FROM GLOBAL TO LOCAL:MODELING LOW EMISSIONS DEVELOPMENT STRATEGIES IN COLOMBIA Globally, agriculture is responsible for 10 – 14% of GHG emissions and largest source of no-CO2 GHG emissions. Countries can choose among technologies with different emission characteristics and we believe it's less costly to avoid high-emissions lock-in than replace them, so EFFORT TO ENCOURAGE LEDS is key.

1. FROM GLOBAL TO LOCAL:
MODELING LOW EMISSIONS
DEVELOPMENT STRATEGIES IN
COLOMBIA
Dr. Alex De Pinto - Senior Research Fellow
Dr. Tim Thomas - Research Fellow
Dr. Man Li - Research Fellow
Dr. Ho-Young Kwon - Research Fellow
Ms. Akiko Haruna - Research Analyst
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

2. CLIMATE CHANGE BASICS
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

3. The drivers of food security challenges
 Demand
• The number of people,
• Their control over financial and physical
resources,
• Their dietary desires,
• Their location.
 Supply
• Our capacity to sustainably meet these
demands.

4. Food security challenges are
unprecedented
 On the demand side
• More people
 50 percent more people between 2000 and
2050
 Almost all in fragile economies.
• With more income
 More demand for high valued food (meat,
fish, fruits, vegetables).
• Climate change – exacerbates existing threats,
generates new ones.
Page 4

5. Greenhouse gas emissions have been
rising
From
‘agriculture
’
Page 5

6. … and likely to rise more
Figure 2 in Peters et al. (2012)
Page 6

7. It has been getting warmer…
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8. … and could get a lot warmer!
SRES scenario
differences
small until after
2050 (but GCM
differences can be large)
Source: Figure 10.4 in Meehl, et al. (2007)

9. Yield Effects, Rainfed Maize, CSIRO A1B
(% change 2000 climate to 2050 climate)
Source: Nelson et al, 2010.

10. Yield Effects, Rainfed Maize, MIROC A1B
(% change 2000 climate to 2050 climate)
Source: Nelson et al, 2010.
Page 10

11. And it gets much worse after 2050
Climate change impacts on wheat yields with
2030, 2050, and 2080 climate (percent change
from 2000)
Year
Developed
Developing
Rainfed Irrigated Rainfed Irrigated
2030
-1.3
-4.3
-2.2
-9.0
2050
-4.2
-6.8
-4.1
-12.0
2080
-14.3
-29.0
-18.6
-29.0
Source: Nelson et al, 2010.

12. Income and population growth drive prices
higher
(price increase (%), 2010 – 2050, Baseline economy and demography)
Source: Nelson et al,
2010.

13. Climate change increases prices even
more
(price increase (%), 2010 – 2050, Baseline economy and demography)
Maize price
mean
increase is
101 %
Minimum and
maximum effect from
four climate
scenarios
Rice price
mean
increase is
55%
Wheat price
mean
increase is
54%
Source: Nelson et al,
2010.

14. Food security, farming, and climate change
to 2050
 Ag prices increase with GDP and
population growth.
 Prices increase even more because of
climate change.
 International trade is critical for
adaptation.

15. GLOBAL FORCES, LOCAL
REACTION:
LOW EMISSION
DEVELOPMENT STRATEGIES
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

16. Low Emission Development Strategies
 Globally, agriculture is responsible for 10 –
14% of GHG emissions and largest source of
no-CO2 GHG emissions.
 Countries can choose among a portfolio of
growth-inducing technologies with different
emission characteristics.
 We believe that is less costly to avoid highemissions lock-in than replace high-emissions
technologies. EFFORT TO ENCOURAGE
LEDS.

17. Low Emission Development Strategies
 Main goal of USAID funded project: Create a
tool for the objective evaluation of LEDS
involving agriculture and forestry sectors.
 Analysis and modeling based on IFPRI
expertise and in-country knowledge coming
from existing country programs in the CGIAR
system and other local institutions
 LEDS project includes four countries:
Colombia, Vietnam, Bangladesh, Zambia

18. Low Emission Development Strategies
 Since countries are part of a global economic
system, it is critical that LEDS are devised
based both on national characteristics and
needs, and with a recognition of the role of
the international economic environment.
 Output
• Simulations that show the long term effect on emissions
and sequestration trends of policy reforms,
infrastructure investments and/or new technologies that
affect the drivers of land use-related emissions and
sequestration.
• Consistent with global outcomes.

19. Technical Approach
 Combines and reconciles
• Limited spatial resolution of macro-level economic models that
operate through equilibrium-driven relationships at a subnational
or national level with
• Detailed models of biophysical processes at high spatial
resolution.
 Essential components are:
• a spatially-explicit model of land use choices which captures the
main drivers of land use change
• IMPACT model: a global partial equilibrium agriculture model that
allows policy and agricultural productivity investment simulations
• Crop model to simulate yield, GHG emissions, and changes in soil
organic carbon
Output: spatially explicit country-level
results that are embedded in a framework
that enforces consistency with global
outcomes.

20. Conclusion
This approach allows us to:
 Determine land use choices trends, pressure for
change in land uses and tension forest/
agriculture
 Simulate policy scenarios, their viability and the
role of market forces
 Simulate the long term effect on emissions and
sequestration trends of the identified policy
reforms in relation to global price changes and
trade policies
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21. MUCHIAS GRACIAS
THANK YOU
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

22. IFPRI’s Approach
Modeling Setting and Data
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23. Technical Approach
 Combines and reconciles
• Limited spatial resolution of macro-level economic models that
operate through equilibrium-driven relationships at a subnational
or national level with
• Detailed models of biophysical processes at high spatial
resolution.
 Essential components are:
• a spatially-explicit model of land use choices which captures the
main drivers of land use change
• IMPACT model: a global partial equilibrium agriculture model that
allows policy and agricultural productivity investment simulations;
• Crop model to simulate changes in yields and GHG emissions
given different agricultural practices
Output: spatially explicit country-level
results that are embedded in a framework
that enforces consistency with global
outcomes.

24. Satellite data
Model of
Land Use
Choices
Ancillary data:
Ex. Soil type, climate, road
network, slope, population,
local ag. statistics
Macroeconomic scenario:
Future commodity
prices and
rate of growth of
crop areas
IMPACT model
Ex. GDP and population
growth
Model of
Land Use
Choices
General Circulation Model
Climate scenario:
Ex. Precipitation and
temperature
Parameter
estimates for
determinants of
land use change
Land use
change
Baseline
Crop Model
Change in carbon stock
and GHG emissions
Policy Simulation
Policy scenario:
Ex. land use allocation
targets, infrastructure,
adoption of low-emission
agronomic practices
Land use
change
Crop Model
Change in carbon
stock and GHG
emissions.
Economic trade-offs

25. Satellite data
Ancillary data:
Ex. Soil type, climate, road
network, slope, population,
local ag. statistics
Model of
Land Use
Choices
Parameter
estimates for
determinants of
land use change

26. Macroeconomic scenario:
Ex. GDP and population
growth
General Circulation Model
Climate scenario:
Ex. Precipitation and
temperature
IMPACT model
Future commodity
prices, yields, and
rate of growth of
crop areas

27. Satellite data
Ancillary data:
Ex. Soil type, climate, road
network, slope, population,
local ag. statistics
Macroeconomic scenario:
Ex. GDP and population
growth
General Circulation Model
Climate scenario:
Ex. Precipitation and
temperature
Parameter
estimates for
determinants of
land use change
Model of
Land Use
Choices
Future commodity
prices and
rate of growth of
crop areas
IMPACT model
Model of
Land Use
Choices
Land use
change
Baseline
Crop Model
Change in carbon stock
and GHG emissions

28. Satellite data
Parameter
estimates for
determinants of
land use change
Model of
Land Use
Choices
Ancillary data:
Ex. Soil type, climate, road
network, slope, population,
local ag. statistics
Macroeconomic scenario:
Future commodity
prices and
rate of growth of
crop areas
IMPACT model
Ex. GDP and population growth
General Circulation Model
Model of
Land Use
Choices
Climate scenario:
Ex. Precipitation and
temperature
Land use
change
Baseline
Crop Model
Change in carbon stock
and GHG emissions
Policy Simulation
Policy scenario:
Ex. land use allocation
targets, infrastructure,
adoption of low-emission
agronomic practices
Land use
change
Crop Model
Change in carbon
stock and GHG
emissions.
Economic trade-offs

29. The IMPACT Model
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

30. The IMPACT Model
 Global, partial-equilibrium, multi-commodity
agricultural sector model
 Global coverage over 115 countries or
regions.
 The 115 country and regional spatial units are
intersected with 126 river basins: results for
281 Food Producing Units (FPUs).
 World food prices are determined annually at
levels that clear international commodity
markets

31. Global Food Production Units
(281 FPUs)

32. The IMPACT Model
 Economic and demographic drivers
• GDP growth
• Population growth
 Technological, management, and infrastructural
drivers
•
•
•
•
•
•
Productivity growth
Agricultural area and irrigated area growth
Livestock feed ratios
Changes in nonagricultural water demand
Supply and demand elasticity systems
Policy drivers: commodity price policy (taxes and
subsidies), drivers affecting child malnutrition, and food
demand preferences, crop feedstock demand for biofuels

33. The IMPACT Model
 Output:
• Annual levels of food supply
• International food prices
• Calorie availability, and share and number of
malnourished children
• Water supply and demand
• For each FPU: area and yield for each considered
crop
 Prices are used to determine where, due to
changes in relative profitability, are going to
occur,
 Crop area predicted by IMPACT are spatially
allocated by using the land use model

34. Model of Land Use
Choices
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35. Model Structure: Two-level Nested Logit
Perennial Annual
Crops
Crops
Cocoa
Coffee
Palm
Plantain
Other Perennials
Pasture
Cassava
Maize
Potato
Rice
Sugar Cane
Other Annuals
Forest
Forest
Other
Uses

36. Model Specification, Upper Level
 Choice variable: land use at municipio level
 Explanatory variables
•
•
•
•
•
•
•
•
•
Population density in 2005
Travel time to major cities
Elevation
Terrain slope
Soil PH
Annual precipitation
Annual mean temperature
Cattle density
Meat price

37. Model Specification, Lower Level
 Lower level, choice variable: crop shares in
provinces:
•
•
•
•
•
•
•
Crop suitability
Crop price
Soil PH
Elevation
Slope
Precipitation
Temperature

38. Assessment of Prediction Accuracy for
Colombian Land Use Model
Summary Statistics of Municipal-level Predicted Percent Errors
Crop
Mean
Q1
Median
Q3
Max
Cacao
2%
0%
2%
7%
52%
Coffee
3%
-11%
1%
12%
90%
Palm
9%
0%
1%
12%
88%
-4%
-16%
5%
15%
47%
-10%
-12%
1%
5%
47%
Cassava
-4%
-9%
0%
3%
27%
Maize
-4%
-23%
0%
13%
68%
Potato
2%
0%
0%
2%
74%
Rice
7%
1%
5%
14%
94%
Sugarcane
4%
-2%
2%
15%
88%
Other crops
-4%
-6%
1%
5%
44%
Perennial cropland
0%
-1%
2%
3%
18%
Annual cropland
0%
-1%
1%
3%
23%
Pasture
0%
-12%
-1%
12%
75%
Forests
0%
-7%
2%
7%
61%
Other lands
0%
-6%
4%
10%
53%
Perennial crop (N=927)
Plantain
Other crops
Annual crop (N=1080)
Land Categories (N=1121)

39. Assessment of Prediction Accuracy for
Colombian Land Use Model
Summary Statistics of Municipal-level Predicted Percent Errors
Crop
Mean
Q1
Median
Q3
Max
Cacao
2%
0%
2%
7%
52%
Coffee
3%
-11%
1%
12%
90%
9%
0%
1%
12%
88%
-4%
-16%
5%
15%
47%
-10%
-12%
1%
5%
47%
Cassava
-4%
-9%
0%
3%
27%
Maize
-4%
-23%
0%
13%
68%
Potato
2%
0%
0%
2%
74%
Rice
7%
1%
5%
14%
94%
Sugarcane
4%
-2%
2%
15%
88%
Other crops
-4%
-6%
1%
5%
44%
Perennial cropland
0%
-1%
2%
3%
18%
Annual cropland
0%
-1%
1%
3%
23%
Pasture
0%
-12%
-1%
12%
75%
Forests
0%
-7%
2%
7%
61%
Other lands
0%
-6%
4%
10%
53%
Perennial crop (N=927)
Palm
Plantain
Other crops
Annual crop (N=1080)
Land Categories (N=1121)

40. Assessment of Prediction Accuracy for
Colombian Land Use Model
Summary Statistics of Municipal-level Predicted Percent Errors
Crop
Mean
Q1
Median
Q3
Max
Cacao
2%
0%
2%
7%
52%
Coffee
3%
-11%
1%
12%
90%
Palm
9%
0%
1%
12%
88%
-4%
-16%
5%
15%
47%
-10%
-12%
1%
5%
47%
Cassava
-4%
-9%
0%
3%
27%
Maize
-4%
-23%
0%
13%
68%
Potato
2%
0%
0%
2%
74%
7%
1%
5%
14%
94%
Sugarcane
4%
-2%
2%
15%
88%
Other crops
-4%
-6%
1%
5%
44%
Perennial cropland
0%
-1%
2%
3%
18%
Annual cropland
0%
-1%
1%
3%
23%
Pasture
0%
-12%
-1%
12%
75%
Forests
0%
-7%
2%
7%
61%
Other lands
0%
-6%
4%
10%
53%
Perennial crop (N=927)
Plantain
Other crops
Annual crop (N=1080)
Rice
Land Categories (N=1121)

41. Assessment of Prediction Accuracy for
Colombian Land Use Model
Summary Statistics of Municipal-level Predicted Percent Errors
Crop
Mean
Q1
Median
Q3
Max
Cacao
2%
0%
2%
7%
52%
Coffee
3%
-11%
1%
12%
90%
Palm
9%
0%
1%
12%
88%
-4%
-16%
5%
15%
47%
-10%
-12%
1%
5%
47%
Cassava
-4%
-9%
0%
3%
27%
Maize
-4%
-23%
0%
13%
68%
Potato
2%
0%
0%
2%
74%
Rice
7%
1%
5%
14%
94%
Sugarcane
4%
-2%
2%
15%
88%
Other crops
-4%
-6%
1%
5%
44%
Perennial cropland
0%
-1%
2%
3%
18%
Annual cropland
0%
-1%
1%
3%
23%
Pasture
0%
-12%
-1%
12%
75%
Forests
0%
-7%
2%
7%
61%
Other lands
0%
-6%
4%
10%
53%
Perennial crop (N=927)
Plantain
Other crops
Annual crop (N=1080)
Land Categories (N=1121)

42. Preliminary
Results
The results are still preliminary and subject to
change. They should be interpreted as trends and
pressure for change driven by global changes in
supply, demand, and prices.
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Page 42

43. Baseline Scenario Price Changes
2008-2030
Price (USD/ton)
2008
CACAO
COFFEE
PALM
PLANTAIN
OTHR_PERENNIAL
CASSAVA
MAIZE
POTATO
RICE
SUGAR CANE
OTHR_ANNUAL
2273
2012
747
404
936
206
352
396
482
27
938
Source: IMPACT.
2030
2462
1957
1213
507
1168
329
445
252
411
26
1213
Growth (% )
8.32%
-2.73%
62.38%
25.50%
24.79%
59.71%
26.42%
-36.36%
-14.73%
-3.70%
29.32%
Yield (ton/ha)
2008
0.5
0.9
20.3
8.2
1.9
10.9
2.8
17.6
6.3
100.4
9.5
2030
0.6
1.1
28.5
10.1
2.0
15.0
3.3
20.6
6.8
148.6
11.5
Area
growth
(%)
Growth (%)
13.93%
17.15%
39.94%
24.20%
2.52%
37.51%
19.99%
16.97%
7.51%
48.03%
21.35%
3.41%
2.41%
5.87%
21.24%
18.40%
5.11%
-2.38%
7.37%
2.91%
13.38%
6.35%

44. Land Use Change 2008 - 2030
Baseline scenario
Land Use Category
2008 land area
(Million Hectares)
2030 land area
(Million Hectares)
Change in Area
2008 - 2030
(Million ha)
Perennial cropland
2.1
2.2
0.2
Annual cropland
2.4
2.5
0.1
Pasture
35.6
42.8
7.2
Forests
39.2
29.9
-9.2
Other lands
37.2
38.9
1.8
Total
116.4
116.4

45. Land Use 2008-2030 Baseline Scenario
Land use conversion: Change in forested land.
Year 2008 – 2030
Land use conversion: Change in pasture
Year 2008 – 2030

46. Land Use 2030 – Baseline scenario
2009 area
2030 area
Change in Area
(1000 ha)
Crops
(1000 ha)
2009 – 2030
(1000 ha)
CACAO
189
196
6
COFFEE
826
846
20
PALM
345
366
20
PLANTAIN
505
612
107
OTHR_PERENNIAL
191
226
35
CASSAVA
238
250
12
MAIZE
781
762
-19
POTATO
186
200
14
RICE
651
670
19
SUGAR CANE
391
444
52
OTHR_ANNUAL
Total
155
165
10
4458
4735
277
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47. Land Use 2030 – Baseline Scenario
Land use conversion: Change in agricultural land.
Year 2009 – 2030

48. Carbon Stock – Changes 2009 - 2030
Land Use
Category
Above
Ground
Biomass
2008
(Tg C)
Below
Ground
Biomass
2008
(Tg C)
Soil
Organic
Carbon
2008
(Tg C)
Above
Ground
Biomass
2030
(Tg C)
Below
Ground
Biomass
2030
(Tg C)
Soil
Organic
Carbon
2030
(Tg C)
Net
Change in
Carbon
Stock
2009 2030
(Tg C)
Cropland
Pasture
Forest
Other Land
Uses
Total
-
-
629.23
4,491.46
226.35
72.43
3,956.59
-
1,067.47
-
4,182.94
2,683.84
1,139.90 12,219.25
4,414.71
-
-
670.27
5,409.77
41.04
978.67
272.08
87.07
3,098.11
-
834.59
-
3,370.19
2,750.88
67.04
921.66 11,994.37 -1,255.87
3,163.46 -2,342.61

49. GHG Emissions Changes 2008 - 2030
Crops
Per ha
GHG
emission
in 2008
2008 total GHG
emission
2030 total GHG
emission
(Tg CO2eq year-1) (Tg CO2eq year-1)
(Mg/ha)
CACAO
COFFEE
COFFEE
PALM
PLANTAIN
OTHER PERENNIAL
CASSAVA
MAIZE
POTATO
RICE
SUGAR CANE
OTHER ANNUAL
Difference in
total GHG
emission for
2008 - 2030
(Mg CO2eq)
1.20
990,000
1,020,000
20,000
5.84
3,800,000
4,490,000
690,000

50. WHAT TO DO WITH THIS
INFORMATION
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

51. Policy Simulations –
An Example from Vietnam
Land use policy scenario from Decision No. 124/QD-TTg and
Decision on 3119/QD-BNN-KHCN and alternative agricultural
management practices
Scenario 1
Total forest cover increased to 45% of land area by 2030
Scenario 2
Cropland allocated to Rice cultivation kept constant at 3.8
million hectares.
Scenario 3
Adoption of Alternate Wet and Dry (AWD) in rice paddy:
Scenario 4
Replace conventional fertilizer in rice paddy with ammonium
sulfate.
Scenario 5
Introduce manure compost in rice paddy in place of farmyard
manure.

52. Emissions and Carbon Stock (CO2 eq.)
Alternatives to baseline: 2009 - 2030
Carbon stock baseline
Emissions baseline
D
Carbon stock alternative policy
Emissions alternative policy
C
A
B
2009
2030
Time

53. Policy Simulation Comparison
Cropland allocated to
Rice cultivation kept
constant at 3.8 million
hectares.
Adoption of Alternate
Wet and Dry (AWD) in
rice paddy:
Change in GHG
Emissions
(Tg CO2 eq)
Change in Total
Revenue
(Million USD)
513.8
-114.4
-6600
16.23
69.73
-68
-1800
27.53
0
-1550
-2700
2.27
0
Total forest cover
increased to 45% of land
area by 2030
Change C Stock
(Tg CO2 eq)
Lower bound
compensation for gain
in C stock and/or
reduction of emissions
(USD)
-260
-5300
25.58
0
-102
1200
0.00
Introduce manure
compost in rice paddy.
Replace conventional
fertilizer in rice paddy
with ammonium sulfate.
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54. Conclusion
Where do we go from here:
 Need to validate current results and “fine tune”
the model
 Complete the computation of changes in carbon
stock and GHG emissions from agriculture
 Determine what policies should be the object of
simulation. These must be policies that the
country is currently considering for
implementation or are already scheduled to be
implemented.
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55. Conclusion
All LEDS come with costs and benefits
up to the local government to decide
which one is the best option
We can help making educated
decisions
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56. Page 56
THANK YOU
MUCHIAS GRACIAS POR
VUESTRA ATENÇION
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE