Reporting 2021 Evidences Evidences Study #2069 Contributing Projects:      ● P42 - Tailored Agro-Climate Services and food security information for better decision making in Latin America      ● P58 - Putting climate into extension services: Climate-Site-Specific Management Systems (CSMS) for grounding climate smart agriculture to farm rice systems Part I: Public communications Type: OICR: Outcome Impact Case Report Status: On-going Year: 2017 Title: 10 farmers’ organizations and public institutions in LAM empowered with tools to identify CSA options in line with seasonal forecasts Short outcome/impact statement: 10 farmer organizations in 6 counties in LAM now have fully dedicated agro-climatic prediction and climate-site-specific agronomy analysis teams, sustainably producing climate-site-specific agronomic recommendations and seasonal agro-climatic forecasts in connection with national meteorological services. Two farmer organizations in Colombia adopted novel agro-climatic prediction practices; two national meteorological services strengthen their seasonal prediction capacity by automating the Climate Predictability Tool (CPT); 6 organizations use climate-site-specific agronomy to identify best CSA options. Outcome story for communications use: CIAT/CCAFS and partners have been doing comms around this outcome. The reference list contains the communications material. Links to any communications materials relating to this outcome: Part II: CGIAR system level reporting Link to Common Results Reporting Indicator of Policies : Yes Policies contribution: Stage of maturity of change reported: Stage 3 Links to the Strategic Results Framework: Sub-IDOs:     ● Enhanced adaptive capacity to climate risks (More sustainably managed agro-ecosystems)     ● Closed yield gaps through improved agronomic and animal husbandry practices Is this OICR linked to some SRF 2022/2030 target?: Yes SRF 2022/2030 targets:     ● Increased rate of yield for major food staples from current 1%/year Description of activity / study: This report was generated on 2022-08-19 at 08:00 (GMT+0) 1 Reporting 2021 Evidences Geographic scope:     ● Multi-national Country(ies):      ● Mexico      ● Peru      ● Honduras      ● Argentina      ● Nicaragua      ● Colombia Comments: Key Contributors: Contributing CRPs/Platforms:      ● CCAFS - Climate Change, Agriculture and Food Security Contributing Flagships:      ● FP4: Climate services and safety nets      ● FP2: Climate-Smart Technologies and Practices Contributing Regional programs:      ● LAM: Latin America Contributing external partners:      ● FENALCE - Federación Nacional de Cultivadores de Cereales y Leguminosas      ● FLAR - Fondo Latinoamericano para Arroz de Riego      ● IDEAM - Instituto de Hidrología, Meteorología y Estudios Ambientales (Colombia)      ● CIMMYT - Centro Internacional de Mejoramiento de Maíz y Trigo / International Maize and Wheat Improvement Center      ● COPECO - Comisión Permanente de Contingencias (Honduras)      ● FEDEARROZ - Federación Nacional de Arroceros      ● INTA - National Institute of Agricultural Technology / Instituto Nacional de Tecnología Agropecuaria      ● SAG - Secretaría de Agricultura y Ganadería (Honduras) CGIAR innovation(s) or findings that have resulted in this outcome or impact: Innovations: This report was generated on 2022-08-19 at 08:00 (GMT+0) 2 Reporting 2021 Evidences Elaboration of Outcome/Impact Statement: In 2017, CCAFS scientists from projects CSMS (P58) and Agroclimas (P42) projects have enhanced the capacity of institutions across LAM to identify CSA options through the co-design, and co-development of predictive approaches for yield and yield-limiting factors [1–5,], and the co-development and scaling of an agro-climatic services platform [6,7, 23, 25, 28, 29,33 ]. Fenalce, Fedearroz, and ASOHOFRUCOL in Colombia have integrated data capture, storage and analytics. More than 55,000 cropping events have been analyzed from approximately 20,000 farmers [5,17-24,26-30], with scaling potential to 150,000 farmers across LAM. In addition, the approach attracted more than 820k USD in bilateral funding. The local government of Pereira (Colombia), CIMMYT’s nationwide MasAgro project (Mexico), and FLAR partners in Argentina, Uruguay, and Perú harnessed climate-site-specific agronomy analytics for agricultural planning. Four private companies (Miramontes, Inarosa, Santa Lastenia, Melonicsa) in Nicaragua implemented data-driven agronomy [30]. These agricultural organizations and extension services used the outputs to modernize and enhance their support to farmers at large scale [5,26,27]. For instance, work has enabled Fedearroz to provide recommendations about what to grow in specific regions. Machine learning is also linked to agronomic decisions and local climate forecasts, to help farmers determine when and what to plant [16, 17]. The latter are now provided in the pronosticos.aclimatecolombia.org platform [6,7]. The platform provides recommendations to farmers in 22 localities across 8 departments of Colombia (see letters from Fedearroz and Fenalce) [8]. As part of the work in linking agronomic decisions to climate forecasts, novel climate and crop prediction practices have been mainstreamed into institutions. The national meteorological services of Honduras and Colombia have improved their forecast practice by automating CPT and better linking seasonal predictions with agriculture [9–15]. In Honduras, SAG now uses climate-soil zonification and crop modelling connected to seasonal forecasts to produce forecast-based calendars for maize and beans (see letter from SAG). Additionally climate-site-specific agronomy analysis opened the opportunity to scale out climate services in Mexico (see highlights). Other major changes at the institutional level include (i) Fedearroz, Fenalce and ASOHOFRUCOL strategic objectives now include big data analysis and/or agro-climatic prediction; and (ii) both institutions now have agro-climatic prediction staff [5,17, 19,26,27,31,32]. These are linked to IDEAM, and locally to rice and maize farmers through technicians that are trained to understand model outputs and use these to produce recommendations to farmers (see letters from Fenalce and Fedearroz). This report was generated on 2022-08-19 at 08:00 (GMT+0) 3 Reporting 2021 Evidences References cited: 1. Big Data Innovation Challenge: Pioneering Approaches to Data-Driven Development. https://openknowledge.worldbank.org/handle/10986/25102 2. Jiménez D, Dorado H, Cock J, Prager SD, Delerce S, Grillon A, et al. From Observation to Information: Data-Driven Understanding of on Farm Yield Variation. PLoS One [Internet]. 2016;11(3):e0150015. Available from: http://dx.plos.org/10.1371/journal.pone.0150015 3. Delerce S, Dorado H, Grillon A, Rebolledo MC, Prager SD, Patiño VH, et al. Assessing Weather-Yield Relationships in Rice at Local Scale Using Data Mining Approaches. PLoS One [Internet]. 2016;11(8):e0161620. Available from: http://dx.plos.org/10.1371/journal.pone.0161620 4. Supporting agricultural extension towards Climate-Smart Agriculture An overview of existing tools. http://www.fao.org/3/a-bl361e.pdf 5. ACLÍMATE COLOMBIA. Open Data to Improve Agricultural Resiliency. http://odimpact.org/files/case-aclimate-colombia.pdf 6. AClimateColombia pronósticos. https://pronosticos.aclimatecolombia.org/ 7. https://twitter.com/aclimatecol/status/923948013831192576 8. [usage statistics for aclimatecolombia.org] 9. http://blog.ciat.cgiar.org/es/el-ciat-participa-en-la-presentacion-de-la-perspectiva-climatica-2018-par a-honduras/ 10. http://blog.ciat.cgiar.org/es/cpt-herramienta-climatica-oportuna-y-confiable-para-los-agricultores-ho ndurenos/ 11. http://blog.ciat.cgiar.org/agro-climatic-forecasts-to-the-rescue/ 12. https://ruav.edu.co/clima-a-favor-proyecto-ciat-revoluciona-la-agricultura-colombiana/ 13. http://www.aclimatecolombia.org/la-onu-premia-proyecto-big-data-liderado-por-el-ciat-entre-las-me jores-ideas-del-mundo-para-fortalecer-la-accion-climatica/ 14. https://sites.google.com/view/copeco 15. http://www.resilientcentralamerica.org/honduras-es/ 16. Big Data for climate-smart agriculture. https://ccafs.cgiar.org/bigdata#.WxGrjO4vzX4 17. Big Data for Resilience Storybook. https://www.iisd.org/library/big-data-resilience-storybook 18. Data-driven farming proves fertile ground for Operations research.https://cgspace.cgiar.org/rest/bitstreams/151927/retrieve 19. Data-capture webased tool adopted by Fenalce and nenamed SIRIA: http://siria.fenalce.org/locale.action?countryCode=CO 20. ASOHOFRUCOL monthly briefs on Site-Specific Agriculture https://issuu.com/aesceasohofrucol/docs/bolet__n_aesce_julio__ed 21. Contribution to the 2016 GACSA Compendium. https://www.plantwise.org/Uploads/2016%20Gacsa%20Compendium%20On%20Csa%20And%20Exte nsion.pdf 22. Data capture protocol https://github.com/bigdataciat/Protocolo_captura_de_datos 23. Open access script for data preprocessing https://github.com/bigdataciat/verificacion-de-coordenadas 24. Open access scripts for reproducible analysis ttps://github.com/bigdataciat/Regression-Methods-AEPS 25. Predicción Climática. ACLÍMATE COLOMBIA. https://pronosticos.aclimatecolombia.org/Clima This report was generated on 2022-08-19 at 08:00 (GMT+0) 4 Reporting 2021 Evidences 26. Putting data at the service of agriculture. https://cgiar-my.sharepoint.com/:b:/g/personal/d_jimenez_cgiar_org/EQqEBKjWprdIlkQTy6-iG2kBYwvj LtpRScbE8-R1FPF_MQ?e=Vvl2oe 27. USAID Case Studies: Machine Learning in Context. Data-driven Agronomy and Machine Learning at the International Center for Tropical Agriculture https://cgiar-my.sharepoint.com/:b:/g/personal/d_jimenez_cgiar_org/EXjErvaOHv1JjfpEQ3VzJ3wBjLgX KXd3-3eUhBwVfxurNw?e=KBR0R0 28. Climate services for smarter farming – what’s it all about? http://blog.ciat.cgiar.org/climate-services-for-smarter-farming-whats-it-all-about/ 29. Farmers Associations across Colombia Institutionalized Climate Site-Specific Management. http://ciat.cgiar.org/outcome/farmers-associations-across-colombia-institutionalized-climate-site-spe cific-management/ 30. Big Data, the team that seldom rests. http://blog.ciat.cgiar.org/big-data-the-team-that-seldom-rests/ 31. Value of Climate Data to Farmers” from CSRD on Vimeo. The video is available for your viewing pleasure at https://vimeo.com/2195336 32. Value of Climate Services” from CSRD on Vimeo. The video is available for your viewing pleasure at https://vimeo.com/2186653005 33. Perez et al. Drivers of vulnerability of bean growing households to climate variability in Colombia. Regional Environmental Change. In preparation. Quantification: Gender, Youth, Capacity Development and Climate Change: Gender relevance: 1 - Significant Main achievements with specific Gender relevance: A survey conducted in 2015 found that women and young people had a higher affinity to use ICTs as a means of accessing information and was suggested to strength participation of women and youth. We subscribe to the view of social inclusion (not just of gender, but marginalized groups, youth) Youth relevance: 1 - Significant Main achievements with specific Youth relevance: A survey conducted in 2015 found that women and young people had a higher affinity to use ICTs as a means of accessing information and was suggested to strength participation of women and youth. We subscribe to the view of social inclusion (not just of gender, but marginalized groups, youth) CapDev relevance: 2 - Principal Main achievements with specific CapDev relevance: As it was aforementioned, Eight farmers’ organizations in LAM integrated data capture, storage and analytics for agronomic decision making in their daily operation, and three of these (two in Colombia, one in Honduras) link seasonal agro-climatic forecasts to agronomic decisions through a climate services platform . Two meteorological services in LAM have enhanced their prediction practices and now produce ever more tailored, accurate and efficient seasonal forecasts for agriculture [9–15]. >100 students have been trained on climate-site-specific agronomy tools. Universities: ICESI, Universidad Nacional, Universidad del Rosario, Universidad del Cauca (see list of participants in atachement Project Study #2099) Climate Change relevance: Other cross-cutting dimensions: This report was generated on 2022-08-19 at 08:00 (GMT+0) 5 Reporting 2021 Evidences Other cross-cutting dimensions description: Institutional innovation and empowerment Outcome Impact Case Report link: Study #2069 Contact person: Daniel Jiménez R. Scientist- Coordinator Community of Practice Data-Driven Agronomy. CGIAR platform for Big Data in Agriculture. International Center for Tropical Agriculture (CIAT) . E-mail: d.jimenez@cgiar.org Tel: +57 (2) 4450000 Ext: 3729 Skype: darijiro . Twitter: @drdarijiro LinkedIn: https://co.linkedin.com/in/darijiro This report was generated on 2022-08-19 at 08:00 (GMT+0) 6