Consumers’ WTP for Sustainability Turfgrass Attributes with Consideration of Aesthetic Attributes and Water Conservation Policies

This study evaluates the effectiveness of fine‐tuning a semantic segmentation model to identify and quantify dollar spot in turfgrasses, the most extensively managed and researched disease of turfgrasses worldwide. Using the DeepLabV3+ model, recognized for its capability to segment complex shapes and integrate multi‐scale contextual information, the research leveraged a diverse dataset comprising various turfgrass species, disease stages, and lighting conditions to ensure robust model training. The trained model is able to identify and segment disease instances accurately and precisely, and the results indicate the potential for model‐based assessment to outperform traditional visual assessment methods in speed, accuracy, and consistency. The development of deep learning models on extensive datasets like ImageNet requires significant computational resources. However, by fine‐tuning a pretrained semantic segmentation model, we adapted it for disease segmentation using only a standard personal computer's graphics processing unit. This approach not only conserves resources but also highlights the practicality of deploying advanced deep learning applications in turfgrass pathology with limited computational capacity. The proposed model provides a new tool for turfgrass researchers and professionals to rapidly and accurately quantify this important disease under real‐world growing conditions. Additionally, the findings suggest the potential to apply deep learning algorithms to other turfgrass diseases to support data‐driven decisions. This could enhance disease management practices and improve decision‐making processes for fungicidal treatments, thereby improving the economic and environmental sustainability of turfgrass management.

Leveraging deep learning for dollar spot detection and quantification in turfgrass

Kitchin,

Sneed,

McCall

2024

Maximizing genetic gains across agronomic and consumer preference traits in St. Augustinegrass breeding

Milla‐Lewis,

Gouveia,

Kenworthy

et al. 2024

Combining large multi‐environment trial (MET) datasets to decide which genotypes to move forward in the breeding process can be challenging, especially when dealing with negatively correlated traits. The use of a selection index has long been identified as an effective strategy in these situations. However, the method has found limited application in turfgrass breeding. The objective of this study was to use MET data for St. Augustinegrass [Stenotaphrum secundatum (Walt.) Kuntze] breeding lines evaluated across the southern United States to compare genetic gains achieved with the additive additive genetic index (AI) versus the turf performance index (TPI) incorporating agronomic as well as consumer preference traits. The use of either selection index produced more positive genetic gains across traits than direct selection even in the presence of negative correlations. However, the higher genetic gains obtained with AI versus TPI indicate that the use of an index that weighs traits according to their importance is a better approach for selection. Moreover, under a more stringent selection intensity, none of the best lines identified with AI would have been selected with TPI emphasizing the importance of choosing selection criteria that provide a more nuanced ranking of lines. Additionally, higher heritability values and gains from selection were obtained for turfgrass quality under stress (drought and shade) than under normal conditions indicating that selection under stress environments might be more efficient. Most of the evaluated St. Augustinegrass lines outperformed the checks, further supporting the value of cross‐institutional breeding collaborations.

Why mow?: A review of the resulting ecosystem services and disservices from mowing turfgrass

Patton

2024

Turfgrasses are those grasses that tolerate frequent mowing. The act of mowing turfgrasses, primarily lawn mowing, has received much negative attention primarily due to its labor requirement and the resulting mower emissions. This paper provides a comprehensive review of the benefits and drawbacks of mowing grasses, specifically turfgrasses used on lawns, parks, golf courses, and sports fields, using an ecosystem services and disservices framework. Discussed is the challenge of creating a “one‐size‐fits‐all” approach to selecting the best mowing management practices and sociocultural tensions that exist related to lawn mowing. Discussion also includes the benefits and value gained from mowing turfgrass and strategies to mitigate ecosystem disservices that result from mowing.