Prediction of Agronomic Characteristics and Estimation of Head Weight Based on Head Length and Width in Cabbage (Brassica oleraceae) Grown in the Derived Guinea Savannah

Cabbage is a high-value leaf vegetable. Its productivity is determined by soil and climatic factors, biotic and abiotic stresses. Few investigations have been carried out to predict head weight considering the relationship between head weight, head length, and width. Regression analysis is an inferential statistic that can predict plant growth and development. The production and consumption of cabbage in urban and peri-urban locations is increasing. Information on the prediction of head weight using agronomic traits that are directly related to head weight in rainforest-savannah agroecology is limited. The aim of this research was to determine variability for agronomic and head yield traits, quantify the association between head yield and yield component traits, and predict the relationship between head weight (Y) and head length, head width and plant length. Five hybrids were evaluated during 2022 and 2023 in a randomized complete block design with three replications. Data was analysed for variability, correlation, and regression analyses using Statistical Analysis software (2012). Findings showed that the hybrids responded linearly to the environment (year). ‘Montra‘ and ‘Paske‘ performed best for marketable head weight. Montra is promising for head length and width. Phenotypic improvement in head length will enhance head width and marketable head weight. The coefficient for predicting the marketable head weight was 12.22. If the head length and width are known, they are multiplied by 12.22, then the head weight can be predicted. This model is ideal for the prediction of head weight in commercial farms if the length and width are known.