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Colombia Climate Smart Agricultural Sector - COP21
The document discusses Colombia's efforts towards implementing climate-smart agriculture (CSA) to improve agricultural resilience and productivity while reducing greenhouse gas emissions. It highlights the importance of collaboration among researchers, local farmers, and policymakers to develop and implement sustainable practices tailored to local conditions. Key components include agroclimatic forecasts, adaptation planning, and capacity-building initiatives to enhance food security and environmental sustainability.
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Colombia Climate Smart Agricultural Sector - COP21
- 1. Colombia towards climate-smartness Andy Jarvis FlagshipLeader for Climate Smart Agriculture (CCAFS) Research Area Director, Decision and Policy Analysis (CIAT) Colombia – Senegal Exchange Cauca, Colombia
- 2. ¿What is CCAFS? CCAFSbrings together the world's best researchers in agricultural science, climate science, environmental and social sciences to identify and address the most important interactions, synergies and trade-offs between climate change and agriculture. Alliance Quesungual Agroforestry System, Honduras
- 3. Andy Jarvis, AndyChallinor Jim Hansen Lini Wollemberg Phil Thornton Research Flagships
- 4. What is Climate-SmartAgriculture (CSA)?
- 5. CSA in Colombia– National context Economic Relevance of Agriculture People and Agriculture
- 6. GHG Emissions AgricultureGHG Emissions CSA in Colombia – National context
- 7. Source: Agronet &CRU (http://badc.nerc.ac.uk/data/cru/) Climate and agriculture: hand in hand T-Max T-Max Yield Yield Rice crop
- 8. Neutral years Tiempo térmico=2627°Ciclo T.Max=31°C T. Min=19.3°C Evapotranspiración=712 Precipitación total=532mm + riego Tiempo térmico=2596°Ciclo T. Max=32°C T. Min=19.3°C Evapotranspiración=714 Precipitación total=635mm + riego 0 2 4 6 8 10 RendimientoTon/Ha Genotipo 0 2 4 6 8 10 RendimientoTon/Ha Genotipo Maize – Buga (Valle del Cauca) Location
- 9. (160) (140) (120) (100) (80) (60) (40) (20) - Tolima Casanare Meta Córdoba Huila MillonesdeUSD$ Total: USD$ 427millones al año Sin cambiar el manejo What is at stake for not understanding climate Rice crop
- 10. CIAT/CCAFS-MADR Agreement 1.Avoid croplosses due to climate variability 2.Close yield gaps through appropriate management of the climate 3.Produce food sustainably, synergistically with the environment
- 11. Colombia’s own CCAFSLocal Agroclimatic Committees Improved crop varieties Agroclimatic forecasts Policies & NAMAs Adaptation Plan for the Agricultural Sector Farmers Government Private sector Producers’ associations Socioeconomic Scenarios Climate-Site-Specific Management (CSMS) Climate-Smart Villages GHG measurements methods for smallholders Scaling up activities
- 12. Component Department Municipality RICE TolimaSaldaña, Ibagué y Espinal Huila Palermo, Aipe Norte Santander Cucuta César Valledupar Cordoba Montería, cereté Casanare Yopal, Aguazul Meta Villavicencio, Santa Rosa Antioquia Nechí Sucre Majagual, San Marcos Valle Palmira Guajira Fonseca BEANS Santander Villanueva Antioquia San Vicente Nariño Pasto Cauca Popayán Huila El Pital MAIZE Córdoba Ciénaga de Oro Tolima Espinal Valle del Cauca Buga Quindío Buenavista Santander Sabana de Torres Meta Fuente de oro BANANA Magdalena Santa Marta, Rio Frio y Zona Bananera Guajira Riohacha y Dibulla LIVESTOCK Boyacá San Miguel de Sema, Caldas y Chiquinquirá Cundinamarca Simijaca Atlántico Tubará, Piojó, Baranoa, Manatí y Suán Casanare Aguazul, Monterey, Pore, El Picón y Maní Activities map CIAT/CCAFS-MADR Agreement Phase II
- 13. Córdoba: 56% (Riego) Temperaturaen fase de llenado Meta: 29% (Secano) Distribución de la precipitación en vegetativa Casanare: 32% (Riego) Radiación en fase reproductiva Tolima: 41% (Riego) Radiación en fase de llenado de grano Huila: 28% (Riego) Temperatura en floración How much yield variability can be explained by climate? Meta: 61% (Riego) Temperatura en fase reproductiva
- 14. Mecanizada Manual Radiación solar acumulada(cal/cmt2) Rendimiento(kg/ha) Humedad relativa (%)Rendimiento(kg/ha) Córdoba Department Location
- 16. Climate forecasts insome Colombian agricultural regions
- 17. 0 50 100 150 200 250 300 Noviembre Diciembre EneroFebrero Marzo Precipitación(mm) Promedio_Mensual Limite_Inferior 0 50 100 150 200 250 300 Noviembre Diciembre Enero Febrero Marzo Precipitación(mm) Promedio_Mensual Limite_Inferior Sugarcane Precipitación – La Virginia Precipitación - Guacarí Precipitación - Aeropuerto Valle del Cauca 0 50 100 150 200 250 300 Noviembre Diciembre Enero Febrero Marzo Precipitación(mm) Promedio_Mensual Limite_Inferior Déficit Normal Exceso CONVENCIONES
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- 19. 01-Oct 08-Oct 15-Oct22-Oct 29-Oct 05-Nov 12-Nov 19-Nov 26-Nov Fed2000 5334.1355 5469.197 5641.9365 5705.36 5792.207 5950.608 6185.824 6345.995 6322.914 Fed733 5582.9985 5477.9985 5451.304 5427.0175 5508.1325 5695.915 5873.7705 5980.603 5826.737 4000 4500 5000 5500 6000 6500 7000 7500 Rendimiento(Kg/ha) Épocas de Siembra Fed2000 Fed733 Yield Forecasts Montería-Cerete (Córdoba)
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- 21. Pronósticos Climáticos Modelación agronómica Conocimiento local Recomendaciones para losagricultores de medidas adaptativas a partir de la combinación del conocimiento local y científico Local Technical Agroclimatic Committees ¿Cómo se afectarían los cultivos? ¿Qué variedades sembrar? ¿Qué habría que hacer? ¿Cuándo sembrar? Local Agroclimatic Bulletins
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- 23. (Water Footprint) Partnership betweendifferent stakeholders to determine the impact of production systems in the use and quality of water AMTEC Vs Manejo Convencional Evaluación de híbridos con y sin riego Conservación Vs Convencional Huella hídrica BIODIESEL DE PALMA Lecheria Producción más limpia
- 24. We are lookingfor ... Make more efficient use of resources Para una región como el Departamento del Tolima esto implicaría agua para 28.000 hectáreas más de arroz
- 25. This is howColombia is moving towards Climate-Smart Agriculture
- 26. Integrated approach ofAFOLU sector in Colombia INDCs formulation -baseline analysis and mitigation scenarios models- Countries can choose among a portfolio of growth-inducing technologies with different emission characteristics. Countries are part of a global economic system, it is critical that LEDS are devised based both on national characteristics and needs, and with a recognition of the role of the international economic environment. Objective: Determine what can be achieved given the global economic environment
- 27. Technical Approach a. IMPACTmodel: a global partial equilibrium model for agricultural commodities that provides plausible pressure for change in ag. prices and cropland areas, b. A spatially-explicit model of land use choices: provides likely location of changes in ag. area and other land uses, c. Crop model: provides yields, GHG emissions, and changes in soil organic. Limited spatial resolution of macro-level economic models that operate through equilibrium-driven relationships at a global or national level with detailed models of biophysical processes at high spatial resolution. Combines and reconciles: * Essential components are: *
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- 29. Policy Scenarios Land usepolicy scenarios after consultation with stakeholders Scenario 1 Reduction of pastureland by 10 million hectares Scenario 2 Total halt to deforestation the Amazon Scenario 3 Total land allocated to palm production reaches a total of 1.2 million hectares
- 30. Additional investigation is necessarybut, results unmistakably indicate the centrality of the livestock sector in emission reduction policies. Source: Authors Results include: changes in SOC, above and below ground C caused by land use change; changes in emissions from cropland and livestock caused by land use change excluding burning; changes in revenue from crop and meat production. Policy outcome comparison
- 31. *Includes changes inSOC, Above and Below ground C caused by land use change. **Changes in emissions from cropland and livestock caused by land use change. Exclude burning ***Changes in revenue from crop and meat production Scenario Change C Stock* (TgCO2eq) Change in GHG Emissions** (TgCO2eq) Difference Stock vs Emissions Change in Total Revenue*** (Billion USD) 1 272 25.2 246.8 40.6 2 168 -1.2 169.2 -2.5 3 64 19.9 44.1 -54.6 Results for each scenario
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- 34. Regional adaptation plans, alignedwith national
- 35. Climate change regional planfor Valle del Cauca Capacity strengthening local implementers and institutions on climate change challenges Tools and instruments for CC adaptation and mitigation Promote interinstituional collaboration Link national policies with regional plans and strategies
- 36. Climate-Smart Village Approach A communityapproach towards sustainable agriculture development CCAFS works with the communities to develop Climate-Smart Villages. These are sites where researchers, local partners, farmers and policy makers work together to select and implement technologies and practices based on global knowledge and local conditions with the purpose of: a) increasing sustainable productivity and income, b) building resilience to climate change, c) reducing GHG emissions and d) promoting food security and development goals. CSVClimate-Smart Village
- 37. Climate information services Climate-smart technologies Local adaptation plans Financial incentives and marketaccess Context specific conditions (social, economic, cultural, environmental) Territorydinamics Scaling up and out Policies Private sector Champion cases are used in big initiatives Continuous learning Stakeholder diversity Capacity building Integrated management through a portfolio that responds to context- specific needs in the territory CSVClimate-Smart Village
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Editor's Notes
- #4 FP1: * Improved technologies, practices and portfolios for CSA that meet the needs of farmers, including women and marginalised groups * Methods and approaches for equitable local adaptation planning and governance, including transformative options * Innovative incentives and mechanisms for scaling up and out that address the needs of farmers, including women and marginalised groups. FP2: * Climate-based methods and tools for seasonal agricultural prediction and early warning * Knowledge and methods for climate information and advisory services for smallholder communities * Food security safety nets and policy interventions for dealing with climate-related shocks * Weather-related insurance programs FP3: * Decision support for assessing mitigation priorities, baselines and trade-offs * Methods and data for quantifying small-scale farming emissions and mitigation options * Analysis for improved mitigation implementation mechanisms (NAMAs, climate finance, accountability for sustainable commodities, innovation systems) FP4: * Data, models and scenarios to understand impacts of climate change * Decision support tools for targeting policy development and making investment choices * Analysis of strengths and weaknesses of current and emerging policy * Analysis and experimentation concerning novel decision-making processes
- #7 Questions these indicators can help answer: How much responsibility does the agriculture sector have in producing the nation’s emissions? Where can the biggest gains be made to address mitigation in agriculture? Colombia details Emissions data is from the 2010 UNFCCC communications, but the inventory was from 2004 A new emissions inventory will be conducted next year The 180mt figure here is what was emitted in 2004
- #32 Assumed: 30 grams * (square meter)-1 *(growing season)-1 Assumed SRI emissions: 0.87 of conventional Assumed mid-season drainage emissions: 0.9 of conventional