Method of Selection of Rural Territory in the Development of a Territorial Diagnosis

Abstract

Purpose. The development of general-scale diagnoses is one of the main reasons why policies, lines of action, and strategies do not adequately respond to the dynamics and needs of rural territories. Consequently, there is a recognition of the need to select rural spaces that require a territorial diagnosis due to their particular characteristics and unfavorable conditions toward balanced development. Method. We design four phases in the rural space selection method. In the first phase, of the method’s development, we identify the formation of Technical Working Table(s) as a key factor. The second phase involves a systematic literature review from local sources, tailored to the application context. In the third phase, variables measuring rural space selection are identified and validated, determining the rural zones of interest. Finally, in the fourth phase, two mathematical techniques, the analytic hierarchy process (AHP) and weighted ratings, are proposed. These techniques enable quantification of options and provide information to facilitate the decision-making process for selecting rural spaces. Results. The first phase involves the formation of the Technical Working Table (TWT), identified as a key factor in the design and operation of the proposed research method. The Technical Working Table (TWT) comprises nine institutional actors committed to regional development, who participated in both the design and implementation of the proposed methodology. Similarly, in the phase of systematic literature review, 43 articles are selected, identifying 21, 18, and 26 variables of major significance in the ecological, social, and economic dimensions of the study area. Subsequently, in the third phase, collaboration with the TWT is employed to validate and select the nine variables constituting the criteria for rural space selection. In the final phase, results from applying the two mathematical techniques quantifying the rural space selection are obtained.​ Conclusions. The rural space selection method enhances the development of specific territorial diagnoses, given the unique characteristics and dynamics of the study area.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

Territorial planning plays a crucial role in managing and shaping rural spaces, where the convergence of environmental sustainability, socioeconomic development, and community well-being is essential [1, 2]. Within this context, the formulation of territorial diagnoses stands out as a pivotal tool for understanding the dynamics of these areas and crafting coherent and impactful development strategies [3]. However, creating diagnoses that accurately capture the unique characteristics of each territory encounter significant challenges, primarily due to the inherent heterogeneity of rural spaces [4] and the imperative to address their ecological, social, and economic dimensions in a holistic manner.

Presently, existing methodologies for developing diagnoses often operate within broad frameworks that, in their quest for standardization, overlook the specificities and interconnectedness of the challenges posed by each territory. Figure 1 depicts the primary causes of deficiencies observed in policy design and action plans, highlighting the failure to acknowledge the diversity and complexity of rural territories. This conventionally segmented approach results in partial and, at times, counterproductive solutions that inadequately address the genuine needs of rural spaces and their inhabitants [5].

[figure(s) omitted; refer to PDF]

Similarly, territory is understood as a multidimensional concept encompassing factors and criteria that play a role in the system. In other words, the territory is a space where work, energy, information, and power relations among actors are projected [6]. Consequently, it is a space constructed from social and historical contexts, requiring an approach that acknowledges and articulates the dynamics at play within it. Hence, the participation of actors in the structure of territorial diagnosis is crucial for the quality of the results, as it involves a collective examination and projection of the current and future state of the territory.

Additionally, it is observed that general diagnoses do not adequately respond to the needs, behaviors, and dynamics of rural territories. This is because rural geographical areas are diverse and unique, and in certain situations, they are not taken into account or assumed to behave similarly to other rural spaces. In these cases, some components of ecological, social, and economic development differ in these territories. Given the above, the research question is defined as whether the design of a rural territory selection method allows for reducing the generic approach of territorial diagnoses. Likewise, the proposed method is applied in the Department of Tolima (Colombia), where a rural territory is selected to develop a territorial diagnosis.

Based on the above, the research aims to provide planners, policymakers, and academics with a practical and effective method to guide the selection of rural space(s). This phase should be defined at the beginning of elaborating territorial diagnoses for a region.

Furthermore, it is acknowledged that environmental degradation, rural-urban migration, poverty, and a lack of economic opportunities are phenomena that characterize rural territories. Therefore, there is a need to strengthen lines of action in territorial diagnoses that facilitate progress toward balanced development across ecological, social, and economic dimensions. Sustainable development is identified as a priority goal at the global level [7, 8]. It is crucial that rural development strategies not only aim for short-term economic efficiency but also take into account the preservation of natural resources for future generations and the resilience of communities in the face of change [9].

In this context, the selection of rural territories emerges as a key factor in the development of territorial diagnostics, given that as an indispensable tool not only for input in regional planning and projection but also as a catalyst for community empowerment and participation.

In this context, the process for selecting rural territories plays a crucial role in the development of territorial diagnoses. It defines a set of selection variables applicable to all rural territories within the region, thereby identifying typologies that can be considered in the development of territorial diagnoses. Consequently, the selection process generates valuable information on the development of rural territories, serving as a significant input in the planning and projection processes of regions. This method can be implemented by governmental entities to gather information on rural territories during the planning (diagnosis development) and projection stages.

Furthermore, the active involvement of rural communities in the selection process of rural territory(ies) is essential. The knowledge and experiences of local stakeholders are crucial in the development of the proposed method [10]. Similarly, social inclusion is not only a matter of equity but also of effectiveness, as strategies developed in collaboration with communities tend to be more robust and enduring [11].

From the foregoing, it is identified that the categories shaping the object of study are the selection method, rural territories, and territorial diagnosis. Consequently, a literature review is conducted around these categories. In Table 1, the seven search strings applied in the search phase are presented. Subsequently, Table 2 displays the quantity of documents found with the search strings. The results reveal that none of the documents feature the combination of two keywords in the title. Therefore, the search is expanded to titles, abstracts, and keywords, leading to the identification of 57 articles.

Table 1

Search strings.

No	Search strings
1	(TITLE (method AND of AND selection) AND TITLE (rural AND territory))
2	(TITLE (method AND of AND selection) AND TITLE (territorial AND diagnosis))
3	(TITLE (rural AND territory) AND TITLE (territorial AND diagnosis))
4	TITLE (territorial AND diagnosis) AND (LIMIT-TO (OA, “all”))
5	(TITLE-ABS-KEY (method AND of AND selection) AND TITLE-ABS-KEY (rural AND territory)) AND (LIMIT-TO (OA, “all”))
6	(TITLE-ABS-KEY (method AND of AND selection) AND TITLE-ABS-KEY (territorial AND diagnosis)) AND (LIMIT-TO (OA, “all”))
7	(TITLE-ABS-KEY (rural AND territory) AND TITLE-ABS-KEY (territorial AND diagnosis)) AND (LIMIT-TO (OA, “all”))

Table 2

Articles related to the object of study.

Word 1	Con	Word 2	Search	No. SS	No. of articles	No. of selected articles
Method of selection	And	Rural territory	Article title	1	0
Method of selection		Territorial diagnosis		2	0
Rural territory		Territorial diagnosis		3	0
Territorial diagnosis		—		4	13	6
Method of selection	And	Rural territory	Article title	5	31	4
Method of selection		Territorial diagnosis	Abstract	6	7	0
Rural territory		Territorial diagnosis	Keywords	7	19	4
Total					70	14

Next, the titles of the 70 articles are reviewed, and it is identified that fourteen articles are related to one of the categories of the object of study. Therefore, Table 3 presents the objects of study from the previously selected manuscripts, identifying diagnoses applied in different contexts such as ecological conservation areas and vulnerability to flooding in territories. Similarly, diagnoses are conducted to identify problems and lines of action in rural communities. However, it is acknowledged that some of the results from the objects of study may contribute elements of interest to the development and feedback of diagnoses. From the above, it is observed that the articles do not fully relate to the object of study of the present research, which emphasizes the importance of selecting rural spaces in the development of territorial diagnoses for the region.

Table 3

Objects of study in secondary sources.

No	Year	Ref	Title	Object of study
1	2017	[12]	Participative diagnosis for territorial planning of protected areas: subsidies to the Taim Ecological Station management plan, Brazil	The Taim Ecological Station is a significant wetland area lacking an effective land management plan, primarily due to conflicts that arise between cultural and productive patterns
2	2023	[13]	Diagnosis of key ecological restoration areas in territorial space under the guidance of resilience: A case study of the Chengdu–Chongqing region	Diagnosing key areas for ecological restoration in a surface-line-point framework under resilience guidelines through the construction of an ecological network
3	2022	[14]	Participatory socioterritorial diagnosis: a strategy that articulates the needs of society with university education	Identifying local issues in a rural community in Chile through the application of a participatory socioterritorial diagnosis based on the principles of action research
4	2009	[15]	Nature and mountain sports: comparative diagnosis of territorial resources	A study is conducted that compares the territorial resources activated in the development of economic activities related to nature and mountain sports in the departments of France
5	2020	[16]	Accounting for surface runoff in territorial flood vulnerability diagnoses	Diagnosis of the vulnerability of territories to flooding, including the phenomenon of runoff, in three municipalities
6	2022	[17]	Territorial approach to development: natural dimension and contributions to the diagnosis and surveying of new scenarios	The emerging concept of territorial development suggests the opportunity to generate alternatives for the development of rural and urban spaces. This is why the dimension of nature is discussed in the territorial development approach, providing elements for the formulation of diagnoses
7	2019	[18]	Spatial development of rural territories in Russian regions: growth areas or desolation zones?	A methodology is designed to assess the development of rural areas (both traditional and modern), given the urgent need for accelerated spatial development of agricultural industries
8	2020	[19]	Spatial disproportions in the development of territorial community under conditions of administrative and financial decentralization	The spatial characteristics of the socioeconomic development of territorial communities are identified in the context of administrative and financial decentralization (center-periphery interaction at the level of territorial communities)
9	2022	[20]	Water, land, and air: how do residents of Brazilian remote rural territories travel to access health services?	The study investigates cost factors, distances, and travel times in health transportation means across 27 remote rural municipalities in Brazil
10	2023	[21]	Sustainable development in rural territories within the last decade: A review of the state of the art	A systematic literature review is conducted to identify the main components and variables of sustainable development in rural territories, addressing ecological, social, and economic dimensions
11	2023	[22]	Evaluation of territorial capacity for development: population and employment	The study analyzes whether the evolution of employment and population has become a driver of local development in two municipalities based on a prior diagnosis
12	2020	[23]	Indicator system for a multidimensional characterization of Colombia’s municipalities	A set of indicators is proposed based on the United Nations City Prosperity Index, adapted to the diversity of municipalities and the availability of statistical data in Colombia
13	2014	[24]	The territorial-based basket of goods and services: A useful tool of diagnosis applied to the Algerian Wilaya of Ain Témouchent	The model of a territorial basket of goods and services is applied in a rural territory, studying the advantages and limitations of local resources. The necessary processes to configure a basket of territorial goods are recognized
14	2023	[25]	Diagnosis of rural development processes based on the stock of social capital and social networks: Approach from E-I index	Building upon the European rural policy LEADER, a methodological approach is proposed to analyze and quantify the stock of social capital present in the social networks of rural areas

Similarly, some contributions are identified that study on the typification of territories given the diversity and heterogeneity of spaces. Therefore, the authors of [23] propose a method consisting of the selection of indicators for the territory, economy, infrastructure, society, and governance dimensions previously defined in the research. Based on the above, the method develops a cluster analysis that defines six categories of territory: two urban and four rural in Colombia. The categories correspond to metropolitan municipalities, urban municipalities, rural municipalities, aging rural municipalities, rural municipalities subject to violence, and the most vulnerable rural municipalities. Likewise, the authors of [18] establish a method that classifies rural areas into growth areas and desolation zones. This method begins with the identification of criteria related to population dynamics, transaction costs, agricultural production, bioclimatic potential of the territory, and state support, in which indicators are defined to evaluate the spatial development of rural areas. That is, this technique calculates the indicators for each of the criteria, obtaining a score for each criterion, thereby estimating a total score in each of the rural settlements. Similarly, in [26], the application of the analytic hierarchy process (AHP) methodology is identified, estimating the importance of ten urban planning criteria, which along with geoinformatics tools allow the identification of suitable areas for urban development that are part of territorial planning and sustainability.

According to the aforementioned context, different techniques are identified that are applied in the categorization or typification of territories, which are partly defined by the purpose and scope of the research. Therefore, it is recognized that the categorization of territories is part of the object of study, but, with the difference, the proposed method is designed based on the needs and requirements of territorial diagnoses.

Finally, it is important to specify that this article presents an approach that moves away from traditional segmentation and incorporates a more holistic perspective. It considers the dynamics and peculiarities of rural territories in the development of territorial diagnoses. In this sense, the implementation of the proposed method in the elaboration of diagnoses aims to ensure that policy and strategy designs respond more effectively to the challenges and issues faced by rural areas. This contributes to a body of knowledge that is vital for informed and responsible territorial planning.

2. Materials and Methods

A key factor in the development of territorial diagnostics is to define the geographic space that delineates the territory of interest. Consequently, Figure 2 illustrates the phases of the method for selecting a rural territory, given the diversity and specificity inherent in rural spaces across nations [27]. The following section explains each stage of development.

[figure(s) omitted; refer to PDF]

2.1. Phase I: Establishment of the Technical Working Committee

The involvement of local actors is a key factor in the selection of rural space, as they contribute knowledge of the dynamics, needs, and limitations within their territories. The formation of the Technical Working Committee (TWC) is established, comprising a group of actors committed to a common purpose [28]. Based on the above, Figure 3 illustrates the characteristics of the team formation corresponding to the TWC [29], accompanied by a brief explanation.

[figure(s) omitted; refer to PDF]

Figure 3 shows that one of the main characteristics is the endorsement of the common objective by each team member. This requires expressing the willingness to undertake the necessary activities and efforts to achieve the stated goal. It is recommended that the team size is between five and nine individuals, as this fosters appropriate interpersonal relationships for effective teamwork. Likewise, seamless and transparent communication is a pivotal factor for team functionality, cutting across other characteristics. Subsequently, team members define roles, activities, and responsibilities to advance toward the objective. It is essential that human and interpersonal complementation be developed, involving the integration of contributions from team members even when complete agreement is not reached. Another characteristic is personal involvement, achieved when individual objectives and interests align with those of the team, emphasizing the acceptance of each team member. Finally, the competency to resolve disagreements and conflicts is strengthened, partly arising from the normal wear and fear of everyday relationships [29].

2.2. Phase II: Systematic Literature Review—Local Sources

Systematic literature review (SLR) was conducted using secondary information sources identified within the space and/or territory of interest in the study. Consequently, SLR comprises four phases corresponding to the search, selection, extraction, and analysis of information (see Figure 4). In the first and second phases, the search and selection criteria for information are defined. Subsequently, in the third stage, the identification and extraction of information take place, culminating in the analysis of information that addresses the question or purpose of the SLR [30]. By the foregoing, it is established that the primary outcome of the SLR is the identification of the key variables or criteria defined in the selection of a rural space. According to the above, it is significant to mention that the purpose of the RSL is the identification of the variables that have been investigated in the object of study.

[figure(s) omitted; refer to PDF]

Similarly, guidelines (laws or technical documents) on the typification of territories in the region where the proposed selection method is applied are consulted. Based on this review, the typification criterion that best corresponds to the dynamics of rural territories is selected, and the category that groups rural territories and is most relevant to the research is chosen.

2.3. Phase III: Validation of Variables and Sample of Rural Spaces

This phase involves the selection, validation, and identification of information sources for the variables. In other words, variable selection involves applying Pareto analysis, which identifies the most significant variables through the 80/20 principle. This principle states that 20% of the causes explain 80% of the problem or object of study [31]. Starting with the significant variables obtained in the previous step, validation is initiated with the Technical Working Group (TWG), where the relevance of the variables to the dynamics of rural territories in the region is discussed. Consequently, the result of the preceding steps is to select variables that have the greatest correspondence with the factors that must be considered in the selection of rural territory(ies). Finally, a search for information in secondary sources of the previously selected variables is conducted, as the data from these variables are essential for the application of mathematical models (see Figure 5).

[figure(s) omitted; refer to PDF]

2.4. Phase IV: Application of Mathematical Techniques

In Figure 6, the scheme for applying mathematical techniques corresponding to the analytic hierarchy process (AHP) and weighted factors (FP) is presented. A brief description of each of the methods is as follows:

[figure(s) omitted; refer to PDF]

Analytic hierarchy process (AHP): A mathematical method addresses the uncertainty inherent in multicriteria decision-making, and the AHP evaluates alternatives considering various criteria [32]. It is crucial to note that the AHP is based on “the principle that the experience and knowledge of the actors are as important as the data used in the process” [33], contributing a subjective component integral to the decision’s complexity.

Weighted factors method (WF): This technique quantifies criteria or quantitative and qualitative variables in the comparison of alternatives. In other words, when dealing with a multicriteria decision problem, relevant factors are identified, and each factor is assigned a weight or importance based on the evaluator’s criteria and experience [34, 35].

By the above, this phase begins with the application of the AHP technique, which is a more structured method than the weighted factors (FP) approach. AHP involves a comparison process that is evaluated using a consistency index. Therefore, if the consistency index does not accept the judgments and the advisory team agrees, it is recommended to review the development of the steps in the technique, as more time may be required. Conversely, if the team agrees, the weighted factors method is chosen as a valid technique with a straightforward application.

3. Results and Discussion Deployment of the Proposed Method in a Case Study

In this section, the application of the proposed method is presented in selecting a rural space in the Department of Tolima for the development of a territorial diagnosis.

3.1. Phase I: Formation of the Technical Working Group(s) (TWGs)

The purpose of forming the advisory team is the selection of a rural area in the Tolima Department, which is essential for the development of a territorial diagnosis. In line with this, it is defined that the profile of the MTT members corresponds to institutional actors with the mission and responsibility of leading the regional development of the Tolima Department. The selection criteria applied to the profiles of the MTT members are as follows.

3.1.1. Study Dimensions

The study dimensions are defined based on the main dimensions of sustainable development, characterized as the pursuit of balance among ecological, social, and economic dimensions [36]. Similarly, it is recognized that these three components are part of territorial diagnoses contributing to measuring the current state of spaces and/or territories. Hence, institutional actors are categorized into one of the dimensions based on the mission and primary functionality of the institutions (see Figure 7).

[figure(s) omitted; refer to PDF]

3.1.2. Relevance of Position

Relevance of position corresponds to departmental institutions and the alignment of the position with the development of the region, especially in the rural territories of the Tolima Department.

By the above, invitations are extended to ten institutional actors to form the TWG, and nine actors confirm their attendance at the TWG opening. During the meeting, the research that gave rise to the formation of the TWG is presented and explained. Additionally, the purpose, work plan, shared values, and work methodology are presented and adjusted based on the evolving needs of the team. Subsequently, the actors acknowledge the interest and importance of the research in the region’s development, along with the fundamental role of the WTC. Therefore, nine actors expressed their interest in being part of the team (two actors from the ecological dimension, two actors from the social dimension, and three actors from the economic dimension).

3.2. Phase II: Systematic Review of Literature from Local Sources

From the explanation of the phases of the proposed method, the results of the SLR are presented.

3.2.1. Search Phase

In this stage, preliminary searches are conducted to define keywords. Specifically, Table 4 lists the criteria applied in the Scopus database. Subsequently, the VOSviewer tool is used to obtain keyword occurrence maps, defining the number of keywords of interest in the object of study (see Table 5).

Table 4

Words defined through preliminary searches.

Word 1	Con	Word 2	Search field	Lg	T years	Type document	Document number	The limiting document number	KW occurrence map	KW defined
Sustainable	And	Development	Title	SE	10	ART	29604	2000	366	7
Social	And	Development	Title	SE	5	ART	3542	2000	66	12
Ecological	And	Development	Title	SE	10	ART	1247	1247	75	12
Economic	And	Development	Title	SE	5	ART	4797	2000	164	12
Rural AND zone	Or	Rural AND space	Title	SE	10	ART	613	613	30	8

Lg, language; S, Spanish; E, English; T, time; ART, articles; KW, keywords.

Table 5

Keywords in the search phase.

Sustainable development	Social development	Ecological development	Economic development	Rural space
Sustainability	Social capital	Ecological footprint	Economic growth	Rural areas
Environmental protection	Community development	Ecological environment	Economic activities	Rural territory(ies)
Environmental sustainability	Child development	Ecosystem services	Local economic development	Rural space
Regional planning	Human development	Environmental protection	Entrepreneurship	Rurality
Regional development	Poverty	Protected areas	Regional economic
Rural development	Social interaction	Natural resources	Corruption
Regional development	Social responsibility	Biodiversity	Infrastructure
	Social entrepreneurship	Climate change	Investment
	Education	Land-use change	Innovation
	Leadership	Pollution	Industrial development
	Culture	Environmental degradation	Employment
	Mental health	Ecological deficit	Productivity

Next, articles are selected as the type of document of interest. The search period is set at 10 years since it is identified that the quantity of research is lower in rural areas compared to urban and/or intermediate spaces. Similarly, eleven scientific journals from Latin America are defined as secondary sources of information. These are selected based on the relevance criterion applied to 31 journals identified in the Scimago and Publindex (Colombia) platforms. As a result, 112 articles of interest are recognized in the search phase.

3.2.2. Selection Phase

Five criteria are defined in the selection of articles, which are explained as follows:

(1) Relevance. This criterion measures the degree of correspondence between the contribution of the research and the scope and object of the research project. The criterion applies a measurement scale ranging from 1 to 5, where one indicates that the article is not relevant, and five indicates that the article is highly relevant to the study.

(2) Geographical Scope. This criterion indicates the geographic location or space where the research is conducted. Therefore, a scale of six categories is defined based on the geographical scope of territorial entities, corresponding to the levels of the country (Latin America), departments, zones, urban municipalities, rural municipalities, hamlets, and townships. Consequently, a score is assigned to each of the categories, with a value of two assigned to articles analyzing the study object at the Latin American level and twelve to manuscripts focusing on investigations at the level of rural municipalities, hamlets, and townships in Colombia.

(3) Publication Time. This criterion refers to the number of years that have elapsed since the date of publication of a scientific article. According to this criterion, a scoring scale from 2 to 8 is employed. A score of 2 indicates that the article has been published for more than 10 years, while a score of 8 indicates that the manuscript was published between 1 and 3 years ago.

(4) Abstract Evaluation. The assessment of the 112 article abstracts is conducted based on the structure of a structured abstract. Therefore, Table 6 presents the rating scale that measures the presence and clarity of information in the context, methods, results, and conclusion items.

Table 6

Abstract evaluation rating scale.

Indicator	Score
The abstract provides clear information in the context, methods, results, and conclusions sections	14
The abstract provides clear information in the context, results, and conclusions sections	12
The abstract provides clear information in the sections on background, methodology, and findings	10
The abstract provides clear information in the sections on background, methodology, and conclusions	8
The abstract is unclear or lacks information in two sections (background, methodology, results, and conclusions)	6
The abstract is unclear or lacks information in three sections (background, methodology, results, and conclusions)	4
The abstract provides information but is generally unclear	2

(5) Open Access. This pertains to the free availability of complete article documents. Therefore, if the manuscript includes the full document, it is assigned a score of one; otherwise, it receives a score of zero.

Based on the above, a weight or importance is assigned to each of the selection criteria, and a weighted score is calculated for the contribution of each article. Consequently, weights of 0.35, 0.2, 0.15, 0.2, and 0.1 are estimated for the criteria of relevance, geographic scope, publication time, abstract evaluation, and open access, respectively. Finally, articles with a score greater than or equal to 6 are selected, resulting in 40 manuscripts.

3.2.3. Information Extraction and Analysis Phase

In this stage is record the pertinent information in the database. The information relates to objects of study, methods, and variables applied in the 43 articles. Subsequently, a summary of the information acquisition is provided.

In Table 7, the 16 objects of study in the research are identified. The thirty-eight percent pertain to themes within the social dimension, focusing on the strengthening of rural associativity, the women’s labor market, and challenges in education—persistent components in rural spaces. Subsequently, 25% of the study objects are associated with all three dimensions, recognizing practices or frameworks guiding territories toward sustainable development while also identifying social, ecological, and economic conflicts in the development of exploitation activities. Similarly, it is established that 19% of the objects specifically relate to ecological and economic dimensions, addressing environmental pollution and diagnoses of microwatersheds in production systems.

Table 7

Objects of study in rural territories.

Objects of study	fr	Dimension	Ref
Strengthening rural associativity	5	D2	[37–41]
Practices toward sustainable rural development through social empowerment	5	D1-D2-D3	[42–46]
Labor market for men and women	4	D2	[46–49]
Land legalization/land-use changes	4	D1-D2-D3	[12, 50–52]
Challenges in education	3	D2	[50, 53, 54]
Development of economic activities in the agriculture sector	3	D3	[55–57]
Payments for ecosystem services as a public policy instrument	3	D1	[58–60]
Social, ecological, and economic conflicts in the development of exploitative activities	3	D1-D2-D3	[61–63]
Environmental pollution	2	D1–D3	[64, 65]
Application of the human development approach in regional development	2	D2-D3	[38, 66]
Socioenvironmental diagnosis of microwatersheds and agricultural production systems	2	D1–D3	[67, 68]
Measurement of poverty using an indicator	1	D2	[69]
Challenges in access to water and basic sanitation	1	D2	[70]
Study of individual and collective realities encompassing the peasantry category	1	D2	[71]
Power relations in the advancement of infrastructure projects	1	D2-D3	[72]
Smart territories/Special economic zones (SEZ) models/technological transfer in the agricultural sector	3	D1-D2-D3	[50, 53, 54]
Total	43

D1, ecological dimension; D2, social dimension; D3, economic dimension.

Similarly, Table 8 identifies 56 qualitative methods geared toward the collection of primary information, along with text analysis techniques that allow for the interpretation of the obtained results. From the above, it is observed that interviews, surveys, questionnaires, and observation are the techniques with the most widespread application in the field of study.

Table 8

Qualitative methods applied in research.

Techniques	No	Sources
Interviews	19	[12, 38–47, 55, 57, 59–61, 63, 68, 73]
Survey	8	[40, 41, 43, 45, 46, 56, 62, 73]
Questionnaire	5	[12, 55, 62, 73, 74]
Observation	5	[42, 43, 45, 47, 63]
Ethnography	4	[53, 54, 71, 72]
Case study	4	[38, 39, 47, 75]
Community work (workshops, others)	3	[40, 46, 57]
Expert panel	2	[46–64]
Historical inquiry	2	[71, 72]
Content analysis	2	[42, 55]
Information analysis (collective reflection)	2	[40, 59]
Total	56

Similarly, Table 9 recognizes 31 quantitative methods characterized by measurement through mathematical procedures. Therefore, it is identified that descriptive statistics, the use of composite indices, correlation analysis, and analysis using GIS tools are the quantitative techniques most commonly implemented in research.

Table 9

Quantitative methods applied in research.

Techniques	No	Sources
Descriptive statistics and frequency distribution	9	[41, 46, 55, 56, 62, 64, 69, 70, 73]
Application or creation of composite indices (productive diversity/human development, etc.)	5	[41, 48, 49, 56, 66]
Correlation analysis/association analysis	3	[56, 57, 76]
Application of GIS tools	3	[12, 43, 51]
Multivariate descriptive analysis (principal components, clustering, etc.)	2	[51, 60]
Physical analysis of soil and water	2	[43, 68]
Parametric or nonparametric mean statistical tests	2	[57, 76]
Regression model (logistic or others)	2	[55, 67]
Multicriteria analysis	2	[40, 60]
Simulation model	1	[52]
Total	31

3.3. Selection of Rural Spaces

In this item, rural spaces of interest in the study are selected, which is why the political-administrative division is assumed. This division establishes a hierarchical order through denominations in the territories, with these denominations varying among nations. By the aforementioned, the Colombian territory is divided into departments, districts, municipalities, and indigenous territories [77], with a focus on the territorial entity of municipalities, as a significant portion of rural territory is defined within this category. Based on this, the principal references that Colombia has in the classification of municipalities are mentioned.

The Law 617 of 2000 defines seven categories based on population and current income, concentrating 88.8% of municipalities in category 6. Therefore, it is identified that the categorization contributed to budgetary expense control but did not allow differentiation of characteristics and dynamics in development environments [78]. Similarly, the Law 1551 of 2012 applies two additional criteria and defines three categories: large municipalities, intermediate municipalities, and basic municipalities, with the majority of municipalities (91%) falling into the third category [79]. This acknowledges the same limitations as indicated in the previous law.

Building upon the aforementioned, methodologies that allow for a better grouping of territories are identified. Thus, Table 10 presents two methodologies. The first methodology analyzes six components and twenty variables, establishing three categories of municipality typification, with 6.2% classified as robust development, 64.7% as intermediate development, and 29.1% as early development [78]. Similarly, the second methodology studies three variables due to the quality and timeliness of municipal level information, defining four categories of rurality represented by 10.43% in cities and agglomerations, 27.99% in intermediates, 33.24% in rural areas, and 28.34% in scattered rural areas [79].

Table 10

Typification of Colombian territories.

Source	Description	Points of interest	Applied criteria	No. categories
Technical document (NPD) 2015 [56]	Departmental and municipal typologies: A proposal for understanding Colombian territorial entities	Methodology for the creation of municipal and departmental typologies note: the three categories apply to departments and municipalities	Urban-regional functionality (5 variables)	3 (robust development, intermediate development, and incipient development)
			Economic dynamics (4 variables)
			Quality of life (1 variable)
			Environmental (2 variables)
			Security (4 variables)
			Institutional (4 variables)
Technical document (NPD) 2014 [57]	Mission for the transformation of the countryside. Definition of rural categories	Not all criteria are included due to the lack of quality and timeliness of information at the municipal level	Rurality within the system of cities	4 (cities and agglomerations, intermediate, rural, and dispersed rural)
			Population density
			Urban-rural population relationship

Source: compiled from NPD documents.

Therefore, the second methodology, “Definition of Rurality Categories,” is selected, focusing on municipalities categorized as scattered rural areas, as they significantly exhibit the characteristics and dynamics of rural territories. That is to say, they are “those municipalities and nonmunicipalized areas with small headcounts and low population density (less than 50 inhabitants/km²)” [79], and they are rural spaces where accessing basic services is challenging.

In this regard, a sample is defined, comprising the 15 municipalities categorized as scattered rural areas in the Department of Tolima.

3.4. Phase III: Selection and Validation of Variables

In this phase, the results of the application of variable selection, validation, and information sources are presented.

3.4.1. Selection of Variables

In the ecological dimension, 17 variables have been identified representing the dynamics of natural resources and ecosystem services sustaining human life and the surrounding environment in the study area. Consequently, Figure 8 illustrates that the variables with the highest incidence include areas in forests (V1), protected areas (V2), agricultural water consumption (V3), water resource availability (V4), domestic water consumption (V5), contamination of water streams (V6), areas with timber extraction (V7), types of ecosystem services (V8), and biodiversity loss (V9). Additionally, a group of variables with lower influence is identified; these variables are operational mining titles (V10), anthropic interventions affecting strategic ecosystems (V11), areas with changes (V12), areas affected by forest fires (V13), soil aptitude loss (V14), regional water assessments (V15), average temperature (V16), and types of restoration of natural ecosystems (V17).

[figure(s) omitted; refer to PDF]

In this context, it is observed that in recent years, economic activities have not been conducted appropriately, leading to significant changes in land cover. This situation emphasizes the priority of implementing actions to protect the soil [51, 52]. Similarly, there is a recognition of the importance of raising awareness among the populations in protected areas and encouraging their participation in the design of territorial planning plans. This instrument guides the development of economic activities in the territories [12].

Likewise, it is known that one of the characteristics of rural territories is the low coverage of basic sanitation services, such as access to clean water. This situation becomes more concerning due to the scarcity of water resources in rural areas [62]. Additionally, it is important to mention the impact of climate change on water availability for agricultural use, particularly affecting small-scale farmers with limited resources. Consequently, food availability is compromised due to losses in subsistence crops and cultivation areas lacking water for irrigation [55].

In the social dimension, 21 variables are defined that represent the living conditions of the rural population. Therefore, in Figure 9, it can be observed that the most significant variables in social development are related to education level (V1), population age (V2), leadership of rural women in productive processes (V3), employment (V4), educational institutions (V5), job opportunities for young people and women (V6), criminal and violent behaviors (V7), population affected by armed conflict (V8), and entrepreneurship strengthening (V9). Moreover, a group of variables with lower incidence is recognized; these correspond to the rural illiterate population (V11), household income from government transfers (V12), population’s labor income (V13), strengthened productive units in associative processes (V14), migrant population (V15), population with access to sewerage services (V16), population affiliated with the health system (V17), population diseases (V18), state of cultural heritage (V19), presence of children in the household (V20), and coverage of child care services (V21).

[figure(s) omitted; refer to PDF]

Building upon the aforementioned, education coverage in rural territories remains a challenge. There are rural spaces where accessing education services is not easy due to issues, such as limited capacity, distances, transportation, and the time constraints of young individuals involved in farm activities (households). Consequently, there is a recognized need to establish a comprehensive education policy ensuring quality access to the education system and creating conditions for young people to pursue secondary and higher education [66]. To achieve this, it is essential for education facilities to be located near rural areas, fostering research that addresses the specific needs and characteristics of the region [66].

Similarly, in the social dimension, the variable of the age of the rural population is identified, which is related to the aging index and has increased in recent years. This trend is attributed to the ongoing migration of young people to urban areas in search of employment opportunities and access to services available in these spaces [49]. Consequently, employment is a critical factor in rural territories, characterized by informality, contributing to human development deterioration [66].

Furthermore, cultural barriers persist, limiting opportunities for female employment. This conditions a more informal and lower-quality job market for women compared to men, with cultural variations observed across regions [74]. Unfavorable conditions for rural women contribute to the migration of young rural women [47], jeopardizing the potential for social reproduction in these territories [68].

In the economic dimension, 26 variables are defined that represent the economic activities carried out in the territory and contribute to meeting human needs. Likewise, institutional and governmental development is considered. Based on the above, in Figure 10, it is observed that the most impactful variables correspond to the strengthening of associative processes of productive units (V1), type and area of land (V2), monitoring of public policies (V3), number of rural producer associations (V4), citizen and community participation (V5), rural properties certified with good environmental practices (V6), land tenure (V7), income of informally employed population (V8), institutional performance of public entities (V9), women participating in politics and leadership (V10), scenarios strengthening rural tourism (V11), primary sector education formation (V12), and income of formally employed rural population (V13). Furthermore, a subset of variables with lesser incidence in the economic dimension is acknowledged; these variables encompass locally consumed rural products (V14), population accessing credit (V15), labor demand for women (V16), studies on the carrying capacity of nature tourism destinations (V17), marketing channels (V18), coverage of technical assistance to productive units (V19), labor demand for rural youth (V20), and rural producers with transformation or postharvest requirements (V21).

[figure(s) omitted; refer to PDF]

In this context, it is evident that collective actions have the potential to improve market access conditions for small producers. However, ensuring the continuity and functioning of associative processes is not straightforward, as it requires relationships of trust and commitments from the members [39]. Therefore, there is a recognized need to formulate and create conditions that facilitate the development of associative processes, with the state and other external agents playing a crucial role in compliance and improvement of these conditions regulating such organizations in the territories [37].

Similarly, it is identified that the variable of land access (land tenure, types, and areas of plots) requires strategies to clarify property rights, improve cadastral surveying, and establish a more updated and progressive tax system in the territories. These conditions favor land tenure security for investors [37].

3.4.2. Validation of Variables

Following this, a validation process is conducted with the advisory team, during which an assessment instrument is designed. Team members are then asked to rate the variables on a scale from 1 to 5, where a value of 1 indicates low importance and 5 signifies high importance. According to this, Table 11 lists the top ten variables that are most important according to the average rating by the territorial actors.

Table 11

Variables prioritized by the WTC.

Ecological		Social		Economic
No. var	Name	No. var	Name	No. var	Name
V1	Forest areas	V1	Population education level	V8/V13	Employed or occupied population
V2	Protected areas for conservation	V16	Households with public services	V7	Land tenure
V4	Water resource supply	V6	Job opportunities	V2	Area and type of the UP
V5	Domestic water consumption	V17	Population affiliated to the general social security system in health	V6	UP certified with good environmental practices (GEP)
V3	Agricultural water consumption	V22	Households with own housing	V18	Marketing channels of the productive units (UP)
V8	Types of ecosystem services	V2	Population age	V19	Technical assistance requested by productive units (UP)
V9	Contamination of surface water streams	V7	Criminal and violent behaviors	V12	Educational level in the primary sector
V14	Loss of soil suitability	V8	Population affected by armed conflict	V15	Productive units (UPs) accessing credit
V11	Types of anthropogenic interventions that have affected ecosystems	V3	Rural women leading productive and entrepreneurial processes	V21	Productive units (UP) with transformation or postharvest requirements

3.4.3. Source of Information for Variables

In this section, the information source for the variables is identified, as the application of mathematical model techniques requires data. Based on this, the primary institutional repositories such as Agronet (Ministry of Agriculture), the National Administrative Department of Statistics (DANE), and TerriData (National Planning Department) are explored. The TerriData database is selected as it contains the most extensive information at the municipal level. Consequently, Table 12 outlines the variables with available information that are defined as criteria in the selection of rural spaces.

Table 12

Variables applicable in rural space selection.

Ecological		Social		Economic
No. Var	Name	No. Var	Name	No. Var	Name
V1	Forest areas	V16	Households with public services	V8/V13	Employed or occupied population
V2	Employed or occupied population	V17	Population affiliated to the general social security system in health (GST)	V2-V6-V18-V19	Productive units (UPA) with access to production factors
V4	Water resource supply	V22	Water resource supply	V21	Value added by primary economic activities in rural territories

3.5. Phase IV: Application of Mathematical Techniques

This phase involves the application of two mathematical techniques that guide the decision in the selection of rural space. The results of each of the methods are as follows.

3.5.1. Analytic Hierarchy Process (AHP)

Figure 11 depicts the structure of the hierarchical model that relates the relevant components of the decision, corresponding to the definition of the objective, the identification of criteria, and the definition of alternatives.

[figure(s) omitted; refer to PDF]

(1) Identification of Criteria. This criterion corresponds to the variables defined in the previous phase (see Table 9).

(2) Criteria Weight Estimation. In the criteria weight estimation phase, WTC members engage in pairwise comparisons of criteria matrices. After completing these matrices, individual judgments are organized, and the consistency ratio (CR) is calculated—a statistical measure determining judgment acceptance as a quality indicator for decision-making [18, 67]. Notably, three out of six judgments in the ecological dimension, two out of three in the economic dimension, and none of the four in the social dimension are accepted. WTC members acknowledge the difficulty of estimating criteria qualifications due to the variables’ importance in each study component. Consequently, the WTC decides to discontinue the AHP method, given the unacceptable CR in a significant number of judgments. A second application would demand additional time, not easily accommodated due to members’ busy schedules. The application of the AHP (analytic hierarchy process) method for the criteria of the ecological dimension is outlined below.

Table 13 presents an example of the rating of criteria within the ecological dimension, where each member of the MTT expresses their preference among the criteria as “equally preferred,” “moderately preferred,” “strongly preferred,” “very strongly preferred,” or “extremely preferred.” These qualitative relationships can be assigned numerical values “1,” “3,” “5,” “7,” and “9.”

Table 13

Rating of criteria in the ecological dimension.

i/j	C1	C2	C3
C1	1	0.11	0.33
C2	9	1	8
C3	3	0.125	1
Sum of values	13	1.2	9.3

C1, area in tropical dry forest; C2, total area of strategic ecosystems; C3, water resource supply.

Next, Table 14 presents the normalized matrix corresponding to the vertical sum of values for each column (from the previous matrix). In this matrix, each value is divided by the total sum of the respective column. Additionally, Table 14 shows the summation of each normalized row, resulting in the priority vector, representing the weights or importance of each criterion.

Table 14

Normalized matrix of criteria in the ecological dimension.

i/j	C1	C2	C3	Weight
C1	0.077	0.090	0.036	0.068
C2	0.692	0.809	0.857	0.786
C3	0.231	0.101	0.107	0.146
Suma of values	1	1	1	1

Based on the above, the priority vector or criterion weights are validated by calculating the consistency index (see equation (1)) and the consistency ratio (see equation (2)).$$\begin{array}{}\text{(1)}& \text{CI}=\frac{\text{CA}-n}{n-1},\end{array}$$where CI = consistency index, CA = consistency average, and n = number of criteria.

To determine the consistency average (CA), first, the composite weight of each criterion is calculated. This is achieved through the vector multiplication of the criterion rating (row) against the column vector of weights for all criteria, which is then normalized. Subsequently, the consistency ratio for each criterion is computed as the division of the composite weight of each criterion (obtained in the previous step) by the criterion’s weight. Finally, the CA is estimated as the average of the consistency ratios of the criteria (see Table 15).

Table 15

Consistency index and ratio.

i/j	C × $P^t$	Pro. C
C1	0.204	3.02
C2	2.564	3.26
C3	0.447	3.06
Consistency average (CA)		3.11
Consistency index (IC)		0.06
Random consistency index (RCI)		0.66
Consistency ratio (CR)		0.08
CR < 0.1 it is accepted		Accepted

C × $P^t$: composite weight. Pro. C: consistency ratio.

Following the above, the calculation of the consistency ratio (CR) is continued (see (2)), wherein (3) is used to compute the Random Consistency Index (RCI). The consistency ratio is a statistical measure that determines whether the judgment is acceptable or not, serving as an indicator of decision quality [32, 80]. Consequently, if CR is less than 0.1, the qualification of judgments is accepted (see Table 15).$$\begin{array}{}\text{(2)}& \text{CR}=\frac{\text{CI}}{\text{RCI}},\text{(3)}& \text{RCI}=\frac{1,98\ast n-2}{n}.\end{array}$$

From the above, Table 16 presents the weights of the variables estimated from weighted factors (FP), which are results that are shared and approved within the WTC.

Table 16

Weights of the DS dimension criteria.

Dimensions	Criteria	Definition	Weighting (%)
Ecological	C1	Area in tropical dry forest	33.03
	C2	Total area of strategic ecosystems	53.73
	C3	Hydrological resource supply	13.24
Total			100
Social	C1	Coverage of public services	50.0
	C2	Population covered by the general health security system	5.0
	C3	Qualitative housing deficit	45.0
Total			100
Economic	C1	Formally employed individuals	47.31
	C2	UPA with access to production factors	44.97
	C3	Value added	7.71
Total			100

The total value of 100 corresponds to the sum of the percentages of the criteria in each of the dimensions.

The alternatives correspond to the municipalities previously selected in the previous phase. Therefore, Table 17 shows the results of the weighted ratings for the ecological, social, and economic dimensions for each alternative. That is, for each alternative (municipality), the weighted rating of each dimension is calculated. Subsequently, an overall sustainable development rating is estimated, assigning equal weight to each dimension (1/3), which is why the results of the multiplication of the values of the rating for each dimension by its respective weight are summed.

Table 17

Results of weighted ratings in the selection of rural space.

Municipalities	Category	Weighted ratings
		0.33	0.33	0.33	Sustainable
		Ecological D	Social D	Economic D
Alpujarra	Dispersed rural	0.364	0.576	0.672	0.537
Anzoátegui		0.142	0.293	0.350	0.261
Ataco		0.016	0.185	0.371	0.190
Coello		0.195	0.576	0.280	0.350
Coyaima		0.248	0.217	0.096	0.187
Cunday		0.138	0.138	0.140	0.139
Falan		0.032	0.633	0.332	0.333
Herveo		0.090	0.363	0.349	0.267
Murillo		0.133	0.237	0.269	0.213
Ortega		0.080	0.260	0.201	0.180
Planadas		0.377	0.363	0.458	0.399
Rioblanco		0.579	0.116	0.565	0.420
Roncesvalles		0.257	0.670	0.320	0.416
San Antonio		0.103	0.374	0.451	0.310
San Luis		0.052	0.143	0.303	0.166

The values highlighted in bold correspond to the two municipalities with the lowest rating.

In accordance with the above, those municipalities with the lowest scores are of interest, as it indicates that the territory has low development in the ecological, social, and economic dimensions. Therefore, it is shared with the MTT that the rural municipalities with a lower rating are Cunday and San Luis, for which the team suggests selecting San Luis for its location and cooperative relationships with the territory’s stakeholders. Based on the above, the municipality of San Luis is selected as the rural space.

In the ultimate analysis, some of the primary results of the phases of the proposed method are mentioned. During the search phase, 112 articles were identified, and upon applying the selection criteria, a total of 43 manuscripts were determined. Subsequently, in the acquisition phase, it was found that 38% of the study objects are related to the social dimension; 25% are related to all three dimensions (ecological, social, and economic); 19% are represented in two dimensions, ecological and economic; and 13% are related to the social-economic dimensions. Similarly, in the acquisition phase, 17, 21, and 26 variables representing the ecological, social, and economic dimensions in rural territories are identified. Subsequently, the Pareto diagram analysis is applied, establishing 9, 9, and 13 variables of greater significance in each of the respective study dimensions. It is also noted that there is a limitation in the availability of information for the variables, which is why three variables are selected in each of the dimensions. Subsequently, two mathematical techniques are applied to evaluate the 15 alternatives corresponding to the municipalities, enabling the objective selection of the best alternative (municipality). Finally, it is identified that a key factor in the development of the proposed method is the involvement of territorial actors. The knowledge and experience they possess regarding the territory are crucial in the selection and validation of variables representing the ecological, social, and economic development of the space.

4. Conclusions

The research addresses the research question by proposing a method for the selection of a rural territory that reduces the generic approach of diagnostics. In other words, the proposed method generates valuable information in the development of territorial diagnostics, ensuring that the dynamics and specificities of rural territories are accurately represented in the assessments.

The research provides a rural territory selection method that reduces the generic approach of territorial diagnoses, as rural spaces, due to their diversity and heterogeneity, exhibit particular behaviors in regional development. Consequently, the proposed method is implemented at the beginning of the planning phase for territorial diagnoses, as it provides information on development and identifies rural territories exhibiting specific behaviors that must be considered in territorial diagnoses. This enables the definition of lines and/or programs to address the needs and problems presented by different types of rural territory development.

Similarly, the research offers planners from governmental and nongovernmental entities, local communities, academics, and researchers a method that measures the ecological, social, and economic development of all rural areas within a territorial unit, identifying specific development behaviors and needs of the territories. In this sense, the method guides the generation of valuable input used in the selection of rural territories, which is part of the planning and preparation phase of territorial diagnoses in regions.

One of the limitations is the availability of information on the variables, especially in rural territories classified in the lower categories of development. In future work, the application of the proposed method is envisaged in the planning phase of territorial diagnostics for governmental entities. Specifically, the aim is to establish, based on the method, a group of rural territories that represents the various types of development measured by the method. Consequently, in the planning of diagnostics, it is essential to consider the participation and involvement of this representative group of rural territories in the region.

Acknowledgments

This work was supported by the Universidad Cooperativa de Colombia. The authors extend our gratitude to the stakeholders of the territory for their participation and contributions that have contributed to the development of the research.

References

[1] Eclac, "Planning for sustainable territorial development in Latin America and the caribbean," 2019. https://repositorio.cepal.org/bitstream/handle/11362/44732/1/S1900438_en.pdf

[2] S. Jónsdóttir, G. Gísladóttir, "Land use planning, sustainable food production and rural development: a literature analysis," Geography and Sustainability, vol. 4 no. 4, pp. 391-403, DOI: 10.1016/j.geosus.2023.09.004, 2023.

[3] M. Morales-Guerra, J. d J. Maldonado-Méndez, P. C. Iñiguez, R. Ariza-Flores, "Opportunities for territorial development in the transisthmic corridor; baseline diagnosis in the region of the isthmus of tehuantepec, oaxaca," Agro Productividad, vol. 17 no. 1, pp. 161-172, DOI: 10.32854/agrop.v17i17.2625, 2024.

[4] Cepal, "The environmental dimension in the development of Latin America," 2001. https://repositorio.cepal.org/login

[5] J. Chiarella, K. Yakabi, "Spatial planning and territorial ordering. Considerations from the Peruvian case," Regional Policy and Plannig Journal, vol. 3, pp. 137-158, 2017.

[6] J. Bradbury, P. John, "Territory and power: critiques and reassessments of a classic work," Government and Opposition, vol. 45 no. 3, pp. 295-317, DOI: 10.1111/j.1477-7053.2010.01314.x, 2010.

[7] Asian Development Bank, "Sustainable development concern about the environment is not new," 2012. https://www.adb.org/sites/default/files/publication/29664/world-sustainable-development-timeline.pdf

[8] J. Mensah, "Sustainable development: meaning, history, principles, pillars, and implications for human action: literature review," Cogent Social Sciences, vol. 5 no. 1,DOI: 10.1080/23311886.2019.1653531, 2019.

[9] J. Sánchez, R. Domínguez, M. León, J. Samaniego, O. Sunkel, "Natural resources, environment, and sustainability," CEPAL, vol. 4, 2019. https://repositorio.cepal.org/server/api/core/bitstreams/e43ad745-6b7d-48e4-a016-b753fdd3b659/content

[10] T. Blanco, E. Ramírez, S. Muñoz, "Manual of participatory rural diagnosis with territorial approach. A guide to the elaboration of rural diagnoses," 2017. https://repository.usta.edu.co/handle/11634/34232?show=full

[11] S. Mario, H. González, "Political-institutional and territorial experiences in Colombia," 2018. https://www.academia.edu/37998368/Experiencias_politico_institucionales_y_territoriales_en_Colombia

[12] C. C. D. Eichenberger, P. Tagliani, "Diagnóstico participativo para planejamento territorial de áreas protegidas," Sustentabilidade em Debate, vol. 8 no. 1, pp. 95-107, DOI: 10.18472/SustDeb.v8n1.2017.19281, 2017.

[13] J. Jiang, H. Zhang, Q. Huang, F. Liu, L. Li, H. Qiu, S. Zhou, "Diagnosis of key ecological restoration areas in territorial space under the guidance of resilience: a case study of the chengdu–chongqing region," Land, vol. 12 no. 5,DOI: 10.3390/land12050973, 2023.

[14] C. M. Concha, G. I. Sánchez, C. A. Rojas, "Diagnóstico socio territorial participativo: una estrategia que articula las necesidades de la sociedad con la formación universitaria," Formación universitaria, vol. 15 no. 6, pp. 49-58, DOI: 10.4067/S0718-50062022000600049, 2022.

[15] P. Mao, C. Hautbois, M. Langenbach, "Développement des sports de nature et de montagne en France: diagnostic comparé des ressources territoriales," Géographie Économie Société, vol. 11 no. 4, pp. 301-313, DOI: 10.3166/ges.11.301-313, 2009.

[16] J. Valy, F. Gache, "Les phénomènes de ruissellement dans le diagnostic de vulnérabilité du territoire aux inondations," La Houille Blanche, vol. 106 no. 6, pp. 47-55, DOI: 10.1051/lhb/2021007, 2020.

[17] V. F. Denardin, C. L. B. Alves, A. A. Cazella, N. Fett Junior, P. R. Lopes, "Territorial approach to development: natural dimension and contributions to the diagnosis and surveying of new scenarios," Brazilian Journal of Regional Management and Development, vol. 18 no. 1, pp. 39-60, 2022.

[18] A. A. Urasova, D. A. Balandin, A. N. Pytkin, E. B. Kovaleva, I. Y. Zagoruiko, "Spatial development of rural territories in Russian regions: growth areas or desolation zones?," International Journal of Recent Technology and Engineering, vol. 8 no. 2, pp. 4110-4123, DOI: 10.35940/ijrte.B3083.078219, 2019.

[19] I. Storonyanska, K. Patytska, I. Hrynchyshyn, V. Chemerys, "Spatial disproportions in development of territorial community under conditions of administrative and financial decentralization," Agricultural and Resource Economics: International Scientific E-Journal, vol. 6 no. 4, pp. 43-62, DOI: 10.51599/are.2020.06.04.03, 2020.

[20] P. F. de Almeida, A. M. d Santos, L. M. d Silva Cabral, E. F. d Anjos, M. C. R. Fausto, A. Bousquat, "Water, land, and air: how do residents of Brazilian remote rural territories travel to access Health services?," Archives of Public Health, vol. 80 no. 1, pp. 241-316, DOI: 10.1186/s13690-022-00995-z, 2022.

[21] C. Suárez Roldan, G. A. Méndez Giraldo, E. López Santana, "Sustainable development in rural territories within the last decade: a review of the state of the Art," Heliyon, vol. 9 no. 7,DOI: 10.1016/j.heliyon.2023.e17555, 2023.

[22] R. Calvo-Palomares, J. A. Aguado-Hernández, E. Sigalat-Signes, B. Roig-Merino, "Evaluation of territorial capacity for development: population and employment," Land, vol. 12 no. 9, pp. 1773-1817, DOI: 10.3390/land12091773, 2023.

[23] C. J. Romera, J. M. Piaggio, "Indicator system for a multidimensional characterization of Colombia’s municipalities," Geographical Notebooks, vol. 59 no. 1, pp. 147-170, DOI: 10.30827/cuadgeo.v59i1.8143, 2020.

[24] G. Alvarez Ernesto, O. M. Abella, S. Freitas Caetano, C. D. C. Fresno, S. R. García, "The territorial-based basket of goods and services: a useful tool of diagnosis applied to the Algerian wilaya of ain témouchent," Geography Annals of Complutense University, vol. 34 no. 1, pp. 105-128, DOI: 10.5209/rev_AGUC.2014.v34.n1.45194, 2014.

[25] J. J. Serrano, J. Esparcia, "Diagnosis of rural development processes based on the stock of social capital and social networks: approach from E-I index," Land, vol. 12 no. 4,DOI: 10.3390/land12040850, 2023.

[26] G. F. Ortiz, Identification of Suitable Areas for Urban Development Using Multicriteria Analysis and Geoinformatics Tools, 2021.

[27] A. R. Cummings, "Territorial development planning capacities: a theoretical-conceptual framework," ECA, vol. 70 no. 743, pp. 441-471, DOI: 10.51378/eca.v70i743.3344, 2015.

[28] Graham, "Team development and participative management," 2000. https://servicios.unileon.es/formacion-pdi/files/2013/03/DESARROLLO-DE-EQUIPOS-2014.pdf

[29] A.-E. Ezequiel, M. J. Aguilar, "Teamwork," 2001.

[30] Y. Xiao, M. Watson, "Guidance on conducting a systematic literature review," Journal of Planning Education and Research, vol. 39 no. 1, pp. 93-112, DOI: 10.1177/0739456X17723971, 2019.

[31] A. d J. Anaya Barbosa, C. I. Burgos Rangel, "Aplicación de seis sigmas integradas con AMEF y QFD en el proceso de fabricación y distribución de muebles," Ingeniare, vol. 24, pp. 13-27, DOI: 10.18041/1909-2458/ingeniare.24.5168, 2018.

[32] T. Hurtado, G. Bruno, "The analytical hierarchy process (AHP) as a decision-making tool in supplier selection," Digital Thesis UNMSM, 2005.

[33] J. C. Osorio, J. P. Orejuela, "Analytic hierarchy process and multicriteria decisión making. Application example," Scientia et Technica, vol. 27 no. 39, pp. 247-252, 2008. http://www.redalyc.org/resumen.oa?id=84920503044

[34] N. Acacio-chirino, "Weighted factors in groundwater treatment in falcón state," Chemical Technology, vol. 43 no. 2, pp. 331-351, 2023.

[35] A. Pérez, J. Acosta, M. Cueva, I. Reinoso, E. Naranjo, "Location analysis for an industrial biofertilizer production plant," Pole of Knowledge, vol. 7 no. 5, pp. 1468-1483, DOI: 10.23857/pc.v7i5.4039, 2022.

[36] S. González-García, M. Rama, A. Cortés, F. García-Guaita, A. Núñez, L. G. Louro, M. T. Moreira, G. Feijoo, "Embedding environmental, economic and social indicators in the evaluation of the sustainability of the municipalities of galicia (northwest of Spain)," Journal of Cleaner Production, vol. 234, pp. 27-42, DOI: 10.1016/j.jclepro.2019.06.158, 2019.

[37] J. D. Gutiérrez, "Smallholders’ agricultural cooperatives in Colombia: ¿vehicles for rural development?," Desarrollo y Sociedad, vol. no. 73, pp. 219-271, DOI: 10.13043/DYS.73.7, 2014.

[38] V. Courdin, "Coordination processes of collective action in family livestock farming in Uruguay," Sustainability in Debate, vol. 12 no. 1, pp. 192-205, DOI: 10.18472/SustDeb.v12n1.2021.35078, 2021.

[39] E. Rodríguez, G. Carlos Julián Ramírez, "Sustainability analysis of the strengthening processes of a rural association: asomora a case study," Journal of Agricultural Sciences, vol. 33 no. 1, 2016. http://www.scielo.org.co/pdf/rcia/v33n1/v33n1a02.pdf

[40] H. R. Espinosa, C. J. R. Gómez, "Methodological approach to participatory formulation of agricultural technical assistance plans with territorial approach," Agronomic Act, vol. 64 no. 4, pp. 321-329, DOI: 10.15446/acag.v64n4.45162, 2015.

[41] L. I. Melo Torres, M. M. Melo Torres, D. E. Fonseca Pinto, "The Associativity: a local development strategy for Ocamonte (APCO) coffee growers in Santander, Colombia," Acta Agronómica, vol. 66 no. 4, pp. 538-543, DOI: 10.15446/acag.v66n4.61389, 2017.

[42] M. K. F. D. Abreu, V. S. D. Nascimento, A. A. Brasil, D. P. Andrade, "Protagonismo social e suas contribuições para o desenvolvimento rural sustentável: um estudo de caso no município de Caririaçu-Ceará (Brasil)," Sustainability in Debate, vol. 7, pp. 152-168, DOI: 10.18472/sustdeb.v7n0.2016.18753, 2016.

[43] L. Cerón Wilmar, Y. C. Escobar, Á. J. Á. Díaz, "Agroecological evaluation of agricultural production systems in the Centella watershed (Dagua, Colombia)," Colombian Forestry, vol. 17 no. 2,DOI: 10.14483/udistrital.jour.colomb.for.2014.2.a03, 2014.

[44] G. Y. F. Yepes, P. A. C. Cuartas, "Analysis of socio-environmental benefits from the implementation of cleaner production strategies in the agricultural sector of the middle reaches of the river Chinchiná, Colombia," Agronomic Act, vol. 63 no. 3, pp. 193-203, DOI: 10.15446/acag.v63n3.40255, 2014.

[45] S. Niño, M. Contreras, "Tourist activity at the chicaque natural park as a factor of sustainable development in the chicaque and cascajal zones (cundinamarca, Colombia)," Society and Tourism, vol. 27, pp. 173-203, 2020.

[46] R. Vargas, M. Victoria, E. L. Machado Chaviano, Á. F. O. Ocaña, "Evaluation of territories to develop community tourism in the amazon region of Ecuador," Society and Tourism, vol. 17, pp. 39-62, 2015.

[47] V. Courdin, G. Litre, P. Correa, "Desarrollo sostenible y transformaciones en la organización del trabajo femenino rural:: el caso de las mujeres ganaderas del Uruguay," Sustainability in Debate, vol. 5 no. 2, pp. 55-75, DOI: 10.18472/sustdeb.v5n2.2014.10714, 2014.

[48] L. Martínez, A. M. Vargas, "Spatial analysis of working conditions for women in the agricultural sector in Mexico," Economic Lectures, vol. no. 91, pp. 181-209, DOI: 10.17533/udea.le.n91a06, 2019.

[49] C. Rodrigues Garcés, M. J. Fawaz Yissi, J. A. Muñoz Soto, "Demographic and labor market transformations in rural areas of Chile," Acta Agronómica, vol. 66 no. 2, pp. 200-206, DOI: 10.15446/acag.v66n2.54918, 2017.

[50] M. Rodríguez, J. Correa, "Impacto del contexto municipal sobre el desempeño académico individual," Lecturas de Economía, vol. no. 90, pp. 159-193, DOI: 10.17533/udea.le.n90a06, 2018.

[51] J. M. de Souza, Eduarda Marques da Costa, "Methodological proposal to analyze land use and land cover changes: the case of santa catarina state in Brazil from 2000 to 2010," Sustainability in Debate, vol. 11 no. 3, pp. 485-500, DOI: 10.18472/SustDeb.v11n3.2020.32395, 2020.

[52] D. Gomez, J. Beltrán, "Dynamic modeling of ecosystem goods and services in the thomas van Der hammen forest reserve," Colombian Forestry, vol. 21 no. 2, pp. 188-204, 2018. https://revistas.udistrital.edu.co/ojs/index.php/colfor/index

[53] I. Figueroa, J. Soto, B. Sciolla, "Dinámicas de recepción, apropiación y contextualización del enfoque de la Guía para la inclusión educativa en escuelas municipales de una comuna de la Región Metropolitana," Pensamiento Educativo: Revista de Investigación Educacional Latinoamericana, vol. 53 no. 1,DOI: 10.7764/pel.53.1.2016.4, 2016.

[54] M. A. Cabrera, M. T. Rojas, "Virtuality and digital culture in children’s school experiences: a visual ethnography in context of poverty," Educational Thinking, vol. 57 no. 1,DOI: 10.7764/PEL.57.1.2020.8, 2020.

[55] P. S. Mesquita, H. Wittman, J. Aroudo Mota, "Climate variability, agricultural livelihoods and food security in semiarid Brazil," Sustainability in Debate, vol. 7, pp. 38-51, DOI: 10.18472/sustdeb.v7n0.2016.18749, 2016.

[56] M. L. C. Teixeira, Sónia Maria Carvalho Ribeiro, "Agriculture and sustainable landscapes: agricultural diversification in the state of minas gerais, Brazil," Sustainability in Debate, vol. 11 no. 2, pp. 17-28, DOI: 10.18472/SustDeb.v11n2.2020.28147, 2020.

[57] A. de A. Olival, "Resiliência em Assentamentos Rurais:: uma experiência na região norte de Mato Grosso," Sustainability in Debate, vol. 7 no. 2, pp. 90-113, DOI: 10.18472/sustdeb.v7n2.2016.15320, 2016.

[58] J. L. A. de Moraes, "Pagamento por Serviços Ambientais (PSA) como Instrumento de Política de Desenvolvimento Sustentável dos Territórios Rurais:: O Projeto Protetor Das Águas de Vera Cruz, RS," Sustainability in Debate, vol. 3 no. 1, pp. 43-56, DOI: 10.18472/sustdeb.v3n1.2012.7196, 2012.

[59] R. Narváez, Y. Alejandra, A. Wilmer Javier Paz, G. C. Mirian del Rosario, H. F. Leonel, "Institutional processes in the payment for ecosystem services, pasto river watershed," Journal of Agricultural Sciences, vol. 33 no. 1, pp. 64-72, 2016.

[60] C. Camacho, L. Mario, S. Díaz, W. Gómez-Anaya, J. E. Rojas-Rojas, R. López-Camacho, "Participatory analysis of ecosystem services in a protected area of the tropical dry forest (TDF), Colombia," Colombian Forestry, vol. 24 no. 1, pp. 123-156, DOI: 10.14483/2256201X.16548, 2021.

[61] M. Prieto, "Compromising water, producing indigenous identities and territories: the Chilean water model and the atacameño people of calama," Revista de Estudios Sociales, vol. 35 no. 55, pp. 88-103, DOI: 10.7440/res55.2016.06, 2016.

[62] J. L. D. O. Pinto Filho, R. A. Petta, R. F. D. Souza, R. Franco de Souza, "Caracterização Socioeconômica e Ambiental da População do Campo Petrolífero Canto do Amaro, RN, Brasil," Sustainability in Debate, vol. 7 no. 2, pp. 200-216, DOI: 10.18472/sustdeb.v7n2.2016.17489, 2016.

[63] L. Álvarez, "Socioeconomic and environmental conflicts caused by ecological distribution in the sinifaná coalfield, Colombia," Development Problems, vol. 50 no. 198,DOI: 10.22201/iiec.20078951e.2019.198.65857, 2019.

[64] A. López-Feldman, C. Chávez, M. A. Vélez, H. Bejarano, A. B. Chimeli, J. Féres, J. Robalino, R. Salcedo, C. Viteri, "COVID-19: impacts on the environment and the achievement of the SDGS in Latin America," Revista Desarrollo y Sociedad, vol. 2020 no. 86, pp. 104-132, DOI: 10.13043/DYS.86.4, 2020.

[65] S. M. J. Sione, H. J. Andrade, M. G. Wilson, L. J. Rosenberger, M. C. Sasal, S. G. Ledesma, E. A. Gabioud, "Reducción de emisiones de carbono por deforestación evitada en bosques del Espinal (Entre Ríos, Argentina)," Colombia forestal, vol. 24 no. 2, pp. 39-51, DOI: 10.14483/2256201X.16166, 2021.

[66] J. Ramírez, C. Avellaneda, K. Pineda, "Estimación del Índice de Desarrollo Humano ajustado para los departamentos colombianos," Lecturas de Economía, vol. 86, pp. 135-160, DOI: 10.17533/udea.le.n83a05, 2017.

[67] A. Obregón, D. Andrés, Á. L. O. Rosero, "Regional convergence in the department of Meta, Colombia: a human development approach," Economic Lectures, vol. 89, pp. 221-254, DOI: 10.17533/udea.le.n89a08, 2018.

[68] D. Roboredo, S. M. P. P. Bergamasco, W. Gervázio, "Diagnóstico dos agroecossistemas da Microbacia Hidrográfica Mariana no Território Portal da Amazônia, Mato Grosso, Brasil," Sustentabilidade em Debate, vol. 8 no. 1, pp. 80-94, DOI: 10.18472/SustDeb.v8n1.2017.18840, 2017.

[69] E. O. Benavides, J. J. N. Velázquez, "Contributions for the construction of a global measure of poverty: the case of Colombia 2011-2017," Society and Development, vol. 2019 no. 83, pp. 263-305, DOI: 10.13043/dys.83.7, 2019.

[70] J. O. Moreno, "The challenges of access to drinking water and basic sanitation in rural areas in Colombia," Engineering Journal, vol. 49, pp. 28-37, 2020. https://revistas.uniandes.edu.co/doi/full/10.16924/revinge.49.5

[71] M. Soraya, J. Devine, D. Ojeda, "Current peasant formations in Latin America: conceptualizations, political subjects, and territories in dispute," Antípod, Journal of Anthropology and Archaeology, vol. 2 no. 40, 2020.

[72] S. Uribe, S. Otero-Bahamón, I. Peñaranda, "Making the state: roads, war, and local orders in the farc territories," Revista de Estudios Sociales, vol. 2021 no. 75, pp. 87-100, DOI: 10.7440/res75.2021.08, 2021.

[73] V. J. P. Bucheli, J. C. B. Fuentes, "The farm systems in two indigenous territories of Putumayo (Colombia), a technical economic comparative assessment," Agronomic Act, vol. 63 no. 2, pp. 91-100, DOI: 10.15446/acag.v63n2.29358, 2014.

[74] A. Espino, M. Sauval, "Obstacles to economic empowerment? Limiting factors to women’s job-placement and employment quality: the Chilean case," Revista Desarrollo y Sociedad, vol. 2016 no. 77, pp. 305-360, DOI: 10.13043/DYS.77.8, 2016.

[75] P. Corrales, "Social innovation for smart territories: fiction or reality," Development Problems, vol. 48 no. 190,DOI: 10.22201/iiec.20078951e.2017.190.56950, 2017.

[76] G. Padierna, L. Patricia, C. A. C. Velásquez, G. A. Viana, "Technology transfer policy of the agricultural sector in Colombia with a territorial approach," Economic Lecture, vol. 89, pp. 199-219, DOI: 10.17533/udea.le.n89a07, 2018.

[77] Dane – National Administrative Department of Statistics, "User manual: political-administrative division of Colombia (divipola)," 2007. https://www.dane.gov.co/files/investigaciones/divipola/divipola2007.pdf

[78] National Planning Department (NPD), "Departmental and municipal Typologies: a proposal to understand Colombian territorial entities," 2015. https://colaboracion.dnp.gov.co/CDT/Desarrollo%20Territorial/Tip-FormatoPublicacion%20(tipolog%C3%ADas)%2023.pdf?

[79] WebNational Planning Department (NPD), "Mission for the transformation of the countryside. Definition of categories of rurality," 2014. https://colaboracion.dnp.gov.co/CDT/Estudios%20Econmicos/2015ago6%20Documento%20de%20Ruralidad%20-%20DDRS-MTC.pdf

[80] A. Mendoza, C. Solano, D. Palencia, D. Garcia, "Aplicación del proceso de jerarquía analítica (AHP) para la toma de decisión con juicios de expertos," Ingeniare: Revista Chilena de Ingeniería, vol. 27 no. 3, pp. 348-360, DOI: 10.4067/s0718-33052019000300348, 2019.