Application of MANOVA for simultaneous trait-based optimization ofharvest and ripening stages in pumpkin (Cucurbita moschata)
DOI:
https://doi.org/10.24154/jhs.v21i1.4639Keywords:
MANOVA, principal component analysis, pumpkinAbstract
This study utilized a multivariate analysis of variance (MANOVA) approach to identify the optimal harvest stage and post-harvest ripening treatments for pumpkin (Cucurbita moschata) to maximize seed yield and quality. Twelve treatments as a combination of three harvesting stages (25, 35, and 45 days’ post-pollination) and four post-harvest ripening durations (0, 10, 20, and 30 days) were analyzed in a randomized block design. Data were collected on seven seed yield traits i.e. total seed weight, number of filled and empty seeds, 100-seed weight, total seeds per fruit, seed yield per plant, and seed yield per plot. Principal component analysis (PCA) addressed multi-collinearity among traits by reducing dimensionality and identifying key contributors to variability. MANOVA revealed significant effects of treatments on the traits with treatment 12 (fruit harvested at 45 days after pollination and stored for 30 days) ranked as the most effective based on PCA-derived composite scores integrating all traits. This integrated statistical analysis highlights the potential to optimize agricultural practices for improved seed yield and quality in pumpkin cultivation.
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Chakravarty, H. L., & Kore, D. K. (1979). Role of seed structure in cucurbit classification. Journal of Cytology and Genetics, 14, 323–329.
Kumar, V., Tomar, B. S., Kaddi, G., & Kumar, S. (2014). Effect of stage of harvest and post-harvest ripening of fruits on hybrid seed yield and quality in pumpkin (Cucurbita moschata). Indian Journal of Agricultural Sciences, 84(6), 737–741. https://doi.org/10.56093/ijas.v84i6.41473
Madaan, T. R., & Lal, B. M. (1984). Some studies on the chemical composition of cucurbit kernels and their seedcoats. Plant Foods for Human Nutrition, 34(1), 81–86.
Oehlert, G. W. (2000). A first course in design and analysis of experiments. W. H. Freeman.
Ongala, J., Mwanga, D., & Nuani, F. (2016). On the use of principal component analysis in sugarcane clone selection. Journal of the Indian Society of Agricultural Statistics, 70(1), 33–39.
Porter, H. F., & O'Reilly, P. F. (2017). Multivariate simulation framework reveals performance of multi-trait GWAS methods. Scientific Reports, 7, Article 38837. https://doi.org/10.1038/srep38837
Singh, R., Singh, A. P., Mishra, R. K., & Singh, S. N. (2016). Effect of sowing dates and harvesting on fruit setting, fruit traits, seed yield and profitability of cucumber. Economics, Environment and Conservation, 22(2), 905–909.
Wellbaum, G. E. (1999). Cucurbit seed development and production. HortTechnology, 9(3), 341–348.
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Copyright (c) 2026 S RANEBENNUR, R Venugopalan, K Padmini, H K Ranganath, P Sisira (Author)
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