Information on functional relationships among yield components will enhance research efforts in breeding for high yield in aromatic peppers. The aim of this study was to develop models for predicting fresh fruit yield in aromatic Capsicum annuum through multiple linear regression analyses. Ten genotypes of aromatic pepper were evaluated for three years in the Faculty of Agriculture farm, University of Nigeria Nsukka in a randomized complete block design (RCBD). Yield components that had strong and significant correlation coefficients were regressed to establish relational functions with fruit yield. The predicted and the actual yield values were tested for significance using t statistic. Fruit yield could be predicted using the combined effects of number of nodes per plant, number of leaves per plant and number of fruits per plant with 87.6% accuracy in the 3 year combined analysis. Linear regression for the single effects of each of the yield components were also used to predict fruit yield. The models developed could predict fruit yield in C. annuum with 62.5, 61.7, and 57.2% accuracy using any one of these yield components, number of nodes, number of leaves and number of fruits, respectively. The combined effects gave higher predictive value than the single effects of the traits. The models developed were validated by extrapolating the values and comparing with actual yield data. There were no significant differences between the predicted and actual yield values. Produced model could, therefore, be used in predicting fresh fruit yield in C. annuum. Inferences drawn from the functions developed were discussed as they affect breeding for high yield in aromatic peppers.
Key words: Breeding, multiple regressions, predictive value, relational functions, yield components.
Copyright © 2023 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0