The Nigerian government's policy on agriculture has supported productivity enhancements among smallholder farmers, yet tomato production is constrained by post-harvest losses leading to over 45 % (750,000 metric tons) loss. Various initiatives are constantly being introduced to make technologies and practices available to reduce these losses. This study was carried out to determine the level of awareness and perception of four technologies. A total of 420 tomato farmers were selected in Kaduna State, Nigeria. Awareness and perception were modelled using the Multivariate Probit Model. The results showed that one or more of the independent variables including cooperative affiliation (p<0.001, for awareness of Reusable Plastic Crate {RP} technique), frequency of extension visit (p<0.001, for awareness of RP), and farm area cultivated (p<0.05, for awareness of Refrigerated Truck {RT}/ Machine Drying {MD}) were significant. For perception, some of the independent variables explored and found significant included multiple sources of information for CS/RT, losses through transit/storage (P<0.01) and the number of technologies adopted (P<0.001) for cheapness; credit access (P<0.001) and farm area (P<0.001) for availability; marital status (P<0.01) and losses through storage (P<0.021) for labour saving perceptions. The awareness and perception of the tomato PHL reduction technologies do not provide common determinants. The study concluded that the communication channels such as Farmer to Farmer, Radio and extension agents (57.9%, 9.3%, 33% for RP, respectively), among others, influenced awareness of the new technologies among farmers. The study recommends the need to drive farmers’ awareness using suitable advocacy channels. A better understanding of constraints that influence farmers' perceptions is important while designing and rolling out technologies.
The Nigerian government's policy on agriculture supports productivity enhancements, yet tomato production is constrained by post-harvest losses of up to over 45%. 420 tomato farmers were selected for study in Kaduna State, Nigeria. Multinomial Logit Model was used to determine factors influencing losses while factors influencing adoption and intensity were modelled using Tobit. The results showed the adoption rate of (new technologies) RP was 3.57%, CS = 0.47%, RT = 0.71%, MD =0.71%, CD = 100%. Adoption rate of (traditional method) raffia basket was 100%. For farmers, the highest source of losses was those in storage (70.5%), followed by farm level (14.5%). Results on factors influencing PHL showed that in transit, Modern Technology accentuated losses (p<0.10), while Car/truck ownership mitigated losses (p<0.01) In storage, Modern Technology (p<0.05), Farm Distance (p<0.05), Farm Size (p<0.10), and Own Car/truck ownership (p<0.10) mitigated losses, while only Multiple Cropping (p<0.05) accentuated losses. In marketing, education (p<0.05), modern technology (p<0.10), multiple cropping (P<0.10), and credit access (P<0.10) accentuated losses while age of farmer (p<0.10), years of technology adoption (p<0.10), farm size (p<0.10), and wealth status of farmer (p<0.05) mitigated losses. The results factors influencing adoption and adoption intensity of PHL-reducing technology show that Education (p<0.05), Age (p<0.10), Extension (p<0.10), CS_Information_Sources (p<0.01), RT_Information_Sources (p<0.01), MD_Information_Sources (p<0.05), Labour_sourcesT (p<0.01), Credit_sourcesT (p<0.10), and Farm_Size (p<0.01) were positive and had a significant influence. Education had a quadratic (Education2) negative influence on adoption of PHL reducing technologies. In conclusion, extension services exposure, large farm holding, and multiple information sources positively influenced adoption of post-harvest loss reduction technologies. The field survey also showed a 100% willingness of the farmers to adopt improved/modern technologies. The study recommended using PPP model to make these modern technologies and farm practices within the financial reach of farmers to mitigate post-harvest losses.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.