Technology adoption within centralised-diffusion systems

Since Sub-Section 2.1.2 already gave a complete characterisation of technology adoption and its determinants, this part discusses adoption as a result of different pathways of the centralised-diffusion process. We use adoption as an aggregate term¹¹ that, according to Feder et al. (1985), ‘is measured by the aggregate of a specific new technology within a given geographical a population’ (p.256). In this study, as in the works of Wyckhuys and O’Neil (2007) and Díaz-José et al. (2016), levels of technology adoption are measured as the total number of users and non-users for each agricultural practice.

11 The other type of adoption that Feder et al. (1985) distinguishes is individual (farm-level) adoption, which they define as ‘the degree of use of a new technology in long-run equilibrium when the farmer has full information about the new technology and its potential’ (p. 256).

The previous section argued that formal rules have an important impact on network structures (see Figure 2.4), which in turn may significantly influence the technology adoption of smallholders. Before examining the results of adoption that may result from possible scenarios of diffusion (Figure 2.3a, b and c), the way that networks and their structural characteristics are associated to technology adoption needs to be clarified.

Actors can have an influence on farmers’ decision-making processes of adoption through network relationships. Rogers (2010) argues that individuals can learn of the existence of an innovation and gain an understanding of how it functions through communication networks. Continuing with this idea, he asserts that before decisions take place, a persuasion phase occurs in which individuals form a favourable or unfavourable attitude towards the use of an innovation. Through network relationships, actors are able to help farmers to become aware of an innovation and to persuade them (in terms of forming and changing attitude) to adopt and implement an agricultural practice.

Since the structures of these networks may be characterised in terms of three patterns, namely size and interactions, central positions, and brokerage, they may also affect the technology adoption of smallholders. Regarding size and interactions, studies from the diffusion literature show a positive association between this structural characteristic and technology adoption. Monge et al. (2008) and Spielman et al. (2011), for instance, show that in case studies of Ethiopia and Bolivia, respectively, the more the actors and interactions influence farmers, the more substantial the influence on farmers’ decisions to adopt¹². Darr and Pretzsch (2008) and Monge et al. (2008) suggest that this is explained by the greater access to information through frequent interaction, and large networks connecting farmers and linking agents with farmers.

As for central positions, well-connected actors can exert a significant influence on farmers’

decisions regarding adoption by transferring valued resources from multiple sources.

12 Exceptions to this positive association can be found in the work of Spielman et al. (2008), which shows that similar users and non-users from Solo (Ethiopia) exhibited similar social networks.

Rogers (2010) asserts that centralised structures are likely to accelerate the pace of diffusion thanks to the action of core actors to transfer information, knowledge or technological innovations. Studies in the literature of diffusion of agriculture show an ambiguous effect of this structural characteristic on adoption. Monge et al. (2008) suggested that if the majority of interactions were centred on a main agent, less noise would be evident in these relationships and adoption levels would be higher.

The study of Spielman et al. (2011) in Ethiopia showed that networks of users were more concentrated around a few core actors than in multiple contacts in networks of non-users.

The authors argued that users had greater access to sources of knowledge, information and inputs than non-users, which provided them with greater support for adoption. Unlike this work, the study of Monge et al. (2008) in Bolivia showed that it was rather the multiplicity of contacts that influenced producers’ decisions and stimulated adoption.

When examining brokerage beyond the group of smallholders, we can say that improvement in adoption levels was positively associated to high brokerage (multiple brokers linking smallholders and other groups of information and transferring resources to these farmers, see p. 66 for explanation of this term). The case study of Spielman et al.

(2011) showed that various actors played a critical role as brokers by making a greater diversity of options in accessing resources available to Ethiopian smallholders.

After elaborating on the relationships between some structural characteristics and technology adoption, it is possible to have an insight into the levels of adoption that may result from some scenarios of diffusion (Figure 2.3a, b and c). Turning to Figure 2.4, it is indicated that at least three types of formal rules (i.e. low control, partial control and high control) can have an influence on three types of structural characteristics, which subsequently impact on smallholders’ adoption in different ways. For example, the scenarios in Figure 2.3a showed that unless a central actor intervenes in the provision of inputs and technical services, as occurred in Scenario 2 with the participation of a farmer association, a CFA of low control may have fewer strong interactions (low size and

interactions). They may also have fewer central actors (low central positions) and brokers (low brokerage) than scenarios led by other agreements. As a result, there may not be any guarantee that necessary information and technical assistance from central or different sources will become accessible to all smallholders. Following the impact of structural characteristics on technology adoption, it is likely that the poor access of information in this scenario diminishes smallholders’ adoption of most agricultural practices.

In the contrasting case of partial control, where both an agro-industrial company and other agents participate in a CFA (Figure 2.3c, Scenario 5), smallholders may gain high technical support in the implementation of most agricultural practices. Figure 2.3c implies that more than one central actor and more than one broker transfer information from other sources to smallholders through their multiple and strong interactions. Unlike Scenario 2, the significant access to information and technical support would entail an increase in adoption levels for most types of agricultural practices.

As was seen in Sub-Section 2.1.2, different agricultural practices may not receive the same technical support and attention during a process of technology diffusion and, thereby, do not have the same levels of adoption. According to the immediacy of rewards, one of the attributes of innovations (see Table 2.1), innovations can be ‘preventive’ or ‘incremental’.

Rogers (2010) defines this attribute in the following way:

‘A preventative innovation is a new idea that an individual adopts now in order to lower the probability of some unwanted future event. The desired effect is distant in time […] In contrast, an incremental (that is not preventive) innovation proved a desired outcome in the near-term future’ (pp. 233-234).

Following a similar conceptualisation, the types of agricultural innovations can be categorised according to their immediacy of reward, for example, as short-term practices and long-term practices. The first group of practices refers to agricultural technologies that are implemented after a stress occurs, for instance treatments for pest and disease.

The results in this case are generally expected in the short term.

The second group of practices refers to agricultural technologies that are intended to guarantee the long-term development, growth and yield of agricultural crops. These practices are periodically adopted by producers and final results are not usually seen straight away. This category groups the following practices: water management, fertiliser use, crop protection, soil management and prevention practices of pest and disease management. Failure in implementing preventive practices may reduce the ‘agricultural sustainability’ of the crop in terms of what Hansen (1996) interprets ‘as the ability to continue’ (p. 119)¹³. Under agricultural sustainability, Conway (1985) asserts that

‘Sustainability is the ability of a system to maintain productivity in spite of a major disturbance, such as is caused by intensive stress or a large perturbation’ (p. 35).

Complementing this concept, Hamblin (1992) argues that:

‘Agriculture is sustainable when it remains the dominant land use over time and the resource base can continually support production at levels needed for profitability (cash economy) or survival (subsistence economy)’ (quoted in Hansen, 1996, p. 119).

This implies a long-term vision and, therefore, the use of long-term practices.

The literature on diffusion of agricultural innovations, and especially network studies, helps to understand the effect of the structural characteristics of networks on adoption.

However, except for the works of Wyckhuys and O’Neil (2007) and Díaz-José et al. (2016), this literature does not commonly link the different information sources of farmers with their levels of adoption according to the different attributes of agricultural practices.

These studies used SNA measures to evaluate structural characteristics (e.g. interactions and central actors) in the adoption of agriculture practices. The study of Díaz-José et al.

(2016), developed in the Mexican maize sector, indicated that although farmers were linked to diverse sources of information, the adoption of most practices was influenced by

13 This is one out of the four interpretations of agricultural sustainability identified by Hansen (1996). The other three interpretations are sustainability as an ideology, as a set of strategies and as the ability to fulfil a set of goals.

their communication channels with other peers. The authors show that a few practices, such as the application of bio-fertilisers and use of quality seeds, were significantly influenced by the links farmers had with a research institute and input suppliers, respectively.

Although previous studies related various information sources to different types of practices, they do not classify the adoption according to the attributes of these agricultural practices, such as their immediacy of reward. From this literature, it is difficult to draw conclusions about the tendency of certain structures and central actors to favour the adoption of short-term and long-term practices, which composes the second part of the third research question of this thesis. The next sub-section will build a potential framework that allows this research to explore this gap in the literature.