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 Tim Wheeler, Tom Osborne, Gillian Rose, Walker Institute Sari Kovats, Simon Lloyd, LSHTM email@example.com
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
How can projections of climate-induced changes in crop production be linked to nutrition?
Climate to crops to nutrition calories nutritional contribution to diet food safety / contamination climate energy economy global agriculture crop regulation water resources natural ecosystems pollution soil social systems
Climate to crops to malnutrition Climate Crops (Reading) Food (Southampton) Health (LSHTM) Simon Lloyd, LSHTM
10 – 5 5 – 2.5 2.5 – 0 0 – -2.5 -2.5 – -5 -5 – -10 -10 – -20 Projections of crop impacts Potential change in cereal yields (%) No data Parry et al 2004 World Bank Development Review 2010
1. Underpinning knowledge Research - qualitative impacts across the sector - broad-scale patterns of crop growing areas - responses of plant physiology to climate - site-specific impacts on crop productivity - short-term climate variability - representing uncertainty in impacts - combining detailed local impacts with large spatial coverage good mediocre or patchy poor
2. Spatial and temporal scale global climate model regional climate model crop data
3. Climate variability W. Australia wheat production
Limitations (cont ...) 4. Major crops only (wheat, maize, soyabean, rice) 5. Productivity focus 6. Adaptive capacity of cropping systems underestimated and more ...
Sources of uncertainty Climate model uncertainty GHG emissions uncertainty Crop model uncertainty Ensemble of: climate model X GHG emissions scenario X crop model
Using probabilistic climate forecasts Model average 63 ensemble members 713 kg ha-1 Observed 775 kg ha-1 Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998 from Challinor et al (2005)
Expand modelling system to cover limitations? Appropriate level of complexity
Climate model uncertainty Relative importance of different sources of uncertainty in climate projections of surface air temperature Orange is internal variability (natural variability, ENSO, NAO,…) Green is GHG scenario uncertainty Blue is model uncertainty (with same forcing) from Hawkins and Sutton, 2008
Simple correlations between rainfall and yield Seasonal rainfall and groundnut yield for all India Time trend removed. r2 = 0.52, p < 0.0001 rainfall yield
Patterns of seasonal rainfall and yield of groundnut in India District level groundnut yields (kg ha-1) Mean of 1966 - 1990 Data source: ICRISAT Sub-divisional level seasonal rainfall (JJAS, cm) Mean of 1966 - 1990 Data source: IITM
Intermediate complexity crop model Challinor et al 2004 Osborne, 2010