Location of the study area

From November 2012 to May 2013 field surveys were carried out throughout rural lands of the municipalities of: Salto de Agua, Tila, Tumbalá, Sabanilla, Yajalón, Chilón and Sitalá, located between 16° 04’ and 17° 56’ N and 90° 22’ and 92° 42’ W, between 19 to 1413 m altitude (^{CEIEG, 2012}). This region has 4 673.01 km²; climate types are: warm humid with rains all year in the lower parts of Salto de Agua, and temperate sub-humid with summer rains in the highest parts of Tumbalá (^{Parra et al. 1989}). The region is constituted by ethnic groups of Mayan origin, to the west Tseltales, Chol to the southeast, Tzotsiles to the northeast and a strong presence of mestizo and European descendants, mainly settled in the urbanized communities of Sitalá, Chilo, Yajalón, Tila and Salto de Agua (^{De Vos, 1988}). The main economic activities are agriculture, livestock, forestry and tourism.

Sample size

The study involved the application of 318 interviews to livestock producers scattered across the study area. These were randomly selected throughout the region, taking into account the 6508 producers registered in the regional livestock producers census (^{INEGI, 2007}) as the sampling frame. To set the sample, a simple random sampling was used, each Livestock Production Unit (UPP) was an experimental unit, each representing each livestock producer. For this study was applied the sampling formula proposed by ^{Hernández et al. (2001}).

where N is population size, d is precision (5 %), n is sample size.

Evaluated indicators

The applied questionnaires had 43 study variables. Their results were organized, systematized and analyzed using various multivariate techniques. According to the indicators for these studies (^{García and Calle, 1998}), from the initial variables various indicators were obtained. These allowed a better analysis of the social and technological components applied at the experimental units. These were initially assessed by a size reduction analysis (^{Morrison, 1976}) in order to summarize and explain the information contained in the set of observed variables, which in turn can find another smaller set of unobserved variables.

According to the methodology by ^{García and Calle (1998}), the studied variables were: the agricultural area/animal surface relationship (this is a useful indicator for landuse assessment); the bull/cow ratio (shows the population structure, values close to 1 are set dominated by bullocks and close to 0 for females); bovine density (define the greater or lesser emphasis livestock activity has regard the total agricultural area); the small/medium-large farm ratio (farms under 20 ha were considered small, medium size between 20 and l00 ha, those larger than 100 ha were considered as big. The medium and large farm concentration will give values close to zero, whereas those small will show coefficients close to one); vaccination against bovine tuberculosis and brucellosis, an indicator that helps to determine whether the study units comply with the Official Mexican Standard for the national campaign against tuberculosis and brucellosis; carrying capacity (cattle per hectare); the presence of improved pastures, (this indicator shows if the study units have improved pastures, few hectares and recognizes the importance of its application for producers in the cattle area); rural/urban population (indicator of the concentration of the representatives of the production units in rural or urban areas, or both; the values close to one indicate a predominantly rural population), and the number of pastures (helps to recognize the type of grazing system).

The resulting clusters were evaluated in three theoretical frameworks: 1) rural economy, 2) preempresarial or transitional conventional model, and 3) modern model or corporate agriculture (^{González, 1990}, ^{García and Calle, 1998}, ^{Guadarrama, 2007}).

Statistical procedure

First, a variables correlation, in addition to their descriptive and frequency statistics in order to standardize them, the Kaiser, Meyer and Olkin (KMO) and Bartlett’s sphericity were applied to define the degree of standardization (^{Ruíz et al., 2012}). The Principal Component Analysis method (PCA) was then used to obtain the commonalities and the total variance test that contributed to record the number of minimum components that allowed to create a components matrix and the rotation test with the Varimax normalization method. Then the cluster analysis was made with the Ward linking and ultimately the table summarizing the sets.

The first test allowed the reduction of information and identified variables that explained the grouped sets. The second revealed the data concentrations, for efficient clustering, which finally led to the classification of the systems to identify major differences. All analyzes were performed with SPSS version 19 software (^{SPSS Inc., 2010}).

System 1

This cluster was characterized for concentrating 88.6 % of the studied production or experimental units (61.2 % are experimental units under 20 ha, and 27.4 % are over 20 ha), where the production units that made up this cluster are predominantly located in rural areas (indicator = 0.728). The set has a small/medium-large land relation of 2.23, which means that for every farm bigger than 20 ha two are of less than 20 ha. The agricultural/livestock relationship was of 0.944, indicating that for every livestock hectare 0.944 are agricultural activities. In other words, most of the properties in the studied area dedicated to livestock-farming activities, the last of which is dedicated to offspring production, this is confirmed by the 0.405 bull/cow ratio that clearly show a female predominance. Interestingly, the bovine density was of 1.51 ha^-1, while the carrying capacity was of 2.35 animal units. These is consistent with the carrying capacity of these experimental units for the improved pastures indicator (9.35 ha), i.e, there is an interest and an effective improvement in pasture production in order to maintain the carrying capacity, in extensive free grazing conditions, as the surveyed producers have an average of three paddocks.

System 2

We found that 5.6 % of the studied production or experimental units were brought together on this scheme. In the indicator for the small/medium-large land estates bigger than 20 ha were gathered, with a result on the agricultural/livestock indicator clearly pooled towards livestock production (0.313) and a tendency for calf production, as shown by a 0.459 bull/cow ratio. These units have significant improvements in their pasture quality which support more animals. This is evidenced by the bovine density of 2.01 and the carrying capacity of 2.6, which is consistent with the improved pasture indicator which resulted on average in 49.5 ha improved. Besides, it is interesting to note that for the indicator of rural/urban population (0.651) in this set has a significant presence of representatives of the production units as urban dwellers.

System 3

The third cluster contained 4.7 % of the evaluated experimental units and corresponds to a system characterized by agricultural activities predominance in areas under 20 ha, as seen in the agricultural/livestock indicator of 1.480, i.e, for every hectare dedicated to livestock activities, 1.48 ha are for agricultural activities.

Livestock activities can be described as “oriented to the production of animals for meat” or calves production, as confirmed by the bull/cow indicator ratio (0.238). Also, as suggested by the number of paddocks (that is 2.8); the production is carried out in extensive grazing conditions, while 4.27 ha is from improved pasture. The carrying capacity reaches up 10.48 animal units, which can be explained by the livestock activity, aimed at calves or fattening bulls for certain periods or seasons which are favorable pastures.

System 4

This group concentrated the smaller number of the evaluated studied units (0.9 %), and corresponded, according to the small/medium-large surface indicator, to lands under 20 ha. The land use, according to the agricultural/livestock indicator (2.22), is mainly agricultural with some additional livestock activities focused on bullocks fattening according to the steer/cow ratio indicator (6.66). This production system is done in extensive grazing conditions with the lowest improved pastures surface level (2.33 ha) among all the studied sets. Bovine density in this group is the lowest; however, the carrying capacity is of 4.78 which is only explained by the activity of the land use in specific periods to concentrate livestock animals such as the rainy season. This cluster is also the one that has the largest number of production units representatives who live in urban areas.

Other studies on cattle production systems researchers report a similar methodology to that proposed here. ^{García and Calle (1998}) studied the systems in use at the Santander region, in Colombia, with similar indicators to those used here, in which they were able to establish an acceptable level of standardization and correlation of their variables.

The results of our study show an interesting data on the index that relates the agricultural/livestock production, given that in all clusters the production system is common to both, agricultural and livestock components. In other words, in the four clusters the productive systems perform agricultural and livestock activities; however, systems 3 and 4 showed a predominance of agricultural production over livestock, i.e, livestock production is a complementary activity to the agricultural. This is conversely in the other two predominate livestock production, defined in the system 2. These inferences suggest that the majority of experimental units are not specialized or with business features, and still maintain a significant cattle production.

A relevant fact is that in only 2.2 % of the experimental units carry out milking for milk production. The Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA) recognize this activity as a dual purpose: cattle production of milk and meat for sale. This means that some cows partially milked and the remaining milk is consumed by the calves from the udder (^{Anderson and Wadsworth, 1995}). Thus the region does not tend to milk production and is mainly concentrated in offspring production.

The system 1 results are sufficient to classify this system as livestock/agricultural and suggest a peasant production model, in a region with a strong indigenous population presence. This imply a significant degree of economic and cultural autonomy, as reported by ^{Magdaleno et al. (2014}), it is an important contribution in order to understand this set.

Regard system 2, because of its described characteristics, this conglomerate may be classified as a livestock system, complemented with agricultural activities, which placed it as a production model in transition (pre-business) as it is a set that has an initial level of livestock of specialization. Meanwhile system 3, which has, given its indicator, the largest population concentration in rural areas, with 0.763, can be classified as an agricultural system with its own complementary activities, proper of cattle production, in a production model inserted in an environment with high indigenous population.

For cluster 4, for its characteristics is classified as an agricultural system with cattle related complementary activities, in a pre-business transition scheme, as it is a set that shows an initial level of agricultural specialization.

The systems are similar to those found by ^{García and Calle (1998}), in the sense that they also grouped four clusters that clearly explained the systems in Santander, Colombia. These researches reported that the sets included a large group of livestock producers who had complementary agricultural activities in a peasant production model. Furthermore, ^{Magdaleno et al. (2014}) reported an important set of farmers who produce in a peasant production system in an indigenous environment at Acambay, Estado de Mexico.

The region Tulijá-Tseltal-Chol XIV defined four systems, clearly outlined, that share similarities in some productive activities such as land use for agricultural and livestock activities, improving pastures or interest in maintaining animal health with vaccination. The systems are different, as the production model which they are part of, even while a high percentage of cases are in a production contest of peasant economy in a contexto or indigenous territory, there is the presence of a minority of producers with higher specialization that can be considered as pre-business at a given time.

Another important aspect, worth noting because it is shared by most of the studied units, is the calves production, given that in this region milking is an emerging industry, whose production is low. The mean calculated in our study for milk production was 7.15 million L per year, which coincides with the report by ^{SAGARPA (2010}) of 7,325,410 liters. This places the livestock system in the region focused on the production of calves for sale (in a pre-business scheme) or to meet family or production needs in a typical household savings system model of peasant economy.

The region has a wide range of socio-economic activities, that range from coffee production, “milpa” and fruit trees to conservation areas with native trees and domestic and wild animals. These components determine the systems, these explain production and this in turn defines the organization of the variety of crops and animals present as elements that give the dynamics of the whole system, and that impact on cultural expressions of their inhabitants, as ^{Boege (2006}) mentions, in his study about the biodiversity in southeastern Mexico.