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Revista mexicana de ciencias agrícolas

Print version ISSN 2007-0934

Rev. Mex. Cienc. Agríc vol.7 n.8 Texcoco Nov./Dec. 2016

 

Articles

Potential productivity and profitability of coffee (Coffea arabica L.) in Mexican tropic

José Antonio Espinosa-García1 

Jesús Uresti-Gil2  § 

Alejandra Vélez-Izquierdo1 

Georgel Moctezuma-López3 

Diana Uresti-Durán2 

Sergio Fernando Góngora-González4 

Héctor Daniel Inurreta-Aguirre2 

1CENID FyMA-INIFAP. Carretera a Colón, km 1. CP. 76190. Ajuchitlán, Querétaro. Tel. 01 419 2920249. (espinosa.jose@inifap.gob.mx; velez.alejandra@inifap.gob.mx).

2C. E. Cotaxtla-INIFAP. Carretera Veracruz-Córdoba, km. 34.5. CP. 94270. Medellín de Bravo, Veracruz. Tel. 01 800 088 2222. (uresti.jesus@inifap.gob.mx; uresti.diana@inifap.gob.mx; inurreta.daniel@inifap.gob.mx).

3CENID COMEF- INIFAP. Av. Progreso No. 5, Barrio Sta. Catarina. Delegación Coyoacán, CP. 04010, México, D. F. Tel: 01 55 36268700. (moctezuma.georgel@inifap.gob.mx).

4C. E. Mococha- INIFAP. Carretera Merida-Motul, km 25. CP. 97454. Mococha, Yucatán. Tel: 01 9919162215. (gongora.sergio@inifap.gob.mx).

Abstract

In order to identify the potential of coffee cultivation at hydrologic response unit (HRU) level, basin and state, considering yield and cost-benefit ratio (R B/C), this work was performed in the major producing states from the Tropical Regions of Mexico. To do so it was simulated and mapped the potential yield for cherry coffee bean in 9 states from South-southeast Mexico and identified the areas with the greatest potential for cultivation. Also, establishment costs, production and revenues from the technological package were estimated and the financial profitability of the crop for each region was evaluated. The results show that coffee is profitable when produced more than 4 500 kg of cherries coffee per hectare. Identifying 381 000 has with potential to produce coffee, located in Veracruz, Puebla, Oaxaca and Chiapas, where the Veracruz region had the highest yield of 9 t ha-1, and R B/C of 1.48. It is concluded that the average yields of coffee and estimated profitability indicators allow locating regions with potential to increase the area, production and current competitiveness of the crop.

Keywords: Coffea arabica L.; economic potential; humid tropics; productive potential

Introduction

Coffee (Coffea arabica L.) is a native of Ethiopia, its introduction to Mexico was around 1790 (Medina et al., 2016), is a plantation with a pre-production period of about three years, with a productive life that can reach up to 40 years, grown at altitudes above sea level ranging from 900 and 1 500 m, with a rainfall of 750 to 3 000 mm, temperatures between 16 oC and 22 oC (Ojien et al., 2010). There are more than 500 000 coffee growers located in 12 states of the country, mostly small farmers; nearly 90% of them have less than five hectares (Flores, 2015). 700 000 ha are cultivated, being Chiapas, Veracruz, Puebla and Oaxaca, the main producing states which together account for about 90% of production (SIAP, 2016).

Most of coffee production is generated in the Mexican tropics, since this region has appropriate agro-ecological conditions for the development of perennial crops, despite these conditions, coffee production has been declining in recent years, from 1 836 882.5 t in 2000 to 1 166 025.8 t in 2014, representing a drop of almost 3.7% per year (SIAP, 2016), data showing the crisis of coffee production in Mexico. According to Imagen agropecuaria (2016), Ortega and Ramírez (2013) and Temis et al. (2011) the causes of this crisis are the presence of rust fungus and borer pest, aging of coffee plantations, lack of programs and effective public policies thus low international coffee prices. This situation leads coffee producers to leave the business to migrate to other economic activities (Ortega and Ramírez, 2013).

Despite these problems, the perspectives for this crop are encouraging, since producers are facing a window of opportunity to the growth of coffee consumption globally, according to figures from OIC (2016), per capita consumption of coffee in Mexico grew at an average annual rate of 0.6% during the period 2012-2015, in addition there is a growing demand for coffee with ethical attributes (Aragon et al., 2013), therefore it is necessary to locate new production areas with productivity and profitability criteria.

By using agro-ecological simulation models that are spatially explicit, it is possible to obtain information on coffee yield (Gálvez et al., 2010), allowing to identify areas with potential for the establishment of commercial plantations, as it is the soil and water assessment tool (SWAT), which is a mathematical model of dynamic simulation (Neitsch et al., 2005), initially developed for hydrological modeling that allows to simulate water and sediment production in watersheds, considering the effect that agronomic practices have on water quality by the use of pesticides and fertilizers. (Rivera et al., 2012), thanks to an interface with GIS (ArcSWAT), can be used to estimate yields of perennial crops using agro-climatic information (Inurreta et al., 2013).

Performing an analysis of technical and economic viability of an agricultural activity generates useful information for decision makers as well as for producers, as Barrera et al. (2011) has done it in a study on profitability of vanilla and Espinosa et al. (2015), for the cultivation of cocoa. Analyses of this type are strengthened when combined with information from technological packages, agro-climatic data, market variables of input and output. Therefore the aim of the study was to identify areas with productive and economic potential to establish coffee plantations at hydrologic response unit (HRU), basin and state level in the region from the Mexican humid tropics.

Material and methods

The work was performed in 48 basins, covering an area of 50.7 million hectares, distributed in nine states from the South-southeast region of Mexico: Campeche, Chiapas, Guerrero, Oaxaca, Puebla, Quintana Roo, Tabasco, Veracruz and Yucatan. To simulate coffee yields, the 48 basins were divided into 816 sub basins and 7154 HRU, subsequently the potential profitability of HRU´s with higher yield was estimated.

Yield simulation

It was performed using the simulation model SWAT with its extension ArcSWAT within the ArcGIS 9.3 interface. Because SWAT is a hydrologic model that works at basin level, it was necessary to condition the study area to perform the simulation at the level of this hydrologic unit (Inurreta et al., 2013), the basin to simulate was divided into sub-basins using topographic information from a digital elevation model (DEM) and a map of surface runoff. In each subbasin were identified areas with the same interval slope, soil type and current land use, called HRU's. The slope map is generated by SWAT from DEM, however edaphological and current land use maps were taken from INEGI with a 1: 250 000 scale and fed into the system.

In the model were used climate, soil, coffee physiology and management practices information to simulate the coffee growth, estimate its yield at HRU level and cartographically express the result. This model consists of eight components: a) climate; b) hydrology; c) nutrients and pesticides in soil; d) soil erosion; e) plant growth and ground cover; f) management practices; g) processes in the main drainage channel; and h) water bodies (storage).

Climate information was extracted from a database of 1 145 stations from the SMN, which had full climate information of at least 10 years. With this information and using the weather generator from EPIC model (Sharply and Williams, 1990), generated weather statistics and subsequently daily data for the period 1912-2010. The edaphological information to characterize soil subclasses from the INEGI map was obtained of a report from INEGI with data field from 1 247 agrologic wells. The missing information was generated from the following sources: the hydraulic properties and soil erosion factor (universal soil loss equation) were calculated in relation to texture according to Colin et al. (2013) and Ramírez et al. (2009), and the albedo was calculated from the organic matter in the soil, applying a regression equation obtained from the original information of SWAT with 202 albedo soils and organic matter. Crop physiological parameters required by the model are presented in Table 1. Agronomic management was prepared according to (Méndez, 2011; Themis et al., 2011).

Table 1 Physiological parameters of coffee used to simulate its yield with the SWAT model. 

Parámetro fisiológico Unidades Valor Fuente
Eficiencia en el uso de la radiación (kg ha-1)/(MJ m-2) 10 Jaramillo et al. (2006)
Índice de área foliar máxima m2 hoja/m2 terreno 1.35 Ramírez et al. (2009)
Índice de cosecha Adimensional 0.15 Montoya et al. (2009)
Temperatura optima °C 30 Arcilla et al. (2007)
Temperatura base °C 10 Arcilla et al. (2007)
Altura mínima sobre el nivel del mar (m) 600 Moguel y Toledo (2004)
Altura máxima sobre el nivel del mar (m) 1 600 Moguel y Toledo (2004)

Estimation of economic potential

The methodology to evaluate long term agricultural projects (Gittinger, 1982) through the estimation of profitability indicatiors: cost/benefit ratio (RB/C), net present value (VAN), internal rate of return (TIR) at HRU level, whose mathematical expressions are presented below (Coss, 1984).

RB/C=t=1nFI1+ini=1nFC1+in

VAN=t=1nFI-FC1+in

TIR=t=1nFI-FC1+in=0

Where: FC is the flow of production costs, FI is the revenue flow, i is bank rate and n plantation age.

FC and FI were estimated from the technical coefficients per hectare from the technological package proposed by López (2011) and Themis et al. (2011), considering the crop horizon of 15 years using the following formulas:

FC=TCoE1+i=1n=15CoFP+CoVP

Where: TCoE1 is the total cost of plantation establishment in year 1, calculated by the sum of inputs amount used for the establishment of one hectare of coffee by their respective average market price in the states of Chiapas, Veracruz , Puebla and Oaxaca in 2014; CoFP in the fixed cost of production from year 1 to year 15, calculated by the sum of depreciation costs of the plantation (TCoE minus the salvage value, by the 15-year life) and management costs (3% of income from coffee sales) and CoVP is the variable cost of production of year 1 to year 15, calculated by the sum of the different amounts of inputs for operation and maintenance of the plantation by their respective average market price from the states of Chiapas, Veracruz, Puebla and Oaxaca, during 2014.

FI=i=3n=15Pxkg*RCxha+Pxmad*Qmad25

Where: Pxkg is the market price of coffee cherries from year 3 to 15, RCxha is the coffee cherry yield per ha, Pxmad is the market price of wood in year 15 and Qmad is the amount of timber sold in year 15. the market price of coffee cherries was estimated with data from ICO (2016), whose original values are given in cents per pound of green coffee, therefore converted to Mexican pesos per kilogram of coffee cherries, applying the conversion factor 0.1841 kg of green coffee per kilogram of coffee cherry, timber prices are current average prices from the states of Chiapas, Veracruz, Puebla and Oaxaca during 2014.

Since establishment, production costs and income correspond to the lifetime of the project; these were taken to their current value using a bank rate of 10.4%, proposed by the World Bank as the opportunity cost of capital for public investment projects in Mexico (Coppola et al., 2014). The study of economic potential was complemented with a sensitivity analysis, considering three scenarios: i) decrease in coffee price, considering the international price, assuming that in the long term the domestic market price decrease; ii) increase in fertilizer price, as one of the inputs of higher percentage in the structure of variable cost, caused by a general increase in prices according to the trend of inflation at national level; and iii) the combination of the above two situations, decline in coffee prices and increase of fertilizer price which is considered the most critical case.

Results and discussion

Productive potential of crop of coffee

The surface with potential to produce the crop is presented in Figure 1, in the green, yellow, orange and red area could be produced coffee cherries with higher yield than the national average, which for the period 2000-2014 was 2 000.2 kg ha-1 of coffee cherry (SIAP, 2016) or 368.23 kg ha-1 of green coffee, since a kg of coffee cherries is equal to 0.1841 kg of green coffee (Flores, 2015). Although the competitive potential is to produce coffee placing areas above the world average, for the same period was 691 kg ha-1 of green coffee (FAO, 2016), equivalent to 3 753.4 kg ha-1 of coffee cherry, in Figure 1, it is observed that there are about 1.896 million ha with competitive potential, located in the states of Veracruz, Puebla, Chiapas and Oaxaca, an area 2.5 times higher than that planted of coffee now throughout the country, which is 737 376.5 ha (SIAP, 2016).

Figure 1 Surface with potential to produce coffee in the SouthSoutheast of Mexico. 

The distribution of the surface by yield level of coffee cherry on the main states with potential is shown in Table 2, where can be seen that on 32% of the regional surface can produce coffee, being Guerrero where greater surface was detected, although with lower yields than other states. Veracruz has the lowest percentage of surface with potential, however this state has the regions with higher yields, therefore despite occupying the third place in planted area, ranks second in domestic production (SIAP, 2016), and the other state that follows in surface with potential for showing high yields is Puebla, therefore ranks third in domestic production. Globally the highest coffee yields are obtained by Vietnam and Brazil with an average of 10 and 6 t of coffee cherries respectively (Flores, 2015), yields that can be obtained in Chiapas, Oaxaca, Puebla and Veracruz.

Table 2 Distribution of the surface (thousands of ha) with potential to produce coffee. 

Estado Rendimiento de café cereza (t) Superficie con potencial (%)
<2 2.1-4 4.1-6 6.1-8 8.1-10 10.1-12 12.1-13
Chiapas 1 714 630 221 277 103 12 5 40
Guerrero 2 833 309 43 0 0 0 0 50
Oaxaca 1 457 779 342 156 59 15 11 31
Puebla 738 119 92 64 53 15 18 32
Veracruz 65 107 173 99 71 23 42 8
Total 6 807 1,944 871 596 286 65 76 32

Fuente: estimaciones realizadas aplicando el modelo SWAT.

If Mexico wants to use the comparative advantages that some regions represent, it should produce coffee in 1 million ha where could obtain yields greater than 6 000 kg of coffee cherries ha-1, to obtain similar yields to those from Brazil which is the main producer in the world (Flores, 2015). But it is not enough to be competitive, but to conserve natural resources, to do so it is proposed to discard the 641 000 ha whose current land use is forest (Table 3) and to establish coffee in the remaining 381 000 ha.

Table 3 Current land use in the area with greater potential to produce coffee. 

Estado Superficie con rendimiento de 6-13 toneladas Uso actual del suelo
Agrícola Ganadero Forestal
Chiapas 397 000 86 000 39 000 271 000
Oaxaca 241 000 14 000 8 000 220 000
Puebla 150 000 84 000 12 000 52 000
Veracruz 235 000 91 000 47 000 98 000
Total 1 023 000 275 000 106 000 641 000

Fuente: estimaciones realizadas aplicando el modelo SWAT.

Watersheds with greater length with potential to produce coffee are the Grijalva and Lacantun River, both in the state of Chiapas, comprising 578 HRU and Papaloapan River, comprising the states of Puebla, Oaxaca and Veracruz, with 158 HRU (Table 4). The basin where the highest yields can be obtained 9 000 kg ha-1 is in the Jamapa River and others, followed in importance by the Astata River and others and Nautla River and others.

Table 4 Surface with the greater potential to produce coffee, per hydrologic basin. 

Cuenca Superficie (ha) URH Rendimiento (t ha-1) Estados
Río Grijalva 197 578 246 7.3 Chiapas
Río Lacantún 189 073 332 7.8 Chiapas
Río Papaloapan 163 629 158 7.9 Oaxaca, Puebla y Veracruz
Río Jamapa y otros 89 372 99 9 Veracruz
Río Nautla y otros 79 495 225 8.7 Puebla y Veracruz
Río Atoyac 75 029 137 7.6 Puebla y Oaxaca
Río Copalita y otros 43 000 179 8.3 Oaxaca
Río Tuxpan 38 733 179 7.7 Puebla y Veracruz
Río Cazones 29 553 123 8 Puebla y Veracruz
Río Astata y otros 14 965 67 8.9 Oaxaca
Río Colotepec y otros 14 091 33 8.7 Oaxaca
Río Coatzacoalcos 12 091 4 6.7 Oaxaca y Veracruz
Río Moctezuma 10 608 56 8.4 Veracruz
Otras 10 cuencas 65 781 148 7 Chiapas y Oaxaca
Total 1 023 000 1 986

Fuente: estimaciones realizadas aplicando el modelo SWAT.

The results presented show the usefulness of estimating yields and thereby locate surface with productive potential, as Rivera et al. (2012) did, for the cultivation of cassava (Manihot esculenta Crantz) in Tabasco, where, by the variables: climate, soil, temperature, rainfall, altitude, photoperiod and growth period, were able to locate 171 121 potential hectares in Huimamguillo 70 386 in Balancan and 41 337 in Macuspana, relevant information for the design of policies to support the development of a culture in a region.

Economic potential of the crop

Obtaining high yields is not enough condition to take the decision to produce, it is also requires crop profitability, to know this information the technical coefficients from the technological package were valued, Tables 5 and 6 show the investments and operation costs of an hectare of coffee during the lifetime of a plantation respectively. As seen in Table 5, it is required to invest more than $26 000 pesos per hectare to establish a plantation, being the vegetative material the highest value with 57%.

Table 5 Establishment cost of coffee plantation. 

Actividades Unidad Cantidad Valor ($)
Unitario Total
Desmonte Jornales 20 150.00 3 000.00
Subsoleo Servicio 1 1 200.00 3 000.00
Barbecho Servicio 1 1 200.00 1 200.00
Rastreo Servicio 2 600.00 1 200.00
Material vegetativo café Unidades 2 500 6.00 15 000.00
Mano de obra Jornales 30 150.00 4 500.00
Material vegetativo para sombra de café Unidades 340 5.00 1 700.00
Total 26 100.00

Table 6 Variable costs of coffee production. 

Actividades Unidad Precio ($) Año 1 Año 6 (cultivo estabilizado)
Cantidad Valor ($) Cantidad Valor ($)
Fertilizantes (kg) 8.03 249.65 2 004.69 676 5 428.28
Control de malezas (L) 122 4 488.00 4 488
Control de enfermedades (kg) 320 0 0 3.5 1 120
Aplicación fertilización Jornales 150 8 1 200.00 16 2 400
Control de malezas Jornales 150 20 3 000.00 15 2 250
Control de enfermedades Jornales 150 0 0 5 750
Otras labores culturales Jornales 150 7 1 050.00 8 1 200
Cosecha Jornales 150 0 70 10 500
Total 7 742.69 24 136.28

As for operating costs, Table 6 shows that in year 1 costs are lower, mainly because the crop is growing and there is no production, also the amount of fertilizer applied is lower due to plantation size, costs start increasing each year, to reach the value of $24 136.28 in year 6, and they continue until year 15, the highest concept of cost is harvest, followed in importance by fertilization, this cost structure is similar to that reported by López and Caamal (2009). To operation costs were added $1 392.00 per year for plantation depreciation, plus administration costs that vary according to yield.

To complete the estimate of profitability indicators is necessary to estimate the income of the plantation, which are determined by two variables: yield and price of coffee, the first is taken from the estimated productive potential, whose value depends on the agro-ecological region as can be seen in Figure 1. in the case of price, there is no single value, because coffee is not an homogeneous product, as shown in Figure 2, where can be seen the price behavior from the last 16 years, of the three predominant types of Arabica coffee in the world, plus the indicator price indicated by ICO (2016), as an indicator price of all coffee types, in the same Figure 2 are presented the average rural price of coffee in Mexico, where it can be seen that it has a similar behavior to international price, although are lower by 30% on average, as mentioned by CEDRSSA (2014), so coffee price considered in the study is $6.08 per kilogram. With cost and revenue information estimated and updated the cash flow for 15 years, which in turn allowed estimating the R B/C, VAN and TIR indicators.

Fuente: estimación con datos de ICO, 2016 y SIAP, 2016.

Figure 2 Coffee cheery price in Mexico and the world. (In Mexican pesos). 

The results show that the minimum yield that a coffee plantation requires for a producer to start having profits is 4 500 kg of coffee cherries ha-1, with which a R B/C of 1, a VAN of 0 and a TIR of 10.4%, equal to the bank rate, which are the conditions of equilibrium (Gittinger, 1974), therefore, plantations earning returns above this amount are profitable, situation that is not currently happening since the national average was just 1 670 kg ha-1 in 2014 (SIAP, 2016), this result confirms the approach from CEDRSSA (2014), stating that coffee production has decreased its yield by disease problems and high production costs.

Table 7 presents the financial indicators assessed in six basins with the largest area with potential to produce coffee, it shows that in the basins from Jamapa river and others and from the Nautla river and other have higher potential yields, thus financial indicators are the most valuable, although in all basins these indicators are favorable, so if coffee plantations were established in these basins producers would get 30 to 48 cents for each peso invested, it would also allow them to hire a credit and pay an interest rate between 16% and 28% higher than the opportunity cost of capital.

Table 7 Profitability indicators of cocoa plantations in states with productive potential. 

Cuenca Superficie (ha) Rendimiento (t ha-1) R B/C TIR (%) VAN ($)
Río Grijalva 197 578 7.3 1.33 28.6 71 269
Río Lacantún 189 073 7.8 1.37 31.7 83 879
Río Papaloapan 163 629 7.9 1.38 32.4 86 402
Río Jamapa y otros 89 372 9 1.48 38.9 114 145
Río Nautla y otros 79 495 8.7 1.45 37.1 106 578
Río Atoyac 75 029 7.6 1.36 30.5 78 835
Otras 17 cuencas 65 781 7 1.3 26.7 63 702

As mentioned previously, the area with potential for coffee production is 381 000 ha (72% agricultural and 28% livestock) but varies by state, the main agricultural crops currently grown are corn, sugar cane, beans and coffee the beef production in the case of livestock (SIAP, 2016), so the producer decision to change the current land use would be based is an expectation of higher gain to that currently obtained. In a study conducted in Mexico by Domínguez et al. (2010) mentions that grains are less profitable than fruits and vegetables, however it also requires less investment, so the risk is also lower.

In another study where coffee production both in a traditional system and organic production, coincides that the benefit cost is negative in the traditional system and 1.15 if organic coffee is produced in the state of Chiapas (López and Caamal 2013), confirming the need to establish plantations in productive areas and applying a technological package that ensures returns above the national average. Finally Carrera et al. (2013), studying the behavior of beef production during the period from 1980 to 2009, they found that this activity has become less competitive. The latter allows placing coffee production as a viable activity for producers in the region studied, although it is necessary to establish a supportive policy, aimed at reducing the financial cost of the investment and promote intensive labor as suggested by Barrera et al. (2011), in a similar study.

Table 8 shows the results of the sensitivity analysis under three scenarios, a) decrease in the price of coffee of 25%, which is the lowest value that coffee producers have obtained in Mexico in the last 10 years (Figure 2); and b) an increase in the price of fertilizer by 25%, which was the highest value obtained over a 10 year period analyzed (SE, 2016) and c) a combination of the two. Financial indicators obtained show a high sensitivity to variations in coffee prices since a fall in price of 25%, as happened in late 2010, some watersheds are no longer profitable, situation that is exacerbated when combined with an increase in inputs price.

Table 8 Sensitivity analysis of the profitability indicators of cocoa plantations in states with productive potential. 

Cuenca < 25% precio café > 25% precio fertilizante < 25% precio cacao y > 25% precio fertilizante
R B/C TIR (%) R B/C TIR (%) R B/C TIR (%)
Río Grijalva 1.03 11.1 1.3 27.2 1.01 9.9
Río Lacantún 1.06 13.4 1.35 30.3 1.04 12.1
Río Papaloapan 1.07 13.9 1.36 30.9 1.05 12.6
Río Jamapa y otros 1.14 18.8 1.45 37.4 1.12 17.4
Río Nautla y otros 1.12 17.5 1.43 35.7 1.1 16.1
Río Atoyac 1.05 12.5 1.33 29.1 1.03 11.2
Otras 17 cuencas 1 9.8 1.27 25.3 0.98 8.5

In 2014, 670 316 ha of coffee were planted in the southsoutheast of the country, producing 1 084 249 tons with a yield of 1.6 t ha-1 (SIAP, 2016), so that the area of 381 000 ha with potential has been reported in the four states, which would produce more than 6 t ha-1, it would generate more than twice the current production, under profitable conditions and thereby achieving also to increase the income of coffee farmers in the country.

Conclusions

The combination of agro-climatic, productive and economic information allowed locating regions with globally competitive coffee grain yields and with higher profitability rates than opportunity cost of capital, which makes them with high potential for coffee plantations. Mainly because of its importance in the humid tropic of Mexico, and the problems it faces, therefore it is recommended to use the information to design policies aimed to reactivate coffee production, achieving to increase exports and simultaneously seize the opportunities that international market offers, led by the increase in world coffee consumption.

Literatura citada

Aragón, G. C.; Montero, S. M. J.; Araque, P. R. A. y Gutiérrez, G. L. 2013. Evaluación del valor percibido en el consumo de café con atributos éticos. Agrociencia. (47):195-207. [ Links ]

Arcilla, P. J.; Farfán, V. F.; Moreno, B. A. M.; Salazar, G. L. F. y Hincapié, G. E. 2007. Sistemas de producción de café en Colombia. Cenicafé. Chinachiná, Cladas, Colombia. 309 p. [ Links ]

Barrera, R. A. I.; Jaramillo, V. J. L.; Escobedo, G. J. S. y Herrera, C. B. E. 2011. Rentabilidad y competitividad de los sistemas de producción de vainilla (Vanilla planifolia J.). Agrociencia . 45:625-638. [ Links ]

Carrera, Ch. B.; Bustamante, L. y Tzatzil, I. 2013. ¿Es la ganadería bovina de carne una actividad competitiva en México?. Nóesis. 22(43):19-50. [ Links ]

CEDRSSA. 2014. Producción y mercado de café en el mundo y en México. Reporte del CEDRSSAR. Cámara de Diputados. México, D. F. 18 p. [ Links ]

Colín, G. G.; Ibáñez, C. L. A.; Reyes, S. J. y Arteaga, R. R. 2013. Diagnóstico de la erosión hídrica de la Cuenca del Río Pichucalco. Rev. Ing. Agríc. Bio. 5(1):23-31. [ Links ]

Coppola, A.; Fernholz, F. and Graham, G. 2014. Estimating the economic opportunity cost of capital for public investment proyects, an empirical analysis of the Mexican case. The World Banck. Policy Research Workimg Paper 8616. Washington, DC. 41 p. [ Links ]

Coss, B. R. 1984. Análisis y evaluación de proyectos de inversión. Ed. LIMUSA. México, D. F. 348 p. [ Links ]

Domínguez, A. R.; Brambila, P. J. J.; Mora, F. S. y Martínez, D. M. A. 2010. Valores críticos para evaluar proyectos agrícolas en escenarios de precios estocásticos. Rev. Fitotec. Mex. 33(1):79-83. [ Links ]

Espinosa, G. J. A.; Uresti, G. J.; Vélez, I. A.; Moctezuma, L. G.; Inurreta, A. H. D. y Góngora, G. S. F. 2015. Productividad y rentabilidad potencial del cacao (Theobroma Cacao L.) en el trópico mexicano. Rev. Mex. Cienc. Agríc. 6(5):1051-1063. [ Links ]

FAO. 2016. FAOSTAT. http://www.fao.org/statistics/es/.3/06/2016. [ Links ]

Flores, V. F. 2015. La producción de café en México: ventana de oportunidad para el sector agrícola de Chiapas. Espacio, Innovación más Desarrollo. (7):175-193. [ Links ]

Gálvez, G.; Sigarroa, A.; López, T. y Fernández, J. 2010. Modelación de cultivos agrícolas. Algunos ejemplos. Cultivos Tropicales (Cuba). 31(3):60-65. [ Links ]

Gittinger, J. P. 1982. Análisis económico de proyectos agrícolas. Instituto de Desarrollo Económico (IDE). Banco Mundial. Tecnos, Madrid, España. 241 p. [ Links ]

IA. 2015. Debacle cafetalero mexicano. Imagen agropecuaria, visión del campo y los agronegocios. http://imagenagropecuaria.com/2015/debacle-cafetalera-mexicana/. [ Links ]

Inurreta, A. H. D.; García, P. E.; Uresti, G. J.; Martínez, D. J. P. y Ortiz, L. H. 2013. Potencial para producir jatropha curcas l. Como materia prima para biodiesel en el estado de Veracruz. Tropical and Subtropical Agroecosystems.16:325-339. [ Links ]

Jaramillo, R. A.; Areila, P. J.; Montoya, R. E. y Quíroga, Z. F. 2006. La radiación solar; consideraciones para su estudio en las plantaciones de café (Coffea arabica L.). Meteoro. (10):12-22. [ Links ]

López, A. A. P. 2011. Paquete tecnológico cacao (Theobroma cacao L.). Programa estratégico para el desarrollo rural sustentable de la región sur - sureste de México: Trópico Húmedo 2011. SAGARPA-INIFAP. Huimanguillo, Tabasco. 10 p. [ Links ]

López, L. E. C. y Caamal, C. I. 2009. Los costos de producción del café orgánico del estado de Chiapas y el precio justo en el mercado internacional. Rev. Mex. Ec. Agríc. Rec. Nat. 2(1):175-198. [ Links ]

Medina, M. J. A.; Ruiz, N. R. E.; Gómez, C. J. C.; Sánchez, Y. J. M.; Gómez, A. G. y Pinto, M. O. 2016. Estudio del sistema de producción de café (Coffea arabica L.) en la región Frailesca, Chiapas. CienciaUAT. 10(2):33-43. [ Links ]

Méndez, L. I. 2011. Paquete tecnológico café robusta (Coffea canephora P.). Establecimiento y mantenimiento. Programa Estratégico para el Desarrollo Rural Sustentable de la Región Sur - Sureste de México: Trópico Húmedo 2011. Rosario Izapa, Chiapas. México. 8 p. [ Links ]

Moguel, P. y Toledo, V. M. 2004. Conservar produciendo: biodiversidad, café orgánico y jardines productivos. Conabio Biodiversitas. (55):1-7. [ Links ]

Montoya, R. E. C.; Arcila, P. J.; Jaramillo, R. A.; Riaño, H. N. M. y Quiroga, Z. F. 2009. Modelo para simular la producción potencial del cultivo del café en Colombia. Boletín Técnico No. 33. Cenicafé. Chinachiná, Cladas, Colombia. 52 p. [ Links ]

Neitsch, S. L.; Arnold, J. G.; Kiniry, J. R. and Williams, J. R. 2005. Soil and Water Assessment Tool.Theoretical Documentation. Backland Research Center. Texas Agric. Exp. Center. Texas, USA. 386 p. [ Links ]

Ojien, V. M.; Dauzat J.; Harmand, J. M.; Lawson, G. and Vaast, P. 2010. Coffee agroforestery system in Central America: II. development of a simple process-based model and preliminary result. Agroforesty System. 80:661-378. [ Links ]

OIC. 2016. Informe del mercado del café. Nueva York, USA. 5 p. [ Links ]

Ortega, H. A. y Ramírez, V. B. 2013. Crisis de la cafeticultura y migración en el contexto de pobreza y marginación. El caso de los productores indígenas de Huehuetla, Puebla. Ra Ximhai 9(1):173-186. [ Links ]

Ramírez, O. F. A.; Hincapié, G. E. y Sadeghian, K. S. 2009. Erodabilidad de los suelos de la zona central cafetera del departamento de caldas. Cenicafé. 60(1):58-71. [ Links ]

Rivera, H. B.; Aceves, N. L. A.; Juárez, L. J. F.; Palma, L. D. J.; González, M. R. y González, J. V. 2012. Zonificación agroecológica y estimación del rendimiento potencial del cultivo de la yuca (Manihot esculenta Crantz) en el estado de Tabasco, México. Avances en Investigación Agropecuaria. 16(1):29-47. [ Links ]

Rivera, T. F.; Pérez, N. S.; Ibáñez, C. L. L. A. y Hernández, S. F. R. 2012. Aplicabilidad del modelo SWAT para la estimación de la erosión hídrica en las cuencas de México. Agrociencia . 46:101-105. [ Links ]

SE. 2016. Consulta precios de insumos. http://www.economia-sniim.gob.mx/nuevo/home.aspx?opcion=consultas/mercadosnacionales/preciosdemercado/agricolas/consultainsumos.aspx?subopcion=9%7c0. 3/09/2016. [ Links ]

Sharpley, A. N. and Williams, J. R. 1990. EPIC- erosion/productivity impact calculator. USDA. Agricultural Research Service, Technical Bulletin No. 1768, Washington, D.C. EUA. 235p. [ Links ]

SIAP. 2016. Cierre de la producción agrícola por estado 2014. SIAPSAGARPA. http://www.siap.gob.mx/agrícultura-produccionanual. [ Links ]

Temis, P. A. L.; López, M. A.; Vigil, M. E. y Sosa, M. M. E. 2011. Producción de café (Coffea arabica L.): cultivo, beneficio, plagas y enfermedades. Temas Selectos de Ingeniería de Alimentos. 5(2):54-74. [ Links ]

Received: September 2016; Accepted: December 2016

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