Towards climate-smart decision support systems in the Philippines
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Presentation on agro-climatic information services for the Philippines Department of Agriculture
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Towards climate-smart decision support systems in the Philippines
- 1. Towards climate- smart decision support systems @JulianCIAT
- 2. Why climate-smart decisions? 1. Climate controls 33 % of global yield variation. Up to 45 % in some areas of The Philippines (Ray et al. 2015) 2. Understanding climate variability helps produce actionable information for farmers 3. There is enourmous demand across the tropics 4. Opportunity to do great science in many tropical countries 5. In the long-term, these can become services for farmers (i.e. climate services)
- 3. Part 1: Improving site-specific management through historical data analysis and crop modelling
- 4. Rice in Colombia Data collection Data analysis Insight validation
- 7. Learning from historical observations to determine limiting factors Machine learning analysis with n=1,240 plot-level observations for irrigated rice Data collection Data analysis Insight validation Delerce et al. (2016)
- 8. Learning from historical observations to determine limiting factors Data collection Data analysis Insight validation Yet more interesting. We can determine why some farms are underperforming “Cluster 10” Delerce et al. (2016)
- 9. (c) (d) (e) (b) (a) R2 = 45.79 1. Good drainage and/or higher slopes Maize case 2. At least 25 kg/ha P 3. Mechanical harvest 4. At least 65,000 plants/ha
- 10. Crop simulations to guide management recommendations • Process-based models represent plant processes and crop systems in high level of (mechanistic) detail Models capture • Responses to climate (drought, temperature) • Response to different N, P and/or K levels • Varietal differences in responses CIAT (unpublished data)
- 11. Crop simulations to guide management recommendations • From a set of calibrated varieties we can determine those which consistently perform well. • We could also determine those that do not seem to do very well under certain events (e.g. drought)
- 12. What would be needed? • Systematic data collection directly from farms • Calibration and validation data for crop simulation model • Clear idea of who the users are, and how information should be “packaged” • Historical data analysis toolkit • Working crop simulation models For a given site, with known climate and soil conditions, we can tell the user what variety to plant, when to plant it, and how to manage the land
- 13. Part 2: Seasonal outlooks for better farming decisions
- 14. 21/03/ 2018 14
- 15. Systematic provision of seasonal agro-climatic outlooks Climate data QA/QC Seasonal forecasts Crop models Crop prediction Key variables Formats and graphics Agro- climatic info Adapted from Prager et al. (2017)
- 16. Start with the user: Understanding user needs 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
- 17. 17 Understanding user needs What are the most critical climate-related decisions? • Whether to sow • When to sow? • What to sow?
- 18. Improve climate information databases 1 2 3 Llanos et al. (2016) • Collect data • Review methods for gap filling and interpolation • Develop novel methods where needed • Precipitation • Solar radiation
- 19. 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
- 20. • Forecasts given as probabilities • Need to be ”brought closer” to user by written interpretation
- 21. Response of given variety to a particular climate outlook
- 23. Going back to the users
- 24. • 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 • Farmers systematically receive agro-climatic advice • Continuous funding to process and support to farmer organisations for continued capacity strengthening • Prediction team improved their efficiency thanks to development of automated forecast tools • Sub-direction of Meteorology pushes for National Framework for Climate Services, using agriculture as lighthouse
- 25. 170 farmers – 3,6m USD saved in 2014
- 26. 3. Crop modelling 2. Data driven agronomy 1. Assess local information needs Farmer advisory services Connect to seasonal and weather forecasts Participatory and digital platforms for farmer advisory
- 27. Thank you for your attention! @JulianCIAT
Notas del editor
- The idea here is to say why developing decision support systems aimed at making decisions more climate-smart is important.
- I divide the talk in two sections. Part 1 is about using historical records for “learning from past” and use that to improve crop management.
- 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).
- This is an example with maize. Point here is that data analysis led to 5 clear-cut management recommendations.
- You may know this already. If we know that the crop is constrained by nutrients, we can test in the field the performance of the crop (cassava in this case), we can simulate these responses with models, and these simulations provide the basis for decisions.
- Similarly, if we knew that cultivar is a key factor, we can determine which cultivars outperform others in particular situations, and make recommendations about their use at farm level.
- This should be clear. Green items indicate those we already have from previous projects elsewhere (i.e. Latin America)
- This is the current thinking at CIAT on how to provide agro-climatic information to farmers. We work on “better forecasts”, “improved insights”, “institutional innovation”, and “user oriented communications (or 2-way dialogue)”. These underpin all the activities we do.
- On the technical side, and if we look at this more linearly, we start from data, develop crop predictions, and then provide tailored agro-climatic info for decision making
- 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 is related to the below. We found that the most important for them is: Whether to sow When to sow? What varieties to sow?
- Improving climate and ag. Information base
- 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 is illustrating the nature of the forecast. It’s a probabilistic forecast, and we need to make sure that we translate this information well for users.
- 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.
- This is the agro-climatic information interface that we have developed for Colombia. It is now being used by rice and maize farmer organizations; these organisations are reaching many thousand farmers.
- Finally, these are results of usability testing. Hopefully relatively easy to describe. People basically respond if they agree or not with the statement being given. Here, only 2 questions (out of 10 we asked) are shown.
- This is self explanatory, hopefully. It shows project-level impacts in Colombia
- A final slide showing one major project impact in 2014. We saved many rice farmers from crop failure.