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Modelling pig and poultry production
systems: computational and conceptual
challenges
M. Gilbert (& T. Van Boeckel)
Univer...
Livestock Human population
Spatial epidemiology &
invasion ecology
Catherine Linard
Yann Forget Jean
Artois
Clément Tisseu...
Intensified livestock production systems and
the emergence of Highly Pathogenic Avian
Influenza
Favour infections
High
den...
Intensified livestock production systems and
agricultural antimicrobial use
Favour infections
High
density &
contacts
Gene...
Global trends in livestock numbers
0
500,000,000
1,000,000,000
1,500,000,000
2,000,000,000
2,500,000,000
1961
1965
1969
19...
Global trends in livestock productivity
90
110
130
150
170
190
210
230
250
1961
1965
1969
1973
1977
1981
1985
1989
1993
19...
Outline
Context
• Intensification has taken place rapidly in the past
• Strong projected changes in demand will lead to fu...
Livestock distribution:
Gridded Livestock of the World (GLW 1.0)
• General principle
• Collection of sub-national livestoc...
Livestock distribution:
Gridded Livestock of the World (GLW 2.0)
• Recent developments
• More recent & higher resolution c...
Livestock distribution:
Gridded Livestock of the World (GLW 3.0)
• In progress…
• New machine learning algoritm (Random Fo...
Livestock distribution:
Gridded Livestock of the World (GLW 3.0)
• In progress…
• New machine learning algoritm (Random Fo...
Livestock distribution:
Gridded Livestock of the World (GLW 3.0)
RF (GLW 3.0) vs. Stratified regression (GLW 2.0)
leave-ou...
Livestock distribution:
Gridded Livestock of the World (GLW 3.0)
1 week /
species
GLW 2.0 GLW 3.0
16h
/species
1 month/
sp...
Outline
Context
• Intensification has taken place rapidly in the past
• Strong projected changes in demand will lead to fu...
Conceptual framework (1)
Conceptual framework (2)
Conceptual framework (3)
The % ext. chicken is predicted at
national level by the GDP model
Ext. raised chicken are distri...
Application to chicken
Extensively raised chicken
Intensively raised chicken
Validation: chicken extensive
Van Boeckel, T., W. Thanapongtharm, T. Robinson, L. D’Aietti & M. Gilbert (2012). Predicting...
Validation: chicken intensive
Van Boeckel, T., W. Thanapongtharm, T. Robinson, L. D’Aietti & M. Gilbert (2012). Predicting...
Application to pigs:
extensive, semi-intensive, intensive
Application to pigs:
extensive, semi-intensive, intensive
Pigs: extensive
Pig semi-intensive
Pig intensive
Disaggregating between extensive and
intensive production systems
• Limitations
• Uncertainty in the GDP model (& other im...
Validation: chicken extensive
Van Boeckel, T., W. Thanapongtharm, T. Robinson, L. D’Aietti & M. Gilbert (2012). Predicting...
Discussion (1)
People
• Number
• Weatlh
• Diet
Livestock
• Number
• Production
systems
Impact
• Amonia pollution
• GHG emi...
Drivers of change in
spatial distribution
Drivers of change in
number
Demand
Discussion (2)
People
Livestock
Demography We...
Future work (1)
Livestock
products
Change in
stock
Change in
productivity
Vertical integration and
concentration of
demand...
Future work (2)
2000
log GDP per capita c. $ 2.9
% extensive c. 83 %
2000
2030
2030
log GDP per capita c. $ 3.8
% extensiv...
Future work (3)
Livestock
products
Change in
stock
Change in
productivity
Vertical integration and
concentration of
demand...
Future work (4)
People
• Number
• Weatlh
• Diet
Livestock
• Number
• Production
systems
Impact
• Amonia pollution
• GHG em...
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Modelling pig and poultry production systems: computational and conceptual challenges

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Presentation delivered by Marius Gilbert from Université Libre de Bruxelles during the LSE seminar series on 26 February 2015

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Modelling pig and poultry production systems: computational and conceptual challenges

  1. 1. Modelling pig and poultry production systems: computational and conceptual challenges M. Gilbert (& T. Van Boeckel) Université Libre de Bruxelles http://lubies.ulb.ac.be/Spatepi.html T. Robinson International Livestock Research Institute
  2. 2. Livestock Human population Spatial epidemiology & invasion ecology Catherine Linard Yann Forget Jean Artois Clément Tisseuil Gaëlle Nicolas Weerapong Thanapongtharm Post docs PhD http://lubies.ulb.ac.be/Spatepi.html
  3. 3. Intensified livestock production systems and the emergence of Highly Pathogenic Avian Influenza Favour infections High density & contacts Genetic similarity Living & health condition HPAI emergence mostly documented in intensive poultry production systems
  4. 4. Intensified livestock production systems and agricultural antimicrobial use Favour infections High density & contacts Genetic similarity Living & health condition Marginal gains due higher off-take rates do make a difference over large volume (but see Graham et al. 2007) Feed conversion rate matters Fast prod. cycles High inputs / high outputs Higher use of antimicrobials in intensive systems (preventive, curative, feed additive)
  5. 5. Global trends in livestock numbers 0 500,000,000 1,000,000,000 1,500,000,000 2,000,000,000 2,500,000,000 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 Heads Cattle Chicken (/10) Pork Source: FAOSTAT (2010)
  6. 6. Global trends in livestock productivity 90 110 130 150 170 190 210 230 250 1961 1965 1969 1973 1977 1981 1985 1989 1993 1997 2001 2005 2009 RelativeincreaseinOutput/Input (kgoutputshead-1year-1) Cattle (331) Chicken (1.73) Pork (60.9) Source: FAOSTAT (2010)
  7. 7. Outline Context • Intensification has taken place rapidly in the past • Strong projected changes in demand will lead to further intensification • Changes are structured geographically Objectives • Better document the geographic distribution of intensive livestock production • Develop tools for making projections Methods • Mapping the global distribution of livestock • Disaggregating in production systems
  8. 8. Livestock distribution: Gridded Livestock of the World (GLW 1.0) • General principle • Collection of sub-national livestock census data • Many variables correlated to livestock farming are mapped at high resolution (e.g. land cover). • Statistical models are based on high resolution GIS predictors and applied to downscale census values by pixel (stratified multiple linear regressions) • Previous developments • GLW 1.0 published by FAO in 2007, mostly based on census data < 2005 (Wint & Robinson 2007) • Global extent, 5 km resolution
  9. 9. Livestock distribution: Gridded Livestock of the World (GLW 2.0) • Recent developments • More recent & higher resolution census data • Spatial modelling @ 1km resolution • Automation of the methodology in R • Disseminated through the Livestock GeoWiki • http://www.livestock.geo-wiki.org/ • New species division • Cattle • Pig • Chicken • Duck • Sheep • Goat Robinson, T., W. Wint, T, G. Conchedda, T. P. Van Boeckel, V. Ercoli, E. Palamara, G. Cinardi, L. D’Aietti, & M. Gilbert (2014) Mapping the Global Distribution of Livestock. PLoS ONE 9(5): e96084. doi:10.1371/journal.pone.0096084
  10. 10. Livestock distribution: Gridded Livestock of the World (GLW 3.0) • In progress… • New machine learning algoritm (Random Forest) • Systematic evaluation (years of CPU time in 4 months) • 180 models for Asia chicken and Africa cattle • Processing on ILRI cluster (parrallelized) • Full integration of metadata • Spatial modelling & dissemination @ 1 km & 10 km resolution • Toward global runs instead of continental tiles • Revision of predictor variable to include more anthropogenic factors
  11. 11. Livestock distribution: Gridded Livestock of the World (GLW 3.0) • In progress… • New machine learning algoritm (Random Forest) • Systematic evaluation (years of CPU time in 4 months) • 180 models for Asia chicken and Africa cattle • Processing on ILRI cluster (parrallelized) • Full integration of metadata • Spatial modelling & dissemination @ 1 km & 10 km resolution • Toward global runs instead of continental tiles • Revision of predictor variable to include more anthropogenic factors
  12. 12. Livestock distribution: Gridded Livestock of the World (GLW 3.0) RF (GLW 3.0) vs. Stratified regression (GLW 2.0) leave-out cross validation
  13. 13. Livestock distribution: Gridded Livestock of the World (GLW 3.0) 1 week / species GLW 2.0 GLW 3.0 16h /species 1 month/ species 1-2 days / species
  14. 14. Outline Context • Intensification has taken place rapidly in the past • Strong projected changes in demand will lead to further intensification • Changes are structured geographically Objectives • Better document the geographic distribution of intensive livestock production • Develop tools for making projections Methods • Mapping the global distribution of livestock • Disaggregating in production systems
  15. 15. Conceptual framework (1)
  16. 16. Conceptual framework (2)
  17. 17. Conceptual framework (3) The % ext. chicken is predicted at national level by the GDP model Ext. raised chicken are distributed equally across rural population Intensively raised poultry is estimated by the difference with the total
  18. 18. Application to chicken
  19. 19. Extensively raised chicken
  20. 20. Intensively raised chicken
  21. 21. Validation: chicken extensive Van Boeckel, T., W. Thanapongtharm, T. Robinson, L. D’Aietti & M. Gilbert (2012). Predicting the distribution of intensive poultry farming in Thailand. Agriculture, Ecosystem and Environment. Doi: 10.1016/j.agee.2011.12.019
  22. 22. Validation: chicken intensive Van Boeckel, T., W. Thanapongtharm, T. Robinson, L. D’Aietti & M. Gilbert (2012). Predicting the distribution of intensive poultry farming in Thailand. Agriculture, Ecosystem and Environment. Doi: 10.1016/j.agee.2011.12.019
  23. 23. Application to pigs: extensive, semi-intensive, intensive
  24. 24. Application to pigs: extensive, semi-intensive, intensive
  25. 25. Pigs: extensive
  26. 26. Pig semi-intensive
  27. 27. Pig intensive
  28. 28. Disaggregating between extensive and intensive production systems • Limitations • Uncertainty in the GDP model (& other important variables ?) • Ignore sub-national GDP variations • Assumption of equal number of Ext. Chicken / rural population
  29. 29. Validation: chicken extensive Van Boeckel, T., W. Thanapongtharm, T. Robinson, L. D’Aietti & M. Gilbert (2012). Predicting the distribution of intensive poultry farming in Thailand. Agriculture, Ecosystem and Environment. Doi: 10.1016/j.agee.2011.12.019
  30. 30. Discussion (1) People • Number • Weatlh • Diet Livestock • Number • Production systems Impact • Amonia pollution • GHG emissions • EIDs • Antimicrobial resistance
  31. 31. Drivers of change in spatial distribution Drivers of change in number Demand Discussion (2) People Livestock Demography Wealth # Consumers Dietary preferences Urbanization of consumers Change in stock Change in productivity Urbanization Vertical integration and distribution of inputs and demand
  32. 32. Future work (1) Livestock products Change in stock Change in productivity Vertical integration and concentration of demand •Methodological improvements •Using agricultural population •Using sub-national GDP where appropriate (e.g. China, India) •Forward and backward predictions
  33. 33. Future work (2) 2000 log GDP per capita c. $ 2.9 % extensive c. 83 % 2000 2030 2030 log GDP per capita c. $ 3.8 % extensive c. 18 % Chicken production in China
  34. 34. Future work (3) Livestock products Change in stock Change in productivity Vertical integration and concentration of demand •Methodological improvements •Using agricultural population •Using sub-national GDP where appropriate (e.g. China, India) •Forward and backward predictions •GDP data & projections (national / sub-national) •Spatial concentrations (peri-urban, access to port, founder effect)
  35. 35. Future work (4) People • Number • Weatlh • Diet Livestock • Number • Production systems Impact • Amonia pollution • GHG emissions • EIDs • Antimicrobial resistance

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