Introduction

Programs of agricultural extension applied in Latin America have their origin in the North American model. Their purpose was to “extend” the knowledge from research and universities and to transfer technology that would allow farmers to increase production (^{Aguilar Ávila et al., 2010}, ^{Alarcón and Ruiz 2011}). However, the institutional and personal efforts in place to achieve results through extensionism in México’s rural sector have not had the results expected (^{Aguilar Ávila et al., 2010}).

There have been several causes; ^{Freire (1973)} points to the existence of a strong weight towards “a naïve vision of reality and in the most common case, a clear sense of superiority, dominance, with which the technician approached peasants who are immersed in a traditional agrarian structure”. In his part, ^{Engel (1998)} attributes it to five characteristics of extensionism: 1) linear character; 2) contempt for non-scientific knowledge; 3) lack of direction towards demands from producers and demands from markets; 4) paternalist approach; and 5) attention to the producer individually.

The naivety that Freire mentions continues today; an example are the public programs for technical assistance and training that do not manage to define a target population which they want to impact (^{McMahon et al., 2011}). They start from the assumption that any producer is appropriate to begin actions of technological transference, insofar as he/she belongs to the target population defined. ^{Obreque (2010)} mentions that the transference processes for innovations cannot be performed by just anyone; the person to carry out such a process must have an innovating profile.

To contextualize, according to the Agricultural, Livestock and Forestry Census performed in 2007 (^{INEGI, 2009}), in México there are 5.5 million rural production units (RPUs). Of these, 4.06 million report agricultural or livestock activity, and 1.3 % (52 781) of these received technical assistance services paid for with public resources, and 1.6 % (64 340) received it through private payment.

According to records by the Ministry of Agriculture, Livestock Production, Rural Development, Fishery and Food (Secretaría de Agricultura, Ganadería, Desarrollo Rural Pesca y Alimentación, SAGARPA)¹, an estimate of 11 758 Professional Services Providers (PSPs) is considered, who give technical assistance and training to 52 781 RPUs, where the result is that each consultant offered this service to 4.4 RPUs per year, in average. If it is considered that these 4.4 RPUs were not selected in function of their access and spreading of knowledge they receive, it can be inferred that within a national context, this type of service can achieve the coverage mentioned before. By coverage, we understand the proportion of actors who receive an intervention (direct or indirect) with regard to the totality of actors who are part of a network around a specific productive activity in the rural sector (^{CIESTAAM, 2013}).

In México, currently, the services of training and technical assistance develop under the model of Platform-Module-Extension area, the so-called Hub model (^{SAGARPA, 2012a}). Programs such as Sustainable Modernization of Traditional Agriculture (Modernización Sustentable de la Agricultura Tradicional, MasAgro) and Support to the Productive Chain of Maize and Bean Producers (Apoyo a la Cadena Productiva de los Productores de Maíz y Frijol, PROMAF) use demonstrative modules as a policy strategy to increase the current coverage of such services.

Under the Hub model, the difficulty for the selection of a good demonstrative module that is a source of information (of technologies and innovations) is relevant, since the methods currently used are related to operative and normative parameters. This does not mean that the method is not well-designed for this purpose; however, it should be complemented with the identification of “key” actors who help to make dynamic the transfer of knowledge; that is, that the so-called “relational criteria” are incorporated, understood as the identification and use of the individual position of each member of a local network and the structure of the network in general.

^{Rogers (2003)} mentions that adoption of innovations takes place through the interaction between producers, since the individual and group achievements depend on the internal and external relationships for the exchange of information, knowledge and resources between the diverse actors present in a region; studies by ^{Monge Pérez and Hartwich (2008)} show that producers adopt an innovation once their peers have done it; that is, from those producers who have similar patterns of social connection. Likewise, ^{Leeuwis and Van den Ban (2004)} and ^{Röling (2009)} highlight interdependence between actors in a network, its effects, joint learning, and social interaction as factors in the adoption of innovations.

The objective of this study was to analyze the coverages obtained in two management strategies for innovation (MasAgro and PROMAF) through the tool of innovation network analysis, to propose criteria that complement the current selection of demonstrative modules.

Methodology

Results and Discussion

The coverage that a PSP can achieve in agricultural extension with rural producers is related to the identification of these based on their individual position within a network, whether this is a position from the point of view of being a source of technical information or prestige, or a social reference (Table 1).

Table 1 Descriptors of variables analyzed. Producers of module and extension areas in Chiapas. MasAgro and PROMAF Programs. 2012. 

Source: authors’ elaboration based on field information.

The analysis of correlations shows a relationship between the social coverage and the technical coverage of the producer with an important level of association, a level of significance of p<0.05 and a positive relation; that is, the coverage that the producer has in the technical network is increased as the coverage it has in the social network increases. Likewise, a relationship was found between the coverage in the technical network and the level of INAI of the producer with a low level of association, a level of significance of p<0.01 and a positive relationship (Table 2).

Table 2 Pearson correlation between variables and level of significance. Producers of module and extension areas in Chiapas. MasAgro and PROMAF Programs. 2012. 

^†Correlation with a level of significance of p<0.05. ¶Correlation with a level of significance of p<0.01.

Source: authors’ elaboration based on field information.

Table 2 points to relationships that lead us to believe that the social coverage of the relationships of a producer is related positively with his technical relationships, and that these technical relationships influence the level of innovation of the producer. Therefore, a strategy of innovation management directed at increasing the level of innovation in producers must start from the consideration of his relationships, both technical and social. That is, the combination of technological leadership and social prestige must be criteria for the identification of demonstrative modules. This coincides with what was described by ^{Rogers and Shoemaker (1971)}: technological leadership and social prestige are two requirements considered essential to establish a demonstrative module and to have a fast spread of knowledge through “imitation”.

The comparison of means showed that no significant differences were found in the social network between the variables analyzed. This is explained because the relationships are interpersonal, that is, they are the interactions that individuals normally carry out. That is, the social structure on which PSPs from both programs act is the same.

Concerning the technical network, the variables studied do not show significant differences (Table 3). However, the average INAI and coverage of MasAgro demonstrative modules are higher than the rest. This is explained because the relational criteria are implicitly included in the identification of the MasAgro modules, and the attention takes place directly because it is a requirement for their accreditation as MasAgro Certified Technician. The non-significance of the differences, beyond the statistical basis, can be explained because it is the first year of operation and during which the levels of innovation have not been expressed in the yields or in the coverage that they reach as sources of information.

Table 3 Means comparison of INAI and Coverage variables by type of reference of the producers in the technical network. 

Means with different super-indexes in the same row are significantly different (p<0.05).

Source: authors’ elaboration based on field information.

^{Hobbs et al. (2008)} mention that the AC is a process that considers the minimum removal of soil (non-tillage), permanent coverage (agricultural residues) and crop rotation. In turn, ^{Friedrich and Kasamm (2009)} mention that the farmers from countries where AC is not practiced face a series of barriers of intellectual, social, biophysical and technological, financial, infrastructure and policy nature, which makes the technological adoption of practices in AC difficult, and which result in more complex processes for analysis and intervention. In this research, the maximum level of innovation (INAI) found was of 80 % in producers with more than two cycles working with conservation agriculture; that is, the familiarity they have with innovations (^{Wejnert, 2002}; ^{Rogers, 2003}) is higher than in the rest of the producers.

^{Wejnert (2002)} mentions three main components in the adoption of innovations: 1) the characteristics of the innovation; 2) the characteristics of the innovators; and 3) the characteristics of the environment where the innovators act. ^{Frambachet and Schillewaert (2002)} identify five factors that influence the individual’s adoption decisions: attitude towards innovation, personal characteristics, the organization’s facilitators, innovation capacity and social influences. Thus, the social influences are related to technological aspects.

^{Leeuwis and Van den Ban (2004)} and ^{Röling (2009)} highlight the interdependence between the actors of a network, its effects, the joint learning and the social interaction as factors in the adoption of innovations. In turn, ^{Monge and Hartwich (2008)} point out that the producers adopt an innovation once their peers do, pointing out that the social pattern influences the technological pattern.

As was mentioned previously, the success of the Hub model depends on an adequate identification of demonstrative modules, on the functioning of platforms and the connection between the elements that make up the model, including the extension areas. When identifying actors that are better connected and positioned in the network, greater coverages can be reached. However, for the establishment of demonstrative modules, in addition to the good connection of the producer, it is required that he/she also have a good innovation level.

In this sense, the proposal of complementing the current identification of demonstrative modules is directed towards the use of the Level of Innovation and the Coverage of the actors intervened. This implies complementing the current criteria with which the PSPs and the programs themselves direct when establishing these modules.

Figure 1 shows the location of the producers selected by the strategies studied according to their level of Innovation (INAI) and connectivity within the Social Network.

Source: authors’ elaboration based on field information.

Figure 1 Location of producers according to their level of INAI and coverage within the Social Network. 

Figure 2 shows the location of the producers selected by the MasAgro and PROMAF Certified Technician strategies according to their Level of Innovation (INAI) and connectivity within the technical network. According to the graph, the modules of Certified Technician have better locations than the PROMAF modules, particularly in terms of connectivity.

Source: authors’ elaboration based on field information.

Figure 2 Location of producers according to their level of INAI and coverage within the Technical Network. 

Figure 2 suggests that producers in quadrant I are well located in terms of coverage and level of innovation. Producers located in any other quadrant require being intervened in a different way, so they can express their potential as demonstrative modules.

Table 4 presents the actions that would have to be developed for each type of producer in function of their level of innovation and connectivity. In quadrant I, producers that must be considered as first option to establish demonstrative modules are located. Their characteristics as innovators and connected producers make them ideal to serve both for the validation of technological practices, and to spread their results. Both are substantive characteristics in a demonstrative module.

Table 4 Work proposals according to the producer’s location. 

Source: authors’ elaboration.

Although quadrant I is the most adequate, only 5.8 % of the modules and the extension areas are found in it, and from this the importance of their correct and timely identification.

Quadrant II makes up 15 % of the producers considered as modules and extension areas. These producers are well connected but do not show medium-high levels of innovation. That is, they have the potential to divulge, but so-to-speak, do not have anything to spread from the point of view of innovation.

Producers from quadrant IV show, contrary to those of quadrant II, good levels of innovation, but low connectivity in the network. They have something to spread, but they do not have the channels to do so.

Most of the observations are located in quadrant III (58 %). They are producers without innovation or connection. Since they are the most abundant, the probability of being chosen as modules is higher, despite their lack of the desirable attributes of innovation and connectivity. These producers tend to be the most attentive to the normative fulfillment of the programs, since they constantly seek public subsidies, increasing the probability of being selected as demonstrative modules.

The Professional Service Provider plays an important role in helping producers in quadrant III to move to quadrant I. That is, the role of change agent must be transformed into the role that ^{Howells (2006)} calls “innovation intermediary”, or ^{Klerkx et al. (2009)} call “systemic negotiator”; that is, those organizations that act as intermediary agents between two or more actors in a process of innovation, delivering information about potential collaborators, helping to find consulting, support and financing for the innovating results that come from such collaborations. According to ^{Winch and Courtney (2007)}, the PSP must act as solely another member of the network, who is not focused on the organization or on the implementation of the network, but rather facilitates for producers to innovate or connect more within their network. This is what this study refers to with the move from quadrant III to quadrant I.

Conclusions

Of the cases analyzed, the modules identified with criteria of position in the network (MasAgro) present better coverages than those that only take into account producers willing to be demonstrative modules (PROMAF). The willingness of a producer to be a module is not sufficient condition to cause the demonstration effect that a module is after.

A greater access to the knowledge present in the modules favors a greater coverage of the services of public technical assistance, improving the return of the public investment performed. Therefore, in order to increase the coverage of technical assistance and training under the Hub strategy, the current identification of demonstrative modules (that is, selection with normative criteria) must be complemented with relational criteria.