Trait-Based Clustering and Environmental Responsiveness of Pro-Vitamin A Cassava Genotypes via Finlay-Wilkinson Regression

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.