Data-driven agro-climatic services help farmers adapt to climate risks
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This document discusses using data and climate modeling to provide agricultural climate services. It notes that climate explains 30-60% of yield variation for various crops. It aims to provide farmers with information on critical climate-related decisions like when to sow and what to plant. The services analyze historical climate and crop data to understand limiting factors, predict seasonal climate, and predict crop yields. Formats and participatory forecasting are used to communicate forecasts to farmers. Initial results found the services helped farmers in Colombia save $3.6 million in 2014 and many farmers in Latin America now receive monthly agro-climatic advice.
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Data-driven agro-climatic services help farmers adapt to climate risks
- 1. Data-driven agro-climatic services Julian Ramirez-Villegas CIAT / University of Leeds
- 2. Why climate-smart decisions? Climate drives ~32-39% yield variation: our systems are sensitive to climate, not resilient to it Ray et al. 2015
- 3. 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 Climate explains 30-60 % yield variation Meta: 61% (irrigated) Temperature during reproductive phase
- 4. In this context, farmers plan their crop based on what occurred last year
- 5. Agricultural climate services Climate data QA/QC Better forecasts Crop modelling & big data analytics Crop yield prediction Key variables Graphics and formats Climate service Prager et al. (2017)
- 7. 7 Understanding user needs What are the most critical climate-related decisions? • Whether to sow • When to sow? • What to sow?
- 8. Understanding user capacities 37% participants reported that “format 1” was clear yet DID NOT produce a good interpretation 14% participants reported “format 1” was confusing and consequently gave an erroneous or incomplete interpretation 95 participants from 6 sites: 21 farmers 70 profesionals 4 others Muñoz and Howland (2016) 23% participants reported that “format 3” was clear yet DID NOT produce a good interpretation 23% participants reported “format 3” was confusing and consequently gave an erroneous or incomplete interpretation
- 9. Data-driven agronomy for rice in Colombia Data collection Data analysis Insight validation Delerce et al. (2016)
- 10. Learning from historical observations to determine limiting factors Machine learning analysis with n=1,240 plot-level observations for irrigated rice Delerce et al. (2016) 26.7 % explained variance Note importance of cultivar, and 3 main climate variables
- 11. Learning from historical observations to determine limiting factors Yet more interesting. We can determine why some farms are underperforming “Cluster 10” Delerce et al. (2016) And recommend ’best suited’ cultivars for given climate types
- 12. Perform seasonal predictions 1. Understand predictability 2. Develop local climate predictions for 3-6 months 3. Automate predictions for operational purposes
- 13. Understanding seasonal climate predictability • Understanding what predictive skill we have in different regions and periods • Using that knowledge to systematically inform on conditions for the next 4-6 months
- 14. Yield prediction Support decisions 1. Should I plant? 2. When to plant? 3. Which variety to plant?
- 15. Response of given variety to a particular climate outlook Yield of F2000 for planting between Oct – Nov 2017 (Lorica)
- 16. Towards a climate information service Advisory toolkit Operational seasonal forecast platform Technical agro- climatic committees Agro- climatic bulletins Participatory forecasts (PICSA) FarmersInstitutions
- 17. The impacts – 170 farmers 3,6m USD saved in 2014
- 18. • From not knowing how to manage climate variability, to having a team of 6 people tasked with delivering agro-climatic information • Many of their 24,000 farmers receive monthly agro- climatic advice The impacts • A team of 2 agro-climatologists running models, leading discussion committees, and producing bulletins • Many of their ~7,000 farmers systematically receive agro-climatic advice • We estimate 300,000 farmers in Latin America are currently receiving agro-climatic information resulting from our work
Editor's Notes
- This is related to the below. We found that the most important for them is: Whether to sow When to sow? What varieties to sow?
- From here on the ppt goes step by step: Step 1 is to understand users needs. We carried out workshops with farmers and technicians to understand their capacities and information needs.
- This slide tries to give a sense on how these insights are developed. Here, the analysis shows that cultivar is the most important variable, followed by a bunch of climatic variables. Developing these insights is a continuous process starting from data collection, then data analysis and then validation of these insights with experts
- This slide tries to give a sense on how these insights are developed. Here, the analysis shows that cultivar is the most important variable, followed by a bunch of climatic variables. Developing these insights is a continuous process starting from data collection, then data analysis and then validation of these insights with experts
- Here the idea is to give more detail about the “variety” being most important variable. Not only we are able to say that “variety” is an important factor determining productivity (this may be obvious), but also we are able to determine those farms which are underperforming and why (i.e. because having climates which are similar to other farms, they are growing varieties that do not perform well under those conditions).
- We try to understand the extent to which we can predict rainfall. The idea is that the graph shows that there is variation in Kendall correlation (which tells you how good a climate forecast is).
- This tries to give an idea of yield variation across planting dates. If you focus on explaining the black line (mean yield across a large number of simulations) you could say that the model is allowing us to identify dates in which yield is likely low, and people shouldn’t plant their crop on those dates.
- A final slide showing one major project impact in 2014. We saved many rice farmers from crop failure.
- This is self explanatory, hopefully. It shows project-level impacts in Colombia