Using social system framework for predicting adoption rate of technology in animal farming in Southeast Asia (particularly in Indonesia and Vietnam). Are you interested in technology uptake by animal farmers in Southeast Asia? We are offering a research case in the broader social environment of farmers.
Majority of production in animal farming in Southeast Asia comes from smallholders. These smallholders cannot meet the rising market demand for animal products in the region. If they must stay in the market they need to scale-up. The use of technologies, particularly affordable smart technologies, could allow the farmers to increase their production capacities and product qualities. Yet, the adoption rate of technology is low in Southeast Asia.
This study aims to model social factors affecting farmers’ decision to adopt technologies for smart farming. Research activities will be jointly decided and may include a systematic literature review, ethnographic observations and interviews with farmers and rural stakeholders in the region. This study is part of an interdisciplinary research project on Smart Agriculture in Indonesia supported by INREF. There are possibilities to conduct (part of) the thesis in Indonesia, but this will depend on the corona measures in the Netherlands and Indonesia.
- To investigate the possibility to quantify and model social factors affecting farmers’ decision to adopt technologies for smart farming practices.
- To consider behaviour of farmers in technology adoption model, e.g. pseudo-adopter behaviour.
- To project the use the model for designing transition pathways for adopting smart farming technologies.
The work in this master/bachelor thesis entails:
- Collecting full-text articles or PDFs from SLRs in the technology adoption in animal farming field.
- Assessing the impact of different social factors to technology adoption rate in animal farming.
- Designing and developing a social system model that enables the prediction of technology adoption rate based on baseline found in the scientific literature.
- Pathak, H. S., Brown, P., & Best, T. (2019). A systematic literature review of the factors affecting the precision agriculture adoption process. Precision Agriculture, 20(6), 1292-1316. doi:10.1007/s11119-019-09653-x
- Pierpaoli, E., Carli, G., Pignatti, E., & Canavari, M. (2013). Drivers of Precision Agriculture Technologies Adoption: A Literature Review. Procedia Technology, 8, 61-69. doi: https://doi.org/10.1016/j.protcy.2013.11.010
- Required skills/knowledge: basic data analytics, interest about sustainable agriculture and social systems
Key words: Computational Social Simulation, Data analytics, Information Systems, Quantitative Social Science, Simulation Modelling, Social Mechanics.