Published on March 19, 2014
Overview of climate variability and likely climate change impacts on agriculture across the Greater Mekong Sub-region (GMS) Eitzinger Anton, Giang Linh, Lefroy Rod, Carmona Stephania, Laderach Peter
Project milestones • Assemble database and quantify impact on crop suitability December 2013 • Engage with partners and share methods and first results March 2014 • Incorporate feedback, rerun analysis and finalize results May 2014
Climate grids are constructed for nine climate variables for the period 1901-2009 - Temperature, - Diurnal temperature range, - Daily minimum temperature, - Maximum temperatures, - Precipitation, - Wet-day frequency, - Frost-day frequency, - Vapor pressure, and - Cloud cover. CRU TS climate database 3.10.01
CRU TS climate database 3.10.01 Tmean + 1.8 ˚C to 2 ˚C Tmax +1.7˚C to 2.2˚C Tmin + 1.6˚C to 2.2˚C No clear rainfall change pattern …Temperature increased between 1901 to 2009 ?
http://www.worldclim.org Worldclim stations worldwide 47,554 precipitation 24,542 tmean 14,835 tmax y tmin Sources: •GHCN •FAOCLIM •WMO •CIAT •R-Hydronet •Redes nacionales Sources: •GHCN •FAOCLIM •WMO •CIAT •R-Hydronet •Redes nacionales Climate baseline
Temperature Annual mean temperature, Mean diurnal range, seasonality, Max of warmest month, Min of coldest month, Annual Range, Mean of Wettest/Driest Quarter, Mean of Warmest/Coldest Quarter Rainfall Annual, Rainfall of Wettest/Driest Month, Seasonality, Rainfall of Wettest/Driest/Warmest/Coldest Quarter * current annual mean temperature, current annual rainfall
Not available = natural (forest, wetland, …), protected, water, bare, urban areas LU Change risk = land mixed with pastoralism (forest, herbaceous, wetlands, …) Available = Agriculture (commercial, subsidized, irrigated, …) Land use change at risk for agriculture
Land use change • Vegetation Index (MOD13Q1 MODIS Product , 16 days, 250m) Normalized difference vegetation index (NDVI) … the values are closely related to vegetation type and climatic conditions as well as the predominant land use pattern. Data noise filtering
Crop-Climate-Suitability modeling using Ecocrop
Crop climate- suitability change by 2050 s of D:_modeling_OUTPUTsearun-1.gdbbanana2chg in zones of D:Anton_DAPA_Projects_ongoingSEA-CCAFSgeodatagms_mask.shp KHM 2,000 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 LAO MMR THA VNM CHN Histograms of D:_modeling_OUTPUTsearun-1.gdbpotato2chg in zones of D:Anton_DAPA_Projects_ongoingSEA-CCA KHM 1,000 900 800 700 600 500 400 300 200 100 0 LAO MMR THA VNM CHN Histogram: Banana Potato Cambodia Laos Myanmar Thailand Vietnam China Cambodia Laos Myanmar Thailand Vietnam China
2 step analysis Compare predicted future suitability change from climate models and Ecocrop maps and existing land use data A time-series analysis of Land Use change using satellite images to identify change patterns in land use 1 2
1 risk for land use change
2006 20122000 2
Expansion of of Maize, Sayabouri What can be detected? 2
Engage with partners and share methods and first results
Validation of Ecocrop results • Groupwork and Discussion on suitability maps Actual production area This area is not suitable Temperature suit. Rainfall suit.Climate-suitability Crop x
• Share & discuss methods of climate change impact assessment with partners • Training on EcoCrop modeling using DIVA-GIS
Presentations & Discussion 1. What is the evidence and observed changes in the climate system and how reliable are climate models and scenarios? 2. How to use climate models & future predictions for Agriculture and modeling? 3. How can we adapt agriculture systems to unknown future conditions?
Overview of climate variability and likely climate change impacts on agriculture across the Greater Mekong Sub-region (GMS) Project team: Peter Laderach, Rod Lefroy, Aparna Mani, Linh Giang, Stephania Carmona, Anton Eitzinger Thank you A.Eitzinger@cgiar.org
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