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Sustainability, environment and climate change:
crop production and health impacts
Tim Wheeler, Tom Osborne, Gillian Rose, Walker Institute
Sari Kovats, Simon Lloyd, LSHTM
t.r.wheeler@reading.ac.uk
• 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?
• Current methods and their limitations• Sources of uncertainty in projections• Appropriate levels of complexity
energy
pollution
water resources
economy
natural ecosystems
crop regulation
global agriculture
soil
calories
nutritional contribution to diet
food safety / contamination
Climate to crops to nutrition
climate
social systems
Climate to crops to malnutrition
Probability density function - dietary energy consumption
0 1000 2000 3000 4000 5000 6000
energy (kcal)
P(U
)
Food (Southampton)
Crops (Reading)
Health(LSHTM)
Simon Lloyd, LSHTM
Climate
Climate change
Climate change assessments
Cli
mat
eC
rop
climate
model
cropresponse
cropmodel
impact
Ass
essm
ent
adaptation
Crop responses to climate
Projections of crop impacts
Potential change in cereal yields (%)
No data
10 – 5
0 – -2.5
-5 – -10-2.5 – -5
-10 – -20
2.5 – 05 – 2.5
World Bank Development Review 2010
Parry et al 2004
1. Underpinning knowledge
good
mediocre
or patchy
poor
- 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
Research
2. Spatial and temporal scale
global climate model
crop dataregional climate model
3. Climate variabilityW. Australia wheat production
0
2000
4000
6000
8000
10000
12000
Area (million ha)
Production (*1000 t)
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
0
5
10
15
20
25
200 300 400 500 600 700 800 900 1000 1100 1200
Yield (kg ha-1)
Fre
qu
ency
Using probabilistic climate forecasts
Use of DEMETER multi-model ensemble for groundnut yield in Gujarat, 1998from Challinor et al (2005)
Model average 63 ensemble members
Observed
775 kg ha-1
713 kg ha-1
Appropriate level of complexity
Expand modelling system to cover limitations?
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
Climate model uncertainty
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 - 1990Data source: ICRISAT
Sub-divisional level seasonal rainfall (JJAS, cm) Mean of 1966 - 1990Data source: IITM
Intermediate complexity crop model
400
600
800
1000
1200
1965 1970 1975 1980 1985 1990
Gro
un
dn
ut
yiel
d (
kg h
a-1
) National Yield StatisticsGLAM prediction
Challinor et al 2004 Osborne, 2010
Intermediate complexity crop model
Conclusions
How can projections of climate-related changes in crop production be linked to nutrition?
• 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
Thank you
Contact:Tim Wheeler t.r.wheeler@reading.ac.uk
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