The importance of agricultural data and informatics for adaptation to climate change
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The importance of agricultural data and informatics for adaptation to climate change

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Presentation made in the Generation Challenge Program (GCP) General Research Meeting held in Hyderabad, India in September 2011.

Presentation made in the Generation Challenge Program (GCP) General Research Meeting held in Hyderabad, India in September 2011.

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  • For Lobell map: Values show the linear trend in temperature for the main crop grown in that grid cell, and for the months in which that crop is grown. Values indicate the trend in terms of multiples of the standard deviation of historical year-to-year variation. ** A 1˚C rise tended to lower yields by up to 10% except in high latitude countries, where in particular rice gains from warming. ** In India, warming may explain the recently slowing of yield gains. For yield graph: Estimated net impact of climate trends for 1980-2008 on crop yields for major producers and for global production. Values are expressed as percent of average yield. Gray bars show median estimate and error bars show 5-95% confidence interval from bootstrap resampling with 500 replicates. Red and blue dots show median estimate of impact for T trend and P trend, respectively. ** At the global scale, maize and wheat exhibited negative impacts for several major producers and global net loss of 3.8% and 5.5% relative to what would have been achieved without the climate trends in 1980-2008. In absolute terms, these equal the annual production of maize in Mexico (23 MT) and wheat in France (33 MT), respectively. Source: Climate Trends and Global Crop Production Since 1980 David B. Lobell 1 , , Wolfram Schlenker 2 , 3 , and Justin Costa-Roberts 1 Science magazine
  • Where the bar shows yield gap fractions, so green (0) = no gap between actual production and potential production; and red (1) = complete yield gap.
  • Why focus on Food security And climate change has to be set in the context of growing populations and changing diets 60-70% more food will be needed by 2050 because of population growth and changing diets – and this is in a context where climate change will make agriculture more difficult.
  • The current suitability is closed to 100% because we are only using a range of temperature and precipitation and we don’t consider other parameters as soil,…. We did the ecocrop analysis with the average of annual precipitation and not with taking in account the crop seasonnality (which could be more exact).

The importance of agricultural data and informatics for adaptation to climate change Presentation Transcript

  • 1. The Importance of Agricultural Data and Informatics for Climate Change Adaptation Andy Jarvis, Julian Ramirez, Glenn Hyman, Jacob van Etten CCAFS
  • 2. The Challenge
  • 3. The concentration of GHGs is rising Long-term implications for the climate and for crop suitability
  • 4. Historical impacts on food security % Yield impact for wheat Observed changes in growing season temperature for crop growing regions,1980-2008. Lobell et al (2011)
  • 5. Average projected % change in suitability for 50 crops, to 2050 Crop suitability is changing
  • 6. 0 0.25 0.50 0.75 1 Exacerbating the yield gap From Licker et al, 2010 Climate change will likely pose additional difficulties for resource-poor farmers (e.g., in Africa), thereby increasing the yield gap
  • 7.
    • In order to meet global demands, we will need
    • 60-70%
    • more food
    • by 2050.
    Food security is at risk
  • 8. “ Unchecked climate change will result in a 20% increase in malnourished children by 2050 ,” relative to the full mitigation scenario. -Gerald Nelson, IFPRI/CCAFS
  • 9. Progressive Adaptation
    • THE VISION
    • To adapt farming systems, we need to:
    • Close the production gap by effectively using technologies, practices and policies
    • Increase the bar : develop new ways to increase food production potential
    • Enable policies and institutions, from the farm to national level
  • 10. A heavy reliance on models
    • For a 2030 world, difficult to do work without the aid of models
    • Starts with the Global Climate Models…
    • Through to agricultural impacts models
  • 11. And models need data…
    • On climate
    • Weather
    • Crop distribution
    • Crop productivity
    • Varietal adaptations
    • Genetic traits
    • Social parameters
    • … and the list goes on….
  • 12. And that data should be..
    • Available (publically)
    • Documented and organised
    • Or you’ll have another ClimateGate
  • 13. A selection of data and informatics tools being developed in CCAFS
    • Agricultural trial data
    • GxE analysis with an emphasis on the E
    • Setting breeding priorities for a climate changed world
    • Analogues
    • Pulling it all together under a knowledge management umbrella: the AMKN
  • 14. >> Multi-site agricultural trial database(agtrial.org) 20,000+ maize trials in 123 research sites Effect of +1ºC warming on yield Sites with >23ºC would suffer even if optimally managed More than 20% loss in sites with >20ºC, under drought Lobell et al. 2011
  • 15.
    • Over 3,000 trials
    • 16 crops
    • 20 countries
    • > 15 international and national institutions
    New data >> Multi-site agricultural trial database(agtrials.org)
  • 16. Importance & Potential
    • Collating input climate and agricultural data
    • Design of experiments
    • Calibration, validation and crop model runs
    • Exploration of adaptation options
      • Genetic improvement
      • On-farm management practices
    • Test them via modelling
    • Build “adaptation packages”
    • Assess technology transfer options
    (c) Neil Palmer (CIAT)
  • 17. >> Multi-site agricultural trial database(agtrial.org) 20,000+ maize trials in 123 research sites Effect of +1ºC warming on yield Sites with >23ºC would suffer even if optimally managed More than 20% loss in sites with >20ºC, under drought Lobell et al. 2011
  • 18. GxE: OPV vs hybrid
  • 19. GxE: duration
  • 20.  
  • 21. Current Climatic Suitability
  • 22. Current Climatic Constraints
  • 23. Future Suitability
  • 24. Benefits of breeding options
  • 25. The Analogue Concept
    • We heavily rely on models to tell us what the future holds
      • GCM/RCM projections
      • Crop models, household models, farming system models
    • Few take into account human adaptive capacity, and social and cultural factors that contribute to decision making
  • 26. The Analogue Concept
    • Analogues: Use spatial variability in climate as a means of having a real experiment of what the future holds for a site
    • Where can I find my future projected conditions, TODAY?
  • 27. Karnal (India)
    • Rainy season from June to September
  • 28. Why we think this an important approach
    • Facilitating farmer-to-farmer exchange of knowledge
    • Permitting validation of computational models and trialing of new technologies/techniques
  • 29. http://gismap.ciat.cgiar.org/analogues
  • 30. Adaptation to progressive climate change · 1 >> Spotlight on: The AMKN Platform It links farmers’ realities on the ground with promising scientific research outputs, to inspire new ideas and highlight current challenge. Why is it useful? The Climate Change Adaptation and Mitigation Knowledge Network platform is a portal for accessing and sharing agricultural A&M knowledge. What CCAFS output?
  • 31. AN EXAMPLE OF USING THE SOME OF THESE APPROACHES TO LINK KNOWLEDGE AND DATA
  • 32. Starting site: Kaffrine, Senegal
    • CCAFS site
    • 600 mm annual rainfall
    • Min. Temp. 14.8°C
    • Max. Temp. 39.1°C
    • Main crops:
      • Millet
      • Maize
      • Peanuts
      • Sorghum
      • Sesame
    • Climate Change threats:
      • Erratic Rainfall
    • Socio-economic constraints:
      • High poverty level - Low access to capital
      • No attractive market
    Kaffrine, Senegal (x:-15.54, y:14.106)
  • 33. Change in climate, 2020 – Kaffrine , Senegal Average Climate Change Trends: - Decrease in precipitation from 660 mm to 590.58 mm - Increase of mean temperature of 0.344°C
  • 34. Crop suitability – Kaffrine , Senegal
  • 35.
    • Mean of the dissimilarity index of 24 GCMs between the starting site Kaffrine, Senegal with the entire world
    • - Climate parameters:
    • Monthly temperature - Monthly rainfall
    • Scenario A1B, 2030
    High climate similarity Where can we find a region with similar climatic conditions to Kaffrine, Senegal in 2030? Climate similarity
  • 36. Zoom on high similarity climate of CCAFS sites CCAFS site with minimum value of dissimilarity with the climate of Kaffrine, Senegal = Tougou, Burkina Faso Best consistency between the 24 GCM’s = Fakara , Niger The current climate of Fakara is similar to the future projected climate in Kaffrine Fakara is the most likely analogue of Kaffrine
  • 37. Analogue of Kaffrine, Senegal: Fakara, Niger
    • CCAFS site
    • 500 mm annual rainfall
    • Min. Temp. 15.7°C
    • Max. Temp. 41.3°C
    • Main crops:
      • Millet
      • Beans
      • Leafy vegetables
      • Maize
      • Sorghum
    • Climate Change threats:
      • Drought
    • Socio-economic constraints:
      • Low level of infrastructure
      • Limited access to market
    Fakara, Niger (x:2.687, y:13.517)
  • 38. Comparison of current conditions Current conditions Kaffrine, Senegal Fakara, Niger = Future condition of Kaffrine Zone Transition zone from the Sahelien towards the Sudan Savannah zone Within the Sahel Altitude 15 m 225 m Annual rainfall average 600 mm 500 mm Minimum Temperature 14.8 °C 15.7 °C Maximum Temperature 39.1 °C 41.3 °C Main crops
    • Millet
    • Maize
    • Peanuts Sorghum
    • Sesame
    Millet Beans Leafy vegetables Maize Sorghum Length of Growing period 130 days 95 days Soil type Deep sandy soil Sandy and clay sandy soil Soil FAO Class Ferric Luvisols Luvic Arenosols Socio-economic constraints High poverty level Low access to capital No attractive market Low level of infrastructure Limited access to market
  • 39. Comparison of main crops Kaffrine, Senegal Fakara, Niger
    • Millet
    • Maize
    • Peanuts Sorghum
    • Sesame
    Millet Beans Leafy vegetables Maize Sorghum
  • 40. Agtrial database - Application Kontela, Mali is another potential analogue to Kaffrine, Senegal The sorghum yield data in Kontela, Mali could help us to know the future sorghum yield in Kaffrine, Senegal. Yield data available in the Agtrials database: http://www.agtrials.org:85/ Sorghum yield data Sorghum Variety K (kg/ha) N (kg/ha) P (kg/ha) Lime (kg/ha) Manure (kg/ha) Grain yield (t/ha) CSM63E 0 0 0 0 0 0.68 CSM63E 0 0 0 0 0 0.10 CSM63E 60 0 30 0 0 0.55 CSM63E 60 100 0 0 0 0.33 CSM63E 0 100 30 0 0 0.38 CSM63E 60 100 30 0 0 1.40 CSM63E 60 100 30 0 0 0.54 CSM63E 60 100 30 500 0 1.68 CSM63E 60 100 30 0 10000 1.06 CSM63E 60 100 30 0 0 0.08
  • 41. Agtrial database - Application Senegal Hombolo, Tanzania is another potential analogue to Kaffrine, Senegal Yield data available in the Agtrial database: http://www.agtrials.org:85/ The MILLET yield data in Homboro, Tanzania could help us to know the future millet yield in Kaffrine, Senegal. Millet Yield data Variety name Grain Yield (t/ha) Nyamkombo 0.87 Okashana-2 1.09 PMV-2 0.78 PMV-3 0.86 SDMV89003 0.88 SDMV89007 0.82 SDMV90031 1.16 SDMV91018 0.91 SDMV92033 0.75 SDMV92038 0.82 SDMV95032 1.03 SDMV95033 0.93 SDMV95045 1.13 SDMV96075 0.89 SDMV97007 0.87 SDMV97011 0.87 TSPM91018 0.69 SDMV89005 0.90 SDMV92035 0.51 SDMV92037 1.01 SDMV95009 0.77 SDMV95014 0.68 SDMV95025 0.73 ZPMV92005 0.50 ZPMV94001 0.60
  • 42. stay in touch www.ccafs.cgiar.org sign up for science, policy and news e-bulletins follow us on twitter @cgiarclimate