New Technologies for Evaluating Putting Green Surface Characteristics

<p>Golf course putting greens require a high level of inputs predicated on timely, well informed decisions. Putting green quality is ultimately defined by performance of the turfgrass, and this performance encompasses both (i) the health and vitality of the turfgrass plants, and (ii) the ability of the turfgrass to exist as a playing surface, as it interacts with the golf ball. For golf course superintendents, accurately and efficiently assessing moisture levels and nutrient status are critical for guiding maintenance practices. This research sought to examine new ways for measuring each of these parameters, and compared them to ground-truth data and/or industry standard methodology/devices. Putting green moisture levels are typically measured as volumetric water content (θ) using portable time domain reflectometry (TDR) meters; in this research a common TDR meter (FieldScout TDR300, Spectrum Technologies Inc.) was fit with spacer-blocks, so that new measurement depths closer to the putting surface (1.2 and 2.5 cm depth) could be obtained. For nutrient status (as well as overall turf health), green color is often used as a general (and subjective) assessment; in this research, a new smartphone app (GreenIndex+ Turf, Spectrum Technologies) utilizing the dark green color index (DGCI) was compared to (i) visual color ratings, as well as (ii) standard research methodology using digital image analysis (DIA). The modified TDR 300 produced significant (P < 0.0001) linear prediction equations for 1.2 and 2.5 cm depths. The DGCI from the GreenIndex+ Turf app lacked the consistency of standardized DIA. Based on these results, simple modifications to an existing TDR300 meter can provide additional moisture information very close to the putting surface. For DIA, further research and refinements are necessary to improve ambient light adjustments, in order to produce reliable data across a range of different lighting conditions. </p>

Name thesis:degree_name

Master of Science in Horticulture (MS)

Level thesis:degree_level

Thesis

Year dc:date.available

2017

Author dc:creator

• O'Brien, Daniel Patrick

Advisor dc:contributor.advisor

• Karcher, Douglas E.

Contributors dc:contributor

• Brye, Kristofor R.
• Purcell, Larry C.

• Digital Image Analysis
• Golf
• Putting Green
• Technology
• Time Domain Reflectometry
• Turfgrass
• Agricultural Science
• Agronomy and Crop Sciences
• Horticulture

Repository record dc:identifier

https://scholarworks.uark.edu/etd/2614

OAI identifier oai:identifier

oai:scholarworks.uark.edu:etd-4163