IFPRI Discussion Paper 01790

December 2018

Climate Change, Agriculture, and Adaptation Options for Colombia

Francisco Boshell

Timothy S. Thomas

Vijay Nazareth

Nicola Cenacchi

Environment and Production Technology Division

INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE

The International Food Policy Research Institute (IFPRI), established in 1975, provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition. IFPRI’s strategic research aims to foster a climate-resilient and sustainable food supply; promote healthy diets and nutrition for all; build inclusive and efficient markets, trade systems, and food industries; transform agricultural and rural economies; and strengthen institutions and governance. Gender is integrated in all the Institute’s work. Partnerships, communications, capacity strengthening, and data and knowledge management are essential components to translate IFPRI’s research from action to impact. The Institute’s regional and country programs play a critical role in responding to demand for food policy research and in delivering holistic support for country-led development. IFPRI collaborates with partners around the world.

AUTHORS

Dr. Timothy S. Thomas (tim.thomas@cgiar.org) is a Research Fellow in the Environment and Production Technology Division of the International Food Policy Research Institute (IFPRI), Washington DC.

Francisco Boshell (jfboshell@gmail.com) was the Technical Director at ECOSAGA SAS, Colombia, at the time of writing this discussion paper.

Vijay Nazareth was a Research Analyst in IFPRI’s Environment and Production Technology Division at the time of writing this discussion paper.

Nicola Cenacchi (n.cenacchi@cgiar.org) is a Senior Research Analyst in IFPRI’s Environment and Production Technology Division, Washington DC.

Notices

1 IFPRI Discussion Papers contain preliminary material and research results and are circulated in order to stimulate discussion and critical comment. They have not been subject to a formal external review via IFPRI’s Publications Review Committee. Any opinions stated herein are those of the author(s) and are not necessarily representative of or endorsed by IFPRI.

2 The boundaries and names shown and the designations used on the map(s) herein do not imply official endorsement or acceptance by the International Food Policy Research Institute (IFPRI) or its partners and contributors.

3 Copyright remains with the authors. The authors are free to proceed, without further IFPRI permission, to publish this paper, or any revised version of it, in outlets such as journals, books, and other publications.

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Abstract Climate change is already affecting the global economy between catastrophic losses from extreme weather events to the subtler losses in agricultural productivity. In the decades to come, the effects of climate change will increase. Now is the time for policymakers to better understand the potential impacts of climate change on agriculture so that they might make appropriate investments and implement effective policies to help farmers better adapt to climate change. This discussion paper uses multiple models to assess the impact of climate change on agriculture in Colombia up to 2050. Some of the analysis is at a very fine geographic resolution, while other is at the national level. The biophysical models used here project modest impacts of climate change on rice and maize, at a cost of around 10 percent of national production. Losses to sugarcane are projected to be much higher, at around 28 percent. Recommendations are made for how policymakers might reduce the losses to these crops and others included in the analysis presented in this paper.

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Acknowledgments This work was implemented and undertaken as part of

• The CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors.

• The CGIAR Research Program on Policies, Institutions, and Markets (PIM) led by the International Food Policy Research Institute (IFPRI). PIM is in turn supported by donors. For details please visit http://pim.cgiar.org/donors/.

This work was also supported through funding by

• The Inter-American Development Bank (IDB). • The Bill & Melinda Gates Foundation (BMGF). • The International Center for Tropical Agriculture (CIAT)

We would also like to thank various colleagues who helped in diverse ways, including Keith Wiebe, Tim Sulser, Sherman Robinson, Ricky Robertson, Ho Young Kwon, and Milcah Prasad.

The opinions and views expressed here belong to the authors and may not be attributed to nor be taken to reflect the official opinions of BMGF, CCAFS, CGIAR, CIAT, IDB, IFPRI, PIM, or any of their respective donors or affiliates.

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Introduction Climate change will affect the Colombian agriculture sector in increasing measure in the coming decades, so it is important for policymakers to better understand how agriculture will be impacted and what can be done to help farmers adapt. This discussion paper uses multiple models to give fine-resolution spatial analysis of the effects on various crops and then examine what those changes will amount to at the national level, taking into consideration changes in global supply and demand as all of countries deal with population growth, income growth, and climate change.

Colombia is the gateway to South America. It borders Panama, Ecuador, Peru, Brazil, and Venezuela, as well as the Pacific Ocean and the Caribbean Sea. Colombia is ecologically diverse, ranging from coastal zones to the Andes ranges, and from the grasslands of the Llanos to the forests of the Amazon basin. It has great agricultural potential.

However, Colombia has coped with an internal armed conflict for more than 60 years, leaving a large number of victims.1 The conflict has influenced and been influenced by important political, social, and economic issues, including social inequality, lack of formal governance in parts of the country, drug trafficking, corruption, illegal economic activity, and poverty. A peace agreement between the Colombian government and the Revolutionary Armed Forces of Colombia–People's Army (FARC–EP) was signed at the end of 2016 which was meant not just to end conflict by to address some of the related issues. Progress was made in implementing the agreement in 2017, but 2018 has seen an increase in violence and some uncertainty about how the peace process might proceed.

In the next section, we next look at Colombia’s economic and demographic trends. This is followed by a section giving an overview of its climate in terms of both what Colombia experienced in the second half of the 20th century and what is projected for Colombia through the middle of the 21st century. We then review the current situation for the AFOLU (agriculture, forestry, and other land use) sector and present what the models suggest will happen to these sectors with climate change. As part of this analysis, we make recommendations for adaptation measures to help farmers adjust to climate change.

Economic and Demographic Conditions and Projections Climate change is only one of the important trends that will affect the well-being of the nation by 2050. At the same time the climate is changing, GDP will be growing, and population will also grow, though most likely at a more modest rate than GDP.

Figure 1 shows one system for dividing the country into ecologically defined regions, which include the Pacific region, a relatively narrow swath of land bordering the Pacific Ocean; the Caribbean region, a similarly narrow stretch of land on the Caribbean Sea; the Andes region, which encompasses the Andes ranges, and the valley between; the Orinoco region, known as the Llanos, a large relatively flat, low-lying grassland area around the top of the watershed for the Orinoco River; and the Amazon region, which is a large, mostly forested area at the top of the watershed for the Amazon River and its tributaries.

1 To understand more about the conflict, see Parra (2015), Rangel (1999), Clavijo (1998), and Duncan (2006).

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The 32 departments of Colombia, the country’s highest-level administrative areas, are outlined within the figure. They do not cleanly fit within the ecological regions, though many do lie completely within a given region.

Figure 2 is an elevation map of Colombia. The country rises from sea level at the coast very rapidly to the highest point, Pico Cristóbal Colón, at 5,700 meters, located on the coast and ironically not part of the Andes zone but within the Caribbean zone. Nevertheless, the highest peak in the Andes zone, Volcán Nevado del Huila, is still an impressive 5,470 meters.

The majority of the eastern half of the country is relatively flat, most of which is 300 meters or less in elevation. Other areas with relatively even terrain include most of the coastal areas, as well as the valleys between the Andes ranges.

Population

Past and Present

Figure 3 shows demographic trends for the last four decades. The relatively high population growth rate of 1.9 percent per year over the entire period is slightly slower in the second two decades than in the first two. Also, the rural population growth rate was remarkably slow, rising by only 21 percent over 42 years, or 0.45 percent per year. This slow growth is probably caused by the long-standing civil war and better economic opportunities in urban centers. Colombia’s high total growth is the result of its very high rate of urbanization, which is around 75 percent.

Figure 4 shows the distribution of the population throughout the country. Most of the population lives in the Andes region, with the Caribbean region second, followed by the Pacific region, the Orinoco region, and finally the Amazon region.

Future

Figure 5 shows projections used by the IPCC's AR5 for population growth under the five SSPs developed for the IPCC analysis of how climate change might affect the global economy and people in general. In the figure, though difficult to see, SSP4 and SSP5 share a path very similar to the SSP1, which is the path of slowest population growth. SSP2 shows moderate population growth, while SSP3 shows the fastest population growth.

Given the rapid population growth experienced by Colombia through 2012, one critique of these data is that the projection for future population is unrealistic, at least for the next decade or two. The SSP with the fastest growth projection is SSP3, which has only a 1.1 percent annual growth rate, compared with the 1.6 percent annual growth rate between 1990 and 2012, which has shown little sign of slowing. The SSP1 growth rate, the slowest of the main three, is a very slow, at 0.57 percent per year.

Figure 6 shows that the SSPs with the slowest overall population growth rates have the most rapid urbanization rates. By 2050, SSP1 projects that Colombia will be 90.9 percent urban. Under SSP3, the urbanization rate will reach only 78.8 percent in 2050, after starting at 75.1 percent in 2010.

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Economic Development and Vulnerability

Past and Present

Figure 7 depicts two trends of importance to a discussion of well-being, poverty, and climate change. It shows changes in GDP per capita from 1970 onward, along with the share of GDP from agriculture. GDP growth per capita since at least 1970 has been steady and strong, at 2.0 percent per year, resulting in income per person being 130 percent higher in 2012 than 42 years ago.

At the same time, we note a steady decline in the share of agriculture in GDP, which was resting somewhere near 6.5 percent in 2012 after having been at around 26 percent in 1970. This is a relatively normal part of the demographic transition, though in Colombia's case it was likely hastened by a rural exodus resulting from the extended armed conflict.

Sometimes GDP per capita does not tell a complete story about the well-being of the population. Table 1 contains several indicators that Colombians are doing well: the adult literacy rate and both primary and secondary enrollment rates are fairly high; under-five malnutrition is relatively low, at 3.4 percent; and most of the population has access to electricity. The only indicator that may suggest high vulnerability is the almost 50 percent of the population that has vulnerable employment.

Figure 8 shows two long and positive trends for well-being: the life expectancy rate has been rising steadily since 1970, from around 61 years to around 74 years in 2012, and under-five child mortality has been falling since 1970. The child mortality rate fell rapidly until around 1985, and then a little more gradually since then; it was at 97 per thousand in 1970, and as of 2012, it was only 18 per thousand.

The Socio-Economic Database for Latin America and the Caribbean (SEDLAC) is a data project sponsored by the Center for Distributive, Labor and Social Studies (Centro de Estudios Distributivos, Laborales y Sociales) and the World Bank's Latin America and the Caribbean poverty group. The project team compiled statistics from the National Administrative Department of Statistics (Departamento Administrativo Nacional de Estadística, DANE), National Planning Department (Departamento Nacional de Planeación, DNP), and Mission to Combine Employment, Poverty, and Inequality Series (Misión para el Empalme de las Series de Empleo, Pobreza y Desigualdad, MESEP) on poverty in Colombia, and presents several different measures, all of them indicating positive trends (lowering poverty) through time. Table 2 shows that for the nation as a whole, extreme income poverty decreased from 17.7 percent in 2002 to 10.4 percent in 2012, while moderate income poverty decreased from 49.7 percent to 32.7 percent over the same period.

There is a rural–urban divide, however. In 2002, 12.3 percent of the urban population was classified in the extreme poverty range and 45.5 percent in the moderate range, while 33.1 percent of the rural population was classified in the extreme range and 61.7 percent in the moderate range. All of these measures declined by 2012, with the urban population at 6.6 and 28.4 percent and the rural population at 22.8 and 46.8 percent of the two ranges, respectively.

Using a broader measure of poverty, as shown In Table 3, moderate multidimensional poverty fell from 60.4 percent in 1997 to 27.0 percent in 2012. In 1997, the urban–rural poverty gap was 50.7 percent urban and 86.0 percent rural; by 2012, these percentages fell to 20.6 and 48.3 percent, respectively

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As for regional issues, it is difficult to separate the urban–rural issue from the simply regional issue. For example, Bogotá has the lowest poverty rate, but it is also almost entirely urban. But Bogotá’s poverty rate is about half of the urban poverty rate, which indicates either that there is a strong regional effect, or that the urban poverty rate perhaps varies with urban size, with larger urban areas having lower poverty rates.

It is also noteworthy that the Atlantic region has the highest poverty rate, even higher than the rate for the Amazon and Orinoco regions, which are almost entirely rural.

Figure 9 shows a set of travel time maps for Colombia, which are meant to give rough indicators of connectivity. Of particular concern is whether farming areas are connected enough to markets to be able to sell their produce and acquire inputs like seeds and fertilizer. Even connection to towns as small as 20,000 can be vital for farmers, since towns of that size can readily serve to interact with farmers on these items, along with consumer goods.

The maps in Figure 9 show that most of the farming areas in the Andes and Caribbean regions are fairly well connected not only to small towns, but to larger cities as well. The larger cities can create large domestic markets for farmers' products. However, we note less connectivity in the Pacific region, and even less in the Orinoco and Amazon regions.

Connectivity can be a two-edged sword. On one hand, it facilitates economic development for those who live there, but on the other hand, as development is facilitated, it tends to attract more people. In the case of the Amazon forest and the seeming commitment of the government to preserve the forest, connectivity to the Amazon region is not necessarily a good thing.

However, one wonders whether there could be further "good" economic development in the grasslands of the Llanos if infrastructure were better developed.

Future

Figure 10 shows various projections for GDP growth based on the SSPs for the IPCC's AR5 reports. For the slowest growth in SSP3, per capita GDP is projected to be 90 percent higher in 2050 than in 2010. For both SSP2 and SSP4, we expect it to be 173 percent higher; for SSP1, 268 percent higher; and finally for SSP5, 356 percent higher.

One of the strongest impacts that climate change will have on the AFOLU sector, especially in a highly urbanized country, is rising food prices. Because poor people spend a much larger portion of their household budget on food than do wealthier families, rising food prices without sufficient increases in household income can be devastating for the poor. However, as long as inequality does not rise greatly, the rising per capita GDP should increase the well-being of everyone, including the poorest in the country.

Summary of Public Policy on Climate Change Provisions included in Colombia’s National Development Plan (Plan Nacional de Desarrollo, PND) for two presidential terms—PND 2010–2014 and 2014–2018—indicate that Colombia has been committed since 2010 to the process of formulating comprehensive national climate change policies, which seek to

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integrate the management of climate change into development decisions, thereby increasing resilience and reducing carbon emissions (CRC 2015). The consolidation of this policy will articulate and establish the general guidelines that will orient medium- and long-term actions to mitigate and adapt to climate change in the country. In this sense, the PND 2014–2018 in the chapter on Green Growth states: "… the policy on climate change will be aligned with the definition of a commitment to reduce emissions, adaptation and media implementation that meets criteria of robustness and fairness, commitment that Colombia included in the overall agreement being negotiated internationally under the UNFCCC [United Nations Framework Convention on Climate Change]" (CRC 2015).

Public Policy Highlights on Mitigation

Following principles of transparency and equity, Colombia’s intended nationally determined contributions (INDCs) consider the country’s historical responsibilities and capabilities in terms of greenhouse gas (GHG) emissions, as well as the overall goal of avoiding the increase of global average temperature to 2 degrees Centigrade (°C). In this respect, Colombia’s goal of reducing GHG emissions by 20 percent by 2030, relative to emissions projected following current trends, means that its emissions will continue to grow over time, but at a lower rate.

The Green Growth Strategy of Colombia’s PND 2014–2018 calls for all of the production sectors to adopt practices that generate added value and support economically, socially, and environmentally sustainable growth (CRC 2015). To achieve goals related to development, peace, equality, and education in the long term, it is necessary to identify and develop opportunities for increased competitiveness, productivity, and efficiency, that in turn reduce GHG emissions in the different sectors of the national economy and promote resilience to the adverse effects of climate change. Law 1753 of 2015 requires every production sector to develop and implement plans for mitigating and adapting to climate change (CRC 2015). The plans must contain quantitative sectoral targets for reducing GHG emissions of in the short (2020) and medium (2025 or 2030) terms. This law also requires Colombia to design and implement both the National Strategy for Reducing Emissions from Deforestation and Forest Degradation and a national policy to combat deforestation, which will contain an Action Plan aimed at preventing the loss of natural forests by 2030 (CRC 2015).

Public Policy Highlights on Adaptation

The adaptation component included in Colombia’s INDCs is an answer to the invitation of Decision 1 of the 20th Conference of the Parties (UNFCCC 2014), built under the National Plan for Adaptation to Climate Change (Plan Nacional de Adaptación al Cambio Climático, PNACC) (DNP, MADS, IDEAM, and UNGRD 2014). PNACC defines the guidelines for various sectors and regions of the country to prioritize their actions to reduce vulnerability and to include climate change and variability in their planning processes, through the formulation and implementation of plans of regional and sectoral adaptation.

Through actions specified for the short, medium, and long terms, PNACC aims to promote comprehensive national planning, consistent with climate change and variability, as well as territorial and sectoral development. To achieve this goal, PNACC has the following objectives:

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1. Promote knowledge of climate change and its potential consequences for communities, biodiversity and ecosystem services, and the economy.

2. Incorporate adaptation to climate change into sectoral planning and territorial development.

3. Implement adaptation options in events and processes associated with climate change, with the criteria of competitiveness, sustainable development, and equity.

From the above, Colombia has formulated 11 regional adaptation plans and is ready to begin implementing their priority actions. Furthermore, adaptation plans for two priority sectors—Agriculture and Primary Road Networks—have been developed by their ministries.

Major adaptation goals that the country should reach by 2030 emerge from the progress made in recent years, coupled with the specific efforts of production sectors and territories to develop plans to adapt according to their specific needs and in line with the country's efforts to combat multidimensional poverty and inequality. A summary of these goals from the PNACC follows:

1. The country will have implemented climate change plans for main sectors and territories.

2. Colombia will have a national system of indicators for adaptation that allows for monitoring and evaluating the implementation of adaptation actions.

3. Hydric river basins of the country will have water management instruments that consider climate variability and change.

4. Priority economic sectors (transport, energy, agriculture, housing, health, trade, tourism, and industry) will be implementing innovative adaptation actions articulated with the private sector.

5. Strategies strengthening adaptation will allow for training and public education on climate change, focusing on the different actors of Colombian society.

6. Protection of moors (wetlands located in higher elevations) will be a priority for the conservation of Colombia’s ecosystems.

7. Increased coverage of national protected areas and carbon stocks, and the conservation, management, and restoration of terrestrial and marine strategic ecosystems and coastal protection will also be a priority.

Climate and Climate Change

Current Climate

This section first looks at the climate of the recent past. In Figure 11, which shows annual mean rainfall between 1950 and 2000, the country’s diverse patterns are apparent. The rainiest region in Colombia is the Pacific, with an average volume of more than 5,000 millimeters (mm) a year, and in some parts, rainfall is between 11,000 and 12,000 mm. Rainfall that high would make annual crop cultivation very difficult, so the Pacific is probably best suited for forests and limited cultivation of perennials.

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In contrast, the peninsula of La Guajira, in the northeast corner of the Caribbean region, registers on average less than 500 mm of rain annually. This area presents the driest conditions in the country. In general, much of the Caribbean region registers average annual rainfall volumes of between 800 and 2,000 mm, with a single-mode regime, distributed between six and eight months, usually from April/May to October/November. These conditions are favorable for short-cycle crops (corn, rice, and cotton), annual crops (cassava and yams), and perennial crops (fruit, palm oil, banana, and pastures). This region has swamps belonging to a complex of wetlands and areas protected or under conservation, known as the Sierra Nevada de Santa Marta, where agricultural activities are limited or restricted.

The Andes region has high rainfall variation. In areas delimiting mountainous regions from the rather flat Orinoco and Amazon regions, maximum annual values close to 6,000 mm are recorded, whereas in western sectors of the Eastern Cordillera, annual values between 500 and 800 mm are registered. Rainfall distribution throughout the year corresponds to a bimodal regime, with peaks in April/May and October/November.

Annual average rainfall in the Orinoco is approximately 2,500 mm. In this region, a single-mode regime prevails throughout the year, with a main rainy period from April to November. In the Amazon region, annual precipitation averages almost 3,000 mm.

As a country, year-to-year variation appears to be relatively small. Using AgMERRA data (Ruane, Goldberg, and Chryssanthacopoulos 2015), we found that median rainfall across the years 1980 to 2010 for Colombia was 2,656 millimeters, with the highest average annual rainfall observed in that period being 2,913 millimeters and the lowest 2,276 millimeters.

Figure 12 shows a map of the mean daily maximum temperatures for the warmest month (which may vary across pixels) Temperatures higher than 36°C are present in the Caribbean region, except in the Sierra Nevada de Santa Marta. Similar high temperatures occur also in the Orinoco region, as well as in the Amazon. The lowest maximum temperatures occur in the high areas of the central and eastern cordilleras and the Sierra Nevada de Santa Marta. In some of these areas, snow is permanently present, and in altitudes higher than 3,000 meters above sea level (m.a.s.l., called “páramos”), where native vegetation protects soils, agricultural land use is prohibited by law.

The highest minimum temperatures are recorded in some northern areas, such as the peninsula of La Guajira; the archipelago of San Andrés, Providencia, and Santa Catalina; and in some areas in the eastern part of the country, near the border with Venezuela.

Most of the Caribbean, Orinoco, Pacific, and Inter Magdalena Valley regions have average annual minimum temperatures of 20–22°C. In mountain areas, temperature decreases as altitude increases. The lowest minimum temperatures are normally recorded in the dry season and depend on the type of coverage on the soil surface. In some savannas located at high altitudes in Andean regions, like the savanna of Bogota, absolute minimum temperatures may drop below 0°C in December, January, and February—a condition known as meteorological frost, affecting especially short-cycle crops, such as potatoes and maize, as well as pastures.

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Climate in the Future according to Five International Models

The overview and methodology paper for this series on climate change and agriculture in Central America and the Andean Region (Thomas et al. 2018) gives an overview of climate models in general. Here we examine models used for Colombia to generate the modeling results presented in this discussion paper.

Figure 13 shows possible changes in annual precipitation, taking as a reference the climate of 1960– 1990, and the projected climate of 2050. There are differences among models outputs. The model that stands out the most as being different from the other three is the IPSL, which has the northern half of the country and part of the southern half becoming substantially drier, while the other three models have the country becoming wetter.

Table 4 quantifies the estimated annual rainfall changes by region. As shown in the table, mean annual rainfall for the Caribbean region would decline by about 280 mm according to the IPSL. The Caribbean is already the driest region in the country, so if this model’s results were accurate, the entire dynamics of agriculture in that region would change.

Figure 14 shows changes projected in the mean daily maximum temperature for the warmest month of the year for each pixel. If there were to be short characterizations of results of these models, we might say that the GFDL depicts the coolest projections, with an average change for the nation of only 1.8°C, compared with the other three models, which all are at or above 3°C on average.

The MIROC model is the most variable, with an odd isolated cooling area near the center of the country, and then a very hot Caribbean region, with projected temperature increases of more than 4°C, which if realized, will make for a very hot region.

The IPSL model has the least variation geographically among the three "hot" models, but still has most of the country getting warmer by 3°C. Finally, the HadGEM model shows a large hotspot split between the Orinoco and Amazon regions, making the Orinoco region’s temperature rise on average close to 4°C.

With such variability in results coming from the international climate models, one might expect to have considerable variations in their interpretations.

Future Climate

Climate Models Used at IDEAM—Summary

In Colombia, the institution responsible for carrying out climate monitoring and analysis at the national level is the Institute of Hydrology, Meteorology and Environmental Studies (Instituto de Hidrología, Meteorología y Estudios Ambientales, IDEAM). This institute uses diverse types of models for climate analysis, depending on the objectives and required temporal and spatial scales. For constructing climate change scenarios in Colombia, global and regional as well as dynamic and statistical models are used. Most global models capture reasonably well the main atmospheric conditions, but are not precise on regional scales. One way to solve this problem is by using regional climate models (RCMs) that increase the model resolution for best detail of the atmospheric circulation over a limited area or region. It is relevant to note that these models, in the case of the equatorial zone where Colombia is located, have

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some limitations because they don´t consider the loss component of the Coriolis force in addition to the effect of altitude mountain ranges present at large parts of the national territory. To analyze and forecast weather on a short-term basis, IDEAM uses models as: • Mesoscale and Mircroscale version 5 (MM5)—This model simulates precipitation, humidity, and

clouds for various regions in Colombia.

• Weather Research and Forecasting (WRF)—This RCM is a later version of MM5, is more robust, and was designed to be easily mounted on Linux platforms, as well as on AIX, HP-UX, Sun, and others. WRF is considered a useful tool for forecasting rainfall, temperature, and wind for diverse regions of Colombia.

• Global Forecast System (GFS)—This low-resolution global model is used to simulate rainfall and high winds from the surface to a height of 200-hecto Pascals.

• Community Atmosphere Model (CAM)—This atmospheric GCM was developed in the United States

at the National Center for Atmospheric Research (NCAR). This type of numerical model solves the equations that explain atmospheric circulation over a grid of points covering the entire globe. IDEAM uses CAM on a spatial resolution of 2.5° longitude by 2.5° latitude.

• Climate Weather Research and Forecasting (CWRF)—This numerical simulation model of atmospheric circulation was developed to predict weather and climate with spatial scales ranging from meters to thousands of kilometers and time scales from days to months. IDEAM uses this model on a spatial resolution of 90 by 30 km.

• Capacity Maturity Model version 5—The CMM5 numerical simulation model of atmospheric circulation has spatial scales ranging from meters to thousands of kilometers and a time scale of months, defined by forcing sea surface temperature. CMM5 is based on the fifth-generation NCAR/Pennsylvania State University mesoscale model (MM5). IDEAM uses this model on a spatial resolution of 90 by 30 km.

• Canonical Correlation Analysis (Análisis de Correlación Canónica, ACC)—This model allows seasonal precipitation and temperature predictions based on canonical correlation analysis. ACC uses series of data, such as sea surface temperature, as independent variables, and values of precipitation or temperature as dependent variables. The most reliable prediction emerges from the analysis with periods longer than 30 years. The probabilistic forecast (percentage) represents the probability that certain conditions of temperature or precipitation occur in three categories: below average or below normal; normal; and above average or higher than normal.

• For climate analysis and medium- and long-term prediction purposes, IDEAM uses such models as

the CAM GCM and such RCMs as CWRF and CMM5. Additionally, IDEAM is using the ACC Climate Prediction Tool, a statistical model for climate predictions on monthly and seasonal bases.

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In its recent Third National Communication on Climate Change, IDEAM presents national projections for the period 2011–2100, for rainfall and for average, maximum, and minimum temperature, generated from the new scenarios of radiative forcing RCP2.6, RCP4.5, RCP6.0, and RCP8.5 (IDEAM 2015b). Each of the scenarios was generated using reliability ensemble averaging for future periods. For the projection of climate change scenarios in Colombia, the period 1976–2005 was used as a historical reference, and climate change scenarios were devised for future periods as 2011–2040, 2041–2070 and 2071–2100, on a quarterly basis (December–February, March–May, June–August, and September–November) as well as on an annual basis (Armenta et al. 2014).

Colombia's Climate Change Scenarios

As part of its Third National Communication on Climate Change, which Colombia delivered to the UNFCCC, updated projections were made with regard to climate change scenarios for 2011–2100, as compared with 1975–2006 (IDEAM 2015b). This exercise was developed in more than two years of work by IDEAM, which followed the latest methodologies proposed by the IPCC in AR5 (IPCC 2014). IDEAM generated future scenarios for rainfall and temperature based on the same RCPs mentioned above, using a multimodel and multiscenario ensemble approach that allows for averaging results from the various RCPs.

The projections of annual precipitation for 2011–2040 shown in Table 5 indicate wide fluctuations from decreases close to 20 percent in some areas of the northeastern part of the country, to increases of 10–20 percent in various parts of the Andes region. According to the results in Table 5, during that period annual rainfall would vary in the range of –10 to +10 percent in much of the country. The highest increases in average temperature by 2040 would be about 1.9°C in the Cesar department (located in the Caribbean region), and by 2100, annual mean temperature increases would fluctuate between 2.0 and 2.7°C throughout the country.

It is important to mention that some previous results of national scenarios of climate change submitted to the UNFCCC as part of the Second National Communication, calculated as recommended by the IPCC in its AR4, differ from those projected in the Third Communication (AR5) (IPCC 2007, 2014). For example, agroclimatic analyses conducted by Boshell et al. in 2015 for rural areas located in the department of Tolima showed that annual rainfall would decrease by 30–40 percent according to the Second National Communication’s (AR4’s) A2 and B2 scenarios for the 2011–2040 period. However, according to the results of the new RCP scenarios in the Third National Communication (AR5), in this area annual rainfall would increase by about 10 percent for this same period.

GHG Emissions according to Institutions for Climate Change and the Environment in Colombia

In the Second National Communication to the UNFCCC (AR4), Colombia reported that in 2004, GHG emissions came from diverse sources (IPCC 2007). Agriculture and land-use change were responsible for around 52 percent of all GHG emissions in Colombia.

In the First Biennial Update Report to the UNFCCC, Colombia reported that for 2010, the AFOLU sector was responsible for 55 percent of total GHG emissions, as shown in Figure 15 (IDEAM 2015a). These results were an advance to the Third National Communication (AR5).

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Figure 16 shows a breakdown of emissions in the AFOLU sector, where 54 percent of the emissions from this sector are generated by land use (including change in land use), which includes forest uses, crops, grasslands, wetlands, and settlements. The livestock sector, through enteric fermentation and manure management, is responsible for 21 percent of the emissions. Finally, others sources and noncarbon dioxide emissions from the land—such as burning of savannas, use of nitrogen fertilizer, direct and indirect nitrous oxide (N2O) emissions emitted by the managed soils, manure management, and methane emissions from rice cultivation—generate up to 25 percent of the emissions.

While the focus of this analysis on climate change in Colombia is primarily on adaptation, it is always good to keep an eye on climate-smart agricultural possibilities: policies and activities that can improve adaptation, resilience, and mitigation (reduction of total GHG emissions or at least reduction of emissions per unit of output). It is therefore helpful to realize that emissions will primarily be reduced through forest interventions (reduced deforestation or increased afforestation or reforestation), more efficient use of fertilizing procedures, and greater efficiency in cattle and dairy operations.

Impact of Climate Change on Agriculture, Forestry, and Land Use In a previous section, we reviewed the historical trends for the size of the rural population, and even looked at the projections for future rural populations. While we do not have any data on how many people would be employed in agriculture in the future, there is generally a high correlation in any given country between rural population and the number of people working in agriculture, which would give us some idea. While agriculture is clearly an irreplaceable part of Colombia's economy, because it is responsible for 40 percent of exports, it is not the primary economic driver for the country.

The results from models presented in this section guide us in better understanding the likely impact of climate change on the AFOLU sector, as well as the economy as a whole, so that farmers, policy makers, researchers, and donors can more effectively plan for successful adaptation.

Land Cover

Figure 17 shows the main land cover in each grid cell of approximately 1 km on each side. Trees are the predominant land cover type, followed by extensive grasslands in the large plain of the Orinoco region, with smaller patches of in the Caribbean region, and even smaller patches in the Andes region. Also, cropland is scattered in central and northwestern portions of the country, mostly in the Andes and Caribbean regions.

According to a detailed study on land-use conflict performed by the national Agustin Codazzi Geographic Institute (Instituto Geográfico Agustín Codazzi, IGAC), about 20 percent of the national territory has agricultural potential, but only 4.66 percent is cultivated (IGAC 2012). In contrast, while livestock potential is only 13.3 percent of the national territory, 30.57 percent is cultivated. Colombia’s highest land-use potential is for forestry, at 56.23 percent, of which 53.17 percent is currently used (Table 6).

Every year, DANE conducts a National Agricultural Survey (Encuesta Nacional Agropecuaria, ENA) in the most productive agricultural regions of the country. As shown in Table 7, the 2011 survey indicated that livestock use occupied 29,148,092 hectares; agricultural production, 2,915,425 hectares; and forestry and other uses, the remaining area of 4,600,000 hectares. (DANE 2012).

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Agriculture: Overview

Crops

Figure 18 shows the distribution of cropland in Colombia, with concentrations at the base of the eastern portion of the Andes, in the valleys between the Andes, in the slopes of the western portion of the Andes, and in the coastal areas of the Caribbean region.

Table 8 summarizes recent agricultural production for Colombia. In determining a crop's relative importance, it is always difficult to know which metric to use. Candidates include harvested area, value of production, and export value. Table 8 focuses on harvested area as the important metric, with the five leading crops harvested being coffee, maize, rice, plantains, and sugarcane. These crops account for slightly less than two-thirds of all harvested area in Colombia (FAO 2014). However, sizable productivity changes have taken place between the 1999–2001 and 2010–2012 periods. Both coffee and rice have seen yield declines of nearly 25 percent, while at the same time harvested area for these crops has expanded by more than 6 percent each. On the other hand, maize yield has increased by an impressive 61 percent, followed by sugarcane and plantains at 27 and 15 percent, respectively. All three crops with yield increases had corresponding area declines.

Colombia is the world’s fourth-largest producer of palm oil, and while not explicitly dealt with in the remainder of this report, it seemed worthy to mention that the government has targeted "a six-fold increase in this sector by 2020 through the policy mandate for expansion of biofuels" (Nepstad et al. 2013). In addition to biofuels, Colombia uses around 40 percent of palm oil produced for domestic food manufacturing. However, palm oil is not something that currently benefits small farmers, since "most palm oil plantations are in the hands of vertically integrated private companies" (Nepstad et al. 2013).

Figure 19 shows the percentage of harvested area in Colombia from 2001 to 2013, for some transient and permanent crops. Coffee is the crop that has the largest area; the area cultivated for such crops as corn, rice, and plantain (plantain + banana) decreased for seven years, while the area cultivated for palm oil increased. Other crops presented in Figure 20 and Table 8 have not shown significant changes in harvested area.

Figure 20 shows yield trends over the same period. Rice yields rose by around a third between 1990 and 2000, but declined back to the 1990 level by 2012. Maize rose by a similar percentage between 1990 and 2000, but continued climbing after 2000, rising another 70 percent by 2012. This is almost a 230 percent increase in yield for maize between 1990 and 2012. Plantains and sugarcane increased yields slightly between 1990 and 2012, both by around 20 percent. Finally, coffee yields declined dramatically between 2008 and 2012, after having been mostly stable (apart from fairly large year-to-year variations) between 1990 and 2008.

Livestock

Table 9 shows a count of livestock in Colombia, averaged between 2010 and 2012. The importance of cattle in Colombia is clear, with 25 million head, which is slightly more than one cow for every two people. Chickens are also important, recorded at 159 million in 2012, along with pigs, at slightly more than 5 million.

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Figure 21 shows trends in livestock from 1990 to 2012, with the number of cattle being mostly stable and constant over the entire period. In contrast, the number pigs, which was mostly constant or perhaps declining slightly between 1990 and 2005, rose by 150 percent between 2005 and 2012. The chicken population also showed impressive growth, rising steadily by around 200 percent over the entire period.

The performance of sheep and goats was somewhat unusual, with both having relatively flat growth, followed by fast growth, and then a fast decline. Between 2002 and 2005, the sheep population rose by around 50 percent, but by 2012, it declined to around one-third of its level in 2005.

Valuation of Agricultural Production

Table 10 shows another metric for the importance of agricultural commodities, which is the gross value of production. One of the difficulties of this metric is the valuing of products properly, given that some of the products are consumed by producers rather than sold on the market. The other difficulty is not adjusting for the cost of inputs—that is, not counting value-added, but simply recording gross value. Nevertheless, this metric lets us consider livestock products, which were not considered in the harvested area metric.

As seen in Table 10, livestock are a very important portion of Colombia’s agriculture sector, with the top three products being livestock products: beef (meat), milk, and chicken meat. Sugarcane is the crop with the highest gross value, at almost double the value of the next-highest crop, plantains. Bananas, rice, and coffee follow these two. Then eggs, another livestock product, is ninth in the list. Finally, palm oil rounds out the list at number 10.

Figure 22 shows real trends in the production value of the key livestock products, and Figure 23 shows the same thing for the key crops. Between 1990 and 2012, the value of chicken production rose by around 300 percent, and the value of egg production rose by 200 percent. Cattle products rose much more slowly in value during that period, with milk rising by around 50 percent and beef rising by only 10 percent.

In contrast, the production value of coffee declined in 2012 to about half its 1990 value. While the value of rice production rose by around 40 percent between 1990 and 2004, it declined from then until 2012 to near its 1990 level. The value of maize production declined by around 20 percent between 1990 and 1998, but then doubled by 2012. Bananas, plantains, and sugarcane shared similar trends, with steady growth of around 20–30 percent between 1990 and 2012 (Figure 23).

Another metric used to rank agricultural products is the export value. Table 11 shows the leading export commodities, with coffee and its derivatives dwarfing its nearest competitors in export value. Bananas and sugar follow, and then beef and palm oil with much lower export values.

As shown in Table 12, the two leading imports are maize and soybean products, which are major components of livestock feed. Given the rapid growth of poultry and swine in Colombia, this is not entirely surprising.

Analysis of Crops with Biophysical and Bioeconomical Models

Biophysical models (also called crop models) were used in the AgMIP GGCMI project to determine the effect of climate change—through temperature, precipitation, and solar radiation—on the yields of

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certain crops. For more details on how the AgMIP GGCMI data was used, see Thomas et al. (2018). While it is possible to debate the merits of individual biophysical models, we have a higher degree of confidence in presenting not only the effect of changes in agricultural technology and global demand, but also the effect of climate change. This section considers several important crops for Colombia: maize, rice, and sugarcane.

In addition to the crop models, we also show results from the IMPACT global model of food and agriculture. More details on how that model works and how we use it in this study are found in Thomas et al. (2018)

Previous Use of Crop Models in Colombia—Summary

Crop modeling uses in Colombia include academic, research, and prediction applications, in diverse institutions. More recently, crop model applications include the design of mitigation and adaptation measures to changing climate; national and international research institutions and national academies have evaluated and applied these models.

A relevant use of crop modeling was undertaken under the Food and Agriculture Organization of the United Nations (FAO)/DNP/IDEAM/Ministry of Agricultural and Rural Development (Ministerio de Agricultura y Desarollo Rural, MADR) Technical Cooperation Project TCP/COL/3302 entitled "Using AquaCrop Model to Estimate Crop Yields under the Study of Economic Impacts of Climate Change (EIECC) in Colombia." Conducted in 2012–2013, this study allowed for a first assessment of the FAO-AquaCrop model under field conditions and produced yield predictions as result of climate change scenarios, for such crops as corn, irrigated rice, potatoes, and sugarcane in Colombia (Cortes et al. 2013a, 2013b, 2013c, 2013d).

The study found that under A2 and B2 emission scenarios, corn yields could decrease in growing areas like Córdoba, Tolima, and Valle del Cauca, as a result of changes in rainfall patterns and an increase in maximum air temperatures. From these considerations, adaptation measures emerged, such as the necessity for implementing irrigation systems and adjusting planting dates to obtain proper establishment for the crop and favorable yields.

Other institutions, such as the Colombian Corporation for Agricultural Research (Corporación Colombiana de Investigación Agropecuaria, Corpoica), the International Center for Tropical Agriculture (Centro Internacional de Agricultura Tropical, CIAT), the National University of Colombia, and several national federations of growers of such crops as cereals, rice, and sugarcane are using another well-known model, the Decision Support System for Agrotechnology Transfer (DSSAT). This tool is facilitating climate–crop analysis for such crops as potatoes, maize, rice, sugarcane, and beans in various regions of the country.

Recently, the CGIAR Climate Change, Agriculture and Food Security Program, Colombia’s Ministry of Agriculture, and CIAT have been establishing “Participatory Desks for Agroclimatic Predictions” in various agricultural regions in the country. The use of such models as AquaCrop, DSSAT, and CropWat in these scenarios is extensive for simulating the impacts of predicted seasonal climatic conditions on crop development and yield. The results of these simulations provide the basis for continuously discussing better adaptation actions with the participants in these desks, who are delegates from agricultural

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associations, universities, and research institutions and technical assistants attached to local governments.

Additionally, such models as the DeNitrification-DeComposition (DNDC) model have been applied to quantify GHG emissions from agriculture. For example, Díaz and Boshell (2012) used the DNDC model to estimate soil moisture conditions and N2O fluxes emitted from soils covered with three species of Koronivia grass.

Similarly, a 2011–2013 national study of the economic impacts of climate change in Colombia, led by DNP, sought to identify major vulnerabilities and opportunities in Colombia to address climate change and generate information necessary for decision makers to identify cost-effective adaptation measures (DNP, BID, IDEAM, and CEPAL 2014). This study used input information for the climate change scenarios for Colombia, as elaborated by IDEAM. Rainfall and temperature projections were inputs for the analysis of potential impacts of climate change in such sectors as agriculture, forestry, hydrology basins, fisheries, and transportation.

Maize

Current distribution of production and yield

Maize is the leading annual crop in Colombia by hectares harvested. Most of the maize, 97 percent, is rainfed. As shown in Figure 24 and presented in Table 13, most of the maize is planted in either the Caribbean or the Andes region, in the valleys between ranges. A small amount is planted in the Orinoco and Amazon regions, and very little is grown in the Pacific region.

Figure 24 and Table 13 also show estimated yields of maize. According to SPAM data (You et al. 2014) average rainfed maize yields across the country are 2.6 tons per ha ( – though there is very little irrigated maize. Rainfed productivity is a bit lower between ranges in the Andes, and is highest east of the Andes and in the southern part of the Caribbean region. The major production areas of traditional maize, which is planted in all departments, are Bolívar, Córdoba, Antioquía, Cesar, and Tolima, which produce approximately 55 percent of the grain. At the national level, 72 percent of the maize area is traditional maize and contributes to 45 percent of national production.

Biophysical modeling of climate effects

Figure 25 shows what the AgMIP crop models projected for climate impacts on rainfed maize yield between 2000 and 2050. The largest adverse impact seems to be anticipated in the Caribbean region and in the northernmost part of the Andes. Table 13, which is derived from the data in Figure 25, shows that for the country as a whole, yield would decline by 8 percent between 2000 and 2050 as a result of climate change. Most zones show losses similar to the national average, except for the Amazon region, which is projected to have only a 4 percent loss.

Impact of climate controlling for technological improvements and global changes in supply and demand

In Figure 26, the IMPACT model suggests that without climate change, maize yield would rise by 32 percent between 2010 and 2050. However, the median projection with climate change says it will rise by only 22 percent, while the GCM with the lowest projected yield change says it would rise by only 17

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percent. The median value represents an 8 percent reduction in yield attributable to climate change. This reduction is comparable to the losses for the five Central American countries in this study, but is not at all like the impact on maize in Peru, which is projected to experience gains as a result of climate change. The worst climate case scenario for Colombia represents instead an 11 percent reduction in 2050, compared to the scenario without climate change.

In Figure 26 the IMPACT model also projects that the maize harvested area would not be significantly affected by climate change. With climate change, the median area in 2050 would be only 2 percent higher than the area in 2010, and without climate change, it would only be 4 percent higher than in 2010. Furthermore, there is little variation among the results from different climate models.

Since production is the product of yield and harvested area, and there was very little change in area, Figure 26 shows that projected maize production is similar to projected yields. In fact, without climate change, total production would rise by 37 percent, but with climate change, the median value suggests that in 2050 production would be reduced from the no climate change case by 10 percent; this still represents a 24 percent increase between 2010 and 2050.

In Figure 27, the world price of maize rises under all four climate scenarios between 2010 and 2050. Even the no climate change price shows a 33 percent increase, and the median result for climate change is another 16 percent higher than the no climate change result.

Figure 27 shows that maize imports are likely to increase between 2010 and 2050. Similar increases are noted for the scenario without climate change and the median value with climate change, with an additional 3.7 million metric tons being imported every year above the average around 2010.

Summary of findings

All in all, only modest yield losses are anticipated for rainfed maize, at less than 10 percent over the 40-year period. Investing in new, heat-tolerant varieties could increase productivity, reduce maize imports, and improve food security in farming households dependent on home-grown maize for a substantial portion of their diet.

Rice

Among the annual crops, in terms of harvested area, rice is second, after maize. According to MADR (2014c), in 2013 approximately 45 percent of the total area planted to rice was irrigated, 40 percent of the area was cultivated on mechanized dryland, and the remaining 15 percent was traditionally grown. MADR (2014c) reports that yields in 2012 were around 6.5 tons per hectare for irrigated rice and 4.7 tons per hectare for rainfed rice (Figures 28 and 29). These yields differ slightly from the SPAM (You et al. 2014) data in Tables 14 and 15, which are based on data for 2004–2006, and which are adjusted for levels from FAOSTAT (FAO 2014).

Current distribution of production and yield

Figure 28 shows where irrigated rice is grown in Colombia and the typical yield in each location. Table 14 shows the same data by ecoregion. The largest concentration of irrigated rice is in the Andes region, but other significant clusters are in the Caribbean and Orinoco regions. The Tolima and Huila

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departments are the main cultivators of irrigated rice, Casanare and Meta are major mechanized upland rice growers, and Chocó and Córdoba are the main manual upland rice growers.

Figure 29 shows the same information found in Figure 28, but for rainfed rice, and Table 15 reports that data tabulated by ecoregion. Most rainfed rice appears to be grown on the boundary between the Llanos (Orinoco region) and the Andes region, mostly in Casanare department. Also, some areas in the Caribbean region are adjacent to areas devoted to irrigated rice.

Biophysical modeling of climate effects

Figure 30 shows the results of the biophysical models for the direct impact of climate change on irrigated rice yields, without technological change. As a whole, little change is projected for the country. Yields are projected to decline by less than 10 percent in the Caribbean region, where the largest concentration of irrigated rice is located. The Andes, the other major region for irrigated rice, appears to have gains under climate change, including some areas with yield improvements of up to 5 percent, though much of that area is unchanged.

Table 14 shows the changes to irrigated rice yields from the biophysical models, by region. Overall, the nationwide yield is projected to decline by slightly more than 2 percent, with the highest—very modest—losses being slightly more than 4 percent in the Caribbean region.

Figure 31 shows the results of the biophysical models for rainfed rice. As with irrigated rice, only modest changes are projected. Table 15 shows the tabulations by region. As a whole, climate change is projected to reduce yield by slightly less than 4 percent between 2000 and 2050, not accounting for technological improvements made over that period. Losses are expected to be slightly higher for rainfed rice than for irrigated rice, but the difference is small. As with irrigated rice, projected yield declines are largest in the Caribbean and Andes regions, at slightly more than 5 percent, and slightly less than 4 percent, respectively.

Table 16 combines the results from Tables 14 and 15, resulting in a 2.8 percent decline in total rice yield between 2000 and 2050. The overall yield decline for rice is highest in the Caribbean region, at 4.5 percent, followed by the Orinoco region, at 2.4 percent.

Impact of climate controlling for technological improvements and global changes in supply and demand

Figure 32 shows some of the results of the IMPACT model for rice in Colombia, taking into consideration changes in global supply and demand, as influenced by changes in population, GDP, agricultural productivity growth, and climate. Considering the graphs cover a 40-year period, very little change in yields is expected, except for the worst-case climate model. Without climate change, yield would grow by 6.4 percent during 2010–2050, which would be reduced by 4.8 percent in the median climate scenario.

The harvested area of rice shown in Figure 32 is actually expected to decline by almost 9 percent without climate change, and by 10 percent in the median climate scenario. Together with yield, this implies that production of rice would decline by slightly less than 3 percent between 2010 and 2050 without climate change, by 6 percent with climate change at the median value, and possibly by 7 percent under the worst-case climate scenario.

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Figure 33 shows changes in world price for rice, which is expected to grow by 26 percent between 2010 and 2050 when not taking climate change into consideration (slightly slower than that for maize). With climate change taken into consideration, rice prices rise by 37 percent in the median scenario (while maize prices jump by 13 percent under the same scenario). The climate effect on rice prices ranges from a drop of 1 percent to a rise of 15 percent.

Figure 33 shows that under all scenarios, net imports of rice are expected to increase, because production is expected to decline at the same time that consumer demand is expected to rise. Importation of generally inexpensive rice is not a bad outcome, especially if it allows Colombian farmers to switch to other crops that they are better suited to produce, and that bring greater profits.

Summary of findings

One of the reasons for a projected decline in rice production in Colombia is that growth in yields (excluding the effect of climate change) is projected to be small compared with that projected for other crops, especially maize; at the same time harvested area is expected to decline between 2010 and 2050. There is an indication, therefore, that farmers will respond to economic incentives and shift to producing more profitable crops, such as maize, which would not be bad for the economy or for farmers.

Sugarcane

The sugarcane industry in Colombia produces white sugar, panela (low-grade brown sugar), and ethanol. According to Nepstad et al. (2013), the nation "is the world’s second largest producer of both ethanol and panela … [with] plans to triple the area of land under production by 2019." While ethanol production is concentrated in fewer than 3,000 farms, panela production tends to favor the smallholder and is "distributed among 70,000 farms, employing approximately 120,000 subsistence farmers" (Nepstad et al. 2013).

Current distribution of production and yield

Figures 34 and 35 shows where irrigated and rainfed sugarcane is grown, respectively. According to SPAM (You et al. 2014), only 30 percent of sugarcane in Colombia is irrigated, but the part that is irrigated has roughly a 50 percent higher yield than rainfed sugarcane (compare Table 17 to Table 18).

As shown in both Figures 34 and 35, sugarcane is grown in the departments of Valle del Cauca and Cauca and within areas of the Cauca River Valley in the coffee-growing region. The sugarcane crop is not irrigated throughout the year. Mainly, Valle del Cauca has the infrastructure, technology, and information for implementing irrigation appropriately and efficiently.

Biophysical modeling of climate effects

Figure 36 shows AgMIP results for yield changes with climate change for irrigated sugarcane. Only one crop model was used for sugarcane in the AgMIP GGCMI project. In most locations, irrigated sugarcane is projected to experience negative and sometimes fairly large climate impacts.

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Figure 37, showing AgMIP results for rainfed sugarcane, is almost identical to Figure 36, also predicting very strong, negative climate change impacts on rainfed sugarcane. While Figure 36 and Figure 37 appear to be almost identical, this similarity does not hold for the rest of the world.

Tables 17, 18, and 19 confirm the results that we already noted in the preceding figures and specify that we may have a 27.9 percent decline in irrigated yields and a 24.7 percent decline in rainfed yields, leading to a combined decline of 25.9 percent. Around 98 percent of the nation's rainfed sugarcane is grown in the Andes region, as is roughly 93 percent of the irrigated sugarcane, with almost all of the remaining irrigated sugarcane being grown in the Pacific region.

Impact of climate controlling for technological improvement and global changes in supply and demand

We turn our attention now to the impacts of climate change on sugarcane, once technological change and global demand are taken into consideration. Figure 38 shows yield, harvested area, and production projections for all sugarcane in Colombia, using the IMPACT model. Without climate change, we would expect yield to grow by 23 percent between 2010 and 2050. However, the median effect from the climate model is for yields to be 23 percent lower under climate change than without.

Without climate change, harvested area would expand by 44 percent, but with climate change, the harvested area would increase by 63 percent, as a median value, between 2010 and 2050. This would be about a 13 percent increase by 2050, compared to the no climate change scenario. Changes and yield and area combined would lead to growth in production by 77 percent between 2010 and 2050 without climate change. With climate change, production would be 12 percent lower in 2050.

The change in harvested area is strongly influenced by the changes in the price of sugar. As shown in Figure 39. world prices are expected to rise by 33 percent between 2010 and 2050 without climate change. Under climate change prices grow by 50.6 percent, as a median, between 2010 and 2050. This is about 13 percent larger than the noCC scenario, in 2050.

Figure 39 shows that the model predicts a rapid increase of net exports of sugar between 2010 and 2025, and then another continued but more gradual rise beyond 2025. Without climate change projections show an increase of over 1 million metric tons of sugar exported between 2010 and 2050. Under median climate change this increase would be only about 550,000 metric tons (or a range between 350,000 in the worst climate scenario and 900,000 in the best case).

Summary of findings

Relative to rice and maize, the impact of climate on sugarcane yields is expected to be high. However, the projected rising world prices, reflecting adverse climate impact on sugarcane globally appear to constitute a strong incentive for farmers to expand the area under sugar cane. Projections show that area expansion may somewhat counteract the yield losses, thereby maintaining a substantial production growth between 2010 and 2050, which may also sustain net exports to 2050. If sugarcane truly is that sensitive to high temperatures – as the crop models show – then careful monitoring and support of research efforts to improve its tolerance of heat would be prudent. The worst case would be for most of the rest of the world to use more heat tolerant varieties, yet Colombia using ones that are not heat tolerant. This could push Colombian farmers out of the global market for sugar production.

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Analysis of Crops Lacking Biophysical Models

In the previous section, we presented results from both biophysical and bioeconomic analyses that show the effect of changes in global supply and demand that included GDP and agricultural technology, along with climate change. For the crops presented in this section, we did not have biophysical models available. So to proxy some climate effect, we used a composite of the results of climate impacts on other crops for which we did have biophysical models. In particular, for plantains and coffee, the climate effect used in the bioeconomic model is the area weighted average of barley, cassava, groundnut, rice, soybean, and wheat.

Because this methodology may not perform very well, when possible we also present research from other studies that attempted to model these crops. We believe that the economic analysis for the scenario without climate change is reliable but recommend using some caution in interpreting the climate change effects for these crops, as the impacts were not modeled using their specific biophysical models.

Plantains

Current distribution of production and yield

While some plantains are irrigated in Colombia, most of them are not. Therefore, this report focuses on rainfed plantains. Figure 40 shows where plantains are primarily grown and their yield, with Table 20 reporting the statistics for yield and area harvested by region.

Overall, 55 percent of the plantain-growing area is in the Andes. Slightly more than 20 percent is in the Caribbean region, around 9 percent is in the Orinoco region, and a little more than 7 percent is in the Amazon region. While the variation in productivity is not great among regions, the Andes has the lowest yield, while the Caribbean region is 10 percent higher, the Orinoco region is 23 percent higher, and the Amazon region is 29 percent higher.

Modeling changes in the supply of and demand for plantains

Because plantains were not part of AgMIP, we cannot report on how crop models expect climate change to affect yields. However, plantains are included as part of the IMPACT model, so we can report on some of the projections for plantains, based on assumptions about global supply and demand, as well as technological change.

Figure 41 shows that yield is expected to increase by around 68 percent between 2010 and 2050 without climate change. Under climate change the estimate is virtually identical in the median value, and the range goes from 58 percent increase under the worst climate scenario, to 69 percent increase under the best. Similarly, expansion of harvested area is expected to be at 39 percent between 2010 and 2050, with or without climate change effects. With such large projected increases in both yield and area, production increases dramatically, by 135 percent without climate change, and by 134 percent under the median climate change.

As shown in Figure 42, without climate change, world prices of plantains are projected to rise by 18 percent, while with climate change, they are expected to rise by 23 percent at the median scenario. However, the results for the different climate models vary, with prices projected to range between 3

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percent lower to 17 percent higher compared to the scenarios without climate change. Net exports are expected to grow steadily between 2010 and 2050. Without climate change, or under median climate change, the trends are identical, without an increase of over 2.2 million tons. However, under the worst climate scenario, the increase would be strongly reduced, to only up to about 1.3 million tons.

Summary of findings

Plantains appear not to suffer significantly under climate change, and to have potential to grow in yield and expand in area, leading to a large rise in production. The large productivity increase is mostly a result of a high yield growth rate, which is due to farmers being able to access improved stock and to grow in their ability to support tree productivity.

Coffee

Current distribution of production and yield

We have already mentioned the importance of coffee to the agriculture sector with regard to total area harvested, value of agricultural production, and export value. Figure 43 shows where arabica coffee—the main kind of coffee grown in Colombia—is grown, and its typical yields in each location. This information is also summarized in Table 21. We note that 86 percent of the coffee area is in the Andes region, with almost all of the rest (12 percent) in the Caribbean region. While most coffee is grown in Huila and Antioquía, a small amount of specialty coffee is produced along the Caribbean coast, specifically in the Sierra Nevada de Santa Marta.

Modeling changes in the supply of and demand for coffee

Coffee is included as part of the IMPACT model, so we can report on some of the projections from this model. A shown in Figure 44, coffee yield is projected to expand by 30 percent without climate change and by a similar percentage with climate change; thus, the difference between climate models is minimal. IMPACT also projects that without climate change, harvested area of coffee would increase by 12 percent between 2010 and 2050, and the increase would be about the same with climate change (13 percent for median results). Together, expanded area and yield suggest that total production would grow by about 47 percent, both with and without climate change.

Figure 45 shows that coffee prices should rise by almost 32 percent without climate change between 2010 and 2050, and by only 3 percent more with climate change. Also, without climate change, net exports of coffee would be expected to rise (by about 260,000 tons by 2050). Under median climate change exports would be slightly higher, or about 270,000 metric tons, but under the worst climate change scenario the increase would be only by 190,000 metric tons.

AgMIP did not analyze coffee as one of its crops. Nevertheless, because coffee is sensitive to temperature, we would expect that climate change would have a strong negative impact on coffee if it continues to be grown in the same area as it has been in the past. Läderach et al. (2013) conducted such a study for Central America, predicting that in Central America as a whole, the optimal coffee-growing elevation will shift from 1200 m.a.s.l. currently, to 1600 m.a.s.l. in 2050. If a similar shift is required in Colombia, a glance at the elevation map in Figure 2 suggests that in fact there may be room for expansion to those higher elevations. Ovalle-Rivera et al. (2015) suggest that Colombia could lose only

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10 percent of its coffee-growing area, which is less than the projected losses in many other coffee-growing countries of the world.

There may be constraints to coffee farmers migrating to higher elevation—such as property rights, steep slopes, environmentally sensitive areas, and other issues that one cannot detect simply by looking at elevation on a map. However, with a proper legal framework and technological and advisory services, there is hope that Colombian coffee growers might adapt successfully to climate change.

Summary of findings

The coffee industry in Colombia faces a number of critical challenges resulting from climate change. Yet these challenges appear to be manageable, given a sufficient legal framework for shifting cultivation to higher altitudes, and continued research. In this respect, it may be especially urgent to work on heat-resistant coffee varieties, along with varieties that are pest and disease resistant.

Livestock

Cattle

Current distribution of production

As mentioned earlier, the cattle subsector plays a significant role in the livestock sector, in terms of both total land use and total value of production. But the subsector is not necessarily efficient, in the sense that stocking density can be quite low, at around one head per hectare of pasture. While there are some large landowners, they represent "a small percentage of production. Most landholdings are small and run by rural families; 82% of cattle ranchers have less than 50 animals per farm" (Nepstad et al. 2013).

With low stocking density, productivity and economic gains can be made by improving the efficiency of land use, increasing stocking density, and letting some of the pasture be used for alternative purposes. The Colombian livestock federation (Federación Colombiana de Ganaderos, FEDEGAN) recognized this need and "established the goal to reduce the area of cattle pasture from 38.6 to 28 million ha by 2019 while increasing production" (Nepstad et al. 2013).

Figure 46 shows the percentage of grassland in each pixel inside Colombia, while Table 22 shows the tabulations by region. Almost 50 percent of Orinoco is covered in grasslands, which is the natural vegetation in this area of the country. The Caribbean region comes in second, with 24 percent of its area in grassland, and the Andes coming in third, at 10.1 percent. Very little of the Amazon or Pacific regions is grassland.

Figure 47 shows cattle density from FAO's Gridded Livestock of the World database (FAO 2007). It appears that several large departments in or near the Amazon region have missing data. This becomes obvious when we note the grasslands of the Llanos are on both sides of the border of Casanare and Vichada, yet the cattle are shown to be only on one side.

Despite our doubts about the correctness of the data in the figure, we tabulate the stocking density for cattle in Table 23, noting that we expect the for Orinoco and Amazon regions—along with the nation as a whole—the values reported in Table 23 are lower than actual.

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We did not have a model that estimated the impact of climate change on cattle. Nonetheless, experts project that as temperatures rise in tropical and subtropical areas, livestock production will be hindered. First, higher temperatures can stress the animals, leading to lower weight gain and increased mortality. And second, higher temperatures tend to increase the prevalence and range of livestock diseases, which may increase mortality rates.

Figure 48 shows what the AgMIP crop models projected for climate impacts on grassland yield between 2000 and 2050. Unfortunately, we did not have SPAM projections for grasslands, so we were unable to weight each cell by harvested area, and instead averaged the values without any weights. Table 24 shows that, as a whole, pasture productivity in Colombia should only fall by 1.0 percent. Yet we know from Figure 48 that most grassland is in the Orinoco region, which has projected declines of 7.9 percent. This should adversely affect cattle productivity.

We see that the Caribbean region will be hit even harder, but climate may have a positive impact on the yield of managed grasses in the Andes, Pacific, and Amazon regions.

Modeling changes in the supply of and demand for beef

The IMPACT model was used to estimate livestock production as affected not just by climate change, but also by growth in population, GDP, and technological change. Figure 49 shows that the global production of beef is projected to rise by 62 percent. There appears to be virtually no difference between the median climate change scenario and the no climate change scenario, nor a significant difference between climate models.

Figure 49 also shows that beef prices are anticipated to rise by only around 20 percent between 2010 and 2050. In fact, the peak price is projected to be reached by around 2040, with a decline in prices of around 5 percent between 2040 and 2050.

In contrast, Figure 49 shows that net exports of beef may be decreasing until around 2040, making Colombia a net importer of beef. Net exports are then projected to rise quickly after 2040, and by 2050 Colombia is estimated to be a net exporter of beef with median exports under climate change at 15 thousand tons, higher than the 10 thousand tons projected under a no climate change scenario. While the change in direction from rising net imports to rising net exports might appear sudden in Figure 49, it is important to keep in mind that total production in 2050 is almost 1.4 million tons, while the net exports is around 0.1 percent of that.

Modeling changes in the supply of and demand for milk

As shown in Figure 50, unlike beef production, milk production is anticipated to increase only modestly between 2010 and 2050, by 13 percent, and most of this increase will be realized in the first decade. Because the supply of milk will be increasingly insufficient milk to meet the domestic demand, net imports will rise by 2050 to almost the level of 25 percent of domestic production.

Figure 50 also shows that the world price for milk will rise by only 12 percent over the 2010–2050 period. In all three graphs in the figure, we failed to see any significant difference caused by climate change, or any differences between climate change models.

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Summary of findings

Demand for cattle proteins (beef and milk) will continue to rise in Colombia faster than production will increase. Importing foods that a country is not at a comparative advantage to produce is a very reasonable strategy. On the other hand, with vast land resources and favorable temperature and precipitation in many locations, one wonders whether Colombia is truly at a comparative disadvantage with cattle. However, to attain a comparative advantage, additional investment would need to be made in the sector, which would lead to higher stocking densities, and possibly improved transportation infrastructure to deliver the meat and milk to market.

Chickens

Current distribution of production

Figure 51 shows FAO's (2007) livestock map for chickens. While some clustering appears in various locations (the northern part of the Caribbean region, for example), chicken production is distributed throughout the country. The Caribbean region has the highest density, with 78.2 chickens/km2 (Table 25). However, this concentration is not much higher compared to the Pacific region, which has the lowest density, with 48.4 chickens/km2.

Modeling changes in the supply of and demand for poultry

Figure 52 shows the IMPACT model’s predictions for production of chicken meat in Colombia. We note a 44 percent increase projected between 2010 and 2050, with virtually no difference between the median climate change results and no climate change. Also, Colombia is projected to remain a net importer of chicken meat, and net imports may grow to about 25 percent of domestic production in 2050.

Finally, we note a 24 percent increase in poultry prices between 2010 and 2050, with climate change resulting in prices being 1.5 percent higher than without climate change.

Modeling changes in the supply of and demand for chicken eggs

In Figure 53, egg production is projected to expand by 83 percent between 2010 and 2050, with or without climate change factored in. This will allow for a steady increase in net exports, which by 2050 may reach 15 percent of all eggs produced.

Over 2010–2050, egg prices are expected to rise by only 6 percent without climate change, and by 7 percent with climate change. In fact, prices actually peak between 2035 and 2040, at a couple of percentage points higher.

Summary of findings

Production of chicken meat and eggs is projected to continue to grow for the next few decades, with no major effects expected from the changing climate. Trends show a steep increase in eggs’ exports, which may reach over 150,000 tons by 2050, a very large increase from the 5,000 tons exported in 2010. On the other hand Colombia is expected to substantially increase the imports of chicken meat. This is a direct result of the increase in household demand, which appears to be consistently greater than the domestic production capacity, despite the incentive to production represented by the increasing world price of poultry meat.

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Forests

Figure 54 shows the percentage of tree cover for Colombia. As expected, the tree coverage in the Amazon appears to be higher than elsewhere, though there does appear to be significant coverage in other regions, as well. Table 26 quantifies the coverage by region, and shows that the 71 percent coverage in the Amazon far exceeds that of any other region. The Pacific has the second-highest coverage, at almost 39 percent, and is followed closely by the Andes, at 35 percent. It is somewhat surprising that the Orinoco, which is primarily grassland, has significant tree coverage, at 28 percent. Finally, the Caribbean region has almost 26 percent coverage.

We did not use any models to determine the impact of climate change on forests as part of this study. Indeed, one of the main reasons forests are of interest to this study is not how climate change might affect them, but how they might affect climate change—that is, how forests might store carbon.

Deforestation in Colombia has been declining since the turn of the 21st century, with deforestation rates in the 1990s averaging of 322,757 hectares per year (ha/yr), falling to 273,334 ha/yr between 2000 and 2005, and falling further to 239,198 ha/yr in the remainder of the decade). In the last period, at least 90 percent of the deforestation in the Amazon occurred to create pasture (Nepstad et al. 2013).

In addition to this positive trend in seeing deforestation rates decline, "between 2001 and 2010, woody vegetation increased by 3% from 580,420 km2 to 597,383 km2. This regrowth appears to result from secondary forest recovery in abandoned agricultural areas" (Nepstad et al. 2013).

The National Development Plan (PND) for 2010–2014 included the goals of avoiding deforestation of 200,000 ha and restoring 90,000 ha (PND, Chapter VI; quoted in Nepstad et al. 2013). The PND, however, includes other goals that may conflict with achieving this objective, including expanding mining activities in the areas of coal and oil production (Nepstad et al. 2013). To meet the deforestation reduction goal, it seems necessary to also fine tune long-established policies meant to help the poor obtain land resources to establish a better life by settling on vacant land—policies that also left the door open to land speculators (Nepstad et al. 2013).

Table 27 shows the estimated area sown for commercial forest species between 1965 and 2012. In 2012, approximately 199,842 ha were planted, mostly in the Andes and Caribbean regions.

Figure 55 depicts the parks, reserves, and other areas with protected status of some kind. "In 1959 the Colombian government delimited forest reserves" totaling more than half of the country's land "to support the national forest economy and to promote the conservation of natural resources" (Nepstad et al. 2013). Unfortunately, the delimitation was not carefully executed, and includes urban and otherwise occupied land areas, and subsequently, some of the area originally set aside has been reduced. At the same time, the land that is protected has had little legal standing, so the law has been ignored "over the years, and the institutional capacity to oversee these areas has been disproportionally small. Currently there are 22,000 km2 of authorized mining titles located in forest reserve areas, including areas in the Amazon region" (Nepstad et al. 2013).

An additional problem is Illegal logging, which "accounts for approximately 42% of the wood produced in Colombia" (Nepstad et al. 2013). Finally, production of illegal drugs also results in forest loss, as producers seek the concealment of the forest canopy, but hollow out trees and vegetation underneath.

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As we have mentioned, one of the keys to preserving and even enhancing forests in Colombia is to rationalize economic growth, so that development in nonforest sectors can still take place while at the same time minimizing destruction of the forests. This outcome will likely require better prioritization and coordination across government ministries.

Furthermore, a clearer or stronger legal framework needs to be established to better protect the forests, especially areas that have been set aside for special protection. This legal framework will also need a stronger enforcement agency, with sufficient power on the ground to stop deforesters.

Finally, care must be taken with infrastructure projects, recognizing that providing better access to forested land is one of the primary causes of deforestation.

Changes in Food Security Figure 56 shows projections for both the share of the Colombian population at risk of hunger and the total number at risk. These projections are based on Fischer et al. (2005). Their methodology uses a quadratic formula for kilocalories consumed, which, in turn, are based on income and food prices.

Looking at the no climate change scenario, the share of the population at risk of hunger declines steadily from 10.6 percent to 4.0 percent. However, with climate change the share at risk is projected to be 10 percent higher at the median value of the projections from the four climate models, and 15 percent higher under the most pessimistic scenario.

In terms of number of people at risk of hunger, without climate change we would see a decline of 47 percent between 2010 and 2050. The median climate scenario would result in only a 42 percent decline, meaning that an additional quarter million people would be at risk of hunger.

The decline in the number of people at risk of hunger, with or without climate change, is a positive sign. Investment in increasing the production of smallholders and subsistence farmers would help those households be less vulnerable, but in the model the main cause of a rise in the population at risk of hunger is the global effect of climate change reducing agricultural production around the world and thus raising prices on food items. Higher prices make food less accessible to the poor, both rural and urban. Therefore, a more complete strategy for reducing the severity of climate on the poor is to develop and maintain strong social safety nets that ensure access to food by poor households.

Conclusions The Colombian territory shows a wide diversity of landscapes, determined by their geographical location, where the existence of three large mountain chains and six natural regions derive in a multiplicity of physical and climatic conditions. This situation, combined with the climatic dependence of the economy and problems arising from social and economic inequalities, make the country highly vulnerable to the adverse impacts of climate change.

The aggregate impact of climate change on the national economy will become very negative in the case of not taking appropriate actions to develop adequate adaptation programs. Climate change further exacerbates the higher frequency and intensity of extreme events and limits progress toward sustainable development.

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Adaptation to climate change impacts is not just a local matter. Because of the co-benefits and contributions generated in meeting global goals—such as poverty reduction, food security, access to and availability of drinking water, health, and conservation of ecosystems—adaptation not only increases local and national resilience, but also generates regional and global benefits.

For Colombia, adaptation to climate change is also a matter of national security, in the context of support to current peace-building efforts. Proper adaptation will contribute to improving the welfare of populations in critical areas and transforming the country into a modern, innovative, and globally competitive territory.

The INDCs to be presented to the UNFCCC represent an opportunity to demonstrate the commitment of Colombia to share international efforts for solving global environmental challenges and to mobilize public and private financial resources—both national and international—for compliance.

Livestock and agricultural soils are the largest emitters of GHGs in the country, followed by the conversion of forests and grasslands.

The country still needs to implement more efficient risk management policies for the agriculture sector to face the impacts from severe climatic events. Actions being taken respond more to emergency situations. For example, heavy losses occurred in Colombian agriculture during La Niña 2010–2011 because of a lack of appropriate local adaptation measures.

In this regard, MADR recognizes that available agroclimatic information is scattered and does not allow for appropriate decision making, and that there is no clarity on how to integrate the different sources and types of information according to the needs of the sector.

It is therefore essential to have reliable, relevant, and timely information that enables a broader approach to reduce vulnerability to extreme climate events. It is also necessary to develop systems for monitoring and continuously evaluating the accomplishment of agricultural policies on climate change.

National experience reviewed shows that while there are relevant works on issues related to climate and crop simulation modeling, it is necessary to continue conducting evaluations and analyses under the new IPCC scenarios for climate change (AR5, IPCC 2014).

In Colombia, previous results of national scenarios of climate change, elaborated as part of the Second National Communication (AR4, IPCC 2007), are significantly different from those projected in the Third National Communication (AR5, IPCC 2014). For example, recent agroclimatic analyses of rural areas showed that annual rainfall would decrease by some percentage according to the Second National Communication’s A2 and B2 scenarios for 2011–2040 (RCP-AR4, IPCC 2007). However, according to the results of the new scenarios (RCP-AR5), annual rainfall in those areas would increase (IPCC 2014). These opposite results should be evaluated, and definitive results should be delivered to national and local communities and authorities.

As an ending balance and in light of the considerations presented throughout this document, the INDCs of Colombia stands out within an equitable international context. From a national perspective, there is a real chance of achieving mitigation and adaptation goals, because of the advances realized to date and because they take into account the national capabilities and conditions. These achievements would

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support a balanced move of Colombia toward an innovative and competitive economy, including mitigation and adaptation to climate change, as preponderant elements of development planning.

The biophysical models show modest impacts of climate change on rice and maize, at a cost of around 10 percent of production. Losses to sugarcane are projected to be much higher, at around 28 percent. To reduce losses, investment in developing varieties better suited to hotter temperatures would be a good option.

Furthermore, careful consideration should be given to whether new laws would facilitate the shifting of some agriculture to better-suited areas. For example, coffee will likely need to shift to higher elevations.

In the end, while the threat of climate change to Colombian agriculture is serious and requires action to support farmers, Colombia will be less negatively affected than many other countries in the world.

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FIGURES

Figure 1 Ecologically defined regions of Colombia

Source: IGAC (2012).

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Figure 2 Elevation of Colombia (meters)

Source: GLOBE Task Team et al. (1999).

Figure 3 Population trends in Colombia: total and rural populations (millions of people) and urban population (percent), 1970–2012

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Source: World Development Indicators (World Bank 2014).

Figure 4 Population distribution in Colombia, 2012 (people per square kilometer)

Source: LandScan (ORNL 2013).

Figure 5 Population projections for Colombia, 2010–2050

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Source: Samir and Lutz (2017). Note: SSP = shared socioeconomic pathway.

Figure 6 Urbanization projections for Colombia, 2010–2050

Source: Jiang and O’Neill (2015). Note: SSP = shared socioeconomic pathway.

Figure 7 Per capita GDP in Colombia (constant 2005 US$) and share of GDP from agriculture (percent), 1970–2012

Source: World Development Indicators (World Bank 2014).

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Figure 8 Well-being indicators for Colombia, 1970–2012

Source: World Development Indicators (World Bank 2014).

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Figure 9 Travel time to urban areas in Colombia, 2000

Source: Guo and Cox (2014).

Note: Top left, travel time to cities of 500,000 or more people; top right, travel time to cities of 100,000 or more people; bottom left, travel time to towns and cities of 20,000 or more people.

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Figure 10 GDP per capita in Colombia, future scenarios, 2010–2050

Source: Dellink, Chateau, Lanzi, and Magne (2017).

Note: The gross domestic product value is purchasing power parity. SSP = shared socioeconomic pathway.

Figure 11 Average annual rainfall (millimeters) in Colombia, 1960-1990

Source: WorldClim version 1.4 (Hijmans et al. 2005).

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Figure 12 Mean daily maximum temperatures (degrees Centigrade) for the warmest month in Colombia, 1960-1990

Sources: WorldClim version 1.4 (Hijmans et al. 2005.

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Figure 13 Changes in mean annual precipitation in Colombia between 1960–1990 and 2050, millimeters

Source: Data from Müller and Robertson (2014), which used downscaled versions of four CMIP5 GCMs (Taylor, Stouffer, and Meehl 2012), under RCP8.5, and re-based to WorldClim 1.4 (Hijmans et al. 2005).

Notes: Top left, Geophysical Fluid Dynamics Laboratory (GFDL; see Dunne et al. 2012, 2013); top right, Hadley Centre Global Environmental Model (HadGEM; see Collins et al. 2011; Martin et al. 2011); bottom left, L’Institut Pierre-Simon Laplace (IPSL; see Dufresne et al. 2013); bottom right, Model for Interdisciplinary Research on Climate (MIROC; see Sakamoto et al. 2012).

42

Figure 14 Changes in mean daily maximum temperature for the warmest month in Colombia between 1960–1990 and 2050, 0C

Source: Data from Müller and Robertson (2014), which used downscaled versions of four CMIP5 GCMs (Taylor, Stouffer, and Meehl 2012), under RCP8.5, and re-based to WorldClim 1.4 (Hijmans et al. 2005).

Notes: °C = degrees Centigrade; top left, Geophysical Fluid Dynamics Laboratory (GFDL; see Dunne et al. 2012, 2013); top right, Hadley Centre Global Environmental Model (HadGEM; see Collins et al. 2011; Martin et al. 2011); bottom left, L’Institut Pierre-Simon Laplace (IPSL; see Dufresne et al. 2013); bottom right, Model for Interdisciplinary Research on Climate (MIROC; see Sakamoto et al. 2012).

43

Figure 15 Composition of greenhouse gas (GHG) emissions in Colombia, 2010

Source: Authors' calculations based on data from IDEAM (2015a).

Figure 16 Trends in GHG emission from the AFOLU sector in Colombia, 2010

Source: Authors' calculations based on data from IDEAM (2015a).

44

Figure 17 Dominant land cover in Colombia, 2010

Source: GLC-SHARE (Latham et al. 2014).

45

Figure 18 Percentage of cropland in Colombia, 2010

Source: GLC-SHARE (Latham et al. 2014).

Figure 19 Area harvested (percent) for some major crops in Colombia, 2001–2013

Source: Authors' calculations based on data from MADR (2014c).

0.0

5.0

10.0

15.0

20.0

25.0

Area

(%)

Rice

Maize

Potatoes

Beans

Cassava

Oil palm fruit

Coffee

Plantains

Cocoa beans

Sugar cane

Panela cane

46

Figure 20 Yield trends in Colombia, 1990–2012

Source: Authors, based on FAOSTAT (FAO 2014).

Figure 21 Livestock trends in Colombia, 1990–2012

Source: Authors, based on FAOSTAT (FAO 2014).

47

Figure 22 Trends for gross value of production for key livestock products for Colombia, 1990–2012

Source: Authors, based on FAOSTAT (FAO 2014).

Figure 23 Trends for gross value of production for key crops in Colombia, 1990–2012

Source: Authors, based on FAOSTAT (FAO 2014).

48

Figure 24 Intensity (hectares per pixel) and productivity (kilograms per hectare) of rainfed maize in Colombia, circa 2005

Source: Spatial Production Allocation Model (SPAM, You et al. 2014). Note: Cultivation intensity map is on the left and the productivity map is on the right. A pixel at the equator represents approximately 8,500 hectares.

49

Figure 25 Median yield change (percent) under climate change: rainfed maize in Colombia, 2000–2050

Source: Authors’ calculations based on data from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI; see Rosenzweig et al. 2014).

Note: The median at each pixel was computed from all possible combinations of four crop models and four climate models. All are from AGMIP GGCMI for Representative Concentration Pathway 8.5 (RCP8.5)

50

Figure 26 Yield, harvested area, and production projections from the IMPACT model: maize in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Yield, top left; harvested area, top right; production, bottom left.

51

Figure 27 World price and net exports from the IMPACT model: maize in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: World price, left; net exports, right.

Figure 28 Intensity (hectares per pixel) and productivity of irrigated rice in Colombia, circa 2005

Source: Spatial Production Allocation Model (SPAM, You et al. 2014). Note: Cultivation intensity map is on the left and the productivity map is on the right. A pixel at the equator represents approximately 8,500 hectares.

52

Figure 29 Intensity (hectares per pixel) and productivity (kilograms per hectare) of rainfed rice in Colombia, circa 2005

Source: Spatial Production Allocation Model (SPAM, You et al. 2014). Note: Cultivation intensity map is on the left and the productivity map is on the right. A pixel at the equator represents approximately 8,500 hectares.

53

Figure 30 Median yield change (percent) under climate change: irrigated rice in Colombia, 2010–2050

Source: Authors’ calculations based on data from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI; see Rosenzweig et al. 2014).

Note: The median at each pixel was computed from all possible combinations of three crop models and four climate models. All are from AGMIP GGCMI for Representative Concentration Pathway 8.5 (RCP8.5)

54

Figure 31 Median yield change (percent) under climate change: rainfed rice in Colombia, 2010–2050

Source: Authors’ calculations based on data from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI; see Rosenzweig et al. 2014).

Note: The median at each pixel was computed from all possible combinations of three crop models and four climate models. All are from AGMIP GGCMI for Representative Concentration Pathway 8.5 (RCP8.5)

55

Figure 32 Yield, harvested area, and production projections from the IMPACT model: total rice in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Yield, top left; harvested area, top right; production, bottom left.

56

Figure 33 World price and net exports from the IMPACT model: rice in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: World price, left; net exports, right.

Figure 34 Intensity (hectares per pixel) and productivity (kilograms per hectare) of irrigated sugarcane in Colombia, circa 2005

Source: Spatial Production Allocation Model (SPAM, You et al. 2014). Note: Cultivation intensity map is on the left and the productivity map is on the right. A pixel at the equator represents approximately 8,500 hectares.

57

Figure 35 Intensity (hectares per pixel) and productivity (kilograms per hectare) of rainfed sugarcane in Colombia, circa 2005

Source: Spatial Production Allocation Model (SPAM, You et al. 2014). Note: Cultivation intensity map is on the left and the productivity map is on the right. A pixel at the equator represents approximately 8,500 hectares.

58

Figure 36 Median yield change (percent) under climate change: irrigated sugarcane in Colombia, 2010–2050

Source: Authors’ calculations based on data from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI; see Rosenzweig et al. 2014).

Note: The median at each pixel was computed from four climate models. All are from AGMIP GGCMI for Representative Concentration Pathway 8.5 (RCP8.5)

59

Figure 37 Median yield change (percent) under climate change: rainfed sugarcane in Colombia, 2010–2050

Source: Authors’ calculations based on data from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI; see Rosenzweig et al. 2014).

Note: The median at each pixel was computed from four climate models. All are from AGMIP GGCMI for Representative Concentration Pathway 8.5 (RCP8.5)

60

Figure 38 Yield, harvested area, and production projections from the IMPACT model: total sugarcane in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Yield, top left; harvested area, top right; production, bottom left.

61

Figure 39 World price and net exports from the IMPACT model: sugar in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: World price, left; net exports, right.

Figure 40 Intensity (hectares per pixel) and productivity (kilograms per hectare) of rainfed plantains in Colombia, circa 2005

Source: Spatial Production Allocation Model (SPAM, You et al. 2014). Note: Cultivation intensity map is on the left and the productivity map is on the right. A pixel at the equator represents approximately 8,500 hectares.

62

Figure 41 Yield, harvested area, and production projections from the IMPACT model: plantains in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Yield, top left; harvested area, top right; production, bottom left.

63

Figure 42 World price and net exports from the IMPACT model: plantains in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: World price, left; net exports, right.

Figure 43 Intensity (hectares per pixel) and productivity (kilograms per hectare) of arabica coffee (both rainfed and irrigated) in Colombia, circa 2005

Source: Spatial Production Allocation Model (SPAM, You et al. 2014). Note: Cultivation intensity map is on the left and the productivity map is on the right. A pixel at the equator represents approximately 8,500 hectares.

64

Figure 44 Yield, harvested area, and production projections from the IMPACT model: rainfed coffee in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Yield, top left; harvested area, top right; production, bottom left.

65

Figure 45 World price and net exports from the IMPACT model: coffee in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: World price, left; net exports, right.

Figure 46 Percent grassland in 2010

Source: GLC-SHARE (Latham et al. 2014).

66

Figure 47 Cattle density map (head per square kilometer), 2005

Source: FAO (2007).

67

Figure 48 Median yield change (percent) under climate change: rainfed managed grasses in Colombia, 2000–2050

Source: Authors’ calculations based on data from the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI; see Rosenzweig et al. 2014).

Note: The median at each pixel was computed from four climate models. All are from AGMIP GGCMI for Representative Concentration Pathway 8.5 (RCP8.5)

68

Figure 49 Production, net exports, and world price from the IMPACT model: beef in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Production, top left; net exports, top right; price, bottom left.

69

Figure 50 Production, net exports, and world price from the IMPACT model: milk in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Production, top left; net exports, top right; price, bottom left.

70

Figure 51 Chicken density (animals per square kilometer) in Colombia, 2005

Source: FAO (2007).

71

Figure 52 Production, net exports, and world price from the IMPACT model: chicken meat in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Production, top left; net exports, top right; price, bottom left.

72

Figure 53 Production, net exports, and world price from the IMPACT model: chicken eggs in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Production, top left; net exports, top right; price, bottom left.

73

Figure 54 Percentage of tree cover in Colombia, 2010

Source: GLC-SHARE (Latham et al. 2014).

74

Figure 55 Protected areas in Colombia, 2013

Source: WDPA (2014).

Figure 56 Share of population (percent) and total number of people (millions) at risk of hunger in Colombia, 2010–2050

Source: Authors, using the IMPACT model (Robinson et al. 2015).

Note: Share of population, left; number of people, right.

75

TABLES

Table 1 Education and nutrition statistics for Colombia, 2010–2012

Indicator Year Percent Literacy rate, adult total (% of people ages 15 and above, three-year average) 2011 93.4 School enrollment, primary (% of gross, three-year average) 2012 111.0 School enrollment, secondary (% of gross, three-year average) 2012 95.3 Employment in agriculture (% of total employment, three-year average) 2012 17.6 Vulnerable employment, total (% of total employment, three-year average) 2012 48.7 Malnutrition prevalence, weight for age (% of children under 5) 2010 3.4 Access to electricity (% of population) 2011 97.4

Source: World Development Indicators (World Bank 2014).

Table 2 Income poverty for Colombia at the national level, 2002–2012, percent of population

Extreme poverty Moderate poverty Year National Urban Rural National Urban Rural 2002 17.7 12.2 33.1 49.7 45.5 61.7 2003 15.7 11.2 29.0 48.0 44.9 56.8 2004 14.8 10.0 29.1 47.4 43.7 58.3 2005 13.8 9.1 27.8 45.0 41.1 56.4 2008 16.4 11.2 32.6 42.0 37.4 56.6 2009 14.4 9.9 25.7 40.3 36.0 53.7 2010 12.3 8.3 25.0 37.2 33.3 49.7 2011 10.6 7.0 22.1 34.1 30.3 46.1 2012 10.4 6.6 22.8 32.7 28.4 46.8

Source: SEDLAC (2014).

76

Table 3 Multidimensional poverty at national and regional levels for Colombia, 1997–2012, percent of population

Region 1997 2003 2008 2010 2011 2012 Urban 50.7 39.8 26.9 23.5 22.2 20.6 Rural 86.0 76.8 59.6 53.1 53.1 48.3 National 60.4 49.2 34.7 30.4 29.4 27.0 Atlantic 72.1 61.1 52.8 45.5 41.8 NA Oriental 65.3 48.5 33.2 28.6 29.2 NA Central 66.6 56.2 36.1 31.2 30.7 NA Pacific 58.4 56.9 35.5 32.3 32.0 NA Bogota 40.9 23.5 12.8 12.1 11.9 NA San Andrés 38.1 23.8 30.3 27.8 19.2 NA Amazon and Orinoco 60.9 52.8 37.4 31.7 35.6 NA Antioquia 56.3 45.2 31.7 25.4 25.7 NA

Source: SEDLAC (2014).

Table 4 Summary of climate changes in Colombia from general circulation models

Change in annual precipitation (millimeters)

Change in mean daily maximum temperature for the warmest month

(OC)

Region GFDL HadGEM IPSL MIROC GFDL HadGEM IPSL MIROC

Orinoco 86 67 42 181 1.65 3.91 3.12 2.22 Amazon 255 68 62 162 1.73 3.54 3.29 2.93 Andes 164 186 -89 220 1.87 2.68 2.93 3.08 Caribbean 143 98 –284 200 1.82 2.57 2.56 4.04 Pacific 225 393 –45 227 1.86 2.52 2.54 3.2

Total 187 125 –26 189 1.77 3.2 3.03 2.98

Source: Constructed by authors from data used in Müller and Robertson (2014), which used downscaled versions of four CMIP5 GCMs (Taylor, Stouffer, and Meehl 2012), under RCP8.5, and re-based to WorldClim 1.4 (Hijmans et al. 2005).

Note: °C = degrees Centigrade; GFDL = Geophysical Fluid Dynamics Laboratory (Dunne et al. 2012, 2013); HadGEM = Hadley Centre Global Environmental Model (Collins et al. 2011; Martin et al. 2011); IPSL = L’Institut Pierre-Simon Laplace (Dufresne et al. 2013); MIROC = Model for Interdisciplinary Research on Climate (Sakamoto et al. 2012).

77

Table 5 Summary of climate change scenarios 2011–2100 (RCP-AR5 scenarios) by departments

Department 2011–2040 2041–2070 2071–2100 Average

temperature change (°C)

Changing rain (%)

Average temperature

change (°C) Changing

rain (%)

Average temperature

change (°C) Changing

rain (%) Amazonas 0.7 –14.84 1.5 –12.47 2.4 –14.03 Antioquia 0.8 4.88 1.4 6.91 2.2 9.3 Arauca 0.9 1.09 1.8 2.23 2.6 2.68 Atlántico 1.1 –7.39 1.6 –9.52 2.2 –11.26 Bogotá DC 0.8 6.57 1.4 9.53 2.2 8.27 Bolívar 0.9 –15.09 1.6 –15.22 2.2 –17.13 Boyacá 0.8 5.84 1.6 3.69 2.4 3.19 Caldas 0.9 20.16 1.6 22.61 2.4 28.12 Caquetá 0.8 –18.99 1.5 –19.32 2.2 –17.15 Casanare 0.9 -2.77 1.7 –2.14 2.4 –4.06 Cauca 0.7 16.18 1.4 17.15 2.1 18.4 Cesar 1.1 –15.32 1.9 –16.2 2.5 –19.82 Chocó 0.8 –5.2 1.5 –4.04 2.3 –2.59 Córdoba 0.9 1.56 1.6 1.88 2.2 –1.42 Cundinamarca 0.8 7.99 1.5 9 2.3 8.21 Guainía 0.9 –5.49 1.7 –9.66 2.7 –9.27 Guaviare 0.9 –6.65 1.7 –9.36 2.5 –5.11 Huila 0.8 16.52 1.4 17.74 2.1 17.24 La Guajira 0.9 –14.5 1.6 –16.57 2.3 –20.02 Magdalena 1 –18.65 1.7 –20.83 2.4 –23.24 Meta 0.9 –7.46 1.7 –5.68 2.4 –3.89 Nariño 0.7 13.69 1.4 13.42 2.1 12.03 Norte Santander 0.9 1 1.7 0.21 2.6 –0.35 Putumayo 0.8 4.45 1.5 6.73 2.2 6.74 Quindío 0.8 6.34 1.5 12.2 2.3 24.28 Risaralda 0.8 18.26 1.5 20.32 2.4 28.36 San Andrés 0.8 –30.2 1.4 –32.78 2 –33.01 Santander 0.9 0.54 1.7 –1.29 2.5 –1.15 Sucre 0.9 –11.3 1.6 –13.38 2.1 –16.2 Tolima 0.9 10.54 1.6 13.11 2.3 17.24 Valle del Cauca 0.9 6.59 1.6 6.08 2.4 6.14 Vaupés 1 –20.49 1.9 –22.69 2.6 –23.31 Vichada 0.9 –0.64 1.8 –1.88 2.6 –2.35

Source: IDEAM (2015b).

Note: °C = degrees Centigrade; RCP-AR5 = Representative Concentration Pathways from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

78

Table 6 Land potential versus land use in Colombia

Type of activity Potential vocation Current land use

Area (hectares) % Area (hectares) % Agriculture 22,077,625 19.34 5,315,705 4.66 Livestock 15,192,738 13.31 34,898,456 30.57 Agrosilvopastoral 4,057,776 3.55 5,064,191 4.44 Forest 64,204,294 56.23 60,703,476 53.17 Conservation 6,303,503 5.52 4,332,133 3.79 Other coverage 2,338,864 2.05 3,860,840 3.38

Source: IGAC (2012).

Table 7 Total area of land use, according to use in 22 departments, 2011.

Land use Area (hectares)

Total land use 37,603,381

Agricultural use

Total agricultural use 2,915,425 Transitory 708,370 Fallow 392,552 Transient and fallow 1,100,921 Permanent 1,721,657 Break 92,847

Livestock use

Total livestock 29,148,092 Pasture and forage 19,945,540 Weeds and stubble 6,201,684 Special vegetation 3,000,868

Use in forests

Total forest use 3,650,051 Natural forests 3,236,317 Planted forests 413,734

Other uses Total other uses 949,969 Water bodies 225,180 Wastelands and rocky outcrops

200,639

Other purposes 480,660 Agricultural infrastructure 43,660

Lost area 939,844

Source: DANE (2012).

79

Table 8 Harvested area, production, and yield of leading agricultural commodities in Colombia, mean of 2010–2012, with changes from the mean of 1999–2001

Crop

Rank by area

harvested

Hectares harvested,

mean for 2010–2012

% change 2001– 2012,

hectares harvested

Tons produced, mean for

2010–2012

% change 2001–

2012, tons produced

Yield (tons/ hect), 2010–

2012

% change,

yield Coffee 1 765,183 6.1 489,520 –20.2 0.64 –24.7 Maize 2 510,419 –9.2 1,642,954 46.4 3.22 61.0 Rice 3 487,621 6.5 2,000,863 –19.1 4.10 –24.1 Plantains 4 368,506 –3.2 3,083,631 11.0 8.37 14.7 Sugarcane 5 356,177 –10.9 39,000,000 13.1 109.50 26.9 Cassava 6 204,060 11.4 2,173,883 17.8 10.65 5.8 Oil palm fruit 7 165,500 23.6 3,566,667 43.2 21.55 15.9 Beans 8 116,060 –1.1 133,120 7.7 1.15 9.5 Potatoes 9 102,869 –23.6 1,808,331 –8.7 17.58 19.5 Cocoa beans 10 99,249 14.0 44,428 7.2 0.45 –6.2

Source: FAOSTAT (FAO 2014).

Note: Percentage change is comparing the three-year average for 1999–2001 with the three-year average for 2010– 2012.

Table 9 Livestock in Colombia, mean of 2010–2012

Livestock Quantity Units Cattle 25,326,310 Head Sheep 1,462,233 Head Goats 1,687,145 Head Pigs 5,271,799 Head Chickens 159,333 Thousand head Horses 1,292,392 Head Asses 139,084 Head Mules 213,433 Head Rabbits and hares 138,832 Thousand head Beehives 116,667 Number

Source: FAOSTAT (FAO 2014).

80

Table 10 Gross value of production of leading agricultural commodities in Colombia, 2010–2012, with changes from 1999–2001

Source: FAOSTAT (FAO 2014).

Note: Value of production is for the three-year average for 2010–2012. Value is in thousands of constant international dollars, which holds the price to the 2004–2006 value. "The method assigns a single price to each commodity and country. For example, 1 ton of maize has the same price in whichever country it is produced" (FAO 2014).

Table 11 Value of leading exports of agricultural commodities in Colombia, 2009–2011

Commodity Rank by

value

Value of exports, avg.

2009–2011 Coffee and products 1 2,299,293 Bananas 2 753,096 Sugar and products 3 717,737 Beef 4 207,632 Oil palm and kernel 5 179,404

Source: FAOSTAT (FAO 2014).

Note: Value is the three-year average for 2009–2011. Value is in thousands of constant international dollars, which holds the price to the 2004–2006 value.

Commodity

Rank by gross value of

production

Gross value of production, avg.

2010–2012 Meat indigenous, cattle 1 2,222,352 Milk, whole fresh cow 2 1,981,779 Meat indigenous, chicken 3 1,546,114 Sugarcane 4 1,280,643 Plantains 5 636,640 Bananas 6 567,510 Rice, paddy 7 557,560 Coffee, green 8 525,921 Eggs, hen, in shell 9 514,507 Oil palm 10 386,471

81

Table 12 Value of leading imports of agricultural commodities in Colombia, 2009–2011

Commodity Rank by

value

Value of imports, avg

2009-2011 Maize 1 801,291 Soybean products 2 752,515 Wheat 3 415,779 Cotton lint 6 110,143 Coffee, green 7 104,333 Sugar refined 8 99,168 Oil, palm 9 98,893

Source: FAOSTAT (FAO 2014).

Note: Value is the three-year average for 2009–2011. Value is in thousands of constant international dollars, which holds the price to the 2004–2006 value.

Table 13 Climate impacts on yield of rainfed maize in Colombia, 2000–2050

SPAM AgMIP

Region Area

(hectares) Yield, 2005

(tons/hectare)

Yield change, 2000–2050

(%)

Amazon 30,507 2.83 -3.9 Andes 239,144 2.16 -9.9 Caribbean 263,051 2.88 -9.3 Orinoco 41,371 2.98 -6.5 Pacific 6,416 2.32 -8.9

Colombia 580,489 2.58 -8.3

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014) and the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI, Rosenzweig et al. 2014).

Note: Grid cell values were tabulated using weights from rainfed maize in SPAM.

82

Table 14 Climate impacts on yield of irrigated rice in Colombia, 2000–2050

SPAM AgMIP

Region Area

(hectares) Yield, 2005

(tons/hectare)

Yield change, 2000–2050

(%)

Amazon 709 7.04 5.7 Andes 170,406 7.05 –1.8 Caribbean 62,726 7.12 –4.3 Orinoco 57,974 7.10 –2.9 Pacific 2,224 7.16 –0.1

Colombia 294,039 7.08 –2.3

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014) and the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI, Rosenzweig et al. 2014).

Note: Grid cell values were tabulated using weights from irrigated rice in SPAM.

Table 15 Climate impacts on yield of rainfed rice in Colombia, 2000–2050

SPAM AgMIP

Region Area

(hectares) Yield, 2005

(tons/hectare)

Yield change, 2000–2050

(%)

Amazon 5,476 3.21 –4.4 Andes 27,643 2.34 –3.7 Caribbean 39,539 3.28 –5.4 Orinoco 54,871 3.63 –0.9 Pacific 4,887 3.46 –1.7

Colombia 132,416 3.23 –3.7

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014) and the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI, Rosenzweig et al. 2014).

Note: Grid cell values were tabulated using weights from rainfed rice in SPAM.

83

Table 16 Climate impacts on yield of all rice in Colombia, 2000–2050

SPAM AgMIP

Region Area

(hectares) Yield, 2005

(tons/hectare)

Yield change, 2000_2050

(%)

Amazon 6,185 3.65 0.2 Andes 198,049 6.39 –1.7 Caribbean 102,265 5.63 –4.5 Orinoco 112,845 5.41 –2.4 Pacific 7,111 4.62 –1.3

Colombia 426,455 5.88 –2.8

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014) and the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI, Rosenzweig et al. 2014).

Note: Grid cell values were tabulated using weights from total rice in SPAM

84

Table 17 Climate impacts on yield of irrigated sugarcane in Colombia, 2000–2050

SPAM AgMIP

Region Area

(hectare) Yield, 2005

(tons/hectare)

Yield change, 2000–2050

(%)

Amazon 3 125.67 NA Andes 99,481 126.51 –27.8 Caribbean 55 126.33 NA Orinoco 0 NA NA Pacific 20,424 126.62 –28.1

Colombia 119,963 126.53 –27.9

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014) and the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI, Rosenzweig et al. 2014).

Note: Grid cell values were tabulated using weights from irrigated sugarcane in SPAM. NA means not available.

Table 18 Climate impacts on yield of rainfed sugarcane in Colombia, 2000-2050

SPAM AgMIP

Region Area

(hectares) Yield, 2005

(tons/hectare)

Yield change, 2000–2050

(%)

Amazon 4,162 84.39 NA Andes 278,764 85.04 –24.6 Caribbean 382 84.76 NA Orinoco 18 102.11 NA Pacific 1,344 67.52 –35.3

Colombia 284,670 84.95 –24.7

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014) and the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI, Rosenzweig et al. 2014).

Note: Grid cell values were tabulated using weights from rainfed sugarcane in SPAM. NA means not available.

85

Table 19 Climate impacts on yield of all sugarcane in Colombia, 2000–2050

SPAM AgMIP

Region Area

(hectares) Yield, 2005

(tons/hectare)

Yield change, 2000–2050

(%)

Amazon 4,165 84.42 NA Andes 378,245 95.95 –25.7 Caribbean 437 89.99 NA Orinoco 18 102.11 NA Pacific 21,768 122.97 –28.3

Colombia 404,633 97.28 –25.9

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014) and the Agricultural Model Intercomparison and Improvement Project (AgMIP) Global Gridded Crop Model Intercomparison (GGCMI, Rosenzweig et al. 2014).

Note: Grid cell values were tabulated using weights from total sugarcane in SPAM. NA means not available.

Table 20 Summary by region for production of rainfed plantains in Colombia, 2005

Name Production

(tons/hectare) Area

(hectares) Yield

(tons/hect)

Amazon 242,124 26,967 8.98 Andes 1,465,372 209,693 6.99 Caribbean 582,045 75,811 7.68 Orinoco 276,632 32,240 8.58 Pacific 80,659 11,404 7.07 Colombia 2,646,832 356,115 7.43

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014).

Note: Grid cell values were tabulated using weights from rainfed plantains in SPAM.

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Table 21 Summary by region for production of rainfed arabica coffee in Colombia, 2005

Name Production

(tons/hectare) Area

(hectares) Yield

(tons/hect)

Amazon 5,937 6,492 0.91 Andes 588,984 682,025 0.86 Caribbean 86,704 91,880 0.94 Orinoco 6,405 6,521 0.98 Pacific 268 565 0.47 Colombia 688,299 787,483 0.87

Source: Authors’ calculations from the Spatial Production Allocation Model (SPAM, You et al. 2014).

Note: Grid cell values were tabulated using weights from rainfed coffee in SPAM.

Table 22 Percentage of grassland in 2010 by region

Region Grassland (%) Orinoco 47.6 Amazon 1.2 Andes 10.1 Caribbean 23.9 Pacific 2.6 Colombia 14.2

Source: Authors’ tabulations of GLC-SHARE (Latham et al. 2014).

Table 23 Stocking density of cattle circa 2000 by region

Region

Head per square

kilometer Orinoco 19.6 Amazon 1.2 Andes 28.4 Caribbean 57.9 Pacific 10.8 Colombia 18.5

Source: Authors tabulations of FAOSTAT (FAO 2014). Note: The table is based on a dataset that appears to be missing data for the five large eastern departments.

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Table 24 Climate impacts on yield of rainfed managed grasses in Colombia, 2000–2050

AgMIP

Region

Yield change, 2000–2050

(%)

Amazon 8.4 Andes 2.3 Caribbean -9.5 Orinoco -7.9 Pacific 6.0 Colombia –1.0

Source: Author's calculations from the Agricultural Model Intercomparison and Improvement Project (AgMIP).

Notes: Grid cell values were tabulated without weights.

Table 25 Chicken production in Colombia circa 2000 by region

Region

Stocking density

(head/square kilometer)

Amazon 68.8 Andes 51.7 Caribbean 66.2 Orinoco 78.2 Pacific 48.4

Colombia 61.2

Source: Authors’ tabulations of FAO (2007).

Table 26 Percentage of tree cover in Colombia in 2010 by region

Region Grassland

(%) Orinoco 27.7 Amazon 71.4 Andes 34.7 Caribbean 25.7 Pacific 38.5 Colombia 46.6

Source: Authors tabulations of GLC-SHARE (Latham et al. 2014).

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Table 27 Forest tree species dominance, 1965–2012

Species Area

(hectare) Acacia Mangium 13,594 Bombacopsis quinata 9 Eucalyptus grandis 18,882 Eucalyptus pellita 1,705 Eucalyptus tereticornis 2,446 Gmelina arborea 14,913 Pinus caribaea 1,857 Pinus Maximinoi 6,674 Pinus oocarpa 9,399 Pinus patula 56,301 Pinus tecunnumanii 13,699 Tectona grandis 60,364

Source: MADR (2014b).

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