A typology of adopters and nonadopters of improved sorghum seeds in Tanzania: A deep learning neural network approach. (March 2020)
- Record Type:
- Journal Article
- Title:
- A typology of adopters and nonadopters of improved sorghum seeds in Tanzania: A deep learning neural network approach. (March 2020)
- Main Title:
- A typology of adopters and nonadopters of improved sorghum seeds in Tanzania: A deep learning neural network approach
- Authors:
- Kaliba, Aloyce R.
Mushi, Richard J.
Gongwe, Anne G.
Mazvimavi, Kizito - Abstract:
- Highlights: A neural network with back-propagation performed better than the double hurdle model. The head of households dominates the adoption decision-making process. Macia varierty was the most popular. Farmer tailored technology will produce impactful and sustainable results. Abstract: For more than three decades, the direction of agricultural research and extension efforts have been toward developing improved seeds for agricultural transformation in Sub-Saharan Africa. Despite these efforts and substantial investment in physical and human capital, the adoption of improved seeds has remained marginal. One of the factors constraining adoption is limited choices among heterogeneous small-scale farmers often targeted by fit-for-all agricultural technologies. In this paper, we typify small-scale sorghum producers in Tanzania based on the socio-economic characteristics of farmers that include a propensity for adoption and intensity of adoption. The two variables are predicted using a deep learning neural network with back-propagation. The visualization of identified adopters and nonadopters groups is achieved using t-distributed stochastic neighbor embedding. Knowing the typology of farmers is a critical first step when the goal is scaling-up the adoption process through tailored advisory services. Results show that sorghum producers in Tanzania are heterogeneous, and there is a need for developing targeted agricultural innovations and public policies that serve specificHighlights: A neural network with back-propagation performed better than the double hurdle model. The head of households dominates the adoption decision-making process. Macia varierty was the most popular. Farmer tailored technology will produce impactful and sustainable results. Abstract: For more than three decades, the direction of agricultural research and extension efforts have been toward developing improved seeds for agricultural transformation in Sub-Saharan Africa. Despite these efforts and substantial investment in physical and human capital, the adoption of improved seeds has remained marginal. One of the factors constraining adoption is limited choices among heterogeneous small-scale farmers often targeted by fit-for-all agricultural technologies. In this paper, we typify small-scale sorghum producers in Tanzania based on the socio-economic characteristics of farmers that include a propensity for adoption and intensity of adoption. The two variables are predicted using a deep learning neural network with back-propagation. The visualization of identified adopters and nonadopters groups is achieved using t-distributed stochastic neighbor embedding. Knowing the typology of farmers is a critical first step when the goal is scaling-up the adoption process through tailored advisory services. Results show that sorghum producers in Tanzania are heterogeneous, and there is a need for developing targeted agricultural innovations and public policies that serve specific groups of farmers. Since Tanzania agricultural policies are formulated at the national level, there must be room for adjustment by regional and district levels authorities to reflect local demand for services. … (more)
- Is Part Of:
- World development. Volume 127(2020)
- Journal:
- World development
- Issue:
- Volume 127(2020)
- Issue Display:
- Volume 127, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 127
- Issue:
- 2020
- Issue Sort Value:
- 2020-0127-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-03
- Subjects:
- Double hurdle -- Deep learning -- Neural networks -- Sorghum -- Typology -- t-SNE
Economic history -- 1990- -- Periodicals
Economic assistance -- Developing countries -- Periodicals
330.9 - Journal URLs:
- http://www.sciencedirect.com/science/journal/0305750X ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.worlddev.2019.104839 ↗
- Languages:
- English
- ISSNs:
- 0305-750X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9354.150000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20536.xml