Determinantes del conocimiento y uso de técnicas de procesamiento climáticamente inteligentes entre agroindustrias pequeñas y medianas en Sierra Leona: Un análisis de regresión logística binaria

Abstract

This paper examines the determinants of awareness and use of climate smart processing techniques (CSAPT) among small medium agro-processors in Sierra Leone, based on the premise that agro-processing activities contribute significantly to greenhouse gas emissions (GHG) but with limited empirical evidence. CSAPT represents technologies used in postharvest processing that reduce GHG emissions. A descriptive research design and random sampling was used to select 134 out of 150 available agro-processors, from which data were collected. Awareness was measured on a 3-point scale of not aware (1), aware but not adopted (2), and adopted (3), which was pooled into total scores and transformed to binary coding for awareness and use respectively. The data were analyzed with Statistical Package for Social Sciences Version 30, using frequency counts, percentages, and Binary logistic regression. The models for the determinants of awareness and use of climate smart processing techniques show that variables included in the model were good predictors (Negelkerke R2=0.593 and 0.826) of factors that influence the awareness and use of CSAPT by 59% and 83% respectively. The results of the binary logistic regression showed that the common significant predictors of awareness and use of climate-smart processing techniques are gender, access to financial support, type of products, number of agro-processing activities, total attitude score on CSAPT. Majority of the agro-processors have low awareness and low level of use of climate-smart agro-processing techniques. The policy implications of the findings are that targeted training programs and financial support schemes would improve the use of CSAPT among agro-processors.