Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/240231 
Erscheinungsjahr: 
2018
Quellenangabe: 
[Journal:] Agricultural and Food Economics [ISSN:] 2193-7532 [Volume:] 6 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2018 [Pages:] 1-21
Verlag: 
Springer, Heidelberg
Zusammenfassung: 
Low adoption of agricultural technology is among the main reasons for low farm productivity and high incidence of poverty and food insecurity in sub-Saharan countries including Tanzania. In this study, we examine the factors affecting adoption of improved sorghum varieties using data from 822 randomly selected sample households in northern and central Tanzania. We employ a multiple-hurdle Tobit model to assess the factors affecting adoption after controlling for both capital and information constraints. We also use t-distributed stochastic neighbor embedding to cluster farmers into homogeneous groups. The method allows to reduce the dimensionality while preserving the topology of the dataset, which increases the clustering accuracy. It also superiors for visualization of the clustering results. Results show that radio and other mass media outlets that create awareness will increase adoption among farmers who do not face capital constraint. Some farmers lack basic resources such as land and capital, and subsidies could have a high impact on these farmers. Other farmers simply need assurance on the performance of improved sorghum varieties. Field days, on-farm trials, and demonstration plots could be useful in supporting these farmers. A tailored support system, however, needs a sustained investment in both quantity and quality of services. There is therefore a need to develop a pluralistic research and extension systems that encourage the use of information technologies and community-based organizations to reach specific groups of farmers.
Schlagwörter: 
Adoption
Multiple-hurdle Tobit
Sorghum
t-SNE
Two-step cluster analysis
Tanzania
JEL: 
O33
Q16
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article

Datei(en):
Datei
Größe
1.35 MB





Publikationen in EconStor sind urheberrechtlich geschützt.