Coffee is one of the most widely traded agricultural commodities in the world, produced by 20 to 25 million producers in more than 14 countries [1
]. Vietnam has a 12% to 15% share in the world market and is the second largest exporter of coffee after Brazil [1
]. In 2013, coffee exports accounted for approximately 2% of Vietnam’s gross domestic product (GDP) [4
] and 17% of all commodity exports [3
]. Currently, an area of 500,000 hectares is planted with coffee, with a production of approximately one million tons [3
]. Vietnam produces coffee from two species, Robusta
; 90% of production is from Robusta [5
]. Approximately 80% of coffee is produced in four provinces located in the Central Highlands of Vietnam, i.e., Dak Lak, Lam Dong, Dak Nong and Gia Lai, typically by smallholder farmers that obtain the largest part of their income from coffee production [6
]. Coffee production is important for the Vietnamese economy and crucial for the Central Highlands. However, the unshaded monoculture farming system used by most producers generates harmful environmental impacts. Coffee farming in Vietnam requires high application rates of fertilizers [9
]. High fertilization rates are associated with high rates of emission of greenhouse gases and leaching of nutrients to water bodies. Emission of greenhouse gases contributes to climate change, whereas leaching of nutrients can have adverse effects on biodiversity and water quality [10
]. Chemical pesticides are used to control fungal diseases, pests and a variety of weeds. Improper application of chemical pesticides in coffee production can have adverse effects on the environment, biodiversity, and human health, especially farm workers [11
]. Ensuring adequate production levels requires irrigation of coffee plantations [13
]. Experience in Vietnam shows that farmers often use more water than needed [9
]. Over-irrigation is especially harmful during drought years, when water resources (from groundwater and basins) are depleted [7
]. Scarcity of water may drive additional investments for construction of deeper wells and negatively affect ecosystems.
The profitability of coffee farming is under pressure, due to increasing production costs for fertilizers and labor [8
], as well as the volatility of coffee prices in the world market [3
]. At low coffee price levels, the revenues of coffee production may not cover production costs, and other crops, such as pepper, may then be more profitable. This situation leads to (partial) replacement of coffee plantations by other crops or to the abandonment of coffee farms [3
], impacting, in turn, on the livelihood of rural communities, especially the relatively poor.
The challenges facing coffee production in Vietnam center around the three pillars of sustainability, i.e., environmental, economic, and social. A first step towards achieving sustainable coffee production in Vietnam is an integrated assessment of the current relative sustainability level of coffee farms. A following step is to explore how the socio-economic characteristics and management practices of farmers affect the sustainability level. The most common approach for sustainability assessment is the use of a diverse set of performance indicators, which measure the extent to which sustainability goals are achieved [15
]. Quantification of a set of performance indicators is feasible in many cases. However, when using isolated indicators reflecting different aspects of sustainability, decision makers must use their own weighing factors to evaluate overall sustainability. These weighing factors reflect implicit and complicated trade-offs between sustainability issues that are not normally in decision makers’ mindsets, e.g., greenhouse gas emissions versus profitability [16
]. To overcome these difficulties, several authors have proposed using a single metric, namely monetary value, to express the performance on the different aspects of sustainability [18
]. We follow this approach and propose using an integrated sustainability performance measure labelled social profit, that is, the profit of the farm system (revenues minus production costs) adjusted for the external costs of production (environmental and social dimensions of sustainability) [17
]. Using this approach, a farm that achieves a positive social profit (farm revenues are higher than production and external costs) is considered to perform sustainably. This approach assumes a weak sustainability perspective, implying that man-made capital can replace any component of the natural and social capital [24
]. Social profit is additive, i.e., the social profit of a sector in agriculture (e.g., the coffee sector) is the sum of the social profit scores of the individual farms.
Some studies have assessed the production and external costs of agricultural production, mostly at the country level, see for example [21
]. Moreover, several studies have employed Data Envelopment Analysis (DEA) and the Directional Distance Function (DDF) to measure agricultural total factor productivity, technical inefficiency and eco-efficiency at farm level, see for example [30
]. However, the literature lacks empirical studies assessing social profit inefficiency, which is the extent to which farms fail to obtain the maximum attainable social profit given technology and prices for inputs, outputs and externalities. In this paper, we address this knowledge gap and employ the profit function and the DDF to construct a measure of social profit inefficiency, i.e., the Nerlovian social profit inefficiency (NI
) indicator. A sustainability performance assessment using the NI
indicator would enable the identification of sources of inefficiency in attaining social profit as a first step to highlight potential areas where sustainability of agricultural production can be improved. That is, the identification of the extent to which the current production potential is used, the sub-optimal choice of the scale of the operations, and the sub-optimal allocation of resources and pollution at given prices for outputs and inputs and the economic value of externalities.
Furthermore, socio-economic characteristics (e.g., education, age, number of family members, socio-economic income level group) and management practices (e.g., pruning, weeding, spraying, fertilization), have been shown to be important factors affecting sustainability of farms [7
]. However, the impact of these characteristics on social profit inefficiency has not been investigated so far.
In line with the preceding considerations, the objectives of this study are to: (i) assess the current relative sustainability level of a sample of coffee farms in Vietnam in terms of their social profit and to identify the sources of social profit inefficiency, and (ii) detect statistical associations between a set of socio-economic characteristics and management practices and the relative sustainability level of farms. For this empirical study, we hypothesize that social profit inefficiency and its components are positively associated with the variables related to distance, spraying, and ethnic group, and that the inefficiency scores are negatively associated with family members, hired labor, pruning, fertilizing, weeding, and education.
This assessment will provide insights and will identify opportunities to improve sustainability of coffee production in Vietnam.
Data used in this study were collected between 2007 and 2009 in Chu Se District, Gia Lai province by the project ‘Quality and Sustainability Improvement of Robusta Production and Trade in Gia Lai, Vietnam’, funded by the Douwe Egberts Foundation and conducted by EDE Consulting. Gia Lai province is located in the Central Highlands of Vietnam. It borders to the north with Kon Tun province, to the south with Dak Lak province, to the east with Binh Dinh providence and to the west with Cambodia. The population of Gia Lai is 1.36 million, of which, approximately 80% depend on agriculture [51
]. Coffee production is one of the main economic activities in the region. Gia Lai has 80,000 hectares planted in coffee and accounts for 12% of total Robusta coffee area in Vietnam [52
In Chu Se District, Gia Lai province, 800 coffee growers voluntarily participated in the project, mainly because of their interest on improving productivity and efficiency of input use. The project provided training to these farmers, through 40-trained key farmers and 14 governmental extension staff. Apart of the training, interested farmers kept daily records on field books of frequency of activities such as fertilizing, weeding, pruning and spraying, on the use and costs of inputs such as labor, fertilizer (quantity of Nitrogen, Phosphorous and Potassium) and pesticides, and on the coffee output produced. Information on socio-economic characteristics such as the education level, age of farmer, number of household members, ethnic group and distances of the farm to the city/town center, fertilizer shop and coffee trader/factory were also collected. Key farmers gathered and reviewed the data to check for potential errors. The number of participants decreased from 739 in 2007 to 661 in 2009. Drop-out was mainly because farmers shirted to other crops (especially pepper) and because of farmers finding record keeping rather demanding. Reasons to keep record were due to the requirements by UTZ and 4C (certification bodies). Participating farmers received feedback through annual individual reports, containing detailed analysis of financial and physical performance of their farm, and ‘group’ reports that enabled farmers to compare themselves with their peers. The daily records of farmers were digitized by project staff to make these reports possible [51
Out of the 661 farms in 2009, we selected 361 coffee farms to conduct this study. For these farms, data was selected from field books for the year 2009 and cover one production cycle. The remaining farms were not included in the analysis as field books were incomplete in terms of input-output information. One output (coffee beans)
and four inputs (variable inputs
, and water)
were included. Land was assumed to be a fixed input in coffee production: coffee is a perennial crop that remains fruitful for 20 to 30 years [40
]. The environmental impacts of coffee production in Vietnam are mainly caused by high fertilization, inadequate use of pesticides, deforestation, and depletion of groundwater [5
]. Therefore, the following negative environmental externalities were selected: greenhouse gas emissions, nitrate pollution,
and pesticide toxicity
. Due to data limitations, externalities related to deforestation and groundwater reduction were not included in this assessment.
Coffee farms in this sample are similar in terms of the coffee produced, i.e., Robusta, tree age, and soils conditions, but vary in intensity of input use and farm size. The average farm size planted with coffee in this sample was 1.1 hectares. According to [40
], a farm with planted coffee area of 1.5 hectares or less is classified as a small farm, and a farm with planted coffee area greater than 1.5 hectares is classified a large farm. Therefore, 85% of the sampled farms could be considered small farms and 15% of the farms large farms (Table 1
3.1. Quantity of Outputs, Inputs, and Externalities
The quantity of coffee beans is expressed in tons of green bean equivalents (GBE) produced in a production cycle (one year in Vietnam). The implicit quantity of variable inputs is expressed as annual aggregated expenditures on fertilizers and chemical pesticides (herbicides, insecticides, and fungicides) in 2009 US dollars ($). Labor is measured as the total number of working days used at the farm, including both family and hired labor (a working day equals eight hours of work). Land is defined as the area utilized for coffee production, measured in hectares (ha). The quantity of water used for irrigation is expressed in cubic meters (m3).
The negative externality greenhouse gas emissions
is expressed in CO2
-eq.). Three greenhouse gasses were considered: carbon dioxide (CO2
), methane (CH4
), and nitrous oxide (N2
O). For each greenhouse gas, the annual amount of gas emitted to the atmosphere was multiplied by its global warming potential over a time frame of 100 years, relative to that of CO2
]. These amounts were then summed to obtain the total annual amount of greenhouse gas emissions. Two sources of emissions were considered: (i) emissions that are intrinsically associated with the production of fertilizers and pesticides (embedded emissions); and (ii) N2
O emissions caused by direct and indirect Nitrogen (N) emissions (see below). Emission factors of production of pesticides and fertilizers were obtained from the scientific literature [57
The externality nitrate pollution
captures the amount of N that is released to the environment in the form of nitrates (NO3
-N) and is expressed in kilograms of nitrates as N (kg NO3
-N). This externality was calculated for each coffee farm as the difference between the amount of N that enters the system and the amount of N that leaves the system, as we assumed that coffee farms are in equilibrium with respect to N in the system. N enters the system through the application of fertilizers. The amount of N that leaves the system includes the amount of N that is lost via background emissions (N2
O-N), fertilizer-induced and crop residue emissions (N2
O-N and NO-N), N lost via volatilization (NH3
-N and NO-N), and N that is exported in the harvest material. We assumed steady-state conditions for soil organic matter: organic matter lost through mineralization is compensated by pruning, dying leaves and dying roots that are added to the soil. N inputs from fertilizers were estimated as the quantity of each type of fertilizer (kg of synthetic and organic fertilizer) multiplied by the known (or estimated) N concentration per kilogram of fertilizer. N contents of organic material used as fertilizer are generally not measured and estimates are based on existing literature [60
]. Nitrogen inputs from crop residues were estimated as the annual amount of crop residues (kg of dry matter per year), multiplied by the average estimated N concentration per kilogram of dry matter (% N per kg dry matter) [62
O-N background emissions were calculated based on the emission factor proposed by [56
] for tropical climates on a per-hectare basis. Fertilizer-induced and crop-residue N emissions were estimated using the generic emission factors of [65
], which reflect the percentage of the applied N that is lost via N2
O-N and NO-N emissions. The generic emission factors differ per type of fertilizer. The average emission factor of [65
] is approximately 1% of total N fertilizer. This value is similar to the default value published by [56
] and to the results of N2
O-N emissions found in the field by [63
] in coffee plantations in Costa Rica. N loss via volatilization was estimated using Tier II [56
]. N export through the coffee harvest was estimated using yield data and figures on nutrient removal from harvesting coffee beans [55
The externality pesticide toxicity
is expressed using the environmental impact quotient (EIQ) score and was estimated using the EIQ model developed by [67
] to provide an assessment of the risks involved with pesticide use. The EIQ model does not provide exact measurements of the impact of pesticide application, but allows the comparison of potential impacts from different farm management practices. The model gives an EIQ score to each active ingredient for three components: environment, farm worker and consumer. The EIQ score for the environmental component reflects the impact of the active ingredient on aquatic life, bees, birds, and beneficial insects. The EIQ score for the farm worker component reflects the impact on applicators and pickers, and the EIQ score for the consumer component reflects the impact of the pesticide active ingredient on the consumer, caused by residues in groundwater and food [68
]. The total EIQ score is calculated as the average of the three components and reflects the overall toxicity of each pesticide active ingredient. To estimate the toxicity of the weed, pest, and disease control strategy of each coffee farm, the total EIQ score for each active ingredient used at each farm was multiplied by its application rate (kg of active ingredient). The EIQ scores were then summed over all the active ingredients used at the farm, yielding the externality pesticide toxicity
. Higher scores represent a higher potential impact of the weed, pest, and disease control strategy of a given coffee farm.
shows the descriptive statistics for the quantities of output, inputs, and externalities. The equations, emission and conversion factors, assumptions, and calculations of the three externalities are fully detailed in Supplementary Material 2
3.2. Prices for Outputs, Inputs and Externalities
Prices for outputs and inputs and the economic value of externalities are all expressed in 2009 US dollars ($). If it was necessary, prices were inflated using Consumer Price Indices.
3.2.1. Prices for Outputs and Conventional Inputs
Observed market prices for the output and the conventional inputs were obtained from field book data. The price used for the output coffee beans is the average annual price per ton of Robusta coffee received by coffee farmers in 2009 ($ per ton). The quantities of pesticides and fertilizers are expressed in total expenditures. As these quantities implicitly incorporate farm-specific prices, the price of the variable input was set to one. The price of labor is the daily minimum wage in Vietnam ($ per working day); this implicitly assumes that the shadow price of family labor is equal to the market wage. The social profit indicator is a measure of social welfare and measures the net benefits of the farm system for society. Hence, family labor is taken as a cost in coffee production. The annual rental value of agricultural land is used as the proxy price of land ($ per ha of agricultural land per year). The price of irrigation water in Vietnam is computed as the cost of the fuel, electricity, and labor needed to irrigate one cubic meter of water ($ per m3).
3.2.2. Economic Value of Externalities
The externalities of coffee production are not traded in well-defined markets, and therefore, market price information does not exist. The economic value for the externalities were transferred from empirical studies in published literature and adjusted to the Vietnamese context using the ratio of GDP per capita of Vietnam to the average GDP per capita of the country, where the estimation was made, expressed in purchasing power parities (PPP). This assumes that the willingness to pay (WTP) to avoid or to mitigate the damages is proportional to the per capita income of each country [69
] and is locally determined.
We used the mean economic value for greenhouse gas emissions
reported by [21
]. In this study, the economic value per ton of CO2
-eq. was calculated based on a review of values found in existing literature sources. These values reflect the societal, economic and ecological costs of emitting a unit of CO2
to the atmosphere. Costs are associated with parasitic and vector bone diseases, sea-level rise, climate instabilities, decreased water availability, droughts and biodiversity loss, among others [21
For the economic value of the externality nitrate pollution
, we relied on the study of [70
]. This study provides a comprehensive assessment of the external costs of different N flows for the EU27 in the year 2008, among them, the health-related damages and the ecosystem damages per unit of N leached to the environment. The per capita income in Vietnam is lower than the per capita income of European countries. Therefore, we assumed that Vietnam’s WTP to reduce human health- and ecosystem-related problems is lower compared to the European countries. Accordingly, we used the lower bound of the external costs reported by [70
], adjusted to the Vietnamese context using the ratio of the GDP per capita, expressed in purchasing power parities (PPP).
In the case of the externality pesticide toxicity
, the economic value of an EIQ was estimated using the pesticide environmental accounting (PEA) tool developed by [71
], combined with the approach of [21
]. The PEA tool estimates an economic value per unit of application of an average pesticide active ingredient, by combining the EIQ method and the base values for external pesticides costs in UK, USA and Germany reported by [72
]. The PEA tool also enables the adjustment of the economic value per unit of application of an average pesticide active ingredient to different countries using the GDP per capita of a country relative to the average weighted GDP per capita of the UK, USA and Germany [71
]. The description of the calculation of the externality pesticide toxicity
is fully detailed in Supplementary Material 2
provides the descriptive statistics for the farm-specific (shadow) prices of outputs, inputs, and externalities.
3.3. Determinants of Nerlovian Social Profit Inefficiency
In the efficiency literature, farm inefficiencies relative to the best-practice frontier are commonly attributed to socio-economic characteristics of farm households and to improper farm management practices [38
]. We selected eleven socio-economic and farm management variables (Table 3
) as potential determinants that could be statistically associated with NI
and its components. The socio-economic variables were: (i) distance of the farm to the fertilizer shop (measured in kilometers); (ii) distance of the farm to the closest city/town center (measured in kilometers); (iii) distance of the farm to the closest coffee trader/factory (measured in kilometers); (iv) family members (measured as the number of members); (v) two variables representing the education of the husband and the education of the wife (each measured as a categorical variable, where 0 reflects five years of primary education, 1 reflects an additional 4 years of intermediate education, and 2 reflects an additional 3 years of secondary education); and (vi) ethnic group (binary variable, where 0 refers to the ethnic group Kinh, which is the major group in Vietnam, and 1 to the ethnic group JoRai). Five explanatory variables reflecting farm management practices were included: the frequency of (vii) pruning, (viii) fertilization, (ix) weeding, and (x) pest and disease control (each measured as the number of times that the activity is performed in one production cycle), and (xi) the share of hired labor in total labor (measured as percentage). Only coffee farms with complete information for the selected variables were used in the bootstrap truncated regression: 302 out of the 361 coffee farms in the sample.
We hypothesized that the inefficiency scores (NI, PTI, SI, and AI) are positively associated with the variables related to distance, spraying, and ethnic group, and that the inefficiency scores are negatively associated with family members, hired labor, pruning, fertilizing and weeding. We expect a mixed effect for the variable education. Our hypotheses are based on the following assumptions:
The farther away the farm is from the fertilizer shop, town/city center, or coffee factory/trader, the more difficult or costly it is for farmers to commercialize products and to have access to inputs, credit and extension services, which should, therefore, lead to increased inefficiency scores. For example, in the study of [40
] in coffee farms in Vietnam and [76
] in coffee farms in Cameroon, it was found that farmers with better access to credits and markets have overall lower production costs and lower inefficiency scores;
The Kinh ethnic group has higher profit maximization behavior in comparison to the JoRai group [14
]. In addition to being the major group in Vietnam, this group often farms areas more favorable for coffee production [79
] and is more likely to have better access to extension services, education and technical information [39
]. Therefore we expect Kinh farmers to have lower inefficiency scores;
Coffee growing is labor intensive [7
] and therefore families with more members have more potential labor (time) to allocate to activities such as harvesting, pruning and weeding, which are mostly done by manual labor [7
Hired labor is better qualified to perform (particular) field activities [80
]; hence, a more efficient use of labor is achieved with hired labor, which should lead to lower inefficiency scores. For example, in the study of [40
], it was found that cost inefficiency in coffee production in Vietnam is associated with lower levels of hired labor;
More frequent pruning, fertilizing, and weeding leads to better crop productivity, because the availability and uptake of nutrients is higher, which in turn leads to more efficient use of inputs [55
The larger the number of spraying events, the higher the inefficiency scores of coffee farms. It has been demonstrated that small-scale farmers in different types of farming systems overuse pesticides. Reasons include risk-adversity, limited or lack of knowledge on how to use pesticides and non-proper equipment [81
]. Over-use of pesticides implies more labor allocated to this activity and higher production costs;
For education, a mixed effect can be expected. Higher education levels can lead to lower inefficiency scores because more years of schooling may indicate higher managerial skills, specific professional training, better access to information and adequate farm planning [34
]. Empirical evidence of this effect was found by [7
] in coffee farms in Vietnam, by [76
] in coffee farms in Cameroon, by [39
] in rice in Vietnam, by [77
] in oil palm plantations in Indonesia and, by [78
] in maize in Ethiopia. On the other hand, higher education levels have also been found to have a positive and significant effect on farm inefficiencies. Examples include [74
] in poultry production in Nigeria and [40
] in coffee production in Vietnam. The positive effect of education on farm inefficiencies may indicate that farmers with more years of schooling may have better off-farm options and less time for supervision of their farms.
The social profit inefficiency score that we have used entails a weak sustainability approach [24
]. We believe this practical approach is justified for this specific problem but the weak sustainability assumption must be carefully evaluated if and when this approach is used in other production environments or when different inputs, outputs and/or externalities are considered.
Deforestation and groundwater depletion are important aspects of sustainability of coffee farming in Vietnam [5
]. Most coffee farms are groundwater irrigated [86
]. It is an economic concern that the cost of groundwater irrigation increases as the groundwater level decreases. It is an environmental concern that groundwater depletion takes place at all [87
]. In addition, the application of fertilizer and yield of coffee depend to some extent on the timing and volume of irrigation [6
]. In this paper, we were not able to deal with these issues because of a lack of data. Our analysis would be usefully extended by taking these issues into account.
The results show that the majority of the externalities are below optimal level, indicating that the external costs (greenhouse gas emissions, nitrate pollution and pesticide toxicity) could increase and still yield a lower social profit inefficiency. This outcome is based on the model assumptions. Further development and use of the social profit inefficiency indicator should incorporate critical loads, targets and restrictions (maximum restrictions) for the use of certain inputs, and for the production of externalities.
The findings from this research cannot be directly extrapolated to the entire coffee in Vietnam as the outcomes of our model pertain to the sample (see sub Section 2.2.2
). Nevertheless, the results of this study can help farmers, researchers, and policy makers identify opportunities where the sustainability performance of coffee farms in Vietnam can be improved. At the farm level, the inefficiency in social profit may be greatly reduced by decreasing the inefficient use of nutrients. An optimal use of nutrients would not only positively affect coffee yields, but would also reduce greenhouse gas emissions, reduce the emission of nitrates into soils and water bodies, and lead to a lower need for the application of pesticides. Greater use of pesticide inputs is caused, in some cases, by nutrient deficiencies or over-fertilization of coffee plantations [8
]. Corrective actions to reach an optimal use of fertilizer inputs would reduce expenditures and the amount of labor required to perform activities, such as weeding and spraying. Some of the labor used to perform these activities could then be allocated to other activities, such as pruning, which are negatively associated with social profit inefficiency. Moreover, policies should stimulate proper timing and frequency of spraying through optimal extension services. In Vietnam, it is common that farmers keep applying pesticides in a calendar pattern rather than based on signs of pest outbreak.
At the regional level, we recommend connecting remote areas with extension services and markets. It is expected to: (i) help integrate the coffee chain (producers, traders, and processing companies), (ii) increase the bargaining power of farmers (access to information on coffee prices, traders, and new products and technologies) and, (iii) increase the access of farmers to credit due to improved risk profile.
We also recommend performing an in-depth study on the management of coffee farms by the JoRai ethnic group. These farmers were found to be less inefficient in terms of social profit. The results of the study can be used to identify best management practices; extension services can then disseminate this information and stimulate the adoption of best management practices on more inefficient farms.
Further research should identify additional socio-economic characteristics and management practices of farms (e.g., timing and technology used) that may influence the sustainability performance of coffee farms in Vietnam.
At the methodological level, our approach assumes that socio-economic and farm management practices influence inefficiency of coffee farms. However, it does not allow for the possibility that these characteristics and practices affect the production possibilities [88
]. Therefore, future research could also test whether the second-stage regression is supported by the data [88
Finally, it is worth noting that the variation in farm-level externalities and the variation in the frequency of farm management practices in this study might have some measurement error. This could occur as our sample is a snapshot of coffee farms in a single year and it did not consider what happened in previous years. Thus, for example, it is unlikely that all farms are in the same stage of pruning of their plants, which in turn could affect the nutrient needs of the coffee plantations. The same type of measurement error could emerge with pesticides. In our study, the same pesticide is presumed to have the same externality across all coffee farms, regardless of when it was applied or how it was applied. Therefore, we highly recommend a multiple-year sustainability assessment, as well as the inclusion of other variables related to the farm management practices, such as timing and technology used, to improve the conclusions that were drawn from this assessment.
In conclusion, this paper compared the sustainability performance of a sample of coffee farms in Vietnam using the Nerlovian social profit inefficiency (NI) indicator. Furthermore, this study identified the socio-economic characteristics and management practices that affect social profit inefficiency. The results show that farms, on average, could improve their social profits by almost three times the current social profit levels (84% of the value of the NI denominator). This suggests a large potential for performance improvements. The main source of NI is the allocative inefficiency (55% of the value of the NI denominator), rather than pure technical inefficiency or scale inefficiency. The determination of variable-specific contributions to NI provides evidence of the sources of inefficiency. The comparison between the actual and optimal quantities of each specific output, input, and externality reveals that the low level of coffee production and the under-utilization of inputs, particularly labor and variable inputs, are the main drivers of inefficiency. Most coffee farms have optimum pollution levels, given the economic value of externalities.
The assessment of the external determinants of NI shows that most of the selected variables (socio-economic characteristics and management practices) have statistically association with NI and its components. Farm-specific NI scores are positively associated with the variables distance to city/town center, distance to traders and spraying. Farm-specific NI scores are negatively associated with the following variables: hired labor, ethnic group, pruning, and fertilizing. Corrective actions to ensure the efficient use of inputs and the correct timing and frequency of farm management activities would reduce social profit inefficiency for most of our coffee farms.
Although our study focused on assessing the relative sustainability performance of coffee production at the farm level, this can be extended to include other stages throughout the coffee chain. Future development of this sustainability assessment approach could provide a decision support tool that can be used to translate the concept of sustainability into concrete management actions, thereby helping to maximize the total net benefits to society of food production.