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Article

An Assessment of Sustainability of Dual-Purpose, Dairy and Beef Cattle Production Systems in the Cundinamarca Department (Colombia) Using the MESMIS Framework

by
Fabián Cruz
1,
Dolly Pardo
1,
Alberto Horcada
2 and
Yolanda Mena
2,*
1
Animal Production Department, Veterinary Faculty, Antonio Nariño University, Cra 3 este # 47ª 15., Sede Circunvalar, Bogota 110231, Colombia
2
Agronomy Department, School of Agricultural Engineering, University of Seville, Ctra, Utrera km 1, 41013 Seville, Spain
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(16), 7054; https://doi.org/10.3390/su16167054
Submission received: 28 June 2024 / Revised: 3 August 2024 / Accepted: 12 August 2024 / Published: 16 August 2024
(This article belongs to the Special Issue Sustainable Agri-Food Systems: Environment, Economy, Society and Policy—2nd Edition)
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Abstract

:
The Cundinamarca Department is located in the Colombian Andean region, and features a variety of bovine production systems dedicated to milk, beef or dual-purpose production in cold, warm and temperate climate areas, respectively. This paper analyses the sustainability of a sample of 35 farms (12 dual-purpose, 13 milk production and 10 beef production) located in some of its municipal areas using MESMIS methodology, which evaluated indicators related to social, environmental and economic factors of the systems during 1 year, grouping them by their productivity, adaptability, equity, self-management and resilience. For productivity, adaptability and equity, the dairy systems scored higher than dual-purpose and beef systems, whereas for the indicators of self-management, stability and resilience, the dairy systems scored lowest, while dual-purpose systems were the best. The indicators of economic sustainability increased in proportion to the intensification of the production system, availability of agricultural machinery and added value, resulting in the best scores being obtained by the dairy system and the worst by the beef system. For social sustainability indicators, the best score was obtained by dual-purpose systems, with dairy systems scoring the lowest, while dairy systems scored highest for environmental indicators. The results could be used to endorse public policies to promote the generation of sustainable agricultural systems.

1. Introduction

Livestock represents around 1.4% of the national Gross Domestic Product (GDP) of the Colombian economy [1]. The Cundinamarca Department has 5.2% of the cattle national inventory and is characterized by a variety of productive systems for livestock farming with beef and dairy and for the provision of food to meet the growing national demand [2,3]. Their diversity of climates produces a wide range of agro-ecological conditions for livestock or agriculture production.
In a previous study, Cruz et al. [4] conducted an analysis of agricultural information from 116 municipal areas in Cundinamarca, which enabled them to characterize and classify the bovine production systems into three categories: dual-purpose, dairy and beef production. In order to assess the sustainability of these three categories of bovine production systems, a detailed analysis is required.
At the farm level, an agricultural system is sustainable if it yields positive economic and social results while preserving the natural resources in the ecosystem [5,6]. Zandstra [7] included environmental sustainability values as a function of chemical input levels, considering that excessive input levels degrade natural resources through accumulation, while inadequate levels degrade resources through exhaustion. This concept is in sharp contrast to the decreasing relationship between chemical input levels and sustainability proposed by Stinner and House [8]. Moreover, social values such as equity, tradition, self-sufficiency, conservation of agrarian culture and preference for small privately run farms have also been incorporated into definitions of sustainability [9].
Until now, the concept of sustainability has yet to be put into practice in many agricultural contexts. To achieve sustainable systems of agriculture, it is imperative to carry out a comprehensive assessment that encompasses broader ecological, economic and social dimensions [5]. To measure the sustainability performance of farms, a large number of sustainability assessment tools have been developed [10], which generally integrate a wide range of themes and indicators to obtain a holistic view and are used for different purposes, such as monitoring, certification, consumer information, farm advice and research [11].
Several frameworks which use indicators to assess sustainability have been described in the literature [12]. Sustainability indicators are tools that can be used by farmers at the farm or field level to assess the effects of managerial changes [13]. Nevertheless, any of these are purely theoretical and cannot be used directly by farmers to make decisions. In fact, complex tools that require large data inputs and expert knowledge to provide estimates are generally not suitable for use at the farm level.
Different techniques are available to build sustainability indicators [6,10,12], which give some guidance regarding the selection and construction of composite indicators, involving strict quality criteria and accurate data gathering to calculate empirical values. However, indicators should be used with caution in all cases because they must be regarded as only partial representations of the complex reality [14]. In order to build and develop indicators for the sustainability evaluation of production systems, researchers agree that one key element is that farmers should participate and be consulted [15].
Given the complexity of the concept of sustainability, as well as the diversity of production systems, the information generated based on indicators must be analyzed in an integrated way. To achieve this, a range of different methodologies is available to facilitate our analysis and understanding of the data. Of these, Response-Inducing Sustainability Evaluation (RISE) is an indicator-based sustainability assessment tool developed at the Bern University of Applied Sciences (School of Agricultural, Forest and Food Sciences, HAFL) [16], while the IDEA (Indicateurs de Durabilité des Exploitations Agricoles or ‘Farm Sustainability Indicators’) method is tool which is used widely in Europe. IDEA assesses whole-farm sustainability using agro-ecological (18 indicators), socio-territorial (18 indicators) and economic (six indicators) scales [17]. Van Cauwenbergh et al. [6] proposed a method known as SAFE (Sustainability Assessment of Farming and the Environment Framework), which recommended a hierarchical framework based on the goods and services provided by agricultural ecosystems, with the primary level of the hierarchy being the “principles” that correlate with the three dimensions of sustainability: economic, social and environmental.
A methodology frequently used in Latin America to analyze sustainability in agricultural systems is MESMIS (for its acronym in Spanish—Marco para la evaluación de sistemas de manejo de recursos naturales incorporando indicadores de sustentabilidad). It has an operative structure featuring a six-step cycle, including the description of the systems, the identification of critical points and the selection of specific indicators for the environmental, social and economic dimensions of sustainability. The data obtained by means of the indicators is integrated to obtain a value judgment about the resource management systems and to provide suggestions and insights aimed at improving their socio-environmental profile [12].
The aim of this work was to evaluate the sustainability of farms representative of the range of cattle production systems for dual-purpose, milk production and beef production in Cundinamarca Department (Colombia), using the MESMIS method. Understanding and improving the sustainability of these livestock production systems could contribute to achieving sustainable development goals (SDG), specifically Goal 1 (no poverty), Goal 2 (zero hunger), Goal 12 (responsible consumption and production), Goal 13 (climate action) and Goal 15 (life on land). The results of this research could help to provide strategies for future decision-making in productive, environmental, social or political spheres to improve livestock activity on the region, country and surroundings at large.

2. Materials and Methods

2.1. Area Study and Sample Selection

This study was carried out on farms employing livestock systems in the Department of Cundinamarca (Colombia), covering an area of 24,210 km2, with approximately 3.2 million registered inhabitants living in 116 municipalities distributed over 15 provinces, of whom 8.43% are agricultural producers. The main agricultural products are potatoes (25.4%), sugar cane (19.1%), coffee (15.2%) and other vegetables (39,7%). Notably, this department also provides 10.4% of the national inventory of laying hens, 8.1% of broilers, 6% of cattle and 7% of pigs [2,3].
The present research was based on previous studies reported by Cruz et al. [4]. Thus, with respect to cattle production, the three cattle livestock systems in this area were described as dual-purpose, dairy and beef production (Figure 1). The ‘dual-purpose’ group included 48 municipal areas, in which 64.6% of the farms employed dual-purpose production and assigned 57.5% of the total registered bovines. The average farm size was 5.2 ha, with 14.7 cattle per farm, a carrying capacity (livestock unit of 500 kg per hectare: LSU/ha) of 1.2 LSU/ha and a milk production per cow of dual-purpose cows of 5.9 kg/d. The ‘dairy production’ group includes 36 municipal areas, where 65.9% of the farms and 61.9% of their bovines are involved with dairy production. The farms have on average 3.8 ha, with 31.8 cattle per farm, a carrying capacity of 2.3 LSU/ha and a milk production per cow of 19.9 kg/d in specialized systems and 11 kg/d in traditional systems. Moreover, 66.6% of the farms in the 32 municipal areas located in warm climate are dedicated to beef production, with approximately 20% of their area used for agriculture and 63.6% of bovines for beef production. The farms for cattle breeding have an average size of 8.5 ha, with 22.3 cattle/farm and a carrying capacity of 1.2 LSU/ha. In relation to other non-bovine activity, in the department, there is considerable technical poultry and pig activity, and marginal sheep, goat and horse production. More information can be obtained in Cruz et al. [4].
Description of farm studies: To conduct key case studies, 35 representative farms were selected: 12 for dual-purpose, 13 for dairy and 10 for beef. Farms were selected according to their main productive characteristics, previous experience of the field staff and the researchers’ knowledge of the respective geographical areas. The main characteristics of the farms selected in this study are shown in Table 1.

2.2. MESMIS Framework for Assessing Sustainability

The MESMIS framework enables us to measure and monitor sustainability indicators as part of a systemic, participatory, interdisciplinary and flexible evaluation process. This methodology is well adapted to small farmers, who usually run diversified, small-scale farms, employ family labor and produce both subsistence and commercial goods as crops, livestock or forest products [12].
A complete description of MESMIS is given by López-Ridaura et al. [12] and Masera et al. [18]. Moreover, some of its applications and experiences have been discussed by Speelman et al. [19] and Ripoll-Bosch et al. [20]. The MESMIS program sets five strategic and interrelated goals: (1) to integrate the theory of complex systems into sustainability assessments which are context-based and constrained to a specific spatial and time scale; (2) to develop and update a comprehensive, coherent sustainability assessment framework that can allow stakeholders to make short- and long-term multicriteria comparisons of alternative Natural Resource Management (NRM) strategies; (3) to facilitate the learning processes by which stakeholders can understand, use and give feedback on the concepts, tools and outcomes of such assessments; (4) to validate the framework by promoting a number of case studies; and (5) to promote and facilitate the participation of all stakeholders in the assessment process. In our study, the framework was developed by critically integrating key concepts regarding sustainable development, sustainability, systemic approaches, natural resource management, sustainability evaluation and sustainability indicators [12].
The sustainability analysis of NRM combined with MESMIS methodology is based on the evaluation of seven general systemic attributes: productivity (capacity to generate goods and services), stability (ability to maintain a constant level of productivity under normal conditions), reliability (maintaining productivity at levels close to equilibrium under normal environmental shocks), resilience (a return to equilibrium or productivity levels similar to the initial level after serious disturbance), adaptability or flexibility (ability to find new levels of balance or to continue helping to provide beneficial, long-term changes in the environment), equity (a system’s ability to distribute both intra-and inter- generational benefits and costs fairly) and self-reliance (a system’s ability to regulate and control interactions with the outside world). Of these, stability, reliability and resilience attributes can be grouped as attributes of ‘‘stability’’ to express the ability of the system to cope with change [12]. The selection of indicators to be included in each attribute, and of the specific weight of each of the indicators, requires a participatory process conducted by an interdisciplinary evaluation team. In this context, the Delphi technique, a procedure that organizes and structures debates among various groups of experts, is recognized as a systematic method to analyze and discuss complex issues, helping channel diverse views and opinions into one or more communal notion through an iterative feedback process [21].

2.3. Data Collected and Information Treatment

Following the MESMIS methodology summarized in Section 2.2, the five-stage evaluating process was conducted as follows:
Stage 1: Definition and identification of critical points of the systems evaluated using in-depth interviews with different stakeholders (farmers and technical advisers) to elaborate a SWOT analysis (strengths, weaknesses, opportunities, and threats) [22]. Characteristics taken into account include satisfaction of labor, time taken to travel to markets, availability of basic public services, rest days per year, participation of women in technical decisions or family’s involvement in the farm activities in the medium term (15 years).
Stage 2: Selection of the indicators. A group of experts composed of technical advisers and researchers, using the previous SWOT analysis, selected the indicators, and their correspondence was used to establish system sustainability attributes and sustainability pillars.
Stage 3: Monitoring of 35 farms and collection of the data needed to calculate the indicators. In this study, data were collected during a 12-month period between 2022 and 2023.
Stage 4: Data obtained in the field were entered into a matrix, which included the average of each indicator for each of the three types of cattle production (dual-purpose, dairy and beef). Likewise, the optimal reference value for each indicator was included. To obtain this, optimal values were defined by 8 experts (veterinarians, animal scientists and ranchers) consulting references in the literature referring to each region. Finally, the degree of approximation of each system to this optimal value was calculated and expressed through an index of approximation to sustainability, which could range between 0% (furthest from the value considered sustainable) and 100% (maximum value of sustainability for that indicator). The approximation indices for each indicator and each farming type were obtained through the following formula: (indicator’s value/optimal value) × 100. In the case of complementary finances, it worked in the opposite way, that is, the best indices were those with the lowest value. In these cases, the formula applied was (optimal value/indicator’s value) × 100. Whatever the case, an index value closer to 100% is better in terms of sustainability.
Stage 5: Conclusions and recommendations for improving sustainability of the animal production systems studied were presented.

3. Results

3.1. SWOT Analysis

The ideas summarizing the main points from the interviews with the stakeholders are set out in Table 2. The main strengths of cattle production lay in the grazing-based production, the breeds used for tropical conditions and the highly diverse surroundings. The key opportunities were related to the development of traditional local products with the designation of origin, functional attributes or the production system. A number of different weaknesses were noted, including little generational relief, few transformation processes on farms and poor use of technology. The most important threats from the general context are the poor socioeconomic development in rural areas and the lack of state socio-economic incentives for production.

3.2. Analysis of Sustainability by Attributes, Pillars and Indicators

With the participation of the group of experts and taking account the SWOT results, 39 indicators were selected, which were grouped into five attributes as follows: productivity (n = 8), adaptability (n = 9), equity (n = 7), self-management (n = 5) and resilience (n = 10) (see Table 3 and Table 4). The average weighted scores obtained by each farm for any sustainability indicator are only given in the aggregate information. For this reason, an analysis of individual indicators is needed to describe the farming systems in detail.
For the classification of sustainability into three pillars, the experts assigned 14 of indicators to the economic pillar (35.0% of total indicators selected), 18 to the social pillar (45.0%) and 8 to the environmental pillar (20.0%). The optimal value for each indicator, evaluated according to local conditions with the participation of professional advisors, is also shown in Table 3 and Table 4.
Positive or negative differences were found between systems for several sustainability indicators, such as net margin per hectare per year (in productivity attribute), availability of agricultural machinery, use of irrigation systems (adaptability attribute), rest days per year, labor profitability (equity), family labor (self-management) and farm continuity in the next 15 years (stability). For self-supplying crops (stability), considerable differences were found in the dual-purpose category.
Table 3 and Table 4 present the percentages obtained for each attribute in each production system. The most notable feature of the attribute of ‘productivity’ was that we found a greater difference in the dairy system compared to the others for the net margin per hectare year indicator. In the same way, for ‘adaptability’, the dairy system was found to be superior for the attributes of ‘renewable energy sources’, ‘availability of agricultural machinery’ and ‘use of irrigation systems’. However, for the attribute of self-management, the dual-purpose system scored best, as well as for stability and resilience, where it was found that dairy systems were inferior to the others, especially for the indicator of ‘continuity in the next 15 years’.
Figure 2 represents graphically the score obtained by the three systems (dual-purpose, dairy and beef), for the sustainability attributes, as defined in Table 3 and Table 4. For productivity, adaptability and equity, the dairy systems scored higher than dual-purpose and beef systems, while for self-management, stability and resilience indicators, the dairy systems obtained the lowest score, with the dual-purpose system the highest.
With respect to the pillars of sustainability, differences among production systems were also considerable. As Figure 3 shows, the economic sustainability indicators increased in line with the intensification of the production system, availability of agricultural machinery and added value, which means that the best score was obtained by the dairy system and the worst by the beef system. For social sustainability indicators, the best score was obtained by dual-purpose systems, due to their family labor and labor profitability, while dairy system scored the lowest. Environmental indicators also showed that the dairy system scored best, probably due to the farms using renewable energy sources.

4. Discussion

4.1. General Approach to the Sustainability of Livestock Systems

The literature on the sustainability assessment of agricultural systems covers the use of integrated and participatory approaches involving various disciplines [23]. These approaches diverge in the different spatial/temporal scales used and the different views taken by the actors in their definition of sustainability [24]. In this context, Bezlepkina et al. [23] and Darnhofer et al. [24] affirmed that policies focusing only on reducing the environmental impact of production systems do not ensure the economic and social reproducibility of farms, and measures taken to improve environmental sustainability need to be implemented at the farm level, with farmers playing a crucial role. On the other hand, at the local level (farmers, technical advisers), greater importance is sometimes given to economic and social issues than to environmental sustainability, and some indicators are perceived as beneficial by some stakeholders and detrimental by others [25].
While the aim of the present research was to compare different livestock systems using representative case studies of farms, the farms following a certain system can be just as heterogeneous as the different systems. It follows, therefore, that it is a challenging task to compare sustainability with multiple indicators across cattle systems, because the critical points of sustainability (and their reference values) can vary across spatial/temporal scales, and therefore, the relevance of certain indicators will not be the same for different systems, agro-ecological regions or socio-economic context, or at different times [26]. Nevertheless, during the participatory process, there is little consensus over reducing the number of indicators and how to distribute individual indicators into particular attributes. In fact, our results and reported by Ripoll-Bosch et al. [20] show how incomes could be assigned to stability or adaptability, or even self-reliance.
Similar average scores for sustainability attributes can also cover up sizeable differences within individual indicators. This means that two systems could have similar scores in one attribute, such as stability, equity or adaptability [20], while the individual indicators of these two systems, such as renewable energy sources or use of irrigation, can vary widely. Binder et al. [27] mentioned that the attributes as defined by MESMIS can also be affected by uncertainty and dynamic aspects of sustainability evaluation, the socio-economic contexts can differ, and there can be trade-offs between attributes and indicators.

4.2. Sustainability Evaluation of Different Types of Cattle Systems in the Department of Cundinamarca by Attributes and Indicators

Productivity attribute: This research found, in beef systems, a meat production of 332.4 kg/ha/year, which is a higher level of productivity than the Colombian average, according to FEDEGAN (Federation of Colombian cattle producers), of 110 kg/year in breeding and fattening systems, and 193 kg/year for the same systems in some improved farms [1].
In regard to the milk produced per animal in the dairy farms evaluated, this research found an average of 5986 kg/year (16.4 kg/day), which is lower in comparison to the 19 kg/day reported in another study by Cruz et al. [4] in specialized dairy and higher than the 11 kg/day reported by the same authors in a peasant dairy. In 2014, the URPA (Regional Unit for Agricultural Planning of Cundinamarca reported an average milk production of 10.25 kg/animal/day for specialized dairy and 7.23 kg/animal/day for traditional dairy [28], while Carulla and Ortega reported an average production in this system of 12 to 14 kg/animal/day [29].
With respect to carrying capacity, between 1.35 LSU/ha/year was found for dual-purpose and 1.91 LSU/ha/year for dairy. Values near 1.2 LSU/ha are considered normal for small and medium livestock producers in Colombia, according to those referenced by González et al. [30]. A higher value in carrying capacity (LSU/ha) is associated with better technological level in the productive systems, which denotes semi-intensive production, characterized by the use of high-yield pastures with electric fences, periodic irrigation and fertilization associated with the food supplementation program systems proposed for Colombia by the CEGA [31]. Carulla and Ortega [29] mentioned that specialized dairy systems in Colombia had an average between 1 and 2 LSU/ha, which is close to the values found in our study. The carrying capacity in beef was 1.2 LSU/ha, which is in line with the complete cycle of breeding and fattening referenced by Mahecha et al. [32] in Colombia.
Adaptability attribute: In Colombia, data on irrigated land is scarce because most irrigation is not registered officially [33]. All the dairy farms evaluated in this research had irrigation systems, probably due to the implementation of improved pastures with a greater demand for water resources, while, in contrast, only 15.4% of dual-purpose farms had them, with none in beef systems, due to its lower technological level and intensification.
In regard to the use of agricultural machinery, all production systems face great challenges in seeking increased efficiency to meet the demands of globalization processes. In fact, specialized dairy systems have sought to optimize processes through the introduction of new technology in milking equipment, the storage and conservation of cold milk and the adoption of programs of good livestock practices in response to the pressure of the dairy industry and to comply with the current regulations [34]. These arguments account for the findings of the present work, with an availability of agricultural machinery in 100% of the dairy systems evaluated. This same variable barely reached 30.7% in dual-purpose systems and 25% in beef livestock, which tends to include areas where it is difficult to use machinery and there is less investment capacity.
The Colombian agricultural sector relies heavily on fossil fuels and suffers from inadequate infrastructure in rural areas, limiting access to energy resources. In fact, the electric power generation capacity in areas not connected to the national grid is supported by diesel in 85.4% of cases, with 14.6% using renewable energy [35]. In many rural areas of Colombia, small milk producers have difficulty accessing electricity networks, or suffer low reliability in their supply. This means that they must generate their own electricity with fuel electric plants in order to keep the temperature of the milk low before to be collected for industrialization and sale in cities. However, solar photovoltaic systems have become a viable option to guarantee a clean, constant supply of energy, taking advantage of the optimum solar radiation conditions that exist in most of the country [36]. Approximately 30% of the dairy producers monitored in this study used some type of alternative energy in their systems, such as for electrifying livestock fences, since in the areas where livestock are kept, there is generally no access to electricity. Solar panels provide the best solution, which are competitively priced and easy to obtain in the market. Another alternative source of energy, although less common in this sector, is to use methane gas from animal manure using anaerobic reactor digestion systems [36]. In our study, we found no farms using biodigesters for energy production, although solar panels for electric fences were found in dual-purpose (7.7% of the farms evaluated) and beef (16.6% of the farms evaluated) systems.
Equity attribute: With respect to the indicator of satisfaction with labor, Beltrán and Tellez [37] reported in technical dairy farming companies in a province in Boyacá (Colombia) that 91.1% of producers were satisfied with living and working in the countryside. The remaining farmers were considering moving to the city due to the difficulties of road access and to improve the educational opportunities for their children. In the present study, the three systems evaluated all scored over 95%, probably due to their tradition as laborers, but those who scored lowest in this variable were those in dual-purpose systems, because the farms evaluated in this category are located far from cities, with fewer opportunities for education and employment for the families.
For the indicator of public basic services, in 2012, in the rural area of the Department of Cundinamarca, about 4% of homes (10,282 houses) did not have electricity and about 1500 had difficulties connecting to the grid due to their remote location, while only 1.4% of rural homes had natural gas [38]. Another study, conducted in 2016 on basic public services [39], reported that the water supply coverage for rural homes was 42.6%, while the sewage coverage was 6.3%. Despite this, the monitoring results observed on farms in 2022 in our study for the three systems analyzed (punctuation over 95%) showed important improvements in public services (electricity and water supply), which has been able to help to improve technological development in local livestock activity.
In general, there are very few studies and little information about gender differences in the livestock sector in Colombia and the rest of Latin America. On the farms evaluated in the present research, producers reported that 44.2% of their workers were women in the dual-purpose system, which was the highest value, while lowest values (40.8 and 34.2%) were found for the dairy and beef systems production, respectively. DANE-ENA [40] reported lower values for the active participation of women in rearing farm animals in Colombia (around 30%). Although it was not confirmed whether they obtained financial remuneration, women were shown to be involved in making decisions on 70% and 91.6% of the farms for dairy and beef, respectively. Arora et al. [41] stated that women in the Department of Cauca participated more actively in the processing and sales of dairy products, while also playing a major role in different livestock and pasture management activities. In the work carried out by Gumucio et al. [42] for Colombian and Costa Rican livestock farms, despite the limited information, it was found that the majority of women worked on their husbands’ farms; however, their work in many cases was not renumerated since it was considered “helping at home”. In the present research, the farmers stated that over 70% of women were involved in taking technical decisions in the three systems evaluated. Finally, regarding the marketing process, in the three systems evaluated, the milk and beef market is usually promoted individually from the place of production to local or regional markets.
Self-management attribute: In the present research, it was found that for dual-purpose and beef systems, family labor is an important factor (75.1% and 64.2% of the total labor in farm, respectively), compared with dairy systems (17.5%). The producers, most of whom were also owners of the land (>81.5% in the three systems evaluated), stated that they generally had autonomy in making production and marketing decisions (>70%). A similar situation was found in a study carried out in the province of Sumapaz in Cundinamarca (Colombia), where it was found that 17% of the properties were managed at the family level [43]. Fonseca-Carreño et al. [44], in an evaluation carried out with MESMIS methodology in the same region, found that between 75% and 100% of the workforce were members of the family, with 80% of those evaluated owners of the land, 60% stating that they made production and marketing decisions as a family and 40% stating that they made them with their partner.
The added value of products is a key indicator of farm self-management. According to DANE-ENA [40], it is estimated that 8.3% of the milk produced daily at the Colombian level is processed in situ, generally for the production of cheese, which is almost entirely sold through informal channels (through intermediaries, informal cheese shops and in market squares). In some cases, on-farm cheese production solves the marketing problem for milk, which is not collected through formal channels, given the large distances from collection centers and difficult access routes. The remaining 31.7% of the milk is sold to intermediaries belonging to the informal channel, with actors of various sizes participating, while 52.6% of the total milk is sold to the industry. In Colombia, around 400 industries collect raw milk from primary producers, in compliance with Colombian government regulations. However, 52% of the formal milk collection [45] is in the hands of only five of these. Meanwhile, beef produced in Colombia is distributed through formal and informal channels. From its production in breeding and fattening, following the complete cycle or on dual-purpose farms, to its arrival at the end consumer, the generic product (meat) can pass through many hands. Firstly, there is the commercialization of live cattle, which can begin its process through collectors, who sell the cattle (lean or pre-fattened) at livestock auctions or fairs for marketing, or directly take them to informal slaughterhouses or plants for slaughtering and, later, marketing the carcass in butcher’s shops and market places. The actors that carry out the transformation in the beef value chain are the animal processing plants or slaughterhouses and the industry dedicated to the production of derivatives. These produce a range of fresh, dried, salted or smoked meat, as well as derivatives such as sausages, black pudding, ham, edible animal offal (innards and giblets) and inedible by-products (skins, tallow and others) [46]. In this study, while the producers of Cundinamarca state that between 15.4 and 20% of milk production is transformed into cheese, in the farms of beef, no transformation process of meat was reported.
Resilience: Regarding continuity on the farm in the next 15 years, in the present work it was found that, in the dual-purpose and beef systems, more than 75% of the farmers wanted to continue their activity, while in the milk production systems, the projected figure barely reaches 30%, probably due to the tough working conditions and the systems having low levels of mechanization and facilities. Beltrán and Téllez [37] reported that 8.9% of dairy producers in a province in Boyacá (Colombia) considered migrating to the city due to the difficult access to roads and the lack of educational opportunities for their children. Regarding the indicator of facilities, the same authors mention that the livestock management activities necessarily take place in the open air, since the animals are kept in grazing systems. For example, milking is carried out at dawn, which exposes the worker to adverse environmental conditions. In dairy systems, the farm worker must remain attentive and available for any eventuality that may happen on the farm, even during their free time, so they cannot mentally disconnect from their work, since the working conditions require it. Consequently, permanent physical and mental fatigue due to the working conditions is a source of dissatisfaction and increases the risk of work accidents [37]. In addition, the farms of small and medium producers only have basic resources for animal management, and it is sometimes non-existent. This was confirmed by Gonzalez-Quintero et al. [47,48] in the study conducted on cattle fattening and breeding in dual-purpose systems in Colombia. Meanwhile, milk production systems usually have better infrastructure and machinery due to the demands that the dairy industry places on product quality [34]. This coincides with the findings in this study, where farms in dairy systems had better infrastructure than dual-purpose and beef systems, mainly for animal management, the storage of raw materials and the processing of products.
The farms of the three systems monitored had technical assistance, with the lowest being those that belonged to the beef systems, with 83.3% stating they received it. In relation to agricultural technical assistance, the Colombian State is responsible for guaranteeing these processes for small- and medium-sized producers, with the secretariats of agriculture in the municipalities charged with carrying out its monitoring and evaluation [49]. Despite several administrative offices running in the Department of Cundinamarca, the provision of services to farmers is reduced [50], and it could be improved by increasing the number of professionals dedicated to technical and administrative advice to farmers.
The changes in economic models has led many underdeveloped nations to reorient their agricultural production processes towards raw material export models [51]. In fact, in these countries, domestic food production has ceased to be a priority, leading to the impoverishment of smallholder farmers with low technological levels and insignificant government aid. This observation has also arisen in Colombia, where the abandonment of primary production is observed in the area to allocating land for export crops and abandoning support for the national food production, leading to a weakening of food sovereignty that also involves a weakening of national food security [52]. This situation is also observed in the specialized beef and milk systems in the Department of Cundinamarca, with under 20% of producers rearing cattle for their own consumption.
For the farms in the three systems evaluated, the extension of silvopasture did not exceed 25% of the total agricultural surface. Despite abundant research favoring and promoting silvopasture systems (SPS), their adoption and use remains at very low levels in Colombia. In fact, UPRA [53] has verified that around 4 × 106 ha has potential for agroforestry, whereas only 216 × 103 ha are currently put to this use, which accounts for only 5% of the potential. This marginal adoption of SPS constitutes a problem not only for Colombia, but also for other Latin American countries, because of its detrimental environmental consequences [54]. Long-term investment, which is required to obtain economic and production benefits, and its high perceived complexity, are still the main barriers hindering the potential adoption of SPS [55], whereby farmers usually adopt several practices, such as the installation of living fences, dispersed trees in paddocks and windbreakers, but few farmers have implemented a fodder bank for animal feed use.
Deforestation is a serious environmental issue in Colombia. In fact, between 2015 and 2016, around 44% more land was deforested [56], with the expansion of agricultural frontiers by extensive cattle farming being the main driver, accounting for 60% of deforested area in Colombia [57]. Livestock farming used 38 million ha, and over 70% is extensively managed with low density tropical cattle farming [53], which has serious environmental consequences, not just in terms of environmental degradation, but also in the low efficiency and profitability of individual farms [58]. On average, less than 15.1% of the three systems monitored in this research practiced forest conservation, with the least conservation corresponding to dairy farming. These figures provide a clear example of the changes to the environment caused by the livestock systems in Cundinamarca, with very few trees included in the systems in this geographical area.
Farmers from Colombia traditionally have several sources of income, with those derived from agricultural activities constituting the majority of the total household income. Colombian national statistics, based on the Household Survey, indicate that agricultural activity is the main source of income and generator of employment in the rural sector of the country for small and medium producers, with only 10% of income coming from remittances, subsidies, pensions or non-agricultural income, such as jobs in nearby towns [59]. This observation contrasts with the situation found in the present work, where between 20 and 27.1% of the producers’ total income in the three systems evaluated came from non-agricultural activities such as nearby jobs, remittances and others.
The majority of the Colombian population lives in the Andean region, one of the most vulnerable regions to the effects of climate change. An evaluation of the impacts of climate change on Colombian agriculture shows that if no adaptation measures are taken, 80% of crops could be impacted in over 60% of the current areas of cultivation [60]. Currently, the regions of three systems evaluated in this research have sufficient water supply for livestock activity and agricultural use, as shown in Table 4 (Resilience attribute). However, in the mid-term, this situation could change. Eitzinger et al. [61] proposed that changes in precipitation patterns caused by climate change would affect soil water availability, possibly reducing yields and quality, and thereby lowering farmers’ incomes. The shift in climate suitability might also force farmers to shift their crops and livestock to higher elevations, the home of the few remaining forests and the alpine tundra system known as ‘páramos’. The effects of this change will be even greater deforestation, which in turn will increase the frequency and intensity of flooding, soil erosion and crop losses due to the changes in the precipitation forecast for the region, together with increased temperatures. As a consequence of the farming systems migrating to higher altitudes (including dairy farms), the ecosystems that provide the population with essential environmental services, like water for rivers and streams, will become threatened.
Traditionally, bovine farming in Colombia is a productive mosaic, not only due to the various forms of production, but also due to the different geographic regions and altitudes in which it is carried out. These frequently include a range of species, which give a mixed character to their farms, combining livestock production of bovines with other bovines (such as buffaloes) and other animals (horses, goats or sheep, poultry or fish) together with agricultural or forestry production. In this process, various forms of pasture management are carried out, using native forage and trees, along with other improved species. Thus, livestock activity has influenced the formation of landscapes made up of grasslands and wild areas [62]. This observation is in line with what was found in the farms in the three systems production we studied (Table 4), where a combination of various species of forage is using for feeding the various species of farm animals.

5. Conclusions

The sustainability evaluation using the MESMIS methodology allowed us to identify that the best attributes of the dual-purpose production systems in Cundinamarca (Colombia) are autonomy and resilience, while for dairy production systems, the best attributes are productivity, adaptability and equity, with beef production systems obtaining high scores for autonomy and equity. With respect to the pillars of sustainability, it was evident how the social dimension is the most important of these systems, and that the economic dimension is the most difficult at the time of the evaluation. The process showed how those systems with better productivity and that carry out agribusiness processes and transformation of their primary products are more sustainable, due to their direct impact on the financial and social aspects.
As recommendations for government and producers to improve sustainability, political measures need to be taken to improve the economic aspects of the dual-purpose and beef systems, through the transformation and industrialization of their products to increase their added value and increase the net margin per hectare. Furthermore, for the environmental pillar, more areas should be dedicated to silvopasture systems, mainly in dairy systems, so that their biodiversity will also improve. The inclusion of renewable energies and improving product diversification, mainly in beef and dual-purpose systems, would certainly contribute to this pillar. As for the social pillar, strategies need to be established that aim at improving the education of producers in the three livestock systems, as well as at establishing systems for the management of their productive and financial information. Productivity is one of the key attributes that must be improved in dual-purpose and beef systems. Here, zootechnical assistance processes are important, so that producers can rear fewer animals with greater productive efficiency and thus increase their net margin per hectare. To improve the attribute of adaptability, access to technological resources and machinery should be encouraged and facilitated, mainly in dual-purpose and beef systems. Finally, aspects like productive and environmental diversity, the most important characteristics of the dual-purpose systems evaluated, must form the backbone of the strategies used to maintain for livestock production in the Department of Cundinamarca, as well as in Colombia in general. Expanding studies to other regions of Colombia is necessary to evaluate the sustainability of their cattle farming systems and generate proposals for their development.

Author Contributions

Conceptualization, analysis, writing—review and editing: F.C., D.P., Y.M. and A.H.; methodology, F.C., Y.M. and A.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data and code used or analyzed in this study are available from the corresponding author upon reasonable request.

Acknowledgments

We would like to thank the farmers who collaborated with their time to provide information for this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of farms sampled in Department of Cundinamarca (Colombia), according to production system.
Figure 1. Location of farms sampled in Department of Cundinamarca (Colombia), according to production system.
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Figure 2. Livestock system sustainability attribute scores.
Figure 2. Livestock system sustainability attribute scores.
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Figure 3. Livestock system sustainability pillar scores.
Figure 3. Livestock system sustainability pillar scores.
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Table 1. Cattle production systems characteristics.
Table 1. Cattle production systems characteristics.
DescriptionDual-PurposeDairyBeef
Number of farms121310
Average land area (ha) *30.2319.951.83
Carrying capacity (LSU/ha/year) *1.351.911.57
Altitude (m.a.s.l) *10502680520
Main speciesCrossed B. indicus–B. taurusB. taurusB. indicus
Main productsRaw milk
Males for rearing and fattening		Raw milkFattening males
* LSU: Livestock unit (500 kg); ha: hectare; m.a.s.l.: meter above sea level.
Table 2. Production system SWOT analysis.
Table 2. Production system SWOT analysis.
Cattle Production SystemStrengthsWeaknesses
Dual-purpose
  • Grazing-based production.
  • Income from the sale of milk and fattened animals.
  • Use of Bos taurus × Bos indicus crossings adapted to climate.
  • Agro-ecological diversity of producing municipal areas.
  • Distance to consumption centers (>3 h).
  • Aging of producers.
  • Few transformation processes on farms.
  • Poor productivity of systems.
  • Poor use of technology.
Dairy
  • Grazing-based production.
  • Use of taurus breeds specialized in milk production.
  • Proximity to consumption centers (<3 h).
  • Specialized systems with good technological support.
  • Heterogenicity in the hygienic quality of milk obtained.
  • Peasant systems with low level of technological development.
  • Few transformation processes on farms.
  • Aging of producers.
Beef
  • Grazing-based production.
  • Use of indicus breeds adapted to tropical conditions.
  • High biodiversity in the environment.
  • Specialized systems in growth and fattening.
  • Distance to consumption centers (>5 h).
  • Little generational relief.
  • Few transformation processes (meat preparation) on farms.
  • Poor productivity of systems.
  • Poor use of technology.
OpportunitiesThreats
Dual-purpose
  • National federation and health institutions that develop programs for producers.
  • Meat exports.
  • Development of traditional local products with designation of origin, functional attributes or production system.
  • Deficient road infrastructure.
  • Lack of state socio-economic incentives for production.
  • Import of dairy products.
  • Little socioeconomic development in rural areas.
Dairy
  • National federation and health institutions that develop programs for producers.
  • Good infrastructure of roads.
  • Proximity to milk industry.
  • Development of traditional local products with designation of origin, functional attributes or production system.
  • Lack of state socio-economic incentives for production.
  • Import of dairy products.
  • Increase in land value due to urbanization processes and proximity to large consumption centers.
Beef
  • National federation and health institutions that develop programs for producers.
  • Opportunities for exporting meat.
  • Development of traditional local products with designation of origin, functional attributes or production system.
  • Deficient road infrastructure.
  • Lack of state socio-economic incentives for production.
  • Poor socio-economic development in rural areas.
Table 3. Production system productivity and adaptability indicators compared to optimal values.
Table 3. Production system productivity and adaptability indicators compared to optimal values.
AttributeIndicatorSPUnitWeightOptimalDual-PurposeDairyBeef
Productivity
(n = 8)		Meat produced per animal/yearEcKg/year5%≥182217.5(100.0%)141.5(77.7%)216.8(100.0%)
Meat produced per hectare/yearEcKg/ha/year5%≥273293.8(100.0%)271.2(99.3%)332.4(100.0%)
Milk produced per animal/yearEckg average/year5%≥30001418(47.3%)5986(100.0%)-(-%)
Milk produced per hectare/yearEcKg/ha/year5%≥45001158(25.7%)6866(100.0%)-(-%)
Average Carrying capacityEcLSU/ha/year20%≥1.51.35(90.0%)1.91(100.0%)1.57(100.0%)
Net margin per kg milk produced/yearEc€/kg/year15%≥0.160.09(56.3%)0.09(56.3%)-(-%)
Net margin per kg meat produced/yearEc€/kg/year15%≥0.720.53(73.6%)0,38(52.8%)0.77(100.0%)
Net margin per hectare/yearEc€/ha/year30%≥1500267.0(17.8%)736.5(49.1%)255.5(17.0%)
Adaptability
(n = 9)		Use information management systemsS% Farmers using management systems5%100%38.4(38.4%)50.0(50.0%)58.3(58.3%)
Education level of farmersSScale a10%21(50.0%)1.1(55.0%)1(50.0%)
Time taken to travel to markets <1 h (cities with over 10,000 inhabitants)S% farms with time taken to travel to markets < 1 h15%100%100(100.0%)100(100.0%)91.6(91.6%)
Time taken to travel to slaughterhousesS% farms with time taken to travel to markets <1 h5%100%53.8(53.8%)100(100.0%)66.6(66.6%)
Diversification of productionEnScale b15%≥32.7(90.0%)1.8(60.0%)1.9(63.3%)
Use of creole breedsEcFarms using local breeds10%>15%7.7(51.3%)0(0.0%)0(0.0%)
Renewable energy sourcesEnFarms using Renewable energy sources10%>25%7.7(30.8%)30.0(100.0%)16.6(66.4%)
Availability of agricultural machineryEc% farmers15%100%30.7(30.7%)100.0(100.0%)25.0(25.0%)
Use of irrigation systemsEn% Farms with irrigation15%100%15.4(15.4%)100.0(100.0%)0.0(0.0%)
SP: Sustainability pillar; Ec (economic), En (environmental) and S (social). a Min: 0 = No education, but agricultural experience; 1 = Basic or intermediate education and agricultural experience; Max: 2 = Higher education and agricultural experience. b Minimum: 1 product; 2 products; Maximum: ≥3 products.
Table 4. Production system equity, self-management and stability indicators compared to optimal values.
Table 4. Production system equity, self-management and stability indicators compared to optimal values.
AttributeIndicatorSPUnitWeightOptimalDual-PurposeDairyBeef
Equity
(n = 7)		Satisfaction with laborSScale a15%>43.8(95.0%)4.0(100.0%)4.6(100.0%)
Public basic servicesSScale b15%32.9(96.7%)3.0(100.0%)2.9(96.7%)
Rest day per yearSDays/year15%≥204.8(24.0%)9.8(49.0%)5.8(29.0%)
Female workersS% female workers13%≥50%44.2(88.4%)40.8(81.6%)34.2(68.4%)
Women’s participation in technical decisionsS% Farms with women as decision-makers12%≥50%84.6(100.0%)70.0(100.0%)91.6(100.0%)
Marketing processEcScale c15%21(50.0%)1.4(70.0%)1(50.0%)
Labor profitabilitySNet margin/WUY15%≥3600 €5355(100.0%)3674(100.0%)4871(100.0%)
Self-management
(n = 5)		Family laborS% WUY familiar17%≥25%75.1(100.0%)17.5(70.0%)64.2(100.0%)
Fodder self-sufficiencyEn% fodder produced17%100%95.4(95.4%)85.5(85.5%)97.1(97.1%)
Autonomy in decisions on production and marketingS% producers with autonomy16%100%84.6(84.6%)70.0(70.0%)91.6(91.6%)
Own farm areaS% own farm16%100%96.1(96.1%)81.5(81.5%)93.3(93.3%)
Added valueEc% farms with product transformation17%100%15.4(15.4%)20.0(20.0%)0.0(0.0%)
Stability, resilience
(n = 10)		Farm continuity in the next 15 yearsS% producers continuing for next 15 years15%100%84.6(84.6%)30.0(30.0%)75.0(75.0%)
Technical assistanceS% producers with technical assistance10%100%100.0(100.0%)90.0(90.0%)83.3(83.3%)
FacilitiesEcNominal d10%31(33.3%)1.7(56.7%)1(33.3%)
Self-supplying cropsS% farms with self-supplied crops10%100%84.6(84.6%)20.0(20.0%)16.6(16.6%)
Animal species producedEnScale e10%31.9(63.3%)1.2(40.0%)1.7(56.7%)
Use of silvopastoral systemsEnNominal f10%31(33.3%)1(33.3%)1(33.3%)
Forage species producedEnScale e10%33(100.0%)2.8(93.3%)3(100.0%)
Complementary financesS% Non-agricultural incomes5%≤15%20.0(75.0%)24.5(61.2%)27.1(55.4%)
Water availability for agricultural useEcScale g10%≥12.2(100.0%)2(100.0%)1.7(100.0%)
Bio-conservationEn% area in forest conservation 10%≥10%13.8(100.0%)4.9(49.0%)15.1(100.0%)
SP: Sustainability pillar; Ec (economic), En (environmental) and S (social). WUY: Working unit year. a Minimum: 1 = Very dissatisfied; 2 = Dissatisfied; 3 = Neutral; 4 = Satisfied; Max: 5 = Very satisfied. b Minimum: 1 = None; 2= Electricity or rural aqueduct; Max: 3 = Electricity and rural aqueduct. c Minimum: 1 = Individual; Max: 2 = Association. d 0 = None; 1 = Only for animal management; 2 = Only for store and transformation; 3 = For animal management, store and transformation. e 1 = 1 species; 2 = 2 species; 3 = 3 or more species. f 0 = None; 1 = less than 25% area; 2 = between 25 and 50%; 3 = more than 50%. g 1 = scarce; 2 = sufficient; 3 = abundant.
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Cruz, F.; Pardo, D.; Horcada, A.; Mena, Y. An Assessment of Sustainability of Dual-Purpose, Dairy and Beef Cattle Production Systems in the Cundinamarca Department (Colombia) Using the MESMIS Framework. Sustainability 2024, 16, 7054. https://doi.org/10.3390/su16167054

AMA Style

Cruz F, Pardo D, Horcada A, Mena Y. An Assessment of Sustainability of Dual-Purpose, Dairy and Beef Cattle Production Systems in the Cundinamarca Department (Colombia) Using the MESMIS Framework. Sustainability. 2024; 16(16):7054. https://doi.org/10.3390/su16167054

Chicago/Turabian Style

Cruz, Fabián, Dolly Pardo, Alberto Horcada, and Yolanda Mena. 2024. "An Assessment of Sustainability of Dual-Purpose, Dairy and Beef Cattle Production Systems in the Cundinamarca Department (Colombia) Using the MESMIS Framework" Sustainability 16, no. 16: 7054. https://doi.org/10.3390/su16167054

APA Style

Cruz, F., Pardo, D., Horcada, A., & Mena, Y. (2024). An Assessment of Sustainability of Dual-Purpose, Dairy and Beef Cattle Production Systems in the Cundinamarca Department (Colombia) Using the MESMIS Framework. Sustainability, 16(16), 7054. https://doi.org/10.3390/su16167054

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