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Turfgrass lawns provide many environmental benefits, including temperature moderation, soil stabilization in urban centers, and surface water runoff reduction in the rapidly urbanizing society. Recently, more sustainable management of urban vegetation have arisen to encourage Florida homeowners to shift from high-input (e.g., fertilizer, water) to low-input landscapes through initiatives such as the Florida Friendly Landscapes and Florida Water Star Programs. To support the water conservation programs in Florida, and better understand homeowners’ lawn choices, the objective of this study is to identify and evaluate homeowners’ preferred turfgrass features for lawn choices and understand the context of promoting sustainable landscaping practices in the state of Florida. Approximately 1,000 Florida homeowners were surveyed online ranked their most preferred turfgrass features out of a set of 12 alternatives. A rank-ordered logit model was utilized to evaluate Florida homeowners’ preferences for different turfgrass features and determine if they influence turfgrass selection for Florida homeowners. The results showed that the top features were drought tolerance, color, frequency of mowing, disease resistance, and pest tolerance. Identification and communication of water proficient, low fertilizer input turfgrass cultivars that are tolerant to environmental stresses (e.g., drought, traffic, shade) are needed in Florida. Policymakers should promote strategies to combat drought tolerant cultivars. Extension agents should educate homeowners on grass dormancy and drought tolerance (affecting color change) as well as promote disease resistance and pest tolerant species through the Florida Friendly Landscape and WaterStar Programs.

Turfgrass consumers trusted information from families, university extension, and garden centers the most.Homeowners ranked garden center/hardware stores, followed by families and then lawn care companies as the most useful information sources.Information from university extension and regional water management authorities were considered as trustworthy but less useful. Consumers need adequate information about turfgrasses attributes and lawn maintenance practices when they choose what types of turfgrasses to grow in their home lawns. We conducted online surveys with 2167 homeowners from the United States to explore what information sources they consider as trustworthy and useful regarding lawn care. We found turfgrass consumers trusted information from families, University extensions, and garden centers/hardware stores the most; these information sources were equally trusted. Whereas, they ranked garden centers/hardware stores, followed by families and then lawn care companies as the most useful information sources. Information from University extensions and regional water management authorities were considered as trustworthy but less useful. Other information sources (such as the Internet, books, manufacturer websites, and third party reviews) were considered as the least trustworthy and least useful sources. Homeowners’ socio‐demographic backgrounds affected their attitude toward lawn care information sources. The results of the study have important marketing implications for turfgrass suppliers and can help guide outreach efforts by University extension educators.

Generation of wind power time series is an important foundational task for assisting electric power system planning and making decision. By analyzing the characteristics of wind power persistence and variation, this paper proposes an improved Markov chain Monte Carlo (MCMC) method, identified as the PV-MC method, for the direct generation of a synthetic series of wind power output. On the basis of the MCMC method, duration time and variation features are concluded in PV-MC method, gaining a more comprehensive reflection of wind power characteristics in the generated wind power time series. First, the wind power state series is generated to meet the state transition matrix based on the definition of the wind power state. Then, the time duration of each state in the series is determined by its respective duration character. Finally, the variation characteristic is used to convert the state series to a wind power time series. A significant amount of simulations are performed based on the PV-MC and MCMC methods and are then compared for 25 wind farms at 6 different locations throughout the world. The simulation results show that the PV-MC method offers an excellent fit for the time domain features (persistence and variation characteristic) while holding other statistic features (mean value, variance, autocorrelation coefficient (ACC) and probability density function (PDF)) close to the MCMC method.

Mining the inherent persistence property of the time series of wind power is crucial for forecasting and controlling wind power. Few common methods exist that can fully depict and quantify the persistence property. Based on the definition of the active power output state of a wind farm, this paper describes the statistical persistence property of the duration time and state transition. Based on the results of our analysis of significant amounts of wind power field measurements, it is found that the duration time of wind power conforms to an inverse Gaussian distribution. Additionally, the state transition matrix of wind power is discovered to yield a ridge property, the gradient of which is related to the time scale of interest. A systemaic methodology is proposed accordingly, allowing the statistical characteristics of the wind power series to be represented appropriately.