Effects of climate change on feed availability and the implications for the livestock sector

P. Havlík, D. Leclere, H. Valin, M. Herrero, E. Schmid, M. Obersteiner

International Institute for Applied Systems Analysis (IIASA), AustriaCommonwealth Scientific and Industrial Research Organisation (CSIRO), AustraliaUniversity of Natural Resources and Life Sciences in Vienna (BOKU), Austria

Mainstreaming Livestock Value Chains: ConferenceAccra, Ghana, November 5-6, 2013

Introduction

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Climate change impacts on livestock Quality and quantity of feed Heat stress Diseases and disease vectors Water

Adaptation response is complex Crop management

(sowing dates, fertilizers, irrigation rates,…) Switch to a different production system

irrigated rainfed grass based mixed crop livestock)

Switch to a different production activity (new crop, livestock species,…)

Change in the volume of production

Modeling tools need to be adapted Climate change is a global phenomenon global context CC impacts vary across space high spatial resolution CC impacts natural environment link to biophysical models Adaptation in production explicit technology

Source: Arblaster et al. 2013 (IPCC AR5)

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Methodological approach

Modelling chain integration

5 GCMs HadGEM2-ES, IPSL-CM5A-LR, GFDL-ESM2M, MIROC-ESM-CHEM, NorESM1-M

x 2 Crop models EPIC, LPJmL

uncertainty across 10 different inputs

General circulation

models:5 GCMs

ΔT°CΔPrec.ΔCO2

Global gridded

crop models:EPIC

LPJmL

Partial equilibrium

model:GLOBIOM

Climate EconomicBiophysical

ΔYieldYield, Area,

Consumption,Trade change

Gridded structure for crop and grass

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Simulation Units (SimU) HRU 50x50km grid Country boundaries

> 200.000 SimUs

Source: Skalský et al. (2008)

PX5

Altitude class, Slope class, Soil Class

PX5

Altitude class (m): 0 – 300, 300 – 600, 600 – 1200, 1200 – 2500 and > 2500;

Slope class (deg): 0 – 3, 3 – 6, 6 – 10, 10 – 15, 15 – 30, 30 – 50 and > 50;

Soil texture class: coarse, medium, fine, stony and peat;

HRU = Altitude & Slope & Soil

Country HRU*PX30

PX5

SimU delineation relatedstatistics on LC classes and

Cropland management systems

reference for geo-coded data on crop management;

input statistical data for LC/LU economic optimization;

LC&LUstat

Cropland productivity from EPIC

EPIC

Rain, Snow, Chemicals

Subsurface Flow

Surface Flow

Below Root Zone

Evaporation and

Transpiration

Grasslands productivity

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Source: EPIC model(t/ha DM)

GLOBIOM: model general structure

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Partial equilibrium model on land use at global scale (endogenous prices balance supply and demand)

Agriculture, forestry and bioenergy 30 economic regions Bilateral trade flows

Supply defined at the grid cell resolution Bottom-up sectoral models for biophysical consistency Explicit technology by production system

All market balanced consistent with FAOSTAT Optimization of the social welfare

Maximizing producer + consumer surplus Non-linear expansion costs Resource and technology constraints

Long term: base year 2000, recursively dynamic (10 year periods)

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Livestock sector coverage

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Livestock categories: Gridded Livestock of World

(Wint and Robinson 2007) Bovines: Dairy & Other Sheep & Goats: Dairy & Other Poultry: Laying hens, Broilers, Mixed Pigs

Production systems: Seré and Steinfeld (1996) classification

Ruminants Grass based: Arid, Humid,

Temperate/Highlands Mixed crop-livestock: Arid, Humid,

Temperate/Highlands Monogastrics

Smallholders Industrial

Production systems parameterization

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Reconciliation with FAOSTAT data

Herrero, Havlík et al., forthcoming

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Results: biophysical impact on feedChange in yield compared to NoCC [%]

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Impact on production

Change compared to 2000 [%]

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Impact on pricesChange compared to NoCC [%]

Change compared to 2000 [%]

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Composition of ruminant diets by production system

World average [%]

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Adaptation through livestock systems switches

Absolute change in percentage shares of ruminants by system

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Land use requirementsLand cover change between 2000 and 2050 [Mha]

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Food availabilityChange to 2000 [%] Change to NoCC [kcal/cap/day]

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Trade Absolute change in share: net trade / (supply+demand)

Conclusions

When looking at feed availability Impact of climate change on livestock is expected to be positive Ruminant production benefits more than monogastric systems Increase pressure from grassland expansion

But still uncertain Depends on crop model and response to CO2 fertilization Adaptation responses matter

Production systems switches Possibilities of reallocation to least affected regions Trade

Further research on this field should benefit from integrated modelling approaches to capture links between grassland, cropland and the livestock production and markets

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Thank you for your attention

Questions…

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