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Revista mexicana de ciencias agrícolas

versión impresa ISSN 2007-0934

Rev. Mex. Cienc. Agríc vol.7 spe 15 Texcoco jun./ago. 2016



Occult coverage measurement services technical assistance and training in rural areas

Bey Jamelyd López Torres1 

Roberto Rendón Medel1  § 

Teodoro Espinosa Solares1 

Paola Torres Díaz Santana1 

Eduardo Santellano Estrada2 

1Universidad Autónoma Chapingo. Carretera México-Texcoco, km 38.5, Chapingo, México. C. P. 56235, (;;

2Universidad Autónoma de Chihuahua, Av. Escorza No. 900, Zona Centro, Chihuahua, Chihuahua. C. P. 31000. (


The coverage change agents can be increased by 22.4% if chosen to producers using proximity and targeting criteria. In turn, coverage can be increased by 32% if you include producers and other actors such as suppliers or other agents of change. Looking to improve efficiency in the use of public resources, the use of a new concept of coverage using criteria of innovation networks is proposed, including targeting, greater diversity of actors and takes account of knowledge transfer two steps.

Keywords: change agent; cocoa; closeness; focus


La cobertura de los agentes de cambio puede incrementarse en 22.4% si se eligen a los productores empleando criterios de cercanía y focalización. A su vez, la cobertura puede incrementarse en 32% si se incluyen a productores y a otros actores como proveedores o incluso otros agentes de cambio. En busca de mejorar la eficiencia en el uso de los recursos públicos, se propone el uso de un nuevo concepto de cobertura que utiliza criterios de redes de innovación, incluye focalización, mayor diversidad de actores y toma en cuenta la transferencia de conocimiento a dos pasos.

Palabras clave: agente de cambio; cacao; cercanía; focalización


Of the 5.5 million existing UPR in Mexico in 2010, 2% had technical assistance, half of these were paid with public funds and other private resources (McMahon and Valdes, 2011). This coverage they have achieved ATyC public services raises the need to improve efficiency through new models aimed at obtaining results, efficient use of resources and accountability. Coverage is a concept that refers to the direct care of a program with respect to certain target population when the program covers 100% of this population is talk of a "universal coverage" (Arenas de Mesa et al., 2001). The National Assessment of Social Policy (CONEVAL) shares this concept and its target population is people or projects, the objective of the program. CONEVAL also evaluates programs SAGARPA, where its main restriction is that for some of these programs is not possible to calculate the coverage, because you do not have the quantification of the target population (CONEVAL, 2011).

The SAGARPA defines the coverage of services ATyC, as the percentage of the UPR receiving these services by professional service providers (PSP) or specialized training institutions (IICA, 2009). This research proposes a new concept of coverage. The coverage ratio actors that access to knowledge results in ATyC respect to all actors who are part of a network is defined.

The actors can be producers, government institutions, educational institutions and research organizations, traders, agro-industries, suppliers and consultants.

The ATyC is defined as the services are provided by technical or institutions with the aim of transferring technology and knowledge to producers.

A network is defined as these to factors and the relationships between them around a specific productive activity in rural areas.

This definition recognizes that through communication links actors in rural areas transmit knowledge, information and technology, which fosters innovation by Muñoz and Altamirano (2008). ATYC services are a means for producers to have access to the use of modern technologies, ie these services seek to transfer technologies. Technology is defined by Zarazúa et al. (2011) as the result of the application of various scientific knowledge to understand, improve or create techniques. The technology can be transferred basically through two models: the linear model of technology transfer and the systemic model of technology transfer.

The linear model of technology transfer is the process by which technology produced or generated in one place is directly applied in another. Researchers involved in this process who develop technologies and innovations; extension, transferring the message standardized developed by researchers, and farmers who simply play the role of those who adopt or reject the technologies developed by the former (Haverkort, 1991). The linear model considers four steps: generation, validation, transfer or diffusion-extension and adoption (Evenson, 1994). Technology transfer along those steps involves the flow of knowledge, skills, organization, values and capital from the point of generation to the adoption (Lall, 2000).

The systemic model of technology transfer is rooted in systems theory (OECD, 1997), where the actors involved in research and technology transfer at different levels involved with the various players involved present. The difference of this model with the linear model is that technological user takes an active role in the learning process and codifying their knowledge and skills (Zarazúa et al., 2011), the stages change of approach and a component is added essential to model agricultural policy (Peterson, 1997).

Analysis of innovation networks

In recent years Mexico has generated findings regarding the use of network analysis in the rural sector. These results come from work and research seeking alternatives interaction with farmers to generate competitiveness through improved processes extensionism and rural innovation (Muñoz et al., 2004; 2007; Zarazúa et al., 2011; Díaz et al., 2013; Sánchez et al., 2013).

Network analysis innovation is a tool to analyze a territory to a network of actors and public relations develop in their social, technical and commercial environment for the purpose of promoting intervention strategies in the rural sector around a production system. Network analysis supports innovation bases in the theory of Social Network Analysis (ARS). This analysis tool allows us to find new ways of intervention to join efforts and existing extensionism ATyC in the rural sector; It has been used for locating key stakeholders to further the process technology transfer (Muñoz et al., 2004; 2007; Zarazúa et al., 2011), adoption of innovations (Muñoz et al., 2004; Díaz et al., 2013; Sánchez et al., 2013), as well as the design, monitoring and evaluation of intervention strategies change agents.

The aim of this research was to analyze the coverage of services ATyC five cases of agents of change, using social network analysis, seeking to improve the efficiency of public and private investment and access to knowledge in rural areas.

Materials and methods

For research were considered exchange five officers who attended directly to 122 cocoa farmers in Mexico between 2010 and 2011 (Table 1).

Table 1. List of cases of change agents considered for research. 

The information used in research comes from the bases corresponding to the diagnostic information of agrifood chains served by AGI that operated between 2010 and 2011- data information was provided by the technical unit specializing in innovation management (UTE-innovation) through its virtually platform.

The database of each AGI has the following elements: identification of UPR direct care (producers that the change agent had detailed monitoring and counted on logbook records), innovation networks (social, technical and commercial) catalog of actors and networks. Note that the networks are coded to be dichotomous and their relationships are directed.

Identification of key players

In rural areas it is possible to identify different types of actors according to some features such as relationships and whether these have indicated or are identified by others as a source of information. Identifies 3 types of actors: source actor, collector actor and an actor articulator. When one of these actors is of some importance by the number of relationships or their position in the network can be called key player. The first two types of actors were included in the study to calculate coverage. The Table 2 defines three types of actors, including its graphical representation where each actor is represented by a node and the relationship is directed, represented by an arrow.

Table 2. Types of actors can be identified in an innovation network. 

To determine which producers are key source and which as collectors in an innovation network is used scope concept developed by Borgatti (2006). For actors sources node degree is considered based on the input area; for collectors’ nodal degree considering the proximity output (Borgatti, 2006).

Coverage estimator

The concept of coverage, this study used is based on the scope defined by indicator Borgatti (2006) shown in the following equation.


K= group of key players or dominant set j is an actor of the network that does not belong to K; dKj= length or minimum distance from any member of the group K to a node j, measured in steps and N= j set of actors; DR = ratio of all nodes reached by the set K, where nodes are weighted by the inverse of the minimum distance.

A range of 100% of the network will be obtained when the set K reaches all actors set N and a range of 0% is obtained when the set K is completely detached from the set N. The distance is defined by Everett and Borgatti (1988) as the number of links or ties between an actor and another, it is possible to calculate minimum and maximum distances.

In the application, the algorithm selects a group of key players K with which a change agent can intervene directly to these through their relationships (steps) with other actors are capable of transmitting knowledge previously received , meaning that they can optimize the dissemination of information. Therefore the coverage, scope is interpreted as two steps an agent of change achieved as a result of attending directly to a particular group of key stakeholders that are part of a network. The research was conducted in two stages, the first to contrast the coverage taking into account as key players only to producers, which was named "selection of actors", while the second contrasts coverage taking into account as key players to producers and other actors so it was called "diversity of actors."

For the two stages of the joint research network resulting from the merger of social, technical and commercial networks each observation was built. To calculate the scope defined by Borgatti (2006) Ucinet software version 6.288 for Windows (Borgatti et al., 2002) was used. This software was possible to plot the networks of the five observations; subsequently, they were transferred in implementing Ucinet: KeyPlayer 2; with the two "harvest" indicators (input range) for source identification actors and "diffuse" (reach out) for the identification of collectors actors were obtained.

Selection of actors

In the step of selecting actors a comparison was performed between the coverage obtained by the potential change agent and coverage, both producers. The obtained coverage represents the percentage of the network to which the producers served directly by the change agent had access to total network. On the other hand, the potential coverage is estimated as a result of choosing the same number of players but considering criteria for identifying networks of actors sources and collectors coverage. For the calculation of coverage obtained they are entered KeyPlayer two actors who had direct care as required actors and with the same calculation process the harvest and diffuse indicators, which were identified source application and what collectors. To calculate coverage potential key producers were identified in the joint network requesting half of source actors and half of collectors actors for each case, with the same number as the direct care at each observation, using only producers actors.

Diversity of actors

In the diversity of actors stage a comparison between the coverage obtained by the agent of change and potential coverage range of stakeholders was made. For this stage only the potential diversity of actors coverage was calculated as the coverage calculated obtained in the previous step was used. To calculate the potential coverage range of stakeholders: stakeholders were identified in the joint network, so that a combination of source and collectors actors in the same ratio was obtained, these may be different producers, with the same number the direct care cared for each observation. Finally indicators of these key actors were calculated and summed to obtain the potential coverage range of stakeholders.


Selection of actors

At the stage of selection of players, the calculation of the indicators allowed to choose closer and better connected to the network producers, where even producers who were treated were selected directly, and other producers which coincided with those who were selected by change agents. In Figure 1 the coverage achieved with elected by the change agents conventionally producers and chosen based on criteria producers network analysis are compared; i.e. shows the comparison between the potential coverage and coverage obtained, the coverage obtained in all cases is less than the potential difference with an average of 22.4%.

Figure 1. Coverage potential and obtained in each network with producers. 

Change agents may have increased their coverage in 22.4% have chosen his actors taking into account the structure of the network and selecting producers network criteria, at least the identification of sources and collectors actors. The maximum increase (32.4% of the network) occurred with the change agent 2.

Diversity of actors

Regarding the stage of diversity of actors, Figure 2 shows the comparison of coverage between a) the coverage obtained with producers direct care b) the potential coverage by selecting all kinds of actors, the latter is the one that could have been achieved considering criteria of connectivity and closeness, but this time with different actors such as government institutions, educational institutions and research organizations, traders, agro-industries, suppliers and consultants.

Figure 2. Coverage of each network by type of actor. 

The average increase of coverage that can be achieved by ATyC services with diverse stakeholders regarding selected only UPR traditionally is 32.2%. The inclusion of diverse actors in an ATyC intervention strategy by the agents of change can reach higher levels of coverage, with spans ranging from 68% to 93% of the actors in the joint network. The smallest increase was in observation 3 with 9.6% and the largest increase was in observation 2 with 50% of the network. Derived from the first two stages of research a new way to calculate the coverage where the inclusion of diversity of actors considered and the selection of key stakeholders that enable knowledge transfer at least two steps is proposed. The Figure 3 shows the agent of change and different combinations of actors who can access directly or indirectly. The calculated so far by SAGARPA coverage takes into account only the coverage achieved directly with actors A (UPR receiving services ATyC), excluding coverage a step with actors B (UPR not receiving services ATyC), and C (other actors) and cover two steps in any combination. That is, only that in calculating the percentage of the UPR receiving services ATyC omitted the rest of the target population has access to knowledge as a result of the intervention of the change agent.

Figure 3. Coverage of a change agent. 

The Figure 3 shows the concept of coverage ATyC services that this research suggests. Directly where the change agent can intervene with actors A, B and C. While indirectly communicating A, B and C with A and B grow other options that they can perform activities as a way: replica, testing, verification, reaffirmation and dissemination of knowledge, information or further collaboration technologies based on the combination of actors, through which these actors access to knowledge.

This proposed coverage calculation takes into account access to knowledge as a result of an intervention by an agent of change. So then direct and indirect intervention of an agent of change is defined. direct intervention is considered when there is a direct relationship (which may be of assistance, training, bonding, collaboration among others) between the change agent and an actor who can be UPR or other actors (government agencies, educational institutions and research organizations, traders, agro-industries, suppliers and consultants), representing intervention coverage agent to a step change. Moreover, intervention is indirect when an actor who has received a direct intervention transmits information or knowledge to a third actor, this intervention represents the coverage change agent two steps. Indirect intervention is important in the participation of actor who has received a direct intervention, as it serves as articulator, "filter" or "information bridge".


The results of the selection stage actors suggest that the success of an intervention in terms of coverage depends on proper selection of actors with which to intervene. It can be seen that smaller increases in the percentage of potential coverage for coverage obtained is given in cases 3 and 5 with 12.7 and 6.4% respectively. One explanation for this is that these networks had been previously handled by an agent of change. This intervention achieved some network configuration and composition of the actors was already known by the agent of change in the intervention study. That is, the actors, which was favorable to intervene, were identified.

Targeting and diversity of actors in the processes of ATyC can effectively increase the network coverage, the suggestion that the participation of diverse actors contribute positively to the process of technology transfer, innovation diffusion and extension is not new. The technological innovation systems (Lundvall, 1988), the Transferred technology systems approach (OECD, 1997), induced innovation Hayami and Ruttan (1989) and the participatory model on technological innovation based on Bruin and Meerman (2001) they are some of the models that have been raised. Even the operating rules of SAGARPA (2013) suggest the involvement of diverse actors in their programs. However, this is not yet reflected in the intervention strategies of so-called agents of change in rural areas. This contrasts with the business sector, currently the success of small and medium enterprises (SMEs) Mexican is based on its management model innovation, as evidenced Solleiro et al. (2006) in a case study of Bioclón Institute. The model used by the institute is based on creating a network of collaboration with research institutions, opinion leaders and health specialists, complemented by internal capacity in technology and knowledge management, such as training, exchange knowledge, market development among others. Include in rural intervention strategies to all kinds of actors as suggested by the results involves a great challenge not only for the agents of change, but for the network in general (Aguilar and Rendón, 2010). However, an institutional strategy to establish and maintain those relationships is necessary. The development and consolidation of such links will increase the impact and efficiency of the whole system of innovation in achieving sectoral targets (McMahon and Valdés, 2011).

In analyzing the first two stages of the investigation it shows that the largest increase in coverage is obtained when considering simultaneously selecting UPR and other actors in the processes of intervention ATyC services. Cuttriss et al. (2013) mention that one of the factors that can influence estimates of coverage is progress in mapping technologies. What this research suggests is that the new mapping methodologies with which already has, to improve how to estimate coverage and access to knowledge, specifically the DCITyER program that aims producers access to knowledge, to be used information and use of innovations in an environment of multiple actors. The Table 3 shows the main differences between the concept of coverage SAGARPA and the concept that this research suggests.

Table 3. Differences between coverage concepts. 

When taken into account the communication links between actors, it favors the knowledge transmitted by a change agent direct way an actor, to be transmitted to other actors indirectly, Choi et al. (2010) mention that it is essential to study the diffusion of innovations know the structures of networks. This vision coverage to two steps also can be seen as a stimulus to promote interaction network between different types of actors and promote innovation among actors, which is defined by Rogers (1983) as a process of individual learning or collective. It is recommended that network analysis is used by technical advisors and other agents of change as a tool for the selection of actors in search of higher coverage of productive network in involved.


The change agents can achieve greater efficiency in the use of public and private resources, and increase the likelihood of achieving the expected impact on schemes ATyC for rural development, properly selecting the producers that promote technologies to spread.

Coverage levels achieved by agents of change through a selection of actors traditionally can be overcome by a focused selection of participants by type of actor and focused on the proximity with respect to the network. This is achieved by identifying and including the main source and network collectors in the intervention strategy change agent actors. The selection of producers targeting criteria allowed an increase of 22.4% coverage.

Most hedge effectiveness is obtained if chosen as key players for the services ATyC also producer to a variety of actors. Agents can increase their coverage at 32.1% result of considering producers and non-producers actors as recipients of the actions of an agent of change. The concept proposed coverage allows for coverage of actors who have access to knowledge in rural areas as a result of ATyC services. This concept of coverage considers three aspects: focus, taking into account a variety of actors and measure the extent to two steps.

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Received: February 2016; Accepted: May 2016

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