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
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 />firstname.lastname@example.org<br />
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. 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 capacity of cropping systems underestimated<br /> and more ...<br />
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 />
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 />
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 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 />
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 />
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 />
Intermediate complexity crop model<br />Challinor et al 2004<br />Osborne, 2010<br />
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
Can explore sources of uncertainty in these projections
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 email@example.com<br />