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Managing Vulnerability and Boosting Productivity in Agriculture Through Weather Risk Mapping

Carlos E. Arce, Edgar Uribe
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About the webinar

On Wednesday, April 8, 2015, from 9:00 to 10:00 am EST, Carlos Arce and Edgar Uribe presented the guide for development practitioners “Managing Vulnerability and Boosting Productivity in Agriculture through Weather Risk Mapping” as part of the FARMD Webinar Series.


Carlos E. Arce

Carlos E. Arce is a lawyer and development economist with over 30 years of experience working on issues of agricultural productivity and risk management.  After working with various international agencies on agriculture and rural development, Carlos joined the World Bank addressing issues related to agricultural risk analysis, weather index-based insurance, and risk mapping applications.  Today, he works as a consultant advising developing economies in agriculture policy and the design of agriculture risk management strategies and applications.

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Edgar Uribe

Edgar Uribe (PhD) is a hydro-climatologist. In 2007, he participated in the design of Agricultural Weather Index Insurance (WII) with AGROASEMEX. In 2009 he developed the first gridded dataset applied for index insurance and the first national flood map for Mexico. He later became consultant with the World Bank’s Agricultural Risk Management Team where he created gridded databases, and also participated in the design of WII for several developing countries in Central America and Africa. Edgar has focused his efforts on hydrometeorological risk management developing: short term hydrometeorological forecasts, seasonal climate forecasts, Agro-Ecological Regionalizations (AEZ), weather databases assessment and improvement, and hydrological and agronomic modeling. He currently works as CAT modeler for MiCRO (Microinsurance Catastrophe Risk Organization) and IFC.

Edgar studied Geophysics and holds M.S. in Atmospheric Sciences, and PhD in Hydrology with a minor in Atmospheric Sciences from the University of Arizona, Tuscon.

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Interviews with the presenters
What is agro-meteorology risk mapping? What are the different technologies used to design weather risk mapping tools? In designing risk mappings, what inputs are essential to getting the most accurate results?

Agro-meteorology risk mapping is a technique to capture maps the frequency of relevant hazards and potential losses. The inputs that are usually necessary are environmental and include climate, soil, crops, land use,  and topography, for example. The different technologies involve physical, mathematical, and statistical modeling but also Geographic Information Systems, Remote sensing, Agronomy, Climatology, Meteorology, Edaphology, etc. It is a multidisciplinary field.

One way to overcome the lack of reliable, consistent, and extensive databases in most developing countries is to work with database proxies. How do you ensure proxies are accurate since they too rely on observational data?

Satellites are proxy examples, and they don’t depend on observational data (e.g. weather stations). However, it is always suggested to evaluate them before using them. On the other hand, in order to have better proxies, it is necessary to have observational data for calibration. Weather services should try to make an effort to share their information with proxy initiatives. In Africa, satellite products are becoming more reliable than in Central America. The reason is that there are initiatives like TAMSAT that collect observations provided by African Weather services and use them to improve satellite products while in Central America Weather Services are reluctant to share information so satellite products are not as reliable.

What is the difference between climatologies and climate & risk maps?

Climatologies are the expected weather conditions in a given place. We can estimate the climatologies of rainfall and temperatures and make a climatology map. Risk maps, on the other hand, involve hazards and assets. They will try to capture the probabilities of suffering losses due to a given hazard. I can make a drought risk map, for example, that will require climatological data but in the end, it will try to assess the probability of suffering losses due to drought.

The line between diagnostic and forecasting products has become increasingly blurred. What is the difference between the two?

Diagnostic climatological products are based on current conditions. They follow the meteorological principle of “Persistence”, which means they assume future conditions will be similar to current conditions. Forecasts, on the other hand, try to guess what will be the conditions in the future using models (physical, mathematical, statistical). A few years ago, most drought early warning systems relied on diagnostics. They would assess current conditions and issue a warning based on them. If drought was in place, their assessment would be that the drought will continue. Nowadays, drought early warning systems also rely on climate simulations of the following months (e.g. based on atmospheric models or statistical relationships with climate drivers, like El Nino), which makes it an actual forecast.

What is agroecological zoning? How does it differ from climatic and agro-climatic zoning? What is assessed and what are some of the benefits to policymakers? Others?

The only difference between these zonings is the type of information and purpose. Climatic zonings are the most basic because they only take into account climatological information. Agro-climatic zonings and AEZs take into account additional environmental factors (e.g. soil, topography, crop behavior) in order to asses what regions are most suitable for crops. The benefits of AEZs are mostly for planning. Once we know what regions are more suitable to which crops, we can asses if the current crop distribution is appropriate or if this distribution can be optimized. Plus, once we identify zones, policymaking becomes much simpler because a single policy design process will cover an entire region.

There is an increasing number of approaches and models to design risk mappings in agriculture, with various degrees of sophistication. What makes some models more successful in achieving accurate results? Are the techniques and tools currently available adaptable to the context of decision making in developing countries?

Physical, mathematical, and statistical models are the most promising methods right now.  The reason we thought a document like this was necessary is that developing countries don’t have the technical resources to develop the showcased products. Most of the responsibility is on the shoulders of weather services, which usually are understaffed, and underfunded. Currently, the products developed at most weather services are not suitable for end-users, their information is abstract, and they don’t reach potential users. On the other hand, development practitioners focus resources on a couple of risk management strategies. Thus, I think it was necessary to point out the existence of additional risk management strategies, which have proved extremely helpful and profitable in countries where they have been implemented.