Analysis of Effects of Adoption of Post-harvest Loss Reduction Technologies on the Welfare of Rural Tomato Smallholder Farmers in Kaduna, Nigeria

Abstract

Increasing local agricultural productivity and bridging import gaps have been a recent concern for the Nigerian government and many developmental institutions. Given the Government's commitment to attaining food self-sufficiency and improving livelihoods, it is imperative to understand the various food chains, especially commodities where the country has a comparative advantage such as tomatoes. Tomato however faces significant challenges due to post-harvest losses and pest-related issues. These losses in Nigeria account for over 45 percent of production, necessitating tomato imports to meet the rising demand. Various technologies have been developed and reported to be available to farmers to mitigate the high levels of post-harvest losses (PHLs) in tomatoes in Nigeria. Still, the effect of these technologies on their welfare and what influences their adoption is highly understudied. This study was designed to analyse the effects of the adoption of tomato post-harvest loss reduction technologies on households’ welfare among rural smallholder farmers in Kaduna state, Nigeria. The specific objectives of the study were to analyse the effects of selected information and communication channels on awareness of tomato post-harvest loss-reduction technologies (PHLRTs); to assess the influence of selected factors on adoption of PHLRTs, and to analyse the effects of the adoption of tomato PHLRTs on household wealth status. The study sampled 420 tomato farmers in Kaduna State using a structured questionnaire. Multivariate Probit Regression (MVP) model was used to model the effects of selected information and communication channels on awareness of tomato PHLRTs. Factors influencing adoption and intensity were modelled using Probit. Logit was applied in modelling the effects of the adoption of tomato post-harvest loss-reduction technologies on household income and welfare, where wealth and poverty indexes were determined for adopters and non-adopters of the technologies. The findings indicated that at least one of the independent variables, such as cooperative affiliation (for knowledge of the Reusable Plastic Crate {RP} technique), frequency of extension visit (for knowledge of RP), and farm area cultivated (for knowledge of the Refrigerated Truck {RT} and Machine Drying {MD}), had a significant impact. The awareness of modern technologies was influenced by several communication channels, including Farmer to Farmer, Radio, and extension agents. The Multinomial Regression model revealed that employing one or more post-harvest loss reduction technologies, selling at the farm gate, owning a radio or TV, and cooperative membership (not statistically significant) contributed to reduced losses across transportation, storage, and marketing categories. The Probit regression model for adoption demonstrated that cooperative membership, higher tomato production, multiple information sources for RPCs and cold storage; and selling in urban markets significantly influenced technology adoption. By employing a Logit regression approach, the study assessed the effect of technology adoption on a derived wealth index, alongside key welfare xiii indicators such as income diversification (non-farm income) and creditworthiness (credit access). The research revealed that adopters exhibit lower poverty headcount, depth and severity compared to non-adopters. The Logit model indicates a positive effect of PHL technology adoption on household wealth status, the significance levels varied but were notably highly significant for rate of adoption, credit access and non-farm income. Overall, the research revealed that adopters had higher education, farming experience, credit access, and cooperative membership. Adopters also displayed favourable changes in asset ownership, indicating increased wealth status and reduced poverty depth and severity. The findings of this study are particularly useful to policymakers and developmental organizations to capitalize on the various factors found to influence adoption, including training, extension exposure, targeting of young individuals and women for access financing, smart subsidies, efficiently utilizing and improving the available information and communication channels for improved coverage. Prioritization of tomato and PHLR in agricultural strategic documents and budgeting should not be overlooked.