PUBLICATIONS Water Resources Research RESEARCH ARTICLE 10.1002/2013WR015200 Key Points: ! Farmers’ attitudes are largely driven by perceived risk and response efficacy ! The majority of farmers ("93%) have a positive attitude toward taking action ! Younger farmers already engaged in conservation have a more positive attitude Correspondence to: R. S. Wilson,
[email protected] Citation: Wilson, R. S., G. Howard, and E. A. Burnett (2014), Improving nutrient management practices in agriculture: The role of risk-based beliefs in understanding farmers’ attitudes toward taking additional action, Water Resour. Res., 50, 6735–6746, doi:10.1002/2013WR015200. Received 20 DEC 2013 Accepted 22 JUL 2014 Accepted article online 25 JUL 2014 Published online 21 AUG 2014
Improving nutrient management practices in agriculture: The role of risk-based beliefs in understanding farmers’ attitudes toward taking additional action Robyn S. Wilson1, Gregory Howard2, and Elizabeth A. Burnett1 1
School of Environment and Natural Resources, Ohio State University, Columbus, Ohio, USA, 2Department of Economics, East Carolina University, Greenville, North Carolina, USA
Abstract A recent increase in the amount of dissolved reactive phosphorus (DRP) entering the western Lake Erie basin is likely due to increased spring storm events in combination with issues related to fertilizer application and timing. These factors in combination with warmer lake temperatures have amplified the spread of toxic algal blooms. We assessed the attitudes of farmers in northwest Ohio toward taking at least one additional action to reduce nutrient loss on their farm. Specifically, we (1) identified to what extent farm and farmer characteristics (e.g., age, gross farm sales) as well as risk-based beliefs (e.g., efficacy, risk perception) influenced attitudes, and (2) assessed how these characteristics and beliefs differ in their predictive ability based on unobservable latent classes of farmers. Risk perception, or a belief that negative impacts to profit and water quality from nutrient loss were likely, was the most consistent predictor of farmer attitudes. Response efficacy, or a belief that taking action on one’s farm made a difference, was found to significantly influence attitudes, although this belief was particularly salient for the minority class of farmers who were older and more motivated by profit. Communication efforts should focus on the negative impacts of nutrient loss to both the farm (i.e., profit) and the natural environment (i.e., water quality) to raise individual perceived risk among the majority, while the minority need higher perceived efficacy or more specific information about the economic effectiveness of particular recommended practices.
1. Introduction Harmful algal blooms (HABs) have been a serious issue in Lake Erie since the 1960s. The limiting nutrient in freshwater systems is phosphorus, and as a result, HABs occur when phosphorus levels are high within the lake, seriously impacting the ecosystem services provided by the lake. Specifically, the blooms restrict recreational opportunities, negatively impact aesthetics, change the taste and odor of water supplies, and lead to an increase in neurotoxic microcystin, which is harmful to wildlife and humans [International Joint Commission, 2014]. The decomposition of these algal masses results in anoxic lake conditions and eutrophication, both of which damage economically important fisheries. The use of fertilizers for row crop farming has historically been a major source of phosphorus loading in Lake Erie [Bast et al., 2009; Ohio State University, 2006; Terry, 2006; USDA-National Agricultural Statistics Service, 2007]. In the 1980s, decreases in particulate-based phosphorus as a result of increased adoption of conservation tillage practices, as well as increased regulation of urban point sources of pollution, seemed to temporarily solve the issue of phosphorus and harmful algal blooms in Lake Erie [De Pinto et al., 1986; Dolan, 1993]. However, data collected over the last two decades revealed an increase in dissolved reactive phosphorus (DRP), while total particulate phosphorus decreased. This increase in DRP is thought to be a result of poor nutrient management planning (i.e., over application, poor timing) and the broadcast application of fertilizer without incorporation [Ohio Lake Erie Phosphorus Task Force, 2013; Scavia et al., 2014]. Precision in nutrient management becomes increasingly important as an adaptation strategy under predicted future climate change scenarios where spring-based storm events, a major source of DRP, are expected to occur with higher intensity. Paired with warmer than average temperatures in Lake Erie during the summer months, it is expected that harmful algal blooms could become more common without immediate human intervention [Meehl et al., 2007; Michalak, 2013; Ohio EPA, 2010; Thomson et al., 2005; van de Vijver et al., 2008]. The best management practices currently recommended to decrease DRP in Lake Erie are those meant to improve soil health (e.g., limited tillage and cover cropping), increase nutrient management precision (e.g.,
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grid sampling and variable rate application), and improve the filtration of subsurface drainage (e.g., controlled drainage systems and biofilters) [van de Vijver et al., 2008]. Experts believe an increased adoption of these types of BMPs can serve to diminish DRP loads from agro-ecosystems, thereby decreasing the overall impact of agriculture on water quality [International Joint Commission, 2014; International Plant Nutrition Institute, 2013]. In addition to decreasing the amount of DRP lost through surface runoff and subsurface drainage, these practices can also result in increased crop yields, decreased nutrient costs, and an improvement in overall farm health and resilience. The objectives of the research reported here are to (1) assess the attitudes of farmers in the target watershed toward taking at least one additional action to reduce nutrient loss on their farm, (2) identify to what extent well-known farm and farmer characteristics (e.g., gross farm sales and education) and risk-based beliefs (e.g. risk perception, perceived control, and response efficacy) explain farmer attitudes, and (3) assess how these factors differ in their predictive ability among farmers based on unobservable latent classes. We hypothesize that one’s attitude toward taking action will become more positive with an increase in risk perception, response efficacy, and perceived control, consistent with findings from the motivational literature in psychology and communication. We also hypothesize that a class of farmers with a more positive attitude toward adopting new practices will emerge and be characterized as younger, more educated and operating larger farms with a greater proportion of owned acreage. Specifically, we propose that heterogeneity in the farming population leads to unique factors influencing action among distinct, identifiable groups or classes of farmers. Increased insight into this heterogeneity not only increases our understanding of what might influence an individual farmer’s attitude toward taking additional action, but it also allows policy makers to more carefully design policy and outreach efforts that will be uniquely motivating to distinct segments of the population.
2. Previous Research Past research on farmer adoption of BMPs focuses on both enrollment in incentive-based conservation programs (e.g., the Conservation Reserve Program) [see Camboni and Napier, 1995, for a review] and adoption of specific field-level practices (e.g., soil and nutrient management practices) [see Edwards-Jones, 2006; Prokopy et al., 2008, for a review]. The factors most commonly studied as influential include sociodemographics (i.e., age, education, and income), cognitions and personality traits (i.e., environmental attitudes, perceived benefits of the practice, risk attitudes, etc.), and farm characteristics (i.e., number of acres farmed, land tenure, capital, type of operation, proximity to a stream, etc.). In general, older farmers tend to resist BMP adoption [Soule et al., 2000; Tey and Brindal, 2012], while farmers with higher levels of education tend to have higher adoption rates [Caswell et al., 2001; Larson et al., 2008; Llewellyn et al., 2012; Rahm and Huffman, 1984; Tey and Brindal, 2012]. Higher gross farm income [Gould et al., 1989; Saltiel et al., 1994], greater acreage [Caswell et al., 2001; Robertson et al., 2011], and greater land ownership [Napier and Bridges, 2003] all tend to be associated with greater BMP adoption. In addition, farmers with greater knowledge about a BMP’s benefits and a more positive attitude toward a BMP tend to have higher adoption rates [Camboni and Napier, 1993; Carlson et al., 1994; Ervin and Ervin, 1982; Lynne et al., 1988; Tucker and Napier, 2002] as do farmers with wider social networks [Belknap and Saupe, 1988; Warriner and Moul, 1992]. Although past research has examined a variety of factors relevant to the adoption of different conservation practices, the results are often quite mixed and not well known for each specific type of conservation practice. In addition, less is known about what in general influences a farmer’s attitude toward taking action, or what influences a farmer’s willingness to adopt new practices beyond what he or she already has in place. Specifically, to address the current harmful algal blooms in Lake Erie it is recommended that DRP in the Maumee watershed be reduced by 41% [International Joint Commission, 2014]. Such a reduction translates to about a half a pound of DRP per acre across the Maumee watershed. It is unknown how much soil test P or fertilizer P might need to be reduced per acre to achieve this goal given the spatial and temporal variability in management and source contributions, respectively. As a result, an important first step is to consider what might motivate an individual to voluntarily take additional steps to reduce nutrient loss on their farm regardless of their location in the watershed, and their current practices. In addition, past behavioral or psychological research on farmer adoption of conservation practices tends to be largely atheoretical, and fails to account for interactions or the direct versus indirect effects of various WILSON ET AL.
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factors on adoption [Prokopy et al., 2008]. The study reported here applies a theoretical framework grounded in judgment and decision making, and controls for past predictors of behavior in a hierarchical or stepwise fashion. Specifically, previous research in judgment and decision making and risk communication suggests that individuals are motivated to protect themselves from a hazard when they have a high threat appraisal (i.e., they believe that negative consequences are likely and outweigh the benefits of not taking action, also known as high perceived risk), followed by a high coping appraisal (i.e., they believe that they are able to take action, high self-efficacy, to actually reduce the threat, high response efficacy) [Floyd et al., 2000; Martin et al., 2007; Witte and Allen, 2000]. For the case of nutrient loss, farmers should be motivated to take action if they believe that the costs of not addressing nutrient loss on their farm (i.e., a loss of farm viability through soil degradation, the threat of regulation to improve water quality, an unnecessary increase in input costs, decreases in water quality, etc.) outweigh the benefits of inaction (i.e., continuing with the status quo, avoiding the transaction costs associated with the adoption of new practices, the increased risk of a bad harvest stemming from decreasing nutrient inputs, etc.). If the perceived costs outweigh the perceived benefits of inaction (i.e., high threat appraisal or high risk perception), farmers will engage in the issue and consider whether or not they have affordable and effective tools at their disposal. Those who then judge that their ability to cope with the issue is high (i.e., high efficacy), will be willing to take steps to address nutrient loss. Alternately, individuals who have a low threat appraisal will be unaware there is an issue that needs to be addressed, and those with high threat appraisals but low coping appraisals may deny that there is an issue that needs to be addressed due to their perceived inability to address it effectively. This proposition that one’s attitude toward a particular object (taking additional action in this case) is a function of one’s salient beliefs about the object (e.g., risk-based beliefs, control-based, efficacy-based beliefs, etc.) is consistent with a plethora of research in social psychology [Fishbein, 1963, 1967]. In addition, prior research indicates that one’s attitude toward taking action is a strong predictor of their behavior when the attitude and the behavior are measured at the same level of specificity [Ajzen and FIshbein, 1977, 2005; Weigel and Newman, 1976]. For example, one’s attitude toward taking additional action to reduce nutrient loss should be a predictor of one’s actual adoption of new practices given a suite of potential practices (as opposed to just one practice) are assessed. Typically, attitude-behavior correlations are around 0.6 when the two variables are measured appropriately [Davidson and Jaccard, 1979; Weigel and Newman, 1976]. As a result, understanding who reports a positive attitude toward taking action, and what factors influence this attitude, is an important first step toward understanding future behavior. A prior published report describing farmer beliefs about nutrient loss in the study watershed indicates that threat and coping appraisals for nutrient loss are fairly high [Wilson et al., 2013]. According to this report, the majority of farmers living in the Maumee watershed in the Lake Erie Basin believe that nutrient loss is likely to have both indirect negative impacts, for example, to water quality (79%), and more direct or personal impacts, for example to profit potential (84%) (i.e., high threat appraisal or risk perception). The majority (>85%) also believes that recommended nutrient management practices are effective at reducing nutrient loss and improving water quality (i.e., high response efficacy). However, perceived control over nutrient loss is only moderate as many farmers are concerned about uncontrollable external factors influencing nutrient runoff like the weather (i.e., moderate self-efficacy). In addition, only a slight majority (56%) believes that what they do on their own farm makes much difference in overall water quality (perhaps pointing to the concern that a few bad actors are creating the problem) (i.e., moderate response efficacy). Finally, the majority of farmers (76.7%) believe that their own nutrient management practices are already adequate to protect local water quality, yet a similar majority had a positive attitude toward taking additional action (76%), with specifically 66% believing additional action was necessary on their farm [Wilson et al., 2013]. This look at the raw numbers suggests that, although the majority of farmers perceive a threat or risk associated with nutrient loss, the perceived ability to cope with the hazard may be mixed. Specifically, many in this group believe that they are doing enough on their own farm and that changes on their farm cannot by themselves improve water quality in a significant way, but at the same time they believe that further reducing nutrient loss can have tangible personal benefits and may be necessary. The analyses we present here will tease out the differential effect of these factors on a farmer’s attitude toward taking additional action under separate assumptions of homogeneity (i.e., multiple linear regression) versus heterogeneity (i.e., latent class analysis) in the population.
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3. Methods 3.1. Sampling and Data Collection The target population for this research was corn and soybean farmers in the Maumee watershed in northwest Ohio. The Maumee watershed was selected as the area of interest for a number of reasons. First, the waterways within the Maumee watershed drain into the western Lake Erie basin, where environmental impacts from phosphorus runoff have led to large harmful algal blooms. Second, experts believe that phosphorus runoff into Lake Erie originates primarily from agricultural practices in the Maumee watershed [Ohio EPA, 2010]. Land use in this area is between 60% and 80% agricultural, with conventional corn and soybean production making up the primary farming activities [Ohio EPA, 2010]. A random sample of 2000 Ohio farmers living within the watershed boundary was purchased from CCI Marketing, a private vendor that maintains a list of farmers based on contact information for individuals receiving farm related publications or government payments. A mailed survey was administered between December 2011 and February 2012 following a modified version of Dillman’s Tailored Design Method [Dillman, 2007]. Specifically, we mailed an announcement letter, a survey with cover letter, a reminder postcard, and a final replacement survey with cover letter (no final reminder letter). To increase response rate, each mailing indicated that all responding participants would be entered into a raffle with a prize of two tickets to an Ohio State football game. Responses were tracked by the date, the survey was returned and whether the respondent replied during the first wave (initial survey packet), second wave (reminder letter), or third wave (final replacement survey packet). Recording response status in this way made it possible to assess potential nonresponse bias using extrapolation where later respondents serve as a proxy for nonrespondents [Armstrong and Overton, 1977; Lindner et al., 2001]. The Ohio State University’s Office of Responsible Research Practices approved all the survey materials and procedures (Protocol 2011B0232). 3.2. Survey Development and Operationalization of Variables The dependent variable for this analysis was farmers’ attitudes toward taking at least one additional action to reduce nutrient loss on his/her farm. The independent variables include farmer characteristics (i.e., age, education and farmer networking), farm characteristics (i.e., gross farm sales, planted acres owned, current enrollment in incentive-based conservation programs, and distance from Lake Erie), and farmer risk-based beliefs (i.e., risk perception, perceived control, and response efficacy; see Table 1 for the latent construct measures and reliability results and Table 2 for the observable single-item construct measures). A farmer’s attitude toward taking additional action was measured by asking the question, ‘‘Taking at least one action I do not already do to reduce nutrient loss on my farm would be. . .’’ and then presenting five word pairs (see Table 1). Respondents were asked to choose one word in each pair and indicate how strongly they agreed with that word (e.g., on a scale from 3 to 23 where 3 5 extremely agree with the word on the left, 23 5 extremely agree with the word on the right, and 0 5 agree with neither word). This semantic differential scale is an established measure for attitudes [Osgood et al., 1957]. To measure the importance of environmental stewardship on one’s farm, we asked respondents to assign 100 points to a variety of farm goals. Each goal (i.e., making a profit, being an environmental steward, protecting human health, ensuring farm viability for my children, and maintaining a farming lifestyle) then had a unique number of points, which we used to create a measure of stewardship versus profit importance by subtracting the points assigned to profit from the points assigned to stewardship to create a continuous measure of relative importance for these two primary goals of interest (where positive numbers indicate greater weight on stewardship and negative numbers indicate greater weight on profit, and zero indicates equal weight placed on both goals). 3.3. Statistical Analysis We used SPSS 19 and Latent Gold 4.5 for data analysis. We assessed reliability of the intended survey measures using Cronbach’s alpha, a measure of internal consistency used to assess the reliability of psychometric measures by assessing how closely related a set of items are in a group [Cronbach and Meehl, 1955]. Cronbach’s alpha values of 0.7 or above for a group of items are generally considered acceptable reliability for combining the items into one measure [Nunnaly and Berstein, 1994]. We modeled farmers’ attitudes toward taking additional action using both (1) hierarchical multiple linear regression for assumed homogeneity and (2) latent class analysis, in which we accommodate latent heterogeneity and allow the data to specify what model of WILSON ET AL.
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Table 1. Summary of Latent Variables and Their Reliability Results Reliability Analysis Latent Variable Risk perception
Cronbach’s Alpha
Item Likelihood that nutrient loss from agriculture will negatively impact. . .a Human health Farming profit potential Water quality Row crop farming as viable occupation Livestock farming as viable occupation Seriousness of the negative consequences of nutrient loss to. . .b You and your family Your local community The United States as a whole People all over the world Plants and animals
Alpha If Deleted
0.771 0.741 0.748 0.720 0.702 0.734 0.944 0.940 0.925 0.922 0.938 0.931
Perceived controlc
0.716 Level of control over nutrient loss on your farm Level of control over farm’s impact on water quality Level of difficulty in reducing nutrient loss
0.540 0.591 0.730
Response efficacyd
Farmer networkinge
Attitude toward taking additional actionf
0.522 Variable rate application reduces the amount of fertilizer lost from the farm field Conservation tillage reduces the level of nutrients lost through erosion What I do on my farm doesn’t make much difference in overall water quality (reversed) Nutrient management practices like filter strips and cover crops improve water quality How often do you have conversations with other farmers. . . Within one square mile Over a mile away but within township Outside the township but within county Outside the county but within northwest Ohio Elsewhere in Ohio or the U.S. Taking at least one action I do not already do to reduce nutrient loss on my farm would be. . . Necessary versus low priority Fair versus unfair Beneficial versus harmful Pleasant versus unpleasant Valuable versus worthless
0.424 0.349 0.609 0.426 0.834 0.829 0.774 0.777 0.794 0.823 0.899 0.883 0.882 0.866 0.888 0.866
a
From extremely unlikely (0) to extremely likely (4). From not at all serious (0) to extremely serious (4). c From no control/very difficult (0) to complete control/very easy (6). d From strongly disagree (-2) to strongly agree (2). e From never (0) to once a week or more (4). f From extremely agree with word on the left (3) to extremely agree with word on the right (23). b
Table 2. Summary of Observable Variables Observable Variable Age Education
Total owned acres Annual gross farm sales
Enrollment in conservation programs Distance to Lake Erie
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Item What is your age? (Fill in the blank) How much formal education have you completed? Some high school, no diploma High School degree or equivalent Some college, no degree Associate’s degree Bachelor’s degree Graduate or Professional degree How large is your current operation? For total acres, include cropland, woodland, pasture, land in government programs, etc. (Fill in the blank) In a normal year, what are the annual gross sales from your farm, including farm program payments? Less than $50,000 $50,000–$99,000 $100,000–$249,999 $250,000–$499,999 $500,000 or greater Are you enrolled in any of the following programs? Check all that apply (EQIP, CRP, CREP, CSP) Calculated in meters from the nearest shore of the western basin using the mailing address
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Table 3. Comparison of Sample Distribution for Total Planted Owned Acres and Gross Farm Income Relative to Agricultural Census Data for the Sample Counties Total Planted Owned Acres
Sample Distribution
Census Distribution
1–9 10–49 50–179 180–499 500–7000 Annual Gross Farm Income Less than $50,000 $50,000 to 100,000 Greater than $100,000
9% 20% 37% 19% 15%
10% 28% 31% 18% 8%
heterogeneity (meaning how many heterogeneous classes exist and what portion of the data falls in each class) best fits the data.
4. Results
4.1. Sample and Response Rate We received 705 returns from our initial mailing to 2000 farmers for an initial 39% 64% 15% 10% response rate of 35.25%, and an adjusted 46% 26% response rate of 37.74%. We then removed any respondents who did not complete the dependent variable, resulting in a sample size of 559 for initial analysis. One-way ANOVA’s revealed only one significant difference across the waves of response. Specifically, individuals in the third wave (i.e., the proxy for nonrespondents) were less likely to already be enrolled in incentive-based conservation programs (p 5 0.020) relative to individuals in the first wave. To further assess potential bias, we compared the total planted acres of our sample against census data for the counties in our sample and although our sample slightly underrepresented smaller farmers with less than 50 planted acres and less than $50,000 in annual gross farm income (see Table 3); we chose not to weight the data to adjust for this bias given the focus of the survey on nutrient management and larger scale corn-soybean operations. We also imputed missing data for the main independent variables of interest (i.e., risk perception, perceived control, and response efficacy) using the SPSS multiple imputation procedure for nonmonotonicity (or randomly missing values). This resulted in a final sample size for analysis of 303 complete respondents (additional respondents not included in the final analysis were those who did not answer at least one of the independent variables in our analysis that we did not impute, such as education, total owned acreage, or farmer network size). Our final sample for analysis was 94% male with an average age of 52 years. Approximately 43% of our sample had a high school degree or equivalent, while an additional 48% had at least some college or had completed an Associates or Bachelors degree. Annual gross farm sales were less than $50,000 for 39% of our sample, with 15% of farms grossing between $50,000 and $100,000, 23% grossing between $100,000 and $500,000, and the remaining 23% grossing over $500,000. The average total acres owned were 287, while the average rented acres were 588. (A high proportion of the rented acreage data was missing from the sample, it is likely that many who did not complete this question did not have rented acreage, but we did not feel comfortable imputing this data based on such an assumption. As a result, we did not include rented acreage in our analyses.) 4.2. Reliability Analyses and Measurement We created a measure for a farmer’s attitude toward taking additional action to reduce nutrient loss using all five of the intended items (Cronbach’s alpha 5 0.899). Responses were averaged for each respondent to create a single measure on a scale from 23 (negative) to 3 (positive) where 0 indicates indifference. We used the mean of both the likelihood and seriousness items in the final risk perception measure (Cronbach’s alpha 5 0.774 and 0.942, respectively), which was the product of the two subscales ranging from 0 (no risk) to 16 (high risk). We created the measure for perceived control by averaging all three of the intended items (alpha 5 0.719), resulting in a scale ranging from 0 (no control) to 6 (complete control). We created a measure of farmer networking using all five of the intended items (Cronbach’s alpha 5 0.834). Responses were averaged for each respondent creating a single measure of farmer networking on a scale from 0 (no conversations with other farmers across spatial scales) to 4 (talks with farmers across spatial scales once a week or more). Finally, the intended multi-item measure for response efficacy was not reliable so we chose one general item (i.e., what I do on my farm does not make much difference in overall water quality), and reverse coded it for consistency with the other independent variables for a newly labeled scale ranging from 22 (low response efficacy) to 2 (high response efficacy) where 0 equals indifference. 4.3. Hierarchical Multiple Regression Analyses We tested a hierarchical multiple linear regression model to explain farmers’ attitudes toward taking additional action. We chose a hierarchical analysis based on (1) the desire to control for previously identified WILSON ET AL.
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Table 4. Summary of Hierarchical Multiple Regression Analysis for Variables Predicting a Farmer’s Attitude Toward Taking Additional Action to Reduce Nutrient Loss on Their Farm Assuming Homogeneity Within the Sample (n 5 303) Block 1 Variable Age Education Annual gross farm sales Total acres owned Distance to Lake Erie Conservation program participation Farmer network Risk perception Perceived control Response efficacy R2 F a
Block 2
B
SE(B)
B
SE(B)
20.005 20.033 0.016 0.000 0.000 0.078 0.030
0.005 0.047 0.052 0.000 0.000 0.136 0.082
20.008 20.052 0.015 0.000 0.000 0.050 0.015 0.077b 0.034 0.146a
0.005 0.045 0.051 0.000 0.000 0.129 0.079 0.017 0.066 0.061
20.009 to 0.011 0.549
0.087 to 0.112 4.389b
p < 0.05. p < 0.01.
b
predictors of farmer conservation behavior, and (2) the assumption that the relationship between individual farm and farmer characteristics and one’s attitude toward action would be mediated by higher order cognitions related to risk, efficacy, and control. The first block of variables included many of the farm and farmer characteristics found to influence conservation behavior in prior research (i.e., age, education, gross farm sales, total acres owned, distance to Lake Erie, current enrollment in incentive-based conservation programs, and farmer networking). These variables are believed to be the antecedents of the hypothesized cognitions having only an indirect effect on one’s attitude (e.g., where higher levels of education lead to higher perception of risk which leads to a more positive attitude toward taking action). The second block included the cognitions of primary theoretical interest, which are hypothesized to most directly influence one’s attitude toward taking additional action (e.g., risk perception, perceived control, and response efficacy). Preliminary assumption testing was conducted for both models to check for sample size, multicollinearity and singularity, outliers, normality, linearity, and homoscedasticity with no serious violations in any case [Tabachnick and Fidell, 1987]. The block 1 model was not significant (p > 0.05), explaining approximately 1% of the variance in attitudes toward taking action (Table 4). The full model (block 2) was significant (p < 0.01), explaining 9–11% of the variance in attitudes toward taking action. Specifically, higher response efficacy (Beta 5 0.146, p < 0.05) and greater risk perception toward nutrient loss (Beta 5 0.077, p < 0.01) both significantly increased positive attitudes toward taking additional action. 4.4. Latent Class Analysis Our next analysis allowed for unobserved or latent preference heterogeneity using latent class analysis (LCA) [Bhat, 1997; Columbo et al., 2009]. LCA estimates multiple weighted linear regressions, with each regression representing a separate ‘‘class.’’ Every observation was included in each regression, but observations were weighted by the estimated probability of belonging to the specific class. We used multiple linear regression and the same set of variables that were utilized in the hierarchical regression model and a variable, described in section 2.2, that captures the relative goals of profit and environmental stewardship. We additionally examine whether demographics differ significantly by class. Variable coefficients and probabilities for all classes are jointly estimated using the Expectation-Maximization (EM) algorithm. Farmers are not a homogeneous population, but this group heterogeneity is not derived entirely from observable demographics. It is not the case that old farmers uniformly act differently than young farmers. Although differences may be correlated with observables like age, they are likely to be driven by other unobservable characteristics of the farmer. LCA, as a semiparametric estimation method, allowed us to test for and identify unobserved heterogeneity without assuming it. We did not impose restrictions on the number of latent classes, and thereby the degree of heterogeneity, ex ante. Instead, we allowed the data to determine the number of latent classes by virtue of which number of classes minimizes the Bayesian WILSON ET AL.
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Information Criterion (BIC) [Nylund et al., 2007]. We found that a model with two latent classes provided the best fit for the data. The BIC valMajority Minority Variable Mean Mean pa ues for the 1 class (no preference heterogeneity), 2 class, and 3 class models are 939.54, Agec 50.78 53.61
