SciELO - Scientific Electronic Library Online

 
vol.7 número1Estudio morfométrico de los epidídimos durante el desarrollo postnatal de corderos Barbados BlackbellyPropiedades de crecimiento de las líneas celulares DH82 y RF/6A bajo condiciones normales de laboratorio índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados

Revista

Articulo

Indicadores

Links relacionados

  • No hay artículos similaresSimilares en SciELO

Compartir


Revista mexicana de ciencias pecuarias

versión On-line ISSN 2448-6698versión impresa ISSN 2007-1124

Rev. mex. de cienc. pecuarias vol.7 no.1 Mérida ene./mar. 2016

 

Articles

Typology of dual-purpose cattle production farms in Sinaloa, Mexico

Venancio Cuevas Reyesa  * 

Alfredo Loaiza Mezab 

José Antonio Espinosa Garcíac 

Alejandra Vélez Izquierdoc 

María Denisse Montoya Floresc 

a Programa de Socioeconomía. Campo Experimental Valle de México-INIFAP. México.

b Campo Experimental Valle de Culiacán. INIFAP. México.

c CENID Fisiología. INIFAP. México.

Abstract:

The aim of this study was to conduct a typology of dual purpose cattle farms in the state of Sinaloa, using social, economic and technological variables. Information of 1,165 system producing dual purpose of Sinaloa who participated in the support program SAGARPA 2010 to 2011 were analyzed. Through the use of principal component analysis, cluster analysis and variance analysis were identified and characterized four types of farms; small farms (67 %), medium farms (24 %), large livestock farms (7 %), and large farms with business potential (2 %). The typology obtained can be useful for generating differentiated public policies that increase the use of technological innovations to obtain greater efficiency and productivity of dual cattle purpose in Sinaloa.

Keywords: Differentiated policies; Principal components; Cluster analysis; Innovations

Introduction

Milk production in Mexico is carried out in all States and under different production systems. Four predominant systems have been identified: skilled, semi-skilled, dual-purpose and family run farms, contributing 50.6, 21.3, 18.3 and 9.8 % respectively1. These systems are associated with agro-ecological regions; intensive system, prevalent in the arid and semi-arid zone, are family-based and semi-specialized systems that are predominantly found in temperate zones and are dual-purpose in the tropical regions2, they are characterized by production units aimed at producing and selling milk or animals for artisanal cheese or animals for slaughter, as weaned calves and as worn-out cows3.

One of the representative states with dual-purpose cattle systems is Sinaloa. The state contributed 1.2 % on average to the national production of bovine milk during the period 1980-2013; however, in 2013 this contribution was only 0.85 %4. A situation that reflects that the pace of growth in the state is lower than the national average, despite the potential of production in tropical regions, because of the low levels of productivity and profitability, as well as low levels of technology use5,6,7. Therefore, it becomes important to identify the different strata and characteristics of the producers of these production systems, in order to generate greater impact in the use of innovations through differentiated strategies tailored to the type and level of resources available in the dual-purpose cattle farms.

A classification or typology serves to establish categories of actors, so that they can analyze the characteristics of each one and identify specific solutions to problems that are also specific8. Thus, the typology of producers relates to the identification and development of various groups or strata, which are obtained through the selection of variables representing the observed reality. Hence, the classification provides some notion that summarizes a variety of characteristics, conditions, events or individuals that or who share some obvious or evident and identifiable characteristic that will allow for a differentiated model9.

The use of multivariate methods for classification and identification of production units in the agricultural field has been recurring; several authors10-14 have used multivariate analysis techniques to identify homogeneous groups in farms.

In Mexico, there are studies that have classified and typified domestic agriculture. These have been conducted at a disaggregated level based on census data15,16. Several authors have used multivariate analysis to identify factors that limit the development of the family system of milk production17, to characterize sheep production farms18, to analyze and characterize sheep production associated with rainfed agriculture19 and to classify dairy production systems20, among others.

In Sinaloa there is no information of this nature; therefore the objective of this study was to conduct a typology of dual-purpose cattle production farms in the state of Sinaloa, using social, economic and technological variables.

Material and methods

Study area

The study was conducted in the state of Sinaloa, in a region located in the northwestern of Mexico at 27° 07' and 22° 20' N, and 105° 22' and 109° 30' W. About 48 % of the state has a warm humid climate, while 40 % is dry and semidry, 10 % is very dry, and the remaining 2 % is temperate sub-humid. The average annual temperature is 25 °C, with minimum of 10.5 °C in January and maximum may be higher at 36 °C for May to July21.

Data and variables

Information of 1,165 dual-purpose cattle production systems (DCPS) that participated in the 2010-2011 SAGARPA funded program was analyzed22. The Sinaloa program began in July 2010 and ended in March 2011. During this period an online baseline survey of the production units (PU) participating in the program was carried out. The survey was structured into eight sections: general information of the producer group, general information about the PU, social and economic producer aspects, characteristics of the PU, inventories (livestock, land, plant, machinery and equipment), management practices and technological components, marketing and investment. With this information, a database was constructed in Excel.

To classify producers a great number of variables and methods can be used8,9. The selection of variables for this study was carried out based on the identification of those factors that have been important in the characterization and typology of livestock production12,23, and based on the analysis of technological indicators on the use and adoption of innovations and practices in PU24,25. The technological indices were obtained following the procedure of authors who have worked on this issue of DCPS in Mexico26, in this manner the following indices were considered as analysis variables: general management, genetic improvement, reproductive management, feeding forages, concentrate feeding, health, milking management, facilities and finally use rate of machinery and equipment. The indices obtained reflect the proportion of adoption and implementation of technology innovations in each of the production units; this was based on the frequency of use for the year the information was captured.

Subsequently, variables that have been considered in other studies of stratification of producers were selected, these were social variables10,19 such as: age, number dependent children and dependent adults, economic variables such as size of the herd17, number of hectares devoted to livestock14 and number of adult cows12,23.

Statistical analysis

The selection of variables was performed using descriptive statistics and correlation analysis13. The social and economic variables along with variables related to technological aspects were applied to a correlation analysis to select those with greater representation in the DCPS. Variables that were significantly correlated (P<0.05) were identified, thus reducing from 15 variables to just 5, which grouped representative characteristics of the study population such as age of the producer, number of animal units, machinery and equipment index, general management index and a forage feed index.

Multivariate analysis includes a set of methods and techniques that enables studying a block of a set of variables that are measured or observed in a population of individuals8,9,27. The selected variables were analyzed with the following methods of multivariate analysis: principal component analysis (PCA) and cluster analysis for classifying and further characterization of the PU11,13,14. Principal component analysis allowed to reduce the variables that identify producer groups and generate new variables (factors or components). Cluster analysis, meanwhile, was used to perform a grouping between production units with similar characteristics. To obtain conglomerates Ward’s method and Euclidean distance squared was applied17,20.

Finally, the analysis and description of the groups was performed using analysis of variance to compare means. Statistical tests were performed using SPSS27 and V16 Matlab statistical package.

Results and discussion

Identify relevant factors and types of producers

The principal component analysis28 allowed explanatory factors in order to extract quantitative variables. The factors obtained were named as: social dimension (component 4), availability of resources (component 2), infrastructure and management (component 1) and livestock feed (component 3), which account for 86.8 % of the original variation (Table 1).

Table 1 Rotated components matrix of cattle production farms*. 

* Extraction method: Principal component analysis. Rotation method: Varimax with Kaiser Normalization. The rotation converged in 5 iterations.

InventaryAU= herd; IndMaqyEq= machinery and equipment; IndMGral= general management; IndAliForr=feeding forages.

The Kaiser-Meyer-Olkin (KMO) statistic obtained presented a value of 0.568, indicates a good fit in sampling adequacy for factor analysis. For the Barlett test of sphericity29) a value of 0.000 was obtained, thus the null hypothesis can be rejected and the variables can be considered by a suitable factor analysis. Cluster analysis of the factors obtained clearly identified four clusters or groups of farms (Figure 1).

Figure 1 Dendogram classification of production cattle farms in Sinaloa. 

Cluster 1 consists of 778 PU, and was defined as small cattle farms (SCF). They represent 67 % of the production farms, it is the most representative and among its relevant group characteristics is that they have an average age of 51.5 ± 14.4 yr. They have 21.4 ± 10.1 units of animals and 26.9 ± 16.0 ha are dedicated to livestock production. It is the largest group in the state and has low levels of innovation, infrastructure and management.

Cluster 2 groups consisted of 83 PU, and these were defined as large cattle farms (LCF). The represented just 7 %, which is a relatively small but stands out for having more than 115 animals units (115.4 ± 19.8); in scale, they use a lot of agricultural land, more than 100 ha, the age of the producers is 50.4 ± 12.8 yr.

Cluster 3 was comprised of 285 PU, and was defined as medium-sized cattle farms (MCF). They represent 24 % of farms with an average age of 57.2 ± 11.8, with 55.0 ± 13.9 animal units and have 38.5 ± 24.2 ha dedicated to livestock production. In conjunction with the SCF group they represent 91 % of PU.

The small and medium farms identified in this study is consistent with the results of the study on typology of producers for southern Sinaloa5; these authors point out that the size of the PU typically in the region is 20 ha and 80 % of producers have a herd of cattle of between 21 and 42 heads.

Finally, the cluster 4 consisted of 19 farms and represented 2 %. They were defined as large cattle farms with business potential (LCFBP) due to the level of resources that they have (242.5 ± 76.5 animal units with 117.5 ± 91.1 ha dedicated to cattle ranching). They are producers that stand out for the scale of the production units and younger age of less than 50 yr (47.3 ± 14.2).

Once the types of producer identified, they were characterized according to the identified components, because as reported by some authors, identifying the characteristics that determine the heterogeneity within production systems is the development starting point14.

Social dimension

The number of dependents over 18 yr (close to two dependents) has similarities. The average age of producers fluctuated between 47.3 and 57.2 yr. It is worth noting that significant differences (P<0.05) between SCF and LCFBP for the variable age (51.5 ± 14.4 vs 47.3 ± 14.2 yr) and the variable number of dependent children (one minor in comparison to two members being minors for LCFBP) (Table 2).

Table 2 Social characteristics of the types of producers in Sinaloa. 

SCF= Small catlle farms; MCF= Medium catlle farms; LCF= Large cattle farms; LCFBP= Large cattle farms with business potential.

abc Dissimilar letter in the same row indicate difference (P<0.05).

The results contrast with those reported by other authors30 who found no significant differences in the age of the producer in dual-purpose farms in Venezuela, while another study7 in Sinaloa found that the age variable also showed significant differences.

In contrast, other studies found that the age of the producer has significance (P<0.05) in terms of adopting technological innovations25. In this sense, the fact that the producers of the LPFBP are under 50 might influence their receptivity to proposals of technological change, training and use of innovations.

Availability of resources

The variables of number of animal units and area devoted to raising livestock farms was significant (P<0.05), indicating that these variables may be important for the classification of types of producers in dual-purpose production cattle systems; in other words the level of financial resources (herd size and agricultural area) is decisive in differentiating producers of this production system (Table 3).

Table 3 Availability of resources by type of livestock farms. 

SCF= Small catlle farms; MCF= Medium catlle farms; LCF= Large cattle farms; LCFBP= Large cattle farms with business potential.

abc Dissimilar letter in the same row indicate significant difference (P<0.05).

The selection of the total herd size as a variable differentiate producer groups is consistent with a study on the family system of milk production in Michoacan17, since the total number of animals in the herd and cows in production were the most important variables leading to the formation of clusters of these systems studied by these authors.

LCFBP were defined as large farms with business potential due to the large amount of resources that they have for production; a herd average of 242.5 ± 76.5 with an agricultural area devoted to livestock production of 174.3 ± 246.6. That is, they have a high potential to have closer links with the market and improve their competitiveness through potential increases in production of calves and milk, given its level of resources; improving the competitiveness of milk production systems in tropical Latin America regardless of the location of the farms is directly related to the size of the herd31.

Infrastructure and management

The indices related to infrastructure of livestock farms show differences (P<0.05) between the LCFBP compared with SCF. Small farms have a rate of 0.09 ± 0.09 facilities compared to farms with business potential (0.18 ± 0.22). This same behavior occurs in the index of machinery and equipment, general management and health. The indices related to genetic management, reproductive management and handling of milking did not show significant difference (Table 4).

Table 4 Use of infrastructure and management of livestock by type of farm. 

SCF= Small catlle farms; MCF= Medium catlle farms; LCF= Large cattle farms; LCFBP= Large cattle farms with business potential.

abc Dissimilar letter in the same row indicate difference (P<0.05).

There are many studies examining the use of technological components or factors that limit the application of innovations in livestock production systems32-34. In the present study, we identified that the use of technological innovations is low and very similar between producers (innovations related to food and use fodder less than 16 %, using concentrate was less than 11 %, milk management practices was between 22 and 11 %, innovations related to reproductive management was less than 6 %; Tables 4, 5). The differences in terms of infrastructure correspond to the size of the farms than to differentiation by level of innovation adopted. In this regard, the Agricultural Census VIII, Livestock and Forestry35 indicates that there are 27,022 production units farming in Sinaloa, of which 22,535 use some form of technology. The technology component that is applied to a greater extent is vaccination (1.8 %), followed by ticks baths (1.7 %), deworming (46.2 %), use of mineral salts (0.8 %) and purchase of balanced diets (0.5 %). This shows enormous potential for the promotion and implementation of technological innovations related to aspects of nutrition, reproductive handling, health and milking in these farms with these systems of production of meat and milk in Sinaloa.

Table 5 Indices of feeding management by type of farm. 

SCF= Small catlle farms; MCF= Medium catlle farms; LCF= Large cattle farms; LCFBP= Large cattle farms with business potential.

abc Dissimilar letter in the same row indicate significant difference (P<0.05).

Livestock feed

The variables analyzed regarding the nutritional management showed significant differences (P<0.05) with LCP compared to LCPBP in terms of use of feed, large farms have a rate of 0.16 ± 0.09 compared to 0.11 ± 0.09 of LCFBP; one possible explanation is that the later have large areas of rangeland where to send their cattle to graze. It is worth noting that in this aspect it was found that the use of concentrate showed no significant differences (Table 5).

The main problems facing dual-purpose cattle producers in Sinaloa are lack of fodder during the dry season, livestock malnutrition, degradation (erosion and compaction) of agricultural land and rangeland, and low efficiency of rainwater5. In this sense, the results show that the use of technological innovations to solve this problem is limited for all the farms, hence it is necessary to generate mechanisms and strategies for dissemination and technology transfer for producers to know related innovations in the use and conservation of fodder.

The results obtained allow authorities, researchers, professional service providers and decision makers to know the different types and characteristics that differentiate livestock producers in the dual-purpose cattle production systems of Sinaloa. This can positively impact the definition and development of public policies targeted by type of producer through: planning and implementation of programs for technology transfer, investment and reorientation of state and federal support, defining actions and lines of research, implementation of outreach and training that is “tailored to fit”, i.e., with differentiated policies for each group of producers (individual or organized).

It is recommended that state institutions of development and support establish development strategies for livestock depending on the different types and characteristics of producers. In this sense, the generation of technological packages by type of producer can enable greater efficiency of the involved resources; it is clear that there is a common problem in this production system, feeding during the dry season. However, to achieve a greater impact it is advisable that intervention strategies be differentiated by type of producer, which have the potential to develop and adopt technological innovations. This can allow for targeting and design strategies that are specific to the type of technology transfer based on the level of resources the livestock rancher has, as well as his problems and requirements, and can enhance regional development. For example, the inclusion of strata with business potential with strategies of territorial development through integration with other links in the beef production chain can be used for generating poles of regional development through the creation of a cluster for production of feeder calves, a product in which Mexico has a deficit. In summary, the results obtained can be used to improve dual-purpose cattle production systems in Sinaloa, as well as in other states and developing countries with similar conditions. The use of typology for producers in the agricultural sector is a necessary tool for defining public policies that consider the various actors in the rural territory.

Conclusions and implications

Using multivariate analysis techniques clearly identified four types of producers in the cattle production system dual-purpose: small farms (67 %), medium-sized farms (24 %), large livestock farms (7 %), and livestock farms with great business potential (2 %). The variables that were relevant to its stratification were, herd size (measured in number of animal units), the area devoted to livestock, the rate of general management and rate of use of forages in animal feed. The typology of the farms can be useful for decision-making and differentiated support strategies and contribute to the definition of public policies. It was observed that there is a low level of use and adoption of technology, so there is great potential work in promoting extension models that consider the resources and characteristics of each type of producer to increase the use and adoption of technological innovations that affect and improve welfare of the families that make up the dual-purpose cattle farms in Sinaloa.

Acknowledgments

Thanks to the Specialized Technical Unit for Livestock at INIFAP in Sinaloa for the information provided during the 2010-2011 evaluation cycle of the Funding Program, it served as the basis for this study. As well as the project: Adoption and impact assessment of the technology implemented in dual-purpose cattle systems in Mexico. INIFAP fiscal funds, project number 21541832011.

REFERENCES

1. Villamar AL, Olivera CE. Situación actual y perspectiva de la producción de leche de bovino en México 2005. Coordinación General de Ganadería SAGARPA. México, DF. 2005. [ Links ]

2. Núñez HG, Díaz AE, Espinosa GJA, Ortega RL, Hernández AL, Vera AH, et al. Producción de leche de bovino en el sistema intensivo. Libro Técnico Núm. 23. Veracruz, México: INIFAP. CIRGOC. 2009:373. [ Links ]

3. Urdaneta F, Dios-Palomares R, Cañas JA. Estudio comparativo de la eficiencia técnica de sistemas ganaderos de doble propósito en las zonas agroeconómicas de los municipios zulianos de la Cuenca del Lago de Maracaibo, Venezuela. Rev Cientif FCV-LUZ 2013;23(3):211-219. [ Links ]

4. SIAP. Servicio de Información Agroalimentaria y Pesquera. Cierre de la producción pecuaria por Estado 2014. Secretaria de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación 2014. http://www.siap.gob.mx/ganaderiaproduccion-anual . Consultado 3 Mar, 2015. [ Links ]

5. Perales RMA, Fregoso TLE, Martínez ACO, Cuevas RV, Loaiza MA, Reyes JJE, et al. Evaluación del sistema agrosilvopastoril del sur de Sinaloa. Sustentabilidad y sistemas campesinos: cinco experiencias de evaluación en el México rural. Masera O, López RL editores. México: Edit. Mundiprensa; 2000. [ Links ]

6. FAO. Food and Agriculture Organization of the United Nations. Ayudando a desarrollar una ganadería sustentable en Latinoamérica y el caribe: lecciones a partir de casos exitosos. Oficina Regional para América Latina y el Caribe: Santiago de Chile. 2008:91. [ Links ]

7. Cuevas RV, Baca MJ, Cervantes EF, Espinosa GJA, Aguilar AJ, Loaiza MA. Factores que determinan el uso de innovaciones tecnológicas en la ganadería de doble propósito en Sinaloa. Rev Mex Cienc Pecu 2013;4(1):31-46. [ Links ]

8. Herrera D. Metodología para la elaboración de tipología de actores. IICA. 1998. http://orton.catie.ac.cr/repdoc/A7950E/A7950E.PDF . Consultado 15 Nov, 2013. [ Links ]

9. López RP. La construcción de tipologías: metodología de análisis. Universidad Autónoma de Barcelona. España 1996. http://ddd.uab.cat/pub/papers/02102862n48/02102862n48p9.pdf . Consultado 8 Dic, 2013. [ Links ]

10. Siegmund-Schultze M, Rischkowsky B. Relating household characteristics to urban sheep keeping in West Africa. Agric Syst 2001;67(3):139-152. [ Links ]

11. Castel JM, Mena Y, Delgado-Pertínez M, Camúñez J, Basulto J, Caravaca F, et al. Characterization of semi-extensive goat production systems in southern Spain. Small Ruminant Res 2003;47(2):133-143. [ Links ]

12. Da Silva A, Escobar MD, Colmenares O, Martínez C. Aplicación de métodos multivariados en la clasificación de unidades de producción con vacunos de doble propósito en el Norte del estado de Carabobo, Venezuela. Rev Cient FCV-LUZ 2003;13(6):471-479. [ Links ]

13. Köbrich C, Rehman T, Khan M. Typification of farming systems for constructing representative farm models: two illustrations of the application of multivariate analyses in Chile and Pakistan. Agric Syst 2003;(76):141-157. [ Links ]

14. Castaldo A, Acero R, Perea J, Martos J, Valerio D, Pamio J, García A. Tipología de los sistemas de producción de engorde bovino en la Pampa Argentina. Arch Zootec 2006;55(210):183-193. [ Links ]

15. CEPAL. Comisión Económica para América Latina y el Caribe. Economía campesina y agricultura empresarial: tipología de productores del agro mexicano. México. Edit. Siglo XXI; 1982. [ Links ]

16. Ovando RE. Tipificación de la agricultura en México: como parte de la referencia territorial de una política sectorial diferenciada. [Tesis maestría]. Tijuana, BC: Colegio de la Frontera Norte; 1998. [ Links ]

17. Sánchez GLG, Solorio RJL, Santos FJ. Factores limitativos al desarrollo del sistema familiar de producción de leche, en Michoacán, México. Cuad Des Rur 2006;5(60):133-146. [ Links ]

18. Vázquez MI, Zaragoza RJL, Bustamante GA, Calderón SF, Rojas AJ, Casiano VMA. Tipología de explotaciones ovinas en la sierra norte del estado de Puebla. Téc Pecu Méx 2009;47(4):357-369. [ Links ]

19. Galaviz RJR, Vargas LS, Zaragoza RJL, Bustamante GA, Ramírez BE, Guerrero RJD, Hernández ZS. Evaluación territorial de los sistemas de producción ovina en la región nor-poniente de Tlaxcala. Rev Mex Cien Pecu 2011;2(1):53-68. [ Links ]

20. Hernández MP, Estrada FJG, Avilés VF, Yong AG, López GF, Donají SMA, Castelán OOA. Tipificación de sistemas campesinos del Sur del Estado de México. Univ y Cienc 2013;29(1):19-31. [ Links ]

21. INEGI. Instituto Nacional de Estadística y Geografía. Perspectiva Estadística Sinaloa. México 2011. http://www.inegi.org.mx/est/contenidos/espanol/sistemas/perspectivas/perspectiva-sin.pdf . Consultado 12 Ene, 2015. [ Links ]

22. UTEP-INIFAP. Unidad Técnica Especializada Pecuaria-Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias. México 2011. http://www.utep.inifap.gob.mx . Consultado 17 jul, 2011. [ Links ]

23. Togo JPR, Usandivaras P, Castel JM, Mena Y. Análisis de la diversidad en los sistemas lecheros caprinos y evaluación de los parámetros productivos en la principal cuenca lechera de Argentina. Livest Res Rural Develop 2005;17(1):1-14. [ Links ]

24. Urdaneta F, Materan M, Peña ME, Casanova A. Tipificación tecnológica del sistema de producción con ganadería bovina de doble propósito (Bos Taurus x Bos indicus). Rev Cientif FCV-LUZ 2004;14(3):254-262. [ Links ]

25. Bernués A, Herrero M. Farm intensification and drivers of technology adoption in mixed dairy-crop systems in Santa Cruz, Bolivia. Span J Agric Res 2008;(2):279-293. [ Links ]

26. Montoya FMD, Espejel GA, Vélez IA, Granados ZL, Espinosa JJA. Caracterización de productores del sistema bovino de doble propósito por el uso de tecnologías en el estado de Chiapas, México. Congreso mundial de ganadería tropical. Tamaulipas, México. 2014. [ Links ]

27. Pérez LC. Técnicas estadísticas con SPSS. España: Ed Prentice Hall; 2001. [ Links ]

28. Aluja BT. El análisis de componentes principales, una aproximación al Data Mining. Barcelona, España: Ediciones Universidad de Barcelona; 1999. [ Links ]

29. Snedecor GW, Cochran WG. Statistical methods. Iowa: Iowa State University Press; 1989. [ Links ]

30. Velasco FJ, Ortega SL, Sánchez CE, Urdaneta F. Factores que influyen sobre el nivel tecnológico presente en las fincas ganaderas de doble propósito localizadas en el estado Zulia, Venezuela. Rev Cient FCV-LUZ 2009;19(2):187-195. [ Links ]

31. Holmann F, Rivas L, Carrulla J, Rivera B, Giraldo L, Guzmán S, Martínez M, Medina A, Farrow A. Evolución de los sistemas de producción de leche en el trópico Latinoamericano y su interrelación con los mercados: un análisis del caso Colombiano 2015. http://www.avpa.ula.ve/congresos/seminario_pasto_X/Conferencias/A13-Federico%20Holmann.pdf . Consultado 20 Feb, 2015. [ Links ]

32. Galindo GG. Uso de innovaciones en el grupo de ganaderos para la validación y transferencia de tecnología “Joachin”, Veracruz, México. Terra 2001;(19):385-392. [ Links ]

33. Rehman T, McKemey K, Yates CM, Cooke RJ, Garforth CJ, Tranter RB, et al. Identifying and understanding factors influencing the uptake of new technologies on dairy farms in SW England using the theory of reasoned action. Agric Syst 2007;94(2):281-293. [ Links ]

34. Ward EC, Vestal KM, Doye GD, Lalman LD. Factors affecting adoption of cow-calf production practices in Oklahoma. J Agric Apl Econ 2008;40(3):851-863. [ Links ]

35. INEGI. Instituto Nacional de Estadística y Geografía. VIII Censo Agrícola, Ganadero y Forestal. Aguascalientes 2009. http://www.inegi.org.mx/est/contenidos/proyectos/Agro/ca2007/Resultados_Agricola/default.aspx . Consultado 18 Ago, 2011. [ Links ]

Received: March 10, 2015; Accepted: April 17, 2015

Creative Commons License Este es un artículo publicado en acceso abierto bajo una licencia Creative Commons