Browsing by Author "Kaingo, Jacob"

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Adoption of processing technologies and innovative food preservation techniques: findings from smallholders in the Lindi Region in Tanzania
(Frontiers in Sustainable Food Systems, 2024) Sarr, Malika; Majili, Zahra; Khalili, Niloofar; Matavel, Custodio E.; Mbwana, Hadijah A.; Kaingo, Jacob; Löhr, Katharina; Rybak, Constance
Adopting processing technologies and innovative food preservation is crucial for improving the food security and nutritional status of rural populations in Tanzania and other countries in the Global South. However, low adoption rates among smallholders highlight the need for a better understanding of farmers’ decision-making processes. The aim of this study is to examine extrinsic and intrinsic factors influencing smallholders’ decision-making processes in the adoption of innovative food processing and preservation techniques (specifically, pigeon pea flour-based products, threshers, dehullers) in Mitumbati and Mibure in the Lindi Region in Tanzania. Primary data on 555 farm households were collected using a standardized survey. Extrinsic influential factors were analyzed using binary logistic regression analysis. The results on internal decision-making are based on an analysis of barriers and motivations identified by farmers in relation to the uptake of the different innovations. Training and awareness emerged as the most significant factors positively associated with the adoption of all innovative processing and preservation techniques. Moreover, the results show that the primary drivers for smallholders in the study region to adopt innovative technologies were the potential health benefits and time savings they offered. The main challenge they faced was a lack of knowledge about the innovations. The results indicate that disseminating knowledge is crucial for the successful adoption of innovative processing technology in the study region. Improving and expanding training programs to be more inclusive can help to create incentives and overcome barriers, leading to increased adoption.
Prediction of soil moisture-holding capacity with support vector machines in dry subhumid tropics
(Hindawi, 2018-07) Kaingo, Jacob; Tumbo, Siza D.; Kihupi, Nganga I.; Mbilinyi, Boniface P.
Soil moisture-holding capacity data are required in modelling agrohydrological functions of dry subhumid environments for sustainable crop yields. However, they are hardly sufficient and costly to measure. Mathematical models called pedotransfer functions (PTFs) that use soil physicochemical properties as inputs to estimate soil moisture-holding capacity are an attractive alternative but limited by specificity to pedoenvironments and regression methods. This study explored the support vector machines method in the development of PTFs (SVR-PTFs) for dry subhumid tropics. Comparison with the multiple linear regression method (MLR-PTFs) was done using a soil dataset containing 296 samples of measured moisture content and soil physicochemical properties. Developed SVR-PTFs have a tendency to underestimate moisture content with the root-mean-square error between 0.037 and 0.042 cm 3 ·cm −3 and coefficients of determination (R 2 ) between 56.2% and 67.9%. The SVR-PTFs were marginally better than MLR-PTFs and had better accuracy than published SVR-PTFs. It is held that the adoption of the linear kernel in the calibration process of SVR-PTFs influenced their performance.