Assessment of factors influencing adoption of tomato post-harvest loss-reduction technologies in Kaduna state, Nigeria

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Nnaemeka Korie
Dr. Lucy Karega Njeru
Prof. John Mburu
Prof. Gitau George Karuoya

Keywords

adoption, adoption rate and intensity, Multinomial Logit Model, Post-harvest losses, technologies, Tobit Regression Model

Abstract

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.

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