Ecological Indicators 7 (2007) 133–149 This article is also available online at: www.elsevier.com/locate/ecolind
Assessment of economic and ecological carrying capacity of agricultural crops in Nicaragua M. Cuadra a,b,*, J. Bjo¨rklund c a
b
Universidad Nacional Agraria (UNA), Apdo. 453, Managua, Nicaragua Department of Ecology and Crop Production Science, Swedish University of Agricultural Sciences (SLU), Sweden c Centre for Sustainable Agriculture, SLU, Sweden Received 3 November 2004; accepted 9 November 2005
Abstract The relationships between, and usefulness of, three different analysis methods: (1) economic cost and return estimation (CAR), (2) ecological footprint (EF) and (3) emergy analysis (EA) in assessing economic viability, ecological carrying capacity and sustainability in tropical crop production was the focus for this study. The analyses were conducted on six agricultural crop production systems in Nicaragua: common bean (Phaseolus vulgaris L.), tomato (Lycopersicum esculentum L. Mill), cabbage (Brassica oleraceae L. var. capitata), maize (Zea mays L.), pineapple (Ananas comosus L. Merr.) and coffee (Coffea arabica L.). The economic indices studied were revenues and profitability. The ecological footprint indices were ecological footprint per hectare of crop (EFcrop), ecological footprint per 1000 USD revenues (EFrev) and ecological footprint per gigacalorie of food energy produced (EFGcal). The emergy analysis indices used were emergy-based profitability (EAprof) and emergy-based ecological footprint (EAEF). The study indicated that cabbage and tomato were the most profitable crops, both in economic and emergy terms, and that coffee was the least profitable crop to grow. On the other hand, beans, coffee and maize were most sustainable when sustainability was measured as ecological carrying capacity, assessed by EF or emergy-based EF, while cabbage and tomato were the least sustainable. Moreover, maize turned out to be the crop with the lowest area demand per production of gigacalorie. Profitability assessed in economic terms or in relation to emergy use (EAprof) or to ecological footprint showed similar patterns and gave the same rankings between the crops. However, profitability assessed by CAR was higher than when assessed by EAprof, due to the fact that no environmental appropriation is included in the CAR. Area appropriation assessed with emergy or with ordinary ecological footprint also resulted in mainly the same rankings between the crops, while the actual size of the areas was at most 10 times larger when assessed in emergy than with plain ecological footprint. Our results add to the body of knowledge on the poor coherence between economic profitability and ecological sustainability. However, we argue that these evaluations may be used as methods for quantitatively assessing different production systems, leading to indices weighting together economic and environmental aspects that may be used to make decisions. # 2005 Elsevier Ltd. All rights reserved. Keywords: Environmental indicators; Economic viability; Sustainability; Nicaragua
* Corresponding author. Tel.: +505 2 33 22 71/18 45; fax: +505 2 33 18 45/12 67. E-mail address:
[email protected] (M. Cuadra). 1470-160X/$ – see front matter # 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.ecolind.2005.11.003
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1. Introduction In many regions, and especially in tropical areas, increased agricultural activity and demand for more agricultural land are causing deforestation, soil degradation and loss of biodiversity at an everincreasing rate (Silver et al., 1996). Inadequate cropping practices and economic shortages are two of the main causes (UNA, 1998). To stop this negative trend, in-depth studies of economic, ecological and social implications of agricultural production as well as the conflict between short-term economic benefits and ecological sustainability for long-term social viability are needed. Tools for combined economic and environmental assessment may help organize information to guide appropriate decisions about agricultural policies and extension. The main objective of this study was to evaluate the relationship between, and usefulness of, three different analysis methods to assess economic profitability and ecological carrying capacity as two important aspects for sustainable development in tropical crop production. Profitability was defined as the maximum economic benefit that could be achieved from the use of an area of cropland. Furthermore, the assessment was extended to evaluate the profitability including the environmental work used for non-marketed resources. Ecological carrying capacity has been defined by Chambers et al. (2000, p. 46) as ‘‘the number of animals of a given species that a defined habitat can support infinitely’’, with the consideration that humans may highly adapt their environment and increase their carrying capacity (ibid.). Sustainable development may generally be defined according to the World Commission on Environment and Development as ‘‘development that meets the needs of current generations without compromising the ability of future generations to meet their needs and aspirations’’ (WCED, 1987). In the context of the present study, this involves the improvement of production to increase the long-term economic viability and at the same time stay within the ecological carrying capacity of the area. The methods used in this study were: (1) cost and return estimation, (2) ecological footprint and (3) emergy analysis. These methods were chosen because they reveal different aspects of sustainability (economic or ecological and short- or long-term), they have different scientific backgrounds and system boundaries
and the importance of natural resources is weighted differently. Questions at issue were: what information is obtained from the different methods of analysis? Does each of the methods of analysis add essential information? Are the results comparable? Do they point in the same direction? Are they adequate as a decision basis for local policy action? What additional information would be needed? As study cases, we used six of the most commonly grown crops in Nicaragua: common bean (Phaseolus vulgaris L.), tomato (Lycopersicum esculentum L. Mill), cabbage (Brassica oleraceae L. var. capitata), maize (Zea mays L.), pineapple (Ananas comosus L. Merr.) and coffee (Coffea arabica L.). These crops were chosen because they are important either as cash crops or for farm self-consumption in thewatershed area where the study was carried out. Nicaragua is mainly an agricultural country with most of its agricultural activities in the Pacific region, which has irregularities in water supply. The watershed of the Xolotla´n or Managua Lake is divided into two areas, northern watershed and southern watershed. The southern watershed of Xolotla´n Lake (118570 –128170 N and 868050 –868290 W), where the study was performed, was identified in 1992 as the highest priority area in the country for watershed management and it is the most important in terms of research and study due to the high risks of contamination of the water sources. In 1998, Universidad Nacional Agraria of Nicaragua undertook a study (UNA, 1998) of the southern watershed of Xolotla´n Lake near the capital city of Managua, with the aim of generating information about resources, limitations and strategies that would be helpful in watershed management. That study provided basic and general information on the area and its agricultural systems that we used in this study. Cost and return estimation (CAR) is one of the most common economic analyses used by farmers and extensionists to project and evaluate the economic outcome of a production system (AAEA, 2000). In a CAR, prices within an actual or hypothetical market are used to assign values to different inputs to a studied agricultural system (AAEA, 2000). The analysis is based on economic theory where value is subjective and due to human preferences, the consumer’s perception of utility. Furthermore, it assumes unlimited substitution between man-made and natural capital (Ayres et al., 2001). At a larger scale, economic analysis has recently been extended to allow the
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inclusion, at least partly, of non-market values such as environmental degradation and land use change (Alfsen et al., 1996; Edwards-Jones et al., 2000; Mu¨nier et al., 2004) with results in monetary units. Such studies include an economic evaluation of air pollution impacts on environmental systems (Adams and Horst, 2003). Moreover, Pimentel et al. (1997) and Bra¨uer (2003) discuss the use of economic evaluation for the conservation of biodiversity, while Costanza et al. (1997) and Zhao et al. (2004) assess the value of the world’s ecosystems in generating ecosystem services. Cost analysis has been used in scientific studies in various countries to assess the economic feasibility of crops, e.g. by Nelson et al. (1998) for maize in the Philippines, Alema´n (2001) for beans in Nicaragua, Taylor et al. (2001) for tomato in Israel and Gangwar et al. (2003) for cabbage in India. However, our review of the literature suggests that there are no studies where CAR at farm and crop level has included environmental costs. In contrast to CAR, the ‘‘ecological footprint’’ (EF) includes no economic valuation, yet it has a human centred approach. According to Wackernagel and Rees (1996) the ecological footprint ‘‘accounts for the flows of energy and matter to and from any defined economy and converts these into the corresponding land/water area required from nature to support these flows’’. The theoretical basis for the analysis is to be found in the biological concept of carrying capacity. The concept accounts for the flows of energy and materials to and from any given economy and converts them to the corresponding land or water area required to maintain those flows and also to assimilate the wastes produced. The EF is a measure of consumption of a geographically defined population, expressed on bioproductive land area. Only resources or processes easily converted to an area are measured. The EF as a figure does not tell us whether the consumption is sustainable or not. It becomes a measure of sustainability only after being compared to the carrying capacity (bioproductive) of the same geographic area. This tool is analytical and educational at the same time, as it not only evaluates the sustainability of human activities, but also effectively increases public awareness and helps in the decision making process. Ecological footprint accounts for the embodied energy in the transformation of raw materials in society. Calculations of the EF have been made to calculate the impact at different scales, from personal (Redefining
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Progress, 1999) to city (Wackernagel and Rees, 1996), regional (Wackernagel and Yount, 1998), country (Bicknell et al., 1998; Brown et al., 2000b) and global (WWF, 2002). EF has also been used to assess the impact of organizations and services, ranging from energy sources and water services to education (Chambers et al., 2000). Another use of EF has been the assessment of products such as cola drinks packaging, recycled paper, materials and waste, various foods and passenger transportation (Chambers et al., 2000). EF has also been used to study the sustainability of food systems and agriculture (Deumling et al., 2003), including estimates of EFs for crops such as potatoes and tomatoes, in relation to weight or to food energy produced. Emergy analysis (EA) has an eco-centric approach, since the valuation is based on the environmental work, direct and indirect, needed to generate a resource or a process, irrespective of human preferences (Odum, 1996). Environmental work can be defined as the energy expended by natural phenomena to provide us with the energy and raw materials necessary to keep human systems operating (Odum, 1988; Merriam Webster Online Dictionary, 2005). In an emergy analysis, economic and environmental inputs are assessed on a common basis (e.g. converted to solar energy) according to the environmental energy needed for its generation (Odum, 1996). By using emergy, it is possible to evaluate the major inputs from the human economy, along with non-market resources and services. In the case of raw agricultural products in particular, market price may underestimate the real contribution to an economy’s welfare, because it does not represent the environmental work involved in making that product. The theoretical and conceptual basis for the emergy methodology is grounded in thermodynamics and systems ecology (Brown et al., 2000a). Emergy analysis has been applied to studies in a variety of both temporal and spatial scales including the evaluation of history, the assessment of environmental policies and management, government programmes, international trade, energy policies and simulation models as decision support (see, e.g. Odum and Odum, 1983, 2000, 2001; Doherty et al., 1993; Ulgiati et al., 1994; Odum, 1996). Some studies have been made that include agriculture and agricultural products, e.g. Ulgiati et al. (1993) analyzed the agriculture of Italy, Lagerberg and Brown (1999) made a study of greenhouse tomatoes in Sweden, Brandt-Williams (2001) calculated the emergy
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of Florida agriculture, Rydberg and Janse´n (2002) compared the use of horse and tractor traction in Swedish agriculture and Lefroy and Rydberg (2003) evaluated three different cropping systems in Australia.
2. Materials and methods Given the different theoretical and conceptual backgrounds that each methodology uses and the differences in system boundaries inherent in the methods, each one focuses on certain aspects of sustainability (for definition see above). We used the CAR analysis to assess the short-term economic profitability of agricultural crops with the calculation of costs and revenues, while EF and EA were used to assess the profitability including certain environmental aspects and to assess ecological carrying capacity. 2.1. Description of the area and the crops The agricultural systems in the southern watershed are characterized by the use of large amounts of inputs for crop production, in comparison to farms in other areas of the country (UNA, 1998). Almost 50% of the farmers use fertilizers, insecticides and certified seeds for their crops. Only 9% of the farmers use organic fertilizers and biological insecticides. The most important crops for the economy of the area are: coffee, pineapple and pitahaya (Hylocereus spp.). Management of these crops uses a significant amount of labour, which means seasonal employment for the rural population. There are mainly two growing seasons, the first from May to August and the second from September to December. Beans are grown during both seasons, while tomato, cabbage and maize are only grown during the first growing season. Pineapple is a semi-perennial crop that can have several harvests. In the case of maize, the farmers commonly sell part of the harvest as maize grain and part as fresh corn ears. The current analysis is based on half of the area for each purpose. 2.2. Data used In July and August 2001, data were collected at three sites considered to be representative of agricultural production in the southern watershed. We used the local data to have a real setting as context for the study.
However, to make sure that the comparison between the crops was not obscured by differences that did not arise from the various production practices in the crops official data from the Ministry of Agriculture and Forestry and the Nicaraguan Institute for Transfer of Technology (MAGFOR, 2001; INTA, 1995a,b, 1996, 1999a,b) were used to validate the reliability of the local data. In most cases, it was found that farm data and statistical data were in accordance. In the cases of labour hours needed in production, prices of purchased pesticides and fertilizers and prices for agricultural products sold, the official data and the information from farmers disagreed substantially. To make a comparison between the different crops possible, prices for purchased inputs were adjusted to the official data. However, as it was considered likely that labour demand actually differed depending on the actual crop and that prices for the products sold differed due to where the products were sold we decided to use the farm data instead of the official data for these items. Statistics for prices on sold products were from the national market in Managua, while the farmer sold their products at the local markets. Moreover, for these items the data collected from the farms were more in accordance with other studies reviewed, e.g. UNA (1997) and FAOSTAT (2004), than with the official figures from the Nicaraguan statistics. All flows of resources and money were converted to an annual basis. Data for the crop yield relate to the farm gate. All buildings, infrastructure, materials and tools used in the production systems were converted to annual flows based on their expected useful life. The life length estimated ranged from 1 to 10 years for tools and from 30 to 50 years for buildings. Transportation costs were calculated as an average of the transportation costs for the crops. Full calculations for one of the crops (beans) are shown in Appendix A. Calculations for all crops were published in Cuadra (2005). 2.3. System boundaries System boundaries, as well as inputs and outputs considered in the different analysis methods, were diagrammed using energy systems language (Odum, 1996) (Fig. 1a–c). In all three methods the crop production system included the cropland as well as the machinery and buildings appropriated for the cultivation. The products were assumed to be sold at the local market.
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In the CAR analysis (Fig. 1a), only money flows were considered, such as payments for the services in infrastructure, in purchased inputs and in transportation, as well as payments for labour and other services. In the EF analysis (Fig. 1b), resource use was assessed as appropriated areas. These areas included the actual cropping area, the built-up area used for cropping purposes and the area for sequestration of carbon dioxide as a consequence of direct and indirect energy use for purchased inputs and in transportation, as well as for labour and other services. Furthermore, an estimated area appropriated for biological con-
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servation was added. In EA (Fig. 1c), the environmental work of the sun, wind and rain was included as a source for the growth of the crops. Other sources included were the environmental work on raw resources in infrastructure, for purchased inputs and for transportation. In addition, the indirect services needed to transform the raw resources, e.g. raw oil or iron, into usable inputs were considered, as were the direct services, e.g. extension and labour. Soil lost due to erosion was considered as an outflow and evaluated as emergy used in the production system.
Fig. 1. Diagram of the six crop production systems: (a) cost return estimation (CAR), (b) ecological footprint (EF) and (c) emergy analysis (EA).
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2.4. Cost and return estimation For the CAR analysis, we divided the costs into two categories: purchased inputs (seeds, fertilizers, pesticides, water, etc.) and other costs (labour, harvest transportation, technical assistance, tools and equipment and infrastructure) and expressed them in USD ha1 year1. Income from the sales of the agricultural products was also expressed as USD ha1 year1. The economic indices presented are revenues and profitability. Revenues represent the amount of money received by the farmer after all costs have been paid: RevenuesðUSD ðha cropÞ1 year1 Þ ¼ Gross income Total costs Profitability is the economic benefit received by the farmer and expressed as a percentage of the total cost: Profitabilityð%Þ ¼
Revenues 100 Total costs
2.5. Ecological footprint The EF concept accounts for flows of energy and matter to and from any defined economy and converts these into the corresponding land area required to support these flows (Wackernagel and Rees, 1996). Six land categories are usually included in the procedure: cropland, pasture land and grasslands, productive forest, productive sea space, energy land and degraded land (built-up environment). The EF is usually expressed on an area per capita basis. However, in this study, it was more relevant to express EF as area of direct and indirect land required per hectare of crop grown per year, calculated as follows: EFcrop ðha ðha cropÞ1 year1 Þ ¼ ðCA þ BL þ ELPG þ ELTH þ EFL Þ BDL where CA is the cropland, the land covered by the studied crop, which by definition is set to 1 ha. BL, built-up land, is the area occupied by farm buildings, roads, parking, etc., at the farm, proportionally (on hectare basis) allocated to the production of the studied crop, expressed in hectare per hectare of crop grown per year. ELPG is energy land, which means the area required for assimilation of carbon dioxide,
due to production of purchased goods (ha (ha crop)1 year1).1 ELTH is the energy land due to direct and indirect energy use for harvest transportation (ha (ha crop)1 year1).2 EFL is the ecological footprint for labour, which was calculated from the number of full year workers required per hectare of crop and the estimated EF per capita for Nicaragua (WWF, 2002) expressed in hectare of EF per hectare of crop grown per year. Although in the EF method, human labour is not considered, we included labour in the EF evaluation to make it more comparable to the CAR and emergy analysis methodologies that usually include labour in their evaluation.3 The last term, BDL, the biodiversity land, is set as a fraction of the other area requirements and is an estimation of the area needed for conservation purposes. According to Wackernagel and Rees (1996) and Chambers et al. (2000, p. 65), BLD was set to 1.12, which means 12% of the total area requirement. This figure was originally suggested in the Brundtland Commission’s report (WCED, 1987). All the additional area requirements, beyond the actual cropping area, were calculated according to methods used in other studies. The index of EF per gigacalorie (EFGcal) indicates the EF per crop to produce 1E+09 food calories (Gcal) per year (Deumling et al., 2003): EFGcal ðha Gcal1 Þ ¼ EFCrop
Qcrop Gcal YCrop
where Qcrop Gcal is the quantity of crop needed to produce 1 Gcal and Ycrop is the yield of the crop per P ELPG = (PGi EPG) FA, where PGi (kg year1) is the quantity of purchased goods and fuels of different kinds (i), EPG (J kg1) the embodied energy in production of PG (Chambers et al., 2000) and FA (ha J1) is the forest area with global average production needed for sequestration of CO2 released from fossil-fuel burning (Chambers et al., 2000, pp. 63–72). 2 ELTH = X Q EQ FA ICC, where X (km year1) is the transport distance, Q (l km1) the fuel use, EQ (J l1) the embodied energy in Q (Chambers et al., 2000), FA (ha J1) the forest area with global average production needed for sequestration of CO2 released from fossil-fuel burning (Chambers et al., 2000, pp. 63–72) and ICC is the indirect carbon consumption for car manufacturing and road maintenance, estimated as 45% of the fuel consumption (Wackernagel and Rees, 1996, p. 107). 3 To include labour, the number of workdays per year per crop and hectare was expressed as a fraction of the total number of workdays per year (230 workdays per year in Wackernagel and Rees, 1996, p. 106) and multiplied by the EF for Nicaragua (1.53 ha/capita in WWF, 2002). 1
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hectare. To calculate this, we used the metabolic energy for the different crops (Senser and Scherz, 1991). A new index of EF per 1000 USD revenues (EFrev) was developed to relate the ecological footprint to economic profitability. The index of EFrev indicates the area needed for every crop to obtain revenues of 1000 USD: EFrev ðha ð1000 USD revenuesÞ1 Þ ¼
EFcrop Qcrop 1000 Revenues
The EFrev was considered an indicator of economic carrying capacity adding environmental constraints. The lower the index, the less support area needed to generate income. In an environmental–economic sense, land is a scarce resource, both in respect to cropland, land for energy generation, for conservation purposes and for sequestration of emissions. This index is, as all revenue-based measures, volatile to prices. The index is restricted to comparisons when revenues are obtained under identical market conditions and similar prevailing technologies (if technologies improve, productivity also increases). If these conditions change, the size of the EF will also change. 2.6. Emergy analysis Emergy analysis accounts for the energy intensity embodied in products, including the environmental work and the work of humans in generating products and services (Odum, 1996). Emergy is a useful concept to show how much a certain activity drains a system of energy, it is also able to connect the ecological and economic systems, and it provides with an ecocentric valuation method comparing all resources on a common basis (Hau and Bakshi, 2004). The emergy analysis was performed by calculating the direct and indirect energy flows, converted to solar energy, required for the production of each crop (EAU):
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1983; Doherty, 1995; Odum, 1996; Buranakarn, 1998; Cuadra and Rydberg, 2000) representing the amount of solar energy Joules involved in making one unit of good or service. Basic data were arranged into quantities per year and reported as available energy in Joules, mass in grams, or money flow in USD. Soil erosion was estimated based on published data from areas with similar conditions [Rivas, 1993 in Nicaragua, Zo¨bisch et al., 1995 in Kenya and Poudel et al., 1999 in the Philippines]. The emergy to money ratio was calculated from a ratio of the average emergy flow per unit money flow for Nicaragua, resulting in 2.65E+13 seJ USD1 (Cuadra and Rydberg, 2000; Odum et al., 2000). This ratio relates the human economy to its biophysical basis and is an estimation of the natural resources indirectly needed to generate the human services that each unit of money buys. The emergy indices calculated were: emergy-based profitability (EAprof) and emergy-based EF (EAEF). The index of EAprof indicates the net gain in emergy for the producer in relation to emergy used: EAprof ð%Þ ¼
EAMR EAU EAU
where EAMR is the emergy buying power in the money received in seJ (ha crop)1 year1 calculated from Nicaraguan average emergy to money ratio (see above) and the money received for the harvest sold. The index of EAEF estimates the direct and indirect support area needed based on emergy. In this case, the calculated EF is the area needed if the emergy related to total resources for production of a certain crop were to be generated by only local renewable emergy inflow. The index was calculated as follows: EAEF ðha ðha cropÞ1 year1 Þ ¼
EAU EAlrnw
where EAlrnw is emergy from local renewable sources, such as sun, wind and rain.
EAU ðseJ ðha cropÞ1 year1 Þ X ¼ Item Transformity
3. Results
Appropriate conversion factors (transformities) from previous studies were employed (Odum and Odum,
Economic measures based on the cost and return analysis revealed the economic profitability in a short
3.1. Cost and return analysis
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time perspective. Table 1 gives a summary of the various analyses conducted on the six crop systems evaluated. Under the columns labelled short-term economic benefit, the revenues and profitability are given along with the relative rank of each crop for easy comparison. Cabbage followed by tomato led to the highest revenues and accordingly these crops were also the most profitable (11,450 USD ha1 and 304% for cabbage, and 5084 USD ha1 and 200% for tomato). On the other hand, growing coffee resulted in negative economic returns for the farmer. 3.2. Ecological footprint The index of EFrev of the different crops (Table 1) shows that cabbage was the crop with by far the least area needed to generate an income of 1000 USD (0.7 ha (1000 USD)1), while pineapple, the crop with the largest EFrev, required more than nine times as large an area as cabbage (6.5 ha (1000 USD)1). As there was a negative profitability in growing coffee, calculating an EF for 1000 USD of revenues did not make sense. When comparing the EF to produce 1 Gcal (EFGcal) (Table 1), maize and beans were the most favourable crops, with the lowest EFGcal (0.17 and 0.23 ha Gcal1, respectively) and tomato the least, with a five times higher value (1.0 ha Gcal1). The index of EFcrop (Table 1) shows that growing beans on 1 ha resulted in the smallest additional area (2.4 ha (ha crop)1), while on the other hand, tomato and cabbage required the highest (6.5 and 7.5 ha (ha crop)1, respectively).
3.3. Emergy analysis Profitability based on emergy showed a similar pattern to the economic profitability (Table 1), with cabbage performing best (262% more emergy was gained in the payment for the produce sold than was used in the production). On the other hand, growing coffee resulted in the farmer losing emergy, in that more emergy was used to produce the harvest than was gained when it was sold. The index of EAEF (Table 1) shows that beans may theoretically be produced using only local renewable resources, on an area of 13 ha (ha crop)1, which was by far the smallest area, while cabbage, tomato and pineapple required the largest area (56, 36 and 36 ha (ha crop)1, respectively). 3.4. Ecological footprint and emergy analysis compared The relative importance of the resources used differed substantially between the EF and the emergy-based EF. For example, in the emergy-based footprint the growing area represented on average only 3% of the total area use, in comparison with 22% in the EFcrop (Fig. 2). The transportation of harvest represented an average of 23% of the total EAEF, but less than 1% of the EFcrop. However, the two methods agreed that the biggest footprints were represented by labour (on average 49 and 30% for EFcrop and EAEF, respectively) and purchased goods (on average 19 and 41% for EFcrop and EAEF, respectively).
Table 1 Summary table for the cost and return analysis (CAR), ecological footprint (EF) and emergy analysis (EA) for bean, tomato, cabbage, maize, pineapple and coffee calculated per hectare and year Crop
Short-term economic benefit
Economic carrying capacity
Revenues
EAprof
USD ha1
Beans 1057 9 Tomato 5084 44 Cabbage 11450 100 Maize 1188 10 Pineapple 567 5 Coffee 818 7 a b
Profitability
Relative % ranka
Relative % ranka
134 44 200 66 304 100 66 22 36 12 62 21
The highest ranking is the best. The lowest ranking is the best.
EFrev Relative ha 1000 ranka USD
94 36 165 63 262 100 40 15 13 5 72 28
2.3 1.3 0.7 3.2 6.5 1
Ecological carrying capacity EFGcal
EFcro
EAEF
Relative ha Relative ha Relative ha Relative rankb Gcal1a rankb ha1 rank b ha1 rankb 35 20 10 50 100 –
0.23 0.96 0.37 0.17 0.29 0.31
24 100 38 18 30 33
2.4 6.5 7.5 3.9 3.7 3.8
33 87 100 52 49 51
13 36 56 27 36 19
22 64 100 47 63 34
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Fig. 2. (a) Ecological footprint in hectares per hectare of crop grown showing the proportions of the different categories calculated. (b) Emergybased ecological footprint in hectares per hectare of crop grown showing the proportions of the different categories calculated.
4. Discussion 4.1. Indicators of short-term profitability and profitability with environmental considerations The CAR analysis reveals the economic profitability in a short-term perspective. In our calculations, we found that all crops with the exception of coffee were profitable in economic terms (Table 1). In spite of
this, the farmers did not seem to be improving their economic status. One obvious reflection was that the most profitable crops, such as cabbage and tomato, were only grown on small areas (less than 1 ha) and only during one cropping season. Enlarging these areas would require higher investments for the small farmer, who ultimately does not have good access to credit. Furthermore, diversity of production and food security are important issues for the farmer who wants
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to ensure a diversity of sources of income and nutrition for the family, and these are other reasons for growing crops that are less profitable, such as pineapple and coffee. Tradition also plays an important part in the farmer’s decision to grow, e.g. maize and beans, crops that are usually grown by farmers in Nicaragua. The negative profitability of coffee concluded in the study was not surprising, as it has been pointed out in several assessments that the international prices for coffee are very low, causing the bankruptcy of many small farmers (see, e.g. Robleto, 2000; Ponte, 2002; ICO, 2004; Oxfam, 2004). A separate analysis was carried out in which production of maize grain and production of corn ears were separated. This analysis showed that when maize was produced just for grain, the revenues and profitability were negative, but when all the harvest was sold as corn ears, the revenues and profitability increased by more than 100%. However, despite the high profitability of fresh corn ears, there might only be a limited local market for the consumption of corn ears. While CAR only considers inputs with economic value on the market, emergy also includes the work of the environment in generating the raw resources (Fig. 1a and c). Through this, EA adds information on environmental claims on the production. With a negative value of the emergy-based profitability, as for coffee, more emergy is flowing out from the system than into it. This means that the system is being drained of natural resources that might be better used inside the system, without the economic potential to substitute them (Odum, 1996; Cuadra and Rydberg, in press). This is especially critical if these resources are nonrenewable. Interestingly, profitability based on emergy shows a similar pattern to the economic calculation. However, as the farmers are not paid for local renewable and non-renewable resources used in the production, considered in the emergy analysis, the calculation of EAprof is always lower than in plain economic terms. Several studies have calculated the emergy-based profitability slightly differently4 (Ulgiati et al., 1993; Brandt-Williams, 2001; Lefroy and Rydberg, 2003). This way of calculating leads to similar result as 4
The emergy exchange ratio (EER) = emergy use due to the production divided by the emergy basis for the money received when selling the harvest (Odum, 1996).
EAprof but does not correspond to the equation for profitability in the CAR, making the comparison less transparent. Therefore, it was not useful for the aims of the present study. However, the above-mentioned authors refer to a negative emergy balance in respect to the farms, indicating that more emergy is leaving the farm than is gained. Except for coffee, these findings are not in accordance with ours. EFrev also extends the economic valuation of the crop to the part of the environmental requirement. Resources easily converted to land area such as energy use are included. The EFrev follows the trend of the other economic indices, with only small differences. 4.2. Indicators of ecological carrying capacity The EF per hectare of crop produced (EFcrop) and the footprint based on emergy are assessments of ecological carrying capacity of a certain production as they indicate the extent of the external area needed to support the production. Comparing the two indices, the trend is similar, in that beans, pineapple, maize and coffee are the crops with a higher ecological carrying capacity than cabbage and tomato (Table 1). However, the size of the support areas differs substantially using one or other index. The support area needed to calculate the EFcrop is at maximum 7.5 ha, while for the EAEF, the support area represents 56 ha ha1 grown (Table 1). This large difference in the size of the footprints is caused by two differences between EF and EA. First, EA includes the environmental work in the generation of all resources, while EF does this simply for the part of resource use easily converted to land area, which in effect means only direct demand on crop land, land for energy use and land for biological conservation. Second, EA uses an open systems perspective, and accordingly there is an endeavour to take into account all processes needed to make the service or product, while the EF has its system boundary around biological resources. Furthermore, there are differences in the relative importance of resources used in EF and EAEF (Fig. 2). For example, the importance of cropland is larger in the EF than in the EA, which is due to the fact that the values for other inputs in the EFcrop, e.g. transportation of harvest and built-up land, are very small, increasing the relative importance of the cropping area. EF only includes the embodied energy in the fuel used for
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transportation (plus 45% for the indirect carbon consumption for car manufacturing and road maintenance). On the other hand, as EA also adds the environmental work for the generation of the raw materials in the car, and for the support of the human labour manufacturing the car, transportation represents a larger area in EA. Moreover, EF only measures the area occupied by buildings and roads and does not take into account the work of nature in generating the materials, as well as the labour used for the construction of the buildings and roads, which are included in EA. EF and EAEF also differ in respect to the items included. Soil erosion and water are usually not included in the calculation of EF, while built-up area and biodiversity are, in general, not integrated in EA. It would be possible to include soil erosion in EF, e.g. as an enlargement of cropping area or reduced harvest. The inclusion of water in an EF has been reported by Chambers et al. (2000, pp. 98–100) but the results underestimated the contribution of fresh water, as it only represented the embodied energy for the economic part of the supply of water or was calculated by the use of the catchment areas, in both cases only considering groundwater and not evapotranspiration. Built-up area and biodiversity could be included in an EA as solar emergy from the area required for the buildings and for conservation of biodiversity (Odum, 1996, p. 239). Both ecological footprint and emergy-based footprint express the stress on the ecosystem, with a postulation that a large stress indirectly means less space available for wild species. This means that a large part of the biocapacity of the Earth is used by humans, leaving less space available for wild species and less resources available for other natural functions (Vitousek et al., 1997; Costanza and Farber, 2002). Neither EF nor EA expressed in hectare of support area per hectare of crop addresses the importance of the crops for the farmer, the purchaser or the society. We therefore added the index of EF per gigacalorie as it roughly indicates the area needed to feed a certain number of people. Deumling et al. (2003) used kilograms of harvest as the denominator when assessing the effect on EF of different production methods. When the aim is to compare the EF for different crops, we argue that this index is inappropriate, as it does not reveal the large differences in
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qualities between crops. On the other hand, using EF per gigacalorie makes the comparison unfair in respect to, e.g. maize and beans, which are more balanced in their composition than the other crops. Tomato, for example, contains only 1% protein in 100 g of edible portion while beans contain 21% (Senser and Scherz, 1991). In our analysis, maize turned out to be the crop with the lowest area demand per production of gigacalorie, and in that sense, the one giving the highest ecological carrying capacity. 4.3. Weighting between different indicators Our results add to the body of knowledge on the poor coherence between economic profitability and ecological sustainability. While cabbage and tomato are the most profitable crops in economic terms, they require large support areas per hectare and per gigacalorie (Table 1). For coffee, beans and maize it is the other way around, a low profitability but smaller support areas required for the production. Cabbage seems to be the optimal choice if economic sustainability is the first priority. However, it is critical to observe that its high ranking, using the indices composed of economic and ecological measurements, is due to the large profitability rather than to a low environmental load. In emergy terms cabbage has by far the largest footprint, almost five times larger than beans. This means that large areas of cabbage would impose a heavy environmental load and would probably have deleterious effects on local ecosystems (Odum, 1999). The EAprof and EFrev do not reveal this. 4.4. General conclusions on the different methods No single method or index is adequate and enough, to answer all questions and to include all aspects. For the evaluation of economic profitability considering the effects on the ecological sustainability it is important to complement economic valuation with ecological ones. This could be achieved with the use of different methods such as EF and EA and by using a combination of the indices proposed in this paper. EF is a useful pedagogic instrument to make our dependence on ecosystems visible, in the ambition to ‘‘force the international development debate beyond its focus on GDP growth to include ecological
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reality’’ (Rees and Wackernagel, 1996). Some of the shortcomings are that EFs have arbitrary system boundaries, do not take into account differences in energy qualities and underestimate some inputs and environmental impacts such as water use, contamination and soil erosion (Moffatt, 2000; van Kooten and Bulte, 2000). In the emergy footprint, the system boundaries are not arbitrary since the boundary for the resource analysis is equivalent to the boundary for the global ecosystem, and the time frame, at least in theory, is as long as the age of the Earth (Odum, 1996; Bjo¨rklund, 2000; Brown and Ulgiati, 2001). However, to communicate the results, a general understanding and acceptance of its scientific basis is needed. Using systems ecology as a way of looking at how nature is organized is a new paradigm on which there is disagreement (Bjo¨rklund, 2000).
Acknowledgements We gratefully acknowledge the Swedish International Development Cooperation Agency (Sida) and its Department for Research Cooperation (SAREC) for providing financial support to the cooperation programme ‘UNA-SLU PhD Programme’ which financed this study. We also want to express our gratitude to Dr. Torbjo¨rn Rydberg, Dr. Lars Ohlander and Dr. Henrik Eckersten from SLU, to Dr. Mark T. Brown from the University of Florida and to two anonymous reviewers for constructive comments on the manuscript.
Appendix A See Tables A.1–A.3.
Table A.1 Cost and return analysis for beans Item
Quantity ha1
Unit
Price unit1
USD ha1
Income Beans
3756
kg
0.49
1848.28
Purchased inputs Seed
104.53
kg
1.22
127.04
Fertilizers Complete formula (18-46-0) Foliar fertilizer
261.32 8.54
kg kg
0.28 2.23
74.10 19.06
4.46
25.41
11.90 7.02
67.75 25.41
Insecticides Metamidophos
5.69
l
Fungicides Benomyl Water for pesticide application
5.69 3.62
kg m3
Total Other costs Labour Soil preparation Harvest transportation Technical assistance Tools and equipment Infrastructure Total Total costs ha1 = purchased inputs + other costs
338.76 143 2 3756 1
Man day1 ha1 times kg ha
2.08 21.17 0.016 2.12
297.14 42.35 59.56 2.12 47.07 4.08 452.30 791.07
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Table A.2 Summary of the ecological footprint for beans (ha (ha crop)1) Footprint areas for (1) (2) (3) (4) (5)
Equivalent total (ha)
Purchased goodsa Transportation of harvesta Built-up land on farm Growing area Laboura
0.21 0.00 0.00 1.00 0.95
Biodiversity (12% of overall footprint) Total used (without biodiversity) Total used (including biodiversity)
0.26 2.16 2.42
(1) Purchased goods: (a) Fertilizers = 13.07 GJ/ha, (b) fungicides = 0.23 GJ/ha, (c) insecticides = 0.23 GJ/ha, (d) seeds = 0.10 GJ/ha, (e) tools = 1.93 GJ/ha and (f) buildings = 0.03 GJ/ha. Energy land for embodied energy in purchased goods = 15.59 GJ/ha/71 GJ/ha/year (energy embodied in net imported goods; WWF, 2002) = 0.21 ha. (2) Transportation of harvest: 20 km trip to market. Average gas consumption = 12 l/ 100 km = 3.5 l/trip to market (including 45% more for indirect carbon consumption in car manufacturing and road maintenance). 35 MJ/l of gasoline = 0.122 GJ. Energy land for transportation = 0.122 GJ 71 GJ/ha/year (energy embodied in net imported goods; WWF, 2002) = 0.0017 ha. (3) Built-up land on farm: Area used for buildings + area for parking, roads and other infrastructure = 0.0002 + 0.0002 = 0.0005 ha. (4) Growing area: 1 ha. (5) Labour: Workdays per year = 230 (Wackernagel and Rees, 1996, p. 106). Man day1 ha1 in beans = 142.68 = 0.62 persons/ha (142.68/230). Ecological footprint of Nicaragua = 1.53 ha/capita (WWF, 2002). EF for labour = 0.62 persons/ha 1.53 ha/capita = 0.95 ha. a Land area required for absorbing CO2 from fossil fuel; direct use; in production of the goods or for subsistence of labour force. Table A.3 Emergy analysis of the system of bean production Component
Annual flowa
Transformityb
1 2 3 4
Renewable resource use Solar insolation (J) Wind, kinetic energy (J) Rain, chemical energy (J) Rain, geopotential energy (J)
5.71E+13 4.12E+11 6.50E+10 1.28E+09
1 2513 30574 17620
5
Non-renewable local sources Topsoil loss (J)
5.98E+09
1.06E+05
633
6 7 8 9 10 11 12 13 14 15
Purchased fuels and goods Nitrogen (g) Phosphate (g) Water for pesticides application (J) Pesticides and fungicides (J) Seeds (J) Metals in equipment (g) Plastics in equipment (J) Buildings, wood (J) Buildings, concrete (g) Buildings, adobe and clay roof (g)
4.70E+04 1.20E+05 1.79E+07 5.44E+08 1.38E+09 2.89E+04 1.84E+07 2.69E+07 1.81E+04 3.39E+03
7.73E+09 6.55E+09 8.06E+04 9.42E+04 5.85E+04 1.68E+09 3.46E+05 1.11E+04 2.42E+09 3.36E+09
363 788 1 51 81 49 6
