A Holland Scientific Crop Circle ACS-430 reflectance sensor attached to a fairway mower collects reflectance data while research plots are being mowed. Reflectance measurements can provide data that can be correlated to turfgrass health, visual quality, color or function. Photo by Michael Carlson
The intensity of management required on golf course turfgrass drives demand for turfgrass care products and economic expenditures (6). Golfers expect turfgrass with lush color, manicured surfaces and long ball roll on putting greens due to the influence
of televised golf courses (1). Golf courses are primarily located in urban areas competing for water with other urban uses and can be viewed as a luxury, making them an easy target for restrictions. Precision turfgrass management (PTM), defined as
precise field applications to target irrigation, fertilizer, pesticide or cultural applications (Figure 1) to meet turfgrass function and aesthetic goals, offers a possibility to increase the resiliency of golf course turfgrass.
Water is the largest and most important input for golf course turfgrass growth (3). Golf courses used an estimated 1.9 million acre-feet of water in 2013, a 22% decrease in water applied from 2005 (3). Total irrigation applied on golf courses varies based
on climate differences throughout the United States. Further increases in the efficiency of irrigation with precision management would help golf courses reduce cost and environmental impact while maintaining ideal golf course playability and aesthetics.
Nitrogen is the highest-volume fertilizer applied on golf courses (2). Golf courses have reduced the total nitrogen fertilizer applied by 35% from 2006 to 2014. Only 21% of the reduction in nitrogen fertilizer applied can be attributed to change in application
rates (4). Increasing the efficiency of nutrient applications is important, as the number of golf courses that reported restrictions on nutrients grew from 8% in 2006 to 24% in 2015 (4). Precision management of nitrogen fertilizer applications
would increase nitrogen use efficiency reducing overapplication onto nontarget areas on or off the golf course.
Diseases, weeds and insects reduce the playability and aesthetics of turfgrass, requiring pesticide applications to prevent or reduce the infection of turfgrass. Golf courses have increased their reliance on fungicides and herbicides by 4% and 2% from
2007 to 2015, respectively, and reduced insecticide usage by 4% during the same period (5). During the same period, 2007-2015, the number of golf courses using integrated pest management (IPM) practices increased by 66%. The IPM practices adopted
included scouting for pests, monitoring weather, rotating pesticides, spot treatments and improved plant health. Precision management coupled with IPM could further increase resiliency of golf courses by increasing the precision of targeted pesticide
applications to control pest outbreaks.
Maintaining consistent playability and aesthetics is a goal of PTM by using geo-referenced sensors and geographic information systems (GIS) to increase the micromanagement of resources for efficient irrigation, fertilizer and pesticide applications. Precision
turfgrass management adopts the precision agriculture concept of applying inputs to increase the site-specific efficiency of irrigation, fertilizer and pesticide applications to increase resiliency and reduce environmental impact on golf courses.
The objectives of this review were to summarize peer-reviewed research on PTM, describe current PTM research and adoption, and propose an agenda for future PTM research priorities.
Figure 1. Decisions regarding inputs related to irrigation, fertility and pesticides can benefit from an approach using precision turfgrass management (PTM).
Materials and methods
More than 270 studies were selected for review based on the following criteria: published in a peer-reviewed journal, available in the English language, conducted in the laboratory or field over the course of at least one year, and included precision
management or technologies and remote sensing studied on turfgrass. The review included studies conducted on plot-scale experiments, operational golf courses and in the laboratory. Relevant studies researching remote-sensing technologies on turfgrass
management at golf courses, sports fields and lawn management systems were included. Turfgrass studies from 2000 to 2022 were reviewed concurrent to the start of research defining PTM up to current day. Articles were searched for in three separate
databases.
The peer-reviewed papers were ordered by publication date and selections started with the most recent published papers. Abstracts were read to determine if papers met the objectives of this review focused on precision management and technology. After
selection, 78 articles were included in this review, including articles that were not found in the database searches.
Results
The research reviewed was split into four groups: GPS, remote sensing, imagery and electrical conductivity. These research studies were used to determine gaps in current PTM research and suggest research priorities to increase PTM adoption on golf courses.
Global positioning system research for precision turfgrass management
Increasing the efficiency of input applications requires geo-referenced sensors to measure and locate turfgrass performance and stressors. Research has quantified the spatial variability of soil volumetric water content and soil electrical conductivity
used to measure clay content on golf courses. The spatial variability of annual bluegrass weevils was documented to be higher in rough height than putting green height turfgrass. Hunting billbug larval population spatial variability was quantified
and used to suggest a 13.1-square-foot (1.2-square-meter) sampling grid should accurately quantify the spatial variability.
Geo-referencing locations of insect populations could be used to develop precision insecticide applications.
Locating disease infestations with geo-referenced imagery and sensors could reduce the negative effects of diseases on turfgrass playability or aesthetics. Dollar spot exhibits a stable pattern of spatial aggregation throughout the growing season on creeping
bentgrass and annual bluegrass, similar to the spatial aggregation of large patch on zoysiagrass. Unmanned aerial vehicle (UAV) imagery can classify areas of spring deadspot on fairways, whereas sprayer hardware and image classification software need
to improve for precise applications. Adopting GPS sampling and sprayers enabled with individual nozzle control could reduce application time and product usage, as sprayers would apply inputs only where desired, whereas this has not been reported.
Figure 2. Mean normalized difference red edge vegetation index (NDRE) data from a selected date in July 2022 from fairway No. 5 at the Jim Ager Memorial Junior Golf Course in Lincoln, Neb. The automated processing of reflectance data could alleviate barriers to producing management decisions for golf courses.
Remote sensing research on turfgrass
Canopy reflectance measures reflected visible and nonvisible light in the electromagnetic spectrum from the turfgrass. Reflectance measurements can provide routine and frequent data that can be correlated to turfgrass health, visual quality, color or
function. Multispectral sensors typically measure multiple wide bands of reflectance from the electromagnetic spectrum, whereas hyperspectral sensors measure many narrow bands of canopy reflectance, increasing data processing time. Multispectral sensors
often lack the fine detail needed to differentiate among stressors, whereas the data are easier to process because of the fewer number of bands measured.
Vegetation indices calculated from reflectance are universal methods of measuring turfgrass performance or stressors. Normalized Difference Vegetation Index (NDVI), which measures reflectance in the red and near-infrared wavelengths, is highly correlated
with visual quality and nitrogen status. Differences in NDVI measurements from turfgrass could indicate drought stress up to 47 hours before visual symptoms. The water band index is a better measure of plant water content because it estimates moisture
limitations in a plant canopy. Vegetation indices can detect differences in turfgrass reflectance caused by stressors but cannot distinguish among stressors.
Only a handful of studies developed models to quantify the relationship of reflectance visual quality, biomass production and irrigation that could be used to increase efficiency of input applications. Kentucky bluegrass biomass production and NDVI were
reported to be positively linearly correlated. Turfgrass plant water content, NDVI, soil-adjusted vegetation index, and Visible Atmospherically Resistant Index (VARI) exhibit nonlinear relationships. Models of vegetation index response to turfgrass
growth and stressors are required to develop decision-support system tools for precision management.
Imagery research for precision turfgrass management
Thermal imagers can detect turfgrass drought stress five days before visual symptoms on creeping bentgrass by detecting rises in canopy temperature. Thermal imagery can be used in conjunction with a stress index to identify timing and amount of irrigation
needed to reduce drought stress on turfgrass. Geo-referenced thermal imagers could provide exact locations for irrigation applications to increase efficiency of water applied.
Visual cameras can provide accurate methods to locate disease symptoms on turfgrass and reduce total amount of fungicides applied. Digital image analysis of camera images is more precise in estimating brown patch infestation on tall fescue than visual
estimation, which could aid in creating prescription fungicide application maps. Classifying spring deadspot on bermudagrass fairways using UAV-based digital image analysis resulted in 51% to 65% less fungicide applied.
Camera-based systems can provide affordable, time-saving tools to assess turfgrass performance. The nitrogen status of bermudagrass and visual quality of tall fescue have been reported to be highly correlated with camera images. Camera-based digital
image analysis of ball lie on fairway- and rough-height turfgrass has been reported to have variability as the Lie-N-Eye system. Imagery can be used with reflectance to determine inputs needed to maintain turfgrass playability.
Digital image analysis can discriminate between turfgrass and weeds to develop prescription herbicide applications on golf course turfgrass. Advanced data science techniques applied for rapid image classification systems can classify broadleaf and grass
weeds in bermudagrass and perennial ryegrass. This technology has been reported to be used to create precision herbicide application maps that reduced herbicide inputs from 20% to 60% compared to broadcast herbicide applications. Imagery can increase
the precision of herbicide applications, but user-friendly software needs to be developed to automate data processing.
Figure 3. A workflow of processing reflectance data to make variable rate nitrogen applications using an Arag-Toro GPS sprayer at the Jim Ager Memorial Junior Golf Course in Lincoln, Neb. Further research is needed to develop biological models to implement variable-rate input applications on golf courses.
Electrical conductivity and ground-penetrating radar precision turfgrass management research
Soil apparent electrical conductivity (ECa) quantifies the distribution and variability of soil salinity, leaching and clay and organic matter content. Soil salinity can be an issue for golf courses that receive recycled water or in geographies with saline
and/or sodic soils. Soil ECa can quantify soil salinity and leach potential, which could help superintendents use site-specific remediation efforts to reduce effects of saline soils on the golf course turfgrass. Geo-referenced ECa reduces time and
money spent on destructive soil sampling. Further research on using geo-referenced ECa sensors to develop soil-based site-specific management units was suggested by researchers.
Ground-penetrating radar can map belowground drainage pipe systems on putting greens and can quantify soil volumetric water content of putting green layers. Researchers also reported that ground-penetrating radar systems reported similar soil volumetric
water content as time domain reflectometry in the sand layer of putting greens. Surface hardness can also be quantified using ground-penetrating radar in less time than using a Clegg impact soil tester. Nondestructive sampling of soil volumetric water
content and soil compaction using ground-penetrating radar is another technology that could help locate areas for site-specific irrigation or cultivation to maintain playability and aesthetics.
Gaps toward adoption of precision turfgrass management
Straw et al. reported that no members of a small group of superintendents interviewed understood how to process data required to incorporate PTM methods on their courses (7). The major barriers of PTM adoption were reported to be the lack of hands-on
experience, attitude toward executing elaborate management schedules, insufficient management of physical resources, and skepticism of proposed benefits. Developing simplistic management methods that do not increase the number of applications will
be required for PTM adoption. Future research needs to assess and quantify the benefits and costs of PTM adoption.
New research should develop models and decision-support systems to determine correct product and applications rates for desired playability and aesthetic goals of each golf course. Only one step-by-step method has been published to create site-specific
management units using free GIS software. Research and development should focus on developing software that automates data processing and recommendations for superintendents.
Minimal research programs have focused on educating golf course superintendents on using software to process and interpret geo-referenced sensor data for prescription input maps. Straw et al. reported that superintendents are unsure whether to dedicate
an employee or hire a company to process and interpret sensor data. Companies should develop software to automate sensor data processing, thereby reducing time and labor required for data interpretation (Figure 2).
Automated sensor data processing for site-specific applications using GPS sprayers will not become commonplace until biological models to interpret data are developed (Figure 3). Advanced data science techniques could decrease time to classify pests and
stress and develop models of prescription applications. Education will be needed on new hardware, software and data science to manage an automated and interconnected golf course of the future.
Conclusions
Methods and technologies associated with PTM are proposed to increase resiliency and provide protocols for golf courses to lower inputs while maintaining function and aesthetics. Of the articles reviewed, 94% documented accuracy of sensors to detect turfgrass
performance and stressors before or during visual symptoms. The remaining 6% of the papers developed models or decision-support systems from sensor data to guide management decisions. Current research has not documented adoption rates of PTM on golf
courses. Research has documented the lack of knowledge among superintendents as a barrier to PTM adoption. Current research has not quantified the benefits of PTM, posing an additional barrier to promoting adoption. Future research should develop
decision-support system tools that integrate sensors, models and precision equipment for precision management. Companies and researchers should focus on automating data collection, processing and interpretation for precision input applications by
GPS and computer-guided sprayers.
The research says
- Precision turfgrass management is proposed to increase golf course resiliency through precision application timing, amounts and location of applications.
- Research so far has focused on measuring performance and stressors of turfgrass rather than developing tools for golf course superintendents to use.
- The lack of knowledge among superintendents about precision turfgrass management poses a challenge toward future adoption.
- Future research should focus on developing precision turfgrass management decision-support systems for golf course turfgrass.
Acknowledgements
This work has been adapted from the following publication: Carlson, M.G., R.E. Gaussoin and L.A. Puntel. 2022. A review of precision management for golf course turfgrass. Crop, Forage & Turfgrass Management. 2022: e20183 (https://doi.org/10.1002/cft2.20183).
Literature cited
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- Haydu, J.J., A.W. Hodges and C.R. Hall. 2006. Economic impacts of the turfgrass and lawncare industry in the United States (FE632). Institute of Food and Agricultural Science Extension, University of Florida (https://doi.org/10.32473/edis-fe632-2006).
- Straw, C.M., W.S. Wardrop and B.P. Horgan. 2020. Golf course superintendents’ knowledge of variability within fairways: A tool for precision turfgrass management. Precision Agriculture 21:637-654 (https://doi.org/10.1007/s11119-019-09687-1).
Michael Carlson, Ph.D., (Michael.carlson@greenkeeper app.com) is the research manager for GreenKeeper App, where he manages all the contracts and proprietary research. Carlson received his bachelor’s and master’s from South Dakota State University and his doctorate from the University of Nebraska-Lincoln. Carlson’s research focuses on precision turfgrass management, nutrient management and application of geographic information systems for precision management of golf course turfgrass.