The PIRE Water and Food Security Project team held its annual meeting from January 12-13, 2017. The University of Connecticut team was excited to welcome PIRE team members from the University of Oklahoma and the University of Wisconsin. The meeting was an excellent opportunity to get updates on team members’ work, especially those from varying disciplines, get useful feedback, and to identify opportunities for collaboration.

Day 1 of the meeting began with introductions and a brief project overview from Dr. Emmanouil Anagnostou. Dr. Anagnostou stressed that the project’s overarching goal is to provide relevant forecast information to rural farmers. This information may include weather conditions, hydrological data, and potential crop production.  In addition to farmers, the forecast information will be useful to government agencies, irrigation managers, and reservoir operators.

 

Following Dr. Anagnostu’s presentation, Dr. Elizabeth Holzer presented information on potential research sites in the Amhara region. This was an opportunity for all participants to ask questions and provide feedback on the sites. Social science graduate students will visit the sites this summer to gather follow up information.

The afternoon consisted of presentations on available hydro-meterological data by Dr. Dokou, forecasting and economic analysis by Dr. Block, crop yield modeling by Dr. Wang, and e-PING app development by Dr. Emad. Day 1 concluded with a group dinner where participants had an opportunity to socialize and network with colleagues from other disciplines. Day 2  commenced with two presentations: Agent Based Modeling by Dr. Mellor, and linking climate, water and society by Dr. Block. The rest of the morning primarily consisted of round-table discussions about data requests for social science students visiting Ethiopia this summer and potential research papers for the project.

The meeting was concluded by deliberating project site selection criteria, publication guidelines, project timeline, and milestones. Overall, the PIRE annual meeting was informative and productive, and it was a great opportunity to connect face-to-face with our colleagues from around the country. We are looking forward to continuing this collaborative work, and we look forward to the next meeting.

 

Dr. Belay Simane from Addis Ababa University, Ethiopia is scheduled to deliver a lecture at UConn on January 24, 2017. The talk is entitled, “Building Resilience at the Community Level in Africa: the Role of Climate Smart Villages for Green Growth.” The event is sponsored by UConn PIRE and all are welcome.

Date: January 24, 2017,  12:00-1:00 pm

Location: Harry G. Manchester Hall, Room 344

Climate change and food security are two of the most pressing challenges in Africa and the continent has a long history of failed agricultural development projects. A major reason for these failures is that proposed solutions to development often follow a top-down approach: they are introduced by international organizations and approaches are developed in other cultural and agroecological contexts. Alternatively, they are imposed by government authorities in an attempt to promote large-scale adoption of favored practices. Recognition of the above shortcomings is the basis for launching the ongoing work in Ethiopia that focuses on the problem of agro-ecosystem based “climate-smart landscape management,” prioritizing adaptation measures to achieve food security, reduce land degradation, and improve  water management.

Establishing climate smart villages (CSV) aims to meet the goals of increasing agricultural productivity and smallholder farmer incomes while enabling adaptation and resilience to climate change and reducing emissions along the way. For small scale subsistence farmers, it’s a way of doing agricultural development that uses resources efficiently and wisely so as to reduce the high level of risk threatening their livilihoods. Resilience strategies are tailored in collaboration with communities, rather than one-size-fits-all, and there is enormous scope for farmer-to-farmer learning and a great potential for scaling up CSVs.