ANÁLISIS DE ARN Y MICROARRAYS DE EXPRESIÓN

Based on the importance of personal and contextual factors outlined above, a number of empirical studies which investigated the influence of farmer and farm characteristics on agricultural innovation adoption are presented. These studies are relevant to this research as they establish the variety and type of variables used in the investigation of FMP adoption among dairy farmers. Although

contextual variables (social, cultural, market orientation and geographical) are likely to constrain comparisons between all studies, only three studies conducted in developing countries have been included due to their relatively low relevance to the CDI.

The importance of herd expansion and socio-demographics to adoption was examined by El-Osta and Morehart (1999) using data from the United States Department of Agriculture’s (USDA) 1993 Farm costs and returns survey. El-Osta and Morehart (1999) identified age, farm size, and specialisation in dairy production as important in increasing the likelihood of adopting a capital-intense technology, while education and size of operation positively impacted the decision to adopt a management- intense technology. Age, education, credit reserves, size of operation (and increased usage of hired labour) positively influenced the decision to adopt a combined capital and management intense technology. The researchers noted that farms which adopted technological innovations had higher levels of productivity.

Paudel, Gauthier, Westra and Hall (2008) assessed the impact of socioeconomic factors on the best management practices (BMPs) adoption decisions of dairy farmers in the state of Louisiana, USA. BMPs were defined by Paudel et al. (2008, p. 203) as voluntary practices producers adopt, or structures they build, to manage resources and mitigate environmental pollution from agriculture. Farmer data was collected via a farmer questionnaire mailed to 325 dairy farmers. Paudel et al. (2008) identified that the likelihood of adoption of a specific BMP was related to a set of socio- economic and financial variables which included years of experience in dairy farming, education, presence of a successor, net farm incomes, debt-to-asset ratios, non-agricultural value of the farm, and the farmer’s environmental ethos. Farmers identified the Louisiana State University Agricultural Centre, USDA, Natural Resource Conservation Service, Hoard’s Dairyman3_{, and similar dairy-specific}

publications as the most important sources of information influencing their adoption of BMPs. In Irish agriculture, Howley, O’ Donoghue and Heanue (2012) examined what farm or farmer characteristics affected the probability of dairy farmers using artificial insemination (AI). The data source used was the Irish national farm survey 1995 to 2009. Howley et al. (2012) found that having a successor as well as participation in a farm advisory programme positively affected adoption.

3 _{Hoard’s Dairyman is an American magazine with international circulation first issued in 1885. Also known as}

Age and having off-farm income negatively affected adoption. The authors note that the results suggest significant heterogeneity exists among farm households, both in the characteristics of the farmer as well as structural farm factors, all of which were found to significantly affect the probability of a farmer adopting this particular agricultural innovation.

In an investigation of the adoption of a range of BMPs among dairy farmers in Turkey, Boz, Akbay, Bas and Budak (2011) reported results similar to Paudel et al. (2008). Their results showed that age, income, investment and the owning of improved breeds of animals positively influenced adoption. Use of the internet, contact with extension personnel, veterinarians and members of an agricultural faculty all increased the level of adoption among farmers. Reading of newspapers, use of television and radio, and travels to provincial centres were shown to have no significant impact on adoption. The BMPs included the use of AI, concentrated feeds, vitamins, proteins, silage, veterinary services, and inoculations.

The adoption of innovations among dairy farms in the Menoufia province in Egypt was researched by Shahin (2004). Farmer data was collected via a questionnaire and showed that the amount of labour devoted to crop production (level of specialisation) and farmer age significantly negatively influenced the adoption of most buffalo dairy innovations. Farmer education was positively correlated with the adoption of most innovations as was the use of a veterinarian, and cosmopoliteness4_{. It is noted that}

the influence of cosmopoliteness contrasts with Boz et al.’s (2011) results. Mass media exposure, credit and contact with veterinarians were positive and significant for adoption of some innovations as was herd size, farm size and the effect of milk sales. In contrast with Howley, et al. (2012), Shanin’s (2004) results showed that the effects of extension on farmers did not influence the adoption of most dairy production innovations while additional income positively and significantly influenced the adoption of AI and other innovations.

Rezaei and Bagheri (2011) conducted a comparative analysis of the characteristics of adopters and non-adopters of AI among Iranian farmers. Data analysis revealed that adopters and non-adopters of AI were significantly different in the case of variables such as animal husbandry experience, farm size (pasture), perceived ease of use and perceived usefulness of AI, profitability of AI and the need to use AI. Logistic regression analysis showed that the key determinants of predicting innovation adoption were farmer need and innovation proneness. The results also indicated that non-adopters had more experience than adopters, thought to indicate that experienced farmers are resistant to change.

4 _{Cosmopoliteness refers to an interest in people, topics, and ideas outside one's immediate social system}

(Rogers, 1983, p. 200). Cosmopolites, as opposed to localites, are more likely to travel more extensively, particularly outside of their local region and country, have a diversity of interests, and a diversity of

Investigating innovation adoption in Dutch agriculture, Diederen, van Meijl and Wolters (2003) found that innovation adoption among Dutch farmers in 1998 was positively related to past adoption behaviour, labour resources (which is highly correlated to farm size), access to information and market position, but was negatively related to solvency (thought to indicate that farms with a high solvency rate are risk averse and not eager to innovate) and to the business environment, in

particular the degree of market regulation. In contrast to Howley et al. (2012), Diederen et al. (2003) found that the influence of heterogeneity was limited and was not statistically significant.

A meta-analysis of research literature undertaken by Prokopy, Floress, Klotthor-Weinkauf and Baumgart-Getz (2008) identified that education levels, capital, income, farm size, access to information, utlilisation of social networks, environmental awareness and positive environmental attitudes are generally, positively associated with the adoption of BMPs. However, they noted that none of these factors were consistently positive nor are any of them positive at an overwhelming rate (Prokopy et al., 2008, p. 310). Ghadim and Pannell (1999, p. 145) also note that “the results from different studies are often contradictory regarding the importance and influence of any given

variable”.

A study by Beswell and Kaine (2004) investigated the relationships between the adoption of pest and disease management practices and the characteristics of farmers and their enterprises. No consistent relationships were found across industries and countries between these management practices and variables such as enterprise characteristics and farmers’ characteristics such as age, education and experience. They go on to suggest that farmers learn about, experiment with, and evaluate management options within the particular context of their enterprises (given the constraints imposed by the realities of commercial production) (Beswell & Kaine, 2004, p. 682).

Although not concerning socio-demographics directly, evidence presented by Ormrod (1990) strongly suggests that geographic location, which ultimately influences an adopter’s environmental, social, cultural and economic circumstances, as well as the compatibility of an innovation to a particular location, is also an important consideration when investigating innovation adoption. Eurostat (2013, p. 1) suggests that site-specific agricultural and environmental conditions are likely to influence FMPs and this may explain some of the inconsistencies noted above. Ormord’s (1990) research did not concern agricultural innovations. However, the influence of geographic location, i.e. aspect, topography, climate and resource availability (e.g. water availability) was identified by Pangborn (2012) as a being a major contributor to growth in the CDI.

Table 2.2 Summary of empirical research findings on innovation adoption in agriculture Research details and variables identified

El-Osta & Morehart (1999): USA dairy farmers’ adoption of capital and management intensive

technologies

Age, farm size, and specialisation in dairy production (capital-intensive). Education and size of operation (management-intense). Age, education, credit reserves, size of operation and usage of hired labour (combined capital and management-intense technology).

Paudel, Gauthier, Westra & Hall (2008): USA dairy farmers’ adoption of BMPs

Years of experience, education, presence of a farm successor, net farm incomes, debt-to-asset ratios, non-agricultural value of the farm, farmers’ environmental ethos. Information sources identified as important: Louisiana State University Agricultural Centre, USDA, Natural

Resource Conservation Service (NRCS), dairy-specific publications.

Howley, O Donoghue & Heanue (2012): Irish dairy farmers’ adoption of A.I.

Family circumstances i.e. the presence of an heir, involvement in extension, age, off-farm job.

Boz, Akbay, Bas & Budak (2011): Turkish dairy farmers’ adoption of various innovations.

Age, income, investment and the owning of improved breeds of animals. Use of the internet, contact with extension personnel, veterinarians and members of an agricultural faculty. Reading of newspapers, use of the television and radio and travels to provincial centres were shown to have no significant impact on adoption.

Shahin (2004): Egyptian smallholder buffalo dairy farmers’ adoption of management practice

innovations.

Level of specialisation, farmer age, farmer education, use of a veterinarian and

cosmopoliteness, mass media exposure, credit availability, contact with veterinarians, herd size, farm size, milk sales, additional income, extension.

Rezaei & Bagheri (2011): Iranian dairy farmers’ adoption of A.I.

Animal husbandry experience, agro-pasture landholding size, perceived ease of use and perceived usefulness, farmer need and farmer innovation proneness.

Diederen, van Meijl & Wolters (2003): Dutch farmers’ adoption characteristics

Past adoption behaviour, labour resources (which is highly correlated to farm size), access to information, market position, solvency, the business environment (in particular the degree of market regulation).

Prokopy, Floress, Klotthor-Weinkauf & Baumgart-Getz (2008): Meta-analysis of research

literature on the adoption of BMPs

Education levels, capital, income, farm size, access to information, utilisation of social networks, environmental awareness, and positive environmental attitudes. However, inconsistencies evident.

Beswell & Kaine (2004): Adoption of pest and disease management practices

No consistent relationships found between socio-demographics and adoption of these FMPs suggesting that farmers learn about, evaluate and adopt FMP based on the context of their production systems.