ABSTRACTEighty-two cattle farmers were surveyed to determine the main variables involved in cattle farm heterogeneity for rural extension work in Chunchi canton, Chimborazo province, Ecuador. The dimension reduction method suggested by Cabrera  et al.  (2004) was applied. Eight factors derived from the study: PCA (4) was made  to variables invest-ment in pastures and monthly milk income (F1); cattle fattening time (F2); total area (F3), and related farm members (F4). Whereas MCA (4) included variables missing indispensable service and applicability of state of the art techno l-ogy on the farm (D1); personnel in emergency situations (D2); breed or crossbreds (D3), and farm technology (D4)

Carrasco Carrasco, Robinson

Curbelo Rodríguez, Lino

Figueredo Calvo, Reynaldo

Masaquiza Moposita, Diego

English

Portal de Revistas - Universidad de Camagüey

Journal of Animal Production, 29 (2), 1-5, 2017 I S S N  2 2 2 4 - 7 9 2 0  Characterization of Cattle Farms for Rural Extension Work in Ecuador I. Determination of the main heterogeneities Robinson Ulises Carrasco Carrasco*, Reynaldo Figueredo Calvo**, Lino Curbelo Rodríguez**, Diego Armando Masaquiza Moposita*** *Riobamba Ecuador **Center for Animal Production Studies (CEDEPA), Faculty of Agricultural Sciences, Ignacio Agramonte Loynaz University of Camaguey, Cuba ***Pelileo Ecuador lino.curbelo@reduc.edu.cu ABSTRACT Eighty-two cattle farmers were surveyed to determine the main variables involved in cattle farm heterogeneity for rural extension work in Chunchi canton, Chimborazo province, Ecuador. The dimension reduction method suggested by Cabrera et al. (2004) was applied. Eight factors derived from the study: PCA (4) was made to variables invest-ment in pastures and monthly milk income (F1); cattle fattening time (F2); total area (F3), and related farm members (F4). Whereas MCA (4) included variables missing indispensable service and applicability of state of the art technol-ogy on the farm (D1); personnel in emergency situations (D2); breed or crossbreds (D3), and farm technology (D4). Key words: cattle farms, rural extension work  INTRODUCTION Cattle extension practices in Latin America fac-es big challenges in unfit areas which need im-plementation of proper cattle raising technology, particularly for milk production. In that sense, the study or diagnosis of milk production systems whose basic nutrition relies on pastures and for-ages, provides identification of strengths and weaknesses for sustainable management that al-low for a more efficient use of resources that guarantee sustainability and  food safety (Curbelo et al., 2009). Apollin and Eberhart (1999) said that when the land is the most critical factor in a country´s agri-culture, the general interest (of people and the economy) is to favor more intensive production systems; that is, those that generate the largest values per area unit available. In addition to it, Mora (2011) conceived the intensive system on the basis of large initial investments in machinery, equipment and facilities that favor the use of con-finement and supplementary feeding technologies for cattle. Individuals in a population may gather together in several ways, usually based on apparent fea-tures. In statistical terms, it is given by the rela-tion among shared features of rural extension. The identification of the context in which the relation occurs prevails in determining the pooling possi-bilities of cattle raising systems. The aim of this study is to determine the main variables that ef-fect on the heterogeneity of cattle farms, to im-plement rural extension in Chunchi canton, Chimborazo province, Ecuador. MATERIALS AND METHODS This research took place in the Matriz and Capzo parishes, Chunchi Canton, Chimborazo, Ecuador. The local climate conditions of the Ec-uadoran cities in the Andes are characterized by high plateau areas that favor frequent precipita-tions, with decreasing temperatures as altitude is higher. Besides, there are 40% of slopes. Eighty-two farms were chosen at random for the study; that figure was proportional to the number of parishes. The database included 100 variables, all validated by the Group of Experts at the Center for Animal Production Development (CEDEPA), at the University of Camaguey, Cuba, together with experts of the Ministry of Agriculture, Live-stock and Fishing (MAGAP), in Chunchi, Chim-borazo, Ecuador. The information compiled for the research was from 2014. The surveys (individual and by groups) were applied by MAGAP specialists (Chunchi). The in-formation was corroborated by personalized in-terviews to farm leaders, technicians (members of the rural extension group), and MAGAP mem-bers. The research advisors helped verify the data Characterization of Cattle Farms for Rural Extension Work in Ecuador I. Determination of the main heterogeneities J o u r n a l  o f  A n i m a l  P r o d u c t i o n ,  2 9  ( 2 ) ,  1 - 5 ,  2 0 1 7  from farms and milk collecting centers, at ran-dom.  The diagram in figure 1 shows the differences of two important phases, like variable dimension-al reduction, in which every subsequent procedure is intended to achieve a lower number of varia-bles, that could provide statistical differences among the individuals studied. It will be the basis for a new classification model. The minimum acceptable variation coefficient (VC) for variable selection was 50% (Cabrera et al., 2004). These variables were also considered important for the study with inferior values (Gar-cía and Ramírez, 2011). The variable association degree was measured with a correlation matrix. The minimum correlation value was 0.5. Variable typification: factorial analysis was per-formed through Principal Component Analysis (PCA) for variable dimensional reduction, with varimax rotation for quantitative data. Multiple Correspondence Analysis (MCA) was made for qualitative data. The factors were chosen accord-ing to the total explained variance percent, with a minimum of 70%. RESULTS AND DISCUSSION Dimension reduction Twenty-three variables with discriminating power were achieved and divided in two groups: quantitative (8) and qualitative (15); three varia-bles were removed due to the poor contribution to the factors achieved. The principal component values were collected after the PCA, and the first four components in the list higher than 1 were se-lected; they had accumulated variance of 82.04%. The first component (F1) was observed to sig-nificantly describe pasture investment and month-ly milk income. The effect of the latter on the former could be deducted, as farmers reported about grassland leasing to cover the needs of cat-tle. In that context, Escobar and Berdegué (1990) noted that farmers are more interested in maxim-izing profits and/or production when the market conditions are fair and stable. Ostensibly, when the conditions were precarious and haphazard, the main interest was based on minimizing risks by increasing the use of abundant resources with low or inexistent opportunity costs. The second component (F2) clearly showed a representative value for the variables: the time needed to fatten cattle for sale (months) and the number of trees on the farm, indicating an interest in intensification practices, prioritizing space op-timization in detriment of tress. It occurred be-cause tress were removed in more intensive sys-tems to prevent the negative effect of shadow on grass growth (Pérez, 2006). The marketed cattle would correspond to areas with more space for animal production.  In that sense, the third component (F3) de-scribed the variables of the total farm area and the sale prices of cattle. In production, Benítez et al. (2006) and Vargas et al. (2011) noted that size of the herd and farm extension defined the system´s capacity to feed animals. According to these au-thors, the sterile animals in smaller areas were sold quickly to use the space. In larger areas, the animals stayed longer, until they were ready for the market, at a better price. In the fourth component (F4), an important con-tribution was corroborated in the explanation of related members on the farm or area, the social side of the research. However, there was no sig-nificant value in terms of total area with a low correlation (0.28) between the two variables. A detailed analysis of the variable showed that households of 1-4 members accounted for 73% of the samples, and the higher values (up to 11 members on the farm) accounted for 27%. Addi-tion to it, the Technical Memoirs of Chunchi (MTCCh, 2013) showed a population distribution with technical precision values in the Canton for children (28.28%), teenagers (15.22%), young adults (17.15%), adults (27.83%), and elders (11.52%); with an average age of 29.   MCA produced four dimensions, with a total variance explained of 74%.  The first dimension (D1) showed some influ-ence of the basic service regarded as priority, which is currently absent due to frequent grass-land management. Also, the feasibility to apply state of the art technology accounted for 60%. This confluence might have been explained by the poverty levels observed in the systems studied; the traditional requirement is that innovation de-pends on the capital available. Therefore, it was important to encourage optimum use of resources, through rural extension processes (Gaitán and Lacki, 2014). All the previous must be considered when dealing with the social use given to goods and services, that favor life in communities, not just in isolation (MTCCh, 2013). R. U. Carrasco, R. Figueredo, L. Curbelo, D. A. Masaquiza Journal of Animal Production, 29 (2), 1-5, 2017  In the second dimension (D2), several prefer-ence variables of assistance in emerging produc-tive conditions coincided with the one associated to area deforestation. That relation may have de-rived from the effect of roads to the farms, as well as the effects of long distances from the Canton´s capital. In that sense, permanent planned collec-tive participation of public and private organiza-tions is important (Vargas et al., 2011). Dimension three (D3) detailed the variables of roads and the cattle breed or crossbreds in the ar-ea. This relation may have been caused by the ef-fect of historic inclusion of artificial insemination programs which are sometimes favored in loca-tions where access by road is easy, and offer im-provements in terms of cattle breeds and biotypes. In detail, in 60% of cases, the herd was made up of crossbred animals (Holstein Friesian); 21% of Swiss Brown; and 19% of crossbreds between the two and Jersey. All this coincided with MTCCh (2013), that reported the presence of Criollo and Crossbred cattle for milk production. Dimension four (D4) included three points of view that may have been linked from a sociocul-tural perspective. It concerned high priority and nonexistent services, according to the kind of cat-tle breed and the origin of the technology applied for production; the ones with traditional technolo-gies were less engaged in cattle breeding. Con-cerning service shortages, it was deducted that those with less access to services available, in-cluding technical training to enhance production and implement breeding, had been traditionally excluded by development policies throughout his-tory. Another study determined that herd for-mation and the technological alternative applied had repercussions on the system´s productivity and the environmental situation of the farms (Vargas et al., 2011). CONCLUSIONS The analysis of quantitative variables tackled differences in terms of intensification based on space; however, the qualitative differences were more related to social and cultural issues observed in the population studied. REFERENCES APOLLIN, F. y EBERHART, C. (1999). Análisis y diag-nóstico de los Sistemas de Producción en el medio rural, Guía Metodológica. Sistema de capacitación para el manejo de los recursos naturales renovables. Quito, Ecuador: COSUDE y DGIS-INTERCOOPERATION. BENÍTEZ, D. G.; RAMÍREZ, A.; GUEVARA, O.; DÍAZ, M.; HERNÁNDEZ, M. y GUERRA, J. R. (2006). Or-denamiento de la actividad ganadera en los maci-zos montañosos occidentales. Informe proyecto 0703083.-PNCT desarrollo sostenible de la monta-ña, Bayamo. CABRERA, D. V.; GARCÍA M., A., ACERO DE LA CRUZ, R.; Castaldo, A.; Perea, J. M. y Martos, J. (2004). Metodología para la caracterización y tipificación de sistemas ganaderos. Dpto Producción Animal Universidad de Córdoba Documentos De Trabajo Producción Animal y Gestión. Retrieved on De-cember 13, 2013, from http://www.uco.es/zootecniaygestion/img/pictorex/14_19_10_sistemas2.pdf. CURBELO, L.; LOYOLA, O. y GUEVARA, R. (2009). Ac-ciones para la recuperación y mejoramiento de pas-tizales nativos en las sabanas serpentiníticas del norte de Camagüey. Revista de Producción Animal, 20 (1), 55-58. ESCOBAR, G. y BERDEGUÉ, J. (1990). Tipificación de sistemas de producción agrícola, Rimisp-Centro Latinoamericano para el Desarrollo Rural. Ed. Red Internacional de Metodología de Investigación de Sistemas de Producción (RIMISP) Santiago de Chile. Retrieved on June 30, 2014, from http://idl-bnc.idrc.ca/dspace/bitstream/10625/3969/1/49675.pdf. GAITÁN, J. y LACKI, P. (2014). La modernización de la agricultura; los pequeños también pueden. Comu-nicación personal 23 de junio, email Po-lan.Lacki@onda.com.br o Po-lan.Lacki@uol.com.br. GARCÍA, I. y RAMÍREZ, L. (2011). Tipificación de sis-temas de producción ganadera del Municipio de Bolívar, Valle del Cauca, Colombia. Revista Co-lombiana de Ciencia Animal, 4 (1), 107-113. MORA, S. (2011). Producción de ganadería de leche para la reducción de presiones sobre los ecosiste-mas forestales andinos de la provincia de Napo. Se-rie Investigación y Sistematización (18). Programa Regional ECOBONA-INTERCOOPERATION. Quito: ECOBONA. Retrieved on December 18, 2014, from  http://www.bosquesandinos.info/ECOBONA/18GANADERIANAPO/Ganader%EDa%20verde%20WEB_Parte1.pdf. MTCCh (2013). Proyecto: “Generación de geoinfor-mación para la gestión del territorio a nivel nacio-nal escala. 1: 25 000”. Memorias Técnicas Cantón Chunchi.República del Ecuador: Ministerio de De-fensa Nacional, Ministerio de Agricultura Ganade-ría Acuacultura y Pesca, Instituto Espacial Ecuato-riano, Secretaría Nacional de Planificación y Characterization of Cattle Farms for Rural Extension Work in Ecuador I. Determination of the main heterogeneities J o u r n a l  o f  A n i m a l  P r o d u c t i o n ,  2 9  ( 2 ) ,  1 - 5 ,  2 0 1 7  Desarrollo, Instituto Nacional de Investigación Geológico Minero Metalúrgico. PÉREZ, E. (2006). Caracterización de sistemas silvo-pastoriles y su contribución socioeconómica a pro-ductores ganaderos de Copán, Honduras. Tesis de Maestría, Centro Agronómico Tropical de Investi-gación y Enseñanza (CATIE), Programa de educa-ción para el desarrollo y la conservación, Escuela de posgraduados, Turrialba, Costa Rica. VARGAS, J.; BENÍTEZ, D.; TORRES, V.; VELÁZQUEZ, F. y ERAZO O. (2011). Tipificación de las fincas ga-naderas en el pie de monte de las provincias Los Ríos y Cotopaxi de la República del Ecuador. Re-vista Cubana de Ciencia Agrícola, 45 (4), 381-390. . Received: 1-12-2017 Accepted: 1-20-2017  Source: Cabrera et al. (2004), author´s adaptation Fig. 1. Flowchart of information processing  Table 1. Matrix of principal components rotated Variables Components 1 2 3 4 Investments in pastures .904 .039 -.086 .013 Monthly milk income .751 .116 .502 -.006 Cattle fattening time (months) .104 .888 .032 -.083 Number of trees .041 .693 -.052 .484 Total area .153 -.087 .899 .231 Price of sold cattle -.348 .474 .588 -.390 Family memebers on the farm -.014 .041 .139 .897          Table 2. Contribution of qualitative variables to dimensions Variables  Dimension Mean 1 2 3 4 Inexisting priority service .453 .150 .171 .310 .271 Where to turn to during emergencies .047 .510 .192 .069 .204 Farm road conditions .273 .056 .313 .073 .179 Tree species .383 .285 .165 .178 .253 Water suplly for agriculture .241 .121 .084 .041 .122 Destination of organic matter .262 .001 .288 .023 .143 Deforestation issues .287 .395 .020 .166 .217 Grassland management .451 .110 .054 .224 .210 Grazing .316 .179 .124 .041 .165 Breed or crossbred .061 .297 .386 .320 .266 Destination of milk .181 .179 .015 .081 .114 Farm technology .028 .106 .169 .318 .155 Applicability of state of the art technology  on the farm .405 .000 .001 .019 .106 Total active 3.386 2.388 1.981 1.862 2.404 Variance % 26.043 18.368 15.235 14.321 18.492   

Characterization of Cattle Farms for Rural Extension Work in Ecuador  I. Determination of the main heterogeneities

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Characterization of Cattle Farms for Rural Extension Work in Ecuador I. Determination of the main heterogeneities

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