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)