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Potentials for Cassava Processing in the Littoral Region of Cameroon

Smallholder agriculture is characterized by underemployment during off seasons, low-income earnings and severe post-harvest losses. This study aimed at examining the effects of cassava processing on rural households in the Littoral region of Cameroon; identifying the different processing techniques, the different products derived from transformation, analysing the profitability of the products derived identifying key factors that hinder the downstream development of the cassava sector; and. Data were collected using questionnaires administered to a sample of 140 respondents who were selected through the multistage random sampling technique. Descriptive statistics and budgetary analyses were used to analyse the data. The results from the analysis revealed that, women represent 82.86% of the number of processors and their average age is 44 years. The average household size is 7, while the education level is low; 48.57% had received only primary education and 38.57% were secondary school dropout. Their initial capital came from their personal savings. The following methods of cassava processing amongst others were found in the study area: grating, drying, draining, fermentation, grilling, sieving, extraction, and soaking. Results from the budgetary analysis revealed that, each of the different by- products ′waterfufu′, ′starch′, ′miondo′, ′bobolo′, ′fufu dry′ and ′garri′ generate profit. For ′Bobolo′ the value added is 98 FCFA, ′Miondo′ 95FCFA, starch 90Fcfa, ′waterfufu′ 70Fcfa, ′garri′ 65FCFA and 60 CFA francs for dry ′fufu′. The most profitable product was found to be ′bobolo′, followed by ′miondo′. Based on the various cost/benefit ratios, it is evident that all these products are profitable because the ratios are greater than unity. It was revealed that the cassava sector does not go without difficulties; inadequate equipment, and inadequate training of processors were the main difficulties encountered. The cassava should be industrialized by installing many machines in rural areas and to invest more in training the processors.

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