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Mitigation strategy and the REDD: Application of the
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Presented by Aline Mosnier at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis
June 6-7, 2011 • Dakar, Senegal
For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop
Presentation of Andre Nassar for the "2nd Workshop on the Impact of New Technologies on the Sustainability of the Sugarcane/Bioethanol Production Cycle"
Apresentação de Marcos S. Buckeridge realizada no "2nd Workshop on the Impact of New Technologies on the Sustainability of the Sugarcane/Bioethanol Production Cycle"
Date / Data : Novr 11th - 12th 2009/
11 e 12 de novembro de 2009
Place / Local: CTBE, Campinas, Brazil
Event Website / Website do evento: http://www.bioetanol.org.br/workshop5
Presentation of Gerd Spavorek for the "2nd Workshop on the Impact of New Technologies on the Sustainability of the Sugarcane/Bioethanol Production Cycle"
Apresentação de Gerd Spavorek realizada no "2nd Workshop on the Impact of New Technologies on the Sustainability of the Sugarcane/Bioethanol Production Cycle"
Date / Data : Novr 11th - 12th 2009/
11 e 12 de novembro de 2009
Place / Local: CTBE, Campinas, Brazil
Event Website / Website do evento: http://www.bioetanol.org.br/workshop5
Sustainability and climate protection, the role of bio fuels and biorefinerie...BioMotion Tour
Presentation by Johan Sanders, Professor Valorisation of Plant Production Chains, Wageningen University and Research center.
BioMotion Tour congress at Agritechnica, Hannover - 13 november 2009
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In this file, you can ref useful information about paired comparison method of performance appraisal such as paired comparison method of performance appraisal methods, paired comparison method of performance appraisal tips, paired comparison method of performance appraisal forms, paired comparison method of performance appraisal phrases … If you need more assistant for paired comparison method of performance appraisal, please leave your comment at the end of file.
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Presentation by Bernardo Strassburg, the International Institute for Sustainability, Brazil, at the 2012 Agriculture and Rural Development Day in Rio de Janiero, Learning Event No. 1, Session 1: ‘Mato Grosso: realising the land-sparing potential from increased agricultural productivity’.
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The third in a series of PowerPoint presentation on public policy analysis and decision making. While focusing on criminal justice is applicable to all government fields. The material is geared toward an elective course in Master's Program, or upper division in related government courses.
Paired comparison method of performance appraisalkianramirez765
In this file, you can ref useful information about paired comparison method of performance appraisal such as paired comparison method of performance appraisal methods, paired comparison method of performance appraisal tips, paired comparison method of performance appraisal forms, paired comparison method of performance appraisal phrases … If you need more assistant for paired comparison method of performance appraisal, please leave your comment at the end of file.
This handy guide is for anyone involved in problem solving and improvement activities. It contains guidelines on the use of many of the tools and techniques which can be used as part of a Continuous Improvement process.
http://www,saharconsulting.com
An Educational presentation about Problem solving and decision making using different tools and offering solutions to problem solving, creative thinking and Decision making
ReadySetPresent (Decision Making PowerPoint Presentation Content): 100+ PowerPoint presentation content slides. Successful and effective strategic decision making is a guarantee to increase productivity in every workplace. Decision Making PowerPoint Presentation Content slides include topics such as: the 6 C’s of decision making, inherent personal and system traps, 10+ slides on decision trees, 10+ slides on decision making methods and tips, 4 slides on the GOR approach to decision making, 8 slides on common pitfalls in decision making, 4 slides on effective strategies in making decisions, 35+ slides on the 8 major decision making traps and how to effectively minimize each, 7 slides on different decision making perspectives, 25 slides on the 3 different types of analysis (grid analysis – paired comparison analysis, and cost/benefit analysis), 4 slides on utilizing planning and overarching questions, 4 modes of decision making and 6 factors in decision making and more!
A Stochastic Analysis of Biofuel Policies
Presented by Michael Obersteiner at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis
June 6-7, 2011 • Dakar, Senegal
For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop
Presentation by Bernardo Strassburg, the International Institute for Sustainability, Brazil, at the 2012 Agriculture and Rural Development Day in Rio de Janiero, Learning Event No. 1, Session 1: ‘Mato Grosso: realising the land-sparing potential from increased agricultural productivity’.
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Workshop Trade-off Analysis - CGIAR_19 Feb 2013_CRP 1.2_Bernard Vanlauwe
Workshop Trade-off Analysis - CGIAR_20 Feb 2013_Keynote Petr Havlik
1. Modelling for trade-offs analysis
at regional and global scale
Petr Havlík + >30 collaborators in and outside IIASA
International Institute for Applied Systems Analysis (IIASA), Austria
International Livestock Research Institute (ILRI), Kenya
CGIAR Workshop: Analysis of Trade-offs in Agricultural Systems
WUR Wageningen, February 19, 2013
2. Trade-offs in the land use sectors
Land sparing
Pollution
N2O emissions
Biodiversity Water use Food, feed, fiber, fuel
CO2 sink Soil degradation Farmers income
NATURAL LAND INTENSIFICATION MANAGED LAND
LAND
Havlík et al. Modelling for trade-offs analysis 2
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
3. Outline
1. Model overview
2. Global case study – Sustainable intensification?
a) Rigid system
b) Flexible livestock systems
c) Land productivity
3. Regional case study – Development scenarios
4. Conclusion
Havlík et al. Modelling for trade-offs analysis 3
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
4. 1. Model overview
Havlík et al. Modelling for trade-offs analysis 4
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
5. GLOBIOM: Global Biosphere Management Model
Partial equilibrium model: Agriculture, Forestry, Bioenergy
DEMAND
SUPPLY
Havlík et al. Modelling for trade-offs analysis 5
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
6. GLOBIOM
Spatial equilibrium model a la Takayama & Judge
Maximization of the social welfare (PS + CS)
Recursively dynamic (10 year periods)
Supply functions
implicit – based on spatially explicit Leontief production functions:
production system 1 (grass based) productivity 1 + constant cost 1
production system 2 (mixed) productivity 2 + constant cost 2
Demand functions
1/ e
explicit: linearized non-linear functions p ˆ ˆ
p * (q / q)
Havlík et al. Modelling for trade-offs analysis 6
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
7. Supply Chains
Wood products
Sawn wood
Natural Forests Pulp
Wood Processing Bioenergy
Managed Forests Bioethanol
Biodiesel
Methanol
Heat
Electricity
LAND USE CHANGE
Short Rotation Tree Bioenergy
Biogas
Plantations Processing
Crops
Corn
Wheat
Cropland Cassava
Potatoes
Rapeseed
etc…
Grassland Livestock Feeding
Livestock products
Beef
Lamb
Pork
Poultry
Other natural land Eggs
Milk
Havlík et al. Modelling for trade-offs analysis 7
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
8. Main exogenous drivers:
Population
GDP
Technological change
Bio-energy demand (POLES team)
Diets (FAO, 2006)
Output: Production Q
- land use (change)
- water use
- GHG,
- other environment (nutrient cycle, biodiversity,…)
Consumption Q
Prices
Trade flows
Havlík et al. Modelling for trade-offs analysis 8
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
9. Spatial resolution
Homogeneous response units (HRU) – clusters of 5 arcmin pixels
HRU = Altitude & Slope & Soil
Altitude class, Slope class,
Soil Class
PX5
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;
Source: Skalský et al. (2008)
Havlík et al. Modelling for trade-offs analysis 9
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
10. Spatial resolution
Simulation Units (SimU) = HRU & PX30 & Country zone
LC&LUstat
> 200 000 SimU
Country HRU*PX30
SimU delineation related
statistics on LC classes and
Cropland management systems
PX5
reference for geo-coded data on crop management;
input statistical data for LC/LU economic optimization;
Source: Skalský et al. (2008)
Havlík et al. Modelling for trade-offs analysis 10
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
12. Crops - EPIC
Relative Difference in Means (2050/2100) in Wheat Yields
[Data: Tyndall, Afi Scenario, simulation model: EPIC]
Havlík et al. Modelling for trade-offs analysis 12
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
13. Grasslands – CENTURY/EPIC
Source: EPIC model
(t/ha DM)
Havlík et al. Modelling for trade-offs analysis 13
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
14. Livestock
Gridded Livestock of the World – Robinson et al. (2011)
Havlík et al. Modelling for trade-offs analysis 14
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
15. Livestock production systems distribution
Sere and Steinfeld (1996) classification updated by Robinson et al. (2011)
Havlík et al. Modelling for trade-offs analysis 15
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
16. Livestock sector coverage
Livestock categories:
Bovines: Dairy & Other
Sheep & Goats: Dairy & Other
Poultry: Laying hens, Broilers, Mixed
Pigs
Production systems:
Ruminats
Grass based: Arid, Humid, Temperate/Highlands
Mixed crop-livestock: Arid, Humid, Temperate/Highlands
Monogastrics
Smallholders
Industrial
Havlík et al. Modelling for trade-offs analysis 16
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
17. Production systems parameterization
Herrero, Havlík et al. forthcoming
Havlík et al. Modelling for trade-offs analysis 17
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
18. Forests – G4M
Downscaling FAO country level information and forest growth
functions estimated from yield tables
Source: Kindermann et al. (2008)
Havlík et al. Modelling for trade-offs analysis 18
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
19. 2a. Global case study:
Rigid system – Trade-offs at their best
Havlík et al. Modelling for trade-offs analysis 19
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
20. Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
21. DO NOTHING scenario – Projected forest area
Tropical deforestation (2010-2050)
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
22. REDD policy scenario
Zero Net Deforestation and Forest
Degradation by 2020 (ZNDD)
Alternative futures scenarios
Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
23. Scenario definition
Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
24. Scenario definition
Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
25. Scenario definition
Diet Shift Bioenergy Plus Pro-Nature Pro-Nature Plus
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013 Kapos et al. (2008)
26. Results
Total land cover change (2010-2050)
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
27. Results
Agricultural commodity prices compared to DO NOTHING
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
28. Results
Agricultural input use compared to DO NOTHING
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
29. 2b. Global case study:
Flexible livestock systems
Havlík et al. Modelling for trade-offs analysis 29
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
30. 2 reference scenarios
Systems Herds
REF0 Fixed Fixed
REF1 Flexible Flexible*
* in regions with specialized herds
30
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
31. LPS distribution for different animal types in 2030
31
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
32. Price changes 2000-2030
32
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
33. Annual average GHG emissions over 2020-2030
33
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
34. Mitigation scenarios
Scenario ALL AGR ANM ENT LUC DEF
Livestock
Enteric fermentation CH4 X X X X
Manure management CH4 X X X
Manure management N2O X X X
Manure grassland N2O X X X
Cropland
Crop fertilizer N2O X X
Rice CH4 X X
Land-use change
Deforestation CO2 X X X
Other LUC CO2 X X
34
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
35. Total abatement calorie cost (TACC) curves for different policy options by 2030
35
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
36. 2c. Global case study:
Land productivity growth
(Havlík et al, 2013; Valin et al, forthcoming)
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CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
37. Scenarios
• Alternative crop yield scenarios
– S0: No crop yield increase – B: Baseline - linear historical trend
– S: -50% yield improvement – C: + 100% in developing regions
• Fixed demand on B reference:
no rebound effect
• Fixed demand on B reference no rebound effect
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
38. Results
Commodity price index 2030/2000
Havlík et al. Modelling for trade-offs analysis 38
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
39. Results
Land cover change 2000-2030
Havlík et al. Modelling for trade-offs analysis 39
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
40. Results
Average annual GHG emissions (2000-2030)
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
41. Results
Crop yield increase as a mitigation policy?
MACC_S0
GHG tax Productivity abatment levels
R&D investment cost
Marginal Abatement Cost Curve 120
with S0 crop yields
100
USD per tCO2-eq
versus 80
R&D investment necessary for S, B, C 60
- calculated as in Burney at al. (2010)
40
20
Crop yield growth can be a cost
0
efficient element of the mitigation S00 500 1000 1500 2000
S B C
MtCO2-eq
portfolio
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
42. What kind of intensification?
Productivity assumptions in developing countries
Scenario Crops Ruminants
TREND FAO historic trend 1980-2010 Bouwman et al. (2005) trend
SLOW 50% TREND growth rate 50% TREND growth rate
CONV Closing 50% EPIC yield gap Closing 50% efficiency gap
CONV-C Closing 50% EPIC yield gap TREND
CONV-L TREND Closing 50% efficiency gap
Management assumptions in developing countries
Pathway Crops Ruminants
Fertilizer Other input Non-feed cost
adjustment adjustment adjustment
Conventional Yes Yes Yes
Sust-Intens No Yes Yes
Free-Tech No No No
• Free demand potential rebound effects
Havlík et al. Modelling for trade-offs analysis 42
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
43. Food security x GHG: Trade-offs & Complementarities
Havlík et al. Modelling for trade-offs analysis 43
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
44. 3. Regional case study:
Development scenarios
Havlík et al. Modelling for trade-offs analysis 44
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
45. Four storylines for Eastern Africa
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
46. Storylines quantification
Main drivers:
– GDP
– Crop yields and management systems
– Livestock yield and production systems
– Producer cost
– Land use change limitations
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
47. GDP per capita in EAF [USD]
700.00
600.00
500.00
400.00 Industrious Ants
Herd of Zebra
300.00 Lone Leopards
Sleeping Lions
200.00
100.00
-
2010 2020 2030
Havlík et al. Modelling for trade-offs analysis
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
48. Results
Calorie consumption in EAF [kcal/cap/day] GHG emissions in EAF in 2030 [MtCO2eq/y]
Havlík et al. Modelling for trade-offs analysis 48
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
49. 4. Conclusion
Havlík et al. Modelling for trade-offs analysis 49
CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
50. Strengths
• Bio-economic model (“Integrated assessment”) - consistent coverage of
economic and environmental parameters
• Land use model – solid relationship between production and land
• Bottom-up representation with detailed management systems description
• Multiscale approach – 10x10km – Region – World
• Global coverage – regional trade-offs (leakage)
• Multisectorial representation – trade-offs between agriculture and forestry
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51. Weaknesses
• Partial equilibrium model – no income feedbacks, no other sectors
• Single representative consumer at the region level – poor food security
proxy
• Water resources – economic versus physical irrigation water availability
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CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
52. Key discussion points / challenges
• Global CGIAR agricultural systems classification/parameterization
database?
• Linking between models to bridge the scales in trade-offs analysis?
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CGIAR Trade-offs Analysis, WUR Wageningen, February 19, 2013
54. References
Havlík, P., Valin, H., Mosnier, A., Obersteiner, M., Baker, J. S., Herrero, M., Rufino, M. C. &
Schmid, E. (2013). Crop Productivity and the Global Livestock Sector: Implications for Land Use
Change and Greenhouse Gas Emissions. American Journal of Agricultural Economics 95
(2), 442—448.
Valin, H., Havlík, P., Mosnier, A., Herrero, M., Schmid E. and Obersteiner M. Agricultural
productivity and greenhouse gas emissions: trade-offs or synergies between mitigation and food
security? Environmental Research Letters, under review.
World Wildlife Fund (WWF) 2011. Living Forests Report. Chapter 1.
http://wwf.panda.org/what_we_do/how_we_work/conservation/forests/publications/living_forests
_report/
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Editor's Notes
-Producer prices (indicating regional integration)-Protection of forests, reduction in emissions-Changes between systems lead to higher productivity, also higher production/emission