11.6%, PC IV 8.4%, and PC V 8.20% of the total variation. This study found the presence of high biochemical variability among the tested accessions’ roots and could be used to select accessions with desirable biochemical composition in future breeding work.

The performance of most crop genotypes varies significantly depending on their genotype–environment interactions. Providing information on high-yielding and well-adapted common bean genotypes in a given environment is crucial for small-holder farmers to boost their productivity. A field trial was conducted at the Gendo and Wara locations over two consecutive seasons to understand genotype–environment interactions on grain yield and to identify and select common bean genotypes that were better adapted and had high yield potential for production. Ten genotypes were evaluated for their performance using a randomized complete block design with three replications. The AMMI and GGE models were used to identify superior genotypes regarding their mean grain yield performance. The AMMI analysis of variance results showed that grain yield was significantly (p ≤ 0.05) influenced by genotype–environment interactions. This shows that genotypes varied in grain yield mean performance across environments, indicating the possibility of selecting suitable genotypes with specific and broad adaptability. The sum of squares factor indicated that grain yield variation was contributed by the genotype (61.0%), followed by the environment (29.80%) and the genotype–environment interaction effect (9.20%) of the total variation. The AMMI and GGE biplot analysis results showed that SER-119 and SER-125 were identified as the best genotypes regarding their mean grain yield performance. Additionally, the genotype selection index analysis result indicated that SER-119 and SER-125 were stable and consistently exhibited high mean performance in all evaluated settings. Therefore, SER-119 and SER-125 are prime candidates for production in various agroecologies suitable for common bean cultivation in Dawuro zones. The use of tools like GGE biplots and AMMI analysis helped visualize the genotype–environment interactions, aiding in the selection of appropriate genotypes for both specific and wide adaptability.

Cassava (Manihot esculenta Crantz) is mainly produced to supplement food security by providing food for smallholder farmers year round. However, its production is constrained by various factors. Thus, the aim of this research was to assess cassava utilization patterns, postharvest handling practices, and the factors that influence productivity. Data were gathered from primary and secondary sources, and a multistage sampling procedure was used to select 200 HHs. A multiple regression model and descriptive statistics were used to analyze the data. The regression model revealed that the education level, family size, land holding size, cropping system, crop rotation, earthing up rate, maturity, variety type, training, and plant population variables were significantly and positively correlated with cassava productivity. This implies that if any of these variables increases, cassava productivity will increase while the other variables remain constant. Topography and pest variables showed a significant negative correlation, indicating that an undulating topography or being attacked by pests could reduce cassava productivity by 60.00. The descriptive statistics results for the utilization proportion showed that 51.87 of the farmers utilized for home consumption, 43.68 for the market, and 4.26 for animal feed. The consumption pattern indicated that 46.50 was boiled roots, 15.00 was flour cooked, and 38.50 was boiled roots and flour cooked. As postharvest handling practices showed, 10.00 of the farmers immediately processed to powder, 18.00 immediately processed to sliced (chips), 61.00 left them to root in the soil, and 11.00 did nothing. This indicates that the farmers consumption patterns and processing methods are very traditional. Therefore, the study suggested that the farmers different practices should be further supported by research through the generation of multiple food forms, postharvest handling practices, and production technology. Proper attention should also be given to address the identified productivity-influencing factors as well as postharvest handling practices. These could sustain the farming system of the crop and help to increase cassava productivity for smallholder farmers.

It is a fact that the performance of most crop genotypes varies across environments due to genotype‐environment interactions. Sharing knowledge about maize genotypes that are well‐adapted and high‐yielding in specific environments is essential for smallholder farmers to boost their productivity. A field trial was established in three locations during the 2019 and 2020 growing seasons to identify and select better‐adapted high‐yielding maize genotypes for production. A total of 10 improved varieties were evaluated using a randomized complete block design with three replications. The AMMI analysis of variance showed grain yield significantly (p ≤ 0.001) influenced by genotype‐environment interaction. This suggests that different genotypes showed varying grain yield performance across environments, implying the opportunity to choose effective genotypes suitable for both specific and broad adaptability. Moreover, the combined analysis of variance showed that genotype‐environment interaction significantly influenced plant height, ear height, dry biomass yield, and hundred‐kernel weight. The sum of squares factor explained that grain yield variation was contributed by the genotype (71.22%), followed by the environment (16.54%) and the genotype‐environment interaction effect (12.24%) of the total variation. Genotypes BH‐661, BH‐549, and BH‐546 demonstrated superior performance and stability, surpassing the national maize mean productivity and indicating broad adaptability. The AMMI analysis, GSI, and GGE biplot confirmed their robustness and high yield potential, especially for BH‐661 and BH‐549, making them optimal for variable conditions. Therefore, BH‐661 and BH‐549 are selected as stable genotypes for production in all agroecologies suitable for maize in the Dawuro zone.