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Revista mexicana de ciencias agrícolas

versión impresa ISSN 2007-0934

Rev. Mex. Cienc. Agríc vol.7 no.6 Texcoco ago./sep. 2016



Analysis of financing networks from national agricultural innovation system in Mexico

Venancio Cuevas Reyes1 

Anastacio Espejel García2  § 

Georgel Moctezuma López3 

César A. Rosales Nieto4 

Alfredo Tapia Naranjo5 

1Campo Experimental Valle de México-INIFAP. Los Reyes-Texcoco, km 13.5, A. P. 10, C. P. 56250. Coatlinchán, Texcoco, Estado de México. (

2CONACYT-Universidad Autónoma Chapingo-Posgrado en Ciencia y Tecnología Agroalimentaria (

3COMEF- INIFAP. Av. Progreso No. 5, Barrio Sta. Catarina. Delegación Coyoacán, C. P. 04010, México, D. F. Tel. 01 55 36268700. (

4Campo Experimental San Luis-INIFAP. Ejido Palma de la Cruz, C.P. 7843. San Luis Potosí, San Luis Potosí. (

5Sitio Experimental Querétaro-INIFAP. (


The present study aimed to analyze financing networks between research institutions belonging to the national agricultural research system (SNIA) in Mexico. In 2014, 164 surveys were conducted to educational institutions (IES), public research centers (CPI) and private companies or research institutions (SP) conducting research activities in the field of agriculture in Mexico. Social networking methodology was used to analyze and characterize the financing network of SNIA institutions nationwide. Calculating indicators and identification of actors was performed with Ucinet v.6 and Key Player v.2 software. Networks analyzed have low density; 0.48% (IES) 0.75% (CPI) and 3% (SP) which implies that there is little relationship between government actors. The financing network from SNIA is supported mainly by public resources and has low participation of private investment. It is concluded that there is low density between the nodes in the system, so that the study of the financing network shows a low level of articulation and linkage of public and private actors involved in agricultural research in Mexico.

Keywords: agricultural sector; density; innovation; links; research


El presente trabajo tuvo como objetivo analizar las redes de financiamiento entre las instituciones de investigación pertenecientes al sistema nacional de investigación agropecuario (SNIA) en México. En 2014 fueron realizadas 164 encuestas a instituciones de educación (IES), centros públicos de investigación (CPI) y empresas o instituciones de investigación privado (SP) que realizan acciones de investigación en el ámbito agropecuario en México. Se utilizó la metodología de redes sociales para analizar y caracterizar la red de financiamiento de las instituciones del SNIA a nivel nacional. El cálculo de indicadores e identificación de actores se realizó con el software Ucinet v.6 y Key Player v.2. Las redes analizadas presentan baja densidad; 0.48% (IES) 0.75% (CPI) y 3% (SP) lo cual implica que hay escasa relación entre los actores gubernamentales. La red de financiamiento del SNIA esta soportada principalmente por recursos públicos y tiene baja participación de inversión privada. Se concluye que existe una baja densidad entre los nodos del sistema, por lo que el estudio de la red de financiamiento muestra un bajo nivel de articulación y vinculación de los actores públicos y privados relacionados con la investigación agropecuaria en México.

Palabras clave: ámbito agropecuario; densidad; investigación; innovación; vínculos


An innovation system consists of a network or set of actors or both public and private institutions whose objective is that new products, processes and forms of organization have economic utility, along with institutions and policies that affect both their behavior and visible performance (Niosi et al., 1993; Freeman, 1995; Lundvall, 1992; World Bank, 2007). It is recognized that a strength from the systems approach to innovation is that provides a holistic explanation of how knowledge is produced, disseminated and used, while emphasizing the various actors and processes (Anlló et al., 2010).

"In the Mexican innovation system (SMI) it is possible to find most of the agents identified in what we might call 'Innovation Systems that work well' as are those of developed countries and some emerging countries. However, in SMI is possible to identify some link failures, knowledge transfer and interactive learning. These failures do not allow better interaction between system agents "(Casas et al., 2014; 44). It has been identified that the most important agents in SMI are government agencies and institutions, institutions of higher education (IES), centers and public research institutes (CPI), companies, linking agencies or intermediary institutions and financial sector (Ekboir et al., 2003; Casas et al., 2014).

A significant part from SMI is the so-called national system of Mexican agricultural research (SNIA) referred only to the research sector, which is composed by the network or group of public and private institutions that carry out research, development and innovation (I + D + i) in the primary sector. SNIA is composed of the National Institute of Forestry, Agriculture and Livestock (INIFAP), Graduate School (COLPOS), Universidad Autonoma Chapingo (UACH), Universidad Autonoma Agraria Antonio Narro (UAAAN), Technological Institutes of Agriculture under the Ministry of Public education (SEP), colleges of agriculture and veterinary from different state universities; state councils of science and technology; support groups to research and state foundations to promote and support projects oriented to technology transfer.

Recent studies where the configuration of the innovation system of Mexican Agrifood industry (SNIA) is studied indicates that despite the robust legal framework and institutional effort to articulate public and private actors to detonate innovation in Mexican agricultural sector the efforts from SNIA actors are not sufficiently consistent with its purposes and goals, often because they are too ambitious in light of the resources with which account (Solleiro et al., 2015). Some authors suggest that an innovation system exists independently of the level of government intervention (Hartwich and Jansen, 2007), and that this approach has weaknesses (Molero and Corona, 2008; Anlló et al., 2010).

A first weakness of this perspective focuses on how to measure these systems in order to study the evolution of its capacity for innovation and its results or compare innovation systems from different countries. A second existing weakness in developing countries, is that the identification of the actors involved is limited. So the links, relationships and institutional capacities are unknown to meet the demand of innovations that require different users from the agricultural sector (Anlló et al., 2010). So that the analysis of the links of research institutions and development in the agricultural sector are relevant for the generation of strategy and incentives that improve SNIA, thus the generation, transfer and use of innovations in the productive sector. The aim of this paper is to analyze financing networks and collaboration among institutions of science, technology and innovation belonging to the national agricultural research system (SNIA) in Mexico during 2013.

Material and methods


The analysis of the financing networks of the major players of the national agricultural research system considered the application of 164 surveys to research institutions established in Mexico. The survey was conducted nationwide between September and October 2014. The mechanics to obtain the surveys was to use the infrastructure from INIFAP, INIFAP has national coverage so that in each of the states of the country there is infrastructure and human capital. Thus, the steps followed to implement the survey were:

  • Identification through secondary sources (publications, Internet pages) of those institutions at state and national level that carry out I + D + i activities in the agricultural sector

  • Selecting a preliminary list of institutions in each state of the country

  • Shipment of the survey to a partner in each state

  • Each state partner verified and validated the list of institutions conducting agricultural research, including fishing, in their state

  • Selection of the final state list of institutions to be surveyed

  • Implementation of the survey

  • Shipping the survey to the central team for review and analysis

  • Development of the database in Excel of institutions surveyed

The main question that shaped and network structure of funding of actors was: ¿Did I mention the name of the sources of research funding that was in 2013?

Social network indicators

Data to identify knowledge networks of institutions were captured in Excel and then exported to Microsoft Notepad version 6.1 using the DL protocol and nodelist 1 format as this format allows to capture as a list of nodes to respondent and their respective relational links (Rendon et al., 2007). The file generated in notebook was exported directly to NetDraw 2.097 software. For the calculation of the indicator social network the Ucinet 6.288 software was used with Borgatti et al., (2002) procedures. The graphics and coverage indicators were made using the Key Player 2 software (Borgatti and Dreyfus, 2003), which focuses on the identification and selection of relevant actors, characterized by receiving or sending information to most network actors. The indicators used for the analysis of social networks were: density, network size, degree centrality of entry and degree centrality of output, in addition to identify the level of coverage that a node may have within the network was used the diffuser node indicator. The mathematical formulation to obtain this indicator is as follows:

Network density: Number of relationships between those possible. Where L = number of relationships. n (n-1) = number of possible relationships (Borgatti et al., 2002).

D=2Lnn-1*100 1)

Size: Where Τn is the size of the individual network from node n, and An are the actors directly related with actor n. A larger network suggests that actors or nodes are mostly connected (Borgatti et al., 2002).

Tn=i=1nAn 2)

For the analysis of this study, the degree centrality indicator was used: the degree is the number of points to which the node is adjacent, that is, with which has direct links. From this perspective, centrality would be defined by the potential capability of communication that nodes have. Degree centrality is calculated by recording the amount of links that address to an actor or leaving from the same actor. Calculated as follows:

di=ΣjAij 3)

Degree centrality is defined as the number of links from actor i. Worth noting that this may be a centrality entry (InDegree), referred to the links that reach the actor or the centrality of output (OutDegree), counting only the links that leave the actor. If an actor receives many links can be interpreted that has a greater "prestige" as the others want to relate with him. If an actor has a high degree centrality of output, it can be interpreted that this actor tends to have a greater "influence" as it is capable of bargaining with others or make other to agree with his interests (Freeman, 2000; Williner et al., 2012).

The identification and selection of stakeholders was performed with keyplayer 2 software (Borgatti and Dreyfus, 2003), same that focuses on the identification of a group of nodes characterized by the ability to receive all kinds of information from most nodes of the network. The diffuse are the group of nodes in the actual position of sending information to most nodes. Other key player are the arrangers of the -disrupt- network; i.e. those nodes that in case of disappearing fragment the network (Borgatti, 2003). Diffuser actor: is calculated using the following algorithm. The letter R as abbreviation of reach used in the literature of networks. Considering that distance dij from the last node to any other node is 1, and N is total nodes (Rendon et al., 2007).

R=Σj1dijN 4)

Results and discussion

According to several authors (Hall et al., 2001; Howells, 2006) for SIA to function and increase the capacity for innovation in agricultural sectors from developing country needs to count with shared visions, links and information flows well established among different public and private actors, to count with self-institutional incentives to increase cooperation, in addition to proper market environment, legislative and policy with well-developed human capital. The following sections are analyzed some of these visions, linkages and relationships that exist in the financing network of higher education and research institutions (IES) are studied, public research centers (CPI) and the private sector (SP) as key actors from SNIA in Mexico.

Financing network of higher education institutions (IES)

The approaches on innovation systems emphasize that "organizations and institutions do not innovate in isolation but in the context of a system. Thus, the central idea is that the essence of a SNI that works well is the existence of a dense network of systemic interactions among agents"(Casas et al., 2014). In this sense, the analysis performed to the financing network of actors from the agricultural research system in Mexico shows a low density of interactions between the different agents that make up this system. The network density was 0.38% which means that there are few sources of funding at national level and therefore low SNIA articulation; i.e. few actors keep united the system.

Centrality entry and output quantifies the power exercised by an actor or actors within the network either as referrals or information seekers, the value of centrality entry 18.09% suggests that there is an actor or referred actors whose coverage relation is 18.09%, this centrality entry indicates that there are referred actors in the network with a certain concentration of power. Moreover, centrality output is low (2.15%) which refers to information seekers, this value suggests low prevalence of some actors and under the leadership of institutions, i.e., a large part or all institutions seek various funding sources separately and in a very low proportion together (Table 1).

Table 1. Indicators of financing network from IES in Mexico. 

Indicador Valor
Nodos 278
Relaciones 293
Densidad 0.38%
Centralización de entrada 18.09%
Centralización de salida 2.15%
Colector 20.65%: Consejo Nacional de Ciencia y Tecnología (CONACYT); Recursos propios, Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (SAGARPA)
Difusor 5.07%: ECOSUR Chiapas; Instituto Politécnico Nacional- IPN- Ciencias Biológicas
Estructuradores Valor Delta:0.001, CONACYT; Recursos propios; SAGARPA

The key actors identified (collector and diffusor) are important from the point of view of coverage, the first with a value of 20.65% indicates that three sources of funding CONACYT, Own resources, SAGARPA, are actors who have referred the institutions as main funders, these three have the ability to concentrate about 20% of the relationships between network nodes (Figure 1). The diffusers value of 5.07% suggests that through the links generated through project financing, the ECOSUR Chiapas and Biological Sciences from IPN actors are best suited to play a role as disseminators of information; however, their coverage is low, this is explained because many of the institutions go to funding sources individually and a very low percentage does it together in addition to few existing funding sources in Mexico.

Figure 1. Financing Network from IES in Mexico. 

Network structure is defined as those actors or institutions that when disappear can fragment the network relations shown, as the network is very weak if any of the funding sources CONACYT, own resources and SAGARPA, stop financing research projects the network would fragment almost completely. The funding sources that act as arrangers correspond to the same actors identified as collectors, if these actors disappear from the network there would be no funding sources and also the network would fragment at 99.8%; i.e., there would not be links or network of actors who had relation for the development of research and innovation in the agricultural sector of our country.

This network without being a network with a high concentration of power is too weak because only three actors or institutions (funding) are supporting the lattice structure from SNIA. Recent studies on SNIA indicate that efforts to articulate and detonate innovation in the agricultural sector have not been consistent with the objectives and goals set, it also presents a variety of actors that includes both those linked to the productive chain as government actors, and policy frameworks (Solleiro et al., 2015).

Financing Network from public research centers (CPI)

The financing network from public research centers analyzed shows a more extensive network compared to the network of higher education institutions in research, has more actors involved because each of the centers seek funding partners locally and at regional level. Network indicators indicate a density of 1.24% which is very low; however, considering that each research center has regional coverage and funders, it is not possible that all centers relate to all funders. Centrality entry and output is very low, this suggests a regional concentration of some actors which is not perceived with the national analysis (Table 2).

Table 2. Indicators of financing network from CPI in Mexico. 

Indicador Valor
Nodos 95
Relaciones 111
Densidad 1.24%
Centralización de entrada 5.19%
Centralización de salida 21.32%
Colector 8.6%, el Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT), Comisión Nacional Forestal (CONAFOR)
Estructurador Valor delta 0.005: CIR- CENTRO, CIR- NOROESTE

Research centers that for the amount of projects funded are presented as possible diffusers and in this case could have a coverage of 45.6% between research network actors are the regional research center CIR-CENTRO and Northwest CIR -NOROESTE, both from INIFAP. Research centers that act as support structure for research in higher amount of funds raised corresponds CIR-CENTRO and CIR-NOROESTE, if at some point these research centers ceased to receive funds the financing network would fragment 99.5% this percentage of fragmentation is a weakness for public centers analyzed as it shows the vulnerability (and in the same sense the importance of INIFAP) of the financing system from agricultural and forestry research in Mexico (Figure 2).

Figure 2. Financing network from CPI in Mexico. 

It is important to note that unlike IES, CPI whose primary mandate is research, originating to obtain a network with a large number of funding sources SAGARPA through Fundacion Produce in each of the states, (SEP-CONACYT, SAGARPA-CONACYT, CIMMYT, PRONATURA, and CONAFOR, among others) at national level, with regions that have greater linkages and therefore major projects for the generation of innovations.

Financing network from private sector institutions (SP) conducting research in the agricultural sector in Mexico

The private sector represents a potential in generating research, however is recurrent that project funders of this actor are practically the same as public research funding. In the agrifood industry of Mexico, research results from private sector are virtually unknown to various users, or at least are not public the research products they get, which causes possibly to be private research funded with public funds. The density of private institutions that conduct research is only 4.40%, but also the number of institutions analyzed was low. Centrality both entry and output is low which means that there is no preponderance of private institutions in access to public financing (Table 3).

Table 3. Indicators of financing network from SP institutions in Mexico. 

Indicador Valor
Nodos 20
Relaciones 17
Densidad 4.4%
Centralización de salida 11.9%
Centralización de entrada 11.9%
Colector 33.3%: CONACYT, recursos propios
Difusor 50%: TRIDEGEN S. A. de C. V., Inv. y des. aplicada de Ags.
Estructurador 0.034: recursos propios, Inv. y des. aplicada de Ags.

The actors that can act as information collectors and which are who fund the private sector are CONACYT and own resources, these concentrated 33.30% of the relations on this network. Diffusers that can reach 50% of the actors in the network are two private companies, which take in the same way the role of arrangers (Figure 3).

Figure 3. Financing network of SP in Mexico. 

In accordance with Dutrénit et al. (2010), the funding from private sector in SNIA is virtually absent. The results obtained in the analysis of the funding network to private research institutions in the agricultural sector show a low level of articulation and linkage of the actors in Mexico. Studies conducted at national level indicate that the Mexican Innovation System (SMI) has two main characteristics as interactions refers (Dutrénit et al., 2010 cited in Casas et al., 2014), on one hand, the private sector acts as a isolated agent within the system, maintaining relationships almost exclusively with other companies in the business sector, with the Government through macroeconomic policy and access to incentives and to various funds. And secondly, most interactions occur between public institutions, particularly between CONACYT and Public IES, between CONACYT and CPI, and between public research centers-public research institutes. This study confirms what some authors (Casas et al., 2014; Solleiro et al., 2015) said: "the national innovation system in Mexico disarticulated".

SNIA has a low density of links between its actors, this may contribute to what happens in reality: the existence of a low use of innovations by agricultural, livestock and forestry producers. So it requires greater efforts to increase density, but even more, to ensure that final beneficiaries use innovations that impact their production systems. One possible way is to support through SNIA greater validation processes and technology transfer, as Busse et al. (2015) says the validation process of innovation is a key to increase the use of these, as producers will only use those that are practical, simple and useful for their production processes.

On the other hand, it is important to identify what some authors call innovation intermediaries (Howells, 2006) to generate links that favor the development and increased research and development projects that generate useful innovations for the primary sector in Mexico. An innovation system with weak links between actors that integrate them demand strengthening of intermediary organizations in creating an institutional environment that promotes links and interactions required in order to build dynamic networks within and between research projects and innovation (Klerkx et al., 2009a); in this regard, cooperation between different types of actors is seen as key to success in the generation and adoption of innovation (Howells, 2006; World Bank 2007; Klerx et al., 2009b).

The analysis conducted in this study show that the density of the funding network from the National Agricultural Research System in Mexico is low; 0.38% (IES), 1.24% (CPI) and 4.4% (SP), which implies that there is little relationship and linkages between actors, and also, and perhaps most importantly: the financing network of agricultural research in Mexico is mainly supported by the governmental sector: CONACYT and SAGARPA, with very little participation from private companies.


The financing network analysis of the actors who make up the National Agricultural Research System in Mexico indicates that the density is low, 0.38% for educational institutions, 1.24% for public research centers and 4.4% for private sector. Therefore few links and relationships between agents that make up the network are present. Key agents for creating links, are governmental actors such as the National Council of Science and Technology, the Ministry of Agriculture, Rural Development, Fisheries and Food and the Ministry of Education. The participation of the private sector is limited and self-generated or own resources from institutions are a key factor that allows the financing of research projects.

The nature and specificity of this study on funding sources for agricultural research allows to identify the organizational and institutional arrangement of competitive funds must be sorted into three levels: national, regional and state, as well as strengthening the competiveness between researchers to gain access to these resources, this in order to streamline public resources and to be transformed and reach territories and end users of the research. Research shows that access to financing funds participate both public and private institutions of national and state court; however, there is a low contribution from private sector to public funding of research, which suggests funding of private research with public funds. There is a low density between nodes in the system, so that the study of financing network shows a low level of articulation and linkage from public and private actors involved with agricultural research in Mexico.

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Received: July 2016; Accepted: September 2016

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