Sample Literature Review 2
In order to reach the targeted 50% increase in output from the dairy industry set out in the FH2020 Report, Kelleher (2011) suggests that it will require world-class efficiency and expansion. Increasing the use of advisory services can benefit farm efficiency and help in achieving the targeted 50% increase in dairy production (Bogue, 2013). Bogue’s (2013) evaluation of dairy discussion groups found that farmers actively involved with discussion groups were more efficient and productive, thus indicating the potential for HTR farmers to increase efficiency and productivity through discussion group membership in the dairy sector.
There is agreement that the adoption of agricultural technology depends on a range of personal, social, cultural and economic factors, as well as on the characteristics of the innovation itself (Pannell et al., 2006). Prokopy (2008) shows that education levels, capital, income, farm size, access to information, positive environmental attitudes, environmental awareness and the utilisation of social networks are generally positively, associated with the adoption of best management practices.
The innovation decision process can lead to either adoption, a decision to make full use of an innovation as the best course of action available, or to reject a decision and not to adopt an innovation (Rogers, 2003, p.21). Factors that affect the rate of adoption include personal, social, cultural and economic characteristics, as well as the characteristics of the innovation itself. Adoption will only occur when a person believes that the innovation in question will be able to improve their personal targets. Acquiring new technologies is challenging and is influenced by the diffusion of information about the innovation. Innovations generally require a lengthy time from the development of an idea to the diffusion of knowledge about the idea and the overall adoption of that innovation (Rogers, 2003, p.24).
Diffusion is the process by which an innovation is communicated to members of a social system over time. It is a special type of communication concerned with the spread of new ideas (Rogers et al., 1995). The ultimate goal concerning the diffusion of ideas is their consideration and adoption by members of a particular group. Adoption is the decision to accept or use new ideas or technologies (Severs et al., 1997). The four main elements of diffusion, as stated by Rogers (2003, p.10), are innovation, communication channels, time and the social system. The adoption process consists of five steps, including awareness, interest, evaluation, trial and adoption. It is the role of the extension service to inform, influence and facilitate the adoption of new ideas. The relative speed at which individuals in a group move through the adoption process and adopt new ideas can be used to 23 categorise them into one of five groups, including innovators, early adopters, early majority, late majority and laggards (Rogers et al., 1995).
Figure 2.2 Adopter categorisation on the basis of innovativeness Source: Rogers (2003 p.281). Figure 2.2 charts the adoption of a new innovation within a social system. Rogers (2003 p.281) has discussed the adoption of a new technology among members of a social system. Within this, he ranked the members by the degree to which an individual is relatively earlier in adopting new ideas than other members of a system. Adopters were categorised into five groups and evaluated (Rogers, 2003 p283): · Innovators are individuals who are first to adopt a new innovation. Innovators are willing to take risks by adopting an innovation earliest, with failure being a greater possibility. · Early adopters are the second category of individuals who adopt an innovation. These individuals can be referred to as trend setters. They have the highest degree of opinion leadership among the adopter categories. · Early majority adopt an innovation after a varying degree of time. The adoption of an innovation takes longer than for other innovators and the early adopter categories. 24 ·
The late majority category will adopt an innovation after a significant period of time has elapsed, compared to the average member of society. These individuals approach an innovation with a high degree of scepticism, after the majority of society has adopted the innovation. · Laggards are the individuals who are the last to adopt an innovation. This category of people tends to be traditionally orientated. Agricultural extension agencies play a vital role in how an innovation is diffused and the information being made available to farmers. The agricultural extension model used in Teagasc consists of a research unit, a specialist team and an advisory service which provides the link between research and farmers. This agricultural extension model has been adopted around the world, and Teagasc is the primary agricultural extension model in Ireland (Kelly et al., 2013). Vanclay (2011) highlights 27 social principles which can be used by the agricultural extension model when facilitating the adoption of new practices. The key principles presented include the idea that adoption is not simply a process of communication, but acknowledging farming as a social activity, that recognition should be given to the diversification of farming and that it should be appreciated that adoption is both a sociocultural and a socio-psychological process. Connolly and Woods (2010) examined the adoption of technology by Irish farmers. The study examined the degree to which a number of key factors influence farmers’ adoption and usage of farming software and farming websites such as agfood.ie. The majority of the farmers involved in the study engaged in cattle farming or cattle and other farming. The key factors that influenced a farmer’s intention to adopt and use farming technologies were identified. The most common influential factor in relation to the adoption of technology on a farm was facilitating conditions. Facilitating conditions is the degree to which the adopter believes that an extension service and technical support will provide assistance in the uptake and use of a new technology such as the AgFood online service. While farmers identified how easy a technology was to use as an influential factor, social influence from a neighbouring farmer has a stronger role in influencing farmers in regard to the adoption of technologies.
The difference in the uptake level among farmers was the final determining factor in the adoption of technology. 25 Factors which encouraged the adoption and uptake of artificial insemination (AI) among dairy farmers in Ireland were identified by Howley (2012). The study found that farmers involved with an extension service (e.g. Teagasc) had a positive effect on the uptake of AI by farmers. The size and scale of the farm also had an impact on the adoption of AI. It was established that farms with a higher gross margin were more likely to use AI, while farms with a higher stocking rate are also more likely to use AI.
Hennessy and Heanue (2012), and Bogue (2013), found that being a member of a discussion group had a positive effect on technology adoption amongst dairy farmers in Ireland. The social influence from other group members plays an important role in persuading farmers to uptake advisory technologies such as AI usage, milk recording, and use of Irish Cattle Breeding Federation (ICBF) database, grass budgets and completion of the e-Profit Monitor.
Both studies found that adoption rates were significantly higher among farmers who were involved in discussion groups compared to farmers not involved in discussion groups. 2.7 Reasons for Non-adoption Certain groups of farmers are slower in the uptake and adoption of technologies, and they generally have barriers or reasons for the non-adoption of technologies. Jørgensen (2007) carried out a study on the decision support system (DSS) crop protection online technology, and found that farmers feel there is a lack of time to record data online, and that the time required to use the technology is a major barrier when it comes to the adoption of technologies. The results also found that farmers would rather rely on their own experience than on an online technology.
Vanclay (2011) stated that farmers have genuine reasons for the non-adoption of technology. He identified 12 genuine reasons for non-adoption: Table 2.1 Reasons For Non-Adoption Of New Technology Too complex Not easily divisible into manageable parts Not flexible enough Not compatible with farm and personal objectives Not profitable Too much additional learning is required The risk and uncertainty is too great Capital outlay is too high There is conflicting information They do not recognise that there is a problem 26 Lack of a physical infrastructure Lack of a social infrastructure Source: Vanclay, 2011 Vanclay (2011) listed 12 reasons for the non-adoption of technology which can be related to Reynolds’ (1989) study which identifies 11 reasons for non-adoption when quantifying possible reasons for the non-adoption of pasture and forage crop research findings at farm level.
These include the failure of extension services to transfer the new technologies, a lack of understanding of the factors which influence a farmer’s decision-making process, the farmer’s lack of understanding of new technologies, the failure to appreciate the importance of the level of management required and the failure to provide a sufficient incentive for the farmer to adopt this new technology.
The reasons stated in Vanclay’s and Reynolds study for non-adoption of technology are based around similar principals, whereas the communication between the extension service and farmer is poor, the level of understand and the risks associated with the use of the technology are unclear and the lack of incentives for farmers to adopt technologies they know little or nothing about.
When investigating reasons for the nonadoption of best management practices among cattle producers in Louisiana, Gillespie (2007) discovered that farmers were not aware that best management practices are beneficial to their farm and that information about best management practices had not been effectively diffused to them. 2.8 Farmers’ Attitudes Attitudes are defined as a positive or negative response towards an object, where the object may be a person, idea, concept or physical object (Willock et al., 1999).
Attitudes play a key role in our lives, both affecting and influencing our behaviour towards others. They also have an important influence on decision-making by individuals (Willock et al., 1999). Kahneman and Sudgen (2005) suggested that people have an attitude about all types of objects, including those objects which do not involve them. Attitudes are formed by what an individual perceives to be a possible understanding or belief about an innovation.
This perception may or may not be based upon information and knowledge, but upon an emotional reaction towards the understanding or innovation (Willock et al., 1999). An attitude change can be referred to as a modification of an individual’s general evaluative perception of a person, 27 object or issue (Cacioppo et al., 1994), and people’s attitudes are not stable, being subject to change under a number of individual factors. Jansen et al., (2010) highlights farmers’ attitudes changed favourably towards the mastitis control programme once they realised that a change in mastitis control on their farms would result in a decrease in their bulk milk tank somatic cell count (SCC). This showed that once HTR farmers actively saw a change happening on their farms they recognised the benefits of using a mastitis control programmes.
Factors which influence the product being produced such as SCC, milk price and quota regulations, will influence farmers’ motivation to work, or not to work (Jansen et al., 2010). 2.9 Farm Financial Analysis and Training Most observers of change in agriculture assume that relative economic performance is a critical mechanism determining the survival of individual farms and the overall direction of changes.
To help farmers prosper in an increasingly competitive farm economy, many public sector programmes have been developed to increase the farm financial management skills of farmers (Jackson-Smith, 2004). Byrne et al. (2003) examined the role of extension in business management practices on Irish dairy farms. Their study indicated that Teagasc advisors play a significant role in equipping farmers with farm production advice, but they did not play as important a role in providing them with farm financial advice.
Byrne suggested that the reason for this was that most of the advisors were agricultural science graduates, and that they may have needed additional training in financial analysis. However, farmers still relied primarily on their accountant for annual tax accounts when dealing with financial management issues (Byrne et al. 2003). Studies by Harrison (2006) and Bone et al., (2003) also showed the beneficial impact of financial analysis and benchmarking on farm financial performance on dairy farms in Australia.
Harrison (2006) assessed the financial management practices considered important in explaining farmer satisfaction with their business performance. A survey of 204 dairy farmers in New South Wales (NSW), Australia, identified five financial management dimensions: appraisal practices, accounting, budgeting, financial aid use and analysis practices (Harrison, 2006). Farmers who placed a greater emphasis on financial management were reported to have greater satisfaction with their business performance.
Business analysis, 28 such as assessing the costs and benefits of alternative decision and calculating repayment periods on investments, were found to be particularly important in explaining satisfaction with business performance. This indicates that more comprehensive financial management is beneficial for farmers. Jackson-Smith et al. (2004) assessed the contribution of financial management training and knowledge to dairy farm financial performance. Farmers involved in the Dairy Farm Business Summary (DFBS) programme and in the Wisconsin portion of the National Dairy Community Study (NDCS) were surveyed.
Their results for NDCS farms indicated that women had an important part in gathering and maintaining farm records, but that they were less involved in financial analysis and farm financial management training. Most farmers kept some type of financial records. However, nearly half used them solely for the preparation of tax returns. All people who received farm financial management training indicated it to be useful and suggested that it improved their farm’s financial performance. Balliet et al. (2010) examined the long-term impacts of a specific farm financial analysis training (FFAT) course.
The FFAT course covered fundamental skills and concepts in liquidity, profitability, solvency and efficiency. After the farm financial crisis of the 1980s in the US, farmers with USDA loans were mandated by Congress to complete finance and production training courses. There were four specific goals for the project: to quantify perceived gains in knowledge, to measure changes in management behaviour, to measure change in specific farm assets and profitability, and to assess changes in attitudes regarding farm finance and lending. A FFAT survey indicated that farm financial training provided new and at-risk producers with cost-effective educational materials that would significantly increase their knowledge about financial statements, increase their use of financial management tools, and improve their comfort and attitudes when dealing with agricultural lenders, in addition to increasing farm profit.
Balliet et al. (2010) claimed that “as commodity margins decrease, the need for intensive financial management to maintain profitability is made more critical. Producers need to identify and solve issues detrimental to profit quickly if they are going to survive”. All of the studies referred to in this section clearly outlined the benefits of farm financial analysis.
In particular, farmer financial training was found to be especially important in terms of enhancing farmers’ competence and confidence in appraising business performance.
2.10 Experiential Learning Experiential learning has been an integral part of agricultural education programmes in the US for many years (Roberts and Harlin, 2007). HTR farmers in Jansen’s et al., (2010) study are often open to new information although rarely take the initiative to act, therefore advisors must realise different types of farmers need to be approached in different ways and through different channels with information. Osborne (1994) states that experiential learning made participants better able to transfer knowledge, understand problems in agriculture, develop their self-confidence, connect practice and principle, improved psychomotor skills, develop problem-solving skills, retain more knowledge and become interested in learning. Kolb (1984, p.4) has described a four-stage model of experiential learning, including concrete experience, reflective observation, abstract conceptualisation, and active experimentation.
Concrete experiences are the basis for observations and reflections. These reflections are assimilated and distilled into abstract concepts from which new implications for action can be drawn. Reflective observation involves watching others who are involved in the experience and reflect on what happens, whereas individuals who jump right in and start doing things favour active experimentation.
Abstract conceptualisation is when one perceives, grasps, or takes hold of new information through symbolic representation which involves thinking about, analysing, or systematically planning rather than using sensation as a guide (Kolb, 1984, p201). Kolb’s Experiential Learning Theory relates closely with the four categories of HTR farmers identified through Jansen et al. (2010) with concrete experiences and reflective observations from Kolb’s theory related closely with wait-and-see-ers from Jansen’s study.
Active experimentation from Kolb’s study shares similarities the pro-activists identified in Jansen’s study and abstract conceptualisation from Kolb’s study shares similarities the do-ityourselfers identified in Jansen’s study on HTR farmers. “Participants learn through real-life experiences and experience influences how they learn because experiences shape individuals by building knowledge and past experiences to influences future experiences” (Knobloch et al., 2003).
Kolb’s Experiential Learning theory has many similarities with Jansen et al’s., (2010) categories of HTR farmers based on their openness towards information and trust in external information sources. 30 Figure 2.3 Kolb’s Experiential Learning Theory Source Kolb (1984 p205) 2.11 The Theory of Reasoned Action The Theory of Reasoned Action (TORA) was developed by Ajzen and Fishbein in 1980. TORA examined how a change in an individual’s behaviour develops.
It is based on an assumption that human beings are usually rational and make systematic use of the information available to them. Ajzen and Fishbein (1980) suggested that the ultimate goal of studying attitudes is to predict and understand an individual’s behaviour. Figure 2.4 A Schematic Representation of the Theory of Reasoned Action Source: Ajzen and Fishbein (1980); cited in Garforth et al., (2006) 31 In the TORA framework, there are two main factors influencing a person: an attitude towards the behaviour and the subjective norm. Attitude towards the behaviour refers to the personal judgement a person has to exercise in order to perform a specific behaviour. The subjective norm is the social influence on a person to perform that certain behaviour. “Individuals perform better when it’s found to have a positive benefit on them, and they believe that others feel that performing it is important” (Ajzen and Fishbein, 1980). TORA was criticised for ignoring the importance of social factors that, in real life, could be a factor for individual behaviour (Werner, 2004). Social factors mean all of the influences of the environment surrounding the individual which may influence individual behaviour (Ajzen, 1991). To overcome this criticism, Ajzen (1991) proposed the addition of another factor, perceived control over the behaviour. Perceived behavioural control is an individual perception on how easily a specific behaviour will be performed (Ajzen, 1991). Attitudes and subjective norm, combined with perceived behavioural control, can account for a considerable proportion of variance in behaviour (Ajzen, 1991). Garforth et al., (2004) used the TORA model to examine farmers’ knowledge and attitudes towards a number of management innovations, including methods to improve heat detection, sowing clover and optimising the use of slurry. They firstly used phone calls with the random selection of farmers to identify important outcome beliefs and social referents with respect to a specific behavioral domain, followed by focus groups to bring about change from the norm.
The results showed that farmers’ attitudes towards new technologies clearly had a strong influence on their intentions to adopt them. Garforth et al. (2004) reported that “carefully planned communication could help to reinforce attitudes which support adoption and counteract those which act as barriers. Attitudes varied between farm and farmer types and strategies for knowledge transfer should therefore be tailored to the specific technology and audience”. O’Dwyer and Connolly (2007), meanwhile, examined the attitudes and objectives of dairy farmers who are regular users of the Teagasc Profit Monitor, using the attitude and objective statements used previously by Willock (1999).
They also assessed opinions of the Teagasc Profit Monitor. The key finding from the study was that 84 per cent of respondents indicated that Profit Monitor completion has enabled them to increase their farm income. Those farmers who agreed that completing Profit Monitor was worth the effort also tended to agree “(1) that it is an essential farm financial management tool; and (2) that it helps them to make better decisions for their farm business.
However, they also indicated that farmers had other 32 non-monetary objectives such as sustainability, quality of life and status (O’Dwyer and Connolly 2007). The 2014 E-Profit Monitor analysis indicates that 19% of Teagasc dairy clients completed the Profit Monitor for the 2014 production year”. 2.12 Summary From the literature, it’s clear although new research and technologies may be technically optimal for improving farm management, to be implemented it has to be effectively and consistently communicated to farmers (Chase et al., 2006).
Although Jansen et al’s., (2010) study on HTR farmers states that it can be difficult to reach and engage with farmers who apparently have no demand for information. Willock et al. (1999) found that multiple attitudes and objectives influence farmers’ decision-making.
Vanclay (2004), meanwhile, observed that the first thing to acknowledge when examining adoption in farming is that farming is a way of life, rather than merely a technical activity. HTR farmers often have genuine reasons for non-adoption of new technologies in relation to financial management although these have to be overcome in order for farmers to see the clear benefits of financial management in order to increase farm efficiency (Vanclay, 2004).
Sample Literature Review 2
2.7 Technology Adoption This section will examine the factors which influence farmer’s decision making and technology adoption. It also investigates knowledge transfer methods which encourage technology adoption. 2.7.1 Farmer Attitude Wallace and Moss (2002) stated that the majority of earlier research conducted has modelled farmer decision-making based on one goal – maximising profit.
However, research since has found there are multiple factors both financial and non-financial which influence farmers decision-making (Wise and Brannen, 1983 and Turvey, 1991). In an effort to assess conflicting literature, Ajzen and Fishbein (1975) developed the Theory of Reason Action (TORA) (see Figure 2.6). This framework was initially used in the health sector but more recently in the agricultural industry.
Figure: 2.5: A schematic representation of the Theory of Reasoned Action (Fishbein and Ajzen, 1975) Concepts that are central in the social and behavioural sciences are incorporated into the TORA model, as the theory states that behavioural intention to emit the behaviours is what determines behaviour (Fishbein and Ajzen, 1975).
According to TORA, there are two key factors that determine behaviour; 1) attitudes towards behaviour which relates to an individual’s personal judgement on performing a behaviour and 2) subjective norms 25 which is the surrounding social influence in the environment of the person performing a behaviour (Ajzen and Fishbein, 1975).
Subsequently, Azjen & Fishbein, (1980) stated ‘that an individual will perform a behaviour when they find it to have a positive benefit on them, and they believe that others feel that performing it is important.’ Garforth et al. (2004) performed a study using the TORA model which investigated farmer attitude and behaviour, with a view to improving how knowledge transfer strategies were designed in South West England.
As a result of this study, the findings showed a strong link between attitudes towards technology and the adoption of the particular technology. Willock et al. (1999) also used the model to examine the role attitude played with farmer’s behaviour in Scotland, finding that social and psychological factors have a considerable influence on the behaviour of farmers.
Therefore, these social and psychological factors can also have a considerable influence on the attitude of participants in discussion groups (Willock et al., 1999). The study concluded that that farmers attitude were an influential factor in both environmentally-oriented and businessoriented behaviour, while some attitudes have a direct impact on farmer behaviour (Willock et al., 1999). In another study by Garforth et al. (2006) also using the TORA model, attitudes had a strong influence on farming intentions to adopt heat detection practices, but also a lack of knowledge in a specific area was identified by farmers as a constraining factor in adopting a new innovation.
However, this study also found that the subjective norms which were the perceived idea of others had less of an impact on farmers adopting technologies. Farmers said they were inclined to focus on their personal opinion, experience, and knowledge for uptake of technologies instead of the opinions of others.
This study found that in order to convince farmers towards specific heat detection methods, issues affecting uptake of practices such as cost-effectiveness need to be established (Garforth et al., 2006). The TORA model received criticism for not incorporating social factors which could be a determining factor in an individual’s behaviour (Werner, 2004).
As a result, Ajzen (1991) developed a new model known as the Theory of Planned Behaviour (TPB) (see Figure 2.7) which is an adaption of the TORA model with an additional third factor, ‘perceived control over the behaviour’. This factor accounted for people intending to carry out a behaviour, but the actual carrying out of information is thwarted due to a lack of 26 confidence or perceived control over the behaviour (Ajzen, 1991).
Furthermore, Ajzen (1991) stated that a significant percentage of variance in behaviour can be accountable for by using the three factors incorporated in the model which are attitudes, subjective norms and perceived behavioural control in the TBP model. Moreover, Ajzen (1991) also suggests the theory of planned action is critical in understanding decisions made by farmers regarding adoption of new technologies, along with accurately predicting behavioural intentions.
Figure 2.6: Image showing TPB model (Ajzen, 1991) Wauters et al. (2010) used the TPB theory in an agricultural study to investigate the adoption of soil conservation in Belgium. The purpose of the study was to examine the use and efficiency of the TPB, and secondly to determine the factors explaining engagement in soil erosion. A pre-questionnaire was used to come up with a complete list of all readily accessible outcomes, referents, and control factors and on completion, the final questionnaire was issued to 140 farmers in the region.
The results indicated farmer’s intention towards soil conservation practices was the biggest influence on their behaviour in the study, while also indicating attitude was a significant factor in decision making. However, feedback indicated that perceived behavioural control was not an influential factor in farmer’s behaviour in this study. In a separate study, Garforth and Mc Kemey (2005) assessed the attitudes and intentions of farmers using Estimated Breeding Values 27 (EBV) when purchasing lambs. However, the study findings declared farmers were reluctant to use the technology, despite evidence of an extra £2 sterling profit.
The farmers were anxious that it would be more difficult to manage and finish lambs and so favoured their traditional methods (Garforth and Mc Kemey 2005). This research is a classic case of where perceived behavioural control was a clear determinant in farmer’s behavioural intention and finally the farmer’s behaviour. 2.7.2 Factors Affecting Attitude Farming is a business where decision-making is predominately made by one person (Groenwald, 1987). In comparison to other businesses, farming has a lot more external pressures that influence decision-making (Groenwald, 1987). Economists generally view that the reasoning for the adoption of technologies is an increase in economic viability of the farm (Brown, 1981; Rogers, 1983; Chamala, 1987; Linder, 1987; Reid, McRae, & Brazendale, 1993). However, business managers in contrast to agricultural economists believe innovation is not associated with the adoption of technology but rather with an entrepreneurial spirit (Willock et al., 1991). Hence, farming is an occupation where decisions are not solely aimed at the unique goal of profit (Herath, Hardaker, & Anderson, 1982; Wise & Brannen, 1983, Gartrell & Gartrell, 1985; Anosike & Coughenour, 1990, Turvey, 1991). This is the same view of Lynne and Rola (1988), who stated income alone is not the only significant factor of behaviour and that environment has also to be taken into account. However, there are other studies that disagree with this theory and say profit motives are more of an influence than environmental motives, even if the environmental problems are present (Lynne & Rola, 1988; Newman, Saunders, Pittaway, & Anderson, 1990; Carr & Tait, 1991). Pampel and van Es (1977) reported that the type of innovation adopted is determined by the mind-set of the farmer, for example, a mind-set driven by profit maximisation or sustainability would sway a decision. Farmer attitude in relation to risk is factor in farm decision-making, as it identifies a vital range that includes risk aversion, innovation, diversification, off-farm work, environment, production, management, legislation, stress, pessimism, and satisfaction towards farming (Willock et al., 1999). Although farmers work in inherently uncertain conditions, farmers are slow to accept ideas that are unproven, and so are risk adverse (Guerin & Guerin, 1994), and attitudes towards adopting innovative techniques are closely linked to risk 28 (Driver & Onwana, 1986). Farmers usually feel a greater job satisfaction than other professions, and are happy with farming as a way of life. Farming is a vocation, and this perhaps is a value within itself (Gasson, 1973, 1974; Coughenour & Swanson, 1983; Schroeder, Fliegel, & van Es, 1985; Coughenour &Tweeten, 1986; Coughenour & Swanson, 1988; Ackerman, Jenson, & von Bailey, 1991). Farmers enjoy an unconstrained decision-making process and lifestyle, where a separation does not exist between work of interest and home (Willock et al., 1991). A study carried out in Britain by Gasson (1973) identified four dominant values that farmers deemed important towards their business. The being economic values, these were associated with maximising income of their business. The three other values recognised included social values, independent lifestyle, and enjoyment of work. While Robinson (1983) similarly found the number one objective of farmers was to make a sufficient profit, while second to that was ‘being good at what you do.’ 2.7.3 The Innovation Process The adoption of knowledge and technology can involve an array of varying factors, and over time, numerous methodologies have been used to determine factors associated with adoption. Rogers (1995) defined the rate of adoption as the ‘relative speed with which an innovation is adopted by members of a social system’. He defined an innovation as ‘an idea, practice, or project that is perceived as new by an individual or other unit of adoption’ (Rogers, 2003). Potential adopters undergo an innovation-decision process containing five different stages with regards new ideas and procedures (see Figure 2.8). The first two stages of the process are gaining knowledge of a particular innovation and then going through a persuasion stage. The next stage is making a decision to adopt or decline, and then implementing the new innovation and the final stage is called the confirmation stage where an innovation is incorporated into an individual’s daily routine. Rogers (2003) defined the rate of adoption as the ‘relative speed with which an innovation is adopted by members of a social system’. He further described diffusion as the ‘process of communicating an innovation over time’. Knowledge Stage: The individual is exposed to the innovation and gains knowledge or skills for effective adoption of innovation. 29 Persuasion Stage: An opinion is formed on the innovation and dialogue proceeds with others regarding the innovation. Decision Stage: At this point, a decision to try the innovation or reject the innovation is made. Implementation Stage: An individual seeks additional information on the innovation and continues to use it on a regular basis. Conformation Stage: The individual integrates the innovation into their daily routine, however discontinuing of the innovation may also occur here if there is exposure to conflicting messages (Rogers, 2003). Figure 2.7: A model representing the five stages of the decision process (Rogers 2003) In 2003, Rogers developed a model which provided a basis for numerous ‘adoption of innovation studies’ (see Figure 2.9), identifying five categories of people based on their rate of innovation adoption (Rogers, 2003). According to Rogers, the first category contains ‘innovators’ individuals willing to take risks and are enterprising individuals. The second category is called ‘early adopters’, where individuals are willing to try new 30 ideas, however, are more cautious than ‘innovators’. Opinion leader groups are where the ‘early adopters’ often form. The third category consists of ‘early majority’; individuals that have a cautious attitude towards new ideas, while the fourth category, ‘late majority’ are skeptical when exposed to new ideas and it takes time for this cohort to realise or be convinced of the advantages of adopting technologies. The final categories are called ‘laggards’ as they like to keep their traditional ways of doing things and they are the slowest to adopt new innovation because of their suspicions mindset (Rogers, 2003). Figure 2.8: Distribution of adopter classified by innovativeness (Rogers, 2003) However, the part where Rogers’ (2003) adoption curve fails on, is that specific categories don’t always apply to everyone in all research studies. The often interdependent nature of farmer’s objectives between business and personal issues can be an influencing factor for adoption or non-adoption (Vanclay, 2004). There are other influencing factors that affect adoption or individuals in the ‘laggards’ or ‘late adopters’ categories (Vanclay, 2004). Therefore, it is not always possible to assign people into the categories of the Rogers model. 2.7.4 Factors Affecting Adoption Vanclay (2004) found that legitimate reasons exist for non-adoption of technologies such as technologies being too complicated, or have a lack of compatibility with the farmers or farms objectives. He also stated that occasionally when the capital outlay is too high, this 31 brings too much risk and uncertainty, resulting in little profit, while too much additional learning may be involved with adopting new technologies (Vanclay 2004). In a study of Australian farmers, Guerin and Guerin (1994) examined the restrictions of technology adoption and found that constraints evident from the research findings included, technology complexity, cost, limited information made available, lack of perceived relevance, limited support from advisory services, small financial incentive, farmers attitude, farm size and age of the farmer. Hall and Kahn (2003) also found that factors constraining technology adoption included a lack of time, too costly, or if it was too complicated, then the adoption of the technology is slow. Zander et al. (2013) researched the constraints of technology adoption in Kenya, a lack of farmer knowledge and motivation were the reasons for poor technology adoption. Feder et al. (1985) also noted that arrangements of tenure influence adoption whereby land rental affects adoption, as it is less than average where rental of land is for a short tenure. Buttel et al. (2000) found that farm size can impact technology adoption, with a study of over 1600 dairy farmers in Wisconsin. Larger farms adopted technologies specific to production including the use of milking parlours, three times a day milking, agricultural chemical use, and total mixed ration equipment. In contrast, the study findings found smaller farmers were more likely to adopt management-intensive rotational grazing systems, where 33% of farmers with less than 50 cows practiced this technology, while only 10 % of farmers with more than 100 cows adopted the technology. From another research study carried out in Wisconsin, the rate of adoption by farm size was also a factor (the size of the herd was the method of determining farm size) (Barham et al., 2006). Results showed that 100% of farmers with a herd size of over 200 cows adopted the use of total ration equipment, where only 10% of farms with a herd size of 50 had adopted the technology. The farms consisting of over 200 cows had adoption rates of 80 and 90% for nutrient management plans and computer use, while farms with herd sizes of 50 cows had a far lower adoption rate of 20% and 25% for both technologies respectively (Barham et al., 2006). Schultz (1995) found that human capital impacts technology adoption and in this research the two categories used; 1) allocative ability and 2) worker ability. Allocative ability is defined as a ‘person’s ability to receive, decode and make decisions based on available information,’ it also includes the ability to process new technologies, absorb information efficiently, and contain a high learning capacity. While worker ability is defined as the 32 ability of an individual to physically carry out a technology. Wozniak (1993) showed that educated farmers were more likely to embrace new technologies and to converse with agricultural extension agents, to obtain information on adoption. Fuglie and Kascak (2001) associated education with a more swift adoption of soil nutrient testing. When Läpple et al. (2012) examined data from the Teagasc National Farm Survey (NFS), it was found that farmers with higher levels of education both adopted intensive grazing systems and continued using the technology for a longer period compared to farmers with low education levels. McBride (2004) also concluded that farmers with higher levels of education were more likely to adopt technologies, e.g. recombinant bovine somatotropin, while Putler and Zilberman (1998) found there is a correlation between education, farm size, and farmer’s age, with the ownership of computers. Farmers who have a higher level of education tend to be early adopters (Putler and Zilberman, 1998). Defrancesco et al. (2008) found farmer’s age to be a significant factor with regards risktaking, with younger farmers more likely to adopt technologies. The reasoning for this is that younger farmers generally have higher levels of education and planning horizons with greater distinction. Khanal and Gillespie (2011) reported that young farmers had a higher rate of adoption in technologies such as extending grazing seasons, embryo transplants and sexed semen. Similarly, Howley et al. (2012) concluded that younger farmers were more likely to adopt artificial insemination than elderly farmers. 2.7.5 Knowledge Transfer Maher & Donworth (2012) reported that the transferring of knowledge to a farmer is straightforward, but a change of attitude or the adoption of technologies is challenging. Teagasc have come up with a discussion group model to achieve knowledge transfer and technology adoption. Hennessy & Newman (2010) demonstrated that discussion group members are far more likely to adopt new technologies and increase the overall profit of their farming enterprise. Maher and Donworth (2012) also state that the more focused farmers will naturally adhere to the adoption of new technologies and engage in knowledge transfer processes, and this is not the case with farmers that are less focused. Three factors that influence the uptake of technologies are social, cultural and economic factors (Macken & Walsh, 2010). Cultural capital represents the knowledge and skills people already have, and it can change over a period of time. However, until cultural 33 capital changes, it can have a negative impact on technology adoption (Macken & Walsh, 2010). While social capital refers to social relationships and networks of people, as the adoption of an innovation can result in friction between social network/relationships, and therefore, restricting technology adoption (Macken & Walsh, 2010). Discussion groups are designed to help break down social and cultural barriers by facilitating farmers to share knowledge and gain an insight into other farmer’s experiences on adopting technologies. Kelly (2011) researched the impact Teagasc has on technology adoption on Irish dairy farms. The results showed that farmers who were active members of discussion groups had a higher adoption rate of technologies, such as Herd Plus artificial insemination and bovine viral diarrhoea vaccination. Hennessy and Heanue (2012) carried out research on the effect of discussion groups on technology adoption and profitability on dairy farms using data from the National Farm Survey (NFS, 2009). The study showed that members of discussion groups had a higher rate of technology adoption compared to non-discussion group participants (O’Donovan, 2011). These technologies included milk recording and reseeding. The research also showed that discussion group members tended to have larger farm sizes and herds and discussion group members were also younger than non-discussion members. Rhoades and Booth (1982) also stated the importance of participation in discussion groups and on-farm trials experiencing new innovations which have been proven to improve the acceptance of new research methods. While Creighton et al. (2010), similar to Maher & Donworth (2012) stressed the importance of farmer participation in discussion groups in Ireland, stating that a more considerable effort is needed for facilitating the active involvement of farmers, which can aid in new technologies being shared and acquired by the entire group as a whole. Byrne (1997) reported a direct link between participation in discussion groups and technology adoption. Läpple et al. (2012) found that farmers who adopted the technology of a longer grazing season were members of discussion groups. Eady and Fisher (2004) carried out research with Australian farmers where it was found that ‘being part of a group devoted to dealing with the problem’ and ‘learning from each other’ assisted in farmers changing practices on their farms. Creighton et al. (2011) carried out a study of the level of uptake of grassland management technologies and the results showed the level of adoption was poor. However, technology uptake was improved where farmers were active members of discussion groups. 34 Furthermore Ryan (2012) managed a ‘Grass Roots Project’ where over 200 farmers across 11 counties joined grass groups. These farmers were taught and shown how to measure and manage their grass by an advisor when they met every 3 weeks. Areas of focus for the group included estimating covers of grass in terms of DM/ha, reviewing weekly grassland management decisions made at the previous meeting and interrupting results of the most recent farm walk. Ryan (2012) concluded from the campaign that grass groups aided in improved grassland management, greater grass growth of on average 3 tonnes of DM/ha across the farms, greater confidence to increase herd size and improved animal performance. Newman (2015) carried out a study investigating the low uptake of grass measurement with farmers from the East of Ireland through surveys and case studies. His findings also found that grass groups help ‘educate farmers how to grass measure, use a computer based software programme and make grassland management decisions.’ Vanclay (2004) stated that ‘multiple methods of extension’ are necessary to diffuse messages to a diversity of farmers and to reinforce the message in diverse ways. Pannell et al. (2006) agrees with Vanclay indicating that farmers can develop more confidence in a message if it is reinforced through a number of different channels and sources. Furthermore, Pannell et al. (2006) states that ‘even the most expert and persuasive extension, landholders are not likely to change their management unless they can be convinced that the proposed changes are consistent with their goals.’
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