andy challinor a.j.challinor@leeds.ac.uk
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Andy ChallinorA.J.Challinor@leeds.ac.uk
Forecasting food in China: the influence of climate, composition
and socio-economics
Institute for Climate and Atmospheric Science
Co-authors: Evan Fraser, Steve Arnold, Sanai Li, Elisabeth Simelton
Lobell et al. (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030. Science 319.
China South Asia
Turner, B. et al. (2003) A framework for vulnerability analysis in sustainability science. PNAS. 100,4, 8074-8079.
Progress in modelling food crop production
Linking crop yield and climate prediction models to assess impact
Simulating a range of impacts based on socio-economic scenarios
Agrometeorology
Adaptation, e.g. through choice of crop genotype
Quantifying biophysical uncertainty: climate and crop yield ensembles
Qualitative approach to food systems research
Focus on ways people obtain food.
DFID’s “sustainable livelihoods approach” that looks at how different types of capital are used to obtain food.
Attempts to generalize field studies and link with global drivers.
?
Biophysical Modelling
Qualitative approach to food systems research
Climate impacts and adaptation in China
Can wheat yield be simulated using a crop model driven by regional climate model (PRECIS) output?
What are the drivers of current and future yields?
Is adaptation needed?
Tests at two locations showed better model-observation agreement for rainfed simulations than irrigated
Wheat cultivation in China
Winter wheat is partially irrigated in some regions of China (no quantitative data available)
Comparison of simulated and observed wheat yield (kg/ha) at 0.5o scale across China
(a) Observations (b) Simulations (rainfed), using PRECIS baseline climate
Current climate: simulated wheat yield as a function of seasonal total rainfall in China
Climate change: temperature limitations on yield of winter wheat
Baseline
Grain-filling occurs after flowering
Increase in simulated wheat yield (%) in response to a doubling of CO2 from 350 to 700 ppm in China
Two plausible responses to a doubling of CO2
(No associated climate change)
The ‘net’ effect of climate change in the North China Plain:
Interannual variability of yield: CV up by ~10-20% across NCP.
Without CO2 With CO2
North NCP ~20 to 50+ % increaseSouth NCP ~20% decrease ~20% increase
Results qualitatively similar for A2 and B2 scenarios
Mean yield from:
Winter wheat
Causes of north/south difference:• Increase in the amount of seasonal precipitation in the north
– associated decrease in soil water stress• Lengthening of period between flowering and harvest in the north, decrease in much of the south
– Super-optimal temperatures– Earlier flowering whilst temperatures are increasing => cooler (sub-optimal) post-flowering temperatures
Baseline
Genotypic adaptation to climate change
Which genotypic properties are needed to adapt to climate change?
Do these properties exist in the current germplasm?
Ensemble methods: genotypic adaptation to changes in mean temperature, using QUMP
• Graph suggests 20% increase in TTR is needed• Further simulations and analysis of crop cardinal temperatures suggest a 30% increase may be needed• Simple analysis of field experiments suggests the potential for a 14 to 40% increase within current germplasm
Increase in thermal time requirement
0%
10%
20%
Challinor et al., 2008b
Response to climate change, from over 180,000 crop simulations for one location
Sim
ulat
ion
coun
t
Percentage change in yield
No-adapt
Adapt
Mean T
0
25
50
75
100
Area affected (%)
Looking across India: what is the adaptive capacity contained within current germplasm?
> 50%20-30%< 10%
Yield reduction
Upper estimateArea affected
Challinor (2008): GECAFS proceedings
?
0% - ?%
• Potential for a 14 to 40% increase within current germplasm
No-adapt
Adapt
WaterT extremes
Mean THumidity
All
0
25
50
75
100
Area affected (%)
> 50%20-30%< 10%
Yield reduction
Upper estimateArea affected
Challinor (2008): GECAFS proceedings
?
Looking across India: what is the adaptive capacity contained within current germplasm?
What ‘new’ processes will limit yield in the future?
Crops and atmospheric composition: O3
• Ozone lowers the photosynthetic rate and accelerates leaf senescence ~5% yield reductions currently; 30% in 2050?
• Few crop field studies with O3 carried out in the tropicsSee e.g. Long et al. (2005); Slingo et al. (2005)
• Industrial emissions resulting in increased surface ozone are predicted to rise.
• Predictions for China particularly high.
Future air quality and climate closely linked
How will these processes interact to determine future air quality in China?
Probability of max 8-h O3 > 84 ppbvvs. daily max. T (USA)
Lin et al. (Atm. Env., 2001)
Correlation of high ozone withincreasing temperature is driven by:(1) Stagnation in the boundary layer, (2) biogenic hydrocarbon emissions, (3) chemical reaction rates, (4) deposition
Atmospheric composition modelling at Leeds
• UKCA - Collaboration between universities and Met Office - Coupled climate-chemistry-aerosols - Ozone photochemistry coupled to climate and land-surface - Coupled ozone deposition fluxes and climatic drivers for future
• TOMCAT - State-of-the-art 3D global chemistry-transport model - Offline, so ideal for process studies, comparison with observations, parameterisation development.
TOMCAT surface ozone (23 June 2008)
Composition-climate-crop strategy
TOMCAT ozone fields
GLAM with O3 flux
parameterisation
Climate drivers(analyses)
Stomatal deposition parameterisation for vegetation/crop type
Yield
Offline studies (no climate-chemistry coupling) for evaluation of parameterisations
Composition-climate-crop strategy
From Oct 2008: PhD student joint with Met Office – will work on ozone-vegetation interactions using TOMCAT and UKCA
GLAM with O3 flux
parameterisation
Climate drivers
Yield
Coupled (climate-chemistry) studies for prediction
UKCA
Surface ozone
Land surface scheme
Stomatal ozone flux
Composition-climate-crop strategy
From Oct 2008: PhD student joint with Met Office – will work on ozone-vegetation interactions using TOMCAT and UKCA
GLAM with O3 flux
parameterisation
Climate drivers
Yield
Coupled (climate-chemistry-crop) studies:importance of land use and patterns of deposition
UKCA
Surface ozone
Land surface scheme
Stomatal ozone flux
Qualitative approach to food systems research
Focus on ways people obtain food.
DFID’s “sustainable livelihoods approach” that looks at how different types of capital are used to obtain food.
Attempts to generalize field studies and link with global drivers.
Analyses of socio-economic drivers of crop productivity
• Will farmers have access to the genotypes needed for adaptation?
• What characteristics make a food production system vulnerable or resilient to environmental change?
Exposure (e.g. to droughts of different severity)
Impa
ct o
f env
ironm
enta
l cha
nge
Big problem small impact - managed to adapt
Small problem big impact - did not adapt
Harvest
Impacts
Economic
Impacts
Health impacts
Resilien
t
Vulnerable
RESILIENT
Vulnerable
Electricity
Infrastructure
Invest in other agr activities Double
cropping
Agr production capital,
Invest in agr, GDP share of agr
Fertiliser,Machinery
Rural population
WheatIncreasing exposure
Incr
easi
ng im
pact
Conclusions
Lobell et al. (2008). Prioritizing Climate Change Adaptation Needs for Food Security in 2030. Science 319.
China South Asia
Sustainability Science approach to food
systems
Biophysical Modelling
Qualitative approach to food systems research
Increa
singly
quan
titativ
e
Decrea
singly
site-
spec
ifyIncreasingly relevant
Adapta
tion
More p
roce
sses
(ozo
ne)
Uncer
tainty
Impa
ctsPro
cess
esCor
relat
ions
Starting to happen:• NERC QUEST• ESRC Centre for Climate Change Economics and Policy
Increase in CO2 ppm
Increasee in yield (%)
Methods Source
330-660 37 Glasshouse or growth chambers
Kimball, 1983
350-700 31 Estimated by cubic equation from multiple experiments
Amthor, 2000
350-700 28 Linear extrapolation of FACE experiment
Easterling et al., 2005
370-550 7 -23 FACE experiment
Kimball,2002
330-660 25 CERES for C3 crops Boote,1994350-700 16-30 GLAM model
Summary of observed and modeled increase in wheat yield in response to elevated CO2
Vulnerability trend 1960s-2001
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Anhui
Beijing
Fujian
Gansu
GuangdongGuangxi
Guizhou
Hebei
Heilongjiang
Henan
Hubei
Hunan
Jiangsu
Jiangxi
Jilin
Liaoning
Inner Mongolia
NingxiaQinghaiShaanxi
Shandong
Shanghai
Shanxi
Sichuan
T ianjin
Yunnan
Zhejian
VI-trend (RI>1) wheat
_ 0,025
_ 0,0125_ 0,0025
Wheat
No increase in double cropping (only land increase) Low rural labour - inefficient land use. Land conversion projects: from wheat to rice
Lowest per capita investments in agriculture (highest double cropping). Guangxi highest mean VI (wheat) of all.
“Food prices are rising on a mix of strong demand from developing countries; a rising global population; more frequent floods and droughts caused by climate change; and the biofuel industry’s appetite for grains, analysts say.”Also: rising input prices (oil, fertiliser) and speculation (e.g. based on expected demand for biofuel)
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