Sustainability, environment and climate change:crop production and health impacts<br />Tim Wheeler, Tom Osborne, Gillian R...
Climate variability and change present threats and opportunities to the environment and sustainability of food systems, an...
Current methods and their limitations
Sources of uncertainty in projections
Appropriate levels of complexity</li></li></ul><li>Climate to crops to nutrition<br />calories<br />nutritional contributi...
Climate to crops to malnutrition<br />Climate<br />Crops (Reading)<br />Food (Southampton)<br />Health<br />(LSHTM)<br />S...
Climate change<br />
Climate change assessments<br />climate<br />model<br />Climate<br />Assessment<br />crop<br />response<br />crop<br />mod...
Crop responses to climate<br />
10 – 5<br />5 – 2.5<br />2.5 – 0<br />0 – -2.5<br />-2.5 – -5<br />-5 – -10<br />-10 – -20<br />Projections of crop impact...
1. Underpinning knowledge<br />Research<br />- qualitative impacts across the sector<br />- broad-scale patterns of crop g...
2. Spatial and temporal scale<br />global climate model<br />regional climate model<br />crop data<br />
3. Climate variability<br />W. Australia wheat production<br />
Limitations (cont ...)<br />4. Major crops only (wheat, maize, soyabean, rice)<br />5. Productivity focus<br />6. Adaptive...
Sources of uncertainty<br />Climate model uncertainty<br />GHG emissions uncertainty<br />Crop model uncertainty<br />Ense...
Using probabilistic climate forecasts<br />Model average<br />63 ensemble members<br />713 kg ha-1<br />Observed<br />775 ...
Expand modelling system to cover limitations?<br />Appropriate level of complexity<br />
Climate model uncertainty<br />Relative importance of different sources of uncertainty in climate projections of surface a...
Simple correlations between rainfall and yield<br />Seasonal rainfall and groundnut yield for all India<br />Time trend re...
Patterns of seasonal rainfall and yield of groundnut in India<br />District level groundnut yields (kg ha-1)<br />Mean of ...
Intermediate complexity crop model<br />Challinor et al 2004<br />Osborne, 2010<br />
Intermediate complexity crop model<br />
Adaptation & uncertainty<br />A1B, 2050<br />
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Sustainability, Environment and Climate Change: Crop Production and Health Impacts - Professor Tim Wheeler, University of Reading

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  • Undernutrition: when the body contains lower than normal amounts of one or more nutrients, i.e. deficiencies in macronutrients and/or micronutrients. ‘Undernutrition encompasses stunting, wasting and deficiencies of essential vitamins and minerals (collectively referred to as micronutrients).’Malnutrition: an abnormal physiological condition caused by deficiencies, excesses or imbalances in energy, protein and/or other nutrients.
  • Some studies are global in extentParry: regression fits to crop model at many sites. Extrapolate to countries. HadCM3 climate changeWBDR: ?Cline: Parry approach AND panel regression. Several climate models. Consensus result.
  • Currently crop simulations are available for a small number of major crops – wheat, maize, soybean, rice and groundnut. Although these crops make up around 85% of the global diet, it still leaves 15% unaccounted for, contributes to the nutritional quality of the diet.
  • Without adaptation, climate model uncertainty affects the magnitude of CC impact.With adaptation, climate model uncertainty affects sign of change.
  • Sustainability, Environment and Climate Change: Crop Production and Health Impacts - Professor Tim Wheeler, University of Reading

    1. 1. Sustainability, environment and climate change:crop production and health impacts<br />Tim Wheeler, Tom Osborne, Gillian Rose, Walker Institute<br />Sari Kovats, Simon Lloyd, LSHTM<br />t.r.wheeler@reading.ac.uk<br />
    2. 2. Climate variability and change present threats and opportunities to the environment and sustainability of food systems, and hence will affect links between agriculture, nutrition and health<br /><ul><li>How can projections of climate-induced changes in crop production be linked to nutrition?
    3. 3. Current methods and their limitations
    4. 4. Sources of uncertainty in projections
    5. 5. Appropriate levels of complexity</li></li></ul><li>Climate to crops to nutrition<br />calories<br />nutritional contribution to diet<br />food safety / contamination<br />climate<br />energy <br />economy <br />global agriculture<br />crop<br />regulation <br />water resources <br />natural ecosystems <br />pollution <br />soil <br />social systems <br />
    6. 6. Climate to crops to malnutrition<br />Climate<br />Crops (Reading)<br />Food (Southampton)<br />Health<br />(LSHTM)<br />Simon Lloyd, LSHTM<br />
    7. 7. Climate change<br />
    8. 8. Climate change assessments<br />climate<br />model<br />Climate<br />Assessment<br />crop<br />response<br />crop<br />model<br />Crop<br />impact<br />adaptation<br />
    9. 9. Crop responses to climate<br />
    10. 10. 10 – 5<br />5 – 2.5<br />2.5 – 0<br />0 – -2.5<br />-2.5 – -5<br />-5 – -10<br />-10 – -20<br />Projections of crop impacts<br />Potential change in cereal yields (%)<br />No data<br />Parry et al 2004<br />World Bank Development Review 2010<br />
    11. 11. 1. Underpinning knowledge<br />Research<br />- qualitative impacts across the sector<br />- broad-scale patterns of crop growing areas <br />- responses of plant physiology to climate<br />- site-specific impacts on crop productivity<br />- short-term climate variability<br />- representing uncertainty in impacts<br />- combining detailed local impacts with large spatial coverage<br />good<br />mediocre<br />or patchy<br />poor<br />
    12. 12. 2. Spatial and temporal scale<br />global climate model<br />regional climate model<br />crop data<br />
    13. 13. 3. Climate variability<br />W. Australia wheat production<br />
    14. 14. Limitations (cont ...)<br />4. Major crops only (wheat, maize, soyabean, rice)<br />5. Productivity focus<br />6. Adaptive capacity of cropping systems underestimated<br /> and more ...<br />
    15. 15. Sources of uncertainty<br />Climate model uncertainty<br />GHG emissions uncertainty<br />Crop model uncertainty<br />Ensemble of:<br />climate model X GHG emissions scenario X crop model<br />
    16. 16. Using probabilistic climate forecasts<br />Model average<br />63 ensemble members<br />713 kg ha-1<br />Observed<br />775 kg ha-1<br />Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998 from Challinor et al (2005)<br />
    17. 17. Expand modelling system to cover limitations?<br />Appropriate level of complexity<br />
    18. 18. Climate model uncertainty<br />Relative importance of different sources of uncertainty in climate projections of surface air temperature<br />Orange is internal variability<br />(natural variability, ENSO, NAO,…)<br />Green is GHG scenario uncertainty<br />Blue is model uncertainty<br />(with same forcing)<br />from Hawkins and Sutton, 2008<br />
    19. 19. Simple correlations between rainfall and yield<br />Seasonal rainfall and groundnut yield for all India<br />Time trend removed. r2 = 0.52, p < 0.0001 rainfall yield<br />
    20. 20. Patterns of seasonal rainfall and yield of groundnut in India<br />District level groundnut yields (kg ha-1)<br />Mean of 1966 - 1990<br />Data source: ICRISAT<br />Sub-divisional level seasonal rainfall (JJAS, cm) <br />Mean of 1966 - 1990<br />Data source: IITM<br />
    21. 21. Intermediate complexity crop model<br />Challinor et al 2004<br />Osborne, 2010<br />
    22. 22. Intermediate complexity crop model<br />
    23. 23. Adaptation & uncertainty<br />A1B, 2050<br />
    24. 24. Conclusions<br />How can projections of climate-related changes in crop production be linked to nutrition?<br /><ul><li>Link quantitative models. These can only represent a very simple view of food baskets
    25. 25. Can explore sources of uncertainty in these projections
    26. 26. The challenge for research is to expand the modeling system, whilst using the appropriate level of complexity within the model</li></li></ul><li>Thank you<br />Contact:<br />Tim Wheeler t.r.wheeler@reading.ac.uk<br />

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