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Article

The Adoption of Low-Input Turfgrasses in the Midwestern US: The Case of Fine Fescues and Tall Fescue

by
Sanchez Philocles
1,
Ariana P. Torres
1,2,*,
Aaron J. Patton
1 and
Eric Watkins
3
1
Department of Horticulture and Landscape Architecture, Purdue University, 625 Agriculture Mall Drive, West Lafayette, IN 47907, USA
2
Department of Agriculture Economics, Purdue University, 625 Agriculture Mall Drive, West Lafayette, IN 47907, USA
3
Department of Horticultural Science, University of Minnesota, 1970 Folwell Avenue, St. Paul, MN 55108, USA
*
Author to whom correspondence should be addressed.
Horticulturae 2023, 9(5), 550; https://doi.org/10.3390/horticulturae9050550
Submission received: 20 March 2023 / Revised: 6 April 2023 / Accepted: 26 April 2023 / Published: 3 May 2023
(This article belongs to the Special Issue Advances in Horticultural Economics, Policy, Business Management and Marketing)
Review Reports Versions Notes

Abstract

:
Fine fescues (Festuca spp.) and tall fescue (Schedonorus arundinaceus) are low-input turfgrass species that perform well under less water, pesticides, and fertilizers when compared to commonly cultivated species in the Midwestern US. There are numerous benefits in increasing the use of low-input turfgrasses: lowering resource usage, reducing maintenance costs, improving the landscape aesthetic, and contributing to residents’ health and general wellbeing. However, increasing the market share of these grasses requires an understanding of what influences buyers to purchase these turfgrasses. These grasses are usually purchased by consumers as seed; however, sod is a preferred method of establishment for many professional end users. To better understand the economic potential of low-input turfgrass sod, we surveyed sod buyers (landscapers, golf courses, sports turf managers) who purchased sod in 2020 to investigate (1) the factors impacting them to purchase low-input turfgrasses, and (2) the factors influencing the quantity purchased of low-input turfgrasses. The results from our model showed that larger businesses are the most likely to purchase low-input turfgrasses, and, once they purchase them, they tend to acquire a larger amount than their smaller counterparts. Landscaping businesses were more likely to purchase low-input turfgrasses, and factors such as availability, distance, drought, and shade tolerance impacted the decision to purchase low-input turfgrasses. Finally, turfgrass density, the ability to purchase directly from the sod grower, and availability impacted the amount of turfgrasses that buyers purchased.

1. Introduction

The US turfgrass industry is expanding due to the aesthetic and functional benefits provided by grasses. The aesthetic benefits of turfgrasses are related to enhancing the beauty of landscapes; in addition, there are social benefits, including quality of life, mental health, and social harmony [1,2]. Lee and Maheswaran [3] showed the correlation between green spaces and a reduction in several determinants of health, such as physical activity, reduction of obesity, and lower stress among users. The functional benefits of turfgrasses are related to the reduction of soil erosion and surface runoff, sequestering carbon, and protecting water quality [4]. However, to maintain their aesthetic and functional values, lawns tend to require inputs (e.g., fertilizers, pesticides, water), which can be detrimental to environmental and human health if overused [5,6]. In addition, fast growing turfgrasses require a high frequency of mowing, which results in an increase of fossil fuel consumption and subsequent emissions [7,8].
Turfgrass is estimated to occupy approximately 40 million acres of land in the US [9], and this amount is expected to increase with the development of urban areas [10]. As the land area covered by lawns continues to grow, it is crucial to select turfgrass species that are well adapted to site conditions and need fewer resources to help mitigate potential adverse environmental impacts. In the northern US, fine fescues and tall fescues have been proposed as turfgrasses that can offer improved abiotic and biotic stress tolerance, and enhanced aesthetic quality under limited inputs. Tall fescue is a single grass species, whereas fine fescues (Festuca spp.) comprise a group of five cool-season grasses used in turfgrass systems: strong creeping red fescue (F. rubra ssp. rubra), slender creeping red fescue (F. rubra ssp. littoralis), Chewings fescue (F. rubra ssp. commutata), hard fescue (F. brevipila), and sheep fescue (F. ovina) [11]. To illustrate their performance as low-input turfgrasses, Watkins et al. [12] demonstrated that fine fescues and tall fescues can maintain visual quality while requiring fewer overall inputs. Fine fescue species have a slower growth rate, which reduces mowing frequency when compared to Kentucky bluegrass (Poa pratensis) [12]. Conversely, tall fescue has deeper roots than Kentucky bluegrass, which allow it to access water deep in the soil profile to avoid drought [13].
Few researchers have investigated the market potential of low-input turfgrasses—most studies are dated and have focused on one category of the turfgrass industry: residential homeowners. In the turfgrass industry, homeowners are categorized as end-users; those at the end of the supply chain enjoying the multiple benefits that turfgrass offers. Homeowners’ preferences may not show the total market potential of low-input turfgrasses because they are typically at the end of the supply chain of the turfgrass industry. Another important decision-maker is the sod buyer, often a professional. Sod buyers are critical in the turfgrass industry because they are the primary buyers of sod. Sod buyers have the influence to dictate the sod species that farmers decide to grow, and as such, their preferences likely influence the sustainability of the sod industry. Since sod buyers are the ones making purchases for end-users, their decision to purchase specific species of turfgrasses is likely to impact the adoption and diffusion of turfgrasses in commercial and residential landscapes. To date, the literature has failed to capture the preferences of sod buyers.
Our study is intended to take a step further from the consumer literature on the turfgrass industry, and build an understanding of the preferences of sod buyers for low-input turfgrasses. Sod buyers in this study include golf courses, athletic facilities, and intermediaries. Major businesses in this category include landscape contractors, garden centers, lawncare services, and general contractors. Understanding the factors affecting the choice of sod buyers to adopt low-input turfgrasses is a prerequisite to the strategic dissemination of more sustainable turfgrasses in the US turf industry.
Findings from this study provide growers, researchers, and extension agents with empirical evidence about key factors in adopting low-input turfgrasses, and how those factors impact the quantity of low-input turfgrasses purchased by buyers. In addition, growers can benefit from knowing which category of buyers is more likely to purchase low-input grasses, the most common types of purchasing agreements among buyers, and the market standards for these species. Using our findings, sod growers and retailers may develop better-targeted strategies to foster trusting relationships with current and potential clients. Finally, researchers can use our results to improve further their understanding of sod buyers’ decision to purchase low-input turfgrasses and continue contributing to US lawns’ long-term sustainability.

2. Literature Review

Researchers have investigated the relationship between adopting new crop varieties and business characteristics. Business characteristics included size, experience (years of operation), and selling agreements. Feder and Slade [14] suggested that size influences the adoption decision among agricultural businesses; however, studies diverge in the unit of measurement of size. We drew from Micheels and Nolan [15] to define business size by the number of employees. It is expected that larger agribusinesses are more likely to innovate than smaller ones. We hypothesized that business size positively impacts sod buyers to purchase low-input turfgrasses.
An important business characteristic influencing the adoption of innovations is a company’s experience [16], which is measured by the company’s age, which is the number of years since the business has been in operation. Researchers [17,18] provided empirical evidence on the positive correlation between the age of a business and innovation, suggesting that agribusinesses with more experience tend to have a higher probability of working to innovate. The explanation is that older companies are more experienced in selecting and employing profitable innovations.
Other factors linking business practices and the adoption of technologies are selling arrangements, including purchasing through contracts and preorders. When modeling the adoption of innovations, contracting tends to reflect the level of risk management in agribusinesses, because contracts facilitate the interaction between stakeholders along the agricultural value chain [19]. Fernandez-Cornejo et al. [20] found that operations using marketing contracts (i.e., verbal, or written agreements between a buyer and a producer) were more likely to be adopters of genetically engineered crops than their counterparts. In the context of low-input turfgrasses, a contract may indicate that sod buyers have specific market standards that they require their suppliers to meet (e.g., order quantity, on-time delivery, purchasing agreements). In addition, contracts can reduce the growers’ uncertainty that these species would be accepted, and buyers can potentially benefit from price fluctuations and supply availability [21]. On the other hand, preordering is defined as the sellers’ practice of accepting orders from buyers before the product is in the market [22]. Huang [23] found preorders to be a major selling arrangement among agribusinesses selling perishable products due to potential long replacement lead times and the short selling periods [24]. Preorders may help sod buyers to manage accurate forecasts, segment the market, and control their inventory before the high peak of the season. Buyers may also benefit in terms of pricing, as preorders are often accompanied by discounts when sellers want to attract customers [24]. To our knowledge, no studies have addressed the impact of purchasing arrangements on the adoption of innovations among sod buyers.
For the turfgrass industry, suppliers’ attributes are critical because suppliers provide sod, which is the commodity used by athletic facilities, golf courses, and intermediaries to render services to their customers. Meijer et al. [25] used renewable energy technologies to showcase that suppliers’ attributes can affect innovation decisions. They found that supplier’s willingness to live up to standards about the timing, quality, and price of delivery can positively impact the adoption of innovation [25,26]. For the turfgrass industry, suppliers’ attributes may include their willingness to negotiate the price of sod, to deliver sod on-time, have different sod species available, and value associated long-term relationships.
The relationship between distance to nearest market and innovation has been reported in the literature [27,28,29,30,31]. Studies have suggested that increasing the distance to the nearest market would negatively impact the adoption of new crops, since increasing distance is associated to lower access to market information, higher transportation costs, and the potential for losses. Conversely, increasing proximity can affect the frequency of communication and enhances the spread of innovative ideas [27]. For the turfgrass industry, the distance between buyer and supplier is critical because of transportation costs and the perishable nature of sod. Sod has a shelf-life of 36–72 h after harvest, and can be damaged by wind, moisture, and temperature [32,33]. Thus, we expect that the proximity between sod suppliers and markets (i.e., buyers) is important for the adoption of low-input turfgrasses.
Previous studies have reported on the influence of sources of information on the adoption of agricultural innovations. The interactions of multiple stakeholders in agriculture and the subsequent emergence of information exchange networks are fundamental for adopting innovative and sustainable practices [34]. Monge and Hartwitch [35] reported on the importance of knowledge and information from extension personnel in influencing decision-making among agribusinesses. Isaac [34] found that agribusinesses that connect with extension agents are more likely to access information, which helps to increase the adoption of new or improved technologies and practices. For the turfgrass industry, Yue et al. [36] investigated homeowners’ attitudes toward different information sources. Homeowners were found to trust information from families, university extensions, and garden centers/hardware stores the most [36].
Lastly, additional factors presented in the literature influencing the adoption of innovation are related to the innovation’s attributes [37]. We expect that the low-maintenance, visual, and functional attributes of low-input turfgrasses can influence their adoption. Hugie et al. [38] reported that homeowners’ preferences are high for low-maintenance attributes such as less mowing, less fertilization, and less irrigation. Visual (density, growth habit, etc.) and functional (disease resistance, tolerance to foot traffic, etc.) attributes are also important attributes of low-input turfgrasses [38].

3. Materials and Methods

3.1. Data Description

The data used in this study came from a 2021 web-based survey of sod buyers located in Midwestern states (CO, DE, IL, IN, IA, KY, MD, MA, MI, MN, MO, ND, OH, PA, SD, TN, VA, WV, and WI) in the US. Sod buyers included landscapers (i.e., garden centers, landscape contractors), golf course superintendents, and sports turf managers. The survey was distributed via Qualtrics between January and April 2021. Participants were contacted via email, and the email addresses of sod buyers were obtained through a two-phased process. First, researchers asked managers of sod buyers’ associations located in the Midwest to share the online survey with their members. The second phase consisted of compiling sod buyers’ email addresses through online searches using publicly available data lists of sod buyers in the Midwest. Based on membership registration, we estimated that the survey was distributed to 1400 different email addresses. The questionnaire was approved by the Institutional Review Board at Purdue University for compliance with ethical standards for human subjects.
The questionnaire took, on average, 30 min to complete. As Dillman et al. [39] suggested, compensation was included in the invitation email as an incentive to increase survey participation. We offered a USD 50 Amazon gift card to the first 100 respondents who completed the survey. Following best practices for data collection [39], three reminders were sent at a 2-week interval to non-respondents. A total of 200 firms completed the survey for a completion rate of 14%, which is an effective rate for studies with similar methodologies [39].
The questionnaire targeted sod buyers who purchased sod in 2020. Participants were asked questions about their business characteristics, supplier’s attributes, preferences for different sod attributes, as well as their perceptions and motivations to purchase turfgrass species. Information collected on business characteristics included quantity of suppliers; number of years of experience by 2020; total sod stored in 2020; if the buyer purchased from sod grower; closest distance from sod supplier to on-site delivery (in miles); season of purchasing the most sod; species of turfgrass purchased; useful sources of information (e.g., university extension, conferences, and turf breeders); number of employees in 2020; and whether or not the buyer is a landscaping business (i.e., landscape contractor, garden center, lawn care, landscape maintenance). We also asked buyers the most common purchasing agreement for sod, including contracts, preordering, spot markets, and retailers.
The survey also included questions related to the preferred supplier attributes, sod attributes, as well as the buyer’s perceptions about and motivations for purchasing turfgrasses. Supplier attributes included the importance of sod availability, distance to their operation, relationship with the supplier, on-time delivery, and the supplier’s willingness to negotiate price. Buyer’s preferences for turfgrasses included maintenance attributes (i.e., mowing frequency, fertilization needs, and weed infestation); visual attributes (i.e., density, color, growth habit); functional attributes (i.e., root development, ability to withstand foot traffic, drought, and shade tolerance); if sod is grown in-state; heat tolerance; summer performance; and disease resistance.
Buyer’s perceptions included whether or not the buyer perceives labor skill, customer retention, environmental regulation, and access to H2B labor as important factors for business success. We used Likert-like scale questions to capture buyer’s perceptions because they are easy to respond to in a web-based survey and can effectively capture farmer perceptions [40]. Perception questions were rated on a five-point Likert-like scale from extremely important (5) to unimportant (1). The variables were dichotomized such that if the participant responds very important (4) and extremely important (5), the variable would be equal to 1, and 0 otherwise.

3.2. Empirical Model Specification

We employed a double-hurdle model to explain sod buyer’s purchase of low-input turfgrasses. The double-hurdle model is a two-stage process that estimated (1) the factors influencing the decision to purchase low-input turfgrasses, and (2) the factors influencing the amount of low-input turfgrasses purchased. The double-hurdle model is an extension of the standard Tobit model, estimating what drives sod buyers to first determine whether they want to purchase low-input turfgrasses (i.e., participation decision), and then the amount of low-input turfgrasses purchased (i.e., the quantity decision). Josephson and Marshall [41] used a similar methodology to investigate the success of small businesses in Mississippi to obtain loans funds to repair damage caused by Hurricane Katrina.
The adoption of low-input turfgrasses refers to the purchase of species that are considered low-input species. In our model, we used the combined purchase of fine fescues or tall fescues as the dependent variable, as those two species were proven to perform well under a lower quantity of inputs in the Midwest [12]. To this end, Equation (1) illustrates the double-hurdle model with the two-stage decision-making process [42], in which a sod buyer hurdles to the second stage if she or he has first purchased fine fescues or tall fescues in 2020 (i.e., the first stage).
f y 2 | x = P r | y 1 = 0 x P r | y 1 = 1 x   f ( y 2 | y 1 = 1 , x )
We followed Duan et al. [43] and Cameron and Trivedi [44] in defining the first decision as a probit regression (Equation (1)) to estimate the probability of a sod buyer to purchase fine fescues and tall fescues (i.e., low-input turfgrass). Equation (2) is a normally distributed probability regression, where y 1 equals 1 if the buyer purchased low-input turfgrass and 0 otherwise; and x 1 is the vector of business characteristics, supplier’s attributes, sod attributes, and buyer’s perceptions.
In the second stage (Equation (3)), we used a continuous value of the combined amount of purchased fine fescue and tall fescue ( y 2 ). The dependent variable y 2 in Equation (3) is observed only if y 1 = 1 in the first stage. Equation (3) used a log-normal least-squares regression for the amount of tall fescue and/or fine fescue purchased, where x 1 is the vector of business characteristics, supplier’s attributes, sod attributes, buyer’s perceptions, and buyer’s motivation to purchase low-input turfgrasses similar to Equation (2) and x 2 is the vector of buyer’s motivation to purchase low-input turfgrasses. Buyer’s motivations to purchase low-input turfgrasses included whether the buyer purchased fine fescues and tall fescues because clients demanded it, to catch up with competitors, to provide new products, and to diversify product mix; or whether they purchased low-input species because of the lower irrigation, fertilization, and mowing requirements. A summary of the variables used in the analysis and descriptive statistics is presented in Table 1.
P r ( y 1 = 1 | x ) = θ x 1 β 1
( l n y 2 | y 1 = 1 , x ) = x 1 β 1 + x 2 β 2 + v

4. Results

4.1. Descriptive Statistics

Descriptive statistics of our study show that less than half of the sod buyers in our sample purchased fine fescues or tall fescue sod in 2020 (47%; N = 94). As expected, our results show that fine fescues and tall fescues are not the most common cool-season turfgrasses in the Midwestern US, compared to Kentucky bluegrass. Buyers, on average, purchased around 91,200 ft2 of fine fescues and tall fescues combined, which is only 3% of total cool-season sod purchased (3,427,236 ft2). On average, the participants in this study purchased sod from two suppliers, and 89% of them had sod growers as their main suppliers. Our results suggest that buyers prefer to buy sod directly from growers instead of using other suppliers such as big-box retailers and independent garden centers. Establishing trusted relationships and the ability to negotiate purchasing agreements may be driving the choice of sod growers as the main suppliers.
The most common purchasing agreement for sod was preordering. About 40% of buyers purchased most of their sod through preordering arrangements, while 34% purchased most of their sod through contract agreements. These results reflect buyers’ preferences to buy sod based on their time of need, instead of having a fixed contract, since preordering agreements may allow buyers to purchase sod when the need arises. The closest distance from sod suppliers to on-site delivery was 57.6 km (35.8 miles). It seems that sod buyers prefer to buy sod from suppliers near their delivery sites, likely buying from suppliers located within their state of operation. Most participants considered the availability of the sod desired (89%) and on-time delivery (90%) to be very or extremely important attributes of sod suppliers. The most important turfgrass attributes valued among sod buyers were the lack of weed infestation (90%), high density (86%), good root development (85%), and the ability to withstand foot traffic (82%).
In terms of perceptions, 67% of buyers considered customer retention important for business success. Moreover, most sod buyers (61%) perceived access to skilled labor to be important for their business success. Conversely, only a few sod buyers considered H2B workers important for business success (16%). The importance of environmental regulations and housing construction was low among sod buyers, as only 42% and 25% perceived it to be important for their business success, respectively. In terms of useful sources of information, most sod buyers in our sample perceived university extension services (68%), which is consistent with Yue et al. [38], who found that homeowners trusted information from university extensions. Finally, more than half of sod buyers indicated that conference and trade expos (59%) are useful sources of information for their business, while only 41% of sod buyers considered turf breeders to be a useful source of information.

4.2. Regression Results

In this section, we present the results of the two-stage, decision-making process of adopting low-input turfgrasses: stage 1 modeled in the purchase of low-input turfgrasses, while stage 2 modeled the amount purchased of low-input turfgrasses. The results of the double-hurdle model addressing the two research questions are presented in Table 2.

4.2.1. Factors Influencing the Purchase of Low-Input Turfgrasses among Sod Buyers

Using a probit model, the first regression investigated the factors influencing the probability of buyers to purchase low-input turfgrasses. Results showed that purchasing sod from an additional supplier increased the probability of purchasing low-input turfgrass species by 4% (p < 0.10). We expect that an additional supplier would increase a buyer’s exposure to different turfgrasses, in addition to the one(s) they tend to procure. From our results, it seems that exposure to new suppliers and increasing the business network may increase the probability of sod buyers adopting new varieties.
Buyers with a sod grower as their main supplier were 27% more likely to purchase fine fescues and tall fescues (p < 0.01). These results suggest that sod growers may be critical drivers for increasing the spreading of low-input sod species in the turfgrass market. In addition, the results show that buyers who purchased most of their sod through contract agreements had a higher probability (16%) of purchasing low-input turfgrasses (p < 0.10). This result is consistent with previous studies, suggesting that contracting can help foster trusting relationships between growers and buyers, which may be crucial to adopting agricultural innovations [45]. Another explanation may be that since fine fescues and tall fescues are specialty turfgrasses, it may be easier for a buyer to set market standards for sod through contract agreements, which may shed light on the preferred characteristics of sod suppliers. As adopters prefer contract agreements, growers should be willing to measure up to the market standards set by buyers.
The results from the probit model illustrate that increasing the quantity of sod stored by 1000 ft2 for over 24 h increased the probability of low-input turfgrass adoption by 0.4% (p < 0.05). We suspect this result might be related to buyer storage capacity, as storage capacity is largely a function of business size [46]. To illustrate, Holder et al. [47] found that bigger operations were more likely to have bigger storage facilities. We propose that storage capacity can be used as a proxy for size, which may explain why bigger operations may be more likely to purchase low-input turfgrasses. We expect that bigger firms may have enough space to store more turfgrasses, or to acquire technologies such as refrigerated trucks to improve turf shelf-life.
Table 2 illustrates that sod buyers who purchased most of their sod during the spring were 13% more likely to purchase tall and fine fescues (p < 0.05). This result suggests that spring may be the best season of the year to market and advertise low-input turfgrasses. This pattern is consistent with the building construction industry, which experiences peak activities during spring and summer seasons [48]. Moreover, tall fescue planted in late spring may do better in the summer than other species, such as Kentucky bluegrass, because of its drought tolerance.
Buyers who tend to primarily purchase Kentucky bluegrass were 20% less likely to purchase low-input turfgrass species (p < 0.01). This may be due to multiple reasons. Firstly, Kentucky bluegrass is a popular turfgrass for lawns in the northern US [49,50], which is the geographic zone of the participants in our study. Secondly, 40% of participants in our survey are golf courses and sports turf managers, who may get better value from Kentucky bluegrass because of its functional attributes, such as cold and traffic tolerance, and the ability to recover quickly after foot traffic. Thirdly, buyers may not feel that low-input turfgrasses would work at their location without first seeing a demonstration of tall fescues and fine fescues being successfully used; buyers would not take the risk of innovating and adding more complexity to their crop portfolio.
Table 2 indicates that landscapers were 19% more likely to adopt low-input turfgrasses than golf courses, sports turf managers, and municipal parks (p < 0.01). Golf courses and sports turf manager have different uses for grasses. Grasses with high traffic tolerance and a quick ability to recover are needed because of the buyers’ athletic purposes. The positive correlation between landscaping businesses and the adoption of fine fescues and tall fescues could be the result of two motives. First, landscaping businesses typically develop close relationships with homeowners, who were found to have preferences for turfgrasses that require lower inputs for maintenance [38,51,52]. Thus, the direct feedback from homeowners may drive landscapers to purchase low-input turfgrass species. Second, landscapers offer different services to a diverse range of clients, such as homeowners, parks, companies, and schools. This heterogeneous demand would motivate landscapers to purchase a wide variety of turfgrasses to cater to the heterogenous clientele, including low-input turfgrasses. This is consistent with Chatterjee and Eliashberg [53], who reported that heterogeneity in clientele could significantly affect the adoption of innovation. In addition, golf courses and sports turf managers have different uses for grasses.
We also found that increasing the distance between suppliers and on-site delivery negatively impacts the adoption of low-input turfgrasses (Table 2). Ten additional miles (16.1 km) from on-site delivery to the supplier decreased the probability of purchasing low-input turfgrasses by 2% (p < 0.01). We expect that longer distances may limit the interaction between buyers and suppliers, which, in turn, decreases the exchange of information, including innovations. For the turfgrass industry, more specifically, distance is a critical factor. To illustrate, the more distant the markets are, the more expensive sod is to ship and the more significant potential for harvest losses [49].
The next set of explanatory variables comprises turfgrass attributes that buyers find important when buying sod. We found that prioritizing drought and shade tolerance attributes increased the probability of low-input turfgrass adoption by 15% and by 11%, respectively (p < 0.10). The importance of these two attributes has been recognized in the literature of non-traditional turfgrasses in the US, which suggests that tolerance to environmental stress is one of the main traits for developing fine fescues and tall fescues among plant breeders [10,51,54]. Specifically, fine fescues and tall fescues are ranked as more shade tolerant [55] and drought resistant and tolerant [56] than Kentucky bluegrass.
Lastly, buyers who reported that their access to skilled labor is important for business success were 14% less likely to purchase low-input turfgrasses (p < 0.05). Hiring and retaining skilled workers is a significant issue in the agricultural sector [57,58], and sod buyers do not seem exempt from this issue. Business managers may perceive a need for additional skilled labor to handle tall and fine fescues because they are specialty turfgrasses, and less information is available on their growing and handling practices compared to Kentucky bluegrass. Therefore, if buyers are already facing issues with skilled labor availability, they seem to be less willing to add a new product to their turfgrass portfolio that may require additional skills or experience.

4.2.2. How Much Low-Input Turfgrasses Did Buyers Purchase?

The second stage of the double-hurdle model investigated the factors influencing the quantity of fine fescues and tall fescues purchased by sod buyers. The results showed that increasing the number of employees (p < 0.01) and the quantity of sod stored for over 24 h (p < 0.01) increased the amount of low-input turfgrasses purchased by sod buyers. This result is intuitive because these two variables were used as proxy for size, implying thar larger companies of sod buyers purchase more low-input turfgrasses. One explanation for this is that larger businesses may have the technologies and resources necessary to deal with the uncertainty of buying more specialized turfgrasses.
Purchasing directly from sod growers (p < 0.01) and prioritizing buying from suppliers with turfgrass availability in the marketplace (p < 0.01) positively impacted the decision to purchase higher quantity of fine fescues and tall fescues. This result reveals the major role that growers play regarding the adoption of sustainable turfgrasses. We expect that sod buyers will purchase more low-input turfgrasses if they are more widely available on the market.
Kentucky bluegrass buyers purchased a lower quantity of low-input turfgrasses (p < 0.01). Since Kentucky bluegrass is the most used cool-season species in the region where we distributed the survey, we expect these buyers would prioritize buying Kentucky bluegrass due to its familiarity, rather than buying other turfgrass species. In addition, buyers who value turfgrass density were less likely to purchase larger quantities of fine fescues and tall fescues (p < 0.10). This response is likely directed specifically at tall fescues and not fine fescues. Density is the measure of the number of aerial shoots per unit area [59]. Under optimum growth conditions, Kentucky bluegrass provides very high turf density with overall high quality aesthetics. Among these three turfgrass species, tiller density is highest amongst fine fescues, followed by Kentucky bluegrass, with tall fescue being the least dense. Thus, buyers see the lower tiller density of tall fescue as problematic and decide against buying this low-input turfgrass.
Finally, sod buyers who purchased low-input turfgrasses to catch up with competitors purchased 6% less low-input turfgrasses than their counterparts (p < 0.01). We expect that competitors represent an external pressure for firms to adopt low-input turfgrasses, which may be driving the lower impact of this variable on the amount of low-input turfgrasses purchased, when compared to other factors in our model.

5. Discussion

Using an online survey, this study provides empirical evidence on (1) the factors that influenced sod buyers to purchase low-input turfgrasses, and (2) how these factors influenced the amount of low-input turfgrasses purchased. Overall, our results showed that the adoption and quantity of fine fescues and tall fescues purchased are positively impacted by firm size, sod suppliers’ attributes, and low-input turfgrass availability. In contrast, the deterring factors for the adoption and the quantity of low-input turfgrasses purchased included distance from sod supplier to on-site delivery, turfgrass density, and the purchase of Kentucky bluegrass.
We found that landscaping businesses were more likely to purchase low-input turfgrasses, when compared to other sod buyers. This result suggests an opportunity to leverage landscaping businesses as key facilitators and disseminators of the benefits of purchasing and planting low-input turfgrasses. Federal and local governments interested in sustainability (e.g., the State Department of Agriculture, watershed districts) should target the landscaping businesses as their potential allies to achieve sustainability goals by increasing the adoption of low-input sod species. For example, the state of Maryland has watershed implementation plan in which they required the use of slow-release N fertilizer on lawns, reduced P in turfgrass fertilizer applications to 1.5%, and taxed lawn fertilizer to disincentive homeowners to purchase fertilizer [60]. One other option could be incentivizing landscapers who do sod installation to use some percentage of low-input turfgrasses in lawns.
Barton and Behe [61] found that 87% of retailers in the green industry are using some traditional media for advertising. Thus, growers can use newsletters, gardening publications, radio/television to promote their low-input turfgrasses. In addition, Lupo [62] found that landscape industry businesses have successfully used social media marketing to help ensure business viability; therefore, social media represents a low-cost tool that growers can use to promote their low-input sod species and acquire some potential clients. Yue et al. [52] found that 30% of US homeowners are low-input conscious. Since homeowners tend to lack professional knowledge about turfgrass maintenance [55,63], growers and landscapers can use social media to promote the attributes of low-input turfgrasses to expand the market share of low-input sod species.
Market availability is critical for the spreading of low-input turfgrasses. Thus, an effective strategy for more dissemination of low-input turfgrasses could be increasing the awareness of fine fescues and tall fescues among sod growers. Blackwell et al. [64] suggested that the more information a grower has regarding new crops and their attributes, the better their abilities to assess the benefits of incorporating new crops in their farms. As a result, the dissemination of studies on the production economics and agronomic practices of low-input sod species could be a relevant strategy to increase the market availability of low-input turfgrasses. Researchers recently published several papers on the agronomic production (i.e., soils type most suited) of low-input turfgrasses compared to Kentucky bluegrass [11,33,65,66,67], and Philocles et al. [68] studied the production economics of tall fescue sod production so growers could understand the capital needs, as well as the financial risk and uncertainty of growing tall fescue. The combination of disseminating financial and agronomic knowledge could help address growers’ barriers for low-input sod. University extension-led trainings and workshops may be a good means of improving the flow of information from researchers to growers.
The fact that sod maintenance attributes did not impact buyers’ decision to purchase low-input turfgrasses is interesting. Our findings are inconsistent with the end-user (i.e., homeowners) literature, in which researchers found that multiple turfgrass attributes (e.g., low mowing frequency, low fertilizing rate, and low water usage for irrigation) could drive the purchase of low-input turfgrasses [38,51,52,69]. It seems that end-users have a demand for low-input turfgrasses, but sod buyers may not seem to be catering to their needs, and rather buying grasses they are more familiar with (e.g., Kentucky bluegrass). Thus, we propose that industry stakeholders and policymakers advocating for the adoption of low-input turfgrasses raise more awareness among buyers about the environmental and economic benefits of low-input turfgrass sod. This could be effective, as studies have suggested that policy pressure and change of public value orientations can influence the behavior of turf managers [70,71].
Our study presents some limitations that should be acknowledged. Some sod species are more available than others in certain states of the US. Although we controlled geographical location in our model, our results may have suffered from the non-exposure bias. The non-exposure bias results from the fact that sod buyers who have not been exposed to low-input turfgrasses could not adopt them, even if they might have done so if their supplier had it available. The observed proportion of buyers who have adopted low-input turfgrasses may not consistently estimate the true population adoption rate because of non-exposure bias, even with a random sample [72]. In addition, we asked participants which species of turfgrasses they bought in 2020. Therefore, the year 2020 was our critical year to determine whether a buyer adopted low-input turfgrasses or not. It is possible that we may have excluded the purchase of low-input turfgrasses that may have occurred before this year. Despite these limitations, we believe this analysis provided key insights into the most salient constraints and drivers influencing the purchase of fine fescues and tall fescues, which can help turfgrass breeders and industry supply chain members better understand the market opportunities and bottlenecks for low-input turfgrasses adoption.

Author Contributions

Conceptualization, S.P., A.P.T., A.J.P. and E.W.; methodology, S.P. and A.P.T.; software, S.P. and A.P.T.; validation, S.P., A.P.T., A.J.P. and E.W.; formal analysis, S.P. and A.P.T.; investigation, S.P. and A.P.T.; resources, S.P. and A.P.T.; data curation, S.P. and A.T; writing—original draft preparation, S.P.; writing—review and editing, S.P., A.P.T., A.J.P. and E.W.; visualization, A.P.T.; supervision, A.P.T.; project administration, A.P.T.; funding acquisition, A.P.T., A.J.P. and E.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The Indiana State Department of Agriculture, through the Specialty Crop Multi-State Program, grant number A337-19-SCMP-18-001.

Data Availability Statement

Subsets of dataset are available at reader’s request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Van den Berg, M.; Wendel-Vos, W.; Van Poppel, M.; Kemper, H.; Van Mechelen, W.; Maas, J. Health benefits of green spaces in the living environment: A systematic review of epidemiological studies. Urban For. Urban Green. 2015, 14, 806–816. [Google Scholar] [CrossRef]
  2. Velarde, M.D.; Fry, G.; Tveit, M. Health effects of viewing landscapes-Landscape types in environmental psychology. Urban For. Urban Green. 2007, 6, 199–212. [Google Scholar] [CrossRef]
  3. Lee, A.C.K.; Maheswaran, R. The health benefits of urban green spaces. A review of the evidence. J. Public Health 2011, 33, 212–222. [Google Scholar] [CrossRef] [PubMed]
  4. Beard, J.B.; Green, R.L. The role of turfgrasses in environmental protection and their benefits to humans. J. Env. Qual. 1994, 23, 452–460. [Google Scholar] [CrossRef]
  5. Carey, R.O.; Hochmuth, G.J.; Martinez, C.J.; Boyer, T.H.; Nair, V.D.; Dukes, M.D.; Toor, G.S.; Shober, A.L.; Cisar, J.L.; Trenholm, L.E.; et al. A review of turfgrass fertilizer management practices: Implications for urban water quality. HortTechnology 2012, 22, 280–291. [Google Scholar] [CrossRef]
  6. Hobbie, S.E.; Finlay, J.C.; Janke, B.D.; Nidzgorski, D.A.; Millet, D.B.; Baker, L.A. Contrasting nitrogen and phosphorous budgets in urban watersheds and implications for managing urban water pollution. Biol. Sci. 2017, 114, 4177–4182. [Google Scholar] [CrossRef]
  7. Kong, L.; Shi, Z.; Chu, L.M. Carbon emission and sequestration of urban turfgrass systems in Hong Kong. Sci. Tot. Environ. 2014, 473–474, 132–138. [Google Scholar] [CrossRef]
  8. Bartlett, M.D.; James, I.T. A model of greenhouse gas emissions from the management of turf on two golf courses. Sci. Total Environ. 2011, 409, 1357–1367. [Google Scholar] [CrossRef]
  9. Milesi, C.; Running, S.W.; Elvidge, C.D.; Dietz, J.B.; Tuttle, B.T.; Nemani, R.R. Mapping and modeling the biogeochemical cycling of turfgrasses in the United States. Environ. Manag. 2005, 36, 426–438. [Google Scholar] [CrossRef]
  10. Ghimire, M.; Boyer, T.A.; Chung, C.; Moss, J.Q. Consumers’ shares of preferences for turfgrass attributes using a discrete choice experiment and the best-worst method. Hortscience 2016, 51, 892–898. [Google Scholar] [CrossRef]
  11. Braun, R.C.; Patton, A.J.; Watkins, E.; Koch, P.L.; Anderson, N.P.; Bonos, S.E.; Brilman, L.A. Fine fescues: A review of the species, their improvement, production, establishment, and management. Crop Sci. 2020, 60, 1142–1187. [Google Scholar] [CrossRef]
  12. Watkins, E.; Fei, S.; Gardner, D.; Stier, J.; Bughrara, S.; Li, D.; Bigelow, C.; Schleicher, L.; Horgan, B.; Diesburg, K. Low-input turfgrasses species for the north central United States. Appl. Turfgrass Sci. 2011, 8, 1–11. [Google Scholar] [CrossRef]
  13. Meyer, W.A.; Watkins, E. Tall fescue. In Turfgrass Biology, Genetics, and Breeding; John Wiley & Sons: Hoboken, NJ, USA, 2003. [Google Scholar]
  14. Feder, G.; Slade, R. The acquisition of information and the adoption of new technology. J. Agric. Appl. Econ. Assoc. 1984, 66, 312–320. [Google Scholar] [CrossRef]
  15. Micheels, E.T.; Nolan, J.F. Examining the effects of absorptive capacity and social capital on the adoption of agricultural innovations: A Canadian prairie case study. Agric. Syst. 2016, 145, 127–138. [Google Scholar] [CrossRef]
  16. Huergo, E.; Jaumandreu, J. Firms’ age, process innovation and productivity growth. Int. J. Ind. Organ. 2004, 22, 541–549. [Google Scholar] [CrossRef]
  17. Teruel-Carrizosa, M. Gilbrat’s law and the learning process. Small Bus. Econ. 2010, 34, 355–373. [Google Scholar] [CrossRef]
  18. Ferreira, J.J.M.; Fernandes, C.I.; Raposo, M.L. The effects of location on firm innovation capacity. J. Knowl. Econ. 2017, 8, 77–96. [Google Scholar] [CrossRef]
  19. Roussy, C.; Ridier, A.; Chaib, K.; Boyet, M. Marketing contracts and risk management for cereal producers. Agribus 2017, 34, 616–630. [Google Scholar] [CrossRef]
  20. Fernandez-Cornejo, J.; Daberkow, S.; McBride, W.D. Decomposing the Size Effect on the Adoption of Innovations: Agrobiotechnology and Precision Farming. In Proceedings of the American Agricultural Economics Association—2001 Annual Meeting, Chicago, IL, USA, 5–8 August 2001. No. 374-2016-19657. [Google Scholar] [CrossRef]
  21. Shi, Z.; Cao, E. Contract farming problems and games under yield uncertainty. Aust. J. Agric. Resour. Econ. 2020, 64, 1210–1238. [Google Scholar] [CrossRef]
  22. Li, C.; Zhang, F. Advance demand information, price discrimination, and preorder strategies. Manuf. Serv. Oper. Manag. 2013, 15, 57–71. [Google Scholar] [CrossRef]
  23. Huang, M.G. Optimal ordering policy for perishable items in an advanced booking system with stochastic reservation cancellations. Int. J. Inf. Manag. Sci. 2015, 26, 271–290. [Google Scholar] [CrossRef]
  24. Tang, C.S.; Rajaram, K.; Alptekinoglu, A.; Ou, J. The benefits of advance booking discount programs: Model and analysis. Manag. Sci. 2004, 50, 465–478. [Google Scholar] [CrossRef]
  25. Meijer, I.S.M.; Hekkert, M.P.; Koppenjan, J.F.M. The influence of perceived uncertainty on entrepreneurial action in emerging renewable energy technology; biomass gasification projects in the Netherlands. Energy Policy 2007, 35, 5836–5854. [Google Scholar] [CrossRef]
  26. Klerkx, L.; Aarts, N.; Leeuwis, C. Adaptive management in agricultural innovation systems: The interactions between innovation networks and their environment. Agric. Syst. 2010, 103, 390–400. [Google Scholar] [CrossRef]
  27. Rogers, E. Diffusion of Innovations, 3rd ed.; The Free Press: New York, NY, USA, 1983. [Google Scholar]
  28. Brown, J.S.; Duguid, P. The Social Life of Information; Havard Business School Press: Boston, MA, USA, 2000. [Google Scholar]
  29. Hagerstand, T. Innovation Diffusion as a Spatial Process; University Chicago of Press: Chicago, IL, USA, 1967; pp. 68–75. [Google Scholar]
  30. Simtowe, F.; Kassie, M.; Diagne, A.; Asfaw, S.; Shiferaw, B.; Silim, S.; Muange, E. Determinants of agricultural technology adoption: The care of improved Pigeonpea varieties in Tanzania. Q. J. Int. Agric. 2011, 50, 325–345. [Google Scholar] [CrossRef]
  31. Mariano, M.J.; Villano, R.; Fleming, E. Factors influencing farmers’ adoption of modern rice technologies and good management practices in the Philippines. Agric. Syst. 2012, 110, 41–53. [Google Scholar] [CrossRef]
  32. Yi, J. Economic Analysis of Turfgrass-Sod Production in Alabama. Master’s Thesis, Auburn University, Auburn, AL, USA, 2012. [Google Scholar]
  33. Braun, R.C.; Watkins, E.; Hollman, A.B.; Mihelich, N.T.; Patton, A.J. Investigation of cool-season species, seeding rate, and nitrogen fertilization in sod production: II. Management and shelf life. Agron. J. 2021, 113, 3460–3474. [Google Scholar] [CrossRef]
  34. Isaac, M.E. Agricultural information exchange and organizational ties: The effect of network topology on managing agrodiversity. Agric. Syst. 2012, 109, 9–15. [Google Scholar] [CrossRef]
  35. Monge, M.; Hartwich, F. Analisis de redes sociales aplicado al estudio de los procesos de innovacion agricola. Redes. Rev. Hisp. Para El Anal. Redes Soc. 2008, 14, 1–31. [Google Scholar] [CrossRef]
  36. Yue, C.; Wang, J.; Watkins, E.; Bonos, S.; Nelson, K.; Murphy, J.A.; Meyer, W.; Horgan, B. Consumer preferences for information sources of turfgrass products and lawn care. Agron. J. 2017, 109, 1726–1733. [Google Scholar] [CrossRef]
  37. Blazy, J.M.; Carpentier, A.; Thomas, A. The willingness to adopt agro-ecological innovations: Application of choice modelling to Caribbean banana planters. Ecol. Econ. 2011, 72, 140–150. [Google Scholar] [CrossRef]
  38. Hugie, K.; Chengyan, Y.; Watkins, E. Consumer preferences for low-input turfgrasses: A conjoint analysis. Hortscience 2012, 47, 1011–1096. [Google Scholar] [CrossRef]
  39. Dillman, D.A.; Smyth, J.D.; Christian, L.M. Internet, Phone, Mail, and Mixed-Mode Surveys: The Tailored Design Method; John Wiley & Sons: New York, NY, USA, 2014. [Google Scholar]
  40. Lusk, J.; Coble, K. Risk perceptions, risk preference, and acceptance of risky food. Am. J. Agric. Econ. 2005, 87, 393–405. [Google Scholar] [CrossRef]
  41. Josephson, A.; Marshall, M.I. The demand for post-Katrina disaster aid: SBA disaster loans and small businesses in Mississippi. J. Contingencies Crisis Manag. 2016, 24, 264–274. [Google Scholar] [CrossRef]
  42. Cragg, J.G. Some statistical models for limited dependent variables with application to the demand for durable goods. Econometrica 1971, 39, 829–844. [Google Scholar] [CrossRef]
  43. Duan, N.; Manning, W.G.; Morris, C.N.; Newhouse, J.P. A comparison of alternative models for the demand of medical care. J. Bus. Econ. Stat. 1983, 1, 115–126. [Google Scholar]
  44. Cameron, A.C.; Trivedi, P.K. Microeconometrics: Methods and Applications; Cambridge University Press: New York, NY, USA, 2009. [Google Scholar]
  45. Kudadjie-Freeman, C.; Richards, P.; Struik, P.C. Unlocking the Potential of Contract Farming: Lessons from Ghana; International Institute for Environment and Development: London, UK, 2008. [Google Scholar]
  46. Greeley, M. Appropriate rural technology: Recent Indian experience with farm-level food grain storage research. Food Policy 1978, 3, 39–49. [Google Scholar] [CrossRef]
  47. Holder, S.H.; Usman, D.S.; Parvin, D.W. Costs of on-Farm Rice Drying-Storage Facilities in Mississippi, 1975; MAFES Research Bulletins; Mississippi Agricultural & Forestry Experiment Station: Verona, MS, USA, 1976; p. 303. [Google Scholar]
  48. Haydu, J.J.; Satterthwaite, L.N.; Cisar, J.L. An Economic and Agronomic Profile of Florida’s Sod Industry; University of Florida: Gainesville, FL, USA, 1998. [Google Scholar]
  49. Su, K.; Bremer, D.J.; Keeley, S.J.; Fry, J.D. Rooting characteristics and canopy responses to drought of turfgrasses including hybrid bluegrasses. Agron. J. 2008, 4, 949–956. [Google Scholar] [CrossRef]
  50. Fry, J.; Huang, B. Applied Turfgrass Science and Physiology; John Wiley & Sons: Hoboken, NJ, USA, 2004. [Google Scholar]
  51. Yue, C.; Hugie, K.; Watkins, E. Are consumers willing to pay more for low-input turfgrasses on residential lawns? Evidence from choice experiments. J. Agr. Appl. Econ. 2012, 44, 549–560. [Google Scholar] [CrossRef]
  52. Yue, C.; Wang, J.; Watkins, E.; Bonos, S.E.; Nelson, K.C.; Murphy, J.A.; Meyer, W.A.; Horgan, B.P. Heterogeneous consumer preferences for turfgrass attributes in the United States and Canada. Can. J. Agric. Econ. 2017, 65, 347–383. [Google Scholar] [CrossRef]
  53. Chatterjee, R.; Eliashberg, J. The innovation diffusion process in the heterogeneous population: A micromodeling approach. Manag. Sci. 1990, 36, 1057–1079. [Google Scholar] [CrossRef]
  54. Yue, C.; Wang, J.; Watkins, E.; Bonos, S.A.; Nelson, K.C.; Murphy, J.A.; Meyer, W.A.; Horgan, B.P. An investigation of trait prioritization in turfgrass breeding programs. HortScience 2017, 52, 1544–1549. [Google Scholar] [CrossRef]
  55. Christians, N.; Patton, A.; Law, Q. Fundamentals of Turfgrass Management; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2017. [Google Scholar]
  56. Braun, R.C.; Bremer, D.J.; Ebdon, J.S.; Fry, J.D.; Patton, A.J. Review of cool-season turfgrass water use and requirements: II. Responses to drought stress. Crop Sci. 2022, 62, 1685–1701. [Google Scholar] [CrossRef]
  57. Schenck, P. The Modern Grounds Maintenance Worker. Master’s Thesis, Montana Technological University, Butte, MT, USA, 2015, unpublished. [Google Scholar]
  58. Taylor, J.E.; Charlton, D.; Yunez-Naude, A. The end of farm labor abundance. Appl. Econ. Perspect. Policy 2012, 34, 587–598. [Google Scholar] [CrossRef]
  59. Turgeon, A.J. Turfgrass Management, 8th ed.; Pearson Prentice Hall: Hoboken, NJ, USA, 2008. [Google Scholar]
  60. Landschoot, P.J.; Carroll, M.J.; Goatley, J.M.; Turner, T.R. Turfgrass nutrient management and regulatory issues in the Cheasapeake bay watershed. Int. Turfgrass Soc. Res. J. 2017, 13, 75–85. [Google Scholar] [CrossRef]
  61. Barton, S.S.; Behe, B.K. Retail promotion and advertising in the green industry: An overview and exploration of the use of digital advertising. Hortscience 2017, 27, 99–107. [Google Scholar] [CrossRef]
  62. Lupo, C.V. Social Media Marketing Strategies in Landscape Industry Small Businesses; Walden University: Minneapolis, MN, USA, 2018. [Google Scholar]
  63. Reicher, Z.; Throssell, C. Should I Hire a Professional Lawn Care Service? Purdue University Extension Pub; Purdue University: West Lafayette, IN, USA, 1998. [Google Scholar]
  64. Blackwell, R.; Miniard, P.; Engel, J. Consumer Behavior; Hartcourt College Publishers: San Diego, CA, USA, 2001. [Google Scholar]
  65. Braun, R.C.; Patton, A.J. Species, clover-inclusion, and nitrogen fertilizer effects on sod tensile strength of fine fescue taxa. Agron. J. 2022, 114, 1705–1716. [Google Scholar] [CrossRef]
  66. Braun, R.C.; Watkins, E.; Hollman, A.B.; Mihelich, N.T.; Patton, A.J. Investigation of cool-season species, seeding rate, and nitrogen fertilization in sod production: I. Establishment and sod tensile strength. Agron. J. 2021, 113, 4176–4189. [Google Scholar] [CrossRef]
  67. Braun, R.C.; Watkins, E.; Hollman, A.B.; Mihelich, N.T.; Patton, A.J. Management, harvest, and storage characteristics of low-input cool-season turfgrass sod mixtures. Agron. J. 2022, 114, 1752–1768. [Google Scholar] [CrossRef]
  68. Philocles, S.; Torres, A.P.; Watkins, E.; Patton, A.J. Economics of Tall Fescue Sod Production in the Midwest; Purdue University Extension Pub; Purdue University: West Lafayette, IN, USA, 2022. [Google Scholar]
  69. Yue, C.; Cui, M.; Watkins, E.; Patton, A. Investigating factors influencing consumer adoption of low-input turfgrasses. Hortscience 2021, 56, 1213–1220. [Google Scholar] [CrossRef]
  70. Wallace, V.H.; Batholomew, C.; Campbell, J.H. Turf manager response to changing pesticide regulation. HortScience 2016, 51, 394–397. [Google Scholar] [CrossRef]
  71. Hoyle, H.; Jorgensen, A.; Warren, P.; Dunnett, N.; Evan, K. “Not in their front yard” The opportunities and challenges of introducing perennial urban meadows: A local authority stakeholder perspective. Urban For. Urban Green. 2017, 25, 139–149. [Google Scholar] [CrossRef]
  72. Diagne, A.; Demont, M. Taking a new look at empirical models of adoption: Average treatment effect estimation of adoption rates and their determinants. Agric. Econ. 2007, 37, 201–210. [Google Scholar] [CrossRef]
Table
Table 1. Descriptive statistics of explanatory variables used to investigate the adoption of low-input turfgrasses among sod buyers in the Midwestern US.
Table 1. Descriptive statistics of explanatory variables used to investigate the adoption of low-input turfgrasses among sod buyers in the Midwestern US.
Variable NameMeanStandard DeviationDescription
Fescue0.470.501 = if the sod buyer has purchased fine fescues or tall fescue in 2020, 0 otherwise
Fescue91,233.82396,615.80Quantity of fine fescues and tall fescues purchased in 2020 in square feet
Suppliers2.181.31Number of suppliers the buyer purchased sod from in 2020
Experience39.1831.50Age of the business in number of years
Employees28.9650.01Number of full-time, part-time, and H2B workers in the business in 2020
Sod growers0.890.311 = if the sod supplier is a sod grower, 0 otherwise
Storage5.6128.64Quantity of sod stored for over 24 h in thousands of square feet
Percentage contract Z0.340.471 = if the sod buyer purchased at least half of their sod through contract agreements, 0 otherwise
Percentage preorder Z0.410.491 = if the sod buyer purchased at least half of their sod through contract agreements, 0 otherwise
Percentage retail Z0.040.201 = if the sod buyer purchased at least half of their sod through retail markets, 0 otherwise
Percentage spot market Z0.110.311 = if the sod buyer purchased at least half of their sod through the spot market, 0 otherwise
Spring Z0.250.431 = if the sod buyer purchased at least half of their sod during spring, 0 otherwise
Summer Z0.250.431 = if the sod buyer purchased at least half of their sod during summer, 0 otherwise
Fall Z0.380.491 = if the sod buyer purchased at least half of their sod during the fall, 0 otherwise
Bermudagrass Z (Bermudagrass is not a recommended grass for much of the Midwestern regions of the US. It is expected that only a small number of respondents would buy the species.)0.120.331 = if the sod buyer purchased bermudagrass in 2020, 0 otherwise
Kentucky bluegrass Z0.450.501 = if the sod buyer purchased Kentucky bluegrass in 2020, 0 otherwise
Landscapers Z0.510.501 = if the sod buyer is a landscape contractor, garden center, landscape maintenance or lawncare professional, 0 otherwise
Close distance35.7760.10The closest distance from the sod supplier to on-site delivery in miles
Midwest Z0.690.461 = if the operation is a business in Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, Wisconsin; 0 otherwise
Suppliers’ attributes
Availability Z0.890.321 = if the buyer considers the availability of sod desired to be a very or extremely important supplier attribute, 0 otherwise
Distance Z0.470.501 = if the buyer considers the supplier’s distance to their operation to be very or extremely important, 0 otherwise
Relation Z0.560.501 = if the buyer considers relationship with supplier to be very or extremely important, 0 otherwise
Delivery Z0.900.311 = if the buyer considers on-time delivery to be very or extremely important, 0 otherwise
Price Z0.200.401 = if the buyer considers willingness to negotiate price to be a very or extremely important supplier attribute, 0 otherwise
Sod attributes
Fertilization Z0.480.501 = if fertilization needs are very or extremely important for the buyer when buying sod, 0 otherwise
Weed Z0.900.301 = if weed infestation is very or extremely important for the buyer when buying sod, 0 otherwise
Mowing frequency Z0.440.501 = if mowing frequency is very or extremely important for the buyer when buying sod, 0 otherwise
Density Z0.860.351 = if density is very or extremely important for the buyer when buying sod, 0 otherwise
Growth Z0.500.501 = if upright growth habit is very or extremely important for buyer when buying sod, 0 otherwise
Root development Z0.850.361 = if root development is a very or extremely important for the buyer when buying sod, 0 otherwise
Drought tolerance Z0.710.451 = if drought tolerance is very or extremely important for the buyer when buying sod, 0 otherwise
Shade Z0.440.501 = if shade tolerance is very or extremely important for the buyer when buying sod, 0 otherwise
Local Z0.520.501 = if locally grown is very or extremely important for the buyer when buying sod, 0 otherwise
Disease resistance Z0.740.441 = if disease resistance is very or extremely important for the buyer when buying sod, 0 otherwise
Traffic Z0.820.391 = if the ability to withstand foot traffic is very or extremely important for the sod buyer when buying sod, 0 otherwise
Color Z0.670.471 = if color is very or extremely important for the sod buyer when buying sod, 0 otherwise
Sun tolerance Z0.620.491 = if full sun tolerance is very or extremely important for the sod buyer when buying sod, 0 otherwise
Heat tolerance Z0.700.461 = if heat tolerance is very or extremely important for the sod buyer when buying sod
Summer performance Z0.790.411 = if summer performance is very or extremely important for the buyer when buying sod, 0 otherwise
Buyer’s Perceptions
Extension Z0.680.471 = if the sod buyer considers university extensions to be very or extremely useful for their business, 0 otherwise
Conference Z0.590.491 = if the sod buyer considers conferences and trade expos to be very or extremely useful for their business, 0 otherwise
Turf breeders Z0.410.491 = if the sod buyer considers turfgrass breeders to be a very or extremely useful source of information for their business, 0 otherwise
Labor skill Z0.610.491 = if the buyer considers access to skilled labor to be an important factor for their business success, 0 otherwise
Customer retention Z0.670.471 = if the buyer considers customer retention to be an important factor for their business success, 0 otherwise
Environment Z0.420.491 = if the buyer considers environmental regulation to be an important factor for their business success, 0 otherwise
H2B Z0.160.371 = if the buyer considers access to H2B labor to be an important factor for their business success, 0 otherwise
Housing Z0.250.431 = if the buyer considers growing construction to be very or extremely important for their business success, 0 otherwise
Buyer’s motivations
Competitors Z0.130.341 = if buyers purchased fine fescues and tall fescues to catch up with competitors, 0 otherwise
Clients Z0.340.481 = if the buyer purchased fine fescues and tall fescues because their clients demanded it, 0 otherwise
Crop mix Z0.360.481 = if the buyer purchased fine fescues and tall fescues to diversify crop mix, 0 otherwise
New products Z0.330.471 = if the buyer purchased fine fescues and tall fescues to provide new products to customers, 0 otherwise
Lower fertilization needs Z0.440.501 = if the buyer purchased fine fescues and tall fescues because of lower fertilization needs, 0 otherwise
Lower irrigation needs Z0.390.491 = if the buyer purchased fine fescues and tall fescues because of lower irrigation needs, 0 otherwise
Lower mowing needs Z0.650.481 = if the buyer purchased fine fescues and tall fescues because of lower mowing needs, 0 otherwise
Z The mean is the percentage of participants with that characteristic.
Table
Table 2. Double-hurdle model results of the factors that influence sod buyers to purchase low-input turfgrasses, and factors influencing the quantity of low-input turfgrasses purchased in the Midwestern US.
Table 2. Double-hurdle model results of the factors that influence sod buyers to purchase low-input turfgrasses, and factors influencing the quantity of low-input turfgrasses purchased in the Midwestern US.
VariableFactors Influencing the Purchase of
Low-Input Turfgrasses		Factors Influencing the Amount of
Low-Input Turfgrasses Purchased
Marginal EffectsRobust Std. Err. CoefficientsRobust Std. Err.
Suppliers3.832.06*−0.400.34
Experience1.260.89 −0.010.10
Employees0.160.58 0.340.09
Sod growers26.609.74***5.101.85***
Storage0.370.16**0.030.01***
Percentage contract16.179.62*0.151.71**
Percentage preorder5.738.60 0.301.54
Percentage retail24.1715.68 −0.472.31
Percentage spot market26.0111.11 −0.771.62
Spring13.307.10**−0.731.06
Summer5.378.16 0.711.33
Fall6.526.84 0.761.01
Bermudagrass1.0911.35 0.061.53
Kentucky bluegrass−19.856.29***−2.291.22*
Landscapers19.407.13***1.541.28
Close distance−0.210.08***0.030.02
Midwest−1.777.18 0.480.86
Availability30.6612.31**3.822.09*
Distance−5.056.35 −0.221.02
Relation10.316.37*−0.190.83
Delivery0.4112.17 0.291.55
Price4.828.11 −0.741.28
Fertilization2.017.08 1.321.19
Weed infestation17.6013.05 2.771.89
Mowing frequency−5.017.18 0.140.86
Density−23.2911.73 −2.641.59*
Growth habit−0.616.44 −0.690.80
Root development−11.139.09 −1.311.24
Drought tolerance14.718.22*−3.361.40
Shade tolerance11.216.86*−1.040.97
Local−9.116.38 −1.351.17
Disease resistance−14.009.12 1.421.38
Traffic1.738.74 0.941.35
Color4.047.36 0.301.24
Sun tolerance−2.757.40 0.510.98
Heat tolerance0.349.13 0.781.37
Summer performance7.999.20 0.681.29
Extension−4.636.47 0.011.01
Conference−8.496.00 0.180.79
Turf breeders−12.746.68 −0.391.14
Labor skill−14.056.18**0.371.03
Customer retention5.607.73 1.781.55
Environment6.715.95 −1.371.00
H2B7.248.67 −0.651.11
Competitor-- −5.591.04***
Clients-- −1.201.02
Diversify-- 0.421.11
New Products-- 1.411.14
Fertilization needs-- −0.350.89
Water needs-- 0.341.21
Mowing needs-- 0.650.98
*, **, ***, indicates significance at p < 0.1, 0.05, or 0.01, respectively; marginal effects provide the effect of each variable on the probability of purchasing low-input turfgrasses; (-) variables were not included in the first-stage equation.
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MDPI and ACS Style

Philocles, S.; Torres, A.P.; Patton, A.J.; Watkins, E. The Adoption of Low-Input Turfgrasses in the Midwestern US: The Case of Fine Fescues and Tall Fescue. Horticulturae 2023, 9, 550. https://doi.org/10.3390/horticulturae9050550

AMA Style

Philocles S, Torres AP, Patton AJ, Watkins E. The Adoption of Low-Input Turfgrasses in the Midwestern US: The Case of Fine Fescues and Tall Fescue. Horticulturae. 2023; 9(5):550. https://doi.org/10.3390/horticulturae9050550

Chicago/Turabian Style

Philocles, Sanchez, Ariana P. Torres, Aaron J. Patton, and Eric Watkins. 2023. "The Adoption of Low-Input Turfgrasses in the Midwestern US: The Case of Fine Fescues and Tall Fescue" Horticulturae 9, no. 5: 550. https://doi.org/10.3390/horticulturae9050550

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