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Cuban Journal of Agricultural Science

versión impresa ISSN 0864-0408versión On-line ISSN 2079-3480

Cuban J. Agric. Sci. vol.56 no.1 Mayabeque ene.-mar. 2022  Epub 01-Mar-2022

 

Animal Science

Determination of typologies of sheep production systems in Ciego de Ávila province

1Facultad de Ciencias Agropecuarias, Universidad de Ciego de Ávila Máximo Gómez Báez (UNICA), Carretera a Morón, km 9 ½, Código Postal 69450, Ciego de Ávila, Cuba

2Instituto de Ciencia Animal, Carretera Central, km 47 ½, San José de las Lajas, Mayabeque, Cuba

3Instituto Superior Politécnico de Huíla (ISPH), Universidad de Mandume Ya Ndemufayo. Angola

4Centro de Estudio de Producción Animal (CEPA), Universidad de Granma (UDG), Carretera de Manzanillo, km 17 ½, Código Postal 85100, Granma, Cuba

5Universidad de Ciego de Ávila Máximo Gómez Báez, Centro de Bioplanta, Laboratorio de mejoramiento y conservación de recursos fitogenéticos. Ciego de Ávila, Código Postal 69450, Cuba

Abstract

In order to determine the typologies of sheep production systems in Ciego de Ávila province, the statistical model of impact measuring (SMIM) was applied, which combines the main components analysis with the hierarchical cluster method. An information matrix of 296 sheep farmers was used to define groups of farms. The main component analysis explained 71.83 % of the variability. In component 1, variables related to herd movement were identified and component 2 was associated with land tenure. Component 3 had to do with training and management, and component 4 with the age and farmers experience. Component 5 included the number of workers and technological aspects, and component 6 was related to the presence of trees. Five groups of sheep farmers were obtained: I) medium farmers with land from the private sector, II) small farmers without tendency of lands from the private sector, III) small farmers with land from both sectors, with a predominance of the private, IV) large farmers with land from both sectors and V) small farmers with land from both sectors with predominance of the state. The measured indicators explained the higher variability of sheep breeding in the province, in each region, and at the municipal level, Chambas, for the northern region, Ciego de Ávila, for the central region, and Venezuela for the southern region, were the municipalities with superior social, productive, technological and environmental indicators.

Key words: characterization; sheep; classification and multivariate analysis

In America, sheep production is generally carried out under an extensive production system, on grazing. This type of system means an advantage in the economy of farmers, since it represents savings in production costs and self-subsistence. They are known as family production systems (Palacios and Barrientos 2014).

The need to characterize production systems in contexts of rural poverty in Latin America is due to the great diversity of biophysical and socioeconomic conditions, since characterization is essential to design biodiverse, resilient and socially just strategies (Altieri and Nicholls 2013). Numerous studies to characterize sheep production systems have been carried out in the world: that of the high tropics in Colombia (Moreno and Grajales 2017), on the southern coast of Peru (Salamanca et al. 2018) and in Ethiopia Kenfo et al. (2017) and Hailu et al. (2020). In Cuba, Herrera and Carmenate (2019) typified and carried out a geospatial analysis of sheep production in Las Tunas municipality and in Ciego de Ávila (Borroto et al. 2011). These authors determined socioeconomic and technological factors of sheep breeding, only in 30 private farms in three municipalities of the province.

Achieving an increase in the production of food from animal origin is a matter of highest priority in Cuba. This situation makes it necessary to diversify agricultural production and offer meat from different animal species. The sheep is promising, it has a high demand in the Cuban population and it is also adequate to promote low-investment production systems. That is why the objective of this study was to determine the typologies in sheep production systems in Ciego de Ávila province.

Materials and Methods

Characterization of the research area. The study was carried out in Ciego de Ávila province, located in the central region of Cuba, with a surface extension of 6971.64 km2 and a mainland area of 6194.90 km2. The main economic activities are agricultural, livestock, forestry and tourist.

Diagnosis and survey. A survey with 40 variables was used: 17 quantitative and 23 qualitative, to evaluate the performance in the technological, environmental and social dimensions of the sheep production systems (SPS) in the province, in an exploratory-descriptive research. The interview and scientific observation were used in a non-experimental, cross-sectional design. The interview was applied from a semi-structured guide and scientific observation, with the purpose of describing variables and analyzing their incidence and interrelation at a given moment.

Sample size. The sample amounts to 296 total farmers. Work was carried out in the state sector with 100 % of the units reported as having sheep production systems in the Centro Nacional de Control Pecuario (CENCOP) from Ciego de Ávila, which were a total of 74. In the private sector, it was worked with 100 % of the population of contracted farmers, according to the information available in the Empresa de Ganado Menor (EGAME), which is a Unidad Empresarial de Base (UEB), which reports 107 farmers, for a total of 181, between state and private, as an initial sample. Other farmers (private) not contracted by the entity (115), recommended by the interviewees, were added to this, for which a total of 296 was reached. Of these, 74 state farmers and 222 private.

Creation of the data matrix. The information obtained in the interviews was tabulated in data matrices organized in Excel spreadsheets, where the visited livestock systems were placed in the rows, and the variables under study in the columns.

Classification of sheep production systems in the province. A main component analysis was performed on the categorical variables, which allowed them to be transformed into four numerical variables (Torres et al. 2021). Subsequently, the statistical model of impact measuring (SMIM) (Torres et al. 2013) was applied to the numerical and categorical ensemble transformations. The assumptions described by Torres et al. (2008) were checked.

The Varimax Normalization rotation method was used and the MCs that had their eigenvalue higher than 1, and the variables with weight or preponderance factors, higher than or equal to 0.5, positive or negative, were selected. The impact index was obtained for each MC in each farm, which depends on the variables with the highest preponderance and allows interpreting the performance or level of the variables of each MC, in each study case. The hierarchical cluster method was applied to determine the groups of farms from the MC impact indices. The groups formed by their means and standard deviations in the quantitative variables were described, and in the qualitative ones, the frequencies by groups of farms. The information processing was performed using the statistical program IBM-SPSS (2013).

Results and Discussion

For the analysis, the Bartlett sphericity test was performed, which was highly significant (P <0.01) and the KMO (Kaiser-Meyer-Olkin) statistic, with a value of 0.51. This shows that the data fulfill the assumptions for a main component analysis.

The degree of structural dependence of the data, from the rotated factorial solution, which is interpreted as the correlation between the selected components and the variables in the sheep production systems, is shown in table 1. A total of six MCs were obtained, which explained 71.83 % of the total variability of the data.

In MC 1, the variables related to the herd size saturate, which are the most important and explain 27.21 % of the variance. The MC 2 explained 14.45 % of the variance and relates land tenure and qualitative variable 1, which was represented by school level, moonlighting, type of sector, land tenure, contracting with EGAME, production objective, castration, records and classification, according to the type of grazing, use of forages and supplementation. The MC 3 revealed 9.78 % of the variability and relates management variables. The qualitative variable 2 was showed by training and facilities. The MC 4 is related to the social aspect: the age of farmers. The MP 5 with the owner sex. The qualitative variable 3 was represented by the degree of importance, the classification according to grazing hours and the type of grass. The MC 6, by qualitative variable 4, tree component associated with SPS (table 1).

Table 1 Matrix of main components and variables 

Variables Main components
1 2 3 4 5 6
Age 0.022 0.016 0.009 0.837 -0.027 -0.156
Years of experience 0.027 -0.190 -0.106 0.826 0.067 0.129
Total men -0.005 0.344 0.168 -0.035 -0.768 -0.003
Total hectares 0.102 0.890 0.053 -0.106 -0.045 0.085
Total hectares for sheep 0.099 0.865 -0.090 0.067 0.032 0.120
Total sheep 0.990 0.052 -0.032 0.005 -0.021 0.036
Total male offspring 0.848 0.094 -0.104 0.084 -0.076 0.028
Total young males 0.651 0.004 -0.160 -0.036 -0.006 0.036
Total breeding animal 0.808 0.030 0.154 0.028 0.027 0.023
Total female offspring 0.871 0.062 0.028 0.052 0.053 0.033
Total young females 0.707 0.129 0.031 -0.099 0.099 -0.064
Total breeders 0.953 0.009 -0.019 0.020 -0.065 0.059
Breeding animal change frecuency 0.017 0.245 0.703 -0.263 0.318 -0.142
Offspring per lambing 0.087 -0.221 0.265 -0.012 0.349 -0.053
Marketing age -0.021 0.104 0.710 0.278 -0.015 0.134
Qualitative variable 1 0.164 0.665 0.179 -0.400 -0.157 -0.207
Qualitative variable 2 0.123 0.177 -0.785 0.255 0.105 -0.011
Qualitative variable 3 -0.057 0.291 0.107 0.040 0.795 0.014
Qualitative variable 4 0.099 0.100 0.040 -0.026 -0.021 0.947
Eigenvalues 5.17 2.74 1.85 1.53 1.32 1.00
Explained variance, % 27.20 14.45 9.78 8.09 6.99 5.29
Cumulative variance, % 27.20 41.66 51.44 59.54 66.54 71.83

The hierarchical cluster analysis allowed the farms to be grouped into five groups and made it easier to know the patterns that describe similarities and differences between groups (table 2). Mohammed et al. (2017) classify the systems in four groups for indigenous sheep, according to phenotypic and genotypic aspects in Ethiopia. Hailu et al. (2020) used similar methods to phenotypically characterize sheep populations from the Tahta and Maichew district of Ethiopia. They base their classification on the agroecological zone, sex and age groups. While, Mestra et al. (2020), obtained two groups in the characterization, when considering the feeding systems of sheep in Córdoba department, Colombia.

Table 2 Quantitative indicators in the classification of sheep production systems in Ciego de Ávila province 

Variables Groups
I (58 cases) II (74 cases) III (77cases) IV (39 cases) V (48 cases)
Mean SD Mean SD Mean SD Mean SD Mean SD
Age, years 54.03 12.23 51.89 8.38 49.91 7.64 47.23 7.56 46.6 5.18
Years of experience, years 19.72 8.82 14.73 5.65 12.97 6.69 8.64 4.43 8.56 3.96
Women, n 0.36 0.55 0.47 0.55 0.55 0.74 0.79 0.8 0.83 0.81
Men, n 0.98 0.55 1.22 0.5 1.12 0.4 1.82 0.85 0.9 0.59
Total hectares, ha 5.24 5.83 0.28 0.97 6.33 4.95 9.63 7.53 5.26 5.69
Total hectares for sheep, ha 1.76 2.45 0.07 0.26 1.77 1.03 2.27 1.77 1.4 1.5
Total sheep, n 83.36 65.05 45.74 23.63 59.53 32.98 139.9 97.41 45.31 20.88
Total male offspring, n 7.74 6.91 3.62 3.49 5.57 4.27 13.77 12.11 3.88 3.33
Total young males, n 8.67 7.51 5.3 3.96 7.14 8.57 13.31 13.41 4.96 5.09
Total breeding animal, n 1.93 1.23 1.3 0.68 1.33 0.77 2.87 1.94 1.44 0.82
Total female offspring, n 9.17 8.78 3.86 3.56 6.5 4.79 15.9 14.43 5.5 3.8
Total young females, n 9.79 8.05 5.69 4.6 8.18 6.99 18.9 24.33 6.42 7.17
Total breeders, n 46.05 37.21 26.15 13.86 31.12 17.65 75.15 52.84 23.44 12.07
Breeding animal change frecuency, years 0.43 1.16 0.18 0.69 0.29 0.91 1.49 1.65 3.04 1.13
Lambing /year 1.05 0.22 1.01 0.12 1 0 1 0 1.02 0.14
Offspring/lambing 1.38 0.56 1.28 0.56 1.11 0.31 1.18 0.39 1.63 0.67
Marketing age 10.97 2.88 10.65 2.12 9.97 1.41 11.05 1.43 11.9 1.15

Group I, identified as medium farmers with land from the private sector, included 19.6 % of the total cases of the study sample. It is represented in the ten municipalities of the province and of 100 % of the farmers that make up this group, 22.4 % are located in the north, 44.8 % in the center and 32.8 % in the south. These SPS had a mean of 83.36 total sheep in the herd. As a whole, the farmers of this group belong to the private sector: 94.8 % have land with a mean of 5.24 ha and, of this total, only 1.76 ha dedicated to SPS. These systems develop continuous grazing, in 75.9 % and 89.7 % of the extensive type, while 8.6 % practice semi-transhumant grazing, in areas outside the farm.

All the farmers from group I (table 3) contract their sales with EGAME and 86.2 % declared sales to this enterprise as their production objectives. The 50 % of the farmers do not integrate trees in their system and 50 do it, it refers to grazing areas, where 100 % is made up of natural grasses. Of the farmers in this group, 100 % do not develop forage areas and 94.8 % do not supply any supplementation.

Table 3 Frequencies of the main qualitative indicators in the classification of sheep production systems in Ciego de Ávila province 

Variables Category Group I n=58 Group II n=74 Group III n=77 Group IV n=39 Group V n=48
n % n % n % n % n %
Sector Private 58 100 74 100 54 70.1 13 33.3 23 47.9
State - - - - 23 29.9 26 66.7 25 52.1
Training Yes 16 27.6 39 52.7 31 40.3 31 79.5 37 77.1
No 42 72.4 35 47.3 46 59.7 8 20.5 11 22.9
Production objective Sale to EGAME 50 86.2 50 67.7 - - 5 12.8 11 22.9
Self- cunsumption 7 12.1 15 20.3 40 51.9 34 87.2 30 62.5
Sale to others 1 1.7 9 12.2 37 48.1 - - 7 14.6
Trees In grazing areas 29 50.0 3 4.1 3 3.9 7 17.9 23 47.9
In living fences - - - - - - 4 10.3 - -
Protein banks - - - - - - 7 17.9 3 6.3
Integration to forestry - - - - 3 3.9 1 2.6 2 4.2
Do no have 29 50.0 71 95.9 71 92.2 20 51.3 20 41.7
Classification according to grazing hours Continuos 58 100 74 100 74 96.1 31 79.5 48 100.0
Semi-stabled - - - - 3 3.9 8 20.5 - -
Classification according to type of grazing Extensive 52 89.7 4 5.4 75 97.4 37 94.9 35 72.9
Rotational - - - - - - 1 2.6 - -
Semitrashumance 5 8.6 70 94.6 - - 1 2.6 13 27.1
Integrated into crops 1 1.7 - - - - - - - -
Agroforestry systems - - - - 2 2.6 - - - -
Grasses type Natural 58 100.0 74 100 77 100.0 22 56.4 48 100.0
Improved or cultivated - - - - - - 17 43.6 - -
Forages Yes - - 1 1.4 1 1.3 25 64.1 11 22.9
No 58 100.0 73 98.6 76 98.7 14 35.9 37 77.1

Group II corresponds to small farmers with no tendency to land from the private sector, with 25 % of the total cases (table 2 and 3). They are represented in all municipalities except Bolivia. Of the 100 % of this group, 21.6 % are located in the north, 56.8 % in the center and 21.6 % in the south. This group had a mean of 45.74 total heads. The 94.6 % do not have land, so their grazing systems are continuous, but semi-transhumance type, in natural grasses areas. They do not have trees in 95.9 %, and do not develop forage areas or supplement, mostly, with products. Only 67.7 % contract their productions with EGAME. As an objective of their production, they declare 20.3 % as self-consumption, and 12.2 % as sale.

Group III, named as small farmers with land from both sectors, with a predominance of the private, included 26 % of the total farmers, similar to 2. It is only not present in Bolivia municipality. Of 100 % of those that make it up, 7.8 % are in the north zone, 33.8 % in the center, and 58.4 % in the south. It has a mean of 59.53 total sheep in the herd. In its totality, it has lands, with a mean of 6.33 ha, and of these 1.77 ha are dedicate to sheep. None have a contract with EGAME, and the objective of their production is self-consumption and other destinations. The systems of this group develop continuous extensive grazing, confined to paddocks in 97.4 %. The rest integrate grazing into forest areas. The grasses are natural and a small number is represented by state enterprises. The supplementation is with crop wastes, vitamins and protein and 98.7 % do not have forage areas.

Group IV represents the large farmers with land in both sectors, with 13.2 % of the total cases under study. It is represented in all the municipalities, except in Primero de Enero. Of 100 % of those who make up the group, 17.9 % are in the north, 33.3 % in the central and 48.7 % in the south. It has a mean of 139.9 total sheep. The 94.9 % have land, with a mean of 9.63 ha, and of these they dedicate 2.27 ha to sheep. It belongs to the state sector, 66.7 % and they do not contract their productions with EGAME, since their objective is self-consumption. Of the total of farmers, 51.3 % do not have trees, while the rest integrate variants of silvopastoral systems: 2.6 % to forestry, 10.3 % to live fences, 17.9 % to protein banks and 17.9 % to trees in grazing areas. As a distinctive characteristic, it refers to improved grasses in 43.6 % of the cases, and 64.1 % establishes forage areas. They are also distinguished by the supplementation with protein plants and crop wastes.

Group V corresponds to small farmers with land from both sectors, with a predominance of the state, and 16.2 % of the total farmers. It is not in the municipalities of Primero de Enero, Ciro Redondo, Majagua and Baraguá. Of 100 % of SPS that comprise it, 77.1 % are located in the north area, 20.8 % in the center and 2.1% in the south. It has a mean of 45.31 total heads of sheep, which places them as small farmers. It has 68.8 % land, with a mean of 5.26 ha, dedicated to sheep only 1.4 ha. Does not contract his productions with EGAME 77.1 %. Continuous, extensive grazing, confined to paddocks, predominates, and 27.1 % is semi-transhumant.

All the grasses are natural. It presents 22.9 % of forage areas, represented by state enterprises. Also 47.9 % integrate trees in grazing areas. In addition, it develops other variants of silvopastoral systems, 4.2 % in integration with forestry and 6.3 % in protein banks. The farmers from the state sector supply protein supplements in 4.2 % and mineral vitamins in 10.4 %. Also 31.3 % integrate agriculture with by-products and crop wastes. Meanwhile, 54.2 %, represented by private sector farmers, do not supplement the herds.

The variable total number of animals in the herd is taken into account to classify the groups. Herrera and Carmenate (2019) group into four typologies and describe three herd sizes: 18, 48 and 73 animals, for Las Tunas municipality, in Cuba. Salamanca et al. (2018), in the characterization of sheep farmers and their production systems on the southern coast of Peru, describe herds of 100 to 300 sheep, with an average of 189 per breeder, being the herd size similar to those studied by Marín- Bernal and Navarro-Ríos (2014) in Segureño sheep from Spain, and small herds of less than 20 sheep, with an average of 13.2 per breeder, while for the Hidalgo state in Mexico, Vieyra et al. (2020) state that there is a variety in the herd size marked by the municipality, and that it ranges from averages of 12.1 to 43.6. Dagnew et al. (2017) describe small and larger scale farmers, with a mean of 17.25 and 90.63 sheep, respectively.

Depending on the production objective, sales to EGAME are carried out with higher weight by groups I and II. However, self-consumption and sale to others is in all the studied typologies. Groups III, IV and V highlighted for their high sales levels. In previous studies, Borroto et al. (2011) refer as advantages of breeding in the province the economic aspects and the possibility of using their productions for family self-consumption. In addition to this criterion, Vieyra et al. (2020), who identified which market is aimed at the transformation of meat into barbecue (79.6 %) and the remaining part is bought by intermediaries (20.4 %) in Hidalgo State.

According to the type of grazing, only in the northern region, there was a rotational grazing system, in Chambas municipality. Those integrated into crops were only in the southern region, in Venezuela municipality. The agro forestry was in the central region, in Ciego de Ávila, and in the south, in the Venezuela and Majagua municipalities. These systems in the analysis by regions show low percentages of presentation. However, semi-transhumance and extensive systems were in the three regions, with a homogeneous distribution. The farmers without land, or with little availability of land, develop semi-transhumance systems, in 30.1 %. Even during the dry season, most farmers practice semi-transhumance, conditions similar to those described by Montesinos et al. (2015), Camacho (2020) and Vieyra et al. (2020). In Ethiopia, Welday et al. (2019) characterized sheep production systems, and understood the restriction of land for grazing as a productive limitation, which allows them to have herds between 10.7 and 17.7 sheep.

In 94.3 % of the cases of the total sample, the grasses were natural, while 5.7 % had improved grasses. The latter were related to systems considered important, and very important, which also coincides with the state sector and land tenure. They are represented in the southern zone, in Venezuela and Baraguá municipalities, and in the central zone, in Ciego de Ávila.

The typologies obtained in this study (table 2) differ from those found by other authors, such as Toro-Mujica et al. (2015), who obtained four groups of sheep production systems in Spain: I) mixed systems with cereals and olive trees, II) subsistence, III) extensive commercial systems with low stocking and forest areas, IV) mixed sheep-pig systems and little area for cereals. Permanent grasses and forest areas are food for sheep. These differ from the sheep systems that are developed in the State of Mexico (Hernández et al. 2019), which are classified in small, medium and capitalized farmers, differentiated by the number of animals, with a mean of 16.2, 24.6 and 71.7, respectively. In this case, the capitalization is not reflected in productivity, since the three groups have a similar number of lambing and offspring mortality.

Other elements that differentiate sheep production systems refer to the type of grazing, depending on the hours. There are systems that transit between the semi-stabled and continuous extensive or with restricted grazing hours at noon or in a day session. Mestra et al. (2020), in Peru, refer that 42 % corresponds to continuous grazing and 27 % to rotational. Vieira et al. (2021), in Mexico, indicate restricted grazing, where the average hours were from 3.4 to 1 hour, as a minimum range, and as a maximum, 10 hours a day.

Training the breeder would allow the incorporation of new technologies, by changing the mentality of having sheep to being a breeder. Herrera and Carmenate (2019) identified poor training programs for breeders in Las Tunas municipality. Salamanca et al. (2018) verified that only the family father participates in the courses, something that must change, since women also take care of the animals, and play a decisive role in agriculture. Vieira et al. (2021) refer to the request for sheep culture courses by breeders in Mexico.

One of the main limitations presented by the study systems lies in the low levels of technical assistance, which influences on the application of knowledge and innovation processes, a characteristic that resembles sheep production systems in the high tropics in Colombia (Moreno and Grajales 2017).

The age of farmers averaged 50.27 years, close to that recorded by Marín-Bernal and Navarro-Ríos (2014) in Spanish breeders, also similar to that of Mexican breeders (Vieyra et al. 2020) and Peru (Salamanca et al. 2018). The average years of experience in sheep breeding was 26.78 years, similar to that described by Salamanca et al. (2018), and is higher to that reported by Rivas et al. (2014) for La Mancha, Spain, which was 15.6 years. In this study, both variables maintained a regular performance in the three regions. This means that in all the municipalities there are farmers with more years of experience, such as farmers recently incorporated into sheep breeding (table 2).

Of the total number of surveyed, male owners were represented by 90.2 %. This value is higher than that reported by Vieyra et al. (2020), who refer that 82, 65 and 53 % respectively, was represented by men. Of the total, 93.24 % take on salaried workers based on the sheep, similar performance that is described for Sierra Sur de España, in some Segureño sheep farms, which had permanent workers for grazing, as well as temporary or permanent, and both, for lambing (Marín-Bernal and Navarro-Ríos 2014). In contrast to the systems described by Dagnew et al. (2017) and Welday et al. (2019), who state that the main reason for farmers to keep sheep is to generate income for food purchase, in another order of importance, the replacement of the mass, meat production and celebrations.

In the systems of this study, the trees that were registered in the paddocks were of natural generation. The presence of trees explained 5.29 % of the variability of the SPS in the province. In groups I, IV and V (table 3), it was possible to see that approximately 50 % of the farmers had some variant of trees in their systems. On the contrary, in groups II and III, 95.9 and 92.2 % of the farmers, respectively, did not have this resource in their farms. The previous explains that in these sheep production systems, farmers focus their efforts on maintaining the herd, based on the practice of extensive and semi-transhumant grazing, mainly, without taking advantage of the benefits of trees as providers of forage and shade. The characteristics of these systems differ from the proposals of silvopastoral systems (Vieira et al. 2021) and systems integrated with trees (Cocos nucifera) (Lins et al. 2021), with favorable effects on behavior and reduction of heat stress in sheep. Other authors (López-Vigoa et al. 2017) highlight that the introduction of trees in the systems is a way to transform the microclimate in the production of ruminants and guarantee animal welfare, since they regulate the solar radiation that directly affects the grazing animals and favors thermal well-being (Sousa et al. 2015).

From a practical point of view, it was important to carry out this study in sheep production systems at municipal level, where the calculation of the factorial index strengthens the results of the multivariate analysis and contributes to interpreting the level or performance of the studied indicators, which can serve as a basis for the design of a productive improvement strategy and extend this study methodology to other provinces and other productive forms.

It is concluded that the SPS in Ciego de Ávila were classified into five groups: I) medium farmers with land from the private sector, II) small farmers with no tendency to land from the private sector, III) small farmers with land from both sectors with a predominance of the private sector, IV) large farmers with land from both sectors and V) small farmers with land from both sectors with predominance of the state.

The indicators of the analysis explained 71.83 % of the variability in five main components. Component 1 was related to herd movement, 2 to land tenure, 3 to training and management, 4 to the age and farmers experience. Likewise, component 5 included the number of workers and technological aspects and component 6 was related to the presence of trees.

The typologies with the greatest effect on sheep breeding were those from groups III and IV. A homogeneous representation of the five groups was evidenced in the three regions, and in each of the municipalities, with the exception of groups II and III in Bolivia municipality, IV and V in Primero de Enero municipality, and group V on Primero de Enero, Ciro Redondo, Majagua and Baraguá. It was determined that the municipalities with superior social, productive, technological and environmental indicators were: Chambas, for the northern region, Ciego de Ávila, for the central, and Venezuela for the southern.

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Received: December 13, 2021; Accepted: January 10, 2022

*Email: jorgeorlayst@gmail.com

Conflicts of interest: The authors declare that there is no conflict of interest between them.

Authors contribution: J. O. Serrano: Conceptualization, Investigation, Formal analysis, Writing – original draft. J. Martínez-Melo: Conceptualization, Formal analysis, Writing – original draft. Verena Torres: Formal analysis, Writing – original draft. A. Villares: Conceptualization, Investigation, Formal analysis. F.D. Manuel: Investigation, Formal analysis. N. Fonseca: Investigation, Formal analysis. J.C. Lorenzo: Conceptualization, Investigation, Formal analysis.

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