climate models

By Piet van Asten

In February, Wageningen University (WUR) was the host of a CGIAR event that shed light on trade-offs in agricultural systems. These trade-offs arise as we strive to achieve greater food security while also dealing with an increasing population, limited resources, a changing climate, and environmental degradation.

The workshop was aimed at understanding system dynamics, tipping points and shocks, and the resulting trade-offs and synergies across temporal and spatial scales. During the workshop, some 30 scientists currently engaged in different CGIAR research programs came together to share lessons learned from their experiences working with the tools available for assessing these trade-offs.

Researchers also discussed how to challenge underlying assumptions and expected outcomes, with Ken Giller, professor of Plant Production Systems at WUR, posing the question: can research really lift people out of poverty or can we at best hope to reduce hunger? “We should often be talking more about alleviating hunger rather than lifting farmers out of poverty,” he said.

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by Alexa Jay and Amor Ines

Crop yield predictions made during the growing season are relevant to many agricultural and food security decisions, including food safety net and relief programs, agricultural insurance, and management of agricultural inputs and credit supplies. The CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) Theme 2: Adaptation Through Managing Climate Risk supports advances in crop forecasting in service of its goal to enhance the resilience of rural livelihoods and food systems to climate-related risk.

A joint CCAFS Theme 2 project between the International Research Institute for Climate and Society at Columbia University (IRI) and NASA’s Jet Propulsion Laboratory (JPL) is investigating the use of satellite data to improve the accuracy of crop yield forecasting. Promising results in homogenous, large-scale farming environments indicate that incorporation of remotely sensed information on vegetation cover and soil moisture into crop models can reduce errors in yield predictions. Read more »

By Catherine Mungai, Maren Radeny and Caity Peterson

Fifty-five year old Rosalia Shemdoe feels empowered. She lives and works in Yamba village in the Lushoto district of northern Tanzania, but she just got back from what could be the longest journey she’s ever taken – one that ended in Mbinga, a district over 1,000 km away.

Rosalia, a single mother to six and grandmother to five, has been having a difficult time providing food and income for her family, a problem she attributes to unpredictable weather patterns and reduced productivity of her land. Her problem is urgent – how to provide food for her family in the days to come? – but she knows that thinking long-term is important too if her farm is to continue to support her and her family in the coming decades. Imagining such a future is no easy task, however. Talks and presentations, scientific data, and complicated maps and figures are all just about as much use as a crystal ball to a practical farmer with concrete questions. Rosalia needs to see what the scientists are talking about. Maybe then she can devise a way to prepare for the changes in store.

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by Sonja Vermeulen

Ten to twenty years is the typical timeframe for designing and implementing many of the interventions most critical to agriculture: new crop varieties, or catchment-wide infrastructure for irrigation and water storage, or siting and establishment of major new processing hubs.  Good design of any of these depends on knowing what the climate will be like once they are up and running.  What planners and policy-makers need are reliable local forecasts, for a decade or two ahead, of key variables such as inter-annual variability in rainfall, or length of the growing season. 

The bad news is that impatient end-users are likely to wait some years before “good-enough” decadal forecasts are available for most regions. Read more »