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Quantitative variation and interrelationship between factors influencing cassava yield
Abstract/Description
Objective: Cassava is a crop with largely unexplored and unexplained potentially valuable genetic variability. This study aimed to investigate interrelationship between factors that control cassava yield. Methodology and results: Fourteen quantitative characteristics were assessed in ten cassava genotypes at three locations (Namulonge, Bulisa and Kapchorwa) during two seasons in Uganda. Highly significant (P<0.001) influence of the environment and genotype by environment interactions were observed in most of the plant traits evaluated. Broad sense heritability was relatively moderate for storage roots and dry matter content (h2=0.39 and 0.56, respectively) while it was high for petiole length (h2=0.82). The most productive in storage root yield was clone Migyera at all locations during the first season. Storage root performance decreased during the second season and clones SS4, TMS82/01635 and TMS I 91/0057 led at Namulonge, Bulisa and Kapchorwa, respectively. Dry yield production per clone was high at Bulisa in season one while it was high at Namulonge during second season. Phenotypic correlations were significant between dry root yield with storage root number (r=0.53, p<0.001), storage root size (r=0.37, p<0.001), storage root girth (r=0.54, p<0.001), stem girth (r=0.38, p<0.05), leaves and stems biomass (r=0.38 and 0.58, p<0.05, respectively). The leaf area, petiole length, storage root number, root size, root girth, stem weight, and starch content gave the best equation for yield prediction (R2=0.69, C (P) =5.6). Conclusion and application of findings: Indirect path analysis revealed that selection of high potential clones could be achieved based on storage root number, storage root size and storage root diameter as the main yield components contributing factors to yield enhancement in cassava, and could be used as selection criteria for higher storage root yield potential. Results from multiple regression and path analysis suggested, however, that the model does not fully explain the complex interrelationship of factors determining cassava yield and this will need additional research to understand better yield factors.