The main objective of this thesis was to identify factors that are associated with
The study faces the following limitations:
1) The survey collected data at the household level, and therefore was unable to capture community variables that are also important to consider when modeling adoption. For example, community or village information such as access to electricity, road conditions, and proximity to market were not collected.
2) The probit model used to model adoption only included several explanatory
variables out of the many to be considered. For example, other crucial variables such as access to market and membership in a farmers group were not included due to large missing values in the dataset.
3) Endogeneity problems could affect the estimation of some of the explanatory variables due to selection bias when the primary household data was collected. For example, households choose whether or not to receive financial or extension services. Households who derive significant benefits from these services are more likely to take part in these activities.
5.3.2 Recommendations for Future Research
Generally, further research is needed to collect more complete household-level data from farming households in these areas. This could include additional details on socio-demographic information as well as village or community data to be incorporated into future models. Evidence from this thesis suggests significant regional and technological differences in the adoption of studied technologies. Therefore, continued research is an important way to better understand the nuances of technology adoption for a specific region and/or technology. One suggestions is that in order to get qualitative data on what drives a farmer to adopt or not adopt studies should include trips to local farmer groups, cooperatives, and extension agencies.
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