INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Marco de Estrategias de Desarrollo Bajo en Emisiones: Resultados finales de escenariode referencia.LEDS: Low Emissions Development Strategies

Dr. Alex De Pinto - Senior Research Fellow

Proyecto de USAID

LEDS: Estrategias de desarrollo económico con bajas emisiones

Herramientas de evaluación de LEDS en el sector de uso de la tierra

Colombia, Vietnam, Bangladesh, Zambia

Colaboración entre IFPRI, CIAT, y sociosnacionales

Marco de Estrategias de DesarrolloBajo en Emisiones

La idea es presentar a los paises un portafolio de tecnologias que ayuden al desarrollo economico, con diferentesemisiones causadas.

Simulación a largo plazo del uso de tierra,

emisiones y secuestro de carbono, debidas a la implementación de politicasque afectan uso del suelo.

Incluye efecto de economía y mercado global

Capaz del modelo en resumen

Some Lessons Learned

To a very complex problem follows a complex analysis, therefore:

Modeling framework and tools should be adaptable

Modeling framework and tools should be flexible so that new information can be incorporated in the analysis as it becomes available

Modeling framework and tools should be fully transparent, so that trust in the results can be built

Parameter estimates for

determinants of land use change

Change in carbon stock and GHG emissions

Policy scenario:Ex. land use allocation targets, infrastructure, adoption of low-emission agronomic practices

Land use change

Future commodity prices and

rate of growth of crop areas

IMPACT modelMacroeconomic scenario: Ex. GDP and population growth

Model of Land Use Choices

Model of Land Use Choices

Ancillary data:Ex. Soil type, climate, road network, slope, population, local ag. statistics

Satellite data

General Circulation Model

Climate scenario:Ex. Precipitation and temperature

Change in carbon stock and GHG

emissions. Economic trade-offs

Land use change

Baseline

Policy Simulation

Crop Model

Crop Model

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

The Theory Behind our Land Use Modeling Approach

Land use:

rice

Land use:

vegetables

Farm price of rice

Farm price of

vegetables

The von

Thünen ModelA Featureless Plain

A Parcel of Land…

Is operated by individual or group to maximize benefits deriving from using the land (utility)

Has an exogenous set of geophysical and socioeconomic characteristics that influence production choices and productivity

Has a set of possible production choices (relates inputs to possible outputs) that determine land use

Benefits at One Location from several possible uses

𝑙– location of a parcel h – possible use at location 𝑙 𝑿𝒍 is vector of factors that affect the stream of

benefits deriving from farming

X= Elevation, slope, soil pH, precipitation, temperature, crop suitability, crop price

f – technology function that relates inputs to each possible output h

𝐵𝑙,ℎ = 𝑓 𝑋𝑙,ℎ

Farmer at One Location Makes a Choice

Farmer chooses the use that returns the highest benefit B

𝐵𝑙,𝑀𝑎𝑖𝑧𝑒 = 𝑓 𝑋𝑙,𝑀𝑎𝑖𝑧𝑒

𝐵𝑙,𝑅𝑖𝑐𝑒 = 𝑓 𝑋𝑙,𝑅𝑖𝑐𝑒

𝐵𝑙,𝐶𝑎𝑠𝑠𝑎𝑣𝑎 = 𝑓 𝑋𝑙,𝐶𝑜𝑓𝑓𝑒𝑒.

.

.

𝐵𝑙,𝐹𝑜𝑟𝑒𝑠𝑡 = 𝑓 𝑋𝑙,𝐹𝑜𝑟𝑒𝑠𝑡

Choose Land Use to Maximize Net Present Value

Net Present

Value (NPV)

Value of

Output

Cost of

Inputs

Discount

effect

One optimization for each possible land use h at location l

at time T

Choose land use (categorical variable) with highest benefit

minusequals

𝐵𝑙ℎ,𝑇 =

𝑡=0

∞

𝑃𝑙ℎ𝑇_𝑡𝑄𝑙ℎ𝑇+𝑡 − 𝐶𝑙ℎ𝑇+𝑡𝑋𝑙ℎ𝑇+𝑡 𝑒𝑖,𝑡𝑑𝑡

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

The Data

Satellite Images as a source of data

An incredible amount of data

Land-use Choices Are Observed

Data from satellite images are processed into land-use maps.

We observe the choices made by whomever has control over the land.

Many Sources of Data

But also statistical data on hectares

allocated to rice, maize, cassava, etc, at

the province or municipio level.

Method of estimation: discrete choice models, e.g. multinomial logit, nested logit, etc.

For each land use we estimate the probability for that use to be chosen

We statistically evaluate the effect of each explanatory variable

.....3

210

lmaizelmaize

lmaizemaizelmaizemaizejlmaize

price

soilslopeB

Prob. Forest

Prob. Agriculture

Prob. Pasture

Model Specification:Two-level Nested Logit

Cocoa

Coffee

Palm

Plantain

Other Perennials

Pasture ForestPerennial

Crops

Annual

Crops

Forest Other

Uses

Cassava

Maize

Potato

Rice

Sugarcane

Other Annuals

Land-use

choice

Crop

choice

Model Specification:Two-level Nested Logit

Prob. Forest

Prob. Agriculture

Prob. Pasture

Prob. MaizeProb. Rice

Prob. Sugarcane

Land-use

choice

Crop

choice

The estimated probabilities are used to

allocate total area changes predicted by

the IMPACT model

Colombia’s Unit of Analysis: Municipio

Variables dependientes de uso de la tierra

Uso de tierra

año Nivel Fuente

Area cultivo

2008 Municipio Ministerio de

Agricultura y

Desarrollo Rural 2008

Area pasto 2007 Municipio IDEAM, IGAC, IAvH, Invemar, I. Sinchi e IIAP

Area bosque

2007 Municipio IDEAM, IGAC, IAvH, Invemar, I. Sinchi e IIAP

Explanatory Variables for Colombia

Explanatory variables

Lower level:

Choice variable: crop shares within

provinces (2008 and 2030)

Crop area, crop suitability, commodity producer price,

elevation, terrain slope, soil pH, annual precipitation,

annual mean temperature

Upper level:

Choice variable: land cover (2008 and

2030)

Land cover, elevation, terrain slope, soil pH, annual

precipitation, annual mean temperature, population

density, travel time to major cities, conserved areas,

indigenous reserves

Variables independientes, capa de usodel suelo (2008)

Variables año Nivel Fuente

Suitabilidad para cultivo 2009 10kmsquare

IIASA/FAO

Altitud 2012 1km square World HarmonizedSoil DB

Gradiente 2012 1km square World HarmonizedSoil DB

PH de suelo 2012 10km square

ISRIC-WISE

Precipitación anual 1950-2000 1km square WorldClim

Temparetura promedioanual

1950-2000 1km square WorldClim

Population density 2000 CIESIN

Tiempo de viaje a ciudades grandes

-2000 1km square JRC-IES-LRM

Precio de carne 2007 nacionál FAO

Valores inclusivos paracultivo

Derived from lower model

Variables independentes, capa de usodel suelo (2008)

Variables año Nivel Fuente

Valores inclusivos paracultivo

Derived from lower model

Inertia variable

Parques nacionáles 2012 250m square

RUNAP / SINAP

Areas conservaciónesregionales

2012 250m square

RUNAP / SINAP

Reservas Naturales de la Sociedad Civil

2012 250m square

RUNAP / SINAP

Reservas forestales 2011 250m square

RUNAP / SINAP

Reserva Indigena 2012 250m square

SIGOT

Areas Afrodescendentes

2012 250m square

SIGOT

Assumptions, Problems, and Shortcomings Stationary state and dynamic processes

• Processes of land use change are inherently dynamic

Property rights• Competitive bidding process breaks down when property rights are

poorly defined or inexistent.

Spatial effects and interdependent behavior• Possible interactions among several decision makers and eco-

biological processes can transcend parcel boundaries

Nonlinearity in the objective function • Problem specifications force a linear relationship between dependent

and independent variables.

Profit/Utility-maximizing operator• Self-sufficiency or risk minimization might be the goal in land use

decisions

Assumptions, Problems, and Shortcomings

Data on carbon stock and GHG emissions that are specific for Colombia

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Thank you

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RESULTS

Enfoque tecnico

Modelo de uso del suelo espacialmenteexplicito

IMPACT: modelo global de equilibrio partial para el sector de agropecuario

DNDC: Modelo cultivo que simula cosecha, emisiones y cambio de almacenamiento de carbono

Combina y reconcilia los modelos

Parameter estimates for

determinants of land use change

Change in carbon stock and GHG emissions

Policy scenario:Ex. land use allocation targets, infrastructure, adoption of low-emission agronomic practices

Land use change

Future commodity prices and

rate of growth of crop areas

IMPACT modelMacroeconomic scenario: Ex. GDP and population growth

Model of Land Use Choices

Model of Land Use Choices

Ancillary data:Ex. Soil type, climate, road network, slope, population, local ag. statistics

Satellite data

General Circulation Model

Climate scenario:Ex. Precipitation and temperature

Change in carbon stock and GHG

emissions. Economic trade-offs

Land use change

Baseline

Policy Simulation

Crop Model

Crop Model

Land Use Model: Assessment of Predicted Land Use Choices

Table 3c. Summary Statistics of Municipal-level Predicted Percent Errors

Crop Mean Min Q1 Median Q3 Max

Perennial crop (N=927)

Cacao 2% -78% 0% 2% 7% 52%

Coffee 3% -74% -11% 1% 12% 90%

Palm 9% -80% 0% 1% 12% 88%

Plantain -4% -94% -16% 5% 15% 47%

Other crops -10% -99% -12% 1% 5% 47%

Annual crop (N=1080)

Cassava -4% -91% -9% 0% 3% 27%

Maize -4% -85% -23% 0% 13% 68%

Potato 2% -90% 0% 0% 2% 74%

Rice 7% -78% 1% 5% 14% 94%

Sugarcane 4% -96% -2% 2% 15% 88%

Other crops -4% -94% -6% 1% 5% 44%

Land (N=1121)

Perennial cropland 0% -59% -1% 2% 4% 21%

Annual cropland 0% -57% -1% 1% 4% 15%

Pasture 0% -69% -13% 2% 13% 60%

Forests 0% -62% -5% 3% 6% 53%

Other lands 0% -70% -13% 1% 14% 56%

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Results: The Baseline

Baseline, IMPACT projectionsCambio de area cultivo 2008 - 2030 Crops Projected

Change in

Price

Area

2030

(1000

Ha)

Cambio

2008 -

2030

(1000 Ha)

Tasa de

Cambio

(Area)

CACAO 25% 188 -3 -2%

COFFEE 30% 837 0 0%

PALM 92% 430 72 20%

PLANTAIN 36% 542 62 13%

OTHR_PERENNIAL 28% 184 6 4%

CASSAVA 39% 251 6 3%

MAIZE 38% 807 -3 0%

POTATO 26% 206 11 6%

RICE 25% 691 -8 -1%

SUGAR CANE 110% 445 106 31%

OTHR_ANNUAL 25% 158 -3 -2%

Total 4737 247

Escenario de referenciaCambio de uso de la tierra 2008 - 2030

Land use

category

2008

area

(Mha)

2030

area

(Mha)

Change

2008–

2030

(Mha)

Change

(%)

Perennial crops 1.9 2.1 0.1 7

Annual crops 2.4 2.5 0.1 5

Pasture 29.2 31.8 2.6 9

Forest 58.7 55.3 -3.4 -6

Other land

uses

22.1 22.7 0.5 2

Total 114.4 114.4

Projected changes in forest and pasture areas at municipality level, 2008–2030

Projected changes in sugarcane and palm area at municipality level, 2008–2030

Projected Changes in Carbon Stock

Land Use

Category

Soil

Organic

Carbon

2008

(Tg C)

Above

and

below

ground

Biomass

2008

(Tg C)

2008

Total

Carbon

stock

(Tg C)

Soil

Organic

Carbon

2030

(Tg C)

Above

and

below

ground

Biomass

2030

(Tg C)

2030

Total

Carbon

stock

(Tg C)

Net

Change

in

Carbon

Stock

(Tg C)

Cropland 530 52 583 557 54 612 29

Pasture 3242 246 3,488 3,481 272 3,753 266

Forest 6,133 5,919 12,052 5,690 5,588 11,279 -774

Other

Land

Uses

2,649 744 3,393 2,742 766 3,508 114

Total 12,554 6,962 19,516 12,470 6,682 19,152 -364

Crops Change in area

2008–2030

(1,000 ha)

Average

per ha GHG

emission in

2008

(Mg ha-1yr-1)

Average

per ha GHG

emission in

2030

(Mg ha-1yr-1)

Change in total

GHG emission

2008–2030

(Tg CO2eq)**

Pasture 2,598 1.7 1.8 85.1Perennial cropCacao -3 0.1* 0.1* 0Coffee 0 0.7* 0.7* 0

Palm 71 4.0 3.8 2.1Plantain 57 3.5 3.0 -0.5Sugarcane 107 1.7* 1.7* 1.8Other perennial 6 - - -

Annual crop

Cassava 6 2.0 1.9 -0.1

Maize -2 1.7 1.6 -0.8

Potato 11 3.5 3.5 0.3

Rice -7 7.2 6.9 -2.5

Other annual -3 - -Total 243 - 85.4

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Policy Simulations

Policy Scenarios

Land use policy scenario from identifies after

consultation with stakeholdersScenario 1 Reduction of pastureland by 10 million hectares

Scenario 3 Total halt to deforestation in the Amazon

Scenario 4 Total land allocated to palm production reaches a total

of 1.3 million hectares

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Scenario 1: Reduction of pastureland by 10 million hectares

Pasture reduction Zero deforestation Palm expansion

Crop Area 2030

Difference

from baseline

2030 Area 2030

Difference

from baseline

2030 Area 2030

Difference

from baseline

2030

Perennial crop 2,505 432 2,067 -6 2,309 236

Cacao 203 34 168 -1 43 -126

Coffee 948 140 806 -2 565 -243

Palm 535 110 423 -1 1,500 1,075

Plantain 593 98 493 -2 156 -339

Other crops 226 50 176 0 45 -131

Annual crop 3,234 725 2,499 -11 2,465 -45

Cassava 330 83 244 -2 241 -5

Maize 1,039 249 785 -4 775 -14

Potato 242 45 196 0 197 0

Rice 898 226 669 -3 652 -20

Sugarcane 529 80 448 0 444 -5

Other crops 197 40 157 0 156 -1

Pasture 21,846 -10,000 30,899 -947 31,761 -85

Forests 59,479 4,161 56,651 1,332 55,289 -30

Other land uses 27,408 4,683 22,357 -368 22,649 -76

Total 114,473 - 114,473 - 114,473 -

Results (1,000 hectares)

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Scenario 2: Total halt to deforestation in the Amazon

Pasture reduction Zero deforestation Palm expansion

Crop Area 2030

Difference

from baseline

2030 Area 2030

Difference

from baseline

2030 Area 2030

Difference

from baseline

2030

Perennial crop 2,505 432 2,067 -6 2,309 236

Cacao 203 34 168 -1 43 -126

Coffee 948 140 806 -2 565 -243

Palm 535 110 423 -1 1,500 1,075

Plantain 593 98 493 -2 156 -339

Other crops 226 50 176 0 45 -131

Annual crop 3,234 725 2,499 -11 2,465 -45

Cassava 330 83 244 -2 241 -5

Maize 1,039 249 785 -4 775 -14

Potato 242 45 196 0 197 0

Rice 898 226 669 -3 652 -20

Sugarcane 529 80 448 0 444 -5

Other crops 197 40 157 0 156 -1

Pasture 21,846 -10,000 30,899 -947 31,761 -85

Forests 59,479 4,161 56,651 1,332 55,289 -30

Other land uses 27,408 4,683 22,357 -368 22,649 -76

Total 114,473 - 114,473 - 114,473 -

Results (1,000 hectares)

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

Scenario 3: Total land allocated to palm production reaches a total of 1.5 million hectares

Pasture reduction Zero deforestation Palm expansion

Crop Area 2030

Difference

from baseline

2030 Area 2030

Difference

from baseline

2030 Area 2030

Difference

from baseline

2030

Perennial crop 2,505 432 2,067 -6 2,309 236

Cacao 203 34 168 -1 43 -126

Coffee 948 140 806 -2 565 -243

Palm 535 110 423 -1 1,500 1,075

Plantain 593 98 493 -2 156 -339

Other crops 226 50 176 0 45 -131

Annual crop 3,234 725 2,499 -11 2,465 -45

Cassava 330 83 244 -2 241 -5

Maize 1,039 249 785 -4 775 -14

Potato 242 45 196 0 197 0

Rice 898 226 669 -3 652 -20

Sugarcane 529 80 448 0 444 -5

Other crops 197 40 157 0 156 -1

Pasture 21,846 -10,000 30,899 -947 31,761 -85

Forests 59,479 4,161 56,651 1,332 55,289 -30

Other land uses 27,408 4,683 22,357 -368 22,649 -76

Total 114,473 - 114,473 - 114,473 -

Results (1,000 hectares)

Results

*Includes changes in SOC, Above and Below ground C caused by land use change.

**Changes in emissions from cropland and livestock caused by land use change. Exclude burning

**Changes in revenue from crop and meat production

Change in C

stock

(Tg C)

Change in

GHG emission

(Tg CO2eq)

Change in

total revenue

(US$ billion)

Reduce

pasture by

10 million

hectares

Cropland 144 26.7 56

Livestock -1,297 -184 -22

Forest 895 - -

Other 660 - -

Total 402 -157 34

Zero

deforestation

in the

Amazon

Cropland 4 -0.8 -1

Livestock -97 -16 -2

Forest 245 - -

Other -41 - -

Total 111 -17 -3

Increase area

allocated to

palm by 1.5

million

hectares

Cropland 18 24.5 -57

Livestock -10 -1 0

Forest -7 - -

Other -11 - -

Total -11 24 -57

Policy Comparison

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MUCHAS GRACIAS POR SUATENCION

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