Scholarly article on topic 'Low Emission Development Strategies in Agriculture. An Agriculture, Forestry, and Other Land Uses (AFOLU) Perspective'

Low Emission Development Strategies in Agriculture. An Agriculture, Forestry, and Other Land Uses (AFOLU) Perspective Academic research paper on "Earth and related environmental sciences"

CC BY
0
0
Share paper
Academic journal
World Development
Keywords
{"climate change" / "land use change" / "low emission development" / AFOLU}

Abstract of research paper on Earth and related environmental sciences, author of scientific article — Alessandro De Pinto, Man Li, Akiko Haruna, Glenn Graham Hyman, Mario Andrés Londoño Martinez, et al.

Summary As countries experience economic growth and choose among available development pathways, they are in a favorable position to adopt natural resource use technologies and production practices that favor efficient use of inputs, healthy soils, and ecosystems. Current emphasis on increasing resilience to climate change and reducing agricultural greenhouse gasses (GHG) emissions strengthens the support for sustainable agricultural production. In fact, reducing losses in soil fertility, reclaiming degraded lands, and promoting synergistic interaction between crop production and forests are generally seen as good climate change policies. In order for decision-makers to develop long-term policies that address these issues, they must have tools at their disposal that evaluate trade-offs, opportunities, and repercussions of the options considered. In this paper, the authors combine and reconcile the output of three models widely accessible to the public to analyze the impacts of policies that target emission reduction in the agricultural sector. We present an application to Colombia which reveals the importance of considering the full scope of interactions among the various land uses. Results indicate that investments in increasing the efficiency and productivity of the livestock sector and reducing land allocated to pasture are preferable to policies that target deforestation alone or target a reduction of emissions in crop production. Investments in livestock productivity and land-carrying capacity would reduce deforestation and provide sufficient gains in carbon stock to offset greater emissions from increased crop production while generating higher revenues.

Academic research paper on topic "Low Emission Development Strategies in Agriculture. An Agriculture, Forestry, and Other Land Uses (AFOLU) Perspective"

World Development Vol. xx, pp. xxx-xxx, 2016 0305-750X/© 2016 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

http://dx.doi.org/10.1016/j.worlddev.2016.06.013

Low Emission Development Strategies in Agriculture. An Agriculture, Forestry, and Other Land Uses (AFOLU) Perspective

ALESSANDRO DE PINTO a, MAN LIa, AKIKO HARUNAa, GLENN GRAHAM HYMANb, MARIO ANDRES LONDONO MARTINEZ c, BERNARDO CREAMER b, HO-YOUNG KWON a, JHON BRAYAN VALENCIA GARCIAb, JEIMAR TAPASCO b and JESUS DAVID MARTINEZ b *

a International Food Policy Research Institute, USA b International Center for Tropical Agriculture, Colombia c Universidad de los Andes, Colombia

Summary. — As countries experience economic growth and choose among available development pathways, they are in a favorable position to adopt natural resource use technologies and production practices that favor efficient use of inputs, healthy soils, and ecosystems. Current emphasis on increasing resilience to climate change and reducing agricultural greenhouse gasses (GHG) emissions strengthens the support for sustainable agricultural production. In fact, reducing losses in soil fertility, reclaiming degraded lands, and promoting synergistic interaction between crop production and forests are generally seen as good climate change policies. In order for decision-makers to develop long-term policies that address these issues, they must have tools at their disposal that evaluate trade-offs, opportunities, and repercussions of the options considered. In this paper, the authors combine and reconcile the output of three models widely accessible to the public to analyze the impacts of policies that target emission reduction in the agricultural sector. We present an application to Colombia which reveals the importance of considering the full scope of interactions among the various land uses. Results indicate that investments in increasing the efficiency and productivity of the livestock sector and reducing land allocated to pasture are preferable to policies that target deforestation alone or target a reduction of emissions in crop production. Investments in livestock productivity and land-carrying capacity would reduce deforestation and provide sufficient gains in carbon stock to offset greater emissions from increased crop production while generating higher revenues.

© 2016 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/li-censes/by/4.0/).

Key words — climate change, land use change, low emission development, AFOLU

ELSEVIER

www.elsevier.com/locate/worlddev

1. INTRODUCTION

Resource use in many developing countries, from crop production to deforestation is responsible for the bulk of greenhouse gasses (GHG) emissions, and there are instances in which the agricultural and forestry sectors can provide low-cost climate change mitigation opportunities (Golub et al., 2013; Lubowski & Rose, 2013; Smith et al., 2007). From a technical point of view, reducing expected increases in GHG emissions in agriculture requires the adoption of transformative approaches in the use of resources. Emphasis has been placed on methods that increase the efficiency in the use of fertilizers, water, and fossil fuels, as well as waste reduction. A growing body of literature analyzes the effects of alternative agricultural practices (Antle & Stoorvogel, 2008; Diagana, Antle, Stoorvogel, & Gray, 2007; Gilhespy et al., 2014; Schneider & Smith, 2008; Smith et al., 2013; Tenningkeit, Kahrl, Wolcke, & Newcombe, 2012; Tschakert, 2007). The livestock sector has also been the target of research on mitigation opportunities (Golub et al., 2013; Li et al., 2012; Schils, Olesen, del Prado, & Soussana, 2007), and the mitigation potential of forests, soil, and other biomass has been amply analyzed as well (Cacho, Marshall, & Milne, 2005; Lubowski & Rose, 2013; Makundi & Sathaye, 2004; Torres, Marchant, Lovett, Smart, & Tipper, 2010). However, from a policy-making perspective, the design of low emission development strategies is an example of multi-objective decision making in which policies target the reduction of GHG emissions while other goals such as increasing agricultural productivity and food security or attaining objectives such as export goals or

economic growth are preserved. It is also important to consider that all countries are part of a global economic system, and therefore it is critical that policies are devised with full recognition of the role of the international economic environment which, with its effects on commodity prices, can significantly affect the long-term viability and the budgetary implications of mitigation policies. The challenge at hand is to reconcile the limited spatial resolution of macro-level economic models that operate at a global or national level with models that function at a higher spatial resolution to properly account for changes in carbon stocks and GHG emissions. The

* The authors thank our colleagues in the Colombian government who kindly answered our questions and gave critical advice, without which we could not have developed this study. We are especially grateful for suggestions and advice from colleagues of the Ministerio de Agricultura (MADR), the Federación Colombiana de Ganaderos (FEDEGAN), and the Sociedad de Agricultores de Colombia (SAC). We are also grateful to many colleagues from the Ministry of Environment (MADS), Universidad Javieriana, Universidad Nacional, National Cereal Growers Federation (FENALCE), the National Potato Growers Association (FEDEPAPA), and the National Forestry Research and Promotion Institute (CONIF). This work was supported by a grant from the United States Agency for International Development (USAID) and received support from two Consultative Group on International Agricultural Research (CGIAR) research programs: Climate Change, Agriculture and Food Security (CCAFS) and Forest, Trees and Agroforestry (FTA). The authors take sole responsibility for the opinions expressed within this report. Final revision accepted: June 14, 2016.

number of analyses that confront this challenge is still small, but it is growing given the importance of the information that these studies can provide to policy makers. Schneider and Smith (2008) estimated mitigation potentials of U.S. agriculture with regionally disaggregated data and changes in welfare within the agricultural sector. Golub et al. (2013) examined the impact on food consumption and income of implementing mitigation policies at national and regional levels. Rutten, van Dijk, van Rooij, and Hilderink (2014) evaluated the effects of a series of climate change and economic growth scenarios on Vietnam's economy. Dace, Muizniece, Blumberga, and Kaczala (2015), used a system dynamic model to assess the effect of a group of policies on agricultural GHG emissions in Latvia. Havlik et al. (2014) estimated the effects of transitioning to a more efficient livestock production system on GHG mitigation and the economy, and Lubowski and Rose (2013) provided a review of a number of studies that model mitigation potentials of the Reducing Emissions from Deforestation and Forest Degradation (REDD) program along with conservation, sustainable management of forests, and enhancement of forest carbon stocks policies.

In this article we demonstrate that different models, all widely accessible to the public, can be brought together to help policymakers in their evaluation of trade-offs, opportunities, and repercussions of alternative mitigation policies in the agricultural sector. While the focus of this work is on Colombia, the analytical framework can be applied to any country interested in exploring country-wide effects and the economic viability of climate change mitigation policies in agriculture. The approach is based on the use of public and widely accessible data and we believe that the flexibility and transparency of the approach proposed in this study can increase decision-makers' trust in the results. Naturally, additional data and targeted surveys can increase the accuracy of the results and the framework does not create barriers for the inclusion of additional input. Nonetheless, it is clear from our analysis that policy-makers need substantial support in their decision-making process as the range of options they face can be very diverse and the effects of their decisions have important, and sometimes unexpected repercussions. The effects of the policies we simulated cover the entire spectrum of potential outcomes. We found win-win policies (reducing land allocated to pasture increases profits and carbon stock and reduces GHG emissions), policies with tradeoffs (limiting deforestation in the Amazon increases carbon stock, decreases emissions, but reduces profits), and policies that could generate clearly inferior results (increasing the area allocated to oil palm cultivation beyond certain amounts reduces carbon stock, increases emissions, and reduces profits). Stakeholders, from government agencies to producer and consumers' organizations to farmers will benefit from policies devised with the support of solid evidence and the effects of which can be investigated and evaluated by all the parties affected.

2. GREENHOUSE GAS EMISSIONS IN COLOMBIA

In 2010, Colombia presented its second National Communication on Climate Change to the United Nations Framework Convention on Climate Change. The report contains data from the last National Greenhouse Gases Inventory carried out in 2004. Colombia contributes 0.37% (180,010 Gg) of the total worldwide emissions of GHG (49 Gt). Emissions are composed of 50% carbon dioxide (CO2), 30% methane (CH4), 19% nitrous oxide (N2O), and the remaining 1% classified as chlorofluorocarbons (CFCs).

According to the last National Greenhouse Gases Inventory, agricultural activities emit 38% of total emissions, and land use, land use change, and forestry account for another 14%. Of the emissions resulting from agricultural activities, 48.5% are due to enteric fermentation, 47.5% from agricultural soil management, and 2% from emissions related to rice cultivation. Traditionally, Colombia has a large number of smallholder farmers and there is also a well-established culture of cattle ranching with both small and large livestock keepers. Urbanization and industrialization have been growing in Colombia, but agricultural and forestry activities are expected to grow and continue to claim a large share of emissions. Although the agriculture sector represents 7% of the gross national product, the sector employs 18% of the population (CIA (Central Intelligence Agency), 2014).

Colombia has developed plans and policies that address climate change mitigation identifying priority sectors with high GHG emission rates. A working group led by the Ministry of Environment and Sustainable Development (MADS) has selected target areas for low emissions development in the agriculture, forestry, and land use sectors. These include reducing emissions from deforestation and forest degradation, oil palm, livestock, forestry, and fertilizers. In December 2015, the government of Colombia presented its Intended Nationally Determined Contributions (INDCs) at the Conference of the Parties in Paris and this document includes contributions from the AFOLU sector.

According to official government statistics (IAvH et al., 2007; IGAC, 2013), 52% of Colombia's 115 million hectares is covered by natural forests, mostly within the Amazon basin but also forests along the Pacific coast and in the northern part of the country. Cultivated pastures and native savanna grasslands make up 26% of the land area. These lands are characterized by cattle grazing with low stocking rates and frequent natural and anthropogenic fires. Cropland is mostly concentrated in the intermountain valleys, making up about 4% of the land surface (see Figure 1).

In 2011, the Instituto Nacional de Hidrología Meteorología y Estudios Ambientales de Colombia (IDEAM) and MADS quantified national deforestation rates and trends (Table 2.1). The average annual deforestation rate over the entire period is some 238,000 hectares and the Amazon and Andes regions appear to be areas particularly at risk.

Prior to 2000, estimates of forest clearing suggested that two-thirds of this clearing was due to the pastureland encroaching into forest and one-third due to cropland expansion (Etter, McAlpine, Wilson, Phinn, & Possingham, 2006). A more recent analysis has suggested that 90% of forest clearing during 2005-10 was due to pastureland development (Nepstad, Tepper, McCann, Stickler, & McGrath, 2013).

Colombia is the fifth largest producer of palm oil and its production area is expected to increase. Official projections (MADR (Ministry of Agriculture & Rural Development), 2011) indicate that there will be little changes in cropland area over the coming decades, with the exception of oil palm. Oil palm is expected to increase substantially after 2016, due to its high demand for food products and biofuels. However, oil palm development mostly occurred on lands that were already cleared of their forests, a trend that according to some studies is expected to continue, at least partially (Castiblanco, Etter, & Aide, 2013). Pasturelands and livestock production may change substantially in the coming years. According to the Colombian Federation of Cattle Ranchers (Federación Colombiana de Ganaderos, FEDEGAN), the Colombian livestock inventory totals 23.5 million head of cattle and 39.2 million hectares of pasture. With less than one head of

LOW EMISSION DEVELOPMENT STRATEGIES IN AGRICULTURE

Figure 1. Main regions in Colombia.

cattle per hectare, livestock occupies 26% of the total land area of Colombia. The livestock area has expanded from 14.6 to 38 million hectares in the past 50 years, mostly at the expense of tropical forest (MADS (Ministry of Environment & Sustainable Development), 2012). As seen in Figure 2.2, the majority of pasture area is located in the eastern plains and Caribbean region. However, in terms of number of animals, the northwestern part of the Amazon region also appears to hold a significant share of pasture area.

3. MODELING FRAMEWORK

In order to evaluate the potential for GHG emission reductions and trade-offs of alternative mitigation policies, we combined and reconciled data and outputs of economic and biophysical models. This approach included the use of the following models:

• The International Model for Policy Analysis of Agricultural Commodities and Trade Model (IMPACT; Robinson et al., 2015), a global partial equilibrium agriculture model that allows for policy and agricultural productivity investment simulations;

• A spatially explicit model of land use choices to determine the possible effects of future changes in the drivers of land use choices (Li, De Pinto, Ulimwengo, You, & Robertson, 2015); and

• DeNitrification-DeComposition crop model (DNDC; Li, 2007) that estimates spatially explicit profiles of GHG emissions from cropland with varying crop genetic productivity shifters, management systems, and climate scenarios.

This suite of models produces a series of country-specific results embedded in a framework consistent with global outcomes. Using the crop model, which incorporates the most updated knowledge on GHG emissions generated by crop production, it is possible to simulate the effects on GHG emissions of current and alternative agricultural management practices, when relevant (Figure 3.1). With the goal of making the results of our analysis as relevant as possible to policy-makers, we chose to work within a relatively short time horizon, approximately 20 years (from 2008 to 2030). The start year of 2008 was dictated by data availability on land use shares at the municipal level.

(a) IMPACT model

IMPACT is a partial equilibrium agricultural model that uses a system of linear and nonlinear equations to approximate the underlying production and demand relationships of world agriculture (Robinson et al. ,2015). The model has a long record of applications and it has been employed in a wide range of analyses, from assessing the potential effects of climate change on global food production (Godfray & Robinson, 2015; Springmann et al., 2016) to evaluating the global effects of biofuels production (Rosegrant, Zhu, Msangi, & Sulser, 2008). The world's food production and consumption is disaggregated into 159 countries and regional groupings with a further disaggregation in many regions to the river basin level and with the basic unit of analysis being the Food Production Unit (Figure 3.2). IMPACT models the global behavior of a competitive agricultural market for crops and livestock and is specified as a set of countries or regions, in which supply, demand, and prices for agricultural commodities are determined. Countries and regions are linked through trade so that the interactions among country-level production, consumption, and commodity prices are captured through net trade flows in global agricultural markets. Demand is a function of price, income, and population growth. Growth in crop production in each country is determined by crop prices

Table 2.1. Deforestation by regions

Period Indicator Natural region

Pacific Orinoquia Caribbean Andes Amazon Total

1990-2000 Forest 1990 (Ha) 5,249,261 2,335,094 2,368,779 12,565,035 41,924,100 64,442,269

Deforestation (Ha) 140,426 240,580 343,019 876,597 1,198,018 2,798,640

2000-05 Forest 2000 (Ha) 5,227,673 2,182,517 2,014,227 11,716,837 40,669,967 61,811,221

Deforestation (Ha) 29,254 28,696 47,313 97,293 112,565 315,121

2005—2010 Forest 2005 (Ha) 5,035,400 2,123,340 1,807,073 11,151,591 40,096,203 60,213,607

Deforestation (Ha) 110,744 46,534 200,090 435,450 398,985 1,191,803

Annual average deforestation 22,149 9,307 40,018 87,090 79,797 238,361

Source: IDEAM (Instituto Nacional de Hidrología Meteorología y Estudios Ambientales de Colombia) 2010.

Figure 2.2.

WORLD DEVELOPMENT

Number of cattle (left) and percent ofpasture area (right) by municipality.. Source: FEDEGAN (2011) and MADR (Ministry of Agriculture &

Rural Development) (2011).

Figure 3.1. Workflow of the modeling approach.

and the rate of productivity growth from agricultural research and development, agricultural extension and education, markets, infrastructure, and irrigation. The model includes 64 commodities including all major cereals, soybeans, roots and tubers, meats, milk, eggs, oils, oilcakes and meals, vegetables, fruits, sugarcane and beets, and cotton.

IMPACT avails itself of the output from several other models. These include Global Circulation Models (GCM)1 which simulate climate scenarios under global climate change; the Decision Support System for Agrotechnology Transfer (DSSAT) crop model suite which estimates yields with varying crop genetic productivity shifters and alternative management

Figure 3.2. IMPACT global partitioning.. Source: Robinson et al. (2015).

systems; the Spatial Production Allocation Model (SPAM) which provides an estimate of current area allocation to crop production and to management techniques such as rainfed/irrigated, and some IMPACT submodels such as the Water Simulation Model (IWSM) and the Global Hydrologic Model (IGHM).

IMPACT output is generally at a regional or national level. While this output provides information regarding changes in crop prices and in crop areas, it does not generate information with respect to the location of these changes and on the origin of new cropland. Given these limitations, it would be difficult to use the IMPACT model to quantify changes in carbon stock and GHG emissions from crop production. In order to do this, it is necessary to know the carbon content of the land uses that transitions in and out of production. Furthermore, GHG emissions from crop production are dependent on the characteristics of the land where farming takes place. It is therefore essential that the modeling output is sufficiently spatially disaggregated to account for the biophysical characteristics of each location.

(b) Land use model

for particular land use to be chosen. These probabilities are used to spatially allocate changes in cropland areas predicted by the IMPACT model.

As shown in Figure 3.3, the model structure consists of two levels of land use choices. The upper level estimates choices among large aggregation categories, such as cropland, pasture, forest, and other uses while the lower level estimates crop choices within a cropland. A detailed description of model specification and estimation method is provided in Appendix 1.

For the upper level model, we used a wide variety of biophysical and socioeconomic variables that influence land use choices, while for the lower level section we employed biophysical variables related to crop production as well as some economic variables such as crop prices and production costs which we considered to be key determinants of land allocation choices among crops (Table 3.1). To determine how land allocations will likely change in the year 2030, we used projections of commodity prices and the climate scenario MIROC General Circulation Model in combination with socioeconomic assumptions: AR5-SSP2 scenario (Shared Socioeconomic Pathway 2—middle of the road scenario).

In order to increase the spatial disaggregation of IMPACT output, we used an econometric model of land use choices. We followed the approach proposed by Li et al. (2015) based on a Maximum Likelihood method and employed a spatially explicit model which captures the main drivers of land use choice. The basic logic of the modeling approach is that the observed choices are the result of a utility-maximization process where each landholder will choose a land use that maximizes the stream of benefit deriving from the use of land. The econometric model, in this case a nested logit, attempts to explain the observed choices using a series of factors that are thought to influence the stream of benefits as explanatory variables. This class of models has been widely used in a variety of contexts (Chomitz & Gray, 1996; De Pinto & Nelson, 2009; Nelson, De Pinto, Harris, & Stone, 2004; Nelson & Hellerstein, 1997 among many) and, given the characteristics of each location, it can be used to estimate the probability

(i) Data for upper level model

We used the Colombian government statistics to determine cropland and forest areas at the municipal level (IGAC (Instituto Geografico Agustin Codazzi), 2013) while for pastureland, we used data from the Instituto de Hidrología, Meteorología y Estudios Ambientales (IAvH (Instituto de Investigación de Recursos Biológicos Alexander Von Humboldt) et al., 2007). Based on these data, we classified the land use in four categories: cropland; forest; pasture; and other land uses which include shrub and secondary vegetation.

Population density data were gathered from the Gridded Population of the World, Version 3, with a spatial resolution of 30 arc-second (~1 km) (CIESIN (Center for International Earth Science Information Network)/Columbia University/CIAT (Centro Internacional de Agricultura Tropical), 2005). We used a lagged-population density value to mitigate endogeneity issues.

WORLD DEVELOPMENT

Figure 3.3. Structure of the land use model.

Table 3.1. Summary of the explanatory variables used for the land use model

Explanatory variables

Lower level: Choice (dependent) variable: crop shares within municipal Upper level: Choice (dependent) variable: land use shares within municipal

Biophysical suitability by crop; commodity producer price by crop; production cost by crop; elevation; terrain slope; soil pH; annual precipitation; annual mean temperature. Price of meat and milk; timber price; production cost for livestock (cattle); population density; travel time to major cities; elevation; coefficient of variation of elevation; terrain slope; soil pH; annual precipitation; annual mean temperature; protected areas; indigenous reserves.

Note: A table with the descriptive statistics for the explanatory variable is provided in Appendix 1.

Data on market accessibility, measured by travel time to major cities (cities of 50,000 or more people in year 2000), were generated from a global map of accessibility with a resolution of 30 arc-second (Uchida and Nelson, 2010). Factors affecting the travel costs include transport network and environmental and political factors (see Nelson, 2000; Uchida and Nelson, 2010 for details).

Elevation and slope data were generated from a resampled Shuttle Radar Topography Mission (SRTM) digital elevation data with a 30 arc-second spatial resolution, produced by the U.S. National Aeronautics and Space Administration (NASA) and the U.S. National Geospatial-Intelligence Agency (NGA). The original SRTM data were available with a 90 m resolution. The original elevation data contain "no-data" observations where water or heavy shadow prevented the quantification of elevation. These observations were further processed by Jarvis, Reuter, Nelson, and Guevara (2008) to fill in the no-data voids.

Climate data, measured by mean precipitation and mean annual temperature, were generated from WorldClim (Hijmans & et al., 2005). Using MIROC Global Climate Model—AR5 SSP2, we applied rates of change to extrapolate the climate condition for the year 2030. Soil pH was collected from Harmonized World Soil Database (HWSD) (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012). Conservation practice and indigenous reserves were included as a dummy variable, extracted using Colombian government maps (SINAP (Sistema Nacional de Areas Protegidas), 2012).

(ii) Data for lower level model

The lower level model combines local agricultural statistics with geo-referenced data. We used a dataset for cropland

physical area at municipality level (in total 1,121 municipalities) for the year 2008 (MADR (Ministry of Agriculture & Rural Development), 2011). Based on these data, we identified the five major annual crops in terms of cultivated areas: cassava; maize; potato; rice; and sugarcane, and four major perennial crops: cacao; coffee; oil palm; and plantain.

Domestic commodity producer prices were collected from FAOSTAT for the year 2008. IMPACT projected rates of change for prices were utilized to extrapolate domestic commodity producer prices for the year 2030. The use of country-level commodity prices substantially reduces concerns about endogeneity between municipal crop choices and local producer prices. We assumed that domestic commodity producer prices can be observed at the major markets located in cities with populations greater than 50,000 and that farmlevel producer prices move synchronously with domestic prices. With these assumptions, we estimated the spatially explicit prices, ppnj, for each location and each crop using a distance decay function:

ppnj = PPj x exp{—accessn/m.axfn0g accessn<),

where PPj represents the domestic producer price of commodity j and accessn is travel time from each location n to its nearest major market.2

Data on biophysical suitability were derived from the global Agro-ecological Zones (GAEZ, v1.0) assessment (Fischer, Shah, van Velthuizen, & Nachtergaele, 2001), which was developed by the International Institute for Applied Systems Analysis (IIASA) and the Food and Agriculture Organization of the United Nations (FAO). Suitability data are available at a resolution of approximately 9 km at the equator. These data

were used as a proxy for the maximum attainable yield in Colombia.

(iii) Crop model

To estimate GHG emissions from crop production, we employed the biogeochemical model—DeNitrification and Decomposition (DNDC) (Li, Frolking, & Frolking, 1992)—along with coarse-resolution remote sensing data (5 arc-minute grids, approximately 10 km x 10 km) and information about local agricultural practices (Table 3.2). Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emitted from soil in cropland are all accounted for. Figure 3.4 illustrates key data required to run the DNDC model and Figure 3.5 shows the process followed for the computation of GHG emissions.

The processes embedded in DNDC have been intensively tested in numerous conditions of soils, of climate, and against observed GHG fluxes with results generally considered sufficiently accurate (Beheydt, Boeckx, Sluetel, Li, & van Cleemput, 2007; Cai et al., 2003; Deng et al., 2011; Giltrap, Li, & Saggar, 2010; Grote et al., 2009; Li, 2007; Pathak, Li, & Wassmann, 2005; Werner, Butterbach-Bahl, Haas, Hickler, & Kiese, 2007).

We first identified cropland and mosaic cropland land use areas categorized by National Aeronautics and Space Administration's Moderate Resolution Imaging Spectroradiometer (MODIS) and further divided them into a 5 arc-minute grid.

For each cell, we applied the DNDC model to estimate crop productivity and GHG emissions. To derive the crop and location-specific data required for simulations in DNDC, information about local cropping systems and nitrogen fertilization rates were obtained through interviews and literature review. Remote sensing data were used to characterize crop calendars (that is, planting and harvesting dates) (Sacks, Deryng, Foley, & Ramankutty, 2010) and soil physical and chemical properties (such as texture, soil organic C (SOC) content, pH, and bulk density) from HWSD (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012). To derive future weather series corresponding to the MIROC climatic scenario and ensure consistency with the IMPACT scenario, we used climatological data developed by the CGIAR Research Program on Climate Change, Agriculture and Food Security and the MarkSim weather generator accompanied by the data (www.ccafs-climate.org/pattern_scaling). Note that this is an essential step given that GHG emissions and changes in SOC vary with soil types and other location-specific biophysical determinants.

Finally, DNDC simulations were run to make forward projections of GHG emissions from four annual crops (maize, rice, cassava, and potato) and two perennial crops (plantain and oil palm) for the period 2008 to 2030. We assumed current crop management practices and input levels stay constant. Global warming potential (GWP) was computed using fluxes in GHG and changes in SOC content. In order to smooth

Table 3.2. Dataset used for crop model

Spatial resolution

Source

Soil texture; soil C; pH; soil bulk density Crop calendar Inorganic N rate

Tillage rate; residue incorporation rate; irrigation rate; rotation; potential yield (for sugarcane, cassava, potato, palm) Precipitation and temperature

30 arc sec grid 5 arc min grid Country level Country level

5 arc min grid

FAO/IIASA/ISRIC/ISS-CAS/JRC (2012) Sacks et al. (2010)

FAO Fertistat (http://www.fao.org/ag/agp/fertistat/index_en.htm) Agronet.gov.co, fedepapa.co, other local institutions

Marksim weather generator (www.ccafs-climate.org/pattern_scaling)

Figure 3.4. Data requirement for the use of the DNDC model.

Figure 3.5. Simulation flow of GHG emissions in DNDC model.

out the effects of single weather event fluctuations such as a year with abnormal precipitation or temperatures we computed the average per-hectare GWP for each crop and for each pixel. Given that total emissions were computed by multiplying per-hectare emissions by crop area estimations, changes in total emission reflect only the effect of change in crop allocation decisions.

4. OTHER GHG EMISSIONS AND CARBON STOCK

For the crops that could not be modeled using DNDC (coffee, cacao, and sugarcane), we used emission factors available in the literature (De Figueiredo, Panosso, Romao, & La Scala, 2010; Hergoualc'h, Skiba, Harmand, & Henault, 2008; Palm et al., 2002). In addition to cropland emissions, we considered GHG emissions from land allocated to pasture such as methane emission from enteric fermentation and manure management, as well as N2O emissions from manure left on the ground. To generate location-specific estimations, we used national statistics for GHG emissions from livestock (FAO (Food & Agriculture Organization of the United Nations), 2014) and IMPACT values for the country herd size to generate per-head emission estimates.3 Using total herd size and total hectares of pasture, we also estimated the stocking rate and therefore emissions per hectare of pasture. Due to lack of data on GHG emissions from standing forest (e.g., burning and decomposition) and other land uses, we disregard emissions from these sources. Above- and below-ground biomass as well as soil organic carbon (SOC) were the pools of carbon stocks estimated. Changes in stock are estimated in proportion to area changes at the municipality level for each land cover type (Figure 4.1). We assume that for uses other than cropland carbon stock does not change through time if land remains in the same land use.

For the categories of forest; grassland; and other land uses, we used a spatial above-ground biomass dataset (Anaya, Chuvieco, & Palacios-Orueta, 2009; Saatchi et al., 2011) to calculate average biomass for each land use and for each

municipality. According to the country's land cover map (IAvH (Instituto de Investigation de Recursos Biologicos Alexander Von Humboldt) et al., 2007), nearly 50% of ''other land uses" category in Colombia consists of shrub and secondary vegetation. Biomass for this land cover category is also taken into account. For the estimation of below-ground biomass, root-shoot ratio for each land cover type is adopted from the available literature (IPCC, 2006; Saatchi et al., 2011). For major perennial crops, we used biomass figures from Dossa, Fernandes, Reid, & Ezui, 2007; Henson, Ruiz, & Romero, 2012; Koskela, Nygren, Berninger, & Luukkanen, 2000. The municipal average SOC per land use type was elaborated using a global SOC dataset (Hiederer & Kochy, 2011), land cover map (IAvH (Instituto de Investigation de Recursos Biologicos Alexander Von Humboldt) et al., 2007), and soil type map (Niels, 2010). Average SOC is calculated by extracting existing combinations of soil type and land cover in each municipality and weighing each combination according to the corresponding area size within the municipality. Table 4.1 provides an overview of all carbon pools and GHG fluxes accounted for in the study.

5. ECONOMIC TRADE-OFFS, BASELINE, AND ALTERNATIVE SCENARIOS

Policies that aim at reducing emissions can generate tradeoffs by reducing or increasing area allocated to crops, pasture, or forests or by affecting yields or production costs. Due to lack of data, we did not consider possible changes in input usage and we only account for changes in profits caused by projected changes in yields, prices, and land allocations. Forest economy was not included due to lack of data. Yields and producer prices from national statistics (FAO (Food & Agriculture Organization of the United Nations), 2014) are used for the year 2008 while growth rates for yields and prices from the IMPACT model are used for year 2030.

Figure 5.1 summarizes the methodology used to compare alternative policy scenarios with the baseline. The modeling

LOW EMISSION DEVELOPMENT STRATEGIES IN AGRICULTURE

Figure 4.1. Calculation of carbon pools and GHG fluxes.

Table 4.1. Carbon pools and GHG fluxes by land use category Land use category Carbon pools GHG fluxes

Aboveground carbon stock Below-ground carbon stock Soil organic carbon CO2 N2O CH4

Cropland YESa YESa YES YES YES YES

Pasturec YES YES YES YES YES

Forest YES YES YES

Other land uses YESb YESb YES

a Figures are only for perennial crops.

b Figures are only for areas with shrub and secondary vegetation. c Emissions consider enteric fermentation and manure management.

Figure 5.1. Computation of changes in GHG emissions and carbon stock for baseline and alternative policy scenarios.

framework we used for the analysis of low emission development strategies generated estimations and projections for the base year (2008) and the end-period (2030). Therefore, the annual rates of change had to be extrapolated from the starting and ending values. The annual rate of change is essential to compute the total amount of GHG emissions generated under a particular scenario.

The stylized example provided in Figure 5.1 is a case where both GHG emission and carbon stock increase in 2030 compared to 2008. Total change in emissions is obtained by accounting for the yearly changes throughout the time period considered (area triangle ABC). Similarly, the computation of changes in GHG emissions from implementing a particular policy is obtained by measuring the area of triangle ACD. Note

that the effect of each policy is computed against the changes in emissions and carbon stock projected for the baseline.

For the baseline, we assumed that all field activities and agronomic practices stay constant, and therefore our computation of GHG emissions is based only on changes in land use due to changes in commodity prices and other underlying assumptions in IMPACT regarding changes in GDP, population growth, and climatic conditions.

Carbon stocks present in the soil and above- and below-ground biomass in each land use were also assumed constant, thus carbon stock accounting also was based only on land use change. Changes in profits were similarly computed as cumulative effects over the 21 years under consideration.

6. BASELINE RESULTS

IMPACT model results, reported in Table 6.1, indicate that global market forces determine significant increases in area for oil palm, plantain, and sugar cane (7%, 7%, and 25% respectively). These results are dependent on assumed population and GDP growth in Colombia (1.1% per year and 4% per year respectively during the period 2008-2030) but also on assumed global population and GDP growth (1.15% and 3.1%, respectively), technology growth, and changes in diets. For a comprehensive review of how the IMPACT model functions, including IMPACT development over time and an explanation for possible differences among published results, we refer the reader to a more detailed analysis available in Robinson et al., 2015 and for comparison of IMPACT results with other global models to Nelson et al., 2014.

These changes in crop areas were used as an input in the land use model. The land use model was used to spatially allocate changes in cropland projected by IMPACT and to determine how these changes affect carbon stock, GHG fluxes, and profits. The technical details on how the projected changes were used in the land use model are provided in Appendix 1. Given that we relied on the land use model for the spatial allocation of the changes in areas, it was important to evaluate how correctly the model predicts land use choices. We found that the model performed well in predicting most land uses (see Appendix 1 for a detailed assessment). As far as the upper level is concerned, the highest and lowest discrepancies were for pasture, whose share was over-predicted by 4.6% and forest which was under-predicted by 5.7%. Regarding the

lower level component, the model tended to overestimate the share of perennial and annual croplands by a total of 16% and 34%, respectively. We considered this level of accuracy sufficient for the model to capture the effects of the main drivers of land use and that the parameter estimates can be used to make projections regarding future land use choices. In order to further evaluate the robustness of our econometric model, we compared different specifications (two alternatives are explored Appendix 1). We noted differences in land share predictions but very little difference in the transitions from one use to the other when policy simulations are performed. A change in these transitions could significantly alter the results, but no significant changes are noted.

Table 6.2 reports the results for the upper level of the land use model and shows how changes in agricultural product prices are projected to affect large land use categories. The most significant change is in forested area: a decrease of 4 million hectares, which is an average loss of some 190,000 hectares per year. Most of the change is caused by an increase in pastureland, 3.6 million hectares, and in lower amounts by growth in perennial and annual crop areas.

Figure 6.1 depicts the location of deforestation and pasture expansion at the municipality level. Observing the Amazon region, it appears that pastureland expansion corresponds with the areas where deforestation occurs. A very similar pattern is apparent in the Andes region.

Cropland (the sum of annual and perennial cropland) is projected to increase by a total of 500,000 hectares. Figure 6.2 shows projected change for two of the crops with the largest growth in area. Most of the growth for these two crops occurs outside the Amazon region and appears concentrated in the Andes and Caribbean regions. Among the annual crops considered, sugarcane shows the greatest projected change in area with a gain of 87,000 hectares. Rice and maize areas are projected to increase by some 36,000, and 26,000 hectares respectively. Area allocated to perennial crops is also projected to increase. For example, coffee, oil palm, and plantain areas increase by 12,000, 28,000, and 32,000 hectares respectively.

Projected changes in land use translate into changes in carbon stocks, and these are reported in Table 6.3. The model results show a net decrease in carbon stock, a total of 599 Tg C. This large reduction in the stock of carbon is due mostly to the decrease in forested area.

Table 6.4 reports the estimated changes in GHG emissions from cropland and pastureland. By far, the greatest change

Table 6.1. IMPACT projected changes in prices and areas for crop and livestock commodities, 2008-30

Crop Projected change in price Area 2008 (1,000 ha) Area 2030 (1,000 ha) Change 2008-30 (1,000 ha) Change (%)

Cacao 24% 183 189 6 3%

Coffee 30% 812 824 12 1%

Oil Palm 85% 377 405 28 7%

Plantain 34% 473 505 32 7%

Other perennial 31% 181 243 62 34%

Cassava 38% 261 275 14 5%

Maize 39% 835 861 26 3%

Potato 26% 191 201 10 5%

Rice 24% 745 781 36 5%

Sugarcane 107% 346 433 87 25%

Other annual 19% 164 166 2 1%

Cow meat 23% - - - -

Cow milk 16% - - - -

Pasture - 36,157 39,724 3,567 10%

Source: IMPACT 3.1.1.

Table 6.2. Predicted land use change, 2008-30

Land use category 2008 area (Mha) 2030 area (Mha) Change 2008-30 (Mha) Change (%)

Perennial crops 2.0 2.2 0.2 10

Annual crops 2.5 2.7 0.2 8

Pasture 36.2 39.7 3.5 10

Forest 55.8 51.7 -4.1 -7

Other land uses 17.9 18.1 0.2 1

Total 114.4 114.4

Note: Both 2008 and 2030 figures are model predictions.

in emissions is connected to pastureland expansion and rice production. The next significant increase in GHG emissions comes from oil palm and plantain cultivation. This is projected to generate an average yearly increase in emissions equivalent to 12 Tg of CO2 eq. Note that these changes are driven completely by changes in land use, and we assumed that land management practices, as well as livestock management practices remain unchanged for the entire period considered.

7. POLICY SIMULATIONS

Colombian public and private sectors are actively involved in finding viable strategies for reducing emissions from the agriculture sector and from land use change. A series of meetings were held with the objective of soliciting opinions and ideas on high-priority objectives from government officials, private-sector representatives, producers' organizations, and researchers. Our goal was to internalize in our simulations

the interests and priorities of a wide range of stakeholders interested or involved in the design of low emission strategies. Given that some of the policy targets identified were clearly aspirational, we simulated two stages of accomplishment, (1) scenario in which the target is fully met and (2) scenario in which only half of the target is achieved. It is essential to note that the policies were evaluated with respect to the baseline which represents our best prediction of what the landscape and emissions will be in 2030.

For all scenarios, we evaluated the effects on carbon stock and GHG emissions and the impact on profits. Computing the impact on profits was particularly challenging given the lack of data on production costs. However, we believe that even limited insights into these effects can provide important information to policymakers who need to decide which policies are best to pursue. In order to accomplish this, data needed to be compiled from multiple sources. We used the change in commodity prices projected by IMPACT (Table 6.1) to compute changes in revenues. Data on production costs

WORLD DEVELOPMENT

Figure 6.2. Projected changes in sugarcane and oil palm area at municipality level, 2008-30.

Table 6.3. Change in carbon stocks by land use, 2008-30

Land use Soil organic Above- and Total carbon Soil organic Above- and Total carbon Net change in

category carbon 2008 (Tg C) below-ground stock 2008 carbon 2030 below-ground stock 2030 carbon stock

biomass 2008 (Tg C) (Tg C) biomass 2030 (Tg C) (Tg C) (Tg C)

(Tg C)

Cropland 555 77 632 596 83 679 47

Pasture 4,190 327 4,517 4,604 359 4,963 446

Forest 6,012 9,369 15,381 5,576 8,690 14,266 -1,115

Other land uses 1,986 584 2,570 2,005 590 2,595 25

Total 12,743 10,358 23,101 12,780 9,721 22,501 -600

Table 6.4. GHG emissions in cropland and pasture, 2008-30

Crops Change in area Average per ha GHG emission Average per ha GHG emission Cumulative GHG emission

2008-30 (1,000 ha) in 2008 (Mg ha-1 yr-1) in 2030 (Mg ha-1 yr-1) 2008-30 (Tg CO2 eq)

Pasture 3,567 1.1 1.1 40.2

Perennial crop

Cacao 6 0.1a 0.1a 0.4

Coffee 12 0.7a 0.7a 12.6

Oil Palm 28 4.0 3.8 32.8

Plantain 32 3.5 3.0 35.9

Other perennial 62 - - -

Annual crop

Cassava 14 2.0 1.9 11.2

Maize 26 1.7 1.8 30.3

Potato 10 3.5 3.6 14.4

Rice 36 7.2 7.2 115.3

Sugarcane 87 1.7a 1.7a 13.8

Other annual 2 - -

a Indicates values derived from existing literature.

Table 7.1. Production costs by crop and region

Region Cocoaa Coffeeb Oil palmc Plantaind Other Perennialse Cassavae Maizef Potatoe Riceg Sugar Canee Other Annuale

($ ha-1) ($ ha-1) ($ ha-1) ($ ha-1) ($ ha-1) ($ ha-1) ($ ha-1) ($ ha-1) ($ ha-1) ($ ha-1) ($ ha-1)

Carribe 2,101 2,106 2,063 1,103 1,843 870 415 2,373 532 1,429 606

Andena 2,577 1,906 2,158 1,772 2,103 1,402 450 2,743 1,141 1,429 998

Amazon 2,189 1,979 1,811 1,943 1,980 1,136 634 2,383 901 1,429 890

Orinoquia 2,189 1,979 1,529 2,954 2,162 1,136 813 2,383 1,029 1,429 993

Pacific 2,101 1,924 1,492 1,943 1,865 1,136 858 2,031 901 1,429 965

a Ministerio de Agricultura, Colombia.

b Corporación Colombiana de Investigación Agropecuaria.

c Fedepalma, Colombia. USAID.

d Colombia, Programa MIDAS.

e Departamento National de Planeation.

f Industria y Comercio Superintendencia.

gFedearroz, Colombia.

related to crop production (Table 7.1) are available relatively well disaggregated by region.4 Unfortunately, key costs associated with other policy simulations are much more uncertain. Costs related to improving the quality of pasture were derived from a single study on the issue (Martinez, 2009) and from direct communications with the cattle producers' association (FEDEGAN). Costs of preventing deforestation were extracted from current studies on Nationally Appropriate Mitigation Actions and projects in the Amazon that aim at the valorization of local production compatible with standing forest.5 The data obtained, however accurate and sound, represent very limited experiences. Therefore, results will have to be revisited as more data become available.

(a) Pasture

The Colombian livestock sector association, Federación Colombiana de Ganaderos (FEDEGAN), proposed a strategy called ''Plan Estrategico de la Ganaderia 2019 (PEGA)", which calls for the national cattle herd to reach 48 million heads while reducing pasture area to 24 million hectares (equivalent to a reduction of more than 10-million-hectare in pastureland) by 2019 (FEDEGAN (Federation Colombiana de Ganaderos), 2006). The Ministry of Agriculture, meanwhile, proposes reducing land allocated to pasture to a total of 13 million hectares. FEDEGAN proposes to return the land to natural vegetation by promoting reforestation and silvopastoral systems (FEDEGAN (Federation Colombiana de Ganaderos), 2006). We simulated the effects of a reduction in pastureland of 10 million hectares, as well as a ''middle of the road" scenario in which only 5 million hectares are removed from pasture. It is unclear how this objective would be met and what incentives would be put in place to reduce the area. It is only with a certain degree of speculation that the costs associated with achieving this goal can be assessed. The impact on profits of reaching these policy targets was evaluated by assuming that the stocking rate is increased on a portion of pastureland so that the total number of cattle in 2030 remains the same as the one projected in the baseline. On this area, stocking rate was assumed to increase to an average of 0.95 heads per hectare from the current value of 0.64 heads per hectare. This is assumed to be achieved with an improvement of pasture quality and rational use of land. The cost of this type of intervention was estimated to be between $400 and $800 per hectare (Caicedo, Escobar, & Tapasco, 2016; FEDEGAN, 2006; Martinez, 2009). We used a mid-point value of $600 per hectare in our simulations. The area on which improved pasture would have to be implemented is about 3.1 million hectare for the ''middle of the road" scenario and 4.3 million hectares to achieve the full target.

(b) Forestry

Prevention of deforestation in the Amazon forest is considered a critical objective for climate change mitigation as well as biodiversity and cultural conservation. Deforestation occurs in other areas besides the Amazon forest according to historical data (Table 2.1) and to our projections (Figure 6.1); however, given its importance, the government puts an emphasis on the Amazon forest. The Colombian Low Carbon Development Strategy proposed by the government has a goal of a complete halt to deforestation in the Amazon region, while lowering the rate of deforestation in the rest of the country.

We simulated two scenarios: a complete stop to deforestation in the Amazon forest and a reduction of deforestation by half. The costs associated with achieving these targets are very uncertain. We relied on data from current studies on Nationally Appropriate Mitigation Actions and projects in the Amazon that aim at halting deforestation (Caicedo et al., 2016). According to their calculations, the costs per hectare vary between $1,600 and $2,000. We used a mid-point value of $1,800 per hectare in our simulations.

(c) Oil palm cultivation

The Colombian government is planning to incentivize the cultivation of strategic crops. The list of strategic crops includes oil palm, sugarcane, and soybeans, the expansion of which has been driven by both biofuel and dietary demands. The expectation is that the majority of the area expansion would occur on pastureland which is widely considered underutilized land. We simulated two policy objectives, (1) an expansion in area allocated to oil palm cultivation by a total of 1.5 million hectares6 and (2) a scenario in which 50% of this goal is achieved.

At the time of the consultations with stakeholders, there was no consensus on what instruments would be used to achieve the desired goals. We therefore simulated all the scenarios by altering the benefits deriving from the targeted land use in comparison to alternative uses in the land use model. In essence, we treated the policy instrument as an unobserved factor specific to the targeted land use and assumed that this policy instrument acts homogeneously across all municipalities. We added this factor to the net benefit equation of the targeted land use (Eqn. (2), Appendix 1) to reach a desired number of hectares allocated to that particular land use.

Table 7.2 shows the changes in land use induced by the implementation of these objectives. Table 7.3 shows the effects on carbon stock, GHG emissions, and profits from agricultural production.

h ci t

i-î O L th ai

> M Fe

OL ess a

Table 7.2. Land use change under alternative policy scenarios

tp p p:// me

/dx ent

w ri or cu rl l du er e.v e. 2A

Policy targets partially met

Policy targets fully met

Pasture reduction

Reduce deforestation

Oil Palm expansion

Pasture reduction

Zero deforestation

Oil Palm expansion

Area 2030 Difference from Area 2030 Difference from Area 2030 Difference from Area 2030 Difference from Area 2030 Difference from Area 2030 Difference from (1,000 ha) baseline 2030 (1,000 ha) baseline 2030 (1,000 ha) baseline 2030 (1,000 ha) baseline 2030 (1,000 ha) baseline 2030 (1,000 ha) baseline 2030 (1,000 ha) (1,000 ha) (1,000 ha) (1,000 ha) (1,000 ha) (1,000 ha)

Perennial crop 2,407 240 2,161 -5 2,238 72 2,667 501 2,156 -10 2,629 463

Cacao 208 20 188 -1 149 -40 229 40 188 -1 92 -97

Coffee 910 86 823 -1 775 -50 1,001 177 822 -3 666 -158

Oil Palm 458 53 404 -1 750 345 518 113 403 -2 1,500 1,095

Plantain 559 54 503 -2 391 -114 616 111 502 -3 266 -239

Other crops 272 29 242 -1 173 -70 303 60 242 -1 104 -139

Annual crop 3,057 340 2,707 -9 2,702 -14 3,438 722 2,699 -18 2,668 -49

Cassava 310 36 273 -2 273 -2 351 76 271 -3 268 -6

Maize 969 108 858 -3 856 -5 1,090 229 854 -7 845 -16

Potato 226 25 201 0 201 0 252 51 201 0 201 0

Rice 890 109 778 -3 775 -7 1,015 234 775 -6 760 -21

Sugarcane 476 44 432 0 432 -1 522 90 432 -1 429 -4

Other crops 185 20 165 0 165 0 207 41 165 0 164 -1

Pasture 34,724 -5,000 38,828 -896 39,599 -125 29,724 -10,000 37,933 -1,792 39,302 - 422

Forests 53,996 2,252 52,947 1,203 51,761 17 56,237 4,493 54,151 2,407 51,737 -7

Other land uses 20,257 2,167 17,796 -294 18,140 51 22,374 4,284 17,502 -587 18,105 15

Total 114,440 - 114,440 - 114,440 - 114,440 - 114,440 - 114,440 -

R L D D

Italics indicate total values for the main land use categories.

Table 7.3. Summary of predicted total changes in carbon stock (C stock), GHG emissions, and profits, 2008-30

Change in C stock (Tg C) Total change in GHG emission (Tg CO2 eq) Total change in profits (US$ billion)

Reduce pasture by 5 million hectares Cropland 80 16.8 2.0

Livestock -625 - 56.7 -1.8

Forest 621 - -

Other 311 - -

Total 387 - 39.9 0.2

Reduce pasture by 10 million hectares Cropland 168 35.0 4.0

Livestock -1,249 -116.0 -2.6

Forest 1,239 - -

Other 614 - -

Total 772 -81.0 1.4

Reduce deforestation in the Amazon by 50% Cropland -2 -0.4 0.0

Livestock -112 -9.9 -0.3

Forest 332 - -0.5

Other -42 - -

Total 176 -10.3 -0.8

Reduce deforestation in the Amazon by 100% Cropland -4 -0.8 0.0

Livestock -224 -20.0 -0.7

Forest 664 - -1.0

Other -84 - -

Total 352 -20.8 -1.7

Increase area allocated to oil palm by 750,000 hectares Cropland 10 7.5 -0.6

Livestock -16 -1.4 0.0

Forest 5 - -

Other 7 - -

Total 6 6.1 -0.6

Increase area allocated to oil palm by 1.5 million hectares Cropland 35 23.1 -1.3

Livestock -43 -4.7 -0.1

Forest -2 - -

Other 2 - -

Total -8 18.4 -1.4

Note: Effects on profits are expressed in 2008 money and discounted using a yearly discount rate of 8% (A sensitivity analysis to the value of the discount rate was carried out and revealed that qualitatively the outcome of each policy simulation remained unchanged. One exception is the "reduction of pastureland by 5 million hectares" policy target. With a greater discount, the additional profits generated by the increased crop production would not offset the costs of improving pasture productivity, which are mostly upfront costs. A discount rate of 12% would generate a loss in profits of $0.3 billion causing a tradeoff between reduction in GHG gasses and profits. The implicit cost of abating one ton of CO2 eq would still be very low: $0.30. Of course, increasing the discount rate high enough would cause the 10-million-hectares-reduction policy to generate tradeoffs. A discount rate of 18% would reduce the profit gains of this policy to zero.).

(d) Policy target 1: reduction in area allocated to pasture

Results of the policy simulations should be interpreted as assessing the changes in land use brought about by a policy that limits expansion and reduces pasture area by a total 10 million hectares compared to the baseline. Baseline results predicted a 3.6-million-hectare expansion to a total of 39.7 million hectares of pastureland, equivalent to a 10% growth during the period 2008-30. As it can be observed in Table 7.2, the effects of a 10-million-hectare reduction in pasture area would cause a growth in cropland (1.2 million hectares), forest (4.5 million hectares), and other land uses (4.2 million hectares). These changes correspond to an estimated increase in carbon stock of some 772 Tg C (Table 7.3). GHG emissions are also affected. Emissions from pastureland and the associated livestock are expected to decrease while emissions from crop production are expected to rise. The net effect would be a reduction of some 81 Tg CO2 eq. Given our assumptions on how the reduction of pastureland was achieved, reaching this objective would lead to a reduction in profits from cattle raising compared to the baseline, which is estimated to be about $2.6 billion over the 21 years considered. However, the $4-billion increase in profits deriving from cropland expansion is expected to compensate for the loss and generate

a net gain of some $1.4 billion. Of course, higher costs of implementing pasture improvements would change the outcome. A cost of $721 per hectare would reduce the profit gains to zero and costs higher than that would generate a tradeoff between economic gains and reduction of emissions. The intermediate scenario, 50% achievement of the policy goal, also returns the double benefit of a reduction of emissions and economic gains. Carbon stock is projected to increase by 387 Tg C while in total GHG emissions are reduced by a total of 39.9 Tg CO2 eq. The expected net change in profits is estimated at $0.2 billion.

(e) Policy target 2: reduction in the rate of deforestation in the Amazon forest

Results for the baseline scenario indicate a decrease in forested area of 4 million hectares during the period 2008-30, equivalent to an annual average loss of some 190,000 hectares. Results also indicate that the loss in carbon stock due to deforestation would be substantial: a total of 1,116 Tg of carbon. The implementation of a policy that stops deforestation in the Amazon would result in an additional 2.4 million hectares of forest compared to the baseline (Table 7.2). However, this would be at the expense of land that would

otherwise transition into pasture and into crop production. The implementation of this policy would result in 1.8 million hectares of pastureland less than the baseline. Considered all together, the changes in land use lead to 352 Tg C of net increase in carbon stock and a total reduction in GHG emissions quantified to be about 21 Tg CO2 eq. The avoided expansion of pasture and cropland into the forest results in a total loss of $1.7 billion in profits (Table 7.3). These losses are caused by costs incurred in protecting the forest, costs related to improving pasture quality to increase the stocking rate, and forgone crop production.7 The scenario in which we simulate an intermediate achievement of the policy goal (deforestation reduced by half) returns similar trade-offs between mitigation and economic returns. The increase in carbon stock, which amounts to 176 Tg C, and the 10 Tg CO2eq reduction in cropland and pasture emissions come at the expense of profits which are reduced by about $0.8 billion. Clearly, this policy generates significant tradeoffs which is not surprising given that it prevents the expansion of land uses that have a direct economic return into forests. However, the gains in carbon stock and reduction of emissions are also significant. In fact, the cost per ton of GHG abated is about $4.8 per Mg C ($1.3 Mg CO2 eq). This is a relatively low cost considering that, despite that estimates for the social cost of carbon vary wildly and might be biased upward (Havranek, Irsova, Janda, & Zilberman, 2015), surveys and meta-analyses find values that are in the order of $43 Mg C (Yohe et al., 2007). Off course, these results are dependent on the recorded costs of preventing deforestation. The greater the costs, the greater the tradeoffs, and in turn the greater the cost of a ton of GHG abated. For example, an increase in the cost of preventing deforestation by 50% would result in a cost per ton of GHG abated of $6.3 per Mg C.

(f) Policy target 3: increase the area allocated to the cultivation of oil palm

The baseline scenario projects a growth in area allocated to oil palm cultivation of about 28,000 hectares by 2030. In order to reach the policy target of 1.5 million hectares, land allocated to oil palm has to increase by more than 1 million hectares. Our simulation reveals that this policy could have significant unintended consequences if not implemented

judiciously. Model results (Table 7.2) indicate that the expansion of oil palm production occurs at the expense of area allocated to perennial crops causing decreases in plantain area (239,000 hectares), coffee area (158,000 hectares), cacao area (97,000 hectares), and other perennials (139,000 hectares). Similarly, annual crops are negatively affected and total area allocated to them is reduced by a total of 49,000 hectares. Changes in land use result in a decrease in carbon stock of 8 Tg C and an increase in GHG emission of 18.4 Tg CO2 eq. It is important to note that this policy objective comes with heavy economic costs. This is because, against conventional wisdom, the expansion of oil palm cultivation only partially occurs on pastureland. The net effect of this expansion is a loss in profits equivalent to $1.4 billion for the period 2008-30.

The intermediate-goal scenario (50% of the policy goal) shows an important difference. While GHG emissions increase and profits decrease, carbon stock increases by 6 Tg C. These results indicate that there might be a role in low emission development for oil palm production but that policymakers ought to be careful to the extent of area expansion and vigilant to the land uses that might be displaced by oil palm production. A higher profit margin in palm production could potentially change results and produce a win-win outcome when area expansion is not too large. However, according to our calculations profits would have to double for this policy (additional 750,000 hectares) to achieve a "win-win" status.

8. DISCUSSION AND CONCLUSIONS

Results reveal the importance of considering the full scope of interactions and changes in the various land uses when planning for GHG reduction policies. As other studies have noted (Burney, Davis, & Lobell, 2010; Gockowski & Sonwa, 2011; Li et al., 2015), the fate of forests matters considerably given that their carbon stock overwhelms potential changes in GHG emissions generated by crop production. Our results indicate that one additional hectare allocated to agriculture increases GHG emissions on average by some 2.5 Mg CO2 eq per year while one hectare of forest lost in the Amazon results in a loss of carbon stock (above- and below-ground biomass) equivalent to some 367 Mg CO2 eq. Shifts in land uses determined by changes in area allocated to agricultural

Change in total emissions and C stock (MillionTons C02 eq.) Figure 7.1. Trade-off between profits and GHG emission reduction.

production can have a great effect on the existing carbon stock and these might be of greater importance than the possible changes in GHG emission from crop cultivation. To provide some perspective on the possible contribution of mitigation activities in crop production, we simulated a hypothetical reduction of GHG emissions by 50% across all crops. By the end of 2030, this would generate a total reduction of about 130 Tg CO2 eq. Our results for the baseline scenario project a total loss of carbon stock equivalent to 2,198 Tg CO2 eq. Therefore, even a drastic reduction in GHG emissions represents only about 6% of the change in carbon stock induced by land use transitions. This is not to say that reduction of emissions in crop production is not important. Reduction in emissions is generally obtained through an increase in efficiency and as such it represents an important goal. However, results indicate that it is paramount to consider the totality of interactions among the different land uses. Figure 7.1 provides a visual representation of the tradeoffs between profits and GHG emissions across the policies simulated. For comparison purposes, we express changes in carbon stock in CO2 eq and add them to changes in GHG gasses. This allows us to compare overall performances for the period under consideration. Of course, one needs to keep in mind that changes in carbon stock are a one-time event while changes in emissions are yearly recurrences. "Win-win" policies are those represented in the upper left quadrant of the graph (decreased GHG emissions and increased profits). The policy that targets a reduction of land allocated to pasture performs better than all others and returns significant mitigation and economic gains. It is particularly important to note that, even though reducing pasture in our simulation is equivalent to increasing costs of production to improve carrying capacity, the increment in profits from other agricultural products offsets the loss and generates a net gain. At the same time, it is clear that halting the expansion of pastureland is key to reducing deforestation and loss of carbon stock. The policy that targets deforestation in the Amazon forest is a typical example of a policy that generates trade-offs. The significant gains in terms of carbon stock and reduction in GHG emissions generate projected losses in profits of about $1.7 billion. Expressed in dollars per ton of GHG abated, these results indicate costs of about $1.5 per Mg CO2 eq. However, even if these costs are not prohibitive, this policy ranks relatively low compared to reducing pastureland: the gain in carbon stock is about one-half of what would be achieved with the pasture reduction policy and profits are reduced. This is because a policy

that targets pasture prevents deforestation over the entirety of the country and it induces a more efficient use of land. Therefore, the choice between the two policies becomes an issue of political feasibility and implementation challenges.

Plans that promote an increase in land allocated to oil palm cultivation need to be considered with extreme caution and investigated further. Model results suggest that the past trend of oil palm cultivation taking over underproductive pasture-land might not continue into the future. Area expansion beyond certain levels could lead to a detrimental reduction in land allocated to other perennial crops. This is not necessarily because oil palm cultivation replaces, for example, coffee, but because a rather complex chain of land use transitions initiated by oil palm expansion takes place. The model indicates that other important factors besides economic profit might underlie the decision of keeping land in pasture and these might impede the desired land use transition. This suggests that additional research on this issue is warranted and that particular attention should be given to the instruments and incentives that need to be put in place to achieve the desired outcome.

Some additional general considerations can be made. Given the complexity of low emission development strategies, modeling approaches, frameworks, and tools should be adaptable, open, and transparent. Modeling frameworks should be adaptable so that policy makers can explore the consequences of using different data sets and incorporate new information as it becomes available. Modeling frameworks and tools should be open to the inclusion of inputs from different models so that the robustness of the results can be assessed. In the case presented in this article, we have chosen models with which the authors were most familiar. Each one of the models chosen comes with its own set of strengths and weaknesses. For example, the use of a partial equilibrium model like IMPACT limits the scope of the analysis to the agricultural sector, and the use of a parametric model to determine land use choices cannot incorporate land uses that are not already present in the area at the time of the analysis. These limitations become more or less severe according to the specific situation and country analyzed. However, the advantage of the approach proposed is that each one of the models does have alternatives and researchers have the option to replace them or integrate them with other models. We believe that this level of transparency and openness is essential to generate trust in the results among end users.

1. IMPACT generates scenarios based on four different Global Circulation Models (GCMs): Hadley, IP SL, MIROC, and GFDL. We work with IMPACT results that use MIROC GCM. While the choice of MIROC is arbitrary, the average difference among IMPACT projections for crop areas and prices for the year 2030 for Colombia using the four GCMs is rather low, about 0.5% (with some larger differences for oil palm and cocoa areas using GFDL: 3% and 2% respectively). MIROC seems to provide a "middle of the road" scenario for Colombia and for the year 2030. It should be noted that while the use of alternative GCMs returns different rates of growth for the prices of the considered agricultural commodities, changes in areas for Colombia are not significantly different across the different GCMs.

2. The specification of distance decay function is arbitrary, but the authors tested other specifications in previous work (Li et al., 2015; De Pinto & Nelson, 2009) and found this particular one to have some

desirable properties. Specifically, in Eqn. (1) producer price decays at a moderate speed (the spatially explicit price is between 0.368PPj and PPj), and the price is never zero which corresponds to a subsistence value at all locations.

3. The herd size is computed using IMPACT model's output on number of slaughtered animals and by assuming a constant ratio of slaughtered animal and herd size.

4. We make the simplifying assumption that the ratio revenue-cost remains constant through time.

5. The Redd Early Mover (REM) Programme active in the Colombian Amazon uses a cost per ton of CO2 eq reduced by about $0.8. The average mitigation per hectare is considered to be about 378 tons CO2 eq. This generates an approximate costs of about $302 per hectare.

6. The government plans indicate a goal of over three million hectares of oil palm plantations by 2020. However, as other authors have indicated (Castiblanco et al., 2013) it is highly unlikely that this goal will be met. Our IMPACT simulations also indicated that if such an increase in production were to be achieved, world prices would be depressed by about five percent, which makes the goal difficult to attain.

7. It is assumed that primary forest does not generate a revenue stream. Although this is not likely true in all cases, lack of data prevents us from accounting for the contribution of forest products to the overall economy.

8. We choose municipal as the unit of analysis in order to keep the units of upper and lower level models consistent. We apply the coefficient of variation of elevation to control potential heterogeneity within a municipal.

REFERENCES

Anaya, J. A., Chuvieco, E., & Palacios-Orueta, A. (2009). Aboveground biomass assessment in Colombia: A remote sensing approach. Forest Ecology and Management, 257(4), 1237-1246.

Antle, J. M., & Stoorvogel, J. J. (2008). Agricultural carbon sequestration, poverty, and sustainability. Environment and Development Economics, 13(03), 327-352.

Beheydt, D., Boeckx, P., Sluetel, S., Li, C., & van Cleemput, O. (2007). Validation of DNDC for 22 long-term nitrous oxide field emission measurements. Atmospheric Environment, 41, 6196-6211.

Burney, J., Davis, S., & Lobell, D. (2010). Greenhouse gas mitigation by agricultural intensification. Proceedings of the National Academy of Sciences of the United States of America, 107(26), 12052-12057.

Cacho, O. J., Marshall, G. R., & Milne, M. (2005). Transaction and abatement costs of carbon-sink projects in developing countries. Environment and Development Economics, 10(05), 597-614.

Caicedo, P., Escobar, D., & Tapasco, J. (2016). Análisis economico y de prefactibilidad. Capítulo 3 En CIAT-MADR, 2016. In Análisis Integral de Sistemas Productivas en Colombia para la Adaptación al Cambio Climatico, Convenio MADR-CIAT 20120382. Bogota, Colombia.

Cai, Z., Sawamoto, T., Li, C., Kang, G., Boonjawat, J., Mosier, A., ... Tsuruta, H. (2003). Field validation of the DNDC model for greenhouse gas emissions in East Asian cropping systems. Global Biogeochemical Cycles, 17(4), 1107. http://dx.doi.org/10.1029/ 2003GB002046.

Castiblanco, C., Etter, A., & Aide, T. M. (2013). Oil palm plantations in Colombia: A model of future expansion. Environmental Science and Policy, 27, 172-183.

CIA (Central Intelligence Agency) (2014). CIA Factbook. <www.cia.gov> Accessed November 14, 2014.

CIESIN (Center for International Earth Science Information Network)/ Columbia University/CIAT (Centro Internacional de Agricultura Tropical) (2005). Gridded population of the world, Version 3 (GPWv3): population density grid, future estimates. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC), <http:// sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-future-estimates> Accessed March 16, 2012.

Chomitz, K., & Gray, D. (1996). Roads, land use, and deforestation: a spatial model applied to Belize. World Bank Economic Review, 10, 487-512.

Dace, E., Muizniece, I., Blumberga, A., & Kaczala, F. (2015). Searching for solutions to mitigate greenhouse gas emissions by agricultural policy decisions—Application of system dynamics modeling for the case of Latvia. Science of the Total Environment, 527, 80-90.

De Figueiredo, E. B., Panosso, A. R., Romao, R., & La Scala, N. (2010). Greenhouse gas emission associated with sugar production in Southern Brazil. Carbon Balance and Management, 5(3), 1-7.

Deng, J., Zhou, Z., Zhu, B., Zheng, X., Li, C., Wang, X., et al. (2011). Modelling nitrogen loading in a small watershed in southwest China using a DNDC model with hydrological enhancements. Biogeo-sciences, 8, 2999-3009.

De Pinto, A., & Nelson, G. (2009). Land use change with spatially explicit data: a dynamic approach. Environmental and Resource Economics, 43, 209-229.

Diagana, B., Antle, J., Stoorvogel, J., & Gray, K. (2007). Economic potential for soil carbon sequestration in the Nioro region of Senegal's Peanut Basin. Agricultural Systems, 94(1), 26-37.

Dossa, E. L., Fernandes, E. C. M., Reid, W. S., & Ezui, K. (2007). Above-and below-ground biomass, nutrient and carbon stocks contrasting an open-grown and a shaded coffee plantation. Agroforestry Systems, 72, 103-115.

Etter, A., McAlpine, C., Wilson, K., Phinn, S., & Possingham, H. (2006). Regional patterns of agricultural land use in deforestation in Colombia. Agriculture, Ecosystems and Environment, 114(2-4), 369-386.

FAO (Food and Agriculture Organization of the United Nations) (2014). FAOSTAT. <www.faostat.fao.org> accessed June 1, 2014.

FAO/IIASA/ISRIC/ISS-CAS/JRC (2012). Harmonized world soil database version 1.2. Rome and Laxenburg, Austria.

FEDEGAN (Federacioi n Colombiana de Ganaderos) (2006). Plan Estratégico de la Ganadería Colombiana 2019. Bogota.

Fischer, G., Shah, M., van Velthuizen, H., & Nachtergaele, F. (2001). Global agro-ecological assessment for agriculture in the 21st century IIASA research report 02-02. Laxenburg, Austria: International Institute for Applied Systems Analysis.

Gilhespy, S. L., Anthony, S., Cardenas, L., Chadwick, D., del Prado, A., Li, C., et al. (2014). First 20 years of DNDC (DeNitrification DeComposition): Model evolution. Ecological Modelling, 292, 51-62.

Giltrap, D. L., Li, C. S., & Saggar, S. (2010). DNDC: A process based model of greenhouse gas fluxes from agricultural soils. Agriculture, Ecosystems & Environment, 136, 292-300.

Gockowski, J., & Sonwa, D. (2011). Cocoa intensification scenarios and their predicted impact on CO2 emissions, biodiversity conservation, and rural livelihoods in the Guinea rain forest of West Africa. Environ Management, 48, 307-321.

Godfray, H. C. J., & Robinson, S. (2015). Contrasting approaches to projecting long-run global food security. Oxford Review of Economic Policy, 31(1), 26-44.

Golub, A. A., Henderson, B. B., Hertel, T. W., Gerber, P. J., Rose, S. K., & Sohngen, B. (2013). Global climate policy impacts on livestock, land use, livelihoods, and food security. Proceedings of the National Academy of Sciences of the United States of America, 110(52), 20894-20899. http://dx.doi.org/10.1073/pnas.1108772109.

Grote, R., Lehmanna, E., Brümmer, C., Brüggemanna, N., Szarzynski, J., Kunstmanna, H., et al. (2009). Modelling and observation of biosphere-atmosphere interactions in natural savannah in Burkina Faso, West Africa. Physics and Chemistry of the Earth, 34, 251-260.

Havranek, T., Irsova, Z., Janda, K., & Zilberman, D. (2015). Selective reporting and the social cost of carbon. Energy Economics, 51, 394-406.

Havlik, P., Valin, H., Herrero, M., Obersteiner, M., Schmid, E., Rufino, M. C., et al. (2014). Climate change mitigation through livestock system transitions. Proceedings of the National Academy of Sciences of the United States of America, 111(10), 3709-3714. http://dx.doi.org/ 10.1073/pnas.1308044111.

Henson, I. E., Ruiz, R., & Romero, H. M. (2012). The greenhouse gas balance of the oil palm industry in Colombia: A preliminary analysis. I. Carbon sequestration and carbon offsets. Agronomia Colombiana, 30 (3), 359-369.

Hergoualc'h, K., Skiba, U., Harmand, J. M., & Henault, C. (2008). Fluxes of greenhouse gases from andosols under coffee in monoculture or shaded by Inga Densiflora in Costa Rica. Biogeochemistry, 89(3), 329-345.

Hiederer, R., & Kochy, M. (2011). Global soil organic carbon estimates and the harmonized world soil database. Italy: European Union.

Hijmans, Robert J. et al. (2005). Very high resolution interpolated climate surfaces for global land areas. International journal of climatology, 25 (15), 1965-1978.

IAvH (Instituto de Investigacioi n de Recursos Bioloi gicos Alexander Von Humboldt) IDEAM (Instituto de Hidrologia, Meteorologia y Estudios Ambientales) IIAP (Instituto de Investigaciones Ambientales del Pacifico Jhon Von Newman) INVEMAR (Instituto de Investigaciones Marinas y Costeras) & SINCHI (Instituto Amazoinico de

Investigaciones Científicas), A. (2007). Informe del Estado del Medio Ambiente y de los Recursos Naturales Renovables. Bogota: Instituto de Hidrología, Meteorología y Estudios Ambientales.

IDEAM (Instituto Nacional de Hidrología Meteorología y Estudios Ambientales de Colombia) (2010). 2nd Comunicación Nacional ante la Convención Marco de las Naciones Unidas sobre Cambio Climatico. Bogotá.

IGAC (Instituto Geografico Agustin Codazzi) (2013). Sistema de Information Geogrfica para la Planeacion y el Ordenamiento Territorial. <http://sigotn.igac.gov.co/sigotn/> Accessed November 18, 2013.

IPCC (Intergovernmental Panel on Climate Change) (2006). 2006 IPCC guidelines for national greenhouse gas inventories. In H. S. Eggleston, L. Buendia, K. Miwa, T. Ngara, & K. Tanabe (Eds.), Prepared by the national greenhouse gas inventories programme. Hayama, Japan: Institute for Global Environmental Strategies.

Jarvis, A., Reuter, H. I., Nelson, A., & Guevara, E. (2008). Hole-filled SRTM for the globe version 4. <www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1> Accessed August 5, 2011.

Koskela, J., Nygren, P., Berninger, F., & Luukkanen, O. (2000). Implications of the Kyoto Protocol for Tropical forest management and land use: prospects and pitfalls. Tropical Forestry Reports 22. Helsinki: University of Helsinki.

Li, C., Frolking, S., & Frolking, T. A. (1992). A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. Journal of Geophysical Research, 97, 9759-9776.

Li, C. (2007). Quantifying greenhouse gas emissions from soils: Scientific basis and modeling approach. Soil Science and Plant Nutrition, 53, 344-352.

Li, C., Salas, W., Zhang, R., Krauter, C., Rotz, A., & Mitloehner, F. (2012). Manure-DNDC: A biogeochemical process model for quantifying greenhouse gas and ammonia emissions from livestock manure systems. Nutrient Cycling in Agroecosystems, 93(2), 163-200. http://dx. doi.org/10.1007/s10705-012-9507-z.

Li, M., De Pinto, A., Ulimwengo, J., You, L., & Robertson, R. (2015). Modeling land-use allocation with mixed-level data: An econometric analysis for the Democratic Republic of the Congo. Environment and Resource Economics, 60, 443-469.

Lubowski, R. N., Plantinga, A. J., & Stavins, R. N. (2006). Land-use change and carbon sinks: Econometric estimation of the carbon sequestration supply function. Journal ofEnvironmental Economics and Management, 51(2), 135-152.

Lubowski, R. N., & Rose, S. K. (2013). The potential for REDD+: Key economic modeling insights and issues. Review of Environmental Economics and Policy, 7(1), 67-90. http://dx.doi.org/10.1093/reep/res024.

MADR (Ministry of Agriculture and Rural Development) (2011). Statistical data of agriculture and rural development 2001-2010. Hanoi: Agricultural Publishing House.

MADS (Ministry of Environment and Sustainable Development) (2012). Estrategia Colombiana de Desarrollo Bajo en Carbono. Bogota.

Makundi, W. R., & Sathaye, J. A. (2004). GHG mitigation potential and cost in tropical forestry - Relative role for agroforestry. Environment, Development and Sustainability, 6, 235-260.

McFadden, D. L. (1977). Modeling the choice of residential location. New Haven, CT: Yale University Cowles Foundation for Research in Economics.

Martínez, D. F. L. (2009). Estudio De La Viabilidad Economica De La Implantacion Del Pastoreo Racional Voisin (Prv) En La Llanura Inundable Del Departamento De Casanare, Colombia Master thesis. Univesidad Politecnica de Valencia.

Nelson, A. (2000). Accessibility, transport and travel time information CIAT hillsides project report. Cali, Colombia: International Center for Tropical Agriculture.

Nelson, G., & Hellerstein, D. (1997). Do roads cause deforestation? Using satellite images in econometric analysis of land use. American Journal of Agricultural Economics, 79, 80-88.

Nelson, G. C., De Pinto, A., Harris, V., & Stone, S. (2004). Land use and road improvements: A spatial perspective. International Regional Science Review, 27, 297-325.

Nelson, G. C., Valin, H., Sands, R. D., Havlik, P., Ahammad, H., Deryng, D., ... Willenbockel, D. (2014). Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences of the United States of America, 111(9), 3274-3279.

Nepstad, D. T. Bezerra, Tepper, D., McCann, K., Stickler, C., & McGrath, D. G. (2013). Addressing agricultural drivers ofdeforestation in Colombia: Increasing land-based production while reducing

deforestation, forest degradation, greenhouse gas emissions and rural poverty Report to the United Kingdom, Foreign and Commonwealth Office and Department of Energy Climate Change, Forests and Climate Change Programme. Earth Innovation Institute.

Niels, H. B. (2010). IPCC default soil classes derived from the harmonized world soil database. Version 1.1. Retrieved from: <http://www.isnc. org/data/ipcc-default-soil-classes-derived-harmonized-world-soil-data-base-ver-11> Accessed May 27, 2014.

Palm, C. A., Alegre, J. C., Arevalo, L., Mutuo, P. K., Mosier, A. R., & Coe, R. (2002). Nitrous oxide and methane fluxes in six different land use systems in the Peruvian Amazon. Global Biogeochemical Cycles, 16 (4), 21-1-21-13.

Pathak, H., Li, C., & Wassmann, R. (2005). Greenhouse gas emissions from Indian rice fields: Calibration and upscaling the DNDC model. Biogeosciences, 2, 113-123.

Robinson, Sherman, D'Croz, Daniel Mason, Islam, Shahnila, Cennachi, Nicola, Creamer, Bernardo, Gueneau, Arthur, Hareau, Guy, Klein-wechter, Ulrich, Mottaleb, Khondoker, Nedumaran, Swamikannu, Robertson, Richard D., Rosegrant, Mark W., Sika, Gbegbelegbe, Sulser, Timothy B., & Wiebe, Keith D. (2015). Climate change adaptation in agriculture: Ex ante analysis of promising and alternative crop technologies using DSSAT and IMPACT IFPRI discussion paper. International Food Policy Research Institute, Available at <http:// www.ifpri.org/publication/climate-change-adaptation-agriculture-ex-ante-analysis-promising-and-alternative-crop>.

Rosegrant, M. W., Zhu, T., Msangi, S., & Sulser, T. B. (2008). The impact of biofuels on world cereal prices. Background brief in support of testimony to United States Congressional briefing on the world food situation. Washington, DC: IFPRI.

Rutten, M., van Dijk, M., van Rooij, W., & Hilderink, H. (2014). Land use dynamics, climate change, and food security in Vietnam: A global-to-local modeling approach. World Development, 59, 29-46.

Saatchi, S. S., Harris, N. L., Brown, S., Lefsky, M., Mitchard, E. T. A., Salas, W., et al. (2011). Benchmark map of forest carbon stocks in tropical regions across three continents. Proceedings of the National Academy of Sciences of the United States of America, 108(24), 9899-9904.

Sacks, W. J., Deryng, D., Foley, J. A., & Ramankutty, N. (2010). Crop planting dates: An analysis of global patterns. Global Ecology and Biogeography, 19, 607-620.

Schils, R. L. M., Olesen, J. E., del Prado, a., & Soussana, J. F. (2007). A review of farm level modelling approaches for mitigating greenhouse gas emissions from ruminant livestock systems. Livestock Science, 112 (3), 240-251.

Schneider, U. A., & Smith, P. (2008). Energy intensities and greenhouse gas emission mitigation in global agriculture. Energy Efficiency, 2(2), 195-206. http://dx.doi.org/10.1007/s12053-008-9035-5.

SINAP (Sistema Nacional de Areas Protegidas). 2012. GISDATA: Parques Nacionales Naturales de Colombia, Reservas Naturales de la Sociedad Civil, Areas protegidas de las categorías regionales, y Reservas forestales protectoras nacionales. Versioí n 2. Data provided by Grupo de Sistemas de Información y Radiocomunicaciones de Parques Nacionales Naturales de Colombia.

Smith, P., Martino, D., Cai, Z., Gwary, D., Janzen, H., Kumar, P., et al. (2007). Policy and technological constraints to implementation of greenhouse gas mitigation options in agriculture. Agriculture, Ecosystems & Environment, 118(1-4), 6-28.

Smith, P., Haberl, H., Popp, A., Erb, K.-H., Lauk, C., Harper, R., et al. (2013). How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Global Change Biology, 19(8), 2285-2302. http://dx.doi.org/ 10.1111/gcb.12160.

Springmann, M., Mason-D'Croz, D., Robinson, S., Garnett, T., Godfray, H. C. J., Gollin, D., ... Scarborough, P. (2016). Global and regional health effects of future food production under climate change: A modelling study. The Lancet. http://dx.doi.org/10.1016/S0140-6736 (15), 01156-3.

Torres, A. B., Marchant, R., Lovett, J. C., Smart, J. C. R., & Tipper, R. (2010). Analysis of the carbon sequestration costs of afforestation and reforestation agroforestry practices and the use of cost curves to evaluate their potential for implementation of climate change mitigation. Ecological Economics, 69(3), 469-477.

Tschakert, P. (2007). Environmental services and poverty reduction: Options for smallholders in the Sahel. Agricultural Systems, 94(1), 75-86.

Tenningkeit, T., Kahrl, F., Wolcke, J., & Newcombe, K. (2012). Economics of agricultural carbon sequestration in Sub-Saharan Africa. In Ev. Wollenberg, A. Nihart, M.-L. Tapio-Bistrom, & M. Grieg-Gran (Eds.), Climate change mitigation and agriculture (pp. 144-158). New York and London: Earthscan. Train, K. (2003). Discrete choice methods with simulation. New York:

Cambridge University Press. Uchida, H., & Nelson, A. (2010). Agglomeration index: Towards a new measure of urban concentration WIDER working paper 2010/29. UNU-WIDER.

Werner, C., Butterbach-Bahl, K., Haas, E., Hickler, T., & Kiese, R. (2007). A global inventory of N2O emissions from tropical rainforest soils using a detailed biogeochemical model. Global Biogeochemical Cycles, 21(3), GB3010. Yohe, G. W., Lasco, R. D., Ahmad, Q. K., Arnell, N. W., Cohen, S. J., Hope, C., Janetos, A. C., & Perez, R. T. (2007). Perspectives on climate change and sustainability. In M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden, & C. E. Hanson (Eds.), Climate change 2007: Impacts, adaptation and vulnerability. Contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change (pp. 811-841). Cambridge: Cambridge University Press.

APPENDIX 1

Conceptual framework of the land use model

Consider how a decision-maker allocates land use in a municipal,8 indexed by n; n = 1, ..., N. Suppose the decision-maker is a risk-neutral landowner; s/he chooses land uses to maximize the present discounted value of the stream of the expected net benefits from the land. The land grid could be allocated to K alternative major uses, indexed by k; k = 1, ..., K. Among these uses, perennial cropland and annual cropland are two nests of crop, within which the decision maker is allowed to select different crops, indexed by j; j = 1, ..., JK. Our data for Colombia include four major perennial crops (cacao, coffee, oil palm, plantain) and five major annual crops (maize, potato, rice, sugarcane, cassava); the remaining crops are lumped into other perennial crops and other annual crops, respectively. Under these simplifying assumptions, the steady-state decision rule that emerges from

Table 10.1. Summary statistics: lower level model

Crop Unit N Min Max Mean STD

Price by crop Cacao US $/kg 1120 1.4057 2.1733 2.1180 0.0861

Coffee 1120 1.1414 1.7647 1.7198 0.0699

Palm 1120 0.4734 0.7319 0.7133 0.0290

Plantain 1120 0.2155 0.3331 0.3247 0.0132

Othr_perennials 1120 0.2309 0.3570 0.3479 0.0141

Cassava 1120 0.1378 0.2131 0.2076 0.0084

Maize 1120 0.1984 0.3067 0.2989 0.0121

Potato 1120 0.1214 0.1878 0.1830 0.0074

Rice 1120 0.2001 0.3093 0.3015 0.0122

Sugarcane 1120 0.0173 0.0268 0.0261 0.0011

Othr_annuals 1120 0.5257 0.8128 0.7921 0.0322

Production cost by crop Cacao Million COP/ha 1120 7.0031 8.5909 8.1235 0.7016

Coffee 1120 6.3541 7.0185 6.5028 0.2587

Palm 1120 4.9742 7.1940 6.9251 0.5828

Plantain 1120 3.6763 9.8456 5.6645 1.2012

Othr_perennials 1120 5.6850 6.4786 6.2684 0.3018

Cassava 1120 2.8991 4.6735 4.2299 0.7047

Maize 1120 1.3837 2.8610 1.5988 0.3573

Potato 1120 6.7701 9.1446 8.7244 0.6600

Rice 1120 1.7749 3.8029 3.3360 0.7901

Sugarcane 1120 4.7644 4.7644 4.7644 0.0000

Othr_annuals 1120 0.8637 7.4355 1.7311 1.6260

Biophysical suitability by crop Cacao 0-1 1120 0.0000 0.9969 0.2284 0.2548

Coffee 1120 0.0000 0.9738 0.3667 0.2543

Palm 1120 0.0000 1.0000 0.1589 0.2756

Plantain 1120 0.0000 0.9442 0.3019 0.2719

Othr_perennials 1120 0.0000 0.8510 0.2169 0.1905

Cassava 1120 0.0000 0.9455 0.3765 0.3123

Maize 1120 0.0000 0.9513 0.3136 0.2224

Potato 1120 0.0000 0.9321 0.1599 0.2048

Rice 1120 0.0000 1.0000 0.3798 0.3264

Sugarcane 1120 0.0000 0.9900 0.3106 0.2604

Othr_annuals 1120 0.0000 1.7468 0.6057 0.3720

Other explanatory variables Elev 1000 m 1120 0.0032 3.7336 1.3248 0.9953

Slope_l Proportion 1120 0.0084 0.8046 0.2166 0.1862

Slope_m Proportion 1120 0.0311 0.9793 0.5356 0.2046

Slope_h Proportion 1120 0.0311 0.9999 0.8338 0.2366

Soilph 1120 0.9380 7.6794 3.8496 1.5338

Rain2010 1000 mm 1120 0.3751 8.0898 2.0661 1.0404

Temp2010 1 degree 1120 5.8861 28.5597 20.7905 5.5052

the related dynamic optimization problem is to put a land parcel to the use generating the greatest present discounted value of net benefit (Lubowski, Plantinga, & Stavins 2006). That is, allocate a parcel of land to use j if

U„kj > Unkf ; Vk' and j-j' if k — k'

where Unk/ is the one-period expected net benefit from allocating land parcel to use j (/ 2 k). The potential value of Unk/ depends on attributes of the parcel, such as land quality, weather conditions, locational characteristics, and economic conditions in the surrounding area, as well as attributes associated with alternative choices, such as price and yield (see Tables 10.1 and 10.2).

A standard practice in the land use modeling literature is to decompose Unk/ into a deterministic component Unk/ and a random error term enk/. Unk/ represents the expected average net benefit from allocating land use in a grid; enk/ represents the deviation from the average net benefit and is often assumed to follow a normal, logit, or type-I extreme value distribution. We further decompose Unk/ into two deterministic components:

U„kj — X„k ^ Y„kj-

Xnk depends on variables that describe the nest k; it differs over nests but not over alternatives within each nest. Ynk/ depends on variables that describe nested use j (/ 2 k); it varies over choices within the nest.

Under the assumption analogous to the nested logit model about fink/, for example, that £„kj is correlated within nest k but uncorrelated across nests, the probability of grid n allocated to alternative j (7 2 k) can be derived as a product of two multinomial logit probabilities (McFadden 1977; Train 2003):

P„k = -

exp(X„k + kk^»k) EK — 1exp(X„k' + kk'Ink0) '

P nkjjk —

exp(Y„kj/kk ) Ej'ekexp(Y»kj'/kk) '

where the parameter kk is a measure of the degree of independence in enk/- among the alternatives in each nest and Ink = ln(X/2kexp(Ynk//kk)), often called inclusive value of nest k. A higher value of kk implies less correlation and kk = 1 indicates complete independence.

Eqn. (3) defines marginal probability of choosing any alternative in nest k and Eqn. (4) defines conditional probability of choosing alternative j given that any alternative in nest k is chosen. We refer to the marginal probability as an upper level model and to the conditional probability as a lower level model, reflecting their relative positions in the hierarchy structure. In Eqn. (4), kk is treated as a scale parameter that scales coefficient parameters implicitly defined in Ynk/. For those nests where there are no alternative choices inside, the conditional probability given in (4) equals 1 and the inclusive value is reduced to Ink = Ynk//kk. Insert Ink into (3) and kk will be canceled. Probabilities (3) and (4) are fundamental equations in the nested logit model.

Land use model specification

The average expected net benefits from allocating every grid n to nest k, Xnk, is specified as Xnk = xnbk, where xn is a vector of location-specific variables describing population density, market accessibility, conservation practice, topography, soil pH level, and weather conditions; bk is vector of coefficients on xn. Within any nest k, the average expected net benefits from allocating each location n to alternative j, Ynk/, is specified as Ynk/ = y .yk/., where y ■ is a vector of crop-specific variables, including the crop prices, production costs, land suitability, topography, soil pH level, and weather conditions, and an inertia variable (a lagged crop share) that captures land use conversion costs; yk/- is a vector of coefficient parameters specific to crop.

Note that if the choice-specific variables perfectly captured the average expected net revenues for each crop at the level of the individual grid, then yk/- should simply reflect the marginal net benefit and would not be expected to differ over crops. We allow this parameter varying across crops in our specification because both crop-specific variables are originally measured at relatively coarse resolutions and hence cannot perfectly capture the average expected net benefits for each

Table 10.2. Summary statistics: upper level model

Crop N Min Max Mean STD

Lagged land use share (explanatory variable) Perennials Proportion 1120 0.0000 0.0949 0.0244 0.0210

Annuals 1120 0.0000 0.2974 0.0371 0.0372

Pasture 1120 0.0000 0.9733 0.2497 0.2455

Forest 1120 0.0000 0.9971 0.2540 0.2232

Others 1120 0.0029 0.9479 0.4348 0.1756

Other explanatory variables Production cost for pastureland Million COP/tonne 1120 1.367 2.0910 1.7021 0.2034

Pop. Density 1000 people/ha 1120 0 7.3498 0.1381 0.4761

Access 1000 minutes 1120 0.0024 6.0203 0.3720 0.6302

Elevation 1000 m 1120 0.0032 3.7336 1.3248 0.9953

Elevation_cv 1120 0.0018 2.2420 0.3850 0.3425

Slope Proportion 1120 0.0084 0.8046 0.2166 0.1862

Soil ph 1120 0.9380 7.6794 3.8496 1.5338

Rain 1000 mm 1120 0.3751 8.0898 2.0661 1.0404

Temp 1 degree 1120 5.8861 28.5597 20.7905 5.5052

Park Proportion 1120 0.0000 0.8635 0.0304 0.1002

Afrodes Proportion 1120 0.000 0.9920 0.0281 0.1397

Meat US $/kg 1120 1.8269 2.8246 2.7528 0.1118

Timber 1000 USD/ha 1120 0.0040 0.8513 0.2239 0.2788

WORLD DEVELOPMENT

crop at each location. To eliminate kk from the lower level model, we introduce ykJ by dividing ykj by kk such that Ckj = Cicj/kk. This scaling facilitates the estimation.

We estimate the model in a "bottom-up" sequential fashion, starting from the estimation of the lower level model and using the estimated coefficients to calculate the inclusive values, which enter the upper level model as explanatory variables. This approach exploits the fact that the choice probabilities can be decomposed into marginal and conditional probabilities that are logit functions.

Crop allocation based on IMPACT baseline projection

Assumption:

(1) The growth rate of crop area between the base year and the projected year is consistent with that generated from IMPACT baseline projection.

(2) The specification of the nested land use model is correct; the parameter estimates of the land use model are consistent.

Allocating projected crop area based on the nested land use model

Let n be municipal index, j be crop index, and t be time period index. At the base year t = 0, share of crop j can be expressed as

Hnj0 = (1 + enj0 )Pnj0 ;

where PnJ0 is the estimated crop probability from land use model and enj0 is the error between the agricultural census (H) and the land use model estimation (P). Rearranging Eqn. (9), we have

enj0 =

_ Hnj0 1

Hence, for any n, the adjusted share of crop j is

P ij0 = Hnj0 (7)

Let's now turn to the projected year t =1. Let gj be the growth rate of crop area between time 0 and 1, derived from IMPACT baseline projection. Then the area of crop j at time 1 can be extrapolated as

Hnj1 = (1 + gj)Hnj0; (8)

Hnj1 = (1 + enj1)Pnj1-

Rearranging Eqn. (13), we have

= Hji_ 1 = (1 + gj)Hnj0 1

For any municipal, the adjusted share of crop j at time 1 is

Pnj1 = (1 + j)Pnj1 = (1 +gjj)Hnj° x Pnj1 = (1 + gj)Hnj0

Table 10.3. Assessment of the predictive power of the upper level model

Land Observed Predicted Deviation from the observed area Deviation from the observed shares

Mha Share Mha Share Mha Percent Mean Median

Perennial cropland 1.7 5% 2.0 5% 0.28 16.1% 0% 2%

Annual cropland 1.9 6% 2.5 6% 0.61 31.4% 0% 1%

Pasture 34.6 43% 36.2 43% 1.59 4.6% 0% 0%

Forests 59.1 21% 55.8 21% -3.34 -5.7% 0% 2%

Other land uses 17.1 26% 17.9 26% 0.87 5.1% 0% 0%

Source: Authors.

Table 10.4. Assessment of the predictive power of the lower level model

Crop Observed Predicted Deviation from the Deviation from the

observed area observed shares

1000 Ha Percent 1000 Ha Percent 1000 Ha Percent Mean Median

Perennial crop

Cacao 115 6% 183 7% 68.4 59.7% 1% 2%

Coffee 770 41% 812 44% 42.1 5.5% 3% 1%

Palm 262 6% 377 16% 114.5 43.6% 9% 1%

Plantain 437 26% 473 23% 35.9 8.2% - 4% 5%

Other perennial crops 160 20% 181 10% 21.3 13.3% -10% 2%

Annual crop

Cassava 183 12% 261 8% 77.4 42.3% - 4% 1%

Maize 592 34% 835 30% 243.0 41.0% - 4% 1%

Potato 150 11% 191 13% 40.7 27.1% 1% 0%

Rice 505 10% 745 16% 239.8 47.5% 7% 3%

Sugarcane 371 20% 346 23% -25.7 -6.9% 4% 3%

Other annual crops 137 14% 164 10% 27.1 19.8% - 4% 1%

Source: Authors.

Table 10.5. Robustness check of land use prediction in thousand hectares for the year 2008

Observed area Spatial price: exponential decay Flat price Spatial price: linear decay

(1) Area (2) Deviation from (1) (3) Area (4) Deviation from (1) (5) Area (6) Deviation from (1) (7)

Perennial crop 1744 2026 16% 1355 -22% 2024 16%

Cacao 115 183 60% 126 10% 158 38%

Coffee 770 812 5% 537 -30% 859 12%

Palm 262 377 44% 248 -5% 375 43%

Plantain 437 473 8% 321 -27% 453 4%

Other crops 160 181 13% 123 -23% 179 12%

Annual crop 1939 2541 31% 2325 20% 2544 31%

Cassava 183 261 42% 238 30% 257 40%

Maize 592 835 41% 765 29% 837 41%

Potato 150 191 27% 171 14% 191 27%

Rice 505 745 47% 681 35% 751 49%

Sugarcane 371 346 -7% 319 -14% 346 -7%

Other crops 137 164 20% 152 11% 161 18%

Pasture 34568 36157 5% 37560 9% 36160 5%

Forests 59134 55790 - 6% 55273 -7% 55790 -6%

Other lands 17054 17925 5% 17926 5% 17923 5%

Total 114440 114440 - 114440 - 114440 -

Source: Authors.

Table 10.6. Robustness check of land use projection in thousand hectares for the year 2030

Spatial price: exponential decay Flat price Spatial price: linear decay

Area Area Deviation from (1) Area Deviation from (1)

(1) (2) (3) (4) (5)

Perennial crop 2166 1449 -33% 2161 -0.3%

Cacao 189 130 -31% 163 -13%

Coffee 824 545 -34% 872 5.7%

Palm 405 266 -34% 403 -0.5%

Plantain 505 343 -32% 483 -4.3%

Other crops 243 165 -32% 239 -1.5%

Annual crop 2716 2486 - 8% 2719 0.1%

Cassava 275 251 -9% 270 -1.5%

Maize 861 789 -8% 863 0.3%

Potato 201 180 -11% 202 0.2%

Rice 781 714 -9% 788 0.8%

Sugarcane 433 400 -8% 433 -0.02%

Other crops 166 153 -8% 163 -1.5%

Pasture 39724 41252 3.8% 39715 - 0.02%

Forests 51744 51150 -1.1% 51759 0.03%

Other lands 18090 18103 0.07% 18087 -0.01%

Total 114440 114440 - 114440 -

Source: Authors.

Therefore, changes in crop share at municipal can be derived

Pj-1 - Pj0 = ( 1 + gj)Hnj0 - Hnj0 = gjHnj0 (12)

Land use model performance assessment

Tables 10.3 and 10.4 present an assessment of the predictive power of the upper and lower levels of the land use model,

respectively. In each table, columns 1 and 2 correspond to the observed land use derived from the data sample; columns 3 and 4 correspond to the predicted land use estimated from the land use model; columns 5 and 6 present the discrepancies by subtracting entries in columns 1 and 2 from entries in columns 3 and 4. We find that the model performs well in predicting most land uses. As far as the upper level is concerned, the model provides generally accurate in-sample predictions. The highest and lowest discrepancies, expressed in percentage

terms, are —0.2% for perennial cropland and —2.3% for forests. Regarding the lower level component, the model tends to overestimate perennial and annual croplands by a total of 0.2% and 0.4%, respectively. These discrepancies arise mainly from overestimating areas allocated to oil palm, cacao, maize, and rice. Even so, the predicted errors of these four crops in terms of percentage are in a reasonable range, from 2.3% to 3.2%. The model performs well in the prediction of the remaining crops in terms of both magnitude and percentage.

Robustness check of the specification of spatially explicit price in the land use model

The major assumption made in the land use model is the functional form of spatially explicit prices with exponential decay assumed in Eqn. (1) on page 11. As a robustness check, we considered two alternative specifications—original country-level "flat prices" and spatial prices with linear decay. With the specification of flat prices, all prices are equal across the country; with the specification of linear decay, prices decline more sharply than the specification of exponential decay as the distance from major markets located in cities increases.

We compared the prediction accuracy of the three models, presenting the results in Table 10.5, Appendix 1. A comparison between the flat-price and the spatial-price specifications indicates (1) both specifications tend to under-predict forested area and over-predict the area of annual cropland, pastureland, and other lands; (2) the models with spatial-price specifications generate smaller prediction errors in both pasture and forestland, two dominant land types in Colombia; (3) the models with flat-price and the spatial-price specifications generate prediction errors in perennial cropland in opposite directions, but the prediction errors generated by the spatial price models are smaller. The balance of evidence, and log-likelihood ratio, suggests the spatial-price specifications perform better than the flat-price specification.

We also compared the projected areas of land use among the three models for the end-period year (2030), presenting the results in Table 10.6. The comparison indicates that the two spatial-price models yield similar results. Given the fact that the exponential distance decay function allows crop prices to decay at a moderate speed and ensures a positive value at every location, this paper uses the functional form of exponential decay for the analysis.

Available online at www.sciencedirect.com

ScienceDirect