Farmers’ climate change beliefs and adaptation strategies for a water scarce future in Australia

Global Environmental Change 23 (2013) 537–547

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Global Environmental Change journal homepage: www.elsevier.com/locate/gloenvcha

Farmers’ climate change beliefs and adaptation strategies for a water scarce future in Australia S. Wheeler a,*, A. Zuo a, H. Bjornlund a,b a b

CRMA, School of Commerce, University of South Australia, Adelaide, SA 5000, Australia Economics Department, University of Lethbridge, Alberta, Canada

A R T I C L E I N F O

A B S T R A C T

Article history: Received 7 July 2012 Received in revised form 13 November 2012 Accepted 19 November 2012

Climate change is likely to require irrigators in Australia’s Murray-Darling Basin to cope with less water, which will require ongoing farm adjustment. Possible incremental adjustment strategies include expansive and accommodating responses, such as irrigators buying land and water, increasing their irrigated area, changing crop mix and adopting efficient infrastructure. Contractive strategies include selling land and water, and decreasing their irrigated area. Using historical surveys we provide a comparison of irrigators’ planned and actual strategies over the past fifteen years, thereby offering a strong foundation to support analysing future adaptation strategies. We explore influences associated with farm adjustment strategies, and in particular the role that climate change beliefs play. Farmers convinced that climate change is occurring are more likely to plan accommodating, but not expansive, strategies. The relationship between climate change belief and adopting various adaptive strategies was found to be often endogenous, especially for accommodating strategies. Such results suggest the need for irrigation farming policies to be targeted at improving irrigators’ adaptability to manage water variability, and its link with farm future viability. ß 2012 Elsevier Ltd. All rights reserved.

Keywords: Irrigators Climate change attitudes Planned behaviour Basin

1. Introduction Farmers across the world need to undertake continual adjustment to their properties’ physical capital, productive capacity and output, to adapt to an uncertain future. This is particularly relevant for irrigators in the Murray-Darling Basin (known as the Basin) in Australia who have experienced severe weather conditions and large scale social changes in the past 14 years (Beilin et al., 2012). Australia has been described as the ‘front line of the battle for climate change adaptation’, and the Basin in particular provides a classic example of an area that is required to adapt to considerable cuts in water allocation (Palutikof, 2010, p. 219). Climate change is forecast to result in higher frequency of drought, lower precipitation and increased evaporation (CSIRO, 2009; Quiggin et al., 2010). The Basin therefore provides a unique opportunity to investigate farmers’ actual and planned adjustment activities, and the influences on these strategies. Such insight will be of significant assistance to policy makers in other jurisdictions facing similar problems when designing policies to improve famers’ capacity to adapt to a changing climate and reduced water availability.

* Corresponding author. Tel.: +61 8 8302 0698; fax: +61 8 8302 7001. E-mail address: [email protected] (S. Wheeler). 0959-3780/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.gloenvcha.2012.11.008

The adaptation literature has mainly focused on biophysical and economic models, including crop yield analysis, crop switching, hedonic valuation, crop and weather responses (e.g. Below et al., 2012; Challinor et al., 2009; Seo and Mendelsohn, 2007; Mendelsohn et al., 1994; Lobell, 2010; Lobell et al., 2007) and more socially driven case study approaches, which often focus on actors as the unit of analysis in a vulnerability or resilience context (Adger et al., 2009; Nelson et al., 2007). Adaptation has been defined as adjustments in human-environmental systems in response to observed or expected climatic changes. Park et al. (2012) outline a theory of Adaptation Action Cycles, where they define the difference between incremental and transformational adaptation. Transformation adaptation occurs when ecological, economic, or social conditions make existing systems untenable, and it signifies a major change in livelihood, location or identity. Incremental adaptation is more related to the adoption of actions that do not require major decisions or information to adopt (Park et al., 2012; Marshall et al., 2012). This type of adaptation is the focus of this paper. The literature on actual farm adaptation behaviour is less developed (Nicholas and Durham, 2012), and has mainly focused on developing countries (e.g. Below et al., 2012; Deressa et al., 2009), with some exceptions (e.g. Nicholas and Durham, 2012; Marshall et al., 2012). The literature suggests that farmers’ responses are highly critical for estimating the economic impact

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of climate change, and understanding adaptation processes is needed for the development of well-targeted policies (Adger and Vincent, 2005; Smit and Wandel, 2006; Marshall, 2010; Nicholas and Durham, 2012; Below et al., 2012; Beilin et al., 2012). Although there has been considerable research on farmer behaviour, surprisingly there has been little empirical quantitative analysis on farmers’ individual adaptation decisions, especially addressing the complex, forward-looking and sitespecific characteristics of adaptation processes (Below et al., 2012), or on how farmers’ climate change beliefs impact on their plans for the future. Farmers have continually needed to adjust to international market forces to make their farms more productive. Most farmers have responded by increasing farm size to capture economies of scale (Barr, 2009). The farm adjustment process will need to continue worldwide because of the extensive pressure on water resources. To ensure the design of sound policies that minimise unintended consequences, it is important to understand the influences underlying irrigators’ intended strategic responses at the micro level; in particular how farmers’ beliefs drive change. This study investigates influences on irrigators’ adaptation strategies using more than one thousand Australian surveys over the last decade. 2. Background The Basin covers over one million km2 or 14% of Australia (ABS, 2008). Average annual rainfall is between 250 and 300 mm and inflows into the system average 508,000GL, with severe reductions in inflows experienced since 2002. In 2005–2006, the Basin produced 39% of the total value of agricultural commodities produced in Australia, whilst using 50% of average annual surface water flows (ABS, 2008; Quiggin et al., 2010). Variability in water availability is expected to increase, with declines in winter and spring rainfall, increases in summer rainfall and temperature and more reductions in rainfall in the southern Basin (Jones et al., 2001). In the 2000s, water allocations (the physical amount of water received seasonally from water entitlements owned) to irrigators fell from historical levels of over 100% to less than a third in some years, with allocations in some regions decreasing to just 18% (Wei et al., 2011). It is likely that future climate change will mean a decrease in future allocations. Consequently, further adaptation by irrigators will be necessary. There has been considerable research into people’s views on climate change worldwide, finding that the majority believe climate change is occurring and that human activity is a contributor (Bostrom et al., 2012; Akter et al., 2012). Climate change continues to be a divisive political issue in Australia (Akter et al., 2012). Leviston and Walker (2011) found that 77% of Australians believe climate change is occurring, consistent with their 2010 findings. Australian farmers are far more sceptical than the general public. Hogan et al. (2011) found 55% of farmers believed in climate change in 2008, while Donnelly et al. (2009) suggested it was 27% in 2009. What is not clear is whether farmers’ belief in climate change is associated with implementation of farm management adaptation strategies. There is also ambiguity around the causal drivers of farm adjustment, in particular in regards to climate change beliefs and succession plans. This study therefore seeks to provide answers to the following four questions: (1) How similar has planned and actual farm adaptation behaviour been in the Basin over the past decade? (2) Is farmer belief in climate change associated with their planned farm management adaptation strategies? (3) Are climate change beliefs the driver of planned adaptation behaviour? and, (4) Does identification of a farm successor drive a more expansive farm strategy?

3. Climate change adaptation literature review There is a considerable amount of literature on the potential economic, sociological and scientific impacts of climate change on agriculture, focusing on how farmers could adapt to various climate scenarios (e.g. Mendelsohn et al., 1994; Mendelsohn and Dinar, 1999, 2003; Risbey et al., 1999; Nhemachena and Hassan, 2007; Wang et al., 2008; Quiggin et al., 2010). Adaptations generally include changes in: (i) production, such as crop mix; (ii) irrigation practice; (iii) time of planting; (iv) locations; (v) dryland and irrigated areas; (vi) irrigation infrastructure; (vii) water use and trade (buying and selling water); (viii) environmental management (e.g. planting trees); and (ix) farm management strategy, such as use of insurance to protect against potential loss (Smit and Skinner, 2002; Marshall, 2010). Although there has been much research on the adoption of individual strategies on farms, such as irrigation infrastructure or adoption of new crops, (e.g. Deressa et al., 2009; Hisali et al., 2011) and sustainable agricultural techniques (e.g. Lynne et al., 1988; Vogel, 1996), there have been relatively few studies that have attempted to model the adoption of adaptation techniques in agriculture; and these have tended to focus on developing countries. Below et al. (2012) is one of the few studies that has attempted to model farm adaptation as a whole, using an adaptation index of 33 farm practices for addressing climate change in Tanzania. Given the wide variety of practices, regional workshops weighted the importance of each practice in regards to effectiveness and feasibility, although the authors recognised the subjectivity of this approach. They found production factors, natural and physical capital, education and gender of household head, and social and financial capital were all significantly associated with adaptive capacity. As well as the need for more empirical quantitative analysis on the complex, forward-looking and site-specific characteristics of adaptation processes (Below et al., 2012), there has been little analysis on how planned farmer adaptation compares with actual behaviour. Planned behaviour is the intention to undertake a certain action, though of course this plan may not be executed if the individual does not have complete control to perform the action (Ajzen, 1991). In the scenario of farmers adopting new strategies to deal with climate change, it is likely that factors such as farm debt, low income and reduced productivity will constrain implementation. While the intention to undertake a certain action is not the same as actual implementation, it is likely to be the best possible indication. Fielding et al. (2008) studied Australian farmers’ planned and actual adoption of sustainable farming practices and found that past behaviour, perceived behavioural control, social identity variables, and attitudes did influence intentions and actual behaviour. In contrast, Va¨re et al. (2010) report a large gap between planned and actual farm investment. This paper continues this investigation of the difference between planned and actual farm adaptive behaviour using a unique set of historical irrigator surveys in the Basin. In their qualitative study of 65 growers and industries in the Australian wine industry, Park et al. (2012) suggest that climate change beliefs were a more important factor driving transformational change rather than incremental adaptation. Below et al. (2012) examined farmers’ perception of weather related problems in the last decade as a possible influence on adaptation, but it was not included in their final regression, suggesting its relative unimportance. Milne et al.’s (2008) study of 148 Australian farmers suggested a positive link between climate change beliefs and farmers’ preparedness for, and management of, climate risks. Hogan et al. (2011) analysed the role that climate change beliefs and noticing evidence of climate change played in two models of farmer adaptation in Australia. They first examined farmers’ stated

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capacity to adapt through a planned risk management approach (made up of multiple strategies such as diversification, improve financial situation, develop risk strategies; develop whole farm plans; undertake training; succession planning); and the second model investigated farmers’ intentions to undertake adaptive practices (made up of strategies such as interest in carbon credits use of new technologies and adoption of sustainable land management practices). Although their regression models found that belief in climate change was not a significant factor in influencing either adaptation model, they did find some evidence that farmers who noticed physical evidence of climate change were less likely to undertake risk management strategies. However, given the methodology employed in Hogan et al. (2011), it is possible that the results may suffer from bias and hence care must be cautioned with any interpretation of results. As Bostrom et al. (2012) point out, although numerous studies have found significant and positive and correlations between climate change beliefs and behavioural changes, only a few studies have focused on how causal thinking systematically influences policy preferences. We are interested in the causal relationship between climate change beliefs and farm adaptation. Previous studies (e.g. Hogan et al., 2011; Park et al., 2012) argue that the causal relationship runs from climate change belief to adaptation, and indeed theory dictates that attitudes should influence behaviour (e.g. Ajzen, 1991), but the true relationship could be endogenous in some situations, with each influencing the other. Leviston and Walker (2011) recognised the causal influence of the Australian public’s climate change attitude and their behavioural response. But as far as we can tell, our paper is one of the few studies that have tested, and then controlled for, an endogenous relationship between belief and adaptation (further explanation is provided in Section 5). Farm succession may also play a role in influencing future choices. The significant literature on farm succession suggests identifying and nominating a successor can trigger development of the business and provide a powerful motive for ongoing investment in the property (Potter and Lobley, 1996; Mishra and El-Osta, 2007; Calus et al., 2008; Wheeler et al., 2012a). However, the true causal relationship between farm succession and future farm strategies remains unknown. Does the identification of a farm successor drive a more adaptive farm strategy, or, is it that more successful farmers, who are more likely to have expansive strategies in place, attract a successor? This study also addresses the succession causality question. Ellis (2000) developed a rural livelihood framework that included different dimensions of farm household vulnerability to structural adjustment. Within this framework, farm households have five types of capital (human, social, natural, physical and financial) and are also influenced by social relationships, institutions and organisations. The different types of capital, as well as external trends and shocks, such as changes in the terms of trade or climate, determine how farm households select their livelihood strategies. Nelson et al. (2005) used these five capital categories to construct a vulnerability index for Australian broadacre agriculture and Hogan et al. (2011) built on these five capital categories. This study adopts and extends this framework by assuming an association between the five categories of capital and farmers’ intended future strategies. Firstly, in terms of Park et al.’s (2012) Adaptation Action Cycles framework, we are only measuring incremental adoption in this paper. All the actions we measure (although some do require extensive information and involve considerable social attachment), are classified as incremental adoptions generally. Transformational adaptation, such as farm exit, is not considered in this paper and is an area future research needs to consider. We grouped irrigators’ planned adaptive strategies into three broad incremental adoption

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categories: (i) expansive, designed to expand efforts and production; (ii) accommodating, designed to accommodate change, by adopting different water management practices; (iii) contractive, designed to reduce the farm effort and resource ownership. All strategies are independent of one another. Expansive strategies refer to plans to purchase land or water entitlements or to increase irrigated area in the next five years. On average, these actions are considered to be positive and general mainstream strategies that Australian farms will need to continue to adopt in the future to capture economies of scale and maintain productivity (Barr, 2009). Accommodating strategies are also generally positive as they are adoption strategies which plan to improve irrigation infrastructure and management practices as well as shifting to more water efficient crops. Such strategies improve farmers’ adaptive capacity to changing climatic conditions. Contractive strategies are negative actions and refer to plans to sell land, reduce irrigated area and sell water. In general it is unlikely, given the need to maintain productivity increases in the future, that farms undertaking these strategies can survive in the longer-term. It is important to note that our index is only a broad indication of climate change adaptation. It is possible that the adoption of any of these strategies may lead to ‘undesirable’ outcomes or may be classified as a ‘maladaptation’. For example, the purchase of further farm land or the increase in irrigation area (without undertaking any other adaptation strategy) may place the farm on a negative trajectory when further future water restrictions severely restrict its’ ability to produce. Similarly, the adoption of expensive irrigation infrastructure, while improving water use efficiency, may also result in increasing irrigators’ energy costs and debt burden (which may result in them exiting in the future). At the same time, implementing a contractive strategy (such as temporarily stopping irrigating and selling some water entitlements) can be a highly profitable and adaptive strategy for some irrigators (especially those who produce annual crops) at particular points in time, although in general these strategies are associated with downscaling and a lack of farm viability. In order to overcome these complexities, we not only model an overall index but also model all strategies individually to better assess the influences associated with adaptation. Fig. 1 provides a simplified representation of the factors influencing farmers’ adaptation behaviour. The type of adjustment adopted by farmers is linked to their planned behaviour, which itself is linked to their attitudes and the extent to which their behaviour is constrained (also by a variety of factors). Our regression methodology utilises five different forms of farm capital as independent variables and controls for a range of constraints/drivers of planned behaviour. 4. Actual and planned behaviour in the southern basin It is important to establish the internal consistency between actual and planned farm adaptive strategies, to provide confidence in the policy interpretations associated with influences on planned strategies. This paper first compares actual and planned irrigator behaviour over 14 years using four years of cross-sectional surveys, and then models planned behaviour. 4.1. Data Data used in this study were collected from historical and current irrigator surveys in three southern Basin states (New South Wales (NSW), South Australia (SA) and Victoria (Vic)). This paper uses historical surveys (n = 1510) that were conducted five years apart (1998–1999, 2003–2004, 2008–2009 and 2010–2011), with the exception of the last survey, in Australia’s largest irrigation

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540

Expansive Strategies

Farmer Atudes

Perceived Behavioural control - Financial - Farm physical - Social - Human - Regional

1. Buy Land 2. Increase Irrigaon Area 3. Buy Water Entlements Accommodang strategies: 1. Improve Irrigaon Efficiency 2. Change Crop mix

Planned Farm Adaptaon Behaviour

Contracve Strategies

Actual Farm Behaviour

1. Sell land 2. Decrease Irrigaon Area 3. Sell water entlements

Fig. 1. Theory of irrigators’ planned behaviour. Adapted from Ajzen (1991).

district, the Goulburn-Murray Irrigation District (Goulburn) to compare actual and planned behaviour. Wheeler et al. (2012a) provides detailed descriptions of the historical surveys. Although not a panel dataset, the samples were sufficiently large to be representative of Goulburn irrigators. We then use the full set of 2010–2011 survey data across the southern Basin to model influences associated with planned behaviour. In 2010–2011 irrigators were randomly sampled from irrigator organisations and commercial farming lists using computer assisted telephone interviews. The survey had a total response rate of 37% (including call-backs), with 946 irrigators surveyed across the southern Basin (642 observations were used in the modelling due to some missing information). Wheeler et al. (2012b) used the 2010–2011 data to analyse behaviour associated with past government water sales, but information on irrigator adaptation strategies have not been used in any previous analysis. The survey collected information on farmers’ strategic plans, socioeconomic characteristics, demographic variables, climate change beliefs and a range of values and attitudes. These were developed from previous attitudinal research (Morrison et al., 2012). 4.2. Actual vs planned behaviour A comparison of farmers’ plans for the next five years with their actual behaviour over the same five years (assessed in retrospect), provides an indication of the consistency between farmers’ intentions and behaviour. It therefore provides some historical insight into the constraints that differentiate irrigators’ intended behaviour from their actual strategic behaviour (Table 1). In 1998–1999 65% of irrigators indicated they planned to make infrastructure improvements in the next five years. Five years later 57% of irrigators had actually made improvements. Similarly, 20% of irrigators planned to purchase water entitlements in 1998– 1999, and five years later 14% had actually purchased water. All differences between actual and planned behaviour are significant (p < 0.05). The measured difference between intention and actual behaviour appears reasonable given the slight differences in the population and the constraints (e.g. debt, farm income) that prevent intended behaviour becoming translated into actual outcomes. Given these factors, the measured differences are not overly large. However, the differences are greatly magnified for the five years between 2003–2004 and 2008–2009. There was a severe drought

over these years; with three years of extremely low water allocations (e.g. Goulburn irrigators received a seasonal allocation of only 29% of their water entitlements in 2006–2007, 57% in 2007– 2008 and 33% in 2008–2009). These conditions were outside irrigators’ control and reasonable expectations, so it is not surprising that their stated intentions in 2003–2004 (which was a year of 100% water allocations) were inconsistent with their actual behaviour five years later. Although 40% stated in 2003– 2004 they planned to improve their irrigation infrastructure over the next five years, by 2008–2009 88% had actually made some improvements. Similarly, in 2003–2004 very few irrigators planned to undertake contractive strategies, such as decreasing irrigation area (5%) or selling water (3%), but by 2008–2009 53% had reduced their irrigation area and 11% had sold water entitlements. The large and highly significant differences between intended and actual behaviour are probably the result of the continuous reduction in water allocations, however other factors such as debt, farm characteristics, commodity returns, irrigation system changes, water prices, etc. may have also played a part. Wheeler et al. (2012b) provides more commentary on the differences between those actually selling water to the federal government and those thinking about selling. Similarly, although care must be cautioned in assessing the difference between planned and actual behaviour between 2008–2009 and 2010–2011 given the gap of only two years, the most significant difference in behaviour was in the sale of water entitlements. Many more farmers bought and sold water entitlements by 2010–2011 than those planning to do so in 2008–2009. This difference in behaviour is probably explained by accumulated farm debt from the drought and the large scale intervention in the water market by the Commonwealth Government from 2008 onwards which significantly increased demand for, and the price of, water entitlements. Irrigators significantly increased their adoption of water markets, especially for water entitlements, over this period (Wheeler et al., 2012b). It seems reasonable to suggest irrigators’ intentions reasonably match with their actual behaviour in situations of average to full water allocations (albeit actual behaviour often seems to be slightly less than intended behaviour), but when water availability is severely restricted, there are consequences for actual behaviour. Our 2010–2011 analysis of what factors influence irrigators’ plans for the next five years is therefore only accurate if conditions do not change unexpectedly. For example, the removal of water trade restrictions in areas will increase the number of farmers who

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Table 1 Goulburn irrigators’ planned vs actual behaviour from 1998–1999 to 2010–2011. Planned behaviour in next five years

Signif.c

Actual behaviour in past five years

1998–1999

No.

%

2003–2004

No.

%

Improve infrastructure next 5 yrs Purchase entitlement next 5 years Change crop mix next 5 years

193 59 118

65 20 39

Improved infrastructure last 5 years Purchased entitlement last 5 years Changed crop mix last 5 years

315 67 123

57 14 22

Planned behaviour in next five years

** ***

Signif.c

Actual behaviour in past five years

2003–2004

No.

%

2008–2009

No.

%

Improve infrastructure next 5 years Purchase entitlement next 5 years Reduce irrigation area next 5 years Purchase land next 5 years Sell entitlement next 5 years

194 75 22 74 17

40 14 5 15 3

Improved infrastructure last 5 years Purchased entitlement last 5 years Reduced irrigation area last 5 years Purchased land last 5 years Sold entitlement last 5 years

263 23 160 71 32

88 8 53 24 11

Planned behaviour in next five years

**

*** *** *** *** ***

Signif.c

Actual behaviour in past five years

2008–2009

No.

%

2010/2011

No.

%

Purchase entitlement next 5 years Sell entitlement next 5 years Increase irrigation area next 5 years Decrease irrigation area next 5 years Purchase land next 5 years Sell land next 5 years Improve infrastructure next 5 years

30 39 64 32 54 36 181

10 13 21 11 18 12 60

Purchased entitlement last five years Sold entitlement last five years Increased irrigation area last 5 years Decreased irrigation area last 5 years Purchased land last 5 years Sold land last 5 years Improved infrastructure last 5 years

74 129 55 182 87 37 253

21 36 15 51 24 10 70

*** *** ** *** *

***

a

Sample sizes are 1998–1999 (n = 300), 2003–2004 (n = 551), 2008–2009 (n = 300) and 2010–2011 (n = 359). The questions on infrastructure improvement are slightly different in the three surveys. The exact questions refer to ‘change, improve or install any of the infrastructure (laser grading, surface drains, off-farm drainage and reuse-system)’ in 1998–1999, ‘improve irrigation and drainage’ in 2003–2004 and ‘irrigation efficiency improvement’ in 2008–2009. c Test on the equality of proportions using large-sample statistics. * p < 0.1. ** p < 0.05. *** p < 0.01. b

choose to sell water entitlements. Similarly, the withdrawal of the Commonwealth Government from the water entitlement market in 2011, and their subsequent return to only ‘targeted’ tenders in 2012, may have influenced water trade behaviour. Alternatively, governments may choose to remove irrigation infrastructure efficiency improvement support, which will reduce irrigators’ continued adoption of irrigation technology. However, irrigators surveyed in 2010–2011 were probably more likely to have taken the impact of future drought into account than those surveyed in 2003– 2004. But, while poor climatic conditions may be better anticipated now than earlier in the 2000s, further advent of climate change, changing water availability and differences in government policy may result in a disconnect between intentions and behaviour. 5. Methodology 5.1. Dependent variables In 2010–2011 irrigators were asked about their strategies regarding water use, irrigated area, farm land, changed crop mix and improved irrigation infrastructure in the past five years, and their plans for the next five years. Fig. 2 illustrates irrigators’ planned adaptive strategies categorised as expansive, accommodating or contractive. There was a slightly higher proportion of farmers planning expansive than contractive strategies, while most planned to adopt more efficient irrigation infrastructure (62%). The least likely strategy was decreasing irrigated area (23%). This suggests a relatively high level of ongoing farm adjustment is planned. An index is used to quantify the extent of irrigators’ planned behaviour for the next five years. This index represents adaptation capacity and farm adjustment. A value of one is assigned to a single expansive (or accommodating) strategy and a value of minus one is assigned to a single contractive strategy. The adaptive index is the sum of all strategies, with a maximum of five (if an irrigator plans

to undertake all five expansive strategies but no contractive strategies) and a minimum of minus three (if an irrigator plans to undertake all of the three contractive strategies but no other strategies). The resulting index is a graduated scale from most contractive to most expansive and can be treated as a continuous variable to be modelled by ordinary least square regression (OLS). Unfortunately data on the intensity of each strategy, such as the size of land/water purchase or sale, is not available. This is a common problem in farm adaptation models (e.g. Below et al., 2012) and is something future research should consider. Appendix B provides further details on the index. Given the heterogeneity of our strategies, we also modelled each strategy separately, using binary probit regression. Where endogeneity of a variable was found, we utilised bivariate probit regression to instrument the explanatory variable. All probit regressions report the marginal effects of the explanatory variables. Although weightings have been utilised in the past for adaptation indexes (e.g. Below et al., 2012), we chose not to do this, following the methodology employed by Hoffmann et al. (2009) who used an unweighted index of 26 possible adaptation measures. Weightings can be inherently biased, and all our adaptive strategies can be equally important, depending on the type of farm, crop and region. 5.2. Independent variables Five forms of farm capital (human, farm, social, financial, regional/physical) were used to model irrigators’ future adaptation strategies (Nelson et al., 2005; Below et al., 2012). Variation in human capital is assumed to be an important factor in the decision making process. Recent work has suggested that farmers’ strategic responses to external change are not entirely explained by profit-maximisation considerations, but also by attitudes and values (Gasson and Errington, 1993; Marshall et al.,

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70% 60% 50% 40% 30% 20% 10%

Accomodating

Expansive

Do none of these strategies

Sell permanent water

Decrease irrigation area

Sell land

Purchase permanent water

Increase irrigation area

Purchase land

Change crop mix

Improve Irrigation efficiency

0%

Contractive

Fig. 2. Southern basin irrigators’ planned adaptation strategies in 2010–2011 (n = 946).

2012). Farmers gain benefits from the value they place on family, community, lifestyle, land and water. In this study five attitudinal factors were derived from factor analysis of 56 statements and the factor scores included as independent variables in the regression (Appendix A provides further details). The four factors were labelled ‘tradition’, ‘commerce’, ‘environment’ and ‘technology’. In addition, we included a climate change belief variable, to test whether belief in climate change directly influenced future plans. Fig. 3 illustrates the overall climate change beliefs of irrigators, as stated in 2010–2011. Only 32% of farmers believed that climate change posed a risk, and 31% were planning for it. Around half of all irrigators stated that they did not believe in climate change and were not planning for it. Using a two-sample t test with equal variance in our sample of 946 irrigators, the results found believing in climate change was associated (p < 0.01) with a farmer being younger, having spent less years farming, having a higher education, not having identified a successor, having less traditional and commerce orientated attitudes and having received less water allocations in the current season and over the previous five years. 60%

50%

40%

30%

No Yes

20%

10%

0% NSW

VIC

SA

NSW

VIC

SA

Do you believe that climate change poses a risk Are you incorporating the potential for climate for your region? change in your farm planning?

Fig. 3. Southern basin irrigators’ climate change beliefs and plans in 2010–2011 (n = 946).

An index of actual adaptation behaviour over the previous five years was also created, following the same methodology used for the dependent variable, and used as an independent variable in the model of overall adaptation index. Within each model of individual planned adoption, we included the relevant past measure of that strategic behaviour as a dummy variable (such as sold/bought land, etc.) as a human capital variable. Consistent with the economic literature on ‘path-dependent’ development, human behaviour literature has found considerable evidence that past behaviour significantly influences planned behaviour (Conner and Armitage, 1998). Finally, the last variables grouped under human capital include farmer characteristics such as age, gender, education and the length of time spent as a farmer. The social capital variables include four measures of possible outside influences on irrigator decision making, such as their membership of various groups (such as Landcare or Waterwatch) and their information sources, which have been shown to be important for Australian farmers (Fielding et al., 2008; Conner and Armitage, 1998). ‘Physical capital’ variables included the size and type of farm, land composition, the technology used, and the size of water entitlement and water received. ‘Financial capital’ variables include productivity change, net farm operating surplus (taking into account costs and revenues received), farm debt and equity and offfarm work. Regional capital variables include rainfall and evaporation, regional location, and the closing water seasonal allocation. Taken together, this represents the most complete micro assessment of Basin irrigators’ attitudes, values and associated adaptation responses yet attempted. The full definition list of variables, and their summary statistics, is provided in Table 2. Restricted and unrestricted versions of the OLS regression models were estimated, and a BIC (Bayesian information criterion) comparison of the two models indicates the restricted model was preferred (Raftery, 1996). The final model only includes variables whose significance level is higher than 0.3. In all our initial models we tested to see if the error term of the regression was correlated with the climate change belief variable (see pages 321–325 and pages 823–825 of Greene (2008) for more detail). In the presence of endogeneity, OLS can produce biased and inconsistent parameter estimates. Endogeneity was able to be tested for in our regressions

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543

Table 2 Definition and summary statistics.

Overall adaptation strategy index in next 5 years Overall adaptation strategy index in past 5 years Buy Water (1 = buy water entitlements in next 5 years; 0 = otherwise) Sell Water (1 = sell water entitlements in next 5 years; 0 = otherwise) Increase Irrig. Area (1 = increase irrigation area in next 5 years; 0 = otherwise) Decrease Irrig. Area (1 = decrease irrigation area in next 5 years; 0 = otherwise) Buy land (1 = buy land in next 5 years; 0 = otherwise) Sold land (1 = sell land in next 5 years; 0 = otherwise) Improve irrig. infrast. efficiency (1 = improve in next 5 years; 0 = otherwise) Change crop mix (1 = change in next 5 years; 0 = otherwise) Buy Water (1 = buy water entitlements in past 5 years; 0 = otherwise) Sell Water (1 = sell water entitlements in past 5 years; 0 = otherwise) Increase Irrig. Area (1 = increase in past 5 years; 0 = otherwise) Decrease Irrig. Area (1 = decrease in past 5 years; 0 = otherwise) Buy land (1 = buy in past 5 years; 0 = otherwise) Sold land (1 = sell in past 5 years; 0 = otherwise) Improve irrig. infrast. efficiency (1 = improve in past 5 years; 0 = otherwise) Change crop mix (1 = change in past 5 years; 0 = otherwise) Farmer’s age Male (1 = male, 0 = female) Years spent farming Successor in place (1 = successor; 0 = otherwise) Climate Beliefs (1 = belief in climate change for their region; 0 = otherwise) Tradition factor score (see Appendix A) Commerce factor score (see Appendix A) Environment factor score (see Appendix A) Technology factor score (see Appendix A) Better health (1 = poor, 2 = fair, 3 = good, 4 = very good, 5 = excellent) Number of full-time equivalent (FTE) employees Dryland size (ha, in 1000s) Irrigated farm size (ha, in 1000s) Total water received for the season (ML, in 1000s) Horticulture (% of area) Annual crops (% of area) Grazing (% of area) Drip infrast. (% of irrig. area covered) Reuse system (% of irrig. area covered) Organic certified farm (1 = organic; 0 = otherwise) Member of environ. Group (1 = group; 0 = otherwise) Obtains info from gov. sources (1 = obtains; 0 = otherwise) Obtains info. from private sources (1 = obtains; 0 = otherwise) Farm net operating surplus ($ for 2009–2010, in 1000s) Farm debt/equity ratio (at time of survey in 2010–2011) % of household income from off-farm work Productivity change last 5 yrs (1 = strongly decreasing to 5 = strongly increasing) Mean end season allocation of the previous 5 years (% weighted by individual ownership of high and low/general water entitlements) Regional net evaporation (evaporation take rainfall) of the respective season from the closest weather stations (in 1000 mm) SA (1 = South Australia, 0 = otherwise) VIC (1 = Victoria, 0 = otherwise)

as we had an exogenous variable (climate plans implemented–the instrument) that was strongly correlated with climate change beliefs and uncorrelated with the error term of the OLS regression. Where endogeneity was found (using 2SLS and Wu-Hausman F and Durbin-Wu-Hausman chi-sq tests for the overall adaptation model and the Wald test after a recursive biprobit estimator for the individual adaptation strategy models), then the final models report the instrumented variable regressions. Overall all models represent a reasonable fit, with no serious multicollinearity (as measured by VIFs and correlation analysis) and were estimated with robust standard errors. 6. Results and discussion The regression results for our overall strategy index model are shown in Table 3. Table 4 reports the results of all the individual strategies. Human capital factors have a very significant influence on farmer adaptability. We found that farmers’ climate change belief

Mean

Std. Dev.

Min

Max

1.4 1.0 0.3 0.4 0.3 0.2 0.3 0.3 0.7 0.6 0.3 0.3 0.1 0.5 0.2 0.1 0.8 0.5 55.1 0.9 34.2 0.4 0.3 0.0 0.0 0.0 0.0 3.6 2.3 0.2 0.3 0.3 33.1 33.3 26.0 30.5 34.7 0.2 0.3 0.1 0.1 6.6 0.4 40.7 2.1 70.3

1.8 1.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.5 0.4 0.3 0.4 0.5 10.8 0.3 13.3 0.5 0.5 1.0 1.0 1.0 1.0 1.0 2.9 0.3 0.8 0.5 46.1 40.0 37.2 43.3 44.2 0.4 0.4 0.3 0.3 5.1 0.4 39.0 1.2 14.6

3.0 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 26 0.0 1.0 0.0 0.0 2.5 2.5 5.4 3.9 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 54.4

5.0 5.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 86 1.0 80 1.0 1.0 2.1 2.8 2.5 2.3 5.0 34 3.5 8.0 8.1 100 100 100 100 100 1.0 1.0 1.0 1.0 11.5 1.0 100 5.0 93

1.4

0.2

0.9

1.6

0.3 0.4

0.5 0.5

0.0 0.0

1.0 1.0

has a significant (albeit only weakly significant) and large impact on our farm adaptation index. However, believing in climate change is negatively associated with our overall adaptive strategy. The more a farmer believes in climate change, the lower they score on the index. More specifically, Table 4 shows that climate change beliefs are most significantly negatively associated with the purchase of further farm land. Farmers who believe in climate change are definitely not planning to expand their farm, and they are also more likely to be planning to decrease their irrigated area (albeit insignificant). However, they are much more likely to plan to change their crop mix and adopt more efficient irrigation infrastructure. The sign on the variable purchase water entitlements was also positive (albeit insignificant), indicating that they believe in the need for more water for their farm for the future. In all our models we tested for causality of the relationship between climate change beliefs and adaptation strategies. Endogeneity was tested for by instrumenting climate change beliefs with climate change plans, and was found in five out of the nine models, indicating that causality between belief and

S. Wheeler et al. / Global Environmental Change 23 (2013) 537–547

544 Table 3 OLS regression of overall adaptation in 2010–2011.

Overall adaptability index Coefficient

Standard error

Human capital Past adaptation index Believes in climate change Tradition factor score Commerce factor score Environment factor score Technology factor score Age More risk loving Better health Years spent farming Successor in place

0.27*** 0.21* 0.12* 0.08 0.10* 0.20*** 0.04*** 0.11** 0.10* 0.02** 0.70***

0.04 0.13 0.06 0.06 0.06 0.06 0.01 0.05 0.06 0.01 0.13

Social capital Obtains info from gov. sources

0.24

0.21

Farm capital Irrigated hectares Dryland hectares Full-time employees Annual crop % Reuse infrastructure %

0.50* 0.10 0.05** 0.003 0.004**

0.27 0.08 0.02 0.002 0.002

Financial capital Productivity change last 5 yrs Off-farm work

0.12** 0.002

0.05 0.002

Regional capital SA Obs. Adjusted R2 F stat

0.65*** 642 0.37 21.13

0.16

*

p < .1. p < .05. *** p < .01. **

behaviour is an important bias that needs to be taken into consideration when modelling climate change beliefs and behaviour. Ignoring the true causal relationship may have implications for the sign and significance of the climate change attitude variable. Further, it is interesting to note what strategies found evidence of endogeneity. Beliefs were not endogenous for adaptive actions that involved undertaking strategies that may be considered more risky or maladaptive if (or when) severe water shortages occur in the Basin in the future (e.g.: selling water entitlements, buying farm land and increasing irrigated area). On the other hand, beliefs did test endogenous for actions that involved implementing strategies to deal with current or future water shortages (changing crop mixes, adopting more efficient infrastructure, selling land and decreasing irrigated area). Given the difference in these types of strategies, such results suggest that the adoption of true water risk management strategies will reinforce the belief that climate change is occurring, but it is not necessarily the first causal driver of the adoption in the first place. Given these results, it is hypothesised that climate change beliefs will be endogenous with true transformative change in irrigation as well, but such a result needs further investigation. The past behaviour of each individual farmer has one of the largest impacts on their future behaviour. The overall adaptation index of past behaviour is a highly significant, positive influence on future adaptation. This variable explained 10% of our explanatory power in the whole adaptation model. In our individual adaptation models, if farmers had undertaken a particular strategy in the past five years, they were more likely to plan on undertaking it again in the next five years. The only exception was selling farm land. Such results suggest that there may be an element of path dependence involved in strategy choice; something sociologists have previously suggested (e.g. Gasson and Errington, 1993). While Va¨re et al.

(2010) did not find such a link with Finnish farmers’ succession plans, Fielding et al. (2008) found such path dependence for Australian farmers’ planned management of riparian zones. Once farmers are on a track of downsizing and contractive behaviour, this does seem to influence their future behaviour (holding other variables constant). Farmers’ future plans are more adaptable if they are younger or have been farming fewer years. In particular, younger farmers are much more likely to be planning to buy more water and land, increase their irrigated area, buy more farm land, change their crop mix and adopt more efficient infrastructure (albeit this was insignificant). Those who have been farming for more years are much more likely to think about selling water and land (and not purchasing water), decreasing irrigation area, and changing crop mix. In terms of the attitudinal constructs, farmers with values more orientated towards the importance of new innovations and technologies, the environment and traditional farming values are more likely to be adapting overall. In particular, technologically orientated farmers plan to adopt more efficient irrigation infrastructure, different crop mixes and increase their irrigated area. Commercially orientated farmers are more likely to plan to sell farm land. Traditionally focused farmers are more likely to plan to adapt more in the future, and in particular are significantly less likely to sell farm land, decrease irrigation area or sell water, probably because of their focus on the land and the need for land and water inputs for succession and farming in the future. Environmentally focused farmers are less likely to decrease their irrigated area and more likely to change their crop mix. Farmers who identified themselves as more risk loving are more likely to score higher on our overall adaptation index (in particular they plan not to sell water). Confirming results from previous research (e.g. Potter and Lobley, 1996; Mishra and El-Osta, 2007; Calus et al., 2008), we found that farmers who had identified a successor were significantly more likely to be planning to adapt more in the future. The successor variable had the largest coefficient in our adaptation index model and had a significant and positive influence on all the expansive strategies and on improving irrigation efficiency. We investigated the causality question, that is, whether a successor drives a more expansive overall farm strategy, by instrumenting our variable of an identified farm successor with farmers’ attitudes towards the importance of succession. Having a successor was found not to be endogenous, suggesting that the naming of a successor is not tied to the relative success of the farm. This result is important, as it provides support to policies which encourage or facilitate farm succession, especially at an early stage. It is also the first time that we know of that the endogeneity issue of succession has been formally tested (e.g. Wheeler et al., 2012a). The last human capital variable with a significant and positive influence on overall adaptability was an increase in farmer health. In particular, farmers in better health were more likely to plan to change crop mix and were less likely to plan to sell water. Physical capital is also associated with future adaptability, but social capital variables had no significant impact on adaptability (although some social capital influence was found in individual models). Farms with a smaller irrigated area, a larger number of employees and a higher percentage of irrigated area with reuse systems were more likely to plan further adaptive strategies. In particular, smaller irrigated farms were more likely to plan on increasing their irrigated area while larger farms were more likely to decrease their irrigated area. This partially confirms the finding by Nicholas and Durham (2012) that smaller vineyards were more likely to be more adaptive and have more informal experimentation than larger farms, which were more uniformly managed using farm plans. Farms with more employees were more likely to plan

S. Wheeler et al. / Global Environmental Change 23 (2013) 537–547

545

Table 4 Marginal effects of bivariate and binary probit models of planned strategies in 2010–2011. Purchase watera

Sell waterb

Increase irrig areab

Decrease irrig areaa

Purchase farm landb

Sell farm landa

Change crop mixa

Improve irrig. effic.a

Human capital Believes in climate change Tradition factor score Commerce factor score Environment factor score Technology factor score Age Male Low Education Whole farm plan More risk loving Health Years farming Successor in place

0.11 – – – – 0.005* 0.15** – 0.08 0.02 0.02 0.004* 0.10**

– 0.04* 0.03 0.02 – – 0.08 – – 0.03* 0.04** 0.003* 0.05

– – – – 0.07*** 0.01*** – 0.07 0.09** – – – 0.15***

0.13 0.04** 0.02 0.03** – – 0.07 – 0.08** 0.02 – 0.004*** –

0.12*** – 0.03 – – 0.02*** 0.08 – – – – – 0.21***

0.12 0.08*** 0.06*** – 0.03 – – – 0.13*** 0.02 – 0.005*** 0.15***

0.26** – – 0.05** 0.07*** 0.01*** – 0.10 0.08 – 0.04* 0.005** –

0.17** – – – 0.10*** 0.004 – 0.11* 0.10** – – 0.003 0.13***

Farm capital Irrigated hectares Dryland hectares Full-time employees Annual crops Horticulture Grazing Purchased water (last 5 yrs) Sold water (last 5 yrs) Increased irrigation area (last 5 yrs) Decreased irrigation area (last 5 yrs) Purchased farm land (last 5 yrs) Change in crop mix (last 5 yrs) Improved irrigation efficiency (last 5 yrs) Water received Reuse area Drip infrastructure area Organic certified farm

– – – 0.001** 0.002** – 0.42*** – – – – – – 0.05 0.002*** 0.001 –

– 0.03 – 0.001* – 0.001 – 0.19*** – – – – – 0.07* – – –

0.31*** – 0.02** 0.001*** – – – – 0.28*** – – – – 0.08 – – –

0.20*** 0.03 0.01* 0.001** – 0.001 – – – 0.14*** – – – 0.13** 0.001** 0.001 0.05

– 0.04 0.01 0.001* – – – – – – 0.16*** – – 0.08* – – 0.11**

– – 0.001 0.001* – – – – – – – – – 0.001 – –

– 0.04 – 0.002* 0.002* 0.001 – – – – – 0.36*** – 0.05 – 0.001 –

– – – 0.001** – 0.002*** – – – – – – 0.22*** 0.15** – – 0.11**

– 0.12 0.10

– 0.19** 0.08

– – 0.09

0.04 – –

0.09** – –

– – –

– – –

– –

Financial capital Positive productivity change Farm operating surplus Farm debt/equity ratio Larger off-farm work

0.06*** – – 0.001

0.0578 – – –

– – – –

0.06*** 0.001 0.10** 0.001

0.04** 0.002*** 0.07 0.001*

– – – –

– –

Regional capital Mean end season allocation Net evaporation SA VIC Pseudo R2 Wald chi2 % correctly predicted

– – – – N.A. 347*** 77

– 0.25** – 0.07 0.07

0.003 0.14 – 0.09** 0.18

– 0.23** – 0.08* N.A. 219*** 76

0.004 0.32** 0.20*** – 0.26

– – 0.12* – N.A. 228*** 72

0.002 – – 0.06 NA 365*** 77

Social capital Member of environ. group Obtains information from gov. sources Obtains information from private sources

56*** 67

113*** 74

150*** 79

0.05*

0.14** 0.03*

0.001*

– – 0.07 –

– – 0.22*** – NA 325*** 76

(–) Indicates the variable was dropped because its significance level is greater than 0.3. N.A. indicates not applicable. n = 642. a Bivariate probit model, climate belief variable tested endogenous. b Probit model, climate belief variable tested not endogenous. * p < .1. ** p < .05. *** p < .01.

to increase their irrigated area while farms with less employees were more likely to plan to decrease their irrigated area. Variables that provide an indication of the influence of the climate on future behaviour include the quantity of water received by the farm and regional net evaporation (evaporation minus rainfall). The more water received (water received is the seasonal allocations derived from their water entitlement), the less likely they plan on decreasing their irrigated area or purchasing farm land, and the more likely they are to plan on improving their irrigation infrastructure efficiency and selling water. The greater the net evaporation in the region, the more likely they are to plan on not selling water and to decrease their irrigated area. Conversely, they are more likely to buy more farm land (includes

dryland). These findings suggest that climate variability across the Basin has caused farmers who have experienced greater stress through lower water allocations and higher net evaporation to be more likely to plan contractive, or diversified, strategies. Financial capital also influences adaptation. The most positive and significant impact was an increase in farm productivity in the past five years. In particular, farmers with increasing productivity are more likely to purchase water, buy more farm land, not decrease their irrigated area and not change their crop mix. Farmers who receive a greater percentage of their farm income from off-farm work are more likely to not plan expansive strategies, and in particular will not purchase land or change their crop mix. Farmers with a high debt/equity ratio are

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S. Wheeler et al. / Global Environmental Change 23 (2013) 537–547

significantly more likely to plan to decrease their irrigated area while farmers with higher farm net income are much more likely to plan to buy more land. In terms of state regional influences, SA irrigators were less likely than NSW or Victorian irrigators to plan to be more adaptive, in particular not to buy land (but they will sell farm land) or adopt more efficient infrastructure. This result is a direct consequence of the historical infrastructure of different regions, with SA having significantly improved its on and off-farm irrigation infrastructure many years before other states.

important in driving behaviour, but behaviour can also drive beliefs. Policy makers will need to consider how to develop effective strategies to communicate water use, climate change and adaptation strategies, along with developing policies to address farm succession, water market inefficiencies, and where necessary, design structural adjustment packages to aid the transition out of farming.

7. Conclusion

The authors would like to acknowledge the helpful and constructive comments of the journal’s reviewers in improving this paper. Martin Shanahan also provided useful comments on a previous manuscript. We are also grateful to the irrigators who participated in this research and for the assistance of Adam Loch in the collection of the survey data. This research was part of a project funded by a National Climate Change Adaptation Research Facility grant, SD116. It was also supported by an Australian Research Council linkage grant and six industry partners: Murray-Darling Basin Authority, Goulburn-Murray Water, NSW Department of Energy and Office of Water, Department of Sustainability and Environment, CSIRO and University of Lethbridge, Canada.

Climate change is expected to result in more frequent drought, reduced and uncertain precipitation, increased evaporation and lower water allocations in the Basin. Irrigators, regardless of their views on climate change, will need to adapt. This paper investigates the influences on southern Basin irrigators’ adaptation planning for the next five years. Our overall index of adaptability was made up of three broad strategies: (a) expansive: those designed to increase efforts and production; (b) accommodating: those that seek to accommodate change by adopting more efficient infrastructure and changing crop mix; and (c) contractive: those that involve a reduction in effort and resource ownership. Given the difference between actual (past) and planned behaviour, it also compared irrigators’ past future plans with their actual past strategies. The capacity for irrigators to cope with, and adapt to, climate change can be influenced. Our research has provided detailed evidence of the influences on adaptive change. Adaptive capacity in this paper is represented by a variety of incremental adoption strategies which will help determine an irrigated farms’ ability to cope with uncertain water supply in the long-run and thereby remain resilient, viable and profitable. Once a farm stops planning for and implementing change, then the chances of future success are limited. Overall, incremental adaptation is positively associated with younger (and healthier) farmers, farms that have identified successors, more productive farms, and more innovative, traditional and/or environmentally focused farmers. Central to this paper, we found that farmers who believe in climate change are less likely to be adapting their farm overall. However, this result is driven solely by farmers who believe in climate change (and who have experienced greater variations in rainfall, temperature and water allocations received) being less likely to plan for more expansive strategies. They do, however, plan to implement more accommodating strategies. Our models controlled for the endogenous relationship between belief and behaviour, a critical issue for other climate change behavioural models to consider. The results suggest that, as well as attitudes influencing behaviour, adaptation behaviour can influence attitudes, and this loop is most likely to occur for true water risk management strategies. On the other hand, identifying a successor and future farm plans were found not to be endogenous in our models, suggesting the key importance of identifying successors for a farm. Policy makers can use all this knowledge to their advantage. Finally, there is an element of path dependence in farmer behaviour. Once farmers are on a certain track of expansionary or contractive behaviour, this will continue to influence planned behaviour. Such results may indicate that educating farmers about the reality of climate change to help them adapt their farm management may not have the desired outcome. Therefore it is important to consider how climate change information is portrayed and communicated to farmers. We suggest that farmers be encouraged to plan adaptation to future water variability, and to change their actual behaviour, by focusing on how adaptation to water scarcity can increase profitability and strengthen the viability of the farm. Farmer beliefs, and past actions, are very

Acknowledgements

Appendix A. Attitudinal questions identified by the factor analysis in 2010–2011 Commerce

Tradition

Environment

Technology

q5. Financial gain is the only reason for my involvement in farming. q6. Dollars and cents is what farming is all about. q7. A maximum annual return from my property is my most important aim. q8. I view my farm as first and foremost a business enterprise. q10. I could never imagine living anywhere other than this area. q11. I want to continue farming for as long as I am able. q12. Farming is the only occupation I can imagine doing. q13. My life would be worse if I moved from this farm. q15. Managing environmental problems on my farm is a very high priority. q16. I am willing to do something about the environmental effects of my farming practices. q18. Knowing about new technology that becomes available is important to me. q19. I am open to new ideas and alternatives about farming.

Notes: Answers to the questions above were given on a five point Likert scale (1 = strongly disagree, 5 = strongly agree). The diagnosis indicated the appropriateness of the retained variables for factor analysis. Specifically the determinant of the correlation matrix is 0.15 (this determinant will equal 1.0 only if all correlations equal 0); Bartlett’s test (null: variables are not intercorrelated) was rejected and the Kaiser–Meyer–Olkin Measure of Sampling Adequacy was 0.71 [unacceptable if below 0.5 (Kaiser, 1974)].

Appendix B. Frequency of the overall planned adaptation index Overall index 5 4 3 2 1 0 1 2 3

Obs.

%

34 52 92 118 138 110 59 33 6

5.3 8.1 14.3 18.4 21.5 17.1 9.2 5.1 0.9

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