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Digital Agriculture: How diffusion of innovations occur from planning to adoption - Understanding how digital technologies, data science and remote sensing can lead to the transfer of agricultural innovation


School of Geosciences

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Dr G Watmough No more applications being accepted Funded PhD Project (UK Students Only)

About the Project

Project background
New technologies and approaches are required if the planet is to achieve the Sustainable Development Goals (SDGs). Policies and approaches to dealing with local trends (declining yields) and shocks (pests, diseases, erratic climate) need to be co-created using local indigenous knowledge, data and models. This data is needed at increasingly fine spatial and temporal resolutions. Decision makers and scientists need to be able to feed important information to farmers on expected impacts from climate models, new farming practices and seed varieties. However, farmers need to be able to trial these and feed-back information on local contextual issues that will render some practices and seeds inappropriate. In 2018, NCDO (formerly DfID) launched its Digital Strategy to increase the capabilities of digital technology for development. This project seeks to contribute to this strategy and will utilise the unique connections that the CASE Partner (Appendix 2) has with local organisations in Indonesia, Kenya and East Africa as well as the NCDO Data Science Campus in Scotland to explore how innovations in agricultural practices can be shared to co-create new knowledge on how to best deal with erratic climate. The project falls under the Living with Environmental Change Theme (LWEC). It will involve interdisciplinary research on how information and communications technologies can be developed to create a two way pipeline of data and information to deal with future climatic and environmental changes that present challenges for rural farmers and decision makers.

Research questions
- How are innovations in agricultural practices currently transferred between stakeholders?
- What are the processes involved in introduction, adoption and scaling up of digital tools in agriculture? & what are the challenges involved in introducing digital tools in agriculture?
- What are the socio-cultural dynamics involved in adoption of digital tools in agriculture?
- How geospatial techniques and big data can be used to develop innovative diffusion practices?

Methodology
Digital Agriculture is fast emerging, but is it about the technology, or the processes and who benefits and who gets left behind? How do we adopt the right technologies in an inclusive manner? This joint project between NIRAS/LTS Data Futures Hub and UoE will answer these questions. The student will identify with the supervisor team and in collaboration with the CASE partners and the data Futures Hubs in Indonesia and Kenya which locations, agricultural system(s) and innovations on which to focus. The project will involve interdisciplinary research into agricultural innovations which will involve local indigenous knowledge systems, precision agriculture, data science and remote sensing. The focus will be on identifying technologies that can be implemented to help the co-creation of information rather than a linear top-down or bottom-up approach.

- Year 1: identify focal point of the research project in collaboration with LTSI, Data Futures Hub and DFID Data Science Campus in East Kilbride. Research into existing agricultural planning and adoption practices in the selected regions.
- Year 2: field work to examine local indigenous knowledge, how this is created and shared in the local area and if it is fed forward to policy making bodies. Focus also on how regional and national level policies are developed and communicated. Theory of Change will be used to co-create aims.
- Year 3: Analysis of the data collected and a prototype of information dissemination developed and tested. This could involve developing with the CASE partner a platform that can take a range of geospatial data (land use, soil quality, rainfall, temperature) and utilised by a range of users from local farmers, land owners, extension workers to regional and national policy makers.

Training
A comprehensive training programme will be provided comprising both specialist scientific training and generic transferable and professional skills. Further training will be provided in co-creation of knowledge, measuring rural poverty and wellbeing. Methods based training will be provided on qualitative and quantitative field methods, remote sensing, geographic information systems.

Requirements
This project would suit a student with an ability to work in an interdisciplinary environment and learn new methods. It would suit students with a wide range of backgrounds, including but not limited to social sciences, informatics, environmental science, agricultural backgrounds, ecology and geography. Students with interests in international development and digital tech in supporting rural livelihoods.

Funding Notes

Eligibility and funding: UK residents only. This studentship is funded by the EPSRC DTP and by LTS international Ltd and covers fees at UK level, 42 months of stipend and research expenses. PLEASE FOLLOW THE INSTITUTION LINK TO APPLY


References

- Lwoga, E.T., Ngulube, P., Stilwell, C. (2014) Managing indigenous knowledge for sustainable agricultural development in developing countries: Knowledge management approaches in the social context, International Information & Library Review, 42(3).
- Cummings et al. (2017) Critical discourse analysis of perspectives on knowledge and the knowledge society within the Sustainable Development Goals, Development Policy Review DOI: 10.1111/dpr.12296
- FAO (2019) Co-creation and sharing of knowledge: agricultural innovations respond better to local challenges when they are co-created through participatory processes, http://www.fao.org/agroecology/knowledge/10-elements/co-creation-knowledge/en/
- Jerven , M. Benefits and costs of the data for development targets for the Post-2015 Development Agenda. United Nations Working Paper, September 16, (2016).
- NIRAS (2019) The Data Futures Hub https://datafutures.org/
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