prediction models for better decisions · ¿what do we do? prediction models for better decisions...
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Outcomesand impact
19LTACs
in the generation, provision and use of agro-climatic information.
institutionsare engaged through
farmers
the LTACs in Colombia and Honduras.
330K
are being reached with climate and/or crop management information through
About Using an analysis of 10 years of weather and crop data connected to a seasonal forecast data, in 2014
avoided losses estimated at
by takingthe advice of
Colombian rice farmers
US $3,6M in climate-smart agriculture.
Climate risk assessments using a combination of participatory methods with crop-climate models
have been empowered with crop, livestock and/or climate modeling toolsfor staple crops including rice, beans, maize, bananas, coffee and livestock.
in Kenyacontributed to enabling investment of
by the
US $25M
in Latin America, Asia, and Africa
15More than An open access
agro-climatic services platform has been adopted by 2 farmers organizations in Colombia, currently covering
22maize- and rice-producing areas in
subnational regions
potentially reaching
10,000farmers.
national partners
We help make better decisions in agriculture using innovative and cutting-edge methods and tools in modeling science, with a focus on the effects of climate variability and change.
An interdisciplinary team of researchers works in developing and implementing modeling tools to help project variations in climate, crop, and livestock patterns, and economic systems. This information helps assess future scenarios and underpin effective responses to future challenges at farm, sub-national, national, and regional levels.
¿What do we do?
Prediction Modelsfor Better Decisions
Agro-climate modeling refers to the creation, improvement and use of models to create seasonal climate and weather forecasts, and long-term climate change projections for use in crop modeling. Some models and tools include: CPT and R-CPT (seasonal forecasting), Global and Regional Climate Models and downscaling methods (ccafs-climate.org), RClimTool, and AClimateColombia (pronosticos.aclimatecolombia.org).
Agro-Climate Modeling
This type of modeling collects the data generated by the climate and crop models and displays future scenarios of how the economy could be impacted by, for example, a rise in temperature and a decrease in a crop yield. Models include IMPACT, Surplus economics and GTAP.
Socio-Economic Modeling
Crop and livestock modeling requires accurate weather and future climate information to understand how different crops will perform under specific climatic conditions. Some models are ORYZA rice model, DSSAT (containing more than 20 crops, including our very own MANIHOT model for cassava), GLAM, species distribution models, and agro-climatic index models.
Crop and Livestock Modeling
Models allow us to work through, and find solutions for, complicated problems and understand complex systems. They are tools that help us predict something like the behavior of climate, crop, or economic systems.
Modeling
200Almost
in Latin America
(Local Technical Agro-Climatic Committees)
1 7 0
(pronosticos.aclimatecolombia.org),
8
CRPS AND PLATFORMS UNDER WHICH WE WORK
Partners – donors
Crops, climate, and the economy all factor into the
livelihoods of millions of people.
Agro-climate modeling
gives farmers key information
to grow their crops in the
face of climate risk.
Socio-economic modeling provides decision makers with future scenarios for better informed solutions.
¿Did you know?
Crop and livestock models help to project productivity under a range of soil, climate and agronomic management conditions.
CONTACT
ciat.cgiar.org March 2019@cgiarclimate
Dr. Steven D. Prager Integrated Modeling Scientist [email protected]
Dr. Julian Ramirez-VillegasClimate Impact Scientist [email protected]
Award for Best Research Result 2018 (CIAT) for the creation ofpronosticos.aclimatecolombia.org
Awards
UN Pulse, 2014, in collaboration with the CIAT Big Data team.This pioneering work combined seasonal forecasts with Big Data analytics to avoid US$3.6m in rice crop losses.
UN Momentum 2017: The collaborative effort of +30 scientists was granted the coveted award by the United Nations Framework Convention on Climate Change. Their trailblazing work used big data methods to develop climate and agricultural forecasts for farmers in Colombia and Honduras.
GenebankPlatform
Key publications
• Challinor A. J. et al. (2016). Current warming will reduce yields unless maize breeding and seed systems adapt immediately. Nature Climate Change 6(6): 954-958. doi: 10.1038/nclimate3061
• Esquivel A. et al. (2018). Predictability of seasonal precipitation across major crop growing areas in Colombia. Climate Services. doi: 10.1016/j.cliser.2018.09.001
• Jarvis A. et al. (2012). Is Cassava the Answer to African Climate Change Adaptation?. Tropical Plant Biology 5(1): 9-29. doi: 10.1007/s12042-012-9096-7
• Martinez-Baron D. et al (2018). Small-scale farmers in a 1.5°C future: The importance of local social dynamics as an enabling factor for implementation and scaling of climate-smart agriculture. Current Opinion in Environmental Sustainability 31: 112-119. doi: 10.1016/j.cosust.2018.02.013
• Ramirez-Villegas J. et al. (2018). Breeding implications of drought stress under future climate for upland rice in Brazil. Global Change Biology 24(5). doi: 10.1111/gcb.14071
• Rippke U; Ramirez-Villegas J. et al. (2016). Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nature Climate Change 6(6): 605-609. doi:10.1038/nclimate2947