The Effect of Contingent Valuation Format on Producers’ Rotational Grazing Adoption Responses
Seon-Ae Kim Jeffrey M. Gillespie Krishna P. Paudel Department of Agricultural Economics and Agribusiness 101 Agricultural Administration Building Louisiana State University Baton Rouge, LA 70808 e-mail:
[email protected] Phone: 1-225-578-2595 Fax: 1-225-578-2716
Selected paper prepared for presentation at the American Agricultural Economics Association annual meeting, Denver, Colorado, August 1-4, 2004
The authors are, respectively, research associate, associate professor and assistant professor, in Department of Agricultural Economics and Agribusiness, Louisiana State University.
Copyright 2004 by Seon-Ae Kim, Jeffrey M. Gillespie, and Krishna P. Paudel. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies.
The Effect of Contingent Valuation Format on Producers’ Rotational Grazing Adoption Responses Abstract Contingent valuation survey was conducted to assess cattle producers’ willingness to adopt rotational grazing. Both dichotomous and polychotomous formats were used. Analyses were conducted to assess the effect of the two formats on the adoption response. Limited evidence suggests that farmers answering under the dichotomous format would be less likely to adopt. Keywords; contingent valuation, ordered probit, probit, rotational grazing
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Best Management Practices (BMPs) have been developed and promoted to conserve soil and water quality while enhancing farm profitability. Rotational grazing is a BMP that has been emphasized by the USDA Natural Resources Conservation Service (NRCS) in Louisiana. Properly managed rotational grazing would result in reduced soil erosion, thus conserving water and soil quality. Fenced paddocks equipped with water facilities would conserve stream or river water quality. While the environmental benefits of rotational grazing have been established, the economic implications at the farm level are less clear. Wyatt et al. have shown decreased net returns under rotational grazing relative to continuous grazing in South Louisiana. Given the higher costs associated with rotational grazing, profit may be reduced under some conditions. Thus, if the federal government desires to entice farmers to adopt rotational grazing, it is likely that an input subsidy in the form of cost-share would be imperative. To assist farmers in adopting BMPs, the federal government is providing farmers with technical and financial assistance through programs such as the Environmental Quality Incentives Program (EQIP). EQIP assists farmers with up to 75 % of the BMP adoption cost. This research examines the rate of adoption of rotational grazing that would be expected at different cost-share proportions using willingness to pay (WTP) format contingent valuation (CV) questions. Previous researches using CV to examine technology adoption have used dichotomous choice formats. In the case of adoption of a management- intensive BMP such as rotational grazing, we were concerned that respondents would have a higher level of uncertainty over whether they would adopt, relative to the typical WTP made over a one-time donation to an environmental cause. Uncertainty would arise due to the potential need for the respondent to collect greater information on the BMP, such as through discussion with experts and reading additional materials on BMPs. After all, adoption would change management practices for a
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number of years in the future. Thus, a polychotomous WTP elicitation format was utilized, similar to that used by Ready et al. (1995). The results from polychotomous choice format were then compared with the dichotomous choice format. The objectives of the study are to 1) assess beef cattle producers’ willingness to adopt rotational grazing under alternative bid offers; 2) determine the difference in farmers’ responses between dichotomous and polychotomous choice formats; and 3) assess reasons for (not) being willing to adopt under a cost-share payment offer. Ready et al. (1995) examined the difference between the dichotomous and polychotomous choice formats when respondents are ambivalent to a WTP question in CV surveys. In their two separate mail surveys (wetland study and horse farm study), respondents were offered either dichotomous or polychotomous choice format WTP questions in the CV settings. In one survey, the polychotomous choice format included the responses, “definitely yes,” “probably yes,” “maybe yes,” “maybe no,” “probably no,” and “definitely no,” while the dichotomous choice format included “yes” and “no.” For the other survey, the polychotomous choice format responses were “strongly prefer program,” “prefer program,” “slightly prefer program,” “slightly prefer no program,” “prefer no program,” and “strongly prefer no program.” Results showed that slightly more usable responses were produced in the polychotomous choice format than dichotomous choice format, with wide ambivalence regions (answers not at the extremes). Responses from dichotomous choice format resulted in a strict “conservatism strategy,” since respondents answered “yes” only if the bid offer was outside of the ambivalence region. Cooper and Keim (1996) applied the Contingent Valuation Method (CVM) using willingness to accept (WTA) elicitation format in their search for determining farmers’ adoption
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of water quality protection practices. The objective of their study was to establish a model that could explain farme rs’ adoption behavior. They used a CV question: “If you don’t use this practice [listed in the question] currently, would you adopt the practice if you were given a $[X] payment per acre?” Six different bid values were selected and randomly given to the respondents. Then the accepted bid value was included as an explanatory variable. The practices used as dependent variables were Integrated Pest Management, Legume Crediting, Manure Testing, Split Applications of Nitrogen, and Soil Moisture Testing. The explanatory variables were bid value in the WTA question, total acres operated, formal education of the operator, estimated market value per acre of land, farm operator’s years of experience in farming, number of days annually the operator worked off the farm, net farm income, whether a soil nitrogen test was conducted, whether crop residues were destroyed for host free zones, farm type (beef, hogs, or sheep), whether grasses and legumes were in a rotation, whether manure was applied to the field, and location. Bid value variables showed expected positive and significant signs at the 1% level for four practices and at the 5% level for one practice. The other objective of Cooper and Keim (1996) was to determine how many acres the farmer would devote to the new practice given the bid value level. Models estimated the acreage on which the practices would be adopted given the choice of whether to accept or not accept the incentive payments estimated. Cooper and Osborn (1998) researched farmers’ willingness to re-enroll in the Conservation Reserve Program (CRP) utilizing a WTA question format. They analyzed two different amounts of bid offers. Scenario one did not allow haying and grazing and offered lower bid levels than scenario two, which allowed haying and grazing. The bidding process included “don’t know” as an answering option. For analysis, ordered probit was utilized for data including
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“don’t know,” and univariate probit was utilized excluding “don’t know” responses. From the results of ordered probit model, the y estimated acreage re-enrollment and compared them to results of univariate probit model. They added simulations on willingness of producers to extend CRP contracts under two scenarios from the univariate and ordered probit model. Cooper (2003) developed a joint framework for analysis of five BMPs. The five practices considered were conservation tillage, integrated pest management, legume crediting, manure testing, and soil moisture testing. Data used was obtained from a farm level survey of 1,000 farmers. A CV method was adopted in the form of WTA an incentive payment. Nonusers of the practices at the time of survey were asked if they would adopt the BMPs with an incentive payment of $[X] per acre. The bid value differed across the respondents from $2 to $24. Using multinomial probit, the study found that identifying producer tendencies to bundle the practices may increase adoption and lower the costs of the program.
Method Suppose there is an unobserved variable y*, which is a cattle producer’s utility difference between choosing to adopt rotational grazing and not choosing to adopt. The latent y* is associated with a set of variables x, which are farm and socio-economic characteristics and attributes of alternative s and an unexplained error term, e. (1)
y * = x 'β + ε What we observe is y =1 if a producer’s utility of choosing to adopt rotational grazing is
greater than that of not choosing it. (2)
y = 1 if y* > 0 y = 0 if y* ≤ 0
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The probit model is a binary choice model commonly used to analyze the choice behavior of an individual facing two alternatives and opting for one. The probability pi of choosing to adopt a rotational grazing system over not choosing it can be expressed as in equation (1), where Φ represents the cumulative distribution of a standard normal random variable and X is the set of
all explanatory variables (Greene, 2003). (3)
pi = prob[Yi = 1| X ] =
∫
xi ' β −∞
(2π )
−1/ 2
t2 exp − dt = Φ ( xi ' β ) 2
The marginal effects for continuous explanatory variables Xk on the probability P(yi=1|X), holding the other variables constant, can be derived as (4) (Greene, 2003); (4)
∂ pi = φ ( xi ' β ) β k ∂ xik
where φ represents the probability density function of a standard normal variable. The marginal effects for dummy variables d can be derived using (5) (Greene, 2003). (5)
Marginal effect of a dummy = Φ ( Xβ , d = 1) − Φ ( Xβ , d = 0) The ordered probit model is an extension of the binomial probit model. In ordered probit
model, y * is a producer’s true unobserved level of certainty on choosing to adopt rotational grazing. The observed are (6)
y= 0
if y * ≤ o ,
y = 1 if 0 < y * ≤ µ 1 , y = 2 if µ 1 < y * ≤ µ 2 , . . . y = J if µ J −1 ≤ y* ,
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where the µ s are unknown parameters to be estimated with β . With normalization of ε (mean equal to zero and variance equal to one ), the following probabilities can be obtained (Greene, 2003). (7)
Pr ob( y = 0| x ) = Φ ( − x' β ) Pr ob( y = 1| x ) = Φ ( µ1 − x ' β ) − Φ ( − x ' β ) , Pr ob( y = 2| x) = Φ ( µ 2 − x' β ) − Φ ( µ 1 − x ' β ) . . . Pr ob( y = J | x ) = 1 − Φ ( µ J −1 − x' β )
The marginal effects of changes in the regressors for continuous Xs are as follows (Greene 2003). (8)
∂ prob ( y = 0| x ) = − φ (x ' β )β , ∂x ∂ prob( y = 1| x ) = [φ ( − x' β ) − φ ( µ − x' β )]β , ∂x ∂ prob ( y = 2| x) = φ ( µ − x ' β )β . ∂x
The marginal effects for discrete variables are estimated as in equation (9). (9)
Pr[ yi = j | x = x, D = 1] − Pr[ yi = j | x = x , D = 0]
Data A pretest survey conducted in May and June of 2003 with 200 farms. After reviewing pretest responses, changes were made, including using single bounded questions for the dichotomous format and six Likert-scale options in the polychotmous choice format. A statewide mail survey of 1,500 beef cattle producers was then conducted in summer, 2003. For both
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surve ys, a stratified sample was drawn according herd size. The National Agricultural Statistics Service drew a stratified sample by herd size of Louisiana beef cattle producers. The size categories were 1-19, 20-49, 50-99, and more than 100. For the main survey, these size categories constituted 26.7 percent, 23.3 percent, 23.3 percent, and 26.7 percent of the sample, respectively. The response rate was 41 percent after deducting 270 who were no longer in the cattle business. However, respondents of the willingness to adopt question were 332 out of 504. Potential respondents to the willingness to adopt rotational grazing question were cattle producers who had not adopted rotational grazing or who answered that they were using rotational grazing, but with less than five paddocks. The cost of adoption was estimated using current market prices of materials necessary to establish a rotational grazing system. They were provided with an extensive description of rotational grazing and then asked, “Suppose that the total cost of establishing a rotational grazing system is $50 per cow, including self- filled troughs, electric fencing, pipeline and labor charges for this installation. Suppose the federal government were to agree to pay X percent ($Y per cow) of the cost. Would you be willing to pay the remainder ($Z per cow) to adopt it?” The chosen cost-share percentages (X) were varied among 60, 70, 80, 90 and 100 percent. The Y and Z figures were calculated accordingly. Half of the respondents received dichotomous choice formats with ‘yes’ and ‘no’ responses. The other half received polychotomous choice formats with six different options to choose from, including “I definitely would not adopt it,” “I probably would not adopt it,” “I would slightly lean towards not adopting it,” “I would slightly lean towards adopting it,” “I probably would adopt it,” and “I definitely would adopt it.” Both treatments were evenly distributed across strata.
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A follow-up question was asked for dichotomous choice format responses as Ready et al. (2001) suggested. If a respondent answered ‘yes’ to the initial adoption question, the follow-up question was, “How sure are you that you would adopt a rotational grazing system given the federal government would cost-share X percent of the adoption expenses?” Choices for this follow-up question were “I definitely would adopt it,” “I probably would adopt it,” “I would slightly lean towards adopting it,” and “I would slightly lean towards not adopting it.” If a respondent answered ‘no’ to the initial adoption question, a similar question with more “no” choices was given. Both versions had questions following the responses asking reasons for adopting or not adopting under the provided cost-share payment. Those willing to adopt faced the following question, “Which of the following best describes your reason(s) for not adopting a rotational grazing system, supposing you would receive a cost-share payment for implementing it?” Nine possible answers were offered for non-adoption. Those who answered that they were willing to adopt faced the following question: “Which of the following best describes your reason(s) for answering that you would adopt a rotational grazing system?” Eight possible choices were offered. Included explanatory variables were bid offers in percentage (BIDOFFER), number of animals in the cattle herd (ANIMALS), presence of purebred or seedstock animals on the farm (PUREBRED), presence of stockers on the farm (STOCKER), number of crops and other livestock operations (CROPTOT), ratio of owned land to total land used in the cattle operation (RATIOLN), number of contacts with NRCS personnel (NRCS) in the past year, number of contacts with Louisiana Cooperative Extension Personnel (LCES), having a stream or river running through the beef cattle farm (STRM), having a family member to take over the farm
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upon retirement (TOVER), tendency to avoid risk when possible in investment decisions (RISKAV), age of the respondent (AGE), holding a college bachelor’s degree (BACHELOR), household net income (HOUINC), percentage of household income from the cattle operation (BEEFINC), debt-asset ratio (DEBT), three different land descriptors (HILLY, MARSH, and RIVBOT), and southern Louisiana location (SOUTH). The choice format (DC) is included in the combined model to determine whether there are significant differences in percentages of respondents indicating they would adopt (Table 1).
Results Three models are presented for the willingness to adopt a rotational grazing system analysis. A total of 332 beef cattle producers answered the question of willingness to adopt rotational grazing. Of the 332, 185 responded in the dichotomous format and 147 in the polychotomous format. The model for the dichotomous questionnaire format had four significant variables (Table 2). Variable BIDOFFER was significant at the five percent level with a positive sign. Holding other variables constant, a one percent increase in cost-share payment would increase the probability of answering ‘yes’ to the willingness to adopt a rotational grazing system question by one percent. Variable RISKAV was negative and significant at the five percent level. Being risk averse toward investment decisions would decrease the probability of being willing to adopt by 24 percent, holding other variables constant. Variable AGE was also negative and significant at the five percent level of significance. A ten-year increase in the farmer’s age would decrease the probability of being willing to adopt by eleven percent, holding other variables constant. Variable DEBT was positive and significant
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at the ten percent level. Surprisingly, a higher debt/asset ratio would increase the probability of being willing to adopt. Table 3 presents results of the ordered probit analysis of the polychotomous choice questionnaire format. The respondents had six options to choose from, including “I definitely would not adopt it,” “I probably would not adopt it,” “I would slightly lean towards not adopting it,” “I would slightly lean towards adopting it,” “I probably would adopt it,” and “I definitely would adopt it.” Among the β coefficients, variables BACHELOR, DEBT and SOUTH were significant, all at the five percent significance level. However, one should pay greater attention to the marginal effects related to these variables. Six sets of marginal effects are presented. M1 represents marginal effects on answer “I definitely would not adopt it.” Variables significant were PUREBRED, LCES, TOVER, BACHELOR, DEBT, and SOUTH. Being engaged in purebred or seedstock operation would decrease the probability of answering that he or she would definitely not adopt a rotational grazing system by four percent, holding other variables constant. Having a family member to take over the farm, holding a college bachelor’s degree, having a greater debt load and being located in South Louisiana would be also associated with decreasing the probability of this response. Variable LCES had a positive relationship with this response. M2 represents marginal effects on “I probably would not adopt it.” Being engaged in a purebred or seedstock operation, having a family member to take over the farm, holding a college degree, having a higher debt load, or being located in South Louisiana would be associated with decreasing the probability of answering that he or she probably would not adopt a rotational grazing system. Having a stocker operation or meeting more frequently with LCES personnel had a positive effect on this response.
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M6 represents marginal effects for answer “I definitely would adopt it.” Seven variables were significant at the ten percent level or better. Having more animals in the beef cattle operation would decrease the probability of responding “I definitely would adopt it.” However, being engaged in a purebred or seedstock, or a stocker operation; having a family member to take over the farm; holding a college bachelor’s degree; having a higher debt-asset ratio; or being located in South Louisiana would positively influence the response, “I definitely would adopt it.” It is surprising that the marginal effect of variable BIDOFFER was not significant in this model. Responses of the dichotomous and polychotomous formats were combined into one model (Table 4). Answers to the polychotomous format were divided into two groups, either ‘yes’ or ‘no.’ Answers “I would slightly lean towards adopting it,” “I probably would adopt it,” and “I definitely would adopt it” were coded one ; otherwise responses were coded as zero. A dummy variable DC was added as an explanatory variable, represent ing the dichotomous format questionnaire. Variables BIDOFFER, TOVER, AGE, DEBT and SOUTH were significant at the ten percent significance level or better. Variable RIVBOT was significant in its marginal effect. Variable DC was almost significant, and had the expected negative sign. Overall, results from the combined model indicate the following: 1) a higher bid offer would lead a farmer to be more willing to adopt a rotational grazing system; increasing the bid offer of the cost-share by one percent would increase the probability of adopting by 0.5 percent, holding other variables constant. 2) Having a family member to take over the farm would lead the farmer to be more likely to adopt; this would increase the probability of adoption by 16 percent, holding other variables constant. 3) Older farmers are less likely willing to adopt; an increase in the farmer’s age by ten years would decrease the probability of acceptance by five
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percent, holding other variables constant. 4) Higher debt producers are more likely willing to adopt; an increase in the debt/asset ratio by 10 percent would increase the probability of adoption by nine percent, holding other variables constant. 5) Being located in South Louisiana would lead to greater probability of adoption; and 6) very limited evidence exists to suggest that farmers answering under the dichotomous format would be less likely to answer that they would adopt. Table 5 shows percentages of bids accepted under given bid offers by beef cattle producers. Using the dichotomous choice format, as bid offer increases, the percentage of producers willing to adopt increased greatly from 21.14 percent to 55.26 percent for the 60 and 100 percent cost-shares, respectively. However, using the polychotomous choice format, and treating answers “I would slightly lean towards adopting it,” “I probably would adopt it,” and “I definitely would adopt it” as ‘yes’ response; the percentage accepted was much higher than with the dichotomous choice format, especially under lower bid rates. Willingness to adopt did not increase with bid offer as significantly as with the dichotomous format. For the 90 percent costshare offer, the accepted percentage was actually lower than the 60, 70 and 80 percent levels. The allowance of “weaker” decisions under the polychotomous choice format (“probably adopting” and “slightly leaning towards adopting”) could account for the higher adoption rates under the lower bids. It is helpful to examine the reasons for answering ‘yes’ or ‘no’ to the willingness to adopt question, as follow-up questions are considered important in determining reasons for adoption/non-adoption. Nine possible reasons were offered to the question, “Which of the following best describes your reason(s) for not adopting a rotational grazing system, supposing you would
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receive a cost-share payment for implementing it?” Respondents were allowed to mark all applicable reasons (Table 6). The highest percentage (40.6%) marked the statement, “I believe I have too few animals to be able to practically use a rotational grazing system.” This is a valid response for producers with fewer cows. The second most common reason (28.9%) was, “I prefer not to deal with the additional management and labor required with a rotational grazing system.” The third most common reason (25.6%) was, “The land I use for beef cattle grazing is not erodible.” Also, many beef cattle producers indicated that they rent the land for their beef cattle operation. This could be a highly valid reason for not answering ‘yes’ on willingness to adopt question; 20.6 percent of producers indicated this. Overall, the descriptive statistics reveal that many beef cattle producers believe they have too few animals to adopt rotational grazing, and many do not prefer to deal with additional management and labor. After all, the enterprise is likely a “hobby” for a substantial portion of the group. For those who answered that they were willing to adopt a rotational grazing system, eight reasons were offered (Table 7). The most frequent reason, at 69.6 percent, was “I believe rotational grazing is a better way of managing grazing land.” The second most commonly indicated reason was, “I believe soil and water conservation is very important,” at 40.9 percent. The third most common reason was, “I believe rotational grazing is profitable under these circumstances.” Other reasons also ha ving high percentages included, “I have heard about the benefits of rotational grazing from farmers, specialists, workshops, magazines, etc.,” (25.4%) and “I was considering implementation of a rotational grazing system before” (23.2%). Reasons aimed to detect strategic and hypothetical biases had relatively low responses.
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Conclusions Socioeconomic factors affecting the willingness to adopt a rotational grazing system were analyzed using probit and ordered probit analyses. A CV technique using WTP elicitation format was adopted to elicit producers’ WTP amount in adopting a rotational grazing system. The results indicated the following: 1. As expected, higher bid offers would lead farmers to be more willing to adopt a rotational grazing system. This suggests that if the federal government desires to enhance adoption of BMPs, the setting of the cost-share rate will significantly affect the adoption rate. 2. Having a family member to take over the farm would lead the farmer to be more likely to adopt. The result indicated that when a farmer has offspring to take over the farm, this effectively extends his planning horizon. This also indicates the farmer’s realization of the positive long term impacts of BMP adoption. 3. Older farmers are less likely willing to adopt, as they expect they would benefit less from the investment, given that the benefits are generally longer term in nature. 4. The polychotomous choice format allowed more detailed information to be gathered on respondents’ opinions of willingness to adopt. 5. Conservatism was found in respondents of the dichotomous choice format, as they answered “yes” to the willingness to adopt question in higher bid offers less frequently than did those answering under the polychotomous choice format. More work, however, needed to verify this result since the differences in willingness to adopt were only marginally (at 10.2 percent) significant. 6. Three frequently cited reasons for not being willing to adopt rotational grazing were the farmers have too few animals to adopt rotational grazing; they preferred not to deal with
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additional management; and they believed the land was not erodible. Three frequently cited reasons for being willing to adopt rotational grazing were they believed rotational grazing was a better way of managing grazing land; soil and water conservation was very important; and they believed it was profitable under these circumstances. These suggest that many beef cattle producers believe rotational grazing is profitable under cost-share programs. However, many may not want to deal with the additional management necessary for rotational grazing. This is consistent with the fact that many cattle farmers are part-time farmers with limited resources to devote to the cattle enterprise. The 2002 Farm Bill increased funding of existing programs such as EQIP and created new programs that assist producers financially in conserving soil and water quality. Higher costshare offers along with educational efforts would induce greater adoption of rotational grazing.
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References Arrow, Kenneth, Robert Solow, Paul R. Portney, Edward E. Leamer, Roy Radner, and Howard Schuman. Report of the NOAA Panel on Contingent Valuation. January 1993. Cooper, Joseph C. “A Joint Framework for Analysis of Agri-Environmental Payment Programs.” American Journal of Agricultural Economics 85(November 2003): 976-987 Cooper, Joseph C., and C. Tim Osborn. “The Effect of Rental Rates on the Extension of Conservation Reserve Program Contracts.” American Journal of Agricultural Economics 80(1998): 184-194. Cooper, Joseph C., and Russ W. Keim. “Incentive Payments to Encourage Farmer Adoption of Water Quality Protection Practices.” American Journal of Agricultural Economics 78(1996): 54-64. Dillman, Don A. Mail and Telephone Surveys: The Total Design Method. Wiley and Sons, Inc. New York. 1978. . Mail and Internet Surveys: The Tailored Design Method. Second Edition. Wiley and Sons, Inc. New York. 1999. Economic Research Service. ERS/USDA Features-The 2002 Farm Bill: Title II-Conservation. 2002. (http://www.ers.usda.gov/Features/farmbill/titles/titleIIconservation.htm) Garrod, Guy, and Kennethe G. Willis. Economic Valuation of the Environment. Edward Elgar Publishing Inc. Northampton, Massachusetts. 1999. Greene, William H. Econometric Analysis. Fifth Edition. Prentice Hall. Upper Saddle River, New Jersey. 2003. . Limdep Version 8.0. Reference Guide. Econometric Software, Inc. Plainview, New York. 2002. . Limdep Version 8.0. Econometric Modeling Guide. Volume 1. Econometric Software, Inc. Plainview, New York. 2002. Ready, Richard C., John C. Whitehead, and Glenn C. Blomquist. “Contingent Valuation When Respondents Are Ambivalent.” Journal of Environmental Economics and Management 29(1995): 181-196. Ready, Richard C., Stale Navrud, and W. Richard Dubourg. “How Do Respondents with Uncertain Willingness to Pay Answer Contingent Valuation Questions?” Land Economics 77(3) (2001): 315-326.
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Waytt, W.E., B.C. Venuto, J.M. Gillespie, and D. C. Blouin. “The Effects of Pasture Stocking Rate and Method on Cow-Calf Production: Calf Performance.” LSU Agricultural Center, Beef Cattle Research Report 32(2003):21-24.
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Table 1. Descriptive Statistics of Explanatory Variables Variables
Units
Std.Dev.1) Min.
ANIMALS2) Number/100
1.276
2.383
0.01
3)
Max. 24.00
PUREBRED
0-1
0.146
0.353
0
1
4)
STOCKER
0-1
0.061
0.239
0
1
CROPTOT5)
Number
1.076
1.099
0
7
RATIOLN 6)
Percent
0.682
0.376
0
1
0-1
0.360
0.481
0
1
LCES
0-1
0.145
0.352
0
1
STRM9)
0-1
0.423
0.495
0
1
0-1
0.274
0.446
0
1
0-1
0.706
0.456
0
1
5.861
1.256
2.3
8.7
BACHELOR12) 0-1
0.315
0.465
0
1
HOUINC13)
1,2,3,4,5
2.445
1.257
1
5
1,2,3,4,5
1.327
0.804
1
5
1,2,3,4,5
1.688
1.007
1
5
7)
NRCS
8)
TOVER10) 11)
RISKAV AGE
Years/10
BEEFINC
14)
15)
DEBT
HILLY16)
0-1
0.416
0.493
0
1
17)
0-1
0.093
0.290
0
1
18)
0-1
0.167
0.373
0
1
0-1
0.527
0.499
0
1
BIDOFFER20) 60,70,80,90,100 80.556 13.760
60
100
0
1
MARSH
RIVBOT SOUTH DC21) 1)
Mean
2)
19)
0-1
0.501 3)
0.501
Standard Deviation; Number of beef animals; 1 if a farm includes purebred or seedstock production and zero otherwise; 4) 1 if a farm includes a stocker production and zero otherwise; 5) Number of crops or other livestock enterprises included on the beef cattle farm; 6) Percentage beef cattle operation land that is owned; 7) 1 for having contact with NRCS personnel at least once in the year, 2002 and zero otherwise; 8) 1 for having contact with LCES personnel at least four times in the year, 2002, and zero otherwise; 9) 1 for a stream or river running through the farm, and zero otherwise; 10) 1 for a family member planning to take over the beef cattle farm, and zero otherwise; 11) 1 for risk averse in investment decisions and zero otherwise; 12) 1 for having a college bachelor’s degree, and zero otherwise; 13) 1 for less than $30,000 of household net income, and 5 for over $120,000; 14) 1 for less than 20 percent of household net income coming from the beef cattle operation, and 5 for between 81-100 percent; 15) 1 for zero debt, and 5 for over a 60 percent debt asset ratio; 16) 1 for hilly land used for beef cattle grazing, and zero otherwise; 17) 1 for marsh land used for beef cattle grazing and zero otherwise; 18) 1 for river bottom land used for beef cattle grazing, and zero otherwise; 19) 1 for land used for the beef cattle operation located in South Louisiana and zero otherwise; 20) percent of bid offer in willingness to adopt question; and 21) 1 for dichotomous choice format, in willingness to adopt question and zero otherwise.
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Table 2. Results of the Probit Analysis of Willingness to Adopt, Dichotomous Choice Format
1
Willingness to adopt a Rotational Grazing System Dichotomous Choice Format Variable B B/St.Er. M M/St.Er CONSTANT BIDOFFER ANIMALS
-0.3806 0.0251**
-0.312 2.430
-0.1496 0.0098**
-0.313 2.433
-0.0213
-0.282
-0.0083
-0.282
0.2110
0.357
0.0838
0.356
#STOCKER
-0.0158
-0.025
-0.0062
-0.025
CROPTOT
-0.2664
-1.601
-0.1047
-1.601
RATIOLN
-0.1911
-0.521
-0.0751
-0.521
#NRCS
0.0757
0.244
0.0298
0.244
#LCES
0.3303
0.715
0.1311
0.718
#STRM
-0.3564
-1.288
-0.1384
-1.315
0.4328
1.441
0.1711
1.454
#PUREBRED
#TOVER #RISKAV
-0.6142**
-0.025
-0.2412**
-2.085
AGE
-0.2884**
-2.342
-0.1134**
-2.336
#BACHELOR 0.0351
0.109
0.0138
0.108
HOUINC
-0.0062
-0.049
-0.0024
-0.049
BEEFINC
-0.0300
-0.142
-0.0118
-0.142
DEBT
0.2837*
1.715
0.1115*
1.714
#HILLY
0.1644
0.576
0.0648
0.575
#MARSH
0.3669
0.870
0.1455
0.876
#RIVBOT
0.1494
0.369
0.0591
0.368
#SOUTH
0.3447
1.314
0.1349
1.328
McFadden R2 Estrella R2 Percentage Correctly Predicted
0.218 0.288 72.34%
B: Values of the Parameters; M: Marginal effects computed at the means of Xs for continuous variables and marginal effects for dummy variables are Pr [y|x=1]-Pr[y|x=0]; # indicates dummy variables. ***: Values significant at 1 % level; **: Values significant at 5 % level; *: Values significant at 10 % level.
1
1) The analysis was conducted using Limdep version 8.0. 2) The model was weighted to adjust for over-sampling larger farms and under-sampling smaller farms.
20
Table 3. Results of the Ordered Probit Analysis of Willingness to Adopt, Polychotomous Choice Format 2 Willingness to adopt a Rotational Grazing System In Polychotomous Choice Format Variable
B
B/St.Er.
M1(Y=0)1
M2(Y=1)2 M3(Y=2)3 M4(Y=3)4 M5(Y=4)5 M6(Y=5)6
CONSTANT
0.4685
0.473
0.0000
0.0000
0.0000
0.0000
0.0000 0.0000
BIDOFFER
0.0017
0.207
-0.0002
-0.0004
0.0000
0.0000
0.0002 0.0004
ANIMALS
-0.0063
-0.075
0.0008
0.0015
0.0001
0.0001
-0.0009 -0.0016***
#PUREBRED
0.3892
1.131
-0.0387*
-0.0910**
-0.0084
-0.0162
0.0455 0.1088***
#STOCKER
0.3236
0.379
-0.0305
0.0755**
-0.0073
-0.0152
0.0357 0.0928**
CROPTOT
-0.0357
-0.305
0.0042
0.0085
0.0006
0.0007
-0.0051 -0.0090
RATIOLN
0.0941
0.271
-0.0112
-0.0224
-0.0017
-0.0019
0.0136 0.0237
#NRCS
0.1211
0.429
-0.0142
-0.0287
-0.0022
-0.0027
0.0172 0.0307
#LCES
-0.2838
-0.755
0.0393***
0.0659***
0.0040
0.0007
-0.0452 -0.0647
#STRM
0.0575
0.256
-0.0069
-0.0136
-0.0010
-0.0012
0.0083 0.0144
#TOVER
0.3973
1.385
-0.0420*
-0.0932***
-0.0082
-0.0139
0.0497 0.1076***
#RISKAV
-0.0931
-0.329
0.0107
0.0221
0.0018
0.0023
-0.0130 -0.0239
AGE
0.0017
0.018
-0.0002
-0.0004
0.0000
0.0000
0.0002 0.0004
#BACHELOR 0.6243** 2.164
-0.0651**
-0.1438***
-0.0128
-0.0231
0.0737 0.1712***
HOUINC
-0.0294
-0.289
0.0035
0.0070
0.0005
0.0006
-0.0042 -0.0074
BEEFINC
-0.0941
-0.405
0.0112
0.0224
0.0017
0.0019
-0.0136 -0.0237
DEBT
0.3071** 2.062
-0.0366*
-0.0729**
-0.0056
-0.0063
0.0442 0.0772*
#HILLY
0.0413
0.157
-0.0049
-0.0098
-0.0008
-0.0009
0.0059 0.0104
#MARSH
-0.1216
-0.234
0.0157
0.0287
0.0020
0.0014
-0.0186 -0.0291
#RIVBOT
-0.0852
-0.263
0.0106
0.0202
0.0015
0.0013
-0.0127 -0.0209
#SOUTH
0.4824** 1.979
-0.0581**
-0.1125***
-0.0085
-0.0099
0.0676 0.1215***
µ1 µ2 µ3 µ4
1.0804*** 8.744 1.2002*** 9.762 1.6880*** 13.366
2.5150*** 16.394 Y=0:‘I would definitely would not adopt it’; 2 Y=1: ‘I probably would not adopt it’; 3 Y=2: ‘I would slightly lean towards not adopting it’; 4 Y=3:‘I would slightly lean towards adopting it’; 5 Y=4: ‘I probably would adopt it’; 6 Y=5: ‘I definitely would adopt it.’ B: Values of the Parameters; M: Marginal effects computed at the means of Xs for continuous variables and marginal effects for dummy variables are Pr [y|x=1]-Pr[y|x=0]; # indicates dummy variables. ***: Values significant at 1 % level; **: Values significant at 5 % level; *: Values significant at 10 % level. 1
2
1) The analysis was conducted using Limdep version 8.0. 2) The model was weighted to adjust for over-sampling larger farms and under-sampling smaller farms.
21
Table 4. Result of the Probit Analysis of Willingness to Adopt, Dichotomous and polychotomous Formats Combined 3 Willingness to Adopt a Rotational Grazing System In Dichotomous and Polychotomous Format Variable B B/St.Er. M M/St.Er CONSTANT
-0.6918
-0.890
-0.2759
-0.890
BIDOFFER
0.0137**
2.226
0.0054**
2.226
#DC
-0.2757
-1.626
-0.1096
-1.636
ANIMALS
-0.0152
-0.264
-0.0060
-0.264
#PUREBRED 0.2296
0.817
0.0908
0.828
#STOCKER
0.1801
0.379
0.0713
0.383
CROPTOT
-0.0938
-1.003
-0.0374
-1.003
RATIOLN
-0.0618
-0.250
-0.0246
-0.250
#NRCS
0.1159
0.588
0.0461
0.590
#LCES
-0.2197
-0.745
-0.0873
-0.752
#STRM
-0.0829
-0.476
-0.0330
-0.476
#TOVER
0.4130**
0.2005 0.1622**
2.062
#RISKAV
-0.2787
-1.432
-0.1104
-1.449
AGE
-0.1265*
-1.703
-0.0504*
-1.703
#BACHELOR 0.2686
1.292
0.1065
1.305
HOUINC
-0.0028
-0.035
-0.0011
-0.035
BEEFINC
-0.0777
-0.502
-0.0310
-0.502
DEBT
0.2360*** 2.254
0.0941**
2.254
#HILLY
0.1544
0.828
0.0615
0.830
#MARSH
-0.0247
-0.085
-0.0098
-0.085
#RIVBOT
0.4380
1.639
0.1708*
1.710
#SOUTH
0.4730*** 2.685
0.1868***
2.736
McFadden R2 Estrella R2 Percentage Correctly Predicted
0.146 0.197 69.40%
B: Values of the Parameters; M: Marginal effects computed at the means of Xs for continuous variables and marginal effects for dummy variables are Pr [y|x=1]-Pr[y|x=0]; # indicates dummy variables. ***: Values significant at 1 % level; **: Values significant at 5 % level; *: Values significant at 10 % level.
3
1) The analysis was conducted using Limdep version 8.0. 2) The model was weighted to adjust for over-sampling larger farms and under-sampling smaller farms.
22
Table 5. Percentages of Producers’ Willing to Adopt Rotational Grazing Under Alternative CostShare Rates Cost-Share Rates DC1 PC2
Accepted Non-Accepted Accepted3 Non-Accepted
60 21.14 75.86 57.14 42.85
70 41.18 58.82 64.70 35.29
1
80 43.18 56.82 55.55 44.44
90 52.50 47.50 52.00 48.00
100 55.26 44.74 70.83 29.17
Total (n) 55.43(82) 44.57(103) 59.86(88) 40.14(59)
Dichotomous Choice Format. 2 Polychotomous Choice Format. 3 Included choices are, ‘I definitely would adopt it,’ ‘I probably would adopt it,’ and ‘I would slightly lean towards adopting it’
23
Table 6. Reasons for Not Being Willing to Adopt a Rotational Grazing System Reasons
Yes
No
I rent the land for my beef cattle operation. Number 37 143 Percentage 20.6 79.4 I believe rotational grazing is not profitable, even if there is a cost-share payment. Number 5 175 Percentage 2.8 97.2 I do not want to go through the paperwork involved in getting the cost-share. Number 29 151 Percentage 16.1 83.9 I believe I have too few animals to be able to practically use a rotational grazing system. Number 73 107 Percentage 40.6 59.4 I prefer not to deal with the additional management and labor required with a rotational grazing system. Number 52 128 Percentage 28.9 71.1 The land I use for beef cattle operation is not erodible. Number 46 134 Percentage 25.6 74.4 I do not like the way the government pays the cost-share payment. Number 9 171 Percentage 5.0 95.0 I need more information on rotational grazing in order to make a decision. Number 16 165 Percentage 8.8 91.2 Other. Number 22 158 Percentage 12.2 87.8
24
Table 7. Reasons for Being Willing to Adopt a Rotational Grazing System Reasons
Yes
No
I believe soil and water conservation is very important. Number 74 107 Percentage 40.9 59.1 I believe rotatio nal grazing is a better way of managing grazing land. Number 126 55 Percentage 69.6 30.4 I believe rotational grazing is profitable under these circumstances. Number 71 110 Percentage 39.2 60.7 I was considering implementation of a rotational grazing system before. Number 42 139 Percentage 23.2 76.8 I have heard about the benefits of rotational grazing from farmers, specialists, workshops, etc. Number 46 135 Percentage 25.4 74.5 1) I am very concerned, but I am not sure I could afford to pay this much. Number 20 160 Percentage 11.1 88.4 I wanted to show support for the government’s funding of EQIP.2) Number 21 160 Percentage 11.6 88.4 Other. Number 5 176 Percentage 2.8 97.2 1)
Hypothetical bias, 2) Strategic Bias, the statements are adopted from Garrod and Willis, 1999.
25
