JRV – Adapting cropping systems climate variability APEC Piura 2016
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Presentation in Piura, Peru during the APEC Climate Symposium
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JRV – Adapting cropping systems climate variability APEC Piura 2016
- 1. Using crop-climate models for adapting cropping systems to climate variability Julian Ramirez-Villegas, et al. Scientist, International Center for Tropical Agriculture, CIAT, Cali, Colombia Research Fellow, University of Leeds, UK
- 2. Adaptation across timescales Ramirez-Villegas and Khoury (2013) Climatic Change
- 3. Agriculture depends on climate Climate drives ~32-39% yield variation: our systems are sensitive to climate, not resilient to it Ray et al. 2015
- 4. Source: Agronet & CRU (http://badc.nerc.ac.uk/data/cru/) At a more local scale… T-Max T-Max Yield Yield Rice crop
- 5. For maize… 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
- 6. CIAT/CCAFS-MADR Agreement 1.Close yield gaps through appropriate management of the climate 2.Avoid crop losses due to climate variability 3.Produce food sustainably, synergistically with the environment
- 7. How to achieve these objectives? Local Agroclimatic Committees Improved crop varieties & better agronomy Agroclimatic forecasts Policies & NAMAs Adaptation Plan for the Agricultural Sector Farmers Government Private sector Producers’ associations Socioeconomic Scenarios Climate-Site-Specific Management (CSMS) GHG measurements methods for smallholders Scaling up activities
- 8. Component Department Municipality RICE Tolima Saldaña, 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
- 9. Climate-smart management to close yield gaps • Mining Big Data from rice farms provides a bottom-up approach to improve rice crop management, and increase yields
- 10. Big data often means noise… so we deal with the noise Delerce et al. (2016) PLoS ONE
- 11. We cannot generalise a site-specific response, but a site-by-variety response Saldaña (irrigated) Villavicencio (rainfed) Delerce et al. (2016) PLoS ONE
- 12. GxE response –but at farm level • We now understand what climate variability can cause to rice across the country • And what aspects of climate are important • For a given condition we develop management recommendations… Delerce et al. (2016) PLoS ONE Saldaña – F733 30 % variance Saldaña – F60 47 % variance Villavicencio– F174 28 % variance
- 13. For example… we look into which varieties perform best in particular climatic conditions Delerce et al. (2016) PLoS ONE Highest yielding environment Moderately high yielding environment
- 14. Córdoba: 56% (irrigated) Temperature in grain filling phase Meta: 29% (rainfed) Rainfall distribution during vegetative phase Casanare: 32% (irrigated) Radiation during reproductive phase Tolima: 41% (irrigated) Radiation during grain filling Huila: 28% (irrigated) Temperature during flowering And… results cover a large portion of the country Meta: 61% (irrigated) Temperature during reproductive phase Currently implemented with 5,000+ farmers, 50,000 observations (not only for rice), and for 6 countries in LAC (CO, PE, NI, AR, UR and MX)
- 15. Years Yield(t/ha) 201519XX Imported technology Regionally adapted agronomy Big Data Methods + Data-driven Agronomy Broadly adapted technology Towards a data-driven agronomy
- 17. Generating agroclimatic seasonal forecast for rice productive regions in Colombia How do we do it? Establish agro-climatic forecasts using seasonal climate prediction models and crop models (mechanistic models) to inform when and what to plant?
- 18. 1. Understand seasonal climate predictability across Colombia • Understanding predictability limits helps to know how much information we can really provide • First predictability study across regions, with focus on agriculture • Four departments that grow rice and maize • Used statistical forecast models with SST as predictor
- 19. Key findings • Inter-Andean valleys (Valle and Tolima) show the greatest predictability on average • Average hides seasonal variability, with lowest predictability around the rainy season for these departments • Clearest teleconnections typically in the Pacific basin, but sometimes also in the Atlantic. • Statistical model skill was not affected significantly by model configuration (i.e. selected predictor region) or lead time
- 20. 2. Calibrate and test crop model (ORYZA2000)
- 21. What do farmers need to know? Agroclimatic forecast: Cordoba and Tolima case studies Identify the most appropriate planting date (with best environmental supply) for rice crop in the period May - Dec 2014. Actions to implement Implement seasonal weather forecasts + mechanistic crop models 0 5000 10000 15000 115 165 215 Biomass(kg/ha) Day in year Projected crop performance to future climate conditions
- 22. Monteria (May – Dec) Pronóstico de precipitación Mayo – Diciembre de 2014 Precipitación(mm) Decreased monthly rainfall Increased monthly temperatures and solar radiation Fecha de siembra 5 May 25 May 19 Jun 14 Jul 08 Aug Rendimiento (kg/ha) 350 0 400 0 450 0 Select the best planting date, as a preventive measure. If farmers make the decision to plant by June 20, the yield obtained can be around 4500 kg/ha. If the crop sowings are delayed, yields will decrease. With this measure: Great economic losses to 170 rice farmers were avoided. 1,800 hectares of rice were saved from being destroyed by the intense drought.
- 23. Turning research into a service for farmers…. National Agroclimatic Bulletin
- 24. Climate forecasts Crop modelling Local knowledge Recomendaciones para los agricultores 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
- 25. Automated web interface to generate and communicate forecasts at local scales • Funded by USAID, under the Climate Services for Resilient Development (CSRD) programme. • Farmer focus groups to understand information and format requirements. • Intense web development and automation of crop- climate model runs • Beta version in Dec (!), and official release in April 2017
Notas do Editor
- Adventurer: Sugarcane:twofold increase in production: Not only farmers but breeders & agronomists using this to better understand site-specific information on the response of crop varieties to climate, soils and crop management in order to improve the management of current varieties and identify the best Climate-Smart Practices (CSP)” - Decision support system for farmers in Latin America that will also include soil and crop management factors