Agriculture accounts for 10-14% of Colombia’s gross domestic product and nearly 20% of their employment. Top commodities include bananas, sugar, and green coffee. Of their agricultural products, 25 of the most important crops and 83% of their agricultural lands are threatened by climate change. The Upper Cauca River Basin is in particular danger as it is involved in the production chain for most of Colombia’s exported agricultural products.
The Agricultural Vulnerability and Adaptation (AVA) project was launched in 2011 to help address climate risks. The AVA began as a partnership between universities, agricultural, and industry organizations. The project seeks to establish methods to monitor, model, and disseminate climate risk information. Unique to this project is the four-dimensional modeling tools under development that include biophysical, political-institutional, economic-productive, and socio-cultural factors. Part of AVA was to develop a tool accessible to many stakeholders. The program faced many challenges that produced lessons learned that are applicable to other research groups trying to provide actionable information to a diverse set of stakeholders, many of whom are very poor. Lessons learned include:
- Interdisciplinary knowledge sharing. Before trying to incorporate the data developed into public policy, the AVA groups shared hydro-meteorological, biophysical, social, and economic knowledge that built value into the model and created trust in their audiences.
- Stakeholder participation builds cross-sectoral action. The strength of the AVA was its multi-disciplinary approach. For example, involving owners of small farms who sought food security and large farms who sought economic risk reduction meant that the successful model brought in broad data sets.
- Commitment is necessary for success. The AVA project operated across several political transitions in national planning priorities. Developing plans that stakeholders could commit to, somewhat irrespective of political forces, was deemed essential.
- Adapting to difficult metrics to measure. Some indicators had large data sets, such as state kept weather data that spanned decades. But some data, especially around long-term regional economic and political factors, was unavailable. The research team made it a priority to be flexible while deeply scrutinizing data for the resources they needed.
- Incorporating uncertainties into an integrated assessment. The four-dimensional model was chosen because, in contrast to one-dimensional models, the uncertainty of the data was integrated into the assessment. It is unlikely that the final models were incredibly accurate in their climate predictions, but even without high-accuracy the modeling results were deemed useful by the stakeholders due to their multi-faceted emphasis.
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