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Agronomía Colombiana

versão impressa ISSN 0120-9965

Agron. colomb. vol.34 no.3 Bogotá set./dez. 2016

https://doi.org/10.15446/agron.colomb.v34n3.58500 

Economy and rural development

Network analysis of knowledge building on rural extension in Colombia

Análisis de redes de generación de conocimiento en la extensión rural en Colombia

Holmes Rodríguez1 

Carlos Julián Ramírez-Gómez1 

Norman Aguilar-Gallegos2 

Jorge Aguilar-Ávila2 

1 Grupo GaMMA, Facultad de Ciencias Agrarias, Universidad de Antioquia. Medellín (Colombia). holmes.rodriguez@udea.edu.co

2 Centro de Investigaciones Económicas, Sociales y Tecnológicas de la Agroindustria y la Agricultura Mundial (CIESTAAM), Universidad Autónoma Chapingo. Texcoco, México.


ABSTRACT

Based on the analysis of scientific papers published on rural extension in Colombia since 2010, an interpretive descriptive study was conducted to identify the level of collaboration be tween authors and institutions in the creation, systematization and dissemination of knowledge in rural extension. Informa tion was gathered from a search in bibliographic databases to identify papers published in rural extension. 50 papers were found. They were organized in a database, and using social network analysis, a review of relational structures and indi cators derived from the scientific collaboration between the authors and institutions involved in the publication conducted. Authors from 28 different institutions have participated in the 50 papers identified, 70% of them have been published by re searchers working in the same institution. The findings of this study support the conclusion that actors building knowledge on rural extension in Colombia have a limited intra and inter-institutional articulation, making it urgent to strength public policies and incentives to foster relationships between research groups and institutions.

Key words: knowledge management; researchers' networks; social network analysis; rural development.

RESUMEN

A partir del análisis de la publicación de artículos científicos sobre extensión rural en Colombia desde el 2010, se realizó un estudio de carácter descriptivo interpretativo para iden tificar el nivel de colaboración entre autores e instituciones en la generación, sistematización y difusión de conocimiento sobre extensión rural. La información se recopiló a partir de la búsqueda en bases de datos bibliográficas para identificar los artículos publicados sobre extensión rural. Se localizaron 50 artículos, los cuales se ordenaron en una base de datos y con el uso del análisis de redes sociales se revisaron las estruc turas relacionales e indicadores derivados de la colaboración científica entre los autores e instituciones involucrados en la publicación. En los 50 artículos identificados, han participado autores de 28 instituciones diferentes; el 70% han sido publica dos por investigadores que pertenecen a la misma institución. Los hallazgos de este estudio permiten concluir que los actores que generan conocimiento sobre extensión rural en Colombia presentan una escasa articulación intra e interinstitucional lo cual hace apremiante el fortalecer las políticas públicas y los incentivos para fomentar los relacionamientos entre los grupos de investigación y entre las instituciones.

Palabras clave: gestión del conocimiento; redes de investiga dores; análisis de redes sociales; desarrollo rural.

Introduction

According to Christoplos (2010), "extension" can be under stood as the systems that facilitate the access of farmers, their organizations, and other market players to knowledge, technologies and information. Extension encourages their integration with research members, teaching, agro-indus try and other institutions; and contributes to the design of practices and technical, management and organizational skills. At an international level, extension services among other factors, are recognized as key points for the develop ment of the agricultural activity (Kilelu et al., 2014; Klerkx and Leeuwis, 2009; Muñoz and Santoyo, 2010).

Rivera and Sulaiman (2009) indicate that extension was originally conceived as part of a "knowledge triangle", formed by research, education and extension. However, today it is addressed in a more comprehensive manner and is valued by various actors participating in rural develop ment, not only in the context of improving productivity, but also for its contribution in strengthening bonds between farmers, researchers, agricultural education institutions, and other actors in society (Faure et al., 2012), that some how form what could be called an "innovation system", through actors interacting in a process of generation, dissemination and use of knowledge in order to increase agricultural production looking for economic and social changes (Hellin, 2012).

In this regard, Universities and National Research Centers as well as public and private institutions have a strategic role within the process of building codified knowledge that is regularly measured through the publication of scientific research products and patents (Rivera-Huerta et al., 2011). Furthermore, in the case of the agricultural sector, these ac tors have the mission of strengthening extension, improv ing their innovation capabilities, through interactions and coordination to create new information articulated with the demand (Klerkx and Leeuwis, 2008; Spielman and Birner, 2008), building networks for strengthening relations and ties, which increase the production and dissemination of knowledge (Aguilar-Gallegos et al., 2015, 2016; Brunori et al., 2013; Vega de Jiménez and Rojo, 2010).

This approach replaces the linear view of knowledge cre ation and innovation, by an interactive process between different actors in agricultural innovation systems (Mu ñoz and Santoyo, 2010). In fact, in the case of Colombia, this approach was adopted by the Ministry of Agriculture and Rural Development (MADR) setting up the National Subsystem of Agricultural Technical Assistance (SSATA), tied to the National System of Agricultural Science and Technology (SNCTA), in order to coordinate actors to improve the development and dissemination of knowledge (MADR, 2012).

At the international level, there is a consensus through university researchers and their research groups about scientific collaboration between universities and other actors playing a key role in the progress of knowledge (De Stefano et al., 2013), since networking allows sharing ideas, methodologies and approaches, which may help provide solutions to common problems. However, in Colombia the empirical evidence indicates a low articulation between actors; therefore, it is important to conduct studies in or der to determine levels of cooperation and coordination between them. According to the findings of the Mission for Rural Transformation (Misión para la Transformación del Campo, in Spanish) (2015), the promotion of networking and development of capabilities, should generate knowledge management strategies to achieve greater impact in rural areas, improving performance of sectors and alternatives to strengthen rural producers.

In this context and in the case of Colombia, this paper aims to analyze the level of collaboration between actors in the creation and dissemination of knowledge on rural exten sion, using social network analysis in order to guide public policy to strengthen rural extension under an innovation system approach.

Methodology

An interpretative and descriptive study was conducted based on the analysis of co-authorship of scientific papers on rural extension, which is a partial indicator of scientific collaboration (Katz and Martin, 1997) between research ers, especially in the publication of papers (Lopaciuk-Gonczaryk, 2016). A social network analysis was used, following other studies of this type (De Stefano et al., 2013; Russell et al., 2009; Valderrama-Zurian et al., 2007; Zazo et al., 2015). Documents considered by Faure et al. (2012) as "gray literature", such as information booklets, sector reports, books, theses or other documents without peer review were not included in the analysis.

Data collection and editing

Information was gathered through bibliographic databases search (Dialnet, Ebsco, Redalyc, Redib, Scholar, SciELO, Scopus, Sciencedirect) to identify all papers published on rural extension in Colombia, based on the definition of rural direct technical assistance provided under Colombian law (Congreso de Colombia, 2000), which includes social, environmental, economic and technical issues; between 2010 and 2015, because during that period, the MADR revived interest in improving the quality and coverage of the ATDR service. Information of Author (Aut), institution of affiliation (Ins), year of publication, and name of the journal were linked to each paper (Art), making the ne cessary adjustments for homonymy and synonymy results (Calero et al., 2006).

Network analysis and collaboration indicators

A social network analysis (SNA) was used as a tool for observing, studying and understanding the relational structures derived from scientific collaboration between authors and institutions involved in the publication of papers on rural extension; the relationship analyzed in this study is the participation of the different authors in the different papers. SNA allows to identify the positions of actors within the network, which partly determines the limitations and opportunities that those actors and the network have in general (Borgatti et al., 2013).

To analyze the participation of authors in each paper, 2-mode networks were used; to analyze collaboration between authors, 1-mode networks were used (Borgatti et al., 2013; Wasserman and Faust, 1994). Adapting the indi cations of Valderrama-Zurian et al. (2007), the number of relationships between authors participating in a paper is calculated as m!/(m-n)!n!, where m is the number of authors in the article and n the number of elements of groups. This analysis approach was also used to analyze the collabora tion between the authors' institutions of affiliation.

Based on Freeman (1979) and Borgatti et al. (2013), the indicators used for the network analysis were as follows: 1. Degree: number of links or relationships that a node has. Thus, the higher the degree, the higher the level of collaboration of an author. 2. Betweenness: frequency measurement of a given node when it is on the shortest path connecting other pairs of nodes. It was only measured for 1-mode networks to make reference to the relative im portance that an author has in connecting other authors. 3. Density: measure of cohesion that makes reference to the number of existing links on the network in relation to possible links, expressed as a percentage. It was only considered for 1-mode networks.

Additionally, homophily - the tendency to bond with indi viduals who have characteristics similar to ours (Lazarsfeld and Merton, 1954) - was calculated. It was only calculated for the 2-mode network using the E-I index (external and internal links) from Krackhardt and Stern (1988), classify ing institutions into three types: public, private and others (NGOs, trade unions and independent). Calculation of indicators and network observation were performed us ing Ucinet (Borgatti et al., 2002) and NetDraw (Borgatti, 2002) software.

Results

Scientific collaboration during the period 2010-2015

115 authors participated in the 50 papers analyzed. Some of them contributed with more than one participation, for a total of 103 different authors (Tab. 1). 74% of the papers have two or more authors. Within the period analyzed, collaboration increased, as in 2010 less than half of papers were written in co-authorship and by 2015 that figure reached nearly 86%.

A growing trend was found in the increase of both pub lications and authors, additionally, there are increasingly more authors involved (Fig. 1). However, the number of new authors involved in publications on rural extension in Colombia is decreasing. The largest increase was seen in 2011, when 15 new authors joined the 19 existing ones. In the last year (2015) there were only 14% new authors. This situation may be considered normal, as the data is cumula tive; however, there are few authors contributing with more than one collaboration, as three authors have published three articles; six have published two, and the remaining 94 have only participated in one paper. This could indicate the insufficiency of critical mass discussing this topic.

TABLE 1 Scientific collaboration in the production of papers 2010-2015. 

Year No. of papers No. of papers written in co-authorship (%) Total No. of authors Authors per paper Maximum No. of authors in a paper New and different authors Increase in new authors (%)
2010 13 6 (46.2) 21 1.6 3 19 -
2011 7 6 (85.7) 16 2.3 3 15 78.9
2012 8 5 (62.5) 19 2.4 5 17 50.0
2013 8 7 (87.5) 21 2.6 4 19 37.3
2014 7 7 (100.0) 20 2.9 5 20 28.6
2015 7 6 (85.7) 18 2.6 4 13 14.4
Total 50 37 (74.0) 115 2.3 5 103

FIGURE 1 Paper publishing and author participation trends. 

Scientific collaboration network between authors

It was found that authors have in general little participa tion in several publications. There are few authors having two or three links to papers; i.e., author 007 (Aut-007), who participates in three papers (003, 027 y 031) (Fig. 2). Likewise, it can be observed that there are more papers with two or more authors than papers published by a single author. 74% of papers have been published in co-authorship, although the participation of the same author in several papers is lower.

FIGURE 2 2-mode network of collaborators. Authors (circles); papers (triangles). 

From the 2-mode network, consisting of authors and papers, it was possible to obtain a 1-mode network (Bor gatti et al., 2013) and therewith a representation of direct collaboration between authors (Fig. 3 ). It was found that 11 authors have individually published a paper; without any collaboration. They have published 13 papers, since an author (Aut-001) published two papers individually, and another author (Aut-017) published one individually and other in collaboration. The maximum number of co-authorship in a paper was 5, in two different cases. Three papers have been published by four authors. The most frequent collaborations occur between two and three au thors. In both cases 16 papers have been published by that number of authors.

FIGURE 3 1-mode network of collaboration between authors. 

Collaboration has been based, almost entirely, on the publication of one article. This occurs because interaction between pairs of authors is limited to a single time (weak ties). Only two pairs of authors (Aut-007 and Aut-008; Aut-010 and Aut-011) have collaborated twice (strong ties) in the publication of two different papers. In both cases, other authors have participated in the publication of those papers.

There are few authors who manage to connect different collaborations. This is the result of publishing different papers with different authors. Take Aut-040 for example, who manages to connect authors 041, 042 and 047. This author published one paper with the first two, and another one with the latter. Only five authors of this type (black nodes) were found across the entire collaboration network.

Network indicators show that 103 authors have managed to establish 200 links between them. Therefore, network density is low, as well as the degree average of each author (Tab. 2). The last indicator shows that, on average, each author has collaborated with nearly two authors. This is supported by the fact that collaboration between two and three authors is quite common. However, the collaboration network is fragmented as there are 40 components. The best connected component links only six authors. This is also the reason why the network diameter is low. In fact, the last indicator is achieved through any of the five nodes that serve as intermediary between different collaborations (Fig. 3, black nodes). That is, although 74% of the 50 papers have been written in collaboration, there is not a real dense collaboration network between all the authors. Likewise, there are no authors having a significant centrality; network centrality is only 3%.

TABLE 2 Basic indicators of the collaboration network between authors. 

Scientific collaboration network between institutions

The participation of authors from 28 different institutions was found. 70% of papers analyzed were published by re searchers belonging to the same institutions, that is, there was no inter-institutional collaboration. The remaining 30% were published in institutional collaboration. It is worth mentioning that inter-institutional collaboration was analyzed, that is, collaboration between different institutions, but intra-institutional collaboration: between different departments, faculties, specialties of the same institution, etc., was not considered, as it is common for authors to include only the main institution to which they are affiliated.

Participation analysis of authors from 28 institutions (circles) with 50 papers (triangles) shows the institutions of affiliation of the authors with most publications (Fig. 4). Authors from Ins-001 have participated 17 publications, out of which 8 have been in collaboration with other institu tions. The participation of each of the authors' institutions of affiliation and their collaboration with other institutions, that is, papers with two or more links, can be observed. This is significant since, just as there are institutions with prolific authors, there are also others with fewer publishing authors, and most of these publications are made in col laboration. Take the case of Ins-025 who has four papers, three out of which were published in collaboration. There is also Ins-007 with three papers, all of them in collabora tion. Finally, there are institutions whose authors do not publish in collaboration (bottom right of the graph) or whose authors have published several papers, but none in collaboration; for example, Ins-002.

FIGURE 4 2-mode network of collaboration. Institutions (circles); papers (triangles). 

Some points of interest were found when turning the 2-mode network (Fig. 4) into 1-mode between institutions (Fig. 5). Nine institutions of affiliation appear isolated, since the papers published by their authors were not written in collaboration. There is a very central institution of affilia tion (Ins-001), who managed to establish links with other 10 institutions from the 8 papers published in collaboration. This fact is related to the importance of the institution in Colombia. Nevertheless, most of the collaborations have oc curred between two institutions of affiliation. There is only one paper with collaborations from three institutions, and another one from four. Only in one case, between Ins-001 and Ins-025, two papers were published in collaboration (thicker line). This implies that inter-institutional research needs strengthening.

FIGURE 5 1-mode network of collaboration between institutions. Color legends: blue nodes: public institutions; red nodes: private institutions; green nodes: other type (NGOs, independent and trade unions). Legend of shapes: diamond nodes: intermediating actors; circular nodes: non-intermediating actors. 

Just as there are authors who manage to link other ac tors, i.e. intermediaries between collaborations, several institutions were also found to play this role with their participation in two or more papers. The diamond-shaped circles represent the institutions that play this role within the network. Out of the 28 institutions, only 6 actors of this type were found.

The calculation of indicators of the previous network revealed that the 28 institutions have established 42 collaborative links; therefore, each one of them has an average of 1.5 links (Tab. 3). Network density also serves as an indicator to measure the level of articulation and the number of existing links, which is 11.1%. This type of indicators would serve as a baseline to analyze in the future the evolution of articulation within the network. In comparison to the network of authors, this one reflects a greater articulation, as there are fewer components. A fact to note is that the best connected component manages to link directly or indirectly 60.7% of all institutions, but this is achieved with a higher network diameter, that is five steps, and also for that reason, the average distance is 2.6. Network centrality is 33.9%, which is very visible because of the importance that the institution of affilia tion Ins-001 has within the network.

TABLE 3 Basic indicators of the collaboration network between institu tions. 

Although the network of institutions of affiliation is denser, it is important to mention that this is partly because the network is smaller, and on the other hand, because it is the result of collaboration between the authors that have published the papers. In fact, if strategies to increase col laboration and the number of authors in each paper were implemented, it would result in a greater collaboration between institutions.

It was found a slight level of homophily on the entire net work, with an E-I index of -0.048 (Tab. 4). Public institu tions have more links between them than with the other types of institutions, however, homophily is more prevalent as the E-I index is -0.286. Besides, there is a higher density of links between them. Meanwhile, there are no links be tween private institutions, i.e. they tend to heterophily (E-I index of 1.000). It is interesting to see that their collabora tive links have been established with public institutions and not with any other type. The group of other institutions has links between them and with public institutions, but not with private ones. However, as density of links is higher to the inside instead of to the outside, then the E-I index of this group is also negative (-0.143).

TABLE 4. Density matrix and E-I index by group of institutions.

DISCUSSION

The results of this study indicate that in Colombia knowl edge building networks on rural extension are limited; the calculated indicators show a low participation of authors and institutions in collaboration networks; neverthe less, the causes of this phenomenon should be deepenly studied. In this sense, authors like Ahrweiler and Keane (2013) find that promoting a networking approach implies complexity, related with the existence of behavior, action and communication models; actors who may present com patibilities and incompatibilities, communicative interest or perhaps different strategic perspectives. Such is the case of homophily as an attribute present in all the structures of the studied networks, and where the phenomenon is evident between universities and other public and private institutions; this behavior could influence structural links, generate processes of social selection, among others. This may hinder processes of building and dissemination of knowledge, as already addressed in other studies (Isaac, 2012; McPherson et al., 2001).

The lack of networking reveals a limitation of institutions of higher education in strengthening innovative capacity of the agricultural productive sector and the direct rural tech nical assistance service due to the lack of generation and dissemination of knowledge on rural extension. On that subject, other authors state that universities are strategic players in the innovation system, thanks to their knowledge of local reality derived from their regional presence. They also have an additional strategic capacity for research, through the theses of their undergraduate and graduate students (Fonseca and Rugeles, 2004); nevertheless, at pres ent, large part of the knowledge generated is not properly systematized and ends up falling into the "gray literature" (Faure et al., 2012). For that reason, the number of scientific papers published is low in comparison with the number of students. Therefore, it is important to implement strategies for the codification of generated knowledge.

The centrality indicator shows the lack of authors that have an important centrality. However, at the level of institu tions, results show a greater connection, probably due to the smaller number of these compared with the number of authors, a better connection, a higher diameter and a higher centrality indicator around the National University of Colombia. This implies an opportunity to implement strategies to promote networking, because the more central actor, as suggested Barrientos-Fuentes and Berg (2013), could generate feedback mechanisms between research institutions, to improve the development and dissemina tion of innovations on rural extension.

Thus, the consolidation of knowledge building networks on rural extension between authors and institutions should be part of the strategies in the implementation of innovation systems within the agricultural sector, in a way that spaces can be generated for collaboration between institutions, and also effective strategies to support networking research programs for generation and dissemination of knowledge on rural extension; because as De Stefano et al. (2013) highlight, scientific collaboration between universities and other actors is important in the progress of knowledge.

In this sense, knowledge building networks can be impor tant for agricultural development. Several Initiatives begin to emerge as the Red Nacional de Extensión Rural [National Network of Rural extension], led by the University of An-tioquia; and the Red de Estudios Rurales [Network of Rural Studies], led by the University of Tolima. Both can serve as platforms for systematizing and publishing successful experiences on rural extension, that can be imitated in other departments of the country, as in the case of the University of Tolima (Ibague, Colombia) with the intern ship program for service delivery of ATDR, financed by the regional government (Serrano et al., 2015).

Accordingly, given the observed shortage of scientific pro duction in matters of extension and technical assistance in Colombia, it would be relevant to conduct a cause analysis, including one of human capital skills in the use of tools for systematization and publication of papers, because many institutions engaged in rural extension does not publish papers. Nevertheless, there is a growing increase in research activities carried out by associations of pro ducers and universities, in the diversification of institu tional structures and the focus of agricultural research. This latter, in the case of the Agronomía Colombiana journal of the National University has led to an increase of 13.64% in the publications in the field of agricultural economy and rural development in the period 2003-2012 (Ligarreto, 2013).

Finally, it is considered important to conduct studies to determine the way in which that knowledge is used by analyzing the effect or impact of such publications, either by the number of citations received or the operability of their proposals. However, this research contributes to con struct baseline indicators that could be used to assess in the future the improvement in the articulation of researchers and institutions addressing this subject.

Conclusions

The findings of this study lead to the conclusion that ac tors that build knowledge on rural extension in Colombia have a poor intra and inter-institutional articulation. For the foregoing, strengthening public policies and incentives becomes urgent to foster relationships between research groups and between institutions. In this way, it will contrib ute to the consolidation of the new collaboration networks that can serve as platforms for dissemination and especially for the use of knowledge on rural extension, strengthening the role of researchers, universities and research centers in shaping the territorial systems of innovation.

The strengthening of mechanisms to connect researchers in matters of extension and technical assistance should start from recognizing their theoretical and methodological capacities. In this regard, the SNA approach can become an important tool for monitoring the impact of implemented actions that seek to strengthen collaboration for building knowledge on rural extension. Although it is a challenge in gathering information and continuous analysis, this type of longitudinal analysis would be a very useful tool for both universities and public institutions responsible for guiding science, technology and innovation policies.

Acknowledgments

The authors thank the Committee for the Development of Research (CODI) for the financial support and to Strategies for Sustainability 2016 of the GaMMA Group, University of Antioquia, Medellin, Colombia.

Literature cited

Aguilar-Gallegos, N., E.G. Martínez-González, J. Aguilar-Ávila, H. Santoyo-Cortés, M. Muñoz-Rodríguez, and E.I. García-Sánchez. 2016. Análisis de redes sociales para catalizar la innovación agrícola: de los vínculos directos a la integración y radialidad. Estudios Gerenciales 32(140), 197-207. Doi: 10.1016/j.estger.2016.06.006 [ Links ]

Aguilar-Gallegos, N., M. Muñoz-Rodríguez, H. Santoyo-Cortés, J. Aguilar-Ávila, and L. Klerkx. 2015. Information networks that generate economic value: A study on clusters of adopters of new or improved technologies and practices among oil palm growers in Mexico. Agric. Syst. 135, 122-132. Doi: 10.1016/j.agsy.2015.01.003 [ Links ]

Ahrweiler, P. and M. Keane. 2013. Innovation networks. Cognit. Stud. Econ. Soc. Sci. 12, 73-90. Doi: 10.1007/s11299-013-0123-7 [ Links ]

Barrientos-Fuentes, J.C. and E. Berg. 2013. Impact assessment of agricultural innovations : a review. Agron. Colomb. 31(1),120-130. [ Links ]

Borgatti, S.P. 2002. Netdraw network visualization. Analytic Tech nologies, Harvard, MA. [ Links ]

Borgatti, S.P., M.G. Everett, and L.C. Freeman. 2002. Ucinet for Windows: software for social network analysis. Analytic Technologies. Harvard, MA. [ Links ]

Borgatti, S.P., M.G. Everett, and J.C. Johnson 2013. Analyzing social networks. SAGE Publications, London, U.K. [ Links ]

Brunori, G., D. Barjolle, J. Ingram, and L. Klerkx. 2013. CAP reform and innovation: the role of learning and innovation networks. EuroChoices 12(2), 27-33. Doi: 10.1111/1746-692X.12025 [ Links ]

Calero, C., R. Buter, C.C. Valdés, and E. Noyons. 2006. How to iden tify research groups using publication analysis: An example in the field of nanotechnology. Scientometrics 66(2), 365-376. Doi: 10.1007/s11192-006-0026-z [ Links ]

Christoplos, I. 2010. Cómo movilizar el potencial de la extensión agraria y rural. Foro mundial sobre servicios de asesoramiento rural. FAO, Rome, Italy. [ Links ]

Congreso de Colombia. 2000. Ley 607 de 2000. In: In: https://www. minagricultura.gov.co/Normatividad/Leyes/ley_607_00.pdf , consulted: January, 2015. [ Links ]

De Stefano, D., V. Fuccella, M.P. Vitale, and S. Zaccarin. 2013. The use of different data sources in the analysis of co-authorship networks and scientific performance. Soc. Networks, 35(3), 370-381. Doi: 10.1016/j.socnet.2013.04.004 [ Links ]

Faure, G., Y. Desjeux, and G. Pierre. 2012. New challenges in agricul tural advisory services from a research perspective: A literature review, synthesis and research agenda. J. Agric. Education Ext. 18(5), 461-492. Doi: 10.1080/1389224X.2012.707063 [ Links ]

Fonseca, S. and L. Rugeles. 2004. De lo agropecuario a lo agroindus-trial: Una visión desde la ciencia, la tecnología y la innovación. CeniRED; Colciencias, Bogotá. [ Links ]

Freeman, L.C. 1979. Centrality in social networks: conceptual clarification. Soc. Networks 1(3), 215-239. Doi: 10.1016/0378-8733(78)90021-7 [ Links ]

Hellin, J. 2012. Agricultural extension, collective action and in novation systems: lessons on network brokering from Peru and Mexico. J. Agric. Education Ext. 18(2), 141-159. Doi: 10.1080/1389224X.2012.655967 [ Links ]

Isaac, M.E. 2012. Agricultural information exchange and organiza tional ties: The effect of network topology on managing agro-diversity. Agric. Syst. 109, 9-15. Doi: 10.1016/j.agsy.2012.01.011 [ Links ]

Katz, J.S. and B.R. Martin. 1997. What is research collaboration? Res. Policy 26(1), 1-18. Doi: 10.1016/S0048-7333(96)00917-1 [ Links ]

Kilelu, C.W., L. Klerkx, and C. Leeuwis. 2014. How dynamics oflearn-ing are linked to innovation support services: Insights from a smallholder commercialization project in Kenya. J. Agric. Edu cation Ext. 20(2), 213-232. Doi: 10.1080/1389224X.2013.823876 [ Links ]

Klerkx, L. and C. Leeuwis. 2008. Matching demand and supply in the agricultural knowledge infrastructure: Experiences with innovation intermediaries. Food Policy 33, 260-276. Doi: 10.1016/j.foodpol.2007.10.001 [ Links ]

Klerkx, L. and C. Leeuwis. 2009. Shaping collective functions in privatized agricultural knowledge and information systems: the positioning and embedding of a network broker in the Dutch dairy sector. J. Agric. Education Ext. 15(1), 81-105. Doi: 10.1080/13892240802617445 [ Links ]

Krackhardt, D. and R.N. Stern. 1988. Informal networks and orga nizational crisis: an experimental simulation. Soc. Psychol. Quart. 51(2), 123-140. Doi: 10.2307/2786835 [ Links ]

Lazarsfeld, P. F. and R.K. Merton. 1954. Friendship as a social pro cess: A substantive and methodological analysis. pp. 18-66. In: Berger, M., T. Abel, and C.H. Page (eds.). Freedom and control in modern society. D. Van Nostrand Co., New York, NY. [ Links ]

Ligarreto, G.A. 2013. Considerations on the research and dissemina tion of agricultural knowledge by the Facultad de Agronomía. Agron. Colomb. 31(2), 243-252. [ Links ]

Lopaciuk-Gonczaryk, B. 2016. Collaboration strategies for publish ing articles in international journals - A study of Polish scien tists in economics. Social Networks 44, 50-63. Doi: 10.1016/j.socnet.2015.07.001 [ Links ]

MADR. 2012. Subsistema nacional de asistencia técnica agro pecuaria. In: In: http://www.adelnarino.org/2011/web/descargas/ Subsistema_Asistencia_Tecnica_Agropecuaria.pdf ; consulted:August, 2015. [ Links ]

Mcpherson, M., L. Smith-Lovin, and J.M. Cook. 2001. Birds of a feather: Homophily in social networks. Ann. Rev. Sociol. 27, 415-444. Doi: 10.1146/annurev.soc.27.1.415 [ Links ]

Misión para la Transformación del Campo. 2015. Estrategia de ciencia, tecnología e innovación agropecuaria y de acompa ñamiento integral. Nuevas Ediciones, Bogotá. [ Links ]

Muñoz, M. and V.H. Santoyo. 2010. Del extensionismo a las redes de innovación. pp. 31-69. In: Aguilar, J., J.R. Altamirano, and R. Rendón (eds.). Del extensionismo a las redes de innovación. CIESTAAM, Universidad Autónoma Chapingo, Texcoco, México. [ Links ]

Rivera, W.M. and R.V. Sulaiman. 2009. Extension: Object of reform, engine for innovation. Outlook Agric. 38(3), 267-273. Doi: 10.5367/000000009789396810 [ Links ]

Rivera-Huerta, R., G. Dutrénit, J.M. Ekboir, J.L. Sampedro, and A.O. Vera-Cruz. 2011. Do linkages between farmers and academic researchers influence researcher productivity? The Mexican case. Res. Policy 40(7), 932-942. Doi: 10.1016/j.respol.2011.05.001 [ Links ]

Russell, J.M., M.J. Madera J., and A. Shirley. 2009. El análisis de redes en el estudio de la colaboración científica. Redes. Rev. Hisp. Análisis Redes Soc. 17(2), 39-47. [ Links ]

Serrano, R., L.E. Guzmán, and M. Jimenéz. 2015. Análisis de un modelo de apoyo a la asistencia técnica agropecuaria caso: municipio de Fresno-Tolima. p. 46. In: Proc. 1er. Congreso Internacional de Estudios Rurales, Congreso Internacional de Estudios Rurales, http://redestudiosrurales.wix.com ; consulted: March, 2016. [ Links ]

Spielman, D. and R. Birner. 2008. How innovative is your agri culture?: Using innovation indicators and benchmarks to strengthen national agricultural innovation systems. The World Bank. Washington, DC. [ Links ]

Valderrama-Zurián, J.C., G. González-Alcaide, F.J. Valderrama-Zurián, R. Aleixandre-Benavent, and A. Miguel-Dasit. 2007. Redes de coautorías y colaboración institucional. Rev. Esp. Cardiol. 60(2), 117-130. Doi: 10.1157/13099458 [ Links ]

Vega de Jiménez, M. and Y. Rojo. 2010. Red: estructura para generar innovación. Rev. Cienc. Soc. 16(4), 699-708. [ Links ]

Wasserman, S. and K. Faust. 1994. Social network analysis: methods and applications. Cambridge University Press, New York, NY. Doi: 10.1017/CBO9780511815478 [ Links ]

Zazo, Á.F., S. Ardines, and E. Castro. 2015. Redes de colaboración de las unidades de investigación de la Universidad de Panamá: investigación, desarrollo e innovación. Redes. Rev. Hisp. Análisis Redes Soc. 26(2), 84-117. [ Links ]

Received: June 18, 2016; Accepted: November 30, 2016

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