Agricultural border displacement as a consequence of climate change. Case - coffee in Colombia

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Agricultural Border Displacement as a Consequence of Climate Change. Case: Coffee in Colombia

Summary

In this document, an estimation of the agricultural border displacement of coffee crops in Colombia due to climate change is proposed. The methodology applied integrates subsoil and topsoil pH and salinity, elevation, temperature, and rainfall to analyze areal disposal of the studied crop. With the integration of GIS and the A2 scenario from the Intergovernmental Panel on Climate Change (IPCC) AR4 report, the climate change is modeled. Three years were selected for the study: 2008, 2040, and 2099. Using ESRI’s ArcGIS 10.2, the potential coffee growing areas were determined for each year based on five different parameters studied. It is concluded that in general in Colombia, climate change would boost the potential growing areas of the coffee, although in some small regions it would be decreased.

Keywords: agricultural border, climate change, coffee.

Resumen

En este documento se propone una estimación del desplazamiento de la frontera agrícola del café en Colombia como consecuencia del cambio climático. La metodología que se llevó a cabo utiliza como parámetros el pH y salinidad del suelo y subsuelo, altura, temperatura y pluviosidad con el fin de analizar la disposición y locación ideal del café. Integrando herramientas SIG y el escenario A2 del Panel Intergubernamental en Cambio Climático (IPCC) propuesto en el reporte AR4, se modela el cambio climático. Tres fechas fueron elegidas para el estudio: 2008, 2040 y 2099. Usando el software ArcGIS 10.2 de ESRI, las áreas de potencial crecimiento de café fueron determinados para cada año, basándose en los cinco parámetros estudiados. Se concluye que en general en Colombia el cambio climático

incrementará las áreas de potencial crecimiento de café a pesar de presentar disminuciones de la misma en algunas regiones menores.

Agricultural Border Displacement as a Consequence of Climate Change. Case: Coffee in Colombia

Colombia is characterized by its diverse geography and by being located near the equator, which makes it a tropical country; that fact tends its climate patterns to be stable, making this country a perfect place for varied agricultural production (Jaramillo, 2002). Given the importance of the primary sector in Colombia, agricultural border displacement results in an important climate phenomenon to analyze, considering that it has been occurring since the 19th century, as Perfetti, Balcázar, Hernández, and Leibovich (2013) have described. Of course the causes of the displacement have been different through the years, but the principle remains: the agricultural border needs to be displaced due to a social or economic fact (Yohe & Schlesinger, 2002).

During the first half of the 20th century agriculture in Colombia was the main source of economic growth, led by coffee since the second half of the 19th century. In the 1950s there was an important change in the importance of agriculture in Colombia’s economy, according to Leibovich, Perfetti, Botello, and Vásquez (2010); agriculture switched its position with the industrial sector. Despite this, the primary sector continued rising all over Colombia. But it was not until the 1990s that agriculture in Colombia was seriously affected as a consequence of an economical aperture (Jaramillo, 2002). This led to a series of

governmental policies in the 21st century that incentivized agriculture, resulting in an increase of the agriculture area (PNUD, 2011).

Agriculture in Colombia is still considered as an important economic sector; for the last 14 years it has represented between 10% to 14% of Colombia’s Gross Domestic Product (GDP) according to PNUD (2009). Furthermore, this activity occupies nearly 7% of

nearly 23% of the cropland (about 650,000 Ha) in 2010 (DANE & CCI, 2010), which made this product an economically relevant crop in the country and the objective crop of this study. Then agricultural border displacement must be directed toward the zones where the farmers’ profit is maximized as Kaminski, Kan, and Fleischer (2012) describe.

Background

Coffee production is sensitive to diverse factors like temperature, precipitation, solar radiation, etc. as the Food and Agriculture Organization of the United Nations (FAO) has determined in its crop database Ecocrop (FAO, 2010). As a perennial horticultural crop, the coffee makes part of a reduced group which, as Glenn, Virginia, Kim, and Ramirez-Villegas (2013) say, “provides important health benefits, serve as income sources, especially in subsistence and smallholder farms, and deliver additional benefits in agroecosystems such as carbon sequestration, erosion protection, biodiversity, and water retention” (p. 51).

The fourth report of the Intergovernmental Panel on Climate Change (IPCC) reveals an imminent global climate change that will affect in diverse ways different ecosystems and socio-economical systems all around the world (Ruiz Murcia & IDEAM, 2010). In Colombia the climate change has been evident in the last few years, considering the seasonal differences that have been happening (Quiroga Mosquera, 2011). The Niña phenomena between 2010 and 2011 have shown how climate variability has a fundamental role in the Colombian

agriculture; more than 800,000 Ha were flooded and nearly 2.7 million people were affected, causing a US $12 million loss (CIAT, 2014).

Climate change is seen as a challenge to many agricultural products because it can cause a yield decrease, although in some cases climate change has been proved to be an impulse to productivity, all relying in the optimal growth ranges. Several studies all around the world have been made in the last few years as a need to act against the future possibility of

a worldwide food shortage, such as the study made by Sommer et al. (2013) about the impact of climate change on wheat productivity in Central Asia, or specific studies on different crops like the one made by Kroschel et al. (2013) in the Andean region or the analysis made by Hassan (2010) about the implications of climate change for agricultural sector performance in Africa.

Recent covenants between the International Center for Tropical Agriculture (CIAT) and the Colombian Agriculture and Rural Development Ministry (MADR) are leading Colombia toward food security research (CIAT, 2014). Different publications supported by the CIAT have been made, such as the paper “A way forward on adaptation to climate change in Colombian agriculture: perspectives towards 2050” (Ramirez-Villegas, Salazar, Jarvis, & Navarro-Racines, 2012) where the agricultural production affection is quantified and

adaptation strategies are proposed.

The temporal and spatial climatic variability of the country represents a challenge to the determination of adaptation policies. The orography in the country contemplates

completely opposite scenarios, from really dry deserts to the most humid region of the planet; from the Andes Mountains at 5,570 meters above sea level (m.a.s.l.) to the coast of two different oceans. The precipitation regime is affected by the location in the Intertropical Convergence Zone (ITCZ). That causes difficulties to modeling the climate in the region.

For administrative and political purposes, Colombia is divided into 32 departments; coffee is being harvested significantly in 20 of them. Figure 1 shows how the coffee

Figure 1. Coffee producer municipalities in Colombia (2008).

Source: (IGAC - Instituto Geográfico Agustín Codazzi, 2008)

Although some departments are practically without any production of coffee, some of them contain at least one municipality in which the coffee production is significant enough to be shown, as happens to the Chocó department in Colombia’s northern pacific coast. On the other hand, departments like Risaralda, Caldas, or Quindío are distinguished for being primarily coffee producers, which makes the region the world known Coffee Triangle in the center of the country in the Andes Mountains.

Methodology

The methodology used was adapted from the methodology that Xiong, Holman, Conway, Lin, and Li (2008) used in their research. First a determination of the model variables is required, which leads to data preparation. The acquisition of the data

corresponding to the pre-specified variables acts as the second step of the methodology. Finally, a cross calibration between all the variables is made to obtain the expected results. Each step is broadly explained in the following pages.

Determination of the Model Variables

The selection of the crop to be studied was based in its social and economic

importance. Five crops were first listed: coffee, oil palms, bananas, rice, and sugar cane, all relevant within the economic and social sectors. The banana crop was first rejected due to the lack of information that can be found about it. Oil palms, rice, and sugar cane were discarded because, despite their social importance, it would take decades for them to reach as strong a social and historical importance as coffee has in Colombia. Then coffee was finally selected as the center of this research.

Coffee’s typical variety Coffea Arabica growing characteristics are summarized in Table 1 and were taken from the Ecocrop database (FAO, 2010). The FAO has made a

classification in Ecocrop according to its productivity and life viability; two classes were used in this research, optimal and absolute. The optimal class refers to a value range for different growing parameters in which the crop would have the best yield possible. On the other hand, the absolute class represents the life range of the crop; if it is exceeded or underachieved the crop will no longer be able to grow in that zone.

Table 1 Optimal and absolute values for different parameters. Source: (FAO, 2010)

Parameter Optimal Absolute

Min Max Min Max

Temperature

required [ºC] 14 28 10 34

Rainfall

(annual) [mm] 1,400 2,300 750 4,200

Elevation

[m.a.s.l.] 1,500 1,900 1,300 2,800

Soil pH 6 7 4 8

Soil Salinity

[dS/m] < 4 < 4

Selection of the different years to run the model. As is known, climate change is

thought to be an increasing problem with the passage of time. A time range had to be considered in this study so that climate change is noticeable enough that an agricultural border displacement can be determined (Ramirez-Villegas et al., 2012). The years 2008, 2040, and 2099 were selected based in the studies made by Ruiz Murcia and IDEAM (2010) where their results showed a clear difference in the precipitation regime all over the country and a temperature increase as well in this time range.

Determination of the time variability of parameters. The time variability of this research implies that some of the variables might be affected by climate change on a temporally basis. Relying on this assumption, each of the parameters was studied to analyze its time variability.

Rainfall and temperature. Several authors as Barba et al. (2010), Ruiz Murcia and IDEAM (2010) and Ramirez-Villegas and Khoury (2013) have studied the climate change in Colombia basing their studies not only on the IPCC reports, but also on the historical records of diverse climatological stations all over the country. All of them agree on the same point: climate change in Colombia is a fact. The temperature has been increasing and will continue increasing, while there had been unusual cycles in precipitation.

Elevation. The elevation is a geometric distance in respect to the mean sea

level. In this research the mean sea level is not considered to change with the time as Darwin, Tsigas, Lewandrowski, and Raneses (1995) considered. Although there are many sea level rising models, the elevation has not been considered to change as a consequence of it.

pH and salinity. Recent studies like the one carried by Singh, Cowie, and Chan (2011) have shown that some important parameters of soil health like pH and salinity are not being affected by the climate change, or at least in a

significant way within the next 100 years, according to the IPCC AR4 report scenarios. Then pH and salinity were not considered to change through the time in the present research.

Data recollection

The required data to properly run the coffee border displacement model is listed in Table 2. Once the required data for the model was listed, its recollection was the next step in this methodology. The data recollected for the model is shown below preceded by the recollection process it required.

Table 2 Border displacement model required data list.

Parameter 2008 2040 2099 Temperature

required [ºC]   

Rainfall

(annual) [mm]   

Elevation

[m.a.s.l.] 

Soil pH 

Soil Salinity

Rainfall and temperature. Climate change scenarios were chosen to be used based on the IPCC Special Report on Emissions Scenarios (SRES). The A2 scenario

developed by the NCAR community (2004) was chosen. The A2 scenario results have the largest response that the NCAR community (2004) modeled; this made it ideal for the extreme climate change scenario that was needed (Barba et al., 2010; Ramirez-Villegas et al., 2012). The data obtained had to be interpolated by the kriging method, because its resolution was of 1.4 degrees which corresponds approximately to 155 km. Data corresponding to mean annual precipitation and temperature was acquired for the 2008, 2040, and 2099. Figures 2 to 7 indicate the annual mean rainfall and temperature data used.

Elevation. Elevation data was obtained from NASA and EOSDIS (2013) who provide satellite information. The ASTER Global Digital Elevation Model GDEM DEM was acquired. A raster mosaic was made in ArcGIS with the 295 raster 30 Km x 30 Km downloaded. The mosaic was later exported as a single raster file from which

Colombia’s shaped mask was extracted. Figure 8 shows Colombia’s DEM with a 30 m x 30 m resolution.

pH and salinity. The International Institute for Applied System Analysis (IIASA) together with the ISRIC-World Soil Information, the Institute of Soil Science – Chinese Academy of Sciences (ISSCAS), the Joint Research Centre of the European Commission (JRC), and the FAO have developed the Harmonized World Soil Database (HWSD) in which different soil parameters like organic carbon, pH, water storage capacity, soil depth, salinity, granulometry, etc. are mapped. The reliability of the HWSD depends on the region, but for Latin America it is considered high (FAO, IIASA, ISRIC, ISSCAS, & JRC, 2012). For the purpose of this research the pH and the salinity of Colombia’s region was extracted for the topsoil and subsoil from the

Data processing

Once all the data was obtained, it was assorted according to the optimal/absolute classification. The maps were properly projected and resampled to the Magna Colombia Bogota system with a 7.5 Km x 7.5 Km cell size (111.32 km2 _{per cell) in order to permit} mathematical operations between them. From the ranges in Table 1, two maps were obtained for each of the parameters (elevation, pH, salinity, temperature, and precipitation). All the following maps are shown by pairs, representing the optimal areas in the first map, and the absolute areas in the second. Figures 11 to 28 show the discussed maps. These maps were then used as the limitations of the model for different purposes.

A reclassification was made in every map (optimal and absolutes) in which every value different from No Data was changed to 1 and the rest maintained as No Data. The reclassification’s purpose was to allow the determination of the absolute

potential coffee growing area.

Analysis and Results

The methodology resulted in five layers per study year, all of them reclassified. When those five layers are summed per year, three absolute potential coffee growing area maps are obtained. The agricultural border displacement results are based on the three maps shown in Figures 29 to 31.The following table summarizes the resulting three figures and their

coverage area.

Table 3 Coffee agro-potential area for each of the studied years

2008 2040 2099

Area [km2] 194.567,02 202.189,35 217.321,08

It is clearly seen that the trend of the coffee in Colombia is to keep increasing its potential area, at least for the next hundred years, considering all of the assumptions made.

As the border displacement had to be determined for two time periods, 2008-2040 and 2040-2099, the following process was applied for both the periods. A reclassification was made to the greater year of the period in which the No Data values were changed to 0 and the values corresponding to 1 were changed for the number 2. The other end of the period is also reclassified by changing every No Data value to 0. Afterward an addition was made between the two maps of the period where the resulting map has only four possible values: 0, 1, 2, and 3; where 0 represents the non-growing area; 1 represents the loss growing areas in the time period; 2 represents the potential growing areas that remain equal during the period; and 3 represents the gained potential growing areas.

Figures 32, 33, and 34 show the resulting maps which had been obtained with the previously described process. The percentile change between 2008 and 2040 is 9.20% and 7.70% between 2040 and 2099. It is seen that an important area gain would occur. Some departments, like Santander, showed an important increase between 2008 and 2099 of more than 100%, while others, like La Guajira, were reduced to zero. All these percentage data are shown in Table 6.

Table 4 summarizes the resulting data from the figures 32 to 34 for each of the classes the classification made in the resulting figures and the following table corresponds to:

 Loss: Area which in the studied time period will be lost.  Gain: Area which in the studied time period will be gained.  Remain: Area which in the studied time period remains the same.

Table 4 Summarized figures 32, 33 and 34

2008 2008 - 2040 2040 - 2099 2008 - 2099

Loss [km2] - 1.411,54 8.356,33 6.944,79

Gain [km2] - 9.033,87 23.488,07 29.698,85

Remain [km2] - 193.155,48 193.833,02 187.622,23

TOTAL [km2] 194.567,02 202.189,35 217.321,08 217.321,08

A deeper analysis was made for each department, considering all the localities in them. A locality is considered to be a piece of territory normally known by its own name where a considerable group of people lives. This analysis is shown in Table 5, where a continuous improvement of the number of potential coffee growing localities is shown despite the loss that some departments would suffer.

From the resulting maps the potential growth area was determined for each of the studied years on a departmental scale. Graphic 1 shows for each of Colombia’s departments the official area and the potential growth area for the different years. All units are in square kilometers.

Graphic 1. Potential departmental area for coffee growing in Colombia.

- 20.000,00 40.000,00 60.000,00 80.000,00 100.000,00 120.000,00

AMAZONAS

ANTIOQUIA

ARAUCA ARCHIP. DE SAN AN…

ATLÁNTICO BOGOTÁ BOLÍVAR BOYACÁ CÓRDOBA CALDAS CAQUETÁ CASANARE CAUCA CESAR CHOCÓ CUNDINAMARCA GUAINÍA GUAVIARE HUILA LA GUAJIRA MAGDALENA META NARIÑO

NORTE DE SANTANDER

PUTUMAYO QUINDÍO RISARALDA SANTANDER SUCRE TOLIMA

VALLE DEL CAUCA

VAUPÉS

VICHADA

POTENTIAL DEPARTMENTAL AREA FOR COFFEE GROWING IN COLOMBIA [KM2_]

OFFICIAL AREA [KM²] POTENTIAL AREA (2008) [KM²]

The potential growing area of some major departments like Antioquia, Risaralda, and Valle del Cauca would be seriously affected in case of a climate change scenario like the one used in this research. On the other hand a bigger number of departments would be

significantly benefitted in the event of a climate change, like Boyacá, Cundinamarca, Norte de Santander, and Santander, while the rest of the departments would tend to maintain their potential coffee areas.

Table 5 Departmental analysis of the affected localities in 2008, 2040 and 2099

DEPARTMENTS NO. OF LOCALITIES POTENTIAL NO. LOCALITIES (2008) POTENTIAL NO. LOCALITIES (2040) POTENTIAL NO. LOCALITIES (2099)

AMAZONAS 15,00 - - -

ANTIOQUIA 164,00 128,00 132,00 122,00

ARAUCA 15,00 3,00 3,00 5,00

ARCHIPIÉLAGO DE SAN ANDRÉS, PROVIDENCIA Y SANTA CATALINA

3,00 - - -

ATLÁNTICO 34,00 - - -

BOGOTÁ 20,00 20,00 20,00 20,00

BOLÍVAR 86,00 10,00 10,00 10,00

BOYACÁ 183,00 97,00 103,00 171,00

CÓRDOBA 57,00 52,00 52,00 52,00

CALDAS 40,00 22,00 22,00 22,00

CAQUETÁ 38,00 22,00 22,00 23,00

CASANARE 74,00 72,00 72,00 72,00

CAUCA 53,00 26,00 26,00 25,00

CESAR 55,00 31,00 32,00 14,00

CHOCÓ 54,00 6,00 7,00 6,00

CUNDINAMARCA 166,00 114,00 122,00 148,00

GUAINÍA 14,00 - - -

GUAVIARE 17,00 - - -

HUILA 62,00 62,00 62,00 62,00

LA GUAJIRA 20,00 3,00 3,00 -

MAGDALENA 60,00 - - 2,00

META 51,00 12,00 15,00 27,00

NARIÑO 80,00 71,00 71,00 71,00

NORTE DE

PUTUMAYO 28,00 17,00 17,00 17,00

QUINDÍO 26,00 26,00 26,00 26,00

RISARALDA 35,00 33,00 34,00 30,00

SANTANDER 135,00 57,00 68,00 120,00

SUCRE 43,00 - - -

TOLIMA 95,00 76,00 76,00 76,00

VALLE DEL

CAUCA 65,00 65,00 65,00 63,00

VAUPÉS 14,00 - - -

VICHADA 15,00 - - -

TOTAL 1.877,00 1.071,00 1.111,00 1.242,00

DEPARTMENTS OFFICIAL AREA [KM²] POTENTIAL AREA (2008) [KM²] POTENTIAL AREA (2040) [KM²] POTENTIAL AREA (2099) [KM²] 2008 -2040 PERC. OF CHANGE 2040 -2099 PERC. OF CHANGE 2008 -2099 PERC. OF CHANGE

AMAZONAS 109.665,00 - - - -

ANTIOQUIA 63.612,00 29.530,00 31.403,39 27.330,63 6,34% -12,97% -7,45%

ARAUCA 23.818,00 900,02 472,33 1.148,49 -47,52% 143,15% 27,61%

ARCHIPIÉLAGO DE SAN ANDRÉS, PROVIDENCIA Y SANTA CATALINA

44,00 - - - -

ATLÁNTICO 3.388,00 - - - -

BOGOTÁ 1.605,00 1.604,75 1.604,75 1.604,75 0,00% 0,00% -

BOLÍVAR 26.383,00 2.724,07 2.724,07 2.724,07 0,00% 0,00% -

BOYACÁ 23.189,00 10.837,41 11.081,24 18.340,47 2,25% 65,51% 69,23%

CÓRDOBA 26.506,00 1.155,61 1.144,91 1.155,61 -0,93% 0,93% -

CALDAS 7.888,00 5.913,39 5.913,39 5.902,52 0,00% -0,18% -0,18%

CAQUETÁ 88.965,00 8.030,32 8.030,32 8.030,32 0,00% 0,00% -

CASANARE 44.640,00 2.466,78 2.418,97 2.489,07 -1,94% 2,90% 0,90%

CAUCA 29.308,00 22.841,41 22.841,41 22.841,41 0,00% 0,00% -

CESAR 22.905,00 3.285,29 3.017,97 2.326,21 -8,14% -22,92% -29,19%

CHOCÓ 46.530,00 2.106,02 2.151,32 772,21 2,15% -64,11% -63,33%

CUNDINAMARCA 22.605,00 12.004,36 13.436,01 17.502,41 11,93% 30,26% 45,80%

GUAINÍA 72.238,00 - - - -

GUAVIARE 53.460,00 - - - -

HUILA 19.890,00 14.783,82 14.783,82 14.783,82 - - -

MAGDALENA 23.188,00 - - 0,82 - - -

META 85.635,00 5.620,17 7.070,54 8.812,14 25,81% 24,63% 56,79%

NARIÑO 33.268,00 14.749,86 14.749,86 14.749,86 0,00% 0,00% -

NORTE DE

SANTANDER 21.658,00 - 9.925,79 11.557,90 - 16,44% -

PUTUMAYO 24.885,00 3.290,78 3.290,78 3.290,78 0,00% 0,00% -

QUINDÍO 1.845,00 1.917,54 1.917,54 1.917,54 0,00% 0,00% -

RISARALDA 4.140,00 3.148,14 3.310,84 2.403,63 5,17% -27,40% -23,65%

SANTANDER 30.537,00 4.859,83 7.295,93 15.421,00 50,13% 111,36% 217,32%

SUCRE 10.917,00 - - - -

TOLIMA 23.562,00 15.758,39 15.758,39 15.758,39 0,00% 0,00% -

VALLE DEL

CAUCA 22.140,00 15.193,30 15.193,30 14.058,77 0,00% -7,47% -7,47%

VAUPÉS 54.135,00 - - - -

VICHADA 100.242,00 - - - -

Conclusions

Colombia’s location in the globe makes it a tropical country. This condition makes it an ideal zone to grow coffee. Even if a dramatic scenario like the A2 scenario from the IPCC AR4 occurs, the geography, soil properties, and climatic conditions of the region will permit acceptable coffee growth for the next hundred years.

Most of the main coffee growing zones in the present, like the Coffee Triangle, will remain practically intact against the imminent climate change. However, big changes are coming in Colombia’s coffee agriculture; new territories need to be viewed now for coffee expansion in the future.

A climate-specific agriculture must be developed in the country by the government and the research centers as an exit to the food security problem that will affect some regions but can be compensated by others. Specific crop land use should be suggested and

incentivized as a measure to prevent a national food shortage, or coffee shortage in this case. As was seen, this is an internationally important issue and Colombia is considered one of the leaders in this field. That leadership must continue not only with coffee, but with other important crops as rice, or oil palms.

Further research is proposed on enhancing the number and type of parameters not only to evaluate the coffee crop, but to evaluate other important crops in Colombia such as oil palms and sugar cane, among others.

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