Adoption of organic farming: Are there differences between early and late adoption?

Ecological Economics 70 (2011) 1406–1414

Contents lists available at ScienceDirect

Ecological Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e c o n

Analysis

Adoption of organic farming: Are there differences between early and late adoption? Doris Läpple a,b,⁎, Tom Van Rensburg b a b

Rural Economy and Development Programme, Teagasc, Athenry, Co Galway, Ireland Department of Economics, The National University of Ireland, Galway, Ireland

a r t i c l e

i n f o

Article history: Received 14 September 2010 Received in revised form 21 February 2011 Accepted 1 March 2011 Available online 30 March 2011 Keywords: Organic farming Early and late adoption Multinomial logit analysis Farmer attitudes

a b s t r a c t Based on the fact that not all farmers adopt a technology at the same time, it is argued in this paper that the distinction between groups is important because early, medium and late adopters respond differently to economic and non-economic factors when they consider whether to take up organic farming or not. The individual effects on adoption between the groups are identified by the use of multinomial logit analysis. The results provide evidence that there are significant differences in the characteristics between the adopter groups. The findings also reveal that the factors that affect adoption play a different role for early, medium and late adopters, particularly with regard to farming intensity, age, information gathering as well as attitudes of the farmer. More specifically, early adopters were the youngest to adopt organic farming and their decisions were found to be less profit related compared to other groups. Late adoption is constrained by risk considerations, while environmental attitudes and social learning were identified to be important determinants for all adopter groups. Overall, the findings strongly suggest, that for policy measures to be effective, the current state of diffusion has to be taken into account. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Organic farming is considered by some to offer solutions to the problems associated with conventional agriculture such as biodiversity loss, nitrate pollution, animal welfare concerns, surplus production or food safety (Häring et al., 2004; Lampkin, 1994; Lynggaard, 2006; Rigby et al., 2001; Van Mansvelt and Mulder, 1993). Thus, the promotion of organic farming has become an essential element of the Common Agricultural Policy (CAP) and several European Member States are eager to increase the size of their organic sectors. This began with the MacSharry reform1 in 1992, which first introduced payments for environmentally friendly farming (including organic farming). Next, the Mid Term review of Agenda 2000 concluded in 2003 with a fundamental reform that involved decoupling of payments from production (CEC, 2002). This encourages extensive farming, therefore further supporting the switch to organic farming. However, regardless of substantial policy support, the organic sector still represents only a small portion of the total utilizable agricultural area (UAA) in most European countries, averaging 4% at the end of 2007 (Willer et al., 2009). Nevertheless, organic farming has been available to the farmer long before it received policy support. Thus, it is crucial that the current diffusion of the sector is accounted for when attempting to explain uptake decisions.

⁎ Corresponding author. Tel.: +353 91 845256; fax: +353 91 844296. E-mail address: [email protected] (D. Läpple). 1 The MacSharry reform was the first major reform of the CAP. The main implication of this reform was the reduction of the level of price support for a number of commodities, while farmers were compensated for the resulting loss in income through increased direct payments (Bromley and Hodge, 1990). 0921-8009/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2011.03.002

For organic farming to be effective, policy makers require an understanding of what persuades conventional farmers to switch to organic farming. Organic farming shares similarities with other agricultural technologies in terms of the adoption and diffusion process. The uptake of new technologies or farming practices has attracted considerable interest over the years. Hence, there is a vast literature on the adoption and diffusion of technologies in agriculture (Feder et al., 1985). Nevertheless, the majority of these studies tend to focus on the classic comparison between adopters and non-adopters of a technology (e.g. Burton et al., 2003; Dadi et al., 2004; DeSouza Filho et al., 1999), with very few empirical studies investigating differences between early and late adoption of new technologies in general and organic farming in particular. Initially, rural sociologists studied the diffusion of technologies. Cumulative adoption was described with an S-shaped curve which results from the fact that only few farmers adopt the new technology in the early stage of the diffusion process (Rogers, 1962). At this stage, only a minority of farmers have acquired full information about the potential advantages of the technology, hence the pace of adoption is slow. Moreover, fear of possible risks associated with the new technology enhances farmers' reluctance to adopt. However, the degree of risk reduces as more farmers adopt, so that the rate of adoption increases. Adoption increases gradually and begins to level off, ultimately reaching an upper ceiling. Obviously, not all individuals in a social system adopt a technology at the same time and based on that, Rogers (1962) divided adopters into five adopter groups: innovators, early adopters, early majority, late majority and laggards. In describing the characteristics of these groups, he suggested that differences exist between adopters at different stages of the distribution curve.

D. Läpple, T. Van Rensburg / Ecological Economics 70 (2011) 1406–1414

Against this background, this study investigates differences between early, medium and late adopters of organic farming and more importantly, whether the factors affecting uptake changed with ongoing diffusion influenced by policy support. The identification of these determinants aims to contribute to an improved understanding of the adoption process and the findings can thereby help to promote the adoption of organic farming. Section 2 gives a brief overview of the organic sector in Ireland; Section 3 provides a review of relevant literature. Section 4 outlines the underlying research hypotheses, while Section 5 explains the use of a multinomial logit model in the context of early, medium or late adoption of organic farming. Section 6 describes the data set, followed by the presentation and discussion of the results in Section 7. Finally, Section 8 provides some concluding remarks. 2. Development of Organic Farming in the Republic of Ireland2 Most of the Directives that followed Ireland's entry into the EEC in 1973 concentrated on stimulating agricultural output and supporting farm incomes (Emerson and Gillmor, 1999). The rural economic and social benefits brought about by the modernisation of Irish agriculture were substantial but there have also been detrimental impacts on the rural environment (Bleasdale and Sheehy-Skeffington, 1995). However, from the early 1980s the CAP has been under increasing public pressure to reform its protectionist policies due to huge budgetary costs, negative welfare measures, distortionary effects on international trade and high environmental costs (European Commission, 1997). This has lead to a process of agricultural reforms which has introduced measures to curb surplus production and protectionist policies, initiated measures to fully decouple income support to farmers from price support to direct income support and the CAP now includes a number of agri-environmental measures (Brouwer and Lowe, 2000; Buller, 2000; Buller et al., 2000; Lowe and Brouwer, 2000). Agri-environment schemes were developed in order to encourage farmers to farm in an environmentally-friendly way in exchange for financial compensation for environmental practices. The main scheme in Ireland is known as the Rural Environment Protection Scheme (REPS) which was introduced in response to the MacSharry reform (Regulation 2078/92). The financial support of organic farming is included as a supplementary measure, which implies that it was compulsory for a farmer to join REPS in order to receive organic subsidy payments. The Irish organic sector was very small during the 1980s and early 1990s, and up until recently the sector has developed without a significant contribution from advisory or educational services. Two policy interventions played an important part in enhancing the development of organic farming in Ireland. First, the sector experienced a significant growth after the introduction of organic support payments for conversion and ongoing organic production in June 1994 by way of the REPS scheme. Second, the decoupling of payments from production which was introduced in Ireland in January 2005 encouraged extensive farming, thus further supporting the switch to organic farming. Recent figures indicate that growth is still ongoing. For example, organic farm numbers increased from 1102 in 2007 to 1315 organic farms in 2009 (DAFF, 2009). Further, the Irish government set a target of 5% of the UAA dedicated to organic farming by 2012; but while currently there is 1.2% of the UAA in organic farming, only about a quarter of the target diffusion rate has been reached. The small scale of the Irish organic sector is somewhat surprising considering the low intensity of Irish conventional farming in comparison with elsewhere in Europe. Moreover, the typical conventional systems of beef, sheep and dairy production in Ireland are most often extensive and mainly grass-based. Therefore, many beef and

2

This study focuses on the Republic of Ireland only, thereafter referred to as Ireland.

1407

sheep farmers in particular could easily adjust to organic production with relatively little entry costs and alterations in farm management or agronomic practices (Reidy, 2006). Hence, it is not surprising that the majority of organic farms in Ireland are engaged in cattle and/or sheep (drystock) farming. Therefore this analysis focuses on drystock farmers since significant numbers, necessary for an empirical analysis, can be found in this sector. However, the combination of low uptake rates and expected easy adjustment to organic practices underlines that the adoption of organic farming is not well understood, which further highlights the need to conduct a study such as this one. 3. Development of Relevant Literature Early research of technology uptake focused on the diffusion process and was undertaken initially by rural sociologists. Ryan and Gross (1943) and Rogers (1962) conducted studies on the diffusion of hybrid corn in Iowa. They observed the S-shaped adoption curve and identified networks of information exchanges between adopters and nonadopters as critical for the diffusion process. The results were used by extension agents to promote new technologies and Ruttan (1996, p.56) claims that “one of the remarkable aspects of the technology diffusion studies by rural-sociologists was how rapidly the results were utilized by practitioners.” Successful agricultural extension can help to overcome the gap between newly invented technologies and changes in the farmer's field. That is, extension specialists supply farmers with the required knowledge, thus assisting in a shift to more efficient production techniques and thereby enhancing the diffusion process of technologies (Birkhaeuser et al., 1991). This process is particularly important in the early stages of the diffusion process. For example, Wozniak (1987) found in his study of early adoption of the cattle feed additive monensin sodium (a feed additive to improve feed efficiency) that education and information on the new technology are very important factors for early adoption. Further, Valente (1996) stresses the effect of social networks in the diffusion of technologies. More specifically, a person's time of adoption is thought to be associated with the proportion of adopters in the social system, and therefore connected to the proportion of adopters in the person's individual network. Due to the observation that not all farmers adopt a new technology at the same time, the diffusion of an innovation follows an S-shaped curve of cumulative adopters and essential differences among farmers can explain this phenomenon (Rogers, 1962). Initially few innovators start using the technology. They are characterized as venturesome, have strong social ties with other innovators but may not be respected by other members in the social system. In addition, they must have the capacity to cope with a high level of risk. The innovators are followed by the early adopters, who are more integrated in the social community and represent a model to follow, which is based on intensive contact with information. The next category, the early majority, comprises of individuals who carefully consider adopting a new idea and, unlike early adopters, rarely have a leadership position. The late majority tends to remain sceptical about the new technology and will wait until the technology is more widely diffused. The last category, the laggards, has traditional values and tends to be the slowest to adopt. This highlights the need to incorporate changes in characteristics of adopters when attempting to explain the uptake of a technology. In order to understand what causes or constrains the adoption of new technologies, several researchers have examined the influence of various determinants on adoption decisions. Hence, there is a vast literature on technology adoption in agriculture. However this is mainly based on the classic comparison between adopters and nonadopters (e.g. Dadi et al., 2004; D'Emden et al., 2006; Feder and Slade, 1984; Sheikh et al., 2003). Compared to the large amount of literature on technology adoption, few empirical studies distinguish between early and late adopters, despite differences among adopter groups over time being well acknowledged in the literature (e.g. Feder et al., 1985; Rogers, 1995). One of the few examples is a study by Barham et

1408

D. Läpple, T. Van Rensburg / Ecological Economics 70 (2011) 1406–1414

al. (2004). The authors explore agricultural biotechnology adoption of Wisconsin dairy farmers and distinguish among non-adopters, early, late and dis-adopters. Their results show, for example, that attitudes toward the technology and location are linked to early adoption, while farm size and complementary technology are important factors for all adopter groups. Further, Diederen et al. (2003), considering a range of innovations, investigate differences between innovators, early adopters and laggards utilising Dutch data. The findings of this study indicate that structural and socio-demographic characteristics explain the difference in adoption behaviour between early and later adopters, while information gathering and active involvement in the development of the new technology explain differences between innovators and early adopters. So far, the only empirical contributions looking at early and late adopters of organic farming are by Flaten et al. (2006) and Best (2008). Flaten et al. (2006) compare farm and farmer characteristics, as well as goals and motives of Norwegian early, mid and late adopters of organic farming. In addition to differences in farming practices between early and later adopters, their results reveal changing motives for conversion over time. Best (2008) compares early and late adopters of organic farming in Germany and test the “conventionalization hypothesis” meaning that organic farming is developing into a modified version of conventional agriculture. His results indicate a development towards more specialized farms, but most farmers still express a high level of environmental concern. Although Flaten et al. (2006) and Best (2008) provide good insight into the adoption process over time, both studies are exploratory in nature, and focus on differences between organic farmers, rather than investigating differences in the adoption between the groups. The following analysis aims to contribute to this gap in the literature by exploring the effect of important determinants on early, medium and late adoption of organic drystock production in Ireland. We further contribute to the literature by focusing on how attitudes and social learning affect the adoption of organic farming. More specifically, we investigate how attitudes toward information gathering, environment, profit and risk differ between the groups. Finally, we specifically add to the literature on the uptake of organic farming by exploring differences in the factors that affect the adoption of organic agriculture over the diffusion process. 4. The Adoption and Diffusion of Organic Farming — Hypotheses The previous outline of relevant literature in combination with the description of the contextual setting of the organic sector in Ireland led to the following set of hypotheses: Hypothesis 1. Significant differences in the farm (i.e. structural) and farmer (i.e. socioeconomic and personal) characteristics between non-adopters, early, medium and late adopters of organic farming can be expected. This arises as a consequence of either non-adoption or adoption at different stages of the diffusion process. Hypothesis 2. It is expected that determinants that are related to adoption play a different role in early, medium and late adoption of organic farming and are of differing importance to each group. Following from Hypothesis 2, we test more precise hypotheses in relation to specific characteristics. Hypothesis 3. Due to the fact that organic subsidy payments were not available for early adopters as well as evidence from the literature that early adopters were mainly driven by non-economic reasons and that factors affecting the adoption of organic farming have changed over time (Flaten et al., 2006; Padel, 2001), it is expected that early adopters are more strongly influenced by environmental considerations and less by profit motives than later adopters. Further, it is anticipated that early adopters are less constrained by risk considerations than later adopters.

Hypothesis 4. Due to the importance of information on technology adoption in general and the fact that organic farming is an information intensive technique in particular, it is expected that both farmers' attitudes towards actively seeking information about the new technology and available information on the new technology is particularly important for early adoption compared to later adoption. Hypothesis 5. It is argued that social learning will positively affect all adopter groups, due to the general consensus that innovators have strong social ties (Rogers, 1962), but later adopters may simply observe or imitate their neighbours (Genius et al., 2006). 5. Methodology Given the assertion that over time there are more than just two identified groups — adopters and non-adopters — requires a more refined distinction of adopters. Therefore, different adopter groups were derived based on the diffusion process, grouped by the date of implementation of relevant policy reforms. Utilising information about the date when farmers adopted organic farming, it was possible to classify adopters into early (‘Pioneers’), medium (‘Followers’) and late adopters (‘Laggards’) of organic farming. In addition, farmers who did not adopt organic farming were categorised as non-adopters. Since there are multiple choices and particular interest lies in the individual effects of explanatory variables on each outcome, the adoption decisions of farmers are modelled using a multinomial logit model. This is an extension of the binary logit model where the unordered response variable has more than two responses. The outcome variable y can take on the values j = 1, 2, …J with J being a positive integer. In particular, the model explains the probability of non-adoption (j = 1) or adoption at an early stage (j = 2), medium stage (j = 3) or at a later stage (j = 4). The determinants associated with each category can be contrasted with the base category, which is non-adoption in this study. The interest lies in how ceteris paribus changes in the elements of x affect the response probabilities, P(yi = j x), j = 1, 2, …J (Wooldridge, 2001). The probability of the J categories is determined by the following equation: P ðyi = k jxi Þ =

expðβk xi Þ   ; j = 1; 2;…; J; 1 exp βj xi

∑Jj =

ð1Þ

where k is one of the j subgroups and P(yi = k) is the probability that the ith farmer belongs to the k subgroup and xi describes farm and farmer characteristics. In order to identify the model, constraints must be imposed. A common approach is to assume that β1 = 0 (Long, 1997). This normalization makes it possible to identify the coefficients relative to the base outcome. Applying the constraint, the model can be written as: P ðyi = k jxi Þ =

expðβk xi Þ   ; for k N 1; 1 + ∑Jj = 2 exp βj xi

ð2Þ

P ðyi = 1 jxi Þ =

1  : 1 + ∑Jj = 2 exp βj xi

ð3Þ

The multinomial logit model is estimated using maximum likelihood with the following equation:   j L β2 ;…; βj jy; X = ∏ ∏

expðβk xi Þ  ; exp βj xi

J k = 1 yi = k ∑ j = 1

ð4Þ

where ∏ yi = k is the product over all cases for which yi = k (Long, 1997).

D. Läpple, T. Van Rensburg / Ecological Economics 70 (2011) 1406–1414

Coefficients are interpreted using the relative risk ratios, which is the relative probability of yi = k, for k N 1to the base category, which are the non-adopters.   P ðy = kÞ = exp βj xi ; for k N 1: P ðy = 1Þ

ð5Þ

As is evident, the relative risk ratio is calculated without reference to the remaining two adopter groups. This shows the underlying assumption of the model of the independence of irrelevant alternatives. Although statistical tests are available to test for this proposition, their use is not recommended due to unreliable test results (Cheng and Long, 2005; Long and Freese, 2006). Thus, the use of a multinomial logit model in this study is based on the recommendation by Amemiya (1981) that the model works well when the alternatives are dissimilar. Overall, the model helps to reveal significant differences between early, medium and late adoption of organic drystock farming in Ireland, relative to non-adoption. 6. Data 6.1. Survey Data The study is based on a nationwide survey of organic and conventional farmers in Ireland, which was conducted between July and November 2008. For the organic farmers complete address lists were available from the organic certification bodies3 and a survey was sent to each farmer on the list. A response rate of 40% was achieved following an announcement of the survey in the Irish Farmers' Journal newspaper and one reminder letter. The data for the conventional farmers were collected through the Teagasc National Farm Survey (NFS; Connolly et al., 2008). The NFS is based on approximately 1100 farms representing 110,000 farms nationally. The NFS data are EU-Farm Accountancy Data Network (FADN) data for Ireland. The data were restricted to farms that have cattle and/or sheep (drystock farms) since significant numbers of organic farmers, necessary for an empirical analysis, can be found in this category. Finally, the overall data set comprises of 546 farms. The very small number of organic farms in Ireland implies that complete random sampling would not have generated a large enough number of organic farms for an empirical analysis. Thus, these farms are well represented in the study, whereas the number of conventional farms in the sample is small considering the proportion of organic farms to the total number of farms in Ireland. Despite not being representative of the general farming population, the sample provides a good representation of the types of farm operators who participate in organic farming in Ireland as well as the conventional drystock operators. The literature on the uptake of environmental schemes and organic farming widely agrees that farmers' decisions depend on a variety of factors, such as economic and structural characteristics of the farm, farmer and household characteristics, such as age, education and household size, personal factors such as attitudes and objectives of the farmer, as well as information provision (e.g. Beedell and Rehman, 2000; Genius et al., 2006; Hynes and Garvey, 2009; Läpple, 2010; Oelofse, et al., 2010; Wynn et al., 2001). Consistent with previous studies the focus is on off-farm income, farm size, livestock density, age, education, as well as information use and attitudes of the farmer. Table 1 provides an overview of the variables used in the analysis. The variables info advisory and info media are an attempt to measure information use of the farmer. Info advisory measures the frequency of consulting a farm advisor, attending an information event and agricultural training course divided by three. Info media captures how frequently the farmer uses magazines/press, TV/radio and the internet as a source of farming information, also divided by three.

3

Organic Trust Ltd. and Irish Organic Farmers and Growers Organisation.

1409

However, particular attention is paid in the present study to the impact of attitudes of the farmer on adoption decisions. While more recent literature agrees on the importance of the attitudes and objectives of the farmer on the adoption decision, this is often incorporated into empirical analysis with the inclusion of only a very few questions in the survey (Burton et al., 2003; Genius et al., 2006). Therefore, in this analysis a comprehensive set of attitudinal statements is used in order to assess farmers' attitudes towards the environment, profit, risk as well as information gathering. A list of the attitudinal statements can be found in Appendix A(Table A1). Based on Fishbein and Ajzen (1975), who underline the importance of measuring attitudes with multiple statements, each attitude was described by five to twelve items and respondents were asked to agree or disagree with each attitudinal statement. Furthermore, in order to keep respondents' attention, statements for the same attitude were randomly scattered within all statements and both, positive and negative phrases were used. For example, seemingly contradictory statements were included to assess farmers' true attitudes as accurately as possible. In order to get the same meaning for all labels, negative statements were reversed for the analysis. Finally, each questionnaire included 35 attitudinal statements and attitudes were empirically identified and confirmed by the use of principal component analysis with varimax rotation. Consequently, the variables included in the analysis are the calculated component scores.

6.2. Descriptive Statistics The way the groups were categorized into different organic farming adopter groups was based on relevant policy reforms: the implementation of the REPS scheme in June 1994 and of the Single Farm Payment Scheme in January 2005. Both schemes had a significant impact on the development of the Irish organic sector, although the Single Farm Payment Scheme to a lesser extent. Table 2 provides descriptive statistics of the explanatory variables divided into the different adopter categories. Non-adopters are conventional farmers accounting for 30% of the sample. The first category is the group of early organic adopters (‘Pioneers’). These are represented by farmers who adopted organic farming before the implementation of REPS in June 1994 and account for 5.3% of the sample. The second category (‘Followers’) accounts for 39.5% of the sample and includes farmers who adopted organic farming techniques after June 1994 and before January 2005, whereas the last category Table 1 Definition of variables. Off-farm income

If the farm household has an off-farm income = 1, = 0 otherwise, Household members The size of the farm household (no.), UAA Utilizable agricultural area of the farm measured in hectares, LU/ha Livestock units per hectare, Info advisory Frequency of consultation with a farm advisor, attendance at information events and agricultural training courses, divided by three, Info media Frequency of using magazines/press, TV/radio and the internet as a source of farming information, divided by three, Knows other organic If the farmer knows another organic farmer = 1, = 0 farmer otherwise, Higher education If the farmer has higher education (second level or higher) = 1, = 0 otherwise, Age Age in years at adoption (in 2008 for non-adopters), Environmental Higher value = higher level of environmental concern, attitude Profit orientation Higher value = higher profit motivation, Risk attitude Higher value = more risk averse, Information Higher value = higher interest in information gathering. gathering attitude

1410

D. Läpple, T. Van Rensburg / Ecological Economics 70 (2011) 1406–1414

Table 2 Descriptive statistics for the sample.

Off-farm income Household members UAA LU/ha Info advisory Info media Knows other organic farmer Higher education Age Environmental attitude Profit orientation Risk attitude Information gathering attitude

Non-adopter (n = 164)

‘Pioneers’ (n = 29)

‘Followers’ (n = 216)

‘Laggards’ (n = 137)

Mean (St. dev.)

Mean (St. dev.)

Mean (St. dev.)

Mean (St. dev.)

0.52 3.31 54.59 1.08 0.68 3.45 0.35 0.65 53.61 − 1.14 0.02 0.23 − 0.18

(0.50) (1.69) (39.95) (0.49) (0.72) (1.33) (0.48) (0.48) (11.25) (0.79) (0.83) (0.82) (0.95)

0.58 2.83 41.09 0.65 0.83 3.40 0.89 0.69 37.38 0.58 − 0.40 − 0.12 0.09

(‘Laggards’) is represented by farmers who adopted organic farming after the implementation of the Single Farm Payment Scheme in January 2005. This group accounts for 25.1% of the sample. 7. Results and Discussion 7.1. Comparison of Adopter Groups Close inspection of Table 2 reveals some notable differences between the adopter groups, which were confirmed using statistical tests. The results of the statistical tests are shown in Table 3. In terms of farm characteristics, non-adopters have significantly larger farms (t = −6.70; p = 0.00) and a higher livestock density (t = −5.89, p = 0.00) than adopters of organic farming. Looking at the age of the farmer at adoption, non-adopters are significantly older than adopters (t= −3.55, p = 0.00), and adopters get significantly older as diffusion of organic farming progresses. For example, the ‘Pioneers’ were found to be significantly younger than later adopters (t= −3.14, p = 0.00), with an average age of 37.4 years, whereas the group of ‘Followers’ were on average 42.8 years of age at adoption. The ‘Laggards’ were on average 45.2 years old when they adopted organic farming. In addition, all adopters of organic farming were also found to be somewhat better educated than the non-adopters (χ2 = 3.89, p = 0.05), while no significant difference in education could be detected between the adopter groups.

(0.50) (1.36) (42.79) (0.35) (0.77) (2.02) (0.31) (0.47) (8.57) (0.67) (1.28) (0.96) (1.27)

0.76 3.53 34.10 0.79 0.86 3.48 0.85 0.72 42.79 0.51 0.01 − 0.08 − 0.04

(0.43) (1.71) (26.68) (0.53) (0.79) (1.67) (0.36) (0.45) (10.54) (0.62) (1.02) (1.05) (1.03)

0.77 3.42 35.75 0.87 1.19 3.62 0.86 0.76 45.23 0.38 0.08 − 0.02 0.22

(0.42) (1.60) (20.18) (0.43) (0.74) (1.67) (0.35) (0.43) (10.62) (0.59) (1.06) (1.00) (0.81)

Some interesting findings emerge when comparing information use between the groups. First, non-adopters use significantly less information (info advisory, t = 4.15, p =0.00) than all adopters and second, ‘Laggards’ utilise significantly more information (info advisory) than earlier adopters (t = 4.11, p = 0.00) and ‘Followers’ (t = − 3.96, p = 0.00). Intensive contact with information is a characteristic of Roger's early adopters, suggesting that the diffusion process of organic farming in Ireland may still be at an early stage. Turning to the attitudinal variables, depicted in the last four rows of Tables 2 and 3, all adopters of organic farming show a stronger proenvironmental orientation than non-adopters (t= 25.53; p = 0.00). Moreover, ‘Laggards’ express a significantly lower level of environmental concern than earlier adopters (t= −2.09, p = 0.00). In terms of profit orientation, ‘Pioneers’ are less profit orientated than all other adopter groups. Indeed, the difference between ‘Pioneers’ and later adopters is significant at the 5% level (t= −2.17). This finding is in accordance with Padel (2001), who reports that motives to adopt organic farming have changed over time to being more economically driven. Next, in line with the general consensus (Feder et al., 1985), non-adopters are found to be more risk averse than adopters of organic farming (t = − 3.28; p = 0.00). In the literature, it is widely agreed that early adopters are more willing to take risks (e.g. Fernandez-Cornejo et al., 1994; Rogers, 1962). However, in the present study, no statistically significant difference with regard to risk attitude could be found between the adopter groups. Finally, non-adopters have a significantly lower level of

Table 3 Comparison of characteristics between adopter groups. Comparison between

Off-farm income Household members UAA LU/ha Info advisory Info media Knows other organic farmer Higher education Age Environmental attitude Profit orientation Risk attitude Information gathering attitude

Non-adopters

Pioneers

Followers

Followers

Laggards

All adopters

Later adopters

Pioneers

Laggards

Earlier adopters

χ2= 26.58⁎⁎⁎ t = 0.81 t = − 6.70⁎⁎⁎ t = − 5.89⁎⁎⁎ t = 4.15⁎⁎⁎

χ2= 4.40⁎⁎ t = − 2.07⁎⁎ t = 1.25 t = − 1.81⁎ t = − 1.08 t = − 0.39 χ2= 0.37

χ2= 3.97⁎⁎ t = − 2.11⁎⁎ t = 1.22 t = − 1.39 t = − 0.23 t = − 0.23 χ2= 0.42

χ2= 0.02 t = 0.61 t = − 0.62 t = − 1.34 t = − 3.96⁎⁎⁎ t = − 0.73 χ2= 0.06

χ2= 0.36 t = − 0.15 t = − 0.29 t = 1.77⁎ t = 4.11⁎⁎⁎

χ2= 0.26 t = − 3.14⁎⁎⁎ t = 1.04 t = − 2.17⁎⁎

χ2= 0.10 t = − 2.45⁎⁎⁎ t = 0.60 t = − 2.01⁎⁎

χ2= 0.74 t = − 2.12⁎⁎⁎ t = 1.92⁎⁎ t = − 2.01⁎⁎

χ2= 0.90 t = 2.75⁎⁎⁎ t = − 2.09⁎⁎

t = 0.47 χ2= 144.14⁎⁎⁎ χ2= 3.89⁎⁎ t = − 3.55⁎⁎⁎ t = 25.53⁎⁎⁎ t = − 0.15 t=−3.28⁎⁎⁎ t = 2.66⁎⁎⁎

t = − 0.32 t = 0.20

t = − 0.19 t = 0.66

t = − 0.55 t = − 2.51⁎⁎

Note: all adopters = Pioneers, Followers and Laggards; later adopters = Followers and Laggards; earlier adopters = Pioneers and Followers. T-tests were used for interval variables, whereas chi-square tests were used for categorical variables. ⁎⁎⁎ p b 0.001. ⁎⁎ p b 0.05. ⁎ p b 0.1.

t = 0.79 χ2= 0.01

t = 1.01 t = − 0.61 t = − 2.35⁎⁎

D. Läpple, T. Van Rensburg / Ecological Economics 70 (2011) 1406–1414

information gathering attitude than adopters (t = 2.66, p = 0.00), confirming that farmers' attitudes toward actively seeking information about farming practices are important for adoption decisions (Genius et al., 2006). Moreover, ‘Laggards’ show a significantly higher level of information gathering attitude than earlier adopters. This is in line with the higher use of information of this group, and could be related to the fact that the provision of information on organic farming in Ireland mainly developed after 2004. 7.2. Results of the Multinomial Logit Model The results of the multinomial logit model are reported as relative risk ratios and are presented in Table 4. In Appendix A Table A2 shows the estimated coefficients. Overall the model correctly predicts 65.6% of adopters in the right category. The model puts 89.0% of the non-adopters in the correct category; in addition 93.1% of ‘Pioneers’, 94.4% of ‘Followers’ and 94.9% of ‘Laggards’ are correctly predicted as adopters of organic farming. Further, a likelihood ratio test rejects the null hypothesis at a 5% level that the alternatives ‘Pioneers’ and ‘Followers’ can be combined (χ2(13) = 23.69), and rejects the null hypothesis at a 1% level that the ‘Pioneers’ and ‘Laggards’ (χ2(13) = 38.98), as well as ‘Followers’ and ‘Laggards’ (χ2(13) = 28.39) can be merged. This suggests that additional information about the adoption of organic farming can be received from separating the farmers into adoption groups over time. In terms of economic factors, the variable denoting whether or not the farm household has an off-farm income is not significantly related to any of the adoption categories. This is consistent with findings by Burton et al. (1999, 2003) and Genius et al. (2006). An increasing number of household members — which have been considered as a proxy for available family labour — are negatively related to early adoption (‘Pioneers’) in comparison to non-adoption. Considering that organic farming is generally associated with higher labour demand (Offermann and Nieberg, 2000) and the majority of Irish farms rely on family labour, this result appears inconsistent. However, an increasing household size may also limit farm business freedom and family considerations may often dominate over farmers' personal wishes in determining farming decisions (Battershill and Gilg, 1997). Farm size is significantly correlated to adoption of organic farming, but no differences are evident between early, medium or late adoption. This implies that farm size is a robust indicator of adoption over time. Table 4 Results of the multinomial logit model. Variables

‘Pioneers’

Off-farm income 0.75 Household members 0.67⁎⁎ UAA 0.98⁎⁎ LU/ha 0.24⁎⁎ Info advisory 1.07 Info media 0.66⁎⁎ Knows other 9.74⁎⁎⁎ organic farmer Higher education 1.76 Age 0.85⁎⁎⁎ Environmental 26.08⁎⁎⁎ attitude Profit orientation 0.48⁎⁎ Risk attitude 0.64 Information 1.22 gathering attitude Log-likelihood: − 404.18 Pseudo-R2: 0.40

(0.47) (0.13) (0.008) (0.16) (0.45) (0.13) (7.63)

‘Followers’

‘Laggards’

1.20 0.90 0.97⁎⁎⁎ 0.53⁎

1.45 0.88 0.97⁎⁎⁎ 0.63 1.99⁎⁎ 0.66⁎⁎⁎ 8.40⁎⁎⁎

(0.57) (0.11) (0.006) (0.19) 1.26 (0.39) 0.66⁎⁎⁎ (0.09) ⁎⁎⁎ 8.41 (3.90)

(1.15) 1.57 (0.76) (0.03) 0.91⁎⁎⁎ (0.02) (12.35) 21.42⁎⁎⁎ (7.40) (0.14) (0.20) (0.39)

0.65⁎ 0.59⁎⁎ 1.19

(0.16) (0.14) (0.28)

(0.70) (0.12) (0.007) (0.23) (0.62) (0.09) (3.99)

1.81 (0.88) 0.94⁎⁎⁎ (0.02) 15.25⁎⁎⁎ (5.25) 0.67 0.60⁎⁎ 1.53⁎

(0.16) (0.15) (0.36)

Number of observations = 546, non-adopters as comparison group, relative risk ratios (RRR) are reported, standard errors (se) of RRRs are reported in parentheses and are calculated:se(RRR)=exp(β) x se(β). ⁎⁎ p b 0.05. ⁎⁎⁎ p b 0.001. ⁎ p b 0.1.

1411

This is in contrast to Diederen et al. (2003) who report that farms that are bigger adopt an innovation earlier. However, in this study, an increasing farm size is negatively related to adoption — in line with Burton et al. (1999) — suggesting that farmers who operate larger farms are less likely to adopt organic farming. Our finding supports the consensus that small farms that rely on family labour generally adopt more labour intensive systems (Hayami and Ruttan, 1985). In the context of organic farming, it is possible that smaller farms are easier to manage, for example in terms of meeting the required organic regulations. The intensity level of the farm has been described by LU/ha. A lower livestock density per hectare appears to be advantageous for early (‘Pioneers’) and medium adoption (‘Followers’) of organic farming, which is suggested by a relative risk ratio below one. For the ‘Laggards’, LU/ha does not show a significant effect on the adoption decision. This may be due to better knowledge of organic farming in recent years, which implies that organic farmers are better able to manage higher livestock densities. Similarly, Flaten et al. (2006) report that late entry organic dairy farmers fed more concentrates and had higher milk production intensities. In terms of information use, the variable info advisory is positively correlated to late adoption (‘Laggards’). Considering that information about organic farming in Ireland mainly developed after 2004, this result is not surprising and it appears that information provision on organic farming has become more important over time. In contrast, Wheeler (2008) reports that organic farmers have often complained about agricultural experts' limited knowledge of organic farming and discouragement to convert. However, widely available information about organic farming in Ireland is still limited, as suggested by the overall negative effect of the variable info media. Due to the absence of widespread information, magazines, internet and TV/radio have a negative effect on adoption of organic farming, which could be due to the fact that these sources mainly provide information about conventional agriculture. The influence of social learning has been described by a proxy denoting whether or not the farmer knows another organic farmer. This appears to be advantageous for all adopter groups, indicating that social networks are equally important over the diffusion process. This finding is confirmed by Lampkin and Padel (1994) who suggest that existing organic farmers are an important source of information and knowledge for farmers involved in converting to organic farming. Further, according to DeSouza Filho et al. (1999) adopters are usually closely related through information networks and a high social integration can enhance adoption. In line with Burton et al. (1999), but in contrast to Hattam and Holloway (2007), the level of education is not significantly related to adoption of organic farming. Although adopters show a higher level of education, which is usually regarded as positively related to technology adoption. Farmer's age is negatively correlated to all adopter groups, but the coefficient for ‘Pioneers’ is significantly smaller than the coefficients for the later adopters. This confirms the general belief that early adopters are the youngest to adopt (Barham et al., 2004). In terms of attitudinal variables, a higher level of environmental concern was found to be significantly and positively related to the adoption of organic farming, consistent with previous literature (Burton et al., 2003; Läpple, 2010). Despite decreasing relative risk ratios of this variable from early to late adopters, no significant differences could be detected between the adopter groups. This suggests that environmental attitude is a consistent and important factor in the adoption diffusion process of organic farming. Similarly, Best (2008) argues that the majority of early and late adopters of organic farmers express strong environmental concern. A higher profit motivation is significantly negatively related to earlier adoption (‘Pioneers’ and ‘Followers’), whereas it was not found to have a significant effect on late adoption. This result is in accordance with policy changes towards increased subsidies for organic farming, suggesting that the availability of organic subsidy payments induced a shift toward more profit oriented conversion. Similarly, Padel (2001) and Flaten et al. (2006) also report higher profit motivated conversion of later adopters.

1412

D. Läpple, T. Van Rensburg / Ecological Economics 70 (2011) 1406–1414

In terms of information gathering attitude, a significant positive relation could be detected for the ‘Laggards’, confirming the influence of information use, a finding similar to Genius et al. (2006). Generally, intensive contact with information is very important for early adopters (e.g. Wozniak, 1987). The finding that this variable is related to the late adoption group in our study could be due to two reasons: first information provision on organic farming mainly developed after 2004 (‘Laggards’) or, alternatively, the intensive contact with information hints towards the assumption that organic farming is still at an early stage of the diffusion process. Being more risk averse is negatively related to later adoption (‘Followers’ and ‘Laggards’), but does not show a significant relation to early adoption. This finding shows that risk attitude has become more important over time, supporting the theory that innovators have a stronger capacity to deal with risk (Rogers, 1962). 8. Concluding Remarks This paper aims to improve our understanding of the adoption of organic farming by taking the diffusion process into account. By utilising information on the date of conversion, the study moves beyond the classic comparison of adopters and non-adopters to a more a refined distinction of non-adopters, early, medium and late adopters. The use of multinomial logit analysis allows for the investigation of factors that are unique to each group. Thereby, we explore whether characteristics related to adoption change over the diffusion process, influenced by policy changes. Our findings confirm significant differences in the characteristics between the adopter groups (Hypothesis 1). Although the main differences were found between adopters and non-adopters, early, medium and late adopters differed significantly with regard to several important characteristics, e.g. information use, age, environmental attitude, profit motives as well as attitude toward information gathering. The results also confirmed Hypothesis 2 that determinants that are related to adoption play a different role for early, medium and late adopters and are of different importance to each group. These general hypotheses lead to more specific hypotheses with regard to attitudes and information. Despite the finding that early adopters express a higher level of environmental concern than late adopters, our empirical findings only partly confirm Hypothesis 3 that environmental considerations are more important and profit motives are less important for early adopters. Indeed, profit motives are negatively related to early adoption (‘Pioneers’ and ‘Followers’), while environmental concerns emerged as a consistent but essential determinant over the whole diffusion process. In terms of risk attitudes, our findings confirm Hypothesis 3 in the sense that risk considerations only constrain later adopters (‘Followers’ and ‘Laggards’) but do not affect early adopters. Against the general consensus in the literature that information gathering and provision has a crucial effect on early adoption (Hypothesis 4) our results indicate that this is a central determinant of late adoption (‘Laggards’). This finding could be due to two reasons: first, the provision of information on organic farming in Ireland developed quite late (after 2004) which explains the effect on our late adopters. Second, this could also hint toward the assumption that the diffusion of the organic sector in Ireland is still in its early stages, which seems plausible given the fact that the organic sector in Ireland is quite small. Finally, Hypothesis 5 has been confirmed, expecting a consistently important effect of social learning on adoption. It is evident from our results that coarsely aggregating all adopters into one group would have lead to the conclusion that all adopters are equally influenced by certain factors. However, upon disaggregating into more nuanced adopter groups, our analysis identified a number of significant differences between the groups, which would not have been revealed otherwise. In terms of the diffusion of an innovation this is an interesting finding since it is possible that because the different groups do not have the same priorities, they adopt organic farming for different reasons. It is also likely that although early adopters are a minority they

may nevertheless play an important role in the diffusion of organic farming because they are less concerned about financial considerations and risk compared with later adopters. Based on our empirical results, we suggest a number of steps be taken to make agricultural policy on organic farming more effective in supporting the adoption of organic farming. First, higher profit motivation of farmers is negatively related to early adoption, but diminishes with increasing policy support. This may suggest a development towards more economically driven conversion which has some policy implications, for example, in highlighting potential economic benefits of organic farming. In this context, access to organic markets is desirable for successful and economically viable organic farming in the long run. Consumer demand and willingness to pay price premiums for organic produce are essential requirements as this provides an opportunity for organic farmers to supplement their incomes. Further, the receipt of price premiums for organic products can reduce dependency of organic farmers on additional support payments. Clearly, organic farmers require access to organic markets in order to receive price premiums. Hence, policy design should be guided by the aim that all organic farmers have access to organic markets and receive price premiums for their produced output. Another policy recommendation, arising from the empirical work in this study, is the importance of improving information provision in order to increase the uptake of organic farming. This is supported by two other points: first, descriptive results of the study suggest that the diffusion process of organic farming in Ireland is still in the early adoption stages, further underlining the importance of information provision. Second, based on the negative effect of risk attitude on later adoption, information provision can be used as a means to reduce the risk of adoption, which is a well known concept in the literature (e.g. Feder and Slade, 1984; Marra et al., 2003), and emerges as a particularly important issue in the context of organic farming. Nevertheless, more insight is needed as to the precise mechanisms of diffusion. Future research that explores whether early adopters persuade later adopters to take up organic farming would be useful. If later adopters are persuaded, is the process more effectively achieved through spatial diffusion or instead through social ties and networks. Are farmers who adopt late convinced by their neighbours who have already adopted or do they join a network and develop social ties with organic farmers who already have experience of organic techniques? Future research could evaluate if the concentration of organic farmers matters and whether high intensity areas lead to greater levels of uptake compared to lower intensity areas (Risgaard et al., 2007). This enquiry could be grounded in social network theory and evaluate if social capital and social networks between farmers are important mechanisms by which organic farmers influence conventional ones. In conclusion, our empirical results indicate that exploring the differences between adopter groups can give new insights into the diffusion process of a technology. Further, research into the adoption of organic is important as organic farming is still at an early stage and attracts only a limited number of participants in Ireland where policy makers are eager to increase the size of the organic sector. There is evidence that increased policy support of organic farming had an impact on the uptake of organic farming, but it also appears that it had an effect on the characteristics that are relevant to adoption. This paper has shown that the factors change depending on the time of conversion. Nevertheless, environmental attitudes emerged as an important characteristic for the adoption of organic farming, and it should be the aim of any further policy changes to underline the likely positive impact of organic farming on the environment. Acknowledgements The authors acknowledge the Teagasc Walsh Fellowship Scheme for funding this research. This paper benefited substantially from the comments of two anonymous referees for which we are grateful.

D. Läpple, T. Van Rensburg / Ecological Economics 70 (2011) 1406–1414

Appendix A Principal Component Analysis of Attitudinal Statements

Table A2 Coefficient estimates for multinomial logit model. Variables

The components were constructed based on the attitudinal statements listed in Table A1. The principal component analysis confirms four attitudes based on Kaiser's criterion (Kaiser, 1960), that only principal components (PC) whose explained variances exceed 1 are to be retained. The four PCs that were retained in the analysis represent about 50% of the variance. Although, Jolliffe (2002) recommends 70 to 90% explained variance as a cut-off point to retain PCs, this can be lower due to practical details in the data set, as it is the case in this study. In some cases 50% of explained variance of the original data set can serve as an adequate summary (Everitt and Dunn, 1991). The first PC has high loadings for environmental attitude statements, therefore representing environmental attitude. The second PC shows high loadings on information gathering statements, thus this PC denotes information gathering attitude of the farmer. The third PC has high loadings on profit statements and on “To survive in farming, a farmer has to adapt to changing and new technologies” indicating that this PC represents innovative profit motives. Finally, the fourth PC has high loadings on risk statements and stands for attitudes towards risk in decision making. Table A1 Attitudinal statements used in the survey. Environmental attitude It is important to be sensitive to the environmental impacts of farming by reducing input use on the farm. The use of chemical inputs has a negative impact on the health of people and animals. It is important to take the environment into consideration, even if it lowers profit. Environmental problems resulting from agricultural activities are exaggerated by the media. It is important to farm in an environmentally friendly way. The impact of fertiliser run-off is worse than generally imagined. Organic farming is better for the environment than conventional farming. The use of chemicals in agriculture makes sense as long as it leads to an increase in profits. It is important to use chemicals sparingly. Maximizing profits is more important than protecting the environment. Organic farming is a fad. Chemical fertilisers have no harmful effects; they promote high-quality production. Profit orientation It is important to receive the highest possible prices for produce. It is important to make the largest possible profit from farming. It is important to try new ways to increase profit. Farming is not just about maximizing profits. Farming is about maximizing profits from the farm business. Risk attitude Before applying different farming practices they first need to be proven on other farms. It is important to be cautious about adopting new ideas. It is important to have accident insurance for your farm. It is important to avoid risky options in farm decision-making. It is important to seek advice before making farm decisions. Off-farm income is important for the financial security of a farmer's family. It is better to stick with current farming practices even if it means giving up on potential profit. Before adopting new ways of doing things it is important to see them working for other people. It is important to keep debt for long term investments as low as possible. Information gathering attitude It is important to discuss farming options with other farmers/friends. It is important to visit other farms to look at their farming methods. There are other farmers whose opinion about the performance of my farming business is important to me. It is important to read about farming practices. It is important to have good contact with other farmers to discuss farm related issues. Successful farmers make decisions on their own. To survive in farming, a farmer has to adapt to changing and new technologies. It is important for farmers to be respected in the local community. It is important to gain recognition among other farmers.

1413

‘Pioneers’

Off-farm income − 0.28 Household − 0.39⁎⁎ members UAA − 0.02⁎⁎ LU/ha − 1.42⁎⁎ Info 1 0.07 Info 2 − 0.41⁎⁎ Knows other 2.28⁎⁎⁎ organic farmer Higher education 0.57 Age at adoption − 0.16⁎⁎⁎ Environmental 3.26⁎⁎⁎ attitude Profit orientation − 0.74⁎⁎ Risk attitude − 0.44 0.19 Information gathering attitude Log-likelihood: − 404.18 Pseudo-R2: 0.40

‘Followers’ (0.62) (0.19)

0.17 − 0.10

‘Laggards’ (0.48) (0.13)

0.37 − 0.12

(0.009) − 0.03⁎⁎⁎ (0.68) − 0.64⁎ (0.42) 0.23 (0.19) − 0.42⁎⁎⁎ (0.78) 2.13⁎⁎⁎

(0.007) − 0.03⁎⁎⁎ (0.37) − 0.46 (0.31) 0.69⁎⁎ (0.14) − 0.41⁎⁎⁎ (0.46) 2.13⁎⁎⁎

(0.65) (0.03) (0.47)

0.45 − 0.09⁎⁎⁎ 3.06⁎⁎⁎

(0.48) (0.02) (0.35)

(0.29) (0.32) (0.32)

− 0.43⁎ (0.24) − 0.521⁎⁎ (0.24) 0.18 (0.23)

(0.49) (0.13) (0.007) (0.37) (0.31) (0.15) (0.48)

0.59 (0.49) − 0.07⁎⁎⁎ (0.02) ⁎⁎⁎ 2.72 (0.34) − 0.40 − 0.51⁎⁎ 0.42⁎

(0.25) (0.24) (0.24)

Number of observations = 546; non-adopters as comparison group, standard errors in parentheses. ⁎⁎ p b 0.05. ⁎⁎⁎ p b 0.01. ⁎ p b 0.1.

References Amemiya, T., 1981. Qualitative response models: a survey. J. Econ. Lit. 19, 1483–1536. Barham, B., Foltz, J., Jackson-Smith, D., Moon, S., 2004. The dynamics of agricultural biotechnology adoption: lessons from rBST use in Wisconsin, 1994–2001. Americ. J. Agric. Econ. 86, 61–72. Battershill, M., Gilg, A., 1997. Socio-economic constraints and environmentally friendly farming in the southwest of England. J. Rural Stud. 13, 213–228. Beedell, J., Rehman, T., 2000. Using social-psychology models to understand farmers' conservation behaviour. J. Rural Stud. 16, 117–127. Best, H., 2008. Organic agriculture and the conventionalization hypothesis: a case study from West Germany. Agric. Hum. Values 25, 95–106. Birkhaeuser, D., Evenson, R.E., Feder, G., 1991. The economic impact of agricultural extension: a review. Econ. Dev. Cult. Change 39, 607–650. Bleasdale, A., Sheehy-Skeffington, M., 1995. The upland vegetation of north-east Connemara in relation to sheep grazing. In: Jefferey, D.W., Jones, M.B., McAdam, J.H. (Eds.), Irish Grasslands. Their Biology and Management. Royal Irish Academy, Dublin, Ireland, pp. 110–124. Bromley, D.W., Hodge, I., 1990. Private property rights and presumptive policy entitlements: reconsidering the premises of rural policy. Europ. Rev. Agric. Econ. 17, 197–214. Brouwer, F., Lowe, P., 2000. CAP and the environment: policy development and the state of research. In: Brouwer, F., Lowe, P. (Eds.), CAP Regimes and the European Countryside: Prospects for Integration Between Agricultural, Regional and Environmental Policies. CABI Publishing, Wallingford, pp. 1–14. Buller, H., 2000. Regulation 2078/92: patterns of implementation. In: Buller, H., Wilson, G.A., Höll, A. (Eds.), Agri-environmental Policy in the European Union. Ashgate Publishing Ltd., Aldershot, pp. 219–254. Buller, H., Wilson, G.A., Höll, A., 2000. Introduction: the emergence of Regulation 2078/ 92. In: Buller, H., Wilson, G.A., Höll, A. (Eds.), Agri-Environmental Policy in the European Union. Ashgate Publishing Ltd., Aldershot, pp. 1–8. Burton, M., Rigby, D., Young, T., 1999. Analysis of the determinants of adoption of organic horticultural techniques in the UK. J. Agric. Econ. 40, 47–63. Burton, M., Rigby, D., Young, T., 2003. Modelling the adoption of organic horticultural technology in the UK using duration analysis. Aust. J Agric. Resour. Econ. 47, 29–54. Cheng, S., Long, J.S. 2005. Testing for IIA in the multinomial logit model, University of Connecticut, Working Paper. Commission of the European Communities (CEC), 2002. Communication from the Commission to the Council and the European Parliament: Mid-Term Review of the Common Agricultural Policy, July 20, 2002. COM, Brussels. Connolly, L., Kinsella, A., Quinlan, G., Moran, B., 2008. National Farm Survey. Teagasc, Rural Economy Research Centre, Athenry, Ireland. Dadi, L., Burton, M., Ozanne, A., 2004. Duration analysis of technological adoption in Ethiopian agriculture. J. Agric. Econ. 55, 613–631. D'Emden, F., Llewellyn, R., Burton, M., 2006. Adoption of conservation tillage in Australian cropping regions: an application of duration analysis. Technol. Forecast. Soc. 73, 630–647. Department of Agriculture, Fisheries and Food (DAFF), 2009. Press Release 29/07/09; available at: http://www.agriculture.gov.ie/press/pressreleases/2009/july/ title,33766,en.html;last accessed December 2009. DeSouza Filho, H., Young, T., Burton, M., 1999. Factors influencing the adoption of sustainable agricultural technologies, evidence from the state of Espirito Santo. Brazil. Technol. Forecast. Soc. 60, 97–112.

1414

D. Läpple, T. Van Rensburg / Ecological Economics 70 (2011) 1406–1414

Diederen, P., van Meijl, H., Wolters, A., Bijak, K., 2003. Innovation adoption in agriculture: innovators, early adopters and laggards. Cah. Econ. Sociol. Rural. 67, 29–50. Emerson, H.J., Gillmor, D.A., 1999. The rural environment protection scheme of the Republic of Ireland. Land Use Policy 16, 235–245. Report to the European Parliament and to the Council of the Application of Regulation (EEC) No. 2078/92 on Agricultural Production Methods Compatible with the Requirements of the Protection of the Environment and the Maintenance of the Countryside. DGVI European Commission, Brussels. Everitt, B., Dunn, G., 1991. Applied Multivariate Data Analysis. Halsted Press, London, UK. Feder, S., Just, R., Zilberman, D., 1985. Adoption of agricultural innovations in developing countries: a survey. Econ. Dev. Cult. Change. 33, 255–298. Feder, G., Slade, R., 1984. The acquisition of information and the adoption of new technology. Americ. J. Agric. Econ. 66, 312–320. Fernandez-Cornejo, J., Beach, E.D., Huang, W., 1994. The adoption of IPM techniques by vegetable growers in Florida, Michigan and Texas. J. Agr. Appl. Econ. 26, 158–172. Fishbein, M., Ajzen, I., 1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading, MA. Flaten, O., Lien, G., Ebbesvik, M., Koesling, M., Valle, P., 2006. Do the new organic producers differ from the ‘old guard’? Empirical results from Norwegian dairy farming. Renew. Agric. Food Syst. 21, 174–182. Genius, M., Pantzios, C., Tzouvelekas, V., 2006. Information acquisition and adoption of organic farming practices. J. Agric. Resour. Econ. 31, 93–113. Häring, A.M., Dabbert, S., Aurbacher, J., Bichler, B., Eichert, C., Gambelli, D., Lampkin, N., Offermann, F., Olmos, S., Tuson, J., Zanoli, R., 2004. Organic Farming and Measures of European Agricultural Policy; Organic Farming in Europe: Economics and Policy, Vol. 11. University of Hohenheim, Stuttgart. Hattam, C., Holloway, G., 2007. Bayes estimates of time to organic certification. Paper Presented at the Agricultural Economics Society's 81st Annual Conference, Reading, UK. Hayami, Y., Ruttan, V.W., 1985. Agricultural Development: An International Perspective. John Hopkins University Press, Baltimore, MD. Hynes, S., Garvey, E., 2009. Modelling farmers' participation in an agri-environmental scheme using panel-data: an application to the Rural Environment Protection Scheme in Ireland. J. Agric. Econ. 60, 546–562. Jolliffe, I.T., 2002. Principal Component Analysis, 2nd edn. Springer Series in Statistics, Springer, New York. Kaiser, H.F., 1960. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151. Läpple, D., 2010. Adoption and abandonment of organic farming: an empirical analysis of the Irish drystock sector. J. Agric. Econ. 61, 697–714. Lampkin, N., 1994. Organic farming: sustainable agriculture in practice. In: Lampkin, N., Padel, S. (Eds.), The Economics of Organic Farming: An International Perspective. CAB International, Oxford, pp. 3–9. Lampkin, N., Padel, S., 1994. The Economics of Organic Farming: An International Perspective. CAB International, Oxford. Long, S., 1997. Regression Models for Categorical and Limited Dependent Variables, Advanced Quantitative Techniques in the Social Sciences. SAGE Publications, London.

Long, S., Freese, J., 2006. Regression Models for Categorical Dependent Variables, second ed. Stata Press, Texas. Lowe, P., Brouwer, F., 2000. Agenda 2000: a wasted opportunity? In: Brouwer, F., Lowe, P. (Eds.), CAP Regimes and the European Countryside. CABI Publishing, Wallingford, pp. 321–334. Lynggaard, K., 2006. The Common Agricultural Policy and Organic Farming: An Institutional Perspective on Continuity and Change. CAB International, Cambridge. Marra, M., Pannell, D., Abadi Ghadim, A., 2003. The economics of risk, uncertainty and learning in the adoption of new technologies: where are we on the learning curve? Agric. Syst. 75, 215–234. Oelofse, M., Hogh-Jensen, H., Abreu, L., Almeida, G., Yu Hui, Q., Sultan, T., de Neergaard, A., 2010. Certified organic agriculture in China and Brazil: market accessibility and outcomes following adoption. Ecol. Econ. 69, 1785–1793. Offermann, F., Nieberg, H., 2000. Economic Perfomance of Organic Farms in Europe; Organic Farming in Europe: Economics and Policy, Volume 5. University of Hohenheim, Stuttgart. Padel, S., 2001. Conversion to organic farming: a typical example of the diffusion of an innovation? Sociol. Rural. 41, 40–62. Reidy, J., 2006. Organic Cattle/Sheep Production, Fact Sheet No. 32. Teagasc, Atehnry, Ireland. Rigby, D., Woodhouse, P., Young, T., Burton, M., 2001. Constructing a farm level indicator of sustainable agricultural practice. Ecol. Econ. 39, 463–478. Risgaard, M.L., Frederiksen, P., Kaltoft, P., 2007. Socio-cultural processes behind the differential distribution of organic farming in Denmark: a case study. Agr. Hum. Values 24, 445–459. Rogers, E.M., 1962. Diffusion of Innovations. Free Press, New York. Rogers, E.M., 1995. Diffusion of Innovations, 4th ed. Free Press, New York. Ruttan, V.W., 1996. What happened to technology adoption–diffusion research? Sociol. Rural. 36, 51–72. Ryan, B., Gross, N.C., 1943. The diffusion of hybrid seed corn in two Iowa communities. Rural Sociol. 8, 14–24. Sheikh, A.D., Rehman, T., Yates, C.M., 2003. Logit models for identifying the factors that influence the uptake of ‘no-tillage’ technologies by farmers in the rice-wheat and the cotton-wheat farming systems of Pakistan's Punjab. Agric. Syst. 75, 79–95. Valente, T.W., 1996. Social network thresholds in the diffusion of innovations. Soc. Netw. 18, 69–89. Van Mansvelt, J.D., Mulder, J., 1993. European features for sustainable development: a contribution to the dialogue. Landscape Urban Plan. 27, 67–90. Wheeler, S.A., 2008. What influences agricultural professionals' views towards organic agriculture? Ecol. Econ. 65, 145–154. Willer, H., Kilcher, L., 2009. The World of Organic Agriculture: Statistics and Emerging Trends 2009, 10th ed. IFOAM, Bonn; FiBL, Frick; ITC, Geneva. Wooldridge, J., 2001. Econometric Analysis of Cross Section and Panel Data. The MIT Press, Cambridge. Wozniak, G.D., 1987. Human capital, information, and the early adoption of new technologies. J. Hum. Resour. 22, 101–112. Wynn, G., Crabtree, B., Potts, J., 2001. Modelling farmer entry into the environmentally sensitive area schemes in Scotland. J. Agric. Econ. 52, 65–82.