Introduction

The socio-economic region XIV called Tulijá-TzeltanTseltal-Chol Cho´l north of Chiapas, in southern Mexico, is identified as one of the regions with the highest bio-cultural diversity of the Mexican southeast. This area brings together three ethnic groups TzeltanTseltal-Chol Cho´l and to a lesser extent TzotzilTsotsil that are conjoined with a mixture of mainly German culture of European descent that arrived in the late nineteenth century¹, where they contributed to the formation of existing cattle production systems in the region. Moreover, the regional ecosystems can support a diversity of production systems, its foundations are centered in the mega diversity and biological heterogeneity - culture^2,3, supporting multiple forms of technical and economic organization of production. The elements that make them up consist of unique qualities and features, product of the biocultural diversity of its components that interact and influence each other, originating a vast productive environment whose roots are possibly derived largely from concepts of the original Mesoamerican system itself^4,5. However, in some respects they also share characteristics and properties that make them similar, their similarities allowing for grouping for different purposes, i.e. for determining the typology of producers. Although it must be recognized that in this region XIV little is known about the characteristics of the types of producers that work in these systems, thus it is essential as mentioned⁶ to know in detail the production situation by identifying patterns of production and their limiting factors.

One of the central premises of this study is to identify the different types of regional livestock producers from the analysis of socio-economic and financial indicators. This is accomplished by carrying out a systematic evaluation within a vision framework whereby solutions are found and designed that are appropriate for the conditions of each of the categories of producers, particularly small-scale farming systems, where economic access to financial resources for the implementation of viable projects present high costs⁷. The objective of this research was to analyze the different socioeconomic characteristics of production systems in the region XIV Tulijá- TzeltanTseltal-Chol Cho´l of Chiapas to determine the different types of cattle producers.

Materials and methods

The classification of types of farmers involved in cattle production systems in this region is shaped by recognizing the biodiversity of multiple components integrated in a dynamic of constant interaction in terms of time and space⁸. Therefore, considering that this variety and interaction of its components form systems with their own functions and arrangements, then the system can be best conceptualized in term of multivariate sets⁹. Thus, the application of a model to study and explore the several variables to facilitate observed structures and functions of the sets or groups derived therefrom, require that the data be managed through various techniques such as multivariate statistical analysis (MSA)^10,11.

The analysis of the different variables to define the types of producers was carried out from November 2012 to May 2013. Tours were conducted in different rural lands in the municipalities of Salto de Agua, Tila, Tumbalá, Sabanilla, Yajalón, Chilón and Sitalá, Chiapas located between 16° 04’ and 17° 56’ N, and 90° 22’ and 92° 42’ W, at 19 to 1,413 m asl¹². This was done in order to implement various surveys and collect socioeconomic and financial information from livestock producers.

This region is characterized as being comprised by people of Mayan ethnic origin, to the west dominated by TzeltanTseltal-Chol Cho´l in the southeast part and towards the northeast by Tzotzil Tsotsil people, with a strong presence of mestizo and European descendants particularly in Chilón, Yajalón, Tila and Salto de Agua¹². It has 4,673 km² and has several types of climate: in the lower parts where the water drains the climate is warm humid with rains all year, and in the highest parts of Tumbalá the climate is subhumid, with rains in the summer¹³. The main economic activities in the region are agriculture, livestock, forestry and tourism.

Interviews with 317 livestock producers selected randomly were applied; the total sample size available was that of 6,508 bovine animal producers based on census data of cattle population in the region¹⁴. A simple random sampling was used and each production unit (PU) was considered as an experimental unit and was represented by each producer. Sampling formula used was¹⁵:

Where: N= population size; d= precision (5%); n= sample size.

A total of 43 variables were studied and the data was organized, systematized and analyzed using various multivariate techniques. The detailed study of the original variables resulted in a selection of 11 variables that were considered most important economically and productively for their contribution to a better analysis of the technological and social components used in the PU. The selected were subjected to a factorial analysis or a dimensional reduction¹⁶, to be summarized and explained with information that contained the full set of observed variables; identifying as appropriate, a smaller number of unobserved variables, called factors. The first statistical tests helped to confirm the 11 original variables that were analyzed by principal component analysis (PCA), and then later by cluster analysis, this permitted identifying the different types of producers¹⁷. The variables studied were, age and level of education of producers, total hectares and cattle area, animal heads and units, income (other income such as complementary professional activities, like employment, etc. were not considered, but only those deriving from livestock rearing), participation in animal health campaigns, equipment, infrastructure and application of mineral salts.

For the analysis of the variables, Pearson’s correlation was applied and then descriptive statistics were used to standardize the variables, KMO and Bartlett test were especially useful to define the degree of standardization¹⁸. Later PCA method was used to obtain the commonalities and test the total variance that recognized the minimum number of components that could confirm the contribution of variables in order to explain the different types. Matrix components as well as the rotation method by using Varimax with Kaiser Normalization. Later cluster analysis with Ward was done, in order to lastly carry out frequency testing. The first allowed for the reduction of information and identified the variables that explain the system, the second revealed data concentrations for efficient clustering and classified production systems by identifying the major differences between types of producers. Analyses were performed using the PSPP program version 19¹⁹.

Results

The analysis of the variables showed that the selected variables are closely correlated (Table 1), while the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.822 indicating that the factor analysis is appropriate because the resulting value match better with higher number of samples. The Bartlett test of sphericity and the approximate chi-square was highly significant (P=0.001), which confirms the correlation between variables and therefore the applicability of factorial analysis. The reduction process factors allowed for visualization of the behavior of the variables and identified four factors that can be grouped. Table 2 shows the commonalities, which expose the proportion of variance explained by the common factors into one variable, while in Table 3 the variances for each variable that account for 70 % of the variability of the original variables are shown.

Table 1 Descriptive statistical analysis of the variables studied for the analysis of components of types of producers in region XIV Tulijá-Tzeltan-Cho’l. 

NOM= Mexican oficial standards.

Table 2 Results of commonalities. 

NOM= Mexican oficial standards.

Table 3 Total explained variance of the variables analyzed. 

ESL= Extraction sums of squared loading.

RSL= Rotation sums of squared loading.

The results of the analysis of the variables and their grouping can be seen in Table 4 and Figure 1. Four groups or types are formed with the following characteristics:

Table 4 Summary of variables and their contribution to the type observed in the region XIV Tulijá-Tzeltan-Cho’l in Chiapas, Mexico. 

NOM= Mexican oficial standards.

Figure 1 Combination of distance clusters rescaled to the study sites 

Discussion

The analysis of the results are reminiscent of those found by other authors, where they also used similar variables to those proposed here and were able to establish an acceptable level of standardization and correlation of their variables, were they also classified four types of producers^7,20. Similar results are observed for the area of extension used for livestock activities; but differences in levels of technological development and arrangement of heads per hectare were found⁷.

This situation may be because the producers of the study region have not adopted the conscious use of modern sustainable technology strategies that are more appropriate to the region and that are useful in bolstering production.

It is important to point out that a variable that the statistical analysis in this study did not consider was production costs. It was not considered because over 70 % of respondents did not provide information; it’s probably because they do not keep or use records, so therefore they do not know their actual production costs, i.e. how much it costs to produce each unit of output, and thus do not know their profit or loss per unit of output. Consequently, their management capabilities are extremely low.

In sum, the region XIV Tulijá-Tzeltan-Cho’l have four profiles types of clearly defined producers, even if they share similarities in some productive activities as the proportion of minerals and participation in animal health campaigns, they are different in some others. Such as educational level that can be seen as a very important difference considering that the range in the first group includes even people without studies, while in the fourth group it includes individuals with higher studies even at the university level.

Another important aspect that is shared by all groups is the low level of technological development. This is alarming as it seems to demonstrate a lack of appreciation of the importance of their use, but one should also take into account the little gross income of most production units is probably impacting the lack of motivation for using modern technology. The application of appropriate technology to the geo-ecological conditions of the region could improve the productive capacity and increase the amount of cattle for production, which apparently seems to be limited by the amount of land. However, the use of modern, sustainable and appropriate technology could help remedy this aspect, even with type I that do not even have corrals for livestock management.

Conclusions and implications

Due to its characteristics, the four types observed and described imply that it is feasible to use this classification to understand and support the resources devoted to bolster livestock production. These must also have specific policies that promote the use of appropriate modern technology, with real applications suitable to the geo-ecological conditions of the region, while also recognizing biodiversity and sustainable production as part of its development strategies.