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
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
PDFAuthors: Taiwo Olawale Adeniji, Tosin Oluyemi Ibidiran, Olutayo, Goodness and Emmanuel Oluwaseyi Ayenigba
Volume/Issue: Volume 28: Issue 1
Published online: 19 May 2025
Pages: 36 - 44
Abstract
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.
Keywords: cabbage, marketable head weight, character association, head length, head width and prediction equation
References
Adelanwa, E. B., & Medugu, J. M. (2015). Variation in the nutrient composition of red and green cabbage (Brassica oleracea) with respect to age at harvest. Journal of Applied Agricultural Research, 7, 183–189.
Adeniji, O. T., Swai, I., Oluoch, M. O., Tanyongana, R. & Aloyce, A. (2010). Evaluation of head yield and participatory selection of horticultural characters in cabbage (Brassica oleracea var. capitata [L.]). Journal of Plant Breeding and Crop Science, 2(8), 243–250.
Getachew, A. S., Muleke, E. M., Saidi, M., Itulya, F. M., Martin, T., & Ngouajio, M. (2022). The assessment of the use of eco-friendly nets to ensure sustainable cabbage seedling production in Africa. Agronomy, 3, 1–12.
Kenneth, V. A. (2012). Evaluation of the performance of three cabbage verities. Gladstone Agricultural Centre, Crop research report, 8, 23–29.
Kibar, B., Karaağaç, O., & Kar, H. (2014). Correlation and path coefficient analysis of yield and yield components in cabbage (Brassica oleracea var. capitata L.). Acta Scientiarum Polonorum Hortorum Cultus, 13(6), 87–97.
Laczi, P. S. R.; Nóbrega, L. H. P., Alves, L. F. A., Cruz-Silva, C. T. A., & Pacheco, F. P. (2016). The insecticidal potential of Foeniculum vulgare Mill, Pimpinella anisum L. and Caryophillus aromaticus L. to control aphid on kale plants. Review Brazilian Plant Medicine, 17, 585–591. https://doi.org/10.1590/1983-084X/14_036
Mwasha, A. M. (2000). Status of vegetable production in Tanzania In: Chada, M.L., Nono-Womdim, R., Swai, I. (Eds). Proceedings of the Second National Vegetable Research and Development Planning Workshop held at HORTI-Tengeru, Arusha, Tanzania, and 25–26 June 1998 (pp. 22–27). AVRDC.
Obidola, S. M., Ibrahim, I. I., & Agwom, R. Z. (2019). Influence of organic manure and inorganic fertilizer on the growth, yield and phytochemical constituents of cabbage (Brassica oleracea). Asian Journal of Agricultural and Horticultural Research, 4(1), 1–9. https://doi.org/10.9734/AJAHR/2019/V4I130012
Olfati, J. A., Peyvast, G. H., Shabani, H., & Nosratie, R. (2010). An estimation leaf area in cabbage and Broccoli using nondestructive Methods. Journal of Agricultural Sciences Technology, 12, 627-632.
Sampson, K. (2010). Groundnut varietal response to spacing in the guinea savannah and forest zones of Ghana. A thesis submitted to the Department of Crop and Soil Sciences, Faculty of Agriculture, College of Agriculture and Renewable Natural Resources, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana.
SAS Institute (2012). SAS system for windows. Release 9.4. SAS Institute Inc. Cary, North Carolina, USA.
USDA (2022). National Nutrient Database for Standard Reference, Legacy Release. Nutrient Data Laboratory, Beltsville Human Nutrition Research Center, ARS, USDA. https://doi.org/10.15482/USDA.ADC/1529216
Williams, P.H. (1985). Black rot: a continuing threat to World Crucifers. Plant Dis., 64, 736–742.