
==== Front
Trop Anim Health Prod
Trop Anim Health Prod
Tropical Animal Health and Production
0049-4747
1573-7438
Springer Netherlands Dordrecht

39230762
4092
10.1007/s11250-024-04092-x
Regular Articles
Socioeconomic determinants of small and medium-sized dairy farms in the Ecuador-Colombia border area
http://orcid.org/0000-0002-3039-7657
Carvajal-Pérez Luis Alfredo luis.carvajal@upec.edu.ec

Montenegro-Arellano Guillermo Fausto
Revelo-Ruales Vinicio Wladimir
Terán-Rosero Gustavo Javier
Urgilés-Urgilés Gladys Primavera
https://ror.org/04msd0t92 grid.442261.7 0000 0004 1762 4979 Universidad Politécnica Estatal del Carchi, Antisana and Universitaria Avenue, Tulcán, Carchi 040101 Ecuador
4 9 2024
4 9 2024
2024
56 7 2542 10 2023
18 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The socioeconomic factors influencing small-scale dairy producers in the border area between Ecuador and Colombia were meticulously identified. Employing a non-experimental design, the study leveraged multivariate statistical analysis to discern key determinants. Data processing was executed using the statistical software SPSS v27, facilitating comprehensive analysis. A random survey was administered to 532 small and medium-scale dairy producers in the Carchi province of Ecuador, employing a structured questionnaire supplemented with a Likert scale for nuanced insights. Based on 35 original variables, seven determining factors were identified in dairy farms: political representation, adequate housing, equipment, innovation, empathy, profitability, social welfare, which combined explain 60.95% of the system’s variability. Such factors affect production, the level of household income, as well as their effect on the standard of living of households. Three groups were formed, the first with a low perception of economic development (Traditionalists 33.3%); the second with a better expectation of economic development (Modernizers 27.6%); and the third, identified with greater economic development (Innovators 10.3%). Each group presents cases with a low to high standard of living perspective. The groups have peculiarities in terms of their performance that can be applied to the entire population. A significant relation was established between socioeconomic factors and standard of living.

Keywords

Milk production
Standard of living
Social welfare
Main components
Clusters
Universidad Politecnica Estatal del CarchiUniversidad Politecnica Estatal del Carchi issue-copyright-statement© Springer Nature B.V. 2024
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pmcIntroduction

Bovine milk extraction is important in several parts of the world, and it is estimated that in emerging economies 80 to 90% of production is generated by small family dairy farms. In 2022, around 544.2 million tons of cow’s milk were produced worldwide, with a decrease of one million tons compared to 2021 (FAO 2023).

Ecuador produces 4.49 million liters of milk per day, providing income for 1.2 million people (INEC 2023). The dairy industry represents around 4% of the agri-food GDP, with a growth of 10.92%, compared to 2020 (CIL Ecuador 2023).

It is considered that dairy production in the province of Carchi is the third largest at the national level and accounts for 8,957 livestock farms (Prefecture of Carchi 2023). Here, a peasant-type family production is developed, with a high presence in the informal market (Morocho et al. 2021). This sector provides employment for 36% of the population (Terán and Cobo 2017).

In Carchi, an extensive livestock system is developed, with traditional practices and a significant presence of local cattle that achieve a yield of 9.4 L per cow per day, higher than the national average of 5.9 L (Carvajal 2014). Likewise, 6% of dairy farms in the area use Holstein cattle and achieve 15 to 18 L per cow per day (Balarezo et al. 2016).

Socio-economic factors (SEF) establish the interactions that people experience in their environment, as well as the positive or negative impact on the way of life and economic activities of producers. According to Benson and Zamora (2023), gender, educational level, age, training, type of work and experience, management efficiency, family structure, culture and beliefs, associativity, technical assistance, availability of basic services, and quality of life are recognized as social factors.

Likewise, economic factors such as the cost of production, income and expenses, investment in technology, financing, land ownership and use, the size and structure of the cattle herd, the volume of production and the quality of milk are also identified (Moncayo et al. 2021; Vásquez et al. 2022).

Furthermore, productive factors such as grazing methods and forage management are important, which have been analyzed in other studies related to dairy farms (Zemarku et al. 2022). In addition, Terán and Cobo (2017) point out that infrastructure, equipment, milking hygiene, biosecurity, and animal welfare are essential factors to achieve technical efficiency.

Peña et al. (2018), on the other hand, argue that the adequate availability of land, the use of water, feed, and veterinary supplies have an impact on the quantity and quality of production, and may vary according to the raising system and automation technologies employed (Tangorra et al. 2022).

Additionally, Alonso et al. (2020) and Moreno (2021) state that innovations incorporated into production make it possible to satisfy essential household needs, provide better access to education and training programs, as well as the possibility of reorganizing work, all with the purpose of increasing productive and economic efficiency and a better standard of living. However, a negative impact of SEF on milk production can be pointed out, which manifests itself through restrictions in the adoption of technology due to the producers advanced age (Vásquez et al. 2022).

On top of that, the scarce availability of surface area, land, and equipment, all of which limit the achievement of higher economic income (Granados et al. 2019); In addition, a low level of technical assistance can lead to a greater presence of diseases, in contrast, sufficient technical assistance allows for increased production, given greater training and frequent contact with experts (Zemarku et al. 2022).

According to Velasteguí (2019), the agricultural production units (APUs) in the study area have milking facilities or barns that have been reduced in accordance with their economic capacity. In addition, smallholders use a land area of 3 hectares for production; medium-sized producers have 7 hectares; and large producers have an average of 120 hectares (Requelme and Bonifaz 2012). In terms of age, producers are approximately 50 years old, with a limited presence of young people and weak generational renewal (Moreno 2018). At the educational level, about 60 per cent of them have primary education, 25 per cent have secondary education and 15 per cent have university education. In addition, the low competitiveness of its production chain affects the adequate performance of production.

It is important to identify the SEF associated with milk production with the use of methodological tools that allow their evaluation. Currently, producers conduct the analysis based on their empirical experience. The objective of this research was to identify the determining SEF in small and medium-sized dairy farms in the border area between Ecuador and Colombia, to understand if there is a significant correlation between educational level, family income, accessibility of resources, among others, and milk production in agricultural communities. In this way, it will be possible to design scientific-methodological criteria for decision-making focused on productive development and well-being.

Materials and methods

The research was carried out in the province of Carchi, located in northern Ecuador, on the border with Colombia. 63% of the territory is located in the humid temperate region, with altitudes of 1,800 to 3,000 m above sea level and temperatures of 12 to 18º C varying in the dry or rainy season (Franco 2016); the remaining 37% is in the very humid sub-temperate region, which corresponds to the low moors, with altitudes of 3,000 to 4,000 m above sea level, temperatures of 6 to 12° C and rainfall of 1,000 to 1,500 mm per year, with no predominance in any month of the year (Requelme and Bonifaz 2012).

The research modality is applied and descriptive, with a exploratory scope. A cross-sectional research design was implemented for the collection and analysis of qualitative and quantitative data. This research design makes it easy to compare more than one group based on their attitudes, opinions, beliefs, and practices (Hernández-Sampieri et al. 2014).

A structured questionnaire was applied to a total number of 532 dairy farmers in the province. The selection of the participants was made by means of a proportional affixation with respect to their representation in the province of Carchi, as shown in Table 1.

Table 1 Proportional distribution of milk producers by Canton according to their participation

Canton	Producers	Participation %	Applied surveys	
Tulcán	2728	30	162	
Montúfar	1734	19	103	
Huaca	1667	19	99	
Espejo	1684	19	100	
Mira	791	9	47	
Bolívar	354	4	21	
Total1	8957	100	532	
1The number of producers was taken from project N°. FIEDS-008-2019

Data collection

Field data collection was carried out in collaboration with Business Administration students from Universidad Politécnica del Carchi (UPEC), Ecuador, during the second semester of 2022.

A survey was designed that contains variables related to dairy activity. The results made it possible to evaluate the relationship of the social and economic variables of production with the state of personal and family well-being.

A total of 44 items were included in the information collection questionnaire, which were identified with the contribution of specialists that have been organized into differentiated segments to obtain the database and thus facilitate its subsequent analysis, as detailed in Table 2.

Table 2 Socio-economic and productive variables of the dairy sector in Carchi

Variables	Indicators	
Social	Age, gender, educational level, training, type of work, experience, family structure, culture and beliefs, associativity, access to basic services, standard of living.	
Economic	Cost, expenses, dairy income, other income, income distribution, investment in equipment, financing	
Productive	Land tenure and use, herd size and structure, pasture management, technology adoption, government support, milk production volume (liters per hectare), productive diversification, milk quality	

Statistical analysis

The Statistical Package for the Social Sciences (SPSS) version 27 was used to verify the normality and homoscedasticity of the variances for quantitative variables using the Shapiro-Wilk test and for qualitative variables the Kruskal-Wallis test (normality assumptions). This allowed the variables to be standardized and the correlation matrix was obtained from which 35 variables that were highly correlated were maintained, shown in Annex 1.

A KMO coefficient was applied with a sample adequacy measure of 0.79, which was considered acceptable for the model, indicating that the observations fit well with a factorial model (Vertti 2019). The value of 7.455 obtained in Bartlett’s sphericity test with 595 degrees of freedom demonstrated a highly significant reliability of 0.000, supporting the decision to reject the null hypothesis of similarity in the correlation matrix. This allowed the continuation of the Principal Component Analysis (PCA) (Roboredo et al. 2018).

Procedure

The population, comprising small and medium-sized dairy producers in the province of Carchi situated on the border with Colombia, was identified. Subsequently, the sample size for the known population was calculated, requiring a total of 532 surveys.

Subsequently, a five-level Likert scale questionnaire was designed. Dichotomous questions and multiple-choice and interval questions were included. The support team was trained in the process of collecting field information, data processing and analysis. The participants used the same information for the development of four undergraduate theses as part of their professional training.

Information preprocessing was performed to eliminate outliers or missing values that could affect the extraction and interpretation of significant results. A file was organized for the analysis of the data and the characterization of the behavior of the variables, for which multivariate analysis tools were used.

An Exploratory Factor Analysis (EFA) was developed for statistical validation. This technique facilitated the identification of the component factors, giving validity and reliability to the study.

The dimensionality of the database was then reduced using a PCA. The Varimax rotation method and the Kaiser-Meyer-Olkin (KMO) normalization were applied. In addition, the sample was adapted using Barlett’s sphericity test (Guiné et al. 2020) to ensure the quality of the results.

Ultimately, the population was divided into three clusters using the Ward hierarchical method with an 80% Euclidean distance threshold on the dendrogram (Albarrán et al. 2019). Subsequently, the K-means method was applied to validate the homogeneity within the groups and the heterogeneity between them, as identified earlier through the hierarchical method. The methodological procedure is illustrated in Fig. 1.

Fig. 1 Methodological procedure used in this research

Results

Among the producers surveyed, 61% are men with an average age of 53, while 39% are women with an average age of 50. The experience in livestock activity is 26 years for men and 23 years for women. This factor influences the production level, as it is estimated that with 10, 20, and 30 years of experience, the production level increases to 5, 10, and 15 L per cow per day, respectively.

In terms of educational level, 11% of producers have no formal education, 46% have primary education, 35% have secondary education and 9% have university education, which makes it difficult to adopt technology, especially in the case of those with a lower level of education. Producers with no formal education, primary, secondary and university education generate average monthly incomes of $250, $420, $670, and $830, respectively. Despite this correlation between educational attainment and income, more than 90% of producers own house-type homes, and approximately 70% of them make efforts to maintain them in good condition.

Regarding households, 44% are economically supported by the husband, 47% by both spouses, and 9% by other relatives. In households composed of up to three people, only 3% correspond to the children, who bear the responsibility for the family’s financial support.

Agricultural and livestock production units (UPA) have an average area dedicated to milk production of 6.04 hectares. Labor in 92% of cases is provided by family members, while 8% involves external labor.

For 47% of those surveyed, milk extraction is considered the primary economic activity, while 21% prioritize agriculture and 11% primarily engage in animal sales.

Regarding the marketing of the milk produced, 67% sell it to intermediaries or milk collectors, 23% allocate it to the industry, 10% sell it to artisanal producers, and only 1% market it to restaurants. On average, milk sales generate monthly income of $648.89 USD.

The correlation between educational attainment and production levels displays an upward trajectory. Producers lacking formal education, those with primary, secondary, and tertiary education, respectively yield an estimated daily production of 30, 40, 40, and 80 L.

Foremost challenges encountered by producers encompass inadequate feed for cattle (28%), diminished soil fertility (20%), dearth of markets for milk and livestock sales (16%), substandard road infrastructure (14%), and supplementary issues of lesser prevalence, including inefficient breeds, absence of specialized veterinary services, and instances of theft or livestock rustling (22%).

Among articulated requirements to enhance their living standards are access to land, potable water, sanitation facilities, irrigation resources, enhanced public infrastructure, credit accessibility, and the shortfall in technical advisory support.

The PCA made it possible to reduce the dimensionality of the dataset while maintaining as much of the original variability of the data as possible. As shown in Table 3, the first eigenvalue (exceeding 1) explained 14.56% of the total variance, while the seventh eigenvalue explained 4.15%.

The components that explain most of the variance, which reveal clustering patterns, were chosen, eliminating multicollinearity. Together, the seven selected components accounted for 60.95% of the system’s variability.

Table 3 Explanation of the total variance of the factors

Component	Initial eigenvalues	Extraction sums
of squared loadings	Rotation sums
of squared loadings	
Total	Variance %	Cumulative %	Total	Variance %	Cumulative %	Total	Variance %	Cumulative %	
1	6.168	17.623	17.623	6.168	17.623	17.623	5.097	14.562	14.562	
2	4.772	13.633	31.256	4.772	13.633	31.256	4.904	14.011	28.572	
3	4.050	11.570	42.827	4.050	11.570	42.827	4.116	11.760	40.333	
4	2.095	5.985	48.812	2.095	5.985	48.812	1.951	5.574	45.906	
5	1.554	4.441	53.253	1.554	4.441	53.253	1.942	5.548	51.454	
6	1.454	4.154	57.407	1.454	4.154	57.407	1.869	5.341	56.795	
7	1.240	3.542	60.949	1.240	3.542	60.949	1.454	4.154	60.949	
8	1.057	3.019	63.968							
9	1.003	2.864	66.832							
…	…	…	…							
35	0.080	0.229	100.000							

Table 4 presents the rotated component matrix, which explains the behavior of the components in the form of vectors in three dimensions. The factors with the highest incidence are considered, with values greater than 0.5. The individual variances explained by each of the seven main components are identified, which explain the integral behavior of the variables. These components are as follows: Political Representation (14.56%), Adequate Housing (14.01%), Equipment (11.76%), Innovation (5.57%), Empathy (5.55%), Profitability (5.34%), and Social Welfare (4.15%).

Table 4 Rotated component matrix

Factor1	Political
representation	Factor1	Adequate
housing	Factor1	Equipment	Factor1	Innovation	
EMPG	0,817	CWH	0,934	UOL	0,863	UICT	0,685	
EMMG	0,816	CSSH	0,931	RTS	0,814	ICF	0,585	
PRIP	0,795	CFH	0,92	GTS	0,809	RCDP	0,378	
EGCL	0,779	CTWH	0,903	URT	0,751	PTDP	0,347	
PRIM	0,764	CRH	0,872	USBS	0,688			
EMNG	0,734	CLSH	0,768	PTUM	0,592			
PRIN	0,726			FST	0,506			
PRIC	0,659			PLA	0,412			
	14,56%		14,01%		11,76%		5,57%	
Factor 1	Empathy	Factor 1	Profitability	Factor 1	Social	Explained variance		
Welfare	
ARSM	0,837	CFS	0,734	SFLS	0,651			
ARMB	0,808	PDB	0,637	PPES	0,395			
IWLF	0,53	IMS	0,572					
		APAA	0,395					
	5,55%		5,34%		4,15%	60,94%		
1Find the description of the factors in Annex 1

Figure 2 shows the distribution of the identified components. The first cluster corresponds to producers who perceive low economic development (Traditionalists), with only some of them located towards the middle or above the scale of improvement in their economic development. You can see those that are to the right, indicating a high standard of living, and those that are to the left, that denote a low standard of living.

Fig. 2 Scatter plot of Economic Development and Standard of Living by Ward, Hierarchical Method

The second cluster is made up of producers who consider themselves to have a better level of economic development (Modernizers), and only a few perceive it as low. Also within this group are producers who are both on the left and on the right in terms of living standards.

Finally, the third cluster includes a small number of producers who tend to perceive a high level of economic development (Innovators).

Table 5 Variables analyzed by each cluster

Variable	Traditionalists	Modernizers	Innovators	
Age	53.08 ± 13.52	50.80 ± 14.44	48.53 ± 15.65	
Family members	3.86 ± 1.43	4.13 ± 1.87	3.47 ± 1.36	
Experience (years)	25.26 ± 13.42	23.23 ± 15.89	24.88 ± 13.42	
Sustaining price	0.53 ± 0.08	0.52 ± 0.09	0.54 ± 0.11	
Price per liter	0.417 ± 0.067	0.406 ± 0.063	0.394 ± 0.056	
Liters per day	50.74 ± 3.41	68.59 ± 4.87	85.82 ± 15.23	
Liters per cow per day	8.68 ± 3.49	10.13 ± 3.68	9.79 ± 3.69	
Liters per hectare per year	4012.98 ± 177.06	4566.06 ± 208.79	6306.41 ± 1183.97	
Monthly Income	562.05 ± 39.55	738.66 ± 50.83	931.41 ± 161.07	
Income per cow per day	3.18 ± 1.36	3.72 ± 1.58	3.54 ± 1.27	
Income per hectare per year	1468.37 ± 65.31	1661.31 ± 77.33	2346.06 ± 471.06	
Cost per hectare per year	711.11 ± 51.70	608.26 ± 47.23	784.94 ± 222.13	
Area (ha)	5.64 ± 0.32	6.29 ± 0.36	6.29 ± 1.03	
Milking cows	6.11 ± 0.33	6.83 ± 0.43	8.41 ± 0.96	
Indebtedness	5704.27 ± 803.96	6497.55 ± 1432.02	29000.00 ± 12771.75	

Regarding the composition of the clusters, it is observed that 33.3% of the producers were classified as Traditionalists, while 27.6% belonged to Modernizers and 10.3% to Innovators.

Table 5 shows that in the Traditionalists cluster, indicators with higher values stand out compared to the Modernizers and Innovators clusters in several aspects. For example, the average age of producers is 53 years old, with 25 years of experience in the activity and a selling price per liter of milk of 0.417 USD.

On the other hand, the Modernizers cluster is characterized by having a greater number of family members with an average of four people, a milk production of 10.13 L per cow per day and an income per cow per day of 3.72 USD.

Finally, the Innovators cluster presents a greater representativeness in certain elements, such as the support price of 0.54 USD, a daily production of 85.82 L, with 6 306 L per cow per year, a monthly income of 931 USD, income per hectare and per year of 2 346 USD, a production cost per hectare per year of 785 USD, an average production area of 6.29 hectares and 8 milking cows, in addition to an indebtedness level of USD 29,000.

Discussion

Among the most relevant SEF are educational attainment, training, access to credit, land tenure, political representation, herd size and structure, access to credit and technology investment, and production costs. These factors influence the volume and quality of production, the income generated and the improvement in the quality of life of livestock farmers in the province of Carchi.

There is supremacy in the participation of men in relation to women in the dairy activity, the average of 53 and 50 years respectively, as well as the experience of 26 years for men and 23 years for women coincides with the findings of Rodríguez-Moreno (2020). It is estimated that producers with more experience can generate a higher level of production. Age correlates with their experience, similar to the findings of Moncayo et al. (2021) and Weltin et al. (2017), older and experienced producers tend to be more restrictive to introduce technological changes (age = 53.08 ± 13.52 years, experience = 25.26 ± 13.42 years).

It is identified that a higher level of education is associated with obtaining more income for the household; Likewise, the level achieved by producers is not directly related to family well-being, a high percentage of producers are concerned about keeping the home in adequate conditions. It was found that 44% of households are economically supported by the husband, and as Zoma-Traoré et al. (2020) point out, the supremacy of male participation in this activity is a fundamental characteristic of a patriarchal society.

The participation of family labor is shown to be much higher than that of workers outside the home, a different situation with the results of Chávez-Pérez (2021), the high dependence on family labor is justified as a source of livelihood different from business administration.

The result of the PCA shows seven factors that managed to explain 60.95% of the variability of the system, Vargas (2015), in his analysis of the determining factors in milk production in the Ecuadorian mountainous area, selected three components that explained 79.9% of the accumulated variance. On the other hand, Segura et al. (2017), in a study of technical efficiency in dairy farms in the Republic of Cuba, used five components explaining 67.4% of the accumulated variance. All of them used the same analysis methodology, which shows that it is a valid technique for evaluating SEF with existing heterogeneity among farms, which show low levels of technology and productivity (Chávez-Pérez et al. 2021). On the other hand, the analysis carried out in the highland area of Pastaza, Ecuador, resulted in the formation of three different groups, with characteristics and behaviors different from those observed in the present study (Carrasco et al. 2017) since they are different contexts in many factors.

The clusters, Traditionalists and Modernizers, have a higher proportion of producers with a high school education (26% and 25%, respectively), while the Innovators cluster includes a significant presence without formal education. Despite this variability, the level of schooling does not have a directly proportional relationship between educational attainment and the increase in production. According to Camacho (2017), schooling can influence other variables, such as income, standard of living, investment capacity, and economic benefit.

Although the Innovators cluster performs better in factors such as production and income, it is a smaller percentage of the total population in the area. In addition, this group shows a higher cost of production and a high level of indebtedness, which influences their expectation of the price received per liter for sustenance. On the other hand, producers in the Traditionalists cluster sell milk at higher prices per liter compared to the global average price of the groups, similar to the study by Habanabakize et al. (2022).

In terms of geographical distribution, the Traditionalist cluster has the greatest presence in the cantons of Tulcán and Montúfar, while the Modernizers cluster stands out in the cantons of Tulcán and Huaca. For its part, the Innovators cluster has a greater stake in the Huaca canton. This confirms that Tulcán, Montúfar and Huaca are the most representative in dairy activity, as pointed out by Terán and Cobo (2017). The Mira canton has less representation of the Traditionalist and Modernizers clusters. The Innovators cluster is represented only in the cantons of Huaca and Espejo.

The variable of income and costs is used to analyze the viability of dairy production, as it allows to determine whether the income is sufficient to compensate for the resources invested in the activity (Silva and Gameiro 2022). However, low financial management is identified in all cluster, which directly affects economic results and has an impact on family well-being (Gazola et al. 2018; Andaleeb et al. 2019; Ramírez et al. 2020; Gavilanes et al. 2022; and Granoble 2022).

The Innovators cluster has had greater access to technical advice compared to the Traditionalists and Modernizers. This indicates that producers face deficiencies when receiving advisory services, which should also be provided by government entities (Camacho et al. 2017).

Dairy farming has become an economic activity of great importance in the province of Carchi. It is characterized by the use of cultural practices passed down from generation to generation. This socioeconomic activity is mixed in nature, as producers engage in various productive activities. However, this dependency varies according to the season, as agriculture is practiced on the same soils on a rotational basis. Although some growers have incorporated technology into their operations, most still do not have the financial capacity to purchase expensive tools. In general, they seek to reduce risks to improve their profits, although they have failed to establish sustainable management practices.

There are annual fluctuations in the availability of inputs, as well as prices and unfavorable environmental conditions, which make it difficult for the producer to make long-term decisions for investments or projections of survival and development.

In a second phase of the research, new data will be collected, after the application of statistical tools to evaluate its economic performance. This will allow a comparative analysis to be carried out using Bootstrap or other Machine Learning tools to predict the results of best practices in livestock activity and their impact on the welfare of producers, which is fundamental for the sustainable development of this economic activity.

Conclusions

The results show that there is heterogeneity between the dairy farms of Carchi with respect to SEF and production. There is poor administrative management, control of exploitation processes and limited investment in technology that reduce livestock efficiency and profitability, which in turn has a negative impact on the well-being of rural producers.

The determining SEF in production were established, which contribute to the obtaining of economic resources and social welfare, such as: political representation, the availability of adequate housing, equipment, innovation, empathy and profitability. These factors were evidenced by cluster analysis, which resulted in three statistically different groups. Of these, the Traditionalists and Modernizers predominate in the province of Carchi. The Innovators have a smaller presence but achieve better productive and economic performance, thanks to the application of good livestock practices and a greater number of cows in production. In spite of this, the perception of those involved in this group in relation to the sale price per liter is lower than that obtained in the other clusters.

In conclusion, the use of multivariate statistical tools, such as the PCA and Clusters, facilitate the understanding of the relevant SEF in dairy farms, however, future studies related to the management of collaborative sustainable practices between groups should be developed, to generate benefits and reduce disadvantages by replicating the best results throughout the province.

Annex 1 Factors considered in exploratory factor analysis

N°	Analyzed factors	Code	
1	Effective management by the provincial government	EMPG	
2	Effective management by the parish / municipal government	EMMG	
3	Politically well-represented interests in the province	PRIP	
4	Effective governance at the community level	EGCL	
5	Politically well-represented interests at the parish / municipal level	PRIM	
6	Effective management by the national government	EMNG	
7	Politically well-represented interests at the national level	PRIN	
8	Politically well-represented interests in the community	PRIC	
9	Condition of windows in housing	CWH	
10	Condition of sanitary services in housing	CSSH	
11	Condition of the flooring in housing	CFH	
12	Condition of the walls in housing	CTWH	
13	Condition of the roof in housing	CRH	
14	Condition of the lighting system in housing	CLSH	
15	Utilization of laboratories	UOL	
16	Receive training services	RTS	
17	Get technical support	GTS	
18	Use of refrigeration tanks	URT	
19	Utilization of savings bank services	USBS	
20	Participation in trade union meetings	PTUM	
21	Frequency of staff training	FST	
22	Performance of laboratory analysis	PLA	
23	Use of ICT in dairy production (PC, internet, cell phone, equipment)	UICT	
24	Implementation of conservation forages	ICF	
25	Has received credit for the last 3 years for dairy production	RCDP	
26	Participates in technological development projects	PTDP	
27	Adequate relationship with suppliers of inputs and medicines	ARSM	
28	Adequate relationship with milk buyers	ARMB	
29	Importance of women’s role in livestock farming	IWLF	
30	Current family situation compared to two years ago	CFS	
31	Profitability of the dairy business	PDB	
32	Income from milk sales allows for investment recovery	IMS	
33	Amount of pasture available for animals	APAA	
34	Incidence of SEF on living standards	SFLS	
35	Post-pandemic economic situation	PPES	

Author contributions

Guillermo Fausto Montenegro-Arellano and Vinicio Wladimir Revelo-Ruales conceptualized and designed the article, Gustavo Javier Terán-Rosero and Gladys Primavera Urgilés-Urgilés preparate the material and analyzed the data. The first draft of the manuscript was written by Luis Alfredo Carvajal-Pérez and all authors commented on previous versions and approved the final manuscript.

Funding

The research has received financial support for projects from Politécnica Estatal del Carchi University, Ecuador / 2023–2024.

Data availability

The authors declare that all the data and materials used in this manuscript comply with field standards and are available on demand.

Declarations

Data publicly available in a repository

The datasets generated by the survey and/or analyzed during the study are available on demand.

Competing interests

Competing Interests: No conflicts of interest have been declared in relation to the research and its findings.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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