ORGANIC MANURE ADOPTION AND ITS IMPLICATIONS FOR REDUCING POSTHARVEST LOSSES IN NIGERIA’S AGRIBUSINESS VALUE CHAIN
Keywords:
Organic, Manure, Adoption, Decision, Production, BottlenecksAbstract
This study evaluated the empirical determinants of organic manure adoption, farm-level production bottlenecks, and value-chain postharvest loss (PHL) implications among smallholder farmers in Gwer East Local Government Area of Benue State, Nigeria. Utilizing a cross-sectional survey design, baseline primary data were collected from a representative sample of 189 registered smallholders (n =189). Analytical methods included descriptive statistics, Recursive Decision Tree Modeling, and Parametric Analysis of Variance (ANOVA). The descriptive results revealed a distinct knowledge-practice gap: while spatial crop rotation achieved universal implementation (100.0%), advanced biomass composting was adopted by only a minority (29.1%) of the population. The Decision Tree recursive outputs established that smallholder choices to adopt organic inputs are primarily driven by expected economic profit margins (59.3%) and anticipated pre-harvest crop durability (40.7%). Concurrently, the parametric variance analysis yielded a statistically non-significant difference in crop yields across different animal manure types (p = 0.626), confirming high input substitution flexibility for producers encountering localized supply shocks. Investigation into supply-chain constraints isolated manual, labor-intensive application as the principal farm-level bottleneck (51.6%), followed by prohibitive procurement-logistics costs (34.7%) and perceived biological handling hazards (13.7%).Based on these findings, the study recommends that the Benue State agricultural extension network deploy localized technical training on standardized composting and introduce affordable, low-cost mechanical spreading tools to mitigate labor constraints and minimize value-chain postharvest losses.
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Copyright (c) 2026 Nigerian Journal of Agriculture and Agricultural Technology

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a CC Attribution-NonCommercial-ShareAlike 4.0

