Consumer Preferences for Sustainable Product Attributes and Farm Program Features

Abstract

Previous literature primarily focused on consumers’ preference for specific sustainable attributes, such as a product being organic, eco-friendly, locally grown, and fair trade. Little is known about consumers’ preference for sustainable program features. We conduct two online choice experiments with U.S. consumers and find that consumers consistently care about farmers’ engagements in sustainable programs, and they are willing to pay a price premium for products from such programs. Consumers also value promoting science in sustainability, establishing concrete measurements of sustainability, and communicating sustainable practices with consumers and downstream industries. We apply the latent class logit model to investigate the potential segmentation of consumers. Three consumer segments are identified based on participants’ heterogeneity in preferences. Our research provides useful information for designing new sustainability programs.

1. Introduction

Sustainable agriculture in the U.S. is defined as agricultural applications that will, in the long turn, satisfy human food needs, enhance environmental quality, preserve nonrenewable resources, and “sustain the economic viability of farm operations and enhance the quality of life for farmers and society as a whole” (U.S. Code Title 7, Section 3103). The Sustainable Agriculture Research and Education program summarizes the common goals of all sustainable agriculture as (1) long term profit, (2) stewardship of natural resources, and (3) quality of life for farmers, ranchers, and their communities. Besides environment responsibilities, the above definition and summary emphasizes the importance of profitability as a key component of sustainable agriculture, and thus understanding consumers’ preference for sustainable agriculture is particularly important.

Previous studies related to sustainable agriculture primarily focused on consumer preferences and purchasing motivation of sustainable agriculture-related attributes, such as being organic, eco-friendly, locally produced, and fair-trade. Organic and eco-friendly attributes are associated with sustainable agriculture’s goal of responsible stewardship of natural resources. Hamzaoui Essoussi and Zahaf [1] and Aertsens et al. [2] found environmental concerns to be one of the motivations for organic consumption. A large body of consumer literature have focused on consumers’ attitudes, perceptions, and willingness-to-pay (WTP) for organic products (see Hemmerling et al. [3] for a review) and found U.S. consumers to be generally willing to pay a premium for organic products [4,5,6,7,8]. For consumer studies on eco-friendly products, Marette et al. [9] studied consumer demand for apples with reduced pesticide use and concluded that messages about pesticide use and pesticide residues significantly impact consumers’ choices. Uchida et al. [10] found that Japanese consumers are willing to pay a 20% price premium for Marine Stewardship Council eco-labeled seafood.

Locally produced and fair-trade attributes are other examples of sustainable agriculture attributes that are studied extensively. These two attributes are closely related to sustainable agriculture’s goal of ensuring the quality of life for farmers, ranchers, and their communities. In addition, they sometimes are associated with eco-friendly agricultural practices. For instance, Feldmann and Hamm [11] conducted a review of consumers’ perceptions and preferences for locally grown products. They found consumers are willing to pay more for locally grown products because they want to support the local community and improve conditions for farmworkers. Perceived environmental benefits and better quality and taste also motive consumers to purchase locally grown products. Onozaka and McFadden [12] studied several sustainability attributes, i.e., organic, fair trade, carbon footprint, and location claims. They found locally grown has the highest consumer WTP value. Hemmerling et al. [3] also found that most studies indicated a higher WTP for locally grown products than organic products. Regarding fair trade products, there is evidence which indicates that fair trade enhances the welfare of farmers and encourages stewardship of natural resources. Dragusanu and Nunn [13] found fair-trade is associated with higher income for farm owners. Dragusanu et al. [14] reviewed the effects of fair-trade and concluded that, on average, fair-trade products receive higher prices, and farmers have greater access to credit, a more stable perceived economic environment, and are more likely to engage in environmentally friendly practices. The demand for fair-trade products is also well documented. Hainmueller et al. [15] conducted an in-store experiment and found when they carried a fair-trade label, sales of the two most popular coffees rise by almost 10%. Dragusanu and Nunn [13] found fair-trade is associated with higher prices, improved sales, and more revenues.

Despite the studies on consumer preferences for specific sustainable attributes such as local, organic, and fair trade, to our knowledge, there is no existing literature investigating consumers’ preferences for the features of sustainable agriculture programs. Due to the broad scope of sustainable agriculture, different sustainability programs focus on different practices. For example, some sustainable agriculture program promotes scientific research and helps farmers finding innovative marketing strategies to reach more consumers (National Institute of Food and Agriculture 2016 Annual Report 2016 (Available at https://nifa.usda.gov/resource/national-institute-food-and-agriculture-2016-annual-report). The Sustainable Iowa Land Trust program (2016) requires farmers to demonstrate the use of sustainable practices, and farms will be inspected once a year based on the checklist. Other programs, such as the Regional Sustainable Development Partnerships in Minnesota, promote farmers’ engagement in sustainability programs and communication between all participating parties.

The broad scope of sustainable agriculture and different practices might lead to consumer confusion and misperception. Therefore, it is crucial to understand what sustainable program features are important to consumers. To fill this knowledge gap, our study investigates what features of sustainability programs consumers like or dislike. Some sustainable farming features may not be perceived as desirable. For instance, some consumers may perceive an independent sustainable certification as being credible, while others may prefer programs that encourage farmers to engage in the certification process. More specifically, we aim to answer the following questions: Do consumers value farmers’ engagements in sustainable farming programs? Do programs promoting farmers’ engagement induce higher consumer WTP? Among existing sustainable farming practices, what are the features likely to induce consumer WTP premiums? Do preferences vary across different consumer groups? To answer the above questions, we conducted two versions of choice experiments using online surveys and employed mixed logit models and a latent class logit model to identify consumer WTP for program features and consumer segmentations differing in their preferences.

2. Choice Experiment and Model

2.1. Choice Experiment

Choice experiment (CE) has been widely used to research consumer behaviors, preferences, and willingness to pay (WTP) for different goods. The method has been applied to various topics, such as studying the effect of additional product information [16] and predicting market performance for new products [7]. CE presents participants with options that have various levels of attributes and ask participants to choose among the alternatives. Attribute levels vary across the alternatives and are designed in the way that ensures there is always a trade-off between alternatives. With different combinations of attributes in each choice set, CEs replicate a consumer’s rational decision-making process and are useful in estimating consumers’ utility function.

One possible drawback for hypothetical CE is that participants do not pay real money when making decisions, and the choices they make are therefore not real. However, the validity of CEs in consumer studies has been proved. Carlsson and Martinsson [17] suggested that CE responses are statistically indistinguishable across hypothetical and non-hypothetical (real purchasing) treatments. Lusk and Schroeder [18] demonstrated that the biases associated with estimated marginal WTP using hypothetical CE are reduced when CE questions are framed in a way that is similar to actual purchasing settings (non-hypothetical settings). They also found that although total WTP (relative to opt-out) was upwardly biased in hypothetical choice experiments, the marginal WTP (the difference in WTP for two products) was consistent. Yue and Tong [6] found the difference between hypothetical and non-hypothetical choice experiment estimation of WTP is 7.5–9.0%, which outperformed most contingent valuation studies examined by List and Gallet [19].

We conducted two versions of CEs in this study. The first CE focuses on consumers’ WTP for general sustainable farming attributes and farmers’ engagement, and the second CE focuses on consumers’ preference for different sustainable agriculture program features. In both versions of CEs, participants were asked to choose between two options of canned sweet corn (15.5oz/3.5 servings) with varying prices. We chose canned sweet corn due to its popularity and its reduced perceived heterogeneity from respondents. Also, although this study focused on a particular product, the majority of the sustainable program features are general.

In the first version of the CE, respondents were asked to choose between canned sweet corn with different production/processing methods, certification methods, food miles, and prices. We used three types of production/processing methods (conventional, organic, and sustainable), four types of certifications, four levels of farm miles, and three levels of prices (The average price of canned sweet corn, according to the US Department of Agriculture (USDA), is about $0.48 per cup in 2016, and is about $0.52 after adjusting to 2019 USD. Thus, the price for a 15.5 oz canned sweet corn is about $1. According to USDA, organic corn prices are generally two to three times higher than conventional corn prices. The prices were chosen to cover the price range of conventional and organic sweet corns.). Respondents were provided with the definition of sustainable agriculture “sustainable agriculture is an integrated system of plant and animal production practices having a site-specific application that will last over the long term (1) enhance environmental quality and the natural resource base upon which the agricultural economy depends, (2) make the most efficient use of nonrenewable resources and on-farm resources and integrate, where appropriate, natural biological cycles and controls, (3) sustain the economic viability of farm operations, and (4) enhance the quality of life for farmers and society as a whole.”

Table 1. Product Attributes and Attribute Levels, Choice Experiment, Version One.

Product Attribute	Attribute Levels	Variable Type
Price of canned sweet corn (15.5oz/3.5 servings)
$1.49		Continuous variable
$1.99
$2.49
Production Method
Conventional		Dummy variables, Conventional as the reference
Sustainable
Organic
Certification
No certification		Dummy variables, No certification as the reference
Independently certified
Participatory certified (farmers participate in certification decision-making)
USDA certified
Food Miles
100 miles		Continuous variable
300 miles
1000 miles
2500 miles

summarizes the levels of the attributes used in the first CE. We include a column that specifies the coding for each variable. Price and food miles are treated as continuous variables, while production methods and certifications are treated as dummy variables, with conventional and no certification as the base level.

In the second version of CEs, respondents were asked to choose between canned sweet corn with five farm program attributes. These attributes covered five major aspects of sustainability programs, i.e., farmers’ engagement, the role of science, marketing promotions, sustainability measurements, and communication on sustainability practices. These five aspects of sustainable agriculture programs are selected based on the sustainable agriculture programs listed as partners of the USDA National Institute of Food and Agriculture (Available at https://nifa.usda.gov/program/sustainable-agriculture-program). Promoting farmers’ engagement is listed as a program feature of the Regional Sustainable Development Partnerships in Minnesota. Communicating and funding sustainable-agriculture-related science is mentioned in the National Institute of Food and Agriculture Annual Report of 2016. Marketing promotion is a common focus of the program marketing guide, such as the farmers’ market guide of North Central Region Sustainable Agriculture Research & Education (SARE) (An example of a farmers market guide can be found at https://www.northcentralsare.org/Resources-and-Learning/Books/The-New-Farmers-Market.). Lastly, several sustainable agriculture programs have developed very specific sustainability measurements and guidelines for sustainability practices (An example of a sustainability guideline can be found at https://silt.org/sustainability-guidelines-and-requirements/). We expect that consumers would demand concrete measures of sustainability and good communication of sustainable practices.

To capture consumer preferences, we designed a choice experiment with various sustainable practices to evaluate if some program practices would induce a WTP premium or not. The attribute levels are presented in

Table 2. Product Attributes and Attribute Levels, Choice Experiment, Version Two.

Product Attribute and Attribute Levels		Variable Type
Price of canned sweet corn (15.5oz/3.5 servings)
$1.00		Continuous Variable
$1.50
$2.00
Farmer engagement in a sweet corn sustainability program
1	Managers tell farmers what is required to participate.	Dummy Variables, 1 as the reference
2	Farmers advise program managers on program requirements and activities.
3	Farmers participate to learn what is required to meet consumer demands.
The role of science in the sustainability program
1	Farmers must seek out scientific information on their own.	Dummy Variables, 1 as the reference
2	Program communicates scientific information to farmers.
3	Program funds science that will increase the sustainability of farmer practices.
Consumer access to sustainably produced processed vegetables
1	Program focuses on helping farmers produce crops more sustainably.	Dummy Variables, 1 as the reference
2	Program helps farmers create new market opportunities.
3	Program helps farmers and processors reach more consumers.
How sustainability is measured on participating farms
1	Farmers declare that they are sustainable with no on-farm measures required.	Dummy Variables, 1 as the reference
2	Farmers in program must demonstrate use of sustainable practices.
3	Measures of on-farm practices and consumer buying decisions are used to measure sustainability.
How the program communicates findings with food processors and grocery chains
1	Program develops grower sustainability scorecards that will be provided to processors and grocery chains.	Dummy Variables, 1 as the reference
2	Program provides popular materials to food processors and grocery chains on sustainability to distribute to consumers.
3	Program provides facts on farmer use of sustainable practices that food processors and grocery chains can communicate to consumers.

. Similar to in Table 1, we also provided the coding for each variable, where the price is treated as a continuous variable, and program features are treated as dummy variables with a certain feature as the base level. The details of the attribute levels are described as follows.

First, regarding farmers’ engagement, the lowest level of farmers’ engagement is that managers dominate the certification process, i.e., managers tell farmers what is required to participate. We included two higher levels of farmers’ engagement, i.e., farmers participate in learning what is required to meet consumer demands and farmers advise program managers on program requirements and activities, and the latter one has the highest level of farmer engagement.

Second, in terms of the role of science, we used the case where the program plays a passive role, i.e., farmers must seek out scientific information related to sustainable agriculture on their own, as the base level. We then include two more levels for the role of science, i.e., the program provides scientific information to farmers, or the program funds science. Both levels were program practices introduced in the National Institute of Food and Agriculture 2016 Annual Report (2016).

Third, regarding marketing promotion, we used the typical practice of sustainable agriculture programs, i.e., helping farmers produce crops more sustainably (without a marketing focus), as the base level. We are interested in what consumers think about a sustainable program that does marketing promotion. Thus, we included two alternative program practices, i.e., the program helps farmers to create new market opportunities, or the program helps farmers and processors reach more consumers.

Fourth, we expected having measurements of sustainable practices to be an essential attribute, as studies demonstrated that the concrete information on sustainable agriculture has a significant impact on consumer preferences [9,10]. Thus, we designed variations in sustainable practice measurement. We set the base level to be that farmers declare that they are sustainable with no on-farm measures required. We then included two more levels, i.e., farmers in the program must demonstrate the use of sustainable practices, or measures of on-farm practices and consumer buying decisions are used to measure sustainability. The last level not only considers farm practice but also marketing performance as sustainability measurement.

Lastly, in addition to communicating program practices to consumers, we expected that consumers may care about how programs communicate with food processors and grocery chains because such communication promotes sustainable program transparency. We thus used the current farm practice, i.e., sustainability scorecards, as the base level, and the included program provides popular materials, or program provides facts on farmer use of sustainable practices, as alternative practices.

To mimic actual shopping, we included an opt-out option for both of the CEs. The two versions of experiments were generated using optimal D-efficiency fractional factorial design with two blocks. Each participant randomly answered one of the two blocks of questions. Each block consisted of 6 or 8 questions for the first and the second version of the CEs, respectively. We presented an example of the first and the second version of CE in

Table 3. Example of Choice Scenario, Choice Experiment Version One.

Attribute	Option A	Option B	Option C
Production/Processing Method	Sustainable	Organic	Neither option A or B
Certification	Participatory certification (producers/processors participate in certification decision-making)	US Department of Agriculture Certification
Food Miles (farm to store)	100 miles	1000 miles
Price of canned sweet corn (15.5oz/3.5 servings)	$2.49	$1.49

and

Table 4. Example of Choice Scenario, Choice Experiment Version Two.

Attribute	Option A	Option B	Option C
Farmer engagement in a sweet corn sustainability program	Farmers advise program managers on program requirements and activities.	Managers tell farmers what is required to participate.	Neither Option A or B
The role of science in the sustainability program	Farmers must seek out relevant scientific information on their own.	Farmers must seek out relevant scientific information on their own.
Consumer access to sustainably produced processed vegetables	The program helps farmers produce crops and find market opportunities.	The program focuses on helping farmers produce crops more sustainably.
How sustainability is measured on participating farms	Farmers declare that they are sustainable with no on-farm measures required.	Measures of on-farm practices and consumer buying decisions are used to measure sustainability.
How the program communicates findings with food processors and grocery chains	The program provides facts on farmer use of sustainable practices that food processors and grocery chains can communicate to consumers.	The program provides facts on farmer use of sustainable practices that food processors and grocery chains can communicate to consumers.
Price of canned sweet corn (15.5oz/3.5 servings)	$1.00	$1.50

, respectively. Besides the choice scenarios, we also asked questions about participants’ socio-demographic backgrounds and economic status. The CEs were conducted online, and participants were randomly selected across the United States recruited by Qualtrics™, a professional survey company. A pretest of the survey was conducted to validate the experiment design. Only consumers who were primary grocery shoppers were allowed to take the surveys.

2.2. Econometrics Model

A mixed logit model was employed to analyze the CE data. A mixed logit model allows consumers’ taste parameter to vary by some distribution, and it does not need the independence of irrelevant alternatives (IIA) assumption [20,21].

We assumed a linear utility function as Equation (1):

$$U_{ijt}=x_{ijt}{\beta }_i+{\epsilon }_{ijt}.$$

(1)

In Equation (1), individual $i \left(i=1,2,\dots ,N\right)$ selects alternative j with the preferred attributes and price combination among a set of $J$ alternatives ($j=1,2,\dots ,J)$. The individual needs to make choices for $t$ $\left(t=1,2,\dots ,T\right)$ choice scenarios. Additionally, $x_{ijt}$ is a vector of observed attributes, including a price level, and ${\beta }_i$ is the random coefficient vector following some density function $f\left(\beta |\theta \right),$ where $\theta$ is a vector of the parameters that define the distribution. In this study, we assume the density function $f\left(\beta |\theta \right)$ is multivariate normal, and thus $\theta$ includes the mean vector and a variance matrix of a multivariate normal distribution. To reduce the number of parameters, we also assumed the variance matrix as being diagonal. ${\epsilon }_{ijt}$ is the random term assumed to be independently and identically distributed following type I extreme value distribution. $y_{ijt}$ denotes individual $i$ choosing alternative $j$ in choice scenario $t$. The probability of individual $i$ choosing alternative $j$ in choice scenario $t$ given ${\beta }_i$ is

$$L_i\left(y_i|x_i,{\beta }_i\right)={\Pi }_{t=1}^T{\Pi }_{j=1}^J{\left[\frac{\mathit{exp}\left(x_{ijt}{\beta }_i\right)}{{{\displaystyle \sum }}_{l=1}^J\mathit{exp}\left(x_{ljt}{\beta }_i\right)}\right]}^{y_{ijt}} .$$

(2)

The likelihood function could then be defined as:

$$L={{\displaystyle \int }}_{\theta \in \Theta }{L}_i\left(y_i|x_i,{\beta }_i\right)f\left({\beta }_i|\theta \right)d{\beta }_i$$

(3)

where $f(\cdot )$ is the density function of a normal distribution with parameter $\theta \in \Theta$. To simulate the integral in Equation (3), we applied 1000 Halton draws of ${\beta }_i$ from the distribution $f\left({\beta }_i|\theta \right)$. The parameters were estimated by using Maximum Likelihood. The mixed logit models were estimated using the R package “mlogit” [22].

The WTP for attribute k was then defined as

$$WT{P}_k=-\frac{{\beta }_k}{{\beta }_p}$$

(4)

where ${\beta }_k$ is the marginal utility of attribute k and ${\beta }_p$ is the marginal disutility from price. Since both ${\beta }_k$ and ${\beta }_p$ were estimated, the standard error of WTP was then estimated using the Delta Method.

Furthermore, the main focus of this study was to understand consumer preferences for sustainable agriculture programs. We employed a latent class logit model (see Boxall and Adamowicz [23] and Greene and Hensher [24]) to identify market segments based on consumers’ preference heterogeneities and demographics. The latent class analysis assumes that consumers can be segmented into a few classes, and the preferences are heterogeneous across different classes, while members of each class have homogeneous preferences. The purpose of the latent class logit model is to identify market segments in terms of consumer preferences for sustainable agriculture programs.

Suppose individual $i$ belongs to class $q\left(q=\left\{1,\dots ,Q\right\}\right)$ with the probability

$${\pi }_{iq}=\frac{\mathit{exp}\left(z_i{\gamma }_q\right)}{{{\displaystyle \sum }}_{l=1}^Q\mathit{exp}\left(z_i{\gamma }_l\right)}.$$

(5)

In Equation (5), $\gamma$ denotes vectors of parameters with ${\gamma }_1$ set to 0, and $z_i$ denotes the demographics of individual $i$. Since individuals within the same group are assumed to have a homogeneous taste parameter, the distribution of taste parameter ${\beta }_{\mathrm{i}}$ for individuals in class $q$ can be specified as $f\left({\beta }_i|\gamma \right)={\pi }_{iq}$. We can then write individual $i$’s contribution to the likelihood function as

$$L_i\left(y_i|x_i,z_i\right)={{\displaystyle \sum }}_{q=1}^Q{\pi }_{iq}\left\{{\Pi }_{t=1}^T{\Pi }_{j=1}^J{\left[\frac{\mathit{exp}\left(x_{ijt}{\beta }_q\right)}{{{\displaystyle \sum }}_{l=1}^J\mathit{exp}\left(x_{ljt}{\beta }_q\right)}\right]}^{y_{ijt}}\right\}.$$

(6)

The parameters were estimated by using maximum likelihood. The latent class logit model was estimated with R package “gmnl” [24].

3. Results

3.1. Summary Statistics

Table 5. Summary Statistics for Choice Experiment Participants.

		CE 1		CE 2		ACS (2018)
Variables		Mean	S.D.	Mean	S.D.
Age		50.58	16.34	49.70	16.39	38.20 (Median)
Household Size		2.56	1.35	2.59	1.35	2.63 (Mean)
		Freq.	%	Freq.	%	%
Female		487	51.10	538	71.07	49.20
Male		466	48.90	219	28.93	50.80
Education	Graduate Degree	147	15.43	88	11.63	12.60
	Some Graduate School	40	4.20	24	3.17
	College Diploma	287	30.12	231	30.52	28.60
	Some College	285	29.91	215	28.40	20.30
	High School Diploma	175	18.36	184	24.31	26.90
	Some High School or Less	19	1.99	15	1.98	11.70
Marital Status	Married	508	53.31	415	54.82	47.75
	Not Married	445	46.69	342	45.18	52.25
Income	$15,000 or under	78	8.67	108	14.27	10.60
	$15,001–$25,000	111	12.33	76	10.04	9.00
	$25,001–$35,000	91	10.11	102	13.47	8.90
	$35,001–$50,000	142	15.78	119	15.72	12.40
	$50,001–$65,000	142	15.78	117	15.46	30.00 ($50,001–$100,000)
	$65,001–$80,000	108	12.00	81	10.70
	$80,001–$100,000	95	10.56	71	9.38
	$100,001–$150,000	84	9.33	64	8.45	15.00
	Over $150,000	49	5.44	19	2.51	14.20
With Children	Yes			220	29.06	37.72
	No			537	70.94	62.28
Employment Status	Full time			267	60.54	60.09
	Part time			105	23.81	17.02
	Others			69	15.65	22.89

presents the summary statistics for participants of the two versions of CEs. Participants of both versions of CEs were consumers who conducted grocery shopping at least twice a year. In order to make Mixed Logit and the latent class analysis comparable, the second version of CE was restricted to participants who provided information on the key demographic variable summarized in Table 5 for CE 2 (After excluding infrequent shoppers, those who did not completed the survey or failed to provide information crucial to the estimation, a total of 1238 participants were included.). As indicated in Table 5, the majority of participants were frequent grocery shoppers, i.e., they shopped groceries at least biweekly. The final sample size of CE version 1 was 1010. The average age was about 51. About half of participants were female, and 53% of participants were married. The average household size was two to three people. Most of participants had less than some college education. The median household income was around $50,000 per year.

The second version of the CE included additional demographic variables, such as employment status and if the household had at least one child or not. The summary statistics for participants in the second version of CE are also shown in Table 5. In total, 757 participants completed the survey. The average age of participants was 50. Among these participants, 71% was female, 55% was married, and 71% had no children under 12 years old living in the same family. The average household size was two to three people. Most of participants had less than some college education, and the income distribution was similar to that of participants who participated in the first CE. Besides, 61% of participants were employed full-time, 24% were part-time.

We also included the census data from the American Community Survey (ACS) in 2018 as the reference. Due to the fact that our samples were restricted to those who were primary shoppers in a household, participants’ demographics in our samples deviated from the census data in certain aspects. For instance, in CE version 2, there were more female participants, and more participants tended to be married.

3.2. Estimation Results

3.2.1. Consumer Preference for Sustainable Certification and Farmers’ Engagements

The first CE was designed to measure consumers’ preference for general sustainable certification and farmers’ engagements in certification programs. To reduce the possibility of numeric overflow of the $\mathit{exp}$ () function (The maximum finite number of a 64-bit machine is approximately exp(709.7827).), we divided farm miles by 1000. For the mixed logit estimation, we allowed all coefficients to be random without correlation.

In

Table 6. Mixed Logit Coefficients, Choice Experiment Version One.

		Coefficients
	Variables	Coef	Std. Error
	Intercept	1.929	0.567	***
Conventional Product	Organic	0.410	0.209	**
	Sustainable	0.475	0.155	***
No Certification	Independently certified	−61.282	732.290
	Participatory certification	0.352	0.161	**
	USDA certified	0.529	0.165	***
	Farm miles	−0.024	0.065
	Price	−0.778	0.243	***
	Standard Deviations
	Intercept	1.267	0.732	*
	sd.Organic	0.007	13.136
	sd.Sustainable	0.548	1.146
	sd.Independently certified	−146.470	1729.100
	sd.Participatory certification	−0.009	7.597
	sd.USDA certified	0.015	7.389
	sd.Farm miles	−0.019	1.887
	sd.Price	0.007	2.208

Note: Log Likelihood = −6358.3. First section of the table summarize the mean coefficients, and the second section summarize the standard deviation coefficients. * p < 0.1, ** p < 0.05, *** p < 0.01.

, we present the results for the mixed-logit model, and all product attribute coefficients were assumed to be random without correlation with each other. Consumers’ marginal utility gain from sustainable agriculture and organic products is statistically significant and positive, and the magnitude is slightly higher for sustainable compared to organic. In addition, although compared to the USDA certified products consumers’ marginal utility gain is smaller for participatory certifications, i.e., the programs that encourage farmers to have a more active role in certification processes, participants have positive marginal utility for participatory certifications; however, independently certified programs do not induce statistically significant marginal utility. Mixed logit was used to investigate if individuals’ taste parameters for the studied attributes varied significantly. The insignificant standard deviations for organic, sustainable, certification, and food miles indicate individuals’ taste parameters do not vary significantly for these attributes; however, we did find the standard deviation for the intercept is significant, which indicates individuals’ preferences and WTP for conventional sweet corns vary significantly.

Table 7. Willingness-to-pay Premium for Attributes, Choice Experiment Version One.

		Estimate	SE	2.50%	97.50%
Conventional Product	Organic	0.528	0.226	0.085	0.971
	Sustainable	0.610	0.159	0.299	0.922
No Certification	Independently Certified	---	---	---	---
	Participatory Certified	0.452	0.150	0.158	0.746
	USDA certified	0.680	0.226	0.237	1.123
	Farm miles (per 1000)	---	---	---	---

Note: The willingness to pay (WTP) standard error was evaluated using the Delta Method. “---” means the WTP is not calculated due to the attribute’s insignificant coefficient.

summarizes the WTP premium for the product attributes. We provided both the mean and the confidence interval at the 95% significance level. We derived the WTP from the mixed logit model presented previously. On average, consumers were willing to pay $0.53 and $0.61 for organic and sustainable certified canned corn compared to the conventional counterpart, respectively. The two WTP premiums were significant at the 95% significance level. The WTP premium for independent certification was statistically insignificant, while the WTP premium for participatory certification and USDA certification were both statistically significant. Consumers were willing to pay, on average, about $0.45 and $0.68 premiums for participatory and USDA certifications, respectively. The WTP for farm miles was statistically insignificant.

3.2.2. Consumer Preference for Specific Farm Program Features

A mixed logit model was employed to analyze the second CE data to understand consumer preference for specific farm program features. All program feature coefficients were assumed to be normal distributions without any correlation between each other. The estimation results for the mixed logit model are shown in

Table 8. Mixed Logit Coefficients, Choice Experiment Version Two.

		Coefficients
Reference	Variables	Coef	Std. Error
	Intercept	104.218	4.186	***
Managers tell farmers what is required to participate.	Farmers advise program managers on program requirements and activities.	8.632	1.645	***
	Farmers participate to learn what is required to meet consumer demands.	8.186	1.682	***
Farmers must seek out scientific information on their own.	The program communicates scientific information to farmers.	6.870	1.495	***
	The program funds science that will increase the sustainability of farmer practices.	8.122	1.960	***
The program focuses on helping farmers produce crops more sustainably.	The program helps farmers create new market opportunities.	−0.421	1.623
	The program helps farmers and processors reach more consumers.	−3.164	2.277
Farmers declare that they are sustainable with no on-farm measures required.	Farmers in program must demonstrate use of sustainable practices.	12.562	1.840	***
	Measures of on-farm practices and consumer buying decisions are used to measure sustainability.	11.301	2.539	***
The program develops grower sustainability scorecards that will be provided to processors and grocery chains.	The program provides popular materials to food processors and grocery chains on sustainability to distribute to consumers.	−4.484	1.694	***
	The program provides facts on farmer use of sustainable practices that food processors and grocery chains can communicate to consumers.	4.336	2.145	**
	Price	−37.909	3.703	***
	Standard Deviations
	Intercept	82.369	4.809	***
	sd. Farmers advise program managers on program requirements and activities.	−9.932	6.109
	sd. Farmers participate to learn what is required to meet consumer demands.	−2.461	7.998
	sd. The program communicates scientific information to farmers.	−43.283	7.508	***
	sd. The program funds science that will increase the sustainability of farmer practices.	1.306	8.972
	sd. The program helps farmers create new market opportunities.	−31.875	6.883	***
	sd. The program helps farmers and processors reach more consumers.	40.361	8.679	***
	sd. Farmers in the program must demonstrate the use of sustainable practices.	1.328	5.682
	sd. Measures of on-farm practices and consumer buying decisions are used to measure sustainability.	−32.123	7.713	***
	sd. The program provides popular materials to food processors and grocery chains on sustainability to distribute to consumers.	−12.288	6.836	*
	sd. The program provides facts on farmer use of sustainable practices that food processors and grocery chains can communicate to consumers.	−0.798	7.595
	Price	38.854	2.971	***

Note: Log Likelihood = −6174.2. First section of the table summarizes the mean coefficients, and the second section summarize the standard deviation coefficients. * p < 0.1, ** p < 0.05, *** p < 0.01.

. As mentioned previously, each program attribute is a dummy variable, and Table 2 shows the base attribute level.

Compared to the base group, the coefficients for two attributes representing a higher level of farmer engagement in the sustainability program were both significant and positive, indicating that consumers value a farmer’s engagement. This result was consistent with what we found using the first CE. The positive and significant coefficients of the attributes related to the role of science suggested that consumers preferred programs to communicate scientific information to farmers or fund scientific research. Furthermore, compared to the program focus on helping consumers to produce more sustainably, programs that focused on marketing did not induce additional utility. This indicates that participants viewed marketing promoting and sustainable producing as being statistically indifferent.

The preference for measurement of sustainability was statistically significant; consumers preferred programs showing that farmers can demonstrate their practices are sustainable, and on-farm practices and consumer buying decisions are used to measure sustainability. Programs that provide popular materials to distribute to consumers did not induce a higher marginal utility, compared to programs that develop sustainability scorecards. On the other hand, programs that provide facts on farmers’ sustainable practices induced a higher utility than sustainability scorecards. The coefficient of price was negative and significant, indicating that price was an important factor when participants make a decision.

The WTPs for program features derived from the mixed-logit model are presented in

Table 9. Willingness-to-pay Premium for Attributes, Choice Experiment Version Two.

Reference Program Attribute	Program Attribute	Estimate	SE	2.50%	97.50%
Managers tell farmers what is required to participate.	Farmers advise program managers on program requirements and activities.	0.228	0.038	0.152	0.303
	Farmers participate to learn what is required to meet consumer demands.	0.216	0.042	0.134	0.298
Farmers must seek out scientific information on their own.	The program communicates scientific information to farmers.	0.181	0.041	0.101	0.261
	The program funds science that will increase the sustainability of farmer practices.	0.214	0.047	0.122	0.306
The program focuses on helping farmers produce crops more sustainably.	The program helps farmers create new market opportunities.	---	---	---	---
	The program helps farmers and processors to reach more consumers.	---	---	---	---
Farmers declare that they are sustainable with no on-farm measures required.	Farmers in the program must demonstrate the use of sustainable practices.	0.331	0.039	0.254	0.409
	Measures of on-farm practices and consumer buying decisions are used to measure sustainability.	0.298	0.062	0.177	0.419
The program develops grower sustainability scorecards that will be provided to processors and grocery chains.	The program provides popular materials to food processors and grocery chains on sustainability to distribute to consumers.	−0.118	0.045	−0.206	−0.031
	The program provides facts on farmer use of sustainable practices that food processors and grocery chains can communicate to consumers.	0.114	0.054	0.009	0.220

Note: WTP is derived from the Mixed-Logit Model. Standard error was evaluated using the Delta Method. “---” means the WTP is not calculated due to the attribute’s insignificant coefficient. 3.2.3. Consumers Segmentation-Latent Class Logit Estimation Results.

. The base levels are listed in the first column of the table. Compared to sweet corns produced by the program where managers tell farmers what is required to participate in a sustainability program, participants were willing to pay $0.23 and $0.22 more per can for sweet corns produced by the programs that encourage farmers to play more active roles. Participants were also willing to pay $0.18 and $0.21 more for canned sweet corn produced with the program communicating scientific information to farmers and program funding science, compared to programs in which farmers seek out scientific information on their own.

Besides, participants were willing to pay premiums of $0.33 and $0.30 for sweet corns produced by the program with explicit measurements of sustainability, compared to self-declared sustainable agriculture programs. Furthermore, the consumer WTP premium for a program that focuses on marketing was insignificantly different from a program that focuses on sustainable agriculture practice. Compared to canned sweet corns produced by a program providing popular materials to consumers, consumers were willing to pay a premium of $0.12 for canned corn by a program with sustainable scorecards, and the WTP was even higher when the program provides facts of sustainable practices, i.e., $0.14 more per can compared to programs with sustainability scorecards.

A latent class logit model was conducted to segment different consumer groups based on their preferences and demographic backgrounds. In empirical studies, information criteria such as the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) are frequently used when choosing the optimal number of latent classes [23]. We found the cases with higher than 4 classes had a singular variance covariance matrix. Thus, in

Table 10. Model Selection for Latent Class Logit Model (Choice Experiment 2).

Classes	LLF	Number of Parameters	AIC2	AIC3	BIC
2	−5691.10	35	11452	11452	11687
3	−5472.00	58	11060	11060	11449

, we show the AIC and the BIC (Nylund et al. [25] showed that the BIC performs better than the AIC, and Dias [26] indicated that the AIC penalty 3 performs better than the AIC penalty 2.) with 2 classes and 3 classes. As Table 10 indicates, the 3 class is preferred than the 2 class, as the AIC2, AIC3, and BIC are lower for 3 classes.

The estimation results of the latent class logit model using 3 classes are presented in

Table 11. Estimation Results from Latent Class Logit Model (Choice Experiment Version 2).

		Class I			Class II			Class III
Base Level of Program Feature Attribute		Coef.	Std. Dev.		Coef.	Std. Dev.		Coef.	Std. Dev.
	Intercept	13.3209	1.7739	***	0.1110	0.3051		1.0197	0.1221	***
	Price	−7.8257	1.1755	***	−1.4755	0.2037	***	−0.0958	0.0752
Managers tell farmers what is required to participate.	Farmers advise program managers on program requirements and activities.	0.5421	0.2654	**	0.2436	0.1758		0.2198	0.0509	***
	Farmers participate to learn what is required to meet consumer demands.	1.2902	0.3314	***	−0.0350	0.2098		0.2591	0.0661	***
Farmers must seek out scientific information on their own.	The program communicates scientific information to farmers.	−0.4611	0.2550	*	0.7099	0.1762	***	0.3179	0.0476	***
	The program funds science that will increase the sustainability of farmer practices.	0.7397	0.4298	*	0.5482	0.1943	***	0.3543	0.0701	***
The program focuses on helping farmers produce crops more sustainably.	The program helps farmers create new market opportunities.	−0.5974	0.2289	***	−0.0669	0.1652		0.0644	0.0499
	The program helps farmers and processors reach more consumers.	−2.0996	0.7387	***	0.0736	0.1762		−0.0287	0.0680
Farmers declare that they are sustainable with no on-farm measures required.	Farmers in the program must demonstrate the use of sustainable practices.	1.9879	0.5849	***	0.4944	0.1605	***	0.3683	0.0461	***
	Measures of on-farm practices and consumer buying decisions are used to measure sustainability.	1.8510	0.6136	***	0.3026	0.2007		0.4037	0.0689	***
The program develops grower sustainability scorecards that will be provided to processors and grocery chains.	The program provides popular materials to food processors and grocery chains on sustainability to distribute to consumers.	−0.3082	0.2810		−0.4356	0.1800	**	−0.1384	0.0544	**
	The program provides facts on farmer use of sustainable practices that food processors and grocery chains can communicate to consumers.	−0.1513	0.3459		−0.0612	0.1794		0.2071	0.0693	***
	Share in the Total Population	19.41%			18.44%			62.15%

Notes: log likelihood = −5472. parentheses * p < 0.1, ** p < 0.05, *** p < 0.01. The reference levels are provided in the first column.

. Similar to Table 9, the column named “base level” lists the base level for the corresponding attribute. Consumers of class I, II, and III consist of 19.41%, 18.44%, and 62.15% of the sample, respectively. First, the majority of participants (81.6%), except class II, preferred farmers to play a more active role in the sustainable program. This result was consistent with our finding in CE version 1 that participants preferred certification that farmers play a more active role than an independent certificate. Second, at a 95% level of significance, participants of class II and III (80.6%) cared about if programs communicate scientific information to farmers or fund sustainable agriculture study. Third, in terms of marketing promotion, none of the three classes preferred programs that focus on promoting market opportunities or reach more consumers to programs that focus on helping farmers produce crops more sustainably. In fact, the program focus on marketing generated negative utility for consumers of class I, and the negative effect was significant at a 1% significance level. However, for participants of class II and III, the marketing-related program coefficients were insignificant, which suggests a statistical indifference between marketing promotion and facilitating sustainable production, rather than an indication that marketing promotion is not important to consumers. Fourth, having a concrete measure of sustainability was the most preferred program practice. Participants of all classes preferred programs that use sustainable practices to programs compared to farmers’ self-declared sustainability. The comprehensive measure of sustainability, which considers both on-farm and consumer buying decisions, was also preferred by participants of class I and III. Lastly, participants in class II and III preferred sustainability scorecards compared to the practice of communicating sustainable measures with processors and grocery chains. Furthermore, providing facts on farmers’ use of sustainable practices was more preferred to sustainability scorecards by consumers in class III. Providing practice facts should be perceived as richer in information by consumers, and this result is consistent to the tendency of demanding more information.

Table 12. Socio-demographic Backgrounds from Membership Function of the Latent Class Logit Model (Choice Experiment 2).

		Class II			Class III
Reference Level	Variables	Coef.	Std. Dev.		Coef.	Std. Dev.
	Intercept	0.5520	0.1919	***	1.1324	0.1635	***
Education: College Degree or Higher	Education: High School or Less	−0.4221	0.1182	***	−0.4017	0.0984	***
	Education: Some College	−0.1179	0.1172		−0.0619	0.0988
Male	Female	−0.1699	0.1043		−0.1315	0.0877
Not Married	Married	−0.0254	0.1067		−0.0401	0.0847
	Household Size	−0.1219	0.0499	**	0.0225	0.0379
High Income ($80,000+)	Low income (<$35,000)	−0.0697	0.1513		0.0677	0.1303
	Median income ($35,001~$80,000)	−0.4851	0.1332	***	−0.3383	0.1135	***
Without Children	With children	0.5184	0.1384	***	0.0993	0.1131
Other Working Status	Working full time	0.1621	0.1156		0.7249	0.0926	***
	Working part time	0.3402	0.1486	**	0.6560	0.1234	***

Notes ** p < 0.05, *** p < 0.01. This table shows differences in demographic backgrounds of the above two groups compared to class I.

presents the effect of demographics on class membership. Table 12 indicates that compared to participants with college or higher degrees, participants with high school or less were less likely to fall into class II and III. Recall that class II and III participants have strong preferences for programs that communicate science with farmers and fund sustainable agriculture studies; such an association between class membership and consumer demographics is reasonable. In addition, classes II and III tended to have higher incomes. Class III tended to have a full-time job. The magnitudes of price coefficients suggested that class II and III were less price sensitive compared to class I, and the price coefficient for class III was not statistically significant.

4. Discussion

Many studies have investigated consumer preferences and WTP for specific sustainable attributes such as local, organic, fair trade, etc. [3,11,27,28]. This study is different from the majority of existing studies because it examines consumer preferences for sustainable agriculture programs. Based on our findings, we make several recommendations on sustainable agriculture programs. First, sustainable agriculture programs should encourage farmers to play a more active role. Although the underlying reason for consumers to encourage inclusiveness may require further studies, previous studies have documented that consumers are motivated to empower farmers. For instance, Hvitsand [29] argued the development of community supported agriculture (CSA) in Norway, as an alternative system to industrialized agriculture and a globalized food system, is driven by the movement of empowering producers and local communities. Second, sustainable agriculture programs should design communication of sustainable agriculture science with farmers and, if possible, fund sustainable agriculture science study as a part of the program mission. Educating farmers about the science of sustainable agriculture is important not only because consumers are willing to pay a premium for such program practices, it is also crucial for promoting sustainable agriculture practices. Mishra et al. [30] studied farmers in Kentucky and found that a lack of knowledge about sustainable farming and associated technology significantly reduces the adoption of sustainable practices. Furthermore, sustainable agriculture programs should have a set of concrete measurements of sustainability and communicate farmers’ sustainable practices to downstream markets. Because sustainable agriculture attributes are largely non-verifiable, provision of the sustainable measures farmers used increases the credibility of the sustainable claims.

Although our results indicate that marking promotions compared to helping farmers produce more sustainably, which supposedly is the focus of sustainable agricultural programs, does not induce consumer WTP premium, this result does not suggest that programs should not take into account the importance of marketing. Instead, our finding should be interpreted as showing that marketing and facilitating sustainable production are not statistically different to the majority of consumers.

5. Conclusions

We conducted two online CEs with U.S. consumers and employed the mixed logit model and the latent class logit model to identify consumer preferences for sustainable attributes and sustainable farm program features. Although we used canned sweet corn as a targeted product, our research provides useful information for designing new sustainability programs for the vegetable industry and beyond.

On average, consumers had a high WTP for sustainable agriculture. We found consumers valued sustainable certification about the same as organic certification. Consumers preferred the certification that encourages farmers’ engagement, while the WTP premium for an independent certification is minimal. Consumers expressed a consistent preference for programs that encourage farmers’ engagements in both versions of CEs. Furthermore, programs promoting science in sustainable agriculture and programs with concrete measurements of sustainable practices were preferred by consumers.

Three segments of consumers were identified based on their preferences and demographic backgrounds in the latent class analysis. The latent class analysis also confirmed that encouraging farmers’ engagement, promoting science, and the majority of the consumers valued concrete measurements of sustainability. As expected, consumers with higher education were more likely to support promoting science on sustainability, and consumers with higher income and education levels were generally less price sensitive.

As a final note, this study is also not without limitations. Because the derived consumers’ WTP are hypothetical, and it might be subject to potential hypothetical bias. However, previous studies have proved the validity of hypothetical CES, and we believe that the main findings, such as consumers’ preference for farmers’ engagement, promotion of science, and concrete measures of sustainable agriculture practice, are robust.