Training on Participatory Integrated Climate Services for Agriculture (PICSA) and Local Technical Agroclimatic Comittes (MTA / LTAC)
•Download as PPTX, PDF•
0 likes•96 views
Training on Participatory Integrated Climate Services for Agriculture (PICSA) and Local Technical Agroclimatic Comittees (MTA / LTAC) to the DeRISK project team. February 11 -19 2020, CIAT Hanoi, Vietnam
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Training on Participatory Integrated Climate Services for Agriculture (PICSA) and Local Technical Agroclimatic Comittes (MTA / LTAC)
Similar to Training on Participatory Integrated Climate Services for Agriculture (PICSA) and Local Technical Agroclimatic Comittes (MTA / LTAC) (20)
More from Decision and Policy Analysis Program
More from Decision and Policy Analysis Program (20)
Recently uploaded
Recently uploaded (20)
Training on Participatory Integrated Climate Services for Agriculture (PICSA) and Local Technical Agroclimatic Comittes (MTA / LTAC)
- 1. Training on PICSA & LTACs (MTA) Feb 10, 2020 – Hanoi, VNM Carlos Navarro-Racines P. Imbach, D. Giraldo, J. Ramírez, D. Martinez, et al DeRISK Southeast Asia – Team Training c.e.navarro@cgiar.org _cenavarro carlitosunal
- 2. Introduction to CS for Agriculture Feb 10, 2020 – Hanoi, VNM Carlos Navarro-Racines P. Imbach, D. Giraldo, H.Dorado, J. Ramírez, D. Martinez, et al PICSA/LTACs (MTA) c.e.navarro@cgiar.org _cenavarro carlitosunal
- 3. Why climate smart decisions? ________________________________________ [2] Total crop yield variability explained due to climate variability over the last three decades (Ray et al., 2015) [2] Context • Climate drives ~ 32-39% of productivity • Our systems are sensitive to climate, not resilient to that.
- 4. Decision making in a risky environment Cultivar selection Soil preparation Seedtime Irrigation quantity Invest in inputs Harvest date Climate variability makes these decisions difficult Farmers must make climate sensitive decisions before the start of the growing season. Context
- 5. Context Gap between information and user
- 6. Climate services Production - Translation - Transfer - Use ________________________________________ [3] Climate Services Partnership [3] = Informed climate decision making CS
- 7. What makes an effective climate service? • A climate service should be useful to next-users – a product that informs decision-making • Tall et al. (2013) describe 5 factors important for effective climate services provision:
- 8. Policies to scale-out and implement the Regional CRM system Integration at the regional scale Policies to integrate climate services into decision making (e.g. scaling LTACs, sustaining and scaling FSDSS) Integration at the national scale POLICY PRACTICE Integration at local level Institutional changes and local policies to enable establishment of climate services Data, information and knowledge for climate risk management
- 9. A conceptual system for Asia ASEAN Climate Outlook Forum http://asmc.asean.org/wp- content/uploads/2019/12/ASEANCOF-13- OutlookBulletin_DJF2019-20_Final.pdf Cascade flow MetServices Ag. Ministry Local Associations Farmers
- 11. Guatemala Colombia Understanding the needs Key 1 Key research here: • Building bridges between supply and demand • Understand Information Flows
- 12. How to build bridges between supply and demand? Key 1
- 13. How the products are connected? https://www.sciencedirect.com/science/article/pii/S2405880719300780
- 14. Knowledge flows Better weather and crop predictions Better predictions 2 Key 2
- 15. Capacity building and co-design ________________________________________ [3] Esquivel et al. (2018). Climate Services. doi: 10.1016/j.cliser.2018.09.001 [3] Key 2 NextGen – AcToday project
- 16. Agro-Climate Forecasts • They combine agricultural and climate information, and then provide adapted recommendations for agriculture. "This weather is so strange, I don't know what variety to sow " Response of some varieties to a forecast Key 2 Yield of F2000 for planting between Oct – Nov 2017 (Lorica)
- 17. Agro-climatic Information Interface for Colombia • MinAgricultura and CCAFS initiative • With FEDEARROZ, Fenalce, IDEAM, Federación Nacional de Cafeteros de Colombia • Used by rice and maize farmer organizations and reaching thousands of farmers
- 18. Agro-climatic Information Interface for Colombia 2/13/2021 18 http://pronosticos.aclimatecolombia.org
- 19. A bigger scale, non experimental data use! BIG DATA
- 20. Many experiments, more data! BIG DATA
- 21. Data Collection – MasAgro Total of 4595 harvest events Information period : 2012 - 2018 Maize temporal system
- 22. What does affect the yield? Identify factors or combination of factors that drives to a high or low productions f ( C , S , M ) = Y + + = Climate Soil Agronomic practices Productivity
- 23. Random Forest, Ranking of variable importance explaining yield variations Explanation of the yield variation in Chiapas: 74.7% About 60.000 seds by HA An optimal measure of 180 kg/ha of N At least 30% rainy days A diurnal range < 11.5OC
- 24. Varieties with high yield potential Hibrid varieties: P4063W y RW4000 high potential in yield From the local varieties: BAAQUIL W6, TUXPENO
- 25. The potential of the predictive model together with the climate forecast Near farms to the station Forecast with 100 simulations 93 harvest cycles 10 km f ( C , S , M ) = Y Variedad Frecuencia P4082W 20 DEKALB 390 13 OTRA (ESPECIFIQUE) 13 DEKALB 7500 7 DEKALB 380 6 P30F32 6 Otras 28
- 26. Yield prediction considering current practicas vs. Optimal practices Max: 6.1 ton/ha Min: 3.61 ton/ha Average: 4.9 ton/ha Sd: 0.51 ton/ha Max: 6.1 ton/ha Min: 5.1 ton/ha Average: 5.4 ton/ha Sd: 0.18 ton/ha + 10.2 % - 64 %
- 27. The impacts – 170 farmers 3,6m USD saved in 2014
- 28. 3. Crop modelling 2. Data driven agronomy 1. Assess local information needs Farmer advisory services in LAM Connect to seasonal and weather forecasts Participatory and digital platforms for farmer advisory
- 30. Teamwork! Key 3
- 32. INTER-INSTITUTIONAL ARRANGEMENT “The MTAs, allow to generate spaces of discussion between actors for the management of local agroclimatic information, in order to identify the best practices of adaptation to climatic phenomena, which are transferred to local technicians and farmers through the Local Agroclimatic Bulletin” Mesas Técnicas Agroclimáticas (MTA) Local Technical Agroclimatic Comitees (LTAC) Key 3
- 34. Local Technical Agro-Climatic Committees • Empowering institutions such as farmer organizations, the private sector, academia, international NGOs and governmental organizations to co- produce forecast-based agronomic recommendations using LTACs. • Strengthening new capacity related to the participatory generation, provision, interpretation and use of forecast-based recommendations for adaptation to climate variability. Key 3
- 35. Local Technical Agroclimatic Comitees (MTA) Lidera y financia MADR, coordina FAO. IDEAM suministra información climática Nacional y los equipo de agro-climatología de los gremios en las MTAs locales. Reuniones y boletines mensuales. Acuerdo de voluntades. Lidera y financia SAG. COPECO suministra información climática. Reuniones y boletines 3 veces al año. Coordinadores locales en cada mesa. Acuerdos de formalización y estatutos por cada mesa. Lidera la mesa Cafenica, Heifer, CIAT. Lidera la Universidad publica CUNORI, Anacafé, CDRO, MAGA-PMA, Helvetas e INSIVUMEH COLOMBIA (10 MTAs) HONDURAS (7 MTAs) NICARAGUA (1 MTAs) GUATEMALA (9 MTAs) Lidera Ministerio de Agricultura PARAGUAY (2 MTAs) & CHILE (1 MTAs) Lidera Ministerio de Desarrollo Agropecuario PANAMÁ (5 MTAs) Lidera Ministerio de Agricultura y Ganadería EL SALVADOR (1 MTAs) Lidera Secretaría de Chiapas MÉXICO (1 MTAs)
- 36. AgroClimatic Bulletins Agroclimatic Bulletins help farmers with recommendations in a specific context to decide over the agronomic activities y and reduce the climate risk.
- 37. How to make climate information useful for farmers? [5] ________________________________________ [4] Dorward P, Clarkson G, Stern R. 2017. Servicios Integrados Participativos de Clima para la Agricultura (PICSA): Manual de campo hdl.handle.net/10568/80548 [4] Key 3
- 38. PICSA in LAM • Participatory Integrated Climate Services for Agriculture developed by the University of Reading • Guatemala, Honduras, Colombia, Nicaragua • Opportunity for direct engagement with farmers for the delivery and use of agro- climatic information from LTACs in Latin America Key 3
- 39. PICSA
- 40. Agroclimatic calendars of main productivity actities: maize, beans, livestock, achiote, cocoa. Plotting precipitation data perception versus registered data in a weather station How to put agroclimatic information in the hands of farmers? Key 3
- 41. Operational seasonal forecast platform Technical agro-climatic committees Agro- climatic bulletins Participatory forecasts (PICSA) Advisory toolkit Farmers Institutions Towards a climate information service
- 42. Outcomes and impact Key 3
- 44. ¡Thank you! Contact Carlos Navarro-Racines Coordinador AgroClimas Fase II c.e.navarro@cgiar.org Dr. Julian Ramirez-Villegas Científico de Impactos de Clima j.r.villegas@cgiar.org Dr. Steven D. Prager Científico de Modelación Integrada s.prager@cgiar.org
Editor's Notes
- CIAT-CCAFS
- CIAT-CCAFS
- Context It is very important to understand that climate drives at least the thirty % of the productivity in the field. In Asia countries, e.g. Laos we have 40-50% of affectations of climate in agriculture. Our agricultural systems are sensitive to climate, but are not resilient to him. This represent a big challenge for the farmers, who depend on the environmental conditions for their livelihoods.
- In general farmers have to take decisions before the growing season starts. Decisions like… The truth is that climate variability makes these decision difficult. It’s not enough that farmers take decisions based on the conditions of the last year, because each year is different. If they can understand the climate variability, they can take better decisions. Fertilizer examuch rain means that the fertilizer will be washed away. Too little rain memple: Deciding when to apply fertilizer requires consideration for when the next rains will come. Too ans that the fertilizer won’t be fully utilized by the plants.
- To meet that we have to solve a main issue. There is a gap between the information produced by the met services and the local farmers. With understandable and contextualized information, farmers can take better decisions.
- The solution is to build climate services for agriculture. CS is not only produce information. It is more a process that involved the production, translation, transfer and use climate information. This works at different levels and areas. The key aspect of this is that users have to be able to make decisions using the information delivered to them
- In Central America, we are trying to implement a system like that. The forecast information comes to the region through the Climate Outlook Forum with the participation of all climate services of the different countries. Then this information goes down to the national level. And finally the information goes down to the local actors. The system also works is two ways, because we try to understand the information need at local level and then scale this need to the upper levels.
- Our work comprises three knowledge flows.
- We evaluate the needs of the different users at a different levels. In this step we analyze the agro-climatic information available, who offer it and who request this information.
- Then we map the actors to understand which actors are linked and which of them are not receiving the climate information products. So then we can easily promote the links between supply and demand and we understand better the information flows. Which are the actors? So we can understand what is the real flux of the information, and who is connected with who e.g. In Colombia IDEAM (met service) it is not connected with key actors. ` Also we can understand which actors needs strengthening Adicionalmente es importante definir el alcance geográfico de la MTA. Mediante un mapa del país o departamento de estudio, y con ayuda de los participantes, se identifica la zona donde trabaja cada institución y los principales cultivos o actividades.
- To that, a focus of our work is to improve the climate forecast capacity. This is possible in a collaborative framework with met services and other institutions like the IRI. A new generation of climate forecast allows to know what are the probabilities of exceed or deficit of some climate thresholds relevant for agriculture. This is being implemented in Colombia, Guatemala, Chile as pilots.
- Then with better predictions we can combine the climate forcast with crop modelling. With this combination we can see the responses of some crops and varieties to the climate forecasts.
- La agricultura tradicionalmente ha sido investigada en el contexto de datos experimentales. Si le damos valor a los datos, observacionales, tales como lo que provienen directamente de fincas podemos tener algunos indicios de lo que afecta a los sistemas productivos
- - Esto debido a que cada fina se altera
- A final slide showing one major project impact in 2014. We saved many rice farmers from crop failure.
- We work toghether with institutions at different levels, but at the end of the day we have to leave ant the institutions remain strengthened, and continues the work.
- We work toghether with institutions at different levels, but at the end of the day we have to leave ant the institutions remain strengthened, and continues the work.
- One mechanism that we use is the LTACs. These are spaces of discussion between different actors in a territory, including: government people, ministries, farmers, people from universities, national associations, reserachers. This allows identify the best practices of adaptation to climatic phenomena, which are transferred to local technicians and farmers through the Local Agroclimatic Bulletin. Recommendations related to: how the crops will be affected, what to sow, when to sow, what are economic impacts of these measures?
- At present we have more that 30 LTACs in 8 countries of LAM. We are aiming to scaling up and strentening the regional capactities.
- We understand that a share information is not enough to make better decisions. We also need other mechanism to that, and we use for example Participatory Integrated Climate Services for Agriculture (developed by the University of Reading) This allow us to reach directly the farmers for the delivery and use of agro-climatic information from LTACs in Latin America
- As a conclusion a good CS for agriculture involves.