Data for: Unlocking Ugandan pumpkin landrace diversity: Integrated morphological and nutritional profiling for food security and breeding innovation

Published: 18 February 2026| Version 1 | DOI: 10.17632/wrpr89yc5k.1
Contributors:
Fred Masika,
,
,
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Description

This dataset comprises comprehensive measurements of fruit, seed, and quality traits in pumpkin landrace accessions, alongside biochemical analysis of total phenolic content (TPC). Structural fruit traits recorded include length, diameter, shape ratio (L/D), flesh thickness, flower scar diameter, weight, neck length, and volume. Quality traits encompass fruit shape, curvature, stem-end and blossom-end profiles, groove presence, skin color (primary/secondary, intensity), waxiness, surface warts, and flesh color. Seed traits were assessed for length, width, and color. Phenotypic diversity across sub-regions was characterized using descriptive statistics (mean, standard deviation, range, median, coefficient of variation), frequency distributions, and proportional analyses. Measurements were obtained with precision digital calipers (±0.01 mm) and calibrated digital bench scales (±0.1 g). Color determination followed the Royal Horticultural Society (RHS) colour chart (6th edition), with harmonized descriptors for consistency across landraces. Biochemical evaluation included quantification of total phenolic content (TPC) in pumpkin mesocarp extracts using the Folin–Ciocalteu colorimetric assay. Absorbance was measured at 765 nm with a UV-Vis spectrophotometer, and results expressed as gallic acid equivalents per kilogram of fresh weight (mg GAE/kg FW). This dataset provides a robust resource for studies on phenotypic diversity, trait correlations, and nutritional quality in pumpkins, supporting crop improvement, germplasm characterization, and sustainable production strategies.

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Steps to reproduce

Study Area & Sampling Select pumpkin-growing districts across Uganda’s agroecological regions (Central, Elgon, East Central, Western, South Western, West Nile, Acholi, Tooro, Bunyoro). Collect physiologically mature fruits (either from farmers’ fields or local markets). Ensure fruits are harvested at peak season and stored ≤7 days before sampling. Record landrace ID, district, market, farmer (if available), and georeference each sample. Morphological Characterization Use ECPGR (2008) descriptors for Cucurbita spp. Measure structural traits: fruit length, diameter, shape ratio (L/D), flesh thickness, flower scar diameter, fruit weight, neck length, fruit volume. Record quality traits: fruit shape, curvature, stem/blossom-end profiles, grooves, skin color (primary/secondary, intensity), waxiness, warts, flesh color. Assess seed traits: length, width, color. Tools: precision digital calipers (±0.01 mm), digital bench scale (±0.1 g), RHS colour chart (6th edition). Replication: 3 fruits per landrace, technical triplicates. Sample Preparation for Nutritional Analysis Collect mesocarp portions (excluding rind and seed cavity). Rinse, slice (~1 cm thick), oven-dry at 60 °C until constant weight. Mill into fine powder; store at −20 °C in airtight, light-proof containers. Proximate Nutritional Composition Dry matter & moisture: gravimetric method (AOAC 934.01). Lipids: Soxhlet extraction with petroleum ether (AOAC 920.39). Protein: Kjeldahl method (AOAC 978.04), conversion factor 6.25. Fiber: sequential acid-alkali digestion. Ash: dry ashing at 550 °C. Carbohydrates: calculated by difference. Sugars: Lane-Eynon titrimetric method. Bioactive Compounds & Antioxidant Activity Total Phenolic Content (TPC): Folin–Ciocalteu assay, absorbance at 765 nm, expressed as mg GAE/kg FW. Antioxidant Activity: DPPH radical scavenging assay, absorbance at 517 nm, % inhibition calculated. Data Analysis Software: R (v4.5.1). Tests: Shapiro-Wilk (normality), Levene’s (homogeneity). Analyses: Descriptive statistics (mean, SD, range, CV). ANOVA (or Kruskal-Wallis if assumptions violated). Tukey’s HSD for post-hoc comparisons. Pearson correlations (trait interrelationships). PCA for trait variation structure. Hierarchical clustering (Ward’s linkage, Euclidean distance). Visualization: ggplot2, corrplot, factoextra, sf, rnaturalearth.

Institutions

Categories

Horticulture, Nutrition, Crop Diversification

Funders

  • Muni University Research and Innovation fund, Government of the Republic of Uganda
    Grant ID: MUNIRIF0218

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