Cassava is an important root crop in sub-Saharan Africa, largely cultivated for its starchy edible roots. Biofortified cassava varieties with enhanced provitamin A carotenoid content (PVAC) developed through conventional breeding provide a solution for vitamin A deficiency among vulnerable communities. The aim of this study was to use diallel analysis of six provitamin A cassava genotypes to determine the combining ability, genetic components, heritability, and heterosis of the most important yield characteristics and total carotenoids. Genetic variability for measured characteristics were evident. Fresh root yield was mainly determined by non-additive genetic effects, while dry matter content and total carotenoids were determined by additive effects. Total carotenoids were negatively correlated with fresh root yield, indicating that selection for higher provitamin A content could reduce yield. Mid and higher parent heterosis was seen in some of the crosses for fresh root yield, dry matter content, and total carotenoids. Narrow sense heritability was moderate for fresh root yield and dry matter content, and was high for total carotenoids. This study indicated that yield and dry matter content can be improved in provitamin A cassava but that increased provitamin A content may carry a yield penalty.

A validated marker system is crucial to running an effective genomics-assisted breeding program. We used 36 Kompetitive Allele-Specific PCR (KASP) markers to genotype 376 clones from the biofortified cassava pipeline, and fingerprinted 93 of these clones with DArTseq markers to characterize breeding materials and evaluate their relationships. The discriminating ability of the 36-quality control (QC) KASP and 6602 DArTseq markers was assessed using 92 clones genotyped in both assays. In addition, trait-specific markers were used to determine the presence or absence of target genomic regions. Hierarchical clustering identified two major groups, and the clusters were consistent with the breeding program origins. There was moderate genetic differentiation and a low degree of variation between the identified groups. The general structure of the population was similar using both assays. Nevertheless, KASP markers had poor resolution when it came to differentiating the genotypes by seed sources and overestimated the prevalence of duplicates. The trait-linked markers did not achieve optimal performance as all markers displayed variable levels of false positive and/or false negative. These findings represent the initial step in the application of genomics-assisted breeding for the biofortified cassava pipeline, and will guide the use of genomic selection in the future.

This study evaluated the yield stability and environmental responsiveness of 42 pro-vitamin A cassava genotypes across multi-season trials using Finlay-Wilkinson (FW) regression and trait-based clustering approaches. Regression parameters-intercept and slope were used to quantify baseline yield potential and sensitivity to environmental variation, respectively. Hierarchical and k-means clustering grouped genotypes into three biologically distinct clusters with clear agronomic relevance. Cluster 2 genotypes exhibited moderate responsiveness and positive yield baselines, indicating broad adaptability and suitability for regional deployment. Cluster 3 showed high environmental sensitivity but low yield potential, suggesting limited resilience under marginal conditions. Cluster 1 comprised highly responsive genotypes with poor baseline productivity, reflecting unstable performance and strong genotype × environment interaction. One-way ANOVA confirmed significant differences among clusters for both slope (F(2,39) = 40.89, p < 0.001) and intercept (F(2,39) = 102.10, p < 0.001), validating the clustering structure. The dendrogram confirmed the cluster structure and provided a basis for selecting key genotypes. The results inform environment-specific breeding strategies and emphasize the importance of integrating multiple traits into future clustering approaches to improve cultivar selection accuracy.

Improving provitamin‑A cassava requires a clear understanding of how key agronomic and nutritional traits respond to seasonal variation and how these traits interact within a multivariate structure. The objectives were to evaluate agronomic and nutritional traits across seasons, apply multivariate analyses to uncover trait domains, and investigate inter‑trait relationships to guide breeding strategies. Forty‑two provitamin‑A cassava accessions were evaluated using a split‑plot randomized complete block design, with genotype as the main‑plot factor and harvest time as the subplot factor, replicated across seasons. Eight traits were measured, with emphasis on DM, FYLD, and TC. Multivariate analyses provided deeper insight into trait structure. Principal component analysis and factor analysis identified distinct trait structures: PCA revealed four trait domains, while factor analysis uncovered three latent trait groupings–Root Productivity (ML2), Vegetative and Compositional Diversity (ML3), and Harvest Efficiency (ML1). Traits such as RTWT and HI exhibited near‑zero uniqueness, reinforcing their roles as anchor indicators of productivity and efficiency, while TC displayed exceptionally high uniqueness, confirming its independence from yield domains and its regulation by distinct metabolic pathways. Biplots highlighted genotype dispersion and trait loadings, hierarchical clustering grouped accessions by combined agronomic and nutritional performance, and a chord diagram confirmed a tightly linked yield complex alongside a separate nutritional domain. Variance component estimates revealed contrasting levels of genetic and environmental influence. DM showed moderate genotypic variance and no significant seasonal effect, underscoring its stability and reliability for processing quality. FYLD exhibited low genotypic variance, high residual variance, and a highly significant season effect, confirming strong environmental sensitivity. TC displayed high genotypic variance and a significant seasonal effect, suggesting that carotenoid accumulation is both genetically controlled and environmentally modulated. The integration of mixed‑model variance partitioning with multivariate and network‑based analyses revealed clear differences in trait stability, genetic control, and inter‑trait relationships. These findings identify DM as a robust trait for selection, FYLD as highly environment‑dependent, and TC as a promising target for biofortification with manageable environmental sensitivity. The results provide a comprehensive model for index‑based selection strategies, guiding breeders toward “stacked” ideotypes that combine high yield potential, stable dry matter content, enhanced carotenoid accumulation, and efficient resource allocation to meet industrial, nutritional, and food security needs across cassava‑growing regions.

This study evaluated the yield stability and environmental responsiveness of 42 pro-vitamin A cassava genotypes across multi-season trials using Finlay-Wilkinson regression and trait-based clustering approaches. Regression parameters-intercept and slope were used to quantify baseline yield potential and sensitivity to environmental variation, respectively. Hierarchical and k-means clustering grouped genotypes into three biologically distinct clusters with clear agronomic relevance. Cluster 2 genotypes exhibited moderate responsiveness and positive yield baselines, indicating broad adaptability and suitability for regional deployment. Cluster 3 showed high environmental sensitivity but low yield potential, suggesting limited resilience under marginal conditions. Cluster 1 comprised highly responsive genotypes with poor baseline productivity, reflecting unstable performance and strong genotype × environment interaction. One-way ANOVA confirmed significant differences among clusters for both slope (F(2,39) = 40.89, P < 0.001) and intercept (F(2,39) = 102.10, P < 0.001), validating the clustering structure. Dendrogram profiling reinforced these classifications, offering a quantitative framework for genotype prioritization. The findings support strategic breeding decisions tailored to agroecological contexts and highlight the need for multi-trait integration in future clustering models to enhance cultivar deployment precision.