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  • Author or Editor: Chanjin Chung x
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This study estimates the influence of the coronavirus disease 2019 (COVID-19) pandemic on consumer preferences for turfgrass attributes by analyzing data from two surveys conducted in Jan 2019 and Apr 2021. First, the study estimated a mixed logit model to account for individual heterogeneity in preferences. Subsequently, estimates of the willingness to pay (WTP) were compared between periods before and after the pandemic. To show the impact of consumers’ risk attitudes with respect to climate change on their preference for turfgrass attributes, we re-estimated the model according to the risk attitude groups (i.e., risk-seeking vs. risk-averse). Finally, to examine how consumers’ demographic characteristics and risk attitudes are related to their WTP for improved turfgrass attributes, we estimated a random-effect panel data model for each attribute. Our results showed that, overall, consumers’ WTP increased during the COVID-19 pandemic. We also found that the WTP of risk-averse consumers were mostly higher than those of risk-seeking consumers during both time periods. Furthermore, the increase in the WTP observed among the risk-averse group was greater than the increase of the WTP of the risk-seeking group. Our findings imply that the demand for drought-tolerant and stress-resistant turfgrasses would increase with possible future climate changes and infectious disease outbreaks.

Open Access

This study examines the effect of social learning on new turfgrass variety adoption decisions using data from 231 turfgrass professionals’ Twitter accounts between 1 Jun 2018 and 31 Dec 2019. To determine the social learning effect, we decompose networking effects into social learning, individual-level and group-level similarities, herd behavior, and clustering effects. Our study estimates a spatial autoregressive probit model that directly incorporates the social network structure to account for unobservable networking effects and potential reflection problem. A Bayesian estimation procedure is used to alleviate the convergence problem caused by the complexity of model specification. Empirical results show that the social learning effect positively influences the new technology adoption and was greater than herd behavior effect. The results also suggest that turf professionals rely more on suggestions and information from online social networking among themselves than recommendations from advisors.

Open Access

This study combines a discrete choice experiment and eye-tracking technology to investigate producers’ preferences for sod attributes including winterkill reduction, shade tolerance, drought tolerance, salinity tolerance, and maintenance cost reduction. Our study results show that sod producers valued drought tolerance the most, followed by shade tolerance, winterkill reduction, salinity tolerance, and lastly, a 10% maintenance cost reduction. Choice survey data revealed the existence of attribute non-attendance, i.e., respondents skipped some attributes, but statistical tests detected no clear evidence about the role of individuals’ attention changes on their willingness-to-accept estimates. Estimates using a scale heterogeneity multinomial logit model indicate an overall learning effect as respondents made choices in the survey. Producers’ willingness-to-accept were generally higher than consumers’ willingness-to-pay for the improved sod variety attributes, except for the drought tolerance attribute. However, the rankings for these attributes were the same between consumers and producers.

Open Access

This study estimates potential economic impacts of developing drought- and shade-tolerant bermudagrass (Cynodon dactylon) turf varieties in five southern states: Texas, Florida, Georgia, Oklahoma, and North Carolina. First, estimates are provided for the market-level crop values of the newly developed two varieties for each state. Then, an economic impact analysis is conducted using an input–output model to assess additional output values (direct, indirect, and induced impacts), value added, and employment due to the new varieties. Our results indicate that the two new varieties would offer significant economic impacts for the central and eastern regions of the United States. Under the assumption of full adoption, the two new products would generate $142.4 million of total output, $91.3 million of value added, and 1258 new jobs. When a lower adoption rate is assumed at 20%, the expected economic impacts would generate $28.5 million of output, $18.3 million of value added, and 252 jobs in the region. Our findings quantify the potential economic benefits of development and adoption of new turfgrass varieties with desirable attributes for residential use. The findings suggest that researchers, producers, and policymakers continue their efforts to meet consumers’ needs, and in doing so, they will also reduce municipal water consumption in regions suited to bermudagrass varieties.

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This study compares preference shares of stress-tolerant, low-maintenance, and low-cost turfgrass attributes in five states (Florida, Georgia, North Carolina, Oklahoma, and Texas) in the southeastern and midsouthern United States using the discrete choice experiment (DCE) and the best–worst method (BWM). An online survey was conducted and a mixed logit model (MLM) was used to determine the homeowners’ relative preferences for turfgrass attributes. The results of a survey of 1179 household consumers indicate that the most preferred attribute using either of the methods was low maintenance cost in all the states. Although the relative importance (preference share) by the DCE and the BWM for each attribute is statistically different, both methods yield a similar preference ordering for low-maintenance, drought-tolerant, and saline-tolerant turf, but a different ordering for shade-tolerant and low purchase–price turf. This study provides a framework for turfgrass researchers and producers to invest and expand outreach on desirable turfgrass attributes for homeowners.

Free access