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Article

Economic Dynamics as the Main Limitation for Agricultural Sustainability in a Colombian Indigenous Community

by
Cintya Ojeda
1,†,
Jhoana P. Romero-Leiton
2,*,†,
Mónica Jhoana Mesa
2,
Juan Zapata
3,
Alvaro Ceballos
4,
Solanyi Ordoñez
5 and
Ivan Felipe Benavides
6,*
1
SENNOVA—Servicio Nacional de Aprendizaje Regional Putumayo, Puerto Asís 862060, Colombia
2
Facultad de Ciencias de la Educación, Universidad del Quindío, Armenia 630004, Colombia
3
Universidad Nacional de Colombia Sede Palmira, Palmira 111321, Colombia
4
Universidad de Nariño, Pasto 520039, Colombia
5
Agencia Nacional de Tierras, Mocoa 860001, Colombia
6
Grupo de Investigación ARENA, Facultad de Ciencias Agrícolas, Universidad de Nariño, Pasto 520002, Colombia
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2024, 16(19), 8611; https://doi.org/10.3390/su16198611
Submission received: 4 July 2024 / Revised: 27 August 2024 / Accepted: 4 September 2024 / Published: 4 October 2024
(This article belongs to the Special Issue Sustainability of the Agricultural System and Agro-Ecological Environment)
Browse Figures
Versions Notes

Abstract

:
Ancient agroecological farms, or chagras, of the Kamëntšá Biyá and Kamëntšá Inga indigenous communities in the Sibundoy Valley of Colombia offer valuable insights into the environmental challenges of intensive agriculture and promote sustainable food production. Sustainability indices have been developed to assess farm-level sustainability and enhance agroecological practices; however, data limitations hinder monitoring and correlation with external factors. This study evaluated sustainability indices in Sibundoy Valley chagras using the holistic evaluation system for farming intensification (HESOFI) interview system with 800 randomly selected chagras, assessing economic, agro-environmental, and sociopolitical–cultural dimensions. The endogenous factors considered included areas managed with diversified agricultural systems, the percentage of transformed products, inputs generated by the chagra, and products intended for sale. Exogenous factors included distance to rivers, roads, cities, and vegetation index ratio. The results showed that all chagras fell below the minimum sustainability threshold (80%), with the economic dimension scoring the lowest. Based on these findings, three strategies were proposed to improve the economic scores and overall sustainability indices of these chagras.

1. Introduction

Agroecology is an effective method for addressing the challenges of agricultural sustainability and is gaining popularity among farmers and growers. This approach combines ecological, social, and economic factors in food production [1]. Transcending disciplinary boundaries promotes a more resilient and sustainable agri–food system, especially in the recent era of economic recovery [2]. This transformation requires a multiscale perspective and careful attention to ensure economic viability [2,3]. Indigenous sustainability focuses on promoting local food systems [4], preserving traditional knowledge and governance structures [5], and integrating indigenous knowledge with modern technology for sustainable development [6,7]. Each indigenous community has distinct needs, beliefs, cultural heritage, and contexts. For instance, the Kamëntšá-Biyá and Kamëntšá-Inga communities in Colombia’s Sibundoy Valley (SV) have experienced significant changes owing to intensive monocropping and cattle rearing practices, altering their landscapes and ancestral farming traditions [8,9].
The Kamëntšá Biyá and Kamëntšá Inga communities in the SV fervently maintain their traditional farming practices within production units known as chagras, which include dwellings, gardens, cultivated crops, and livestock [10]. Through these systems, they maintain a deep connection with the SV’s biodiversity and obtain ecosystem services, such as food, natural medicine, fibers, and cultural assets. Each chagra is unique, with specific needs and roles within the community [11], creating a complex web of interactions that shape Kamëntšá’s living standards. Long-term thoughtful management is essential for sustaining these practices. Chagras function as dynamic agroecosystems in which indigenous farmers cultivate crops, raise livestock, and interact with their environment, including cultural heritage, ecological wisdom, and community resilience [12,13]. Chagras contribute to food security, biodiversity conservation, and soil health by nurturing diverse crops, preserving local seeds, and implementing agroforestry techniques [14]. Chagras can contribute to food security, biodiversity conservation, and soil health [8,15]. Measuring and monitoring these contributions to agricultural sustainability and identifying the endogenous and exogenous factors influencing them are crucial initial phases for improving agricultural practices while preserving traditions [16,17].
A useful tool for measuring farm-scale sustainability levels is the holistic evaluation system for farming intensification (HESOFI) [16]. HESOFI quantifies a sustainability index (SI) by thoroughly examining the structure of three dimensions: agro-environmental, sociopolitical–cultural, and economic. This tool serves as a compass for farmers aiming to achieve sustainable agriculture on their farms and empowers local governments to make informed decisions based on specific SI results and anticipate future scenarios. Moreover, its quantitative and hierarchical structure facilitates correlation with independent external data, aiding the search for endogenous and exogenous drivers of sustainability.
According to [18], various land use planning and management tools are designed to optimize quality of life for populations. These tools allow for the proposal and implementation of projects tailored to the specific needs of each area. Meanwhile, [19] argues that viewing rural areas solely as productive sectors is empirically inadequate, as these spaces also play multiple roles in the restructuring of the socioeconomic system. In other words, land is not just a source of food and raw materials for industry but also a site for the production of artisanal goods, subsistence farming, and other goods and services, with a particular emphasis on resource supply, protection, and conservation.
In this regard, some studies suggest that there is a need to develop tools that allow for the evaluation of a territory’s specific conditions, highlighting works such as those of [20,21,22,23,24,25,26,27], which generally attempt to address problems related to the impact of agricultural or livestock activities on natural resources. Parallel to this, there is an understanding of the lack, limited use, and difficulties in developing an evaluation tool that allows for the identification of critical points in local agroproductive systems.
Therefore, various studies have concluded that indicators are a way to assess agricultural systems, contextualizing the real conditions of the practices being developed and allowing for management analysis and adjustment according to the realities of each productive system [20]. Their construction should aim to identify the versatility of the methodology, allowing for the evaluation of one or two production systems [25]. It should also enable application under a holistic model with multiple characteristics [20], facilitating the use of indicators that integrate three dimensions (ecological, economic, and sociocultural), which will aid in better decision-making in agroproductive systems [20]. Similarly, the inclusion of environmental aspects in productive decisions is an important factor in the sustainability of productive systems [27], leading to a better analysis of trends from the social, ecological, and sociocultural perspectives.
The goal of this study was to measure the SI of Kamëntšá-Biyá and Kamëntšá-Inga chagras in the SV using the HESOFI methodology. Chagras were chosen as the focus of this study because they are the epicenters of agroecological production and decision-making in the region. All high-priority decisions made by indigenous or civil authorities are implemented at the chagra level. Similarly, innovations in agricultural planning have been scaled up from the chagra to the regional level. Thus, chagras represent the success or failure of land use, diversification, profit, and production margins in the SV. This also directly or indirectly reflects the effects of agroecological management on the region’s sustainability [28]. Additionally, we sought to identify potential exogenous and endogenous drivers connected to the SI of chagras. In addition to offering a basis for hypothetical cause-and-effect explanations of SI variability, this approach can provide insights for enhancing the agroecological decision-making framework in the SV and facilitate the optimization of environmental conditions that can most effectively increase regional sustainability [29,30].
This information will serve as a baseline for indigenous authorities and civil governments of the Sibundoy and San Francisco municipalities in the SV, which often face challenges in chagras management amidst ongoing poverty. Additionally, this methodology can be extended and applied to indigenous communities worldwide.

2. Methodology

2.1. Study Area

This study was conducted in the municipalities of Sibundoy (14,000 inhabitants) and San Francisco (7000 inhabitants), located in the SV at altitudes ranging from 2000 to 2800 m in Putumayo province, Colombia (1°09′53″–1°14′45″ N and 77°02′46″–76°51′47″ W). The SV, covering approximately 19,400 hectares, acts as a crucial transition zone between the highland Paramo ecosystems and tropical rainforests of the lower Amazon (see Figure 1). The region experiences annual precipitations of 1400–1500 mm, temperatures of 10–20 °C, and relative humidity levels of 78–83%. Originally dominated by humid Andean forests, the landscape features urban expansion and a mosaic of small agricultural plots interspersed with secondary forest patches. The SV contains 16 distinct soil types, with Andic Dystrudepts and Lithic Udorthents, Typic Hapludands and Typic Placudands, and Typic Udifluvents and Andic Dystrudepts being the most common. These soils, primarily derived from volcanic ash, granodiorites, and monzogranodiorites, exhibit low-to-moderate fertility levels [31].
The main inhabitants of the SV are the Kamëntšá Biyá and Kamëntšá Inga indigenous ethnic groups, whose presence in the area predates the Spanish conquest. These communities have developed unique agricultural traditions and perspectives focusing on biodiversity preservation and environmental stewardship. Within their small farms, known as chagras, families cultivate various crops, medicinal plants, trees for artistic and construction purposes, and animal husbandry. The Kamëntšá people have active roles in their development and have established robust food security and sovereignty standards [32]. Chagras serve as the core of a distinctive agroecological production system, integrating dwellings and cultivated plots where livelihoods are sustained and biodiversity is promoted. Each family’s unique crop and livestock choices, coupled with specific management techniques, contribute to a diverse range of agricultural and ecological practices within these communities [33].

2.2. Sampling and Interviewing

A random sample of 800 chagras was drawn from the study area to participate in semi-structured interviews (see Figure 1). This sample represented 9.2% of the 8688 chagras in the study area, comprising 6830 chagras in Sibundoy and 1858 in San Francisco. Prior to conducting these interviews, thorough discussions were held, plans were made, and explanations were provided to the farmers’ representatives. Subsequently, authorization was obtained from indigenous authorities in San Francisco and Sibundoy. The interviews were conducted in each of the 800 chagras, with questions derived from Table 1. The respondents were always family heads, predominantly women (70%), with men representing the remainder (30%). These interviewees were always the individuals most knowledgeable about and closely associated with the chagras. These interviews encompassed 69 questions (indicators), which were hierarchically nested within three dimensions (Dim) and 12 components (Com), tailored to evaluate the SI (see Table 1 and Figure 2). This phase of the study extended from May to August 2023, with an average interview duration of two hours per chagra.

2.3. Calculation of the Sustainability Index

According to [34], the hierarchical structure and calculations for the SI are divided into three dimensions: social–political–cultural (SPC), agro-environmental (AE), and economic (ECO). The SPC dimension includes four components: culture and territory (CUT), welfare (WEL), internal relations (IRL), and external relations (ERL). The AE dimension consists of six components: animal husbandry (AHS), soil and water (SWA), protection of crops (PRC), biodiversity (BIO), energy (ENE), and landscape (LAND). The economic dimension comprises development (DEV) and efficiency and dynamism (EDM). Each component was assessed using a set of indices derived from interview questions, with predefined multiple-choice answers scored on a scale from 1 to 5 (Table 1). The SI for each chagra was calculated using data from these interviews, along with information on chagra size, geographic coordinates, elevation, family size, family structure, and agroforestry systems. The SI values, ranging from 0% to 100%, reflect the capacity of the agroecosystem to balance ecosystem-derived production and resource preservation across agro-environmental, economic, and sociopolitical dimensions. An SI of 100% indicates optimal sustainability; 96%–99% is excellent; 90%–95% is very good; 80%–89% is goo;, and less than 80% is unacceptable [35]. The scores from the interviews, referred to as endogenous information, were specific to each chagra as collected by the SI system itself.

2.4. Exogenous Environmental Data

Fourteen (14) environmental variables were gathered from diverse public domain sources (Table 2) and employed as independent features in machine learning models to explore insights into the distribution of SI dimensions. These variables, considered exogenous information, were distinct from the SI metrics measured within each chagra, encompassing a range of spatial resolutions (from 10 to 300 m in increments of 10 m) and preprocessing methods, such as alignment, resampling using bilinear interpolation via the ‘raster’ R-package [36], and stacking of polygon layers in their raw form. The variable ‘cov’ was ordinal-scaled to reflect an urbanization gradient from 1 (entirely urban) to 10 (completely natural land cover). This approach aimed to optimize data aggregation and summary at the chagra level, enhancing the efficacy of subsequent unsupervised and supervised machine learning analyses (Table 2).

2.5. Data Analysis

Different data analytics and visualization techniques were applied to answer three basic questions: (1) How are the mean scores of the SI distributed across indices, components, and dimensions for the 800 surveyed chagras, and what are their confidence intervals in the SV population? (2) What are the most critical dimensions, components, and/or indices owing to low and/or uncertain values? (3) Which exogenous environmental variables are reasonable drivers of SI, with a focus on more critical dimensions, components, and indices?
To address questions (1) and (2), we estimated the mean scores and their 95% bootstrapped confidence intervals for each SI, component, and dimension using the ‘boot’ R-package [37]. These results were visually represented through sunburst plots generated using the ‘plotly’ library in Python, from the Google Collaboratory Notebook. (Figure 2). Additionally, a principal component analysis (PCA) with a correlation matrix was conducted on the indices of critical dimensions to uncover the underlying patterns and relationships within the SI. To address question (3), which focused on identifying the exogenous environmental variables influencing the SI, two machine learning techniques were employed. First, unsupervised k-means clustering was used to identify natural clusters in the environmental dataset, enabling the analysis of correlations between the environmental features and SI dimensions. This analysis included comparisons of means and bootstrapped confidence intervals among clusters for continuous features (Figure 3A) and the construction of contingency tables to explore associations with categorical features (Figure 3B).
Statistical assessments were conducted using methods such as the bar overlap technique and Cohen’s f statistic implemented in the ‘sjstats’ R-package [38]. Additionally, a hierarchical cluster analysis was performed on scaled environmental variables using Euclidean distances and the Ward method (‘hclust’ function in the base R-package). The resulting dendrogram (Figure 4) reveals the topological relationships between the clusters identified by k-means clustering.
Second, supervised extreme gradient boosting (XGBoost) modeling was employed to determine the most influential environmental features for differentiating the identified clusters. This approach, facilitated by the ‘caret’ [39] and ‘xgboost’ [40] R-packages, utilizes automated hyperparameter tuning and 30-fold cross-validation to assess model performance and feature importance, highlighting variables crucial for distinguishing between environmental clusters. All the R-packages were run in the software R ver 4.4.1, from the R-studio integrated development environment ver 2023.12.1.

3. Results

3.1. Endogenous Drivers

The observed SI across the 800 chagras sampled in the SV ranged from 2% to 75%, with an average of 42%. Extrapolating to the total of 8688 chagras, the estimated population mean fell between 41% and 44%, with a 95% confidence level, indicating that none exceeded the critical 80% threshold for basic sustainability. It was found that 70% of the chagras had a SI below 40%. Table 3 illustrates confidence intervals by dimension and component, highlighting consistently lower values in the economic (ECO) dimension—33% to 48% less than the agro-environmental (AE) and socio-political–cultural (SPC) dimensions—with significant differences noted, emphasizing ECO as the most critical dimension affecting SI distribution (Figure 2A).
Within the ECO, all components scored below 40%, in contrast to AE, where only biodiversity (BIO) and energy (ENE), and within SPC, only External Relations (ERL) achieved similarly low scores. The PCA identified key endogenous factors influencing ECO, including productivity per technology unit (ppt), protection of particular groups (pif), adherence to instructions (pad), pedagogic performance (anp), and product production (pps). Together, these factors explained 57% of the ECO variance via principal component 1 (PC1), correlating positively (r = 0.6–0.8). Analysis of PC1 loadings and scores revealed distinct patterns, explaining the interactions between the indices, their impact on ECO dynamics, and chagra status across the SV. For instance, chagras in the lowest ECO quartile faced constraints such as minimal production and land use diversification, whereas those in the highest quartile exhibited more robust performance across these indices; however, none reached 80% sustainability. It should be noted that local agricultural production systems are structured according to the combination of various types of relationships and activities developed within their own work [41]. These components include natural resources, the cycling of products and by-products used in agriculture, and the energy required for this activity, such as biogas.
On the other hand, it is highlighted within the analysis of the results that economic alliances in local communities reveal important gaps and challenges in the establishment of strategic collaborations that promote sustainable development. These communities, which traditionally depend on exchange and bartering, face significant limitations in the transformation of their agricultural products. These effective mechanisms for creating added value prevent access to broader and more competitive markets. To overcome these challenges, establishing alliances that facilitate the production of green seals and agroecological certifications is crucial. These certifications, which include the use of organic supplies and adoption of good agricultural practices, can enhance the competitiveness of local products in specialized markets. The formation of strategic economic alliances allows communities to access the technical and financial resources necessary to improve their productive processes and strengthen their organizational abilities. Therefore, identifying and closing these gaps in economic alliances is essential to promoting the sustainable development and resilience of local communities.
At a secondary level within PC1, indices like cultivation productivity (cch), clean tech (pbp), farm items in circulation (eic), and non-farming or simple lifestyle (nml) emerged as drivers of ECO, correlating positively (r = 0.4–0.6) and explaining 32% of its variance. Notably, most chagras do not exceed the local market levels and rely heavily on intermediaries for sales. Structural improvements and market expansion are limited, particularly in lower ECO quartile chagras, reflecting their constraints in enhancing productivity and market access compared to their higher quartile counterparts.

3.2. Exogenous Drivers

The ten (10) continuous environmental features were subjected to z-standardization, whereas four categorical features were subjected to one-hot encoding before k-means clustering. No SI data were included as a feature for k-means to ensure that the resulting clusters were solely based on environmental information independent of the chagras’ surveys. This approach was adopted to ensure that any patterns of association or correlation between the potential endogenous and exogenous drivers of the SI dimensions were identified using unrelated data. Seven clusters were identified to optimize the between-group sum of squares, with the number of chagras per cluster varying from 16 to 361. These clusters were then utilized as categorical variables to analyze variations across each continuous feature and to assess potential associations with each categorical feature (Figure 3). Notably, the primary finding in Figure 3A is the structuring of the seven clusters along the ECO, SPC, and AE dimensions, providing a framework for the subsequent interpretation of environmental features.
Although Cluster 2 exhibited significantly higher means in all dimensions, Cohen’s statistic indicated a larger effect size for ECO (f = 0.35 ± 0.28), whereas it was smaller for SPC (f = 0.22 ± 0.14) and AE (f = 0.24 ± 0.16). This suggests that even if the clustering pattern remains very similar across all three dimensions, the between-cluster variance is significantly higher for ECO. Therefore, Cluster 2 emerged as a group of chagras exhibiting superior scores across all dimensions of the SI (38–50% in ECO, 54–64% in AE, and 52–58% in SPC), representing average increases of 14%, 10%, and 8%, respectively, compared to the overall mean of each dimension. In contrast, Cluster 7 was consistently marked by notably lower scores in all dimensions, representing reductions of 10%, 1%, and 2%, respectively, below the overall mean. For SPC and AE, Clusters 4, 5, and 6 shared similarly lower values than Cluster 7.
Further analysis of the cluster structure of each environmental feature revealed significant correlations or associations between the environmental context of the studied chagras and their SI dimensions. However, throughout the text, we place particular emphasis on ECO, recognizing its status as the weakest dimension. The features ndvi, rvi, and savi exhibit two distinct groups of clusters: Clusters 1, 2, 3, and 4 with mean values lower than 0.5, 0.35, and 0.7, respectively, and Clusters 5, 6, and 7, with significantly higher values. This indicates a negative scaling between these vegetation indices and the SI dimensions, where the chagras with higher values relate to lower vegetation indices and vice versa. An exception is feature evi, whose mean value in Cluster 2 is more closely associated with Clusters 5, 6, and 7, indicating higher vegetation cover measured by this particular index. However, the confidence interval of Cluster 2 for this feature is at least twice as wide as that of the other clusters, necessitating careful consideration before drawing any conclusions.
The second set of environmental features, demonstrating similar cluster structures, involves slo and ele. In both cases, Cluster 2 exhibited significantly higher mean values, exceeding 6% and 2160 m for slo and ele, respectively. Conversely, Cluster 7 recorded the lowest values, falling below 4% and 2100 m for the slo and ele, respectively. This implies a positive correlation between the slo, ele, and SI dimensions, suggesting that chagras situated at higher elevations with a greater proportion of sloped land tend to stand out in terms of the SI. Clusters 4–7, encompassing 74% of the surveyed chagras, were situated in the lowest and flattest areas across the SV, concurrently displaying all their ECO values within the first and second quartiles.
Regarding distance features, both dci and dri exhibit similar clustering structures, wherein clusters with higher SI dimensions tend to aggregate at lower feature values and vice versa. For instance, Clusters 1–4 were, on average, positioned 260 m away from the closest river, whereas Clusters 5–7 were situated 500–1000 m away. Exhibiting a much clearer trend, chagras with the highest SI dimensions (Clusters 1, 2, and 3) were, on average, located less than 6000 m away from cities. Conversely, those with the lowest SI dimensions, particularly Clusters 6 and 7, remained over 6500 and 7500 m, respectively. A contrasting trend is observed with dho and dro, where Cluster 2 stands out with significantly higher values, while the opposite is true for Clusters 4–7. This suggests a negative correlation between both features and the SI, where chagras with high SI scores are notably more distant from other chagras and roads. A notable exception is Cluster 5 for the feature dho, which, despite belonging to the lower SI group, stands out for its high mean value.
The feature cov, representing a gradient from natural cover (0) to complete urbanization (10), displays a variation pattern where Cluster 2 achieves the lowest mean value of 4.1, followed by Clusters 3, 4, and 1, with mean values of 4.8, 5.0, and 5.6, respectively. Conversely, Clusters 5, 6, and 7 had higher values of 6 for this feature. Hence, chagras with intermediate cov values across the entire range were associated with higher SI dimensions. Clusters 5, 6, and 7 were significantly associated with low fertility soils (χ2 = 141, p = 0.0004), fluvial–lacustrine and organic deposits (χ2 = 401, p = 0.0004), and the taxonomic categories Typic Udifluvents, Typic Fluvaquents, and Hydric Haplohemists (χ2 = 401, p = 0.0004). Conversely, Cluster 2 lacked these specific associations, except for fertility, as most of its agricultural plots fell under the low fertility category, although moderate and moderate to high fertility were also represented. Overall, Clusters 1, 2, 3, and 4 exhibited a wider range of variability in the fer, lit, and st categories, with Cluster 2 being the only cluster that was partially associated with volcanic ash, mudstones, and siltstones. For these categorical features, clusters with the lowest SI dimensions were linked to the most unfavorable soil conditions, whereas clusters with the highest SI dimensions encompassed a greater diversity of associations.
Topological relationships between clusters (Figure 4) confirmed the pattern of between-group variability observed for most environmental variables (Figure 3). While Cluster 2 is an outstanding entity, it is closer to Clusters 3, 1, and 4, in that order, within the hyperspace of environmental variability. These clusters have higher mean values across all dimensions, especially for ECO. In contrast, Clusters 5, 6, and 7 formed a separate group, which was characterized by lower mean values in all SI dimensions. The distance between these two main groups in the dendrogram was 83%, whereas the distance between Cluster 2 and the combined group of Clusters 1, 3, and 4 was 75%.
The XGBoost model was trained to measure the influence of the environmental features and achieved average kappa and accuracy values of 99.1 and 99.3%, respectively, highlighting the excellent efficiency of predicting the cluster label and consequently, estimating the feature importance. Figure 5 shows that the most influential environmental features on the clustering patterns are dri and dro with values higher than 60%. The features dci and rvi achieved 22 and 21% importance, respectively, placing them at a secondary level of influence. It is important to note that these values were calculated based on standardized feature values. Therefore, the higher importance observed in Figure 5 cannot be attributed to the larger measurement scales of distance features.

4. Discussion

Our study showed that none of the indigenous chagras of the Kamëntsá Biyá and Kamëntsá Inga in the SV reached a basic level of sustainability (80%). The economic dimension (ECO) was the most critical, as it had the lowest values for multiple indicators. These results are crucial for the initiation of transdisciplinary dialogue to understand the realities of life within indigenous communities and the socio-economic challenges related to their long-term sustainability.
This study highlights important findings related to endogenous factors in the economic dimension, showing low rates in areas such as farm diversification and improvement, production for sale, tourism development, self-sufficiency in inputs, product transformation, commercial channels, market diversification, economic alliances, and certifications. These results reflect the threats and challenges faced by communities to achieve sustainability of the chagras and guarantee food security in the context of global change [42]. Future research should focus on the ECO dimension in order to improve the living conditions of these indigenous communities.
The results obtained in the IS and ECO dimensions underline the importance of evaluating traditional production systems not only to understand their current situation but also to identify areas for improvement and prevent their possible future disappearance. The Kamëntsá Biyá and Kamëntsá Inga indigenous communities have comprehensively documented concerns regarding the status and current reconfiguration of traditional agroecological systems [11]. They seek to shape various conservation strategies and practices that are deeply connected to their territorial context. This highlights the lack of a universally accepted method and the great diversity that exists in aspects such as data collection and analytical techniques, including scale considerations and ultimate objectives. It is argued that addressing the complex and multidimensional nature of agricultural sustainability presents significant obstacles, claiming a methodical and context-specific approach to identifying specific factors, transforming them into indicators, and assessing these factors to diagnose possible improvements [43].
This study, along with previous studies on the applicability of HESOFI, especially those conducted in Central and Latin America [44], highlights the importance of understanding rural activities for decision-making and the creation of effective methodologies to improve agricultural sustainability in the SV. It is essential to understand land distribution, recognize anthropogenic pressures affecting soil quality, and integrate mixed cropping to encourage diversification. Chagras on lands with higher slopes and elevations showed fewer flooding and soil pressure problems. Proximity to rivers and cities improves access to irrigation and connections to markets, which increases sustainability values in chagras but can also lead to sprawling urban development and pollution. This requires a more holistic view of soil suitability [45], incorporation of suitable crops, and the physical, environmental, social, and economic conditions of chagras.
Parameters such as land use constraints, marketing of agricultural products, supply chains, and sociopolitical factors allow for a comprehensive understanding of the rural system and its relationship with crop suitability and the environment. Furthermore, it insists on integrating practices, such as agroforestry, soil conservation, and water and organic input production, which are crucial for developing policies to improve regional planning. This study emphasized the need to interlink local knowledge and sustainable practices in a holistic planning process [46]. Strategies to improve the ECO dimension of chagras include striving for crop industrialization, partnerships, economic certifications, community training and capacity building, and the development of monitoring and evaluation tools.
To strengthen the relationships and indicators between the economic, agro–food, cultural, and environmental dimensions, endogenous development, market diversification, and ecotourism can be promoted [47]. Examples in Latin America, such as the Tilajari Project and Isinche Experiential Tourism initiative, demonstrate the successful integration of agroecology and cultural preservation into tourism. These initiatives underline the potential of agroecological practices to improve food security, economic empowerment, and resilience in the face of socio-environmental pressure and change.
Public policies focused on sustainability, agroecology, and environmental conservation [48] are crucial and require effective political and social coordination to ensure community engagement and co-responsibility for long-term sustainability goals. This research coincides with studies on economic recovery after COVID-19 and highlights the urgent need to invest in agriculture and expand rural areas to ensure food security and promote the socioeconomic development of indigenous communities [49,50,51,52,53].

Author Contributions

Conceptualization, C.O., J.P.R.-L., M.J.M., J.Z., A.C., S.O. and I.F.B.; Methodology, C.O., J.P.R.-L., M.J.M., J.Z., A.C., S.O. and I.F.B.; Software, C.O., J.P.R.-L. and I.F.B.; Writing—original draft, C.O., J.P.R.-L., M.J.M., J.Z., A.C., S.O. and I.F.B.; Methodology, C.O., J.P.R.-L., M.J.M., J.Z., A.C., S.O. and I.F.B.; Writing—review and editing, J.P.R.-L. and I.F.B.; Supervision, I.F.B.; Funding acquisition: J.P.R.-L., M.J.M. and I.F.B. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded in part by the Universidad del Quindío Crossref financing code: 100016837, the MinCiencias Colombia, a project from El Sistema General de Regalías (SGR) BPIN = 2021000100358, internal code 1125, and the Servicio Nacional de Aprendizaje (SENA), code project SGPS-10755-2023.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the last author, Ivan Felipe Benavides.

Acknowledgments

The authors are grateful to the Kamëntšá Biyá and Kamëntšá Inga indigenous communities in the Sibundoy Valley for authorizing the surveys, and to the Centro Agroforestal y Acuícola Arapaima, Datambiente and the Research Group Agroforestería y Recursos Naturales at the Universidad de Nariño in Colombia for providing logistical support. J. Romero and M. Mesa would like to acknowledge the Universidad del Quindío and members from the research groups GEDES, and SIGMA at the Universidad del Quindío for their technical assistance and useful discussions.

Conflicts of Interest

The authors declare no conflicts of interest. The funders did not influence the study design, data collection, analysis, interpretation, manuscript preparation, or the decision to publish the findings.

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Figure 1. The Sibundoy Valley (SV), including 800 surveyed chagras and the cities of Sibundoy (SI) and San Francisco (SaF). Chagra clusters from the k-means algorithm are also displayed to complement the visualization.
Figure 1. The Sibundoy Valley (SV), including 800 surveyed chagras and the cities of Sibundoy (SI) and San Francisco (SaF). Chagra clusters from the k-means algorithm are also displayed to complement the visualization.
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Figure 2. Sunburst plots showing the distribution of (A) mean scores per index, component, and dimension across the 800 surveyed chagras; and (B) their 95% confidence intervals representing the uncertainty level. Both values are presented as percentages.
Figure 2. Sunburst plots showing the distribution of (A) mean scores per index, component, and dimension across the 800 surveyed chagras; and (B) their 95% confidence intervals representing the uncertainty level. Both values are presented as percentages.
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Figure 3. (A) Comparison of mean values (points) and 95% confidence intervals (vertical bars) among the seven clusters for ECO and the 11 continuous exogenous variables, and (B) heatmaps illustrating the level of association (LA) between the seven clusters and the three categorical exogenous variables. ECO: economic, SPC: socio-political–cultural, AE: agro-environmental, ndvi: normalized vegetation index, rvi: ratio vegetation index, evi: enhanced vegetation index, savi: soil adjusted vegetation index, slo: slope, ele: elevation, dri: distance to rivers, dho: distance to houses, dro: distance to roads, cov: CORINE land cover, dci: distance to cities, fer: fertility, lit: lithology, st: soil type.
Figure 3. (A) Comparison of mean values (points) and 95% confidence intervals (vertical bars) among the seven clusters for ECO and the 11 continuous exogenous variables, and (B) heatmaps illustrating the level of association (LA) between the seven clusters and the three categorical exogenous variables. ECO: economic, SPC: socio-political–cultural, AE: agro-environmental, ndvi: normalized vegetation index, rvi: ratio vegetation index, evi: enhanced vegetation index, savi: soil adjusted vegetation index, slo: slope, ele: elevation, dri: distance to rivers, dho: distance to houses, dro: distance to roads, cov: CORINE land cover, dci: distance to cities, fer: fertility, lit: lithology, st: soil type.
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Figure 4. Dendrogram displaying topological relationships among the seven clusters identified using the k-means algorithm. Euclidean distances were calculated between scaled cluster centroids across environmental variables. Numbers in red represent cluster labels.
Figure 4. Dendrogram displaying topological relationships among the seven clusters identified using the k-means algorithm. Euclidean distances were calculated between scaled cluster centroids across environmental variables. Numbers in red represent cluster labels.
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Figure 5. Importance of exogenous environmental variables for the prediction of the seven clusters using the XGBoost classifier.
Figure 5. Importance of exogenous environmental variables for the prediction of the seven clusters using the XGBoost classifier.
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Table 1. Dimensions, components, and indexes that comprise the calculation of the sustainability index (SI) and the scoring assigned to each index from low to high. The color ramp represents the ordinal scale from 1 (red = minimum) to 5 (blue = maximum) for each index. SPC: social–political–cultural, ECO: economic; AE: agro-environmental, WEL: welfare, IRL: internal relationships, ERL: external relationships, CUT: culture and territory, DEV: development, EDM: efficiency and dynamism, BIO: biodiversity, LAND: landscapes, SWA: soil and water, PRC: protection of crops, ENE: energy, AHS: animal husbandry.
Table 1. Dimensions, components, and indexes that comprise the calculation of the sustainability index (SI) and the scoring assigned to each index from low to high. The color ramp represents the ordinal scale from 1 (red = minimum) to 5 (blue = maximum) for each index. SPC: social–political–cultural, ECO: economic; AE: agro-environmental, WEL: welfare, IRL: internal relationships, ERL: external relationships, CUT: culture and territory, DEV: development, EDM: efficiency and dynamism, BIO: biodiversity, LAND: landscapes, SWA: soil and water, PRC: protection of crops, ENE: energy, AHS: animal husbandry.
DimComIndexCodeScoring (Low to High)
SPCWELPercentage of food products intended for preservationppp0up to 2526–5051–75>75
Diversification of the family’s diet (carbohydrates, proteins, and vitamins)didonly carbohydratescarbohydrates and proteinscarbohydrates, proteins and vitamins
Percentage of products for family consumption produced in the chagrapfc0Up to 2526–5051–75>75
Number of basic services: potable water, electricity, medical assistance, school, and means of transportationabsNo service223>3
House conditions: floor (not earthen), walls (not wooden), roof (not thatched), ventilated kitchen, and sanitary facilitieshcobadregulargood
Highest education level in the family (by age groups)helnoneelementary schoolhigh schooltechnical trainingprofessional
IRLPercentage of young individuals involved in chagra workpyi0Up to 2526–5051–75>75
Roles of young individualsryinonepersonalcollaborativeaffiliatedleader
Percentage of women involved in chagra workpww0up to 2526–5051–75>75
Roles of womenrownonepersonalcollaborativeaffiliatedleader
Democracy in internal processes and decision-makingdipnoyes
Land planning: the farmer maintains necessary records, activities, inputs, expenses, and incomelapNo information registeredInformation is registered but not usedInformation used for decision-making
ERLNumber of relationships with public institutions, private entities, and research centersaer012>3
Number of engagements with the local network of cooperatives, associations, movements, unions, organizations, and NGOseln0123>3
Participation of the farmer and family in decisions regarding local collective realitiesplrpassiveactive
Access to media (TV, telephone, internet) and online visibility (website, Facebook, etc.)atmnoneonly radio or tvall media but no visibilityall media and visibility
Sensitization of consumers to production techniquessptnoyes
Number of trainings for the farmer and family in the last two years related to agricultural developmentntf0up to 34–67–10>10
Number of events in the last year attended by the farmer and family related to recreational, religious, community, sports, and cultural issuesnef0Up to 34–67–10>10
CUTThe agricultural techniques raise the awareness of the chagra’s connection with the territory and history, including traditions and knowledgeatanoyes
Land ownershiplaotenantusufructowner
Horizontal transmission of knowledge (number of exchanges of experiences among producers)htknoneup to 34–67–10>10
Transmission of knowledge between generations (type of transmission)tkgritualsdialoguessupport in family activitiestwo or more of the above
Usage of productsuopnoyes
ECODEVPercentage of land area managed with diversified agricultural systemspad0up to 2526–5051–75>75
Number of agricultural and non-agricultural products produced in the chagraanpless than 45–89–1213–17>17
Percentage of production destined for salepps0up to 2526–5051–75>75
Expansion, improvements, or construction of new productive structureseicnoyes
Creation and development of tourism activities on the chagracdtnoyes
EDMLabor forcelafonly hiredfamiliar and hiredonly familiar
Number of market levels achieved
chagra/local/regional/national/international		nml1234>4
Commercial channelscchno salessales by intermediariesdirect sales
Producer’s bargaining power (ability to establish prices and sales conditions)pbpnoyes
Percentage of products transformed in the chagrappt025up–50up to 75>75
Percentage of inputs used in the chagra generated within the chagra itselfpif025up–50up to 75>75
Chagra certifications: organic, fair trade, agroecological, sustainable, biodynamic, small-scale producers, GAP, GAPE, and others that comply with environmental protection requirements for added value related to special marketschcnoyes
Economic allianceseca0123>3
AEBIONumber of agricultural plant speciesnps<34–67–1011–14>14
Percentage of locally cultivated varieties out of the total crop (native and improved native)plv0–1010–2526–5051–75>75
Structural diversity in fence constructionsdfdead fence1–2 integrated tree species+1–2 integrated shrub species+1–2 integrated herb species>3 integrated tree, shrub and herb species
Percentage of self-produced seeds and forestry plants (propagation material)psf0255075100
Typology of plant associationtpanoneAmong herbsAmong herbs and shrubsAmong herbs, shrubs and trees
LANPresence of natural regeneration environmentsnrenoyes
Implementation of traditional practices to improve soil, water, air, and forest plantation managementitpnoyes
SWAPercentage of implemented rotations in the cultivated areapirmonoculture2526–5051–75>75
Efficient water useewunonegravitysprinklermicrosprinklerdrip
Presence of water capture infrastructure (ditches, reservoirs, etc.)wcinoyes
Use of synthetic chemical fertilizersscffrequentrarelynever
Area percentage with incorporation of organic biomassaob0–910–2526–5050–75>75
Fertilization with green manurefgmnoyes
Recycling of organic waste from the chagrarownoyes
PRCUse of chemical pesticidesucpusualunder extreme necessitynever
Percentage of natural pesticides usedpnp0–1011–2526–5051–7576–100
Use of chemical herbicidesuchusualunder extreme necessitynever
Percentage of natural weed control techniques usednwc0–1011–2526–5051–7575–100
Use of synthetic chemical products for post-harvest treatmentcphusualunder extreme necessitynever
Number of natural/alternative products or techniques used for post-harvest treatmentsnphnone123>3
ENEPercentage of renewable energy sources usedrwe0–1011–2526–5051–7576–100
Use of minimal packaging and/or recyclable/recycled materialsmpronly plasticmix (minimum with plastic)only natural and minimum
AHSNumber of animal breedsnab01–34–78–11>11
Percentage of local breeds (native or improved native)nlb0–1011–2526–5051–7576–100
Typology of reproduction implemented in the chagratyrurchased offspringmix between purchased and bred in the chagraon-chagra breeding
Animal grazingangnoYes
Typology of livestock housing structureslhsclosed spacesopen spaces (without roof)semi-open spaces (sleep and milk under a roof)
Origin of animal feedoafall purchasedup to 50% from the chagra>50% from the chagra
Typology of animal feedtafindustrialmix of industrial and naturalnatural
Mutilationmutyesno
Implementation of measures and/or structures for mitigating environmental pollutionmepno actionaction taken
Animal sacrificeansslaughterhouse or processing plantrefrigeratorchagra/cooperative
Table 2. Exogenous environmental variables used to search for insights into the distribution of ECO.
Table 2. Exogenous environmental variables used to search for insights into the distribution of ECO.
Variable (Abbreviation) (Type, Units)Geometry (Extension)Spatial ResolutionSource
Elevation (ele) (continuous, meters)raster (tif)12.5 mAdvanced Land Observation Satellite (ALOS-PALSAR)
https://asf.alaska.edu/datasets/daac/alos-palsar/ (Accessed on 10 November 2020)
Slope (slo) (continuous, %)12.5 m
Normalized vegetation index (ndvi) (continuous, standardized from −1 to 1)10 mSentinel-2
https://www.esa.int/Space_in_Member_States/Spain/SENTINEL_2 (Accessed on 10 November 2020)
Enhanced vegetation index (evi) (continuous, standardized from −1 to 1)
Ratio vegetation index (rvi) (continuous, standardized from −1 to 1)
Soil adjusted vegetation index (savi) (continuous, standardized from −1 to 1)
Distance to houses (dho) (continuous, meters)Colombian cartographic bases from the Instituto Geográfico Agustín Codazzi (IGAC)
https://www.colombiaenmapas.gov.co/ (Accessed on 10 November 2020)
Distance to rivers (dri) (continuous, meters)
Distance to roads (dro) (continuous, meters)
Distance to cities (dci) (continuous, meters)
CORINE land cover (cov) (categorical, 19 categories), transformed to ordinal from 1 to 10 for the data analysespolygon (shp)N/ADatos Abiertos from the subdirección de Agrología, Instituto Geográfico Agustin Codazzi (IGAC)
https://geoportal.igac.gov.co/contenido/datos-abiertos-agrologia (Accessed on 10 November 2020)
Soil type (st) (categorical, 16 categories)
Lithology (lit) (categorical, 14 categories)
Soil fertility (fer) (categorical, 9 categories)
Table 3. The 95% confidence intervals for the means of the dimensions and components of the sustainability index (SI).
Table 3. The 95% confidence intervals for the means of the dimensions and components of the sustainability index (SI).
Dimensions (95% CI for the Mean)Components (95% CI for the Mean)
ECO (27–31%)DEV (26–29%)
EDM (32–33%)
SPC (46–49%)CUT (63–65%)
WEL (52–53%)
IRL (48–52%)
ERL (22–24%)
AE (48–51%)LAN (61–65%)
ENE (38–40%)
BIO (36–39%)
PRC (58–61%)
SWA (50–53%)
AHS (43–46%)
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Ojeda, C.; Romero-Leiton, J.P.; Mesa, M.J.; Zapata, J.; Ceballos, A.; Ordoñez, S.; Benavides, I.F. Economic Dynamics as the Main Limitation for Agricultural Sustainability in a Colombian Indigenous Community. Sustainability 2024, 16, 8611. https://doi.org/10.3390/su16198611

AMA Style

Ojeda C, Romero-Leiton JP, Mesa MJ, Zapata J, Ceballos A, Ordoñez S, Benavides IF. Economic Dynamics as the Main Limitation for Agricultural Sustainability in a Colombian Indigenous Community. Sustainability. 2024; 16(19):8611. https://doi.org/10.3390/su16198611

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Ojeda, Cintya, Jhoana P. Romero-Leiton, Mónica Jhoana Mesa, Juan Zapata, Alvaro Ceballos, Solanyi Ordoñez, and Ivan Felipe Benavides. 2024. "Economic Dynamics as the Main Limitation for Agricultural Sustainability in a Colombian Indigenous Community" Sustainability 16, no. 19: 8611. https://doi.org/10.3390/su16198611

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