how$could$the$cgra$be$organized$in$south$america: data ... ·...
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How could the CGRA be organized in South America: Data, models and others challenges
Argen=na
• Scien=sts and research programs studing the agriculture vulnerability, adapta=on and mi=ga=on to climate change impact;
• Main crop models: DSSAT, STICS, APSIM, CropSys • Main Crops: maize, wheat, soybean, sunflower, sugarcane, intercrops/grass;
• Ins=tu=ons: INTA, Universidad de Buenos Aires and Universidad Nacional del Centro dela Provincia de Buenos Aires.
• good network of meteorological observa=on
Chile
• Chile has carried on impact s=dies in the agricultural sector for the IPCC Second Na=onal Communica=on. Based on es=mated crop yield impacts the economic impacts for the sector were asessed
• There are ongoing ini=a=ves regarding the integra=on sof crop models with hydrological models to stucy impacts of water availability and integrated assessments at a basin scale;
• There are some ac=ve groups working on crop models but experiments for calibra=on and valida=on have not been organized and are not always available to all groups.
• Models: DSSAT, CropSyst and EPIC. • Crops: maize, wheat, and potato. • Ins=tu=ons that could join in: INIA, CIREN, Universidad de Chile and Pon=fic Catholic
Chile University. • Excelen network of meteorological observa=on
Uruguay
• Climate change and variability studies, impacts and adapta=on op=ons. • Development of products and informa=on systems for planning and decision making
support, related with climate in agriculture produc=on. • main studies are: -‐ Impacts and adapta=on of agriculture to climate variability and extreme events (INIA – INTA); -‐ Informa=on systems: soil water, and crops and pasture produc=on monitoring and
forecast, based on climate perspec=ves (INIA – IRI). -‐ Models used: DSSAT, Century, water balance model, PPNA, -‐ Remote sensing (Modis, Landsat, NOAA). -‐ Climate es=ma=on methods (IRI techniques). -‐ good network of meteorological observa=on
Colombia
INSTITUTIONS/PROGRAMS: • Red Interins6tucional de Cambio Climá6co y Seguridad Alimentaria-‐ RICCLISA. • Red Climá6ca Colombia y Modelación de Clima Escenarios -‐ INSTITUTO DE HIDROLOGÍA, METEROROLOGÍA Y
ESTUDIOS AMBIENTALES – IDEAM. • Red Climá6ca Cafetera – CENICAFÉ / Plataforma Agroclimá6ca Cafetera / Xue Mulador. • Red Climá6ca Caña de Azúcar – CENICAÑA / Plataforma de Información. • Uso de modelos de Clima PRECIS – MarkSim – WorldClim -‐ CIAT. • Herramienta para toma de decisiones agropecuarias – AgroNet – MINISTERIO DE AGRICULTURA Y DESARROLLO
RURAL -‐ MADR. CROPS: papa, frijol, yuca, maiz tradicional, maíz tecnificado, platano, caña de azucar, café, cacao, pasturas, forestales. MODELS: CREFT – DSSAT – AQUACROP – APSIM – ARC/APEX good network of meteorological observa=on
Peru
Scien0fic ac0vi0es Clima=c extreme events: methodologies for scenarios development, economy. Climate Change: impact assessment studies for the 07 major economic Peruvian crops. Potato pilot crop modeling: descrip=on of on-‐going ac=vi=es and loca=on sites. The Informa=on technology: infra-‐structure, personnel and ins=tu=on involved were presented. CROPS: potato, rice, yellow corn, coffee, sugarcane, platano and starchy corn. Ins=tu=ons: Senamhi, CONCYTEC, INIA and Ministerio de Agricultura y Riego. good network of meteorological observa=on
Paraguay:
-‐Good network of meteorological observa=on -‐Development of products and informa=on systems for planning and decision making support, related with climate in agriculture produc=on. -‐Needs support to improve crop model structure -‐In=tu=on – MAG-‐IPTA
Bolivie:
-‐Good network of meteorological observa=on -‐Development of products and informa=on systems for planning and decision making support, related with climate in agriculture produc=on. -‐ Needs support to improve crop model structure -‐ Ins=tu=on -‐ INIAF
First Op=on
Labex USA/ARS
CGRA
Second Op=on
Labex USA/ARS
CGRA
Third Op=on
Labex USA
CGRA