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