Precision turf
management: A newwater audit based on
soil moistureT h e application of principles of precision agriculture t o
t u r f g r a s s m a n a g e m e n t has led t o t h e d e v e l o p m e n t of anirrigation audit b a s e d o n soil moisture.
Precision turfgrass management, a relatively
new concept for the golf course industry, isbased on the principles of precision agriculture,
which has been evolving since the early 1990s(2,3,11,12). Foundational principles are:• Site-specific management means that inputs(water, fertilizer, cultivation operations, salinity leaching fraction, etc.) are applied only
where needed, when needed and at the amountneeded. The idea is to foster precise, efficient
application of inputs on smaller areas, such as
the area affected by a single irrigation head.
• Intensive site-specific information is necessary to make wise site-specific decisions. Site
sampling of soil and plant factors takes placeacross the whole area on a close-sample grid inorder to define the degree of spatial variabilityof measured parameters.
• Key soil and plant properties must be measured
to accurately define spatial variability and toinvestigate relationships of parameters (2,3).
• Mobile, multiple-sensor devices are necessary
to measure all factors in a timely manner on

Robert N. Carrow, Ph.D.
Van Cline, Ph.D.

76 GCM August 2011close spacing and across large complex areas
such as golf courses. Mobile devices developed
for precision agriculture are not well adapted
to turfgrass situations, and this has hindered
the development of precision turfgrass management. On smaller areas, such as golf greens,
it is possible to use hand-held units to obtainspatial data.
• All site data are GPS-labeled (global positioning
system), which allows the data to be imported
into powerful geographic information system(GIS) programs for geostatistical analysis, forcomparison of measured parameters at specificlocations to explore relationships, and for development of detailed GIS maps.

A new device
The Toro Co. recently developed a mobile,
multiple-sensor unit specifically designed for use on
turfgrass sites (3,8). The Toro Precision Sense 6000
device has a mapping speed of 2 mph (3.2 kilometers/hour) and covers 2.5 acres (1 hectare)/hour
using an 8 x 8-foot (2.4 x 2.4-meter) sample grid or

Toro Precision Sense 6000 mobile, multiple-sensor device (left) and mapping a golf course fairway (right). Photos courtesy of The
Toro Co.

600 to 700 samples per acre. With a close-sample
grid, GIS programs can accurately calculate theestimated value of measured parameters between
sample locations, which effectively increases thespatial data four-fold. The multiple-sensor device
measures the following GPS-labeled data:• soil moisture as volumetric water content(%VWC) in the surface 0- to 4-inch zone
(0-10 centimeters)• soil salinity in the surface 0 to 4 inches• surface hardness by penetration resistance as
force to insert the probes in the top 0 to 4
inches• plant performance based on spectral reflectance and reported as normalized difference
vegetative index (NDVI), which is a measureof plant density, color and stress• topography slope and aspect at 1-foot (0.3meter) intervals.

The traditional water audit
A new water audit for golf courses based on
soil moisture is a practical field application of precision turfgrass management concepts, in which
the water audit fosters sustainable golf course
management practices though improved wateruse efficiency and conservation. Before discussing this new water audit approach, it is useful tounderstand the traditional water audit method
based on catch-can methodology (13).
The Irrigation Association (9) says thatthrough a traditional irrigation audit (that is, awater audit), "Information about each area's technical characteristics and controller capabilities
should be obtained. An irrigation audit involves
collecting data, such as site maps, irrigation plans
and water use records. Tuning of the irrigation
system is accomplished during the inspection.
Field tests are then conducted to determine thesystem's uniformity and to calculate various zone
precipitation rates."Thus, a water audit has two phases. The firstphase is the collection of existing site informationthat may influence irrigation and the inspectionof the system for repairable malfunctions (1,13).
Information at this stage may include pressure
tests, sources, water-quality tests and flow data.
During system inspection, the Irrigation Association (9) recommends evaluating for:• valve performance• sprinklers that are tilted, sunken, broken,
missing or clogged• nozzles that are incorrect, misaligned or havespray deflected by objects• leak detection and repair• drainage from low sprinkler locations.
For golf courses, many of the items in this firstphase may already be accomplished on a routine
basis, because they are part of a good maintenanceprotocol. However, a high level of system maintenance is not always the case at other turfgrass sites.
As will be addressed later, the new water audit
based on soil-moisture actually aids in implementing the first phase.
The second or field-test phase of a traditional
catch-can water audit is to assess irrigation system
uniformity, where catch-cans are placed in a grid
pattern, the irrigation system is operated under
low wind conditions and water distribution isdetermined. Only selected sites, such as problem
sites, are evaluated in this manner. Major design
issues are of special interest, such as:• improper sprinkler spacing — if only a few
heads are too close or too far apart, they can be
moved, but a consistent pattern of wide spacing that cannot be corrected by installing different heads or nozzles is a major design issue• inadequate zoning or scheduling capability• inadequate piping, pressure or flow rate forsystem operation• outdated or worn-out equipment.
Test results are used to calculate factors important for efficient irrigation operation such as (13):• lower-quartile distribution uniformity (DUl ),a measure of how uniformly water is applied
over an area, where a DU^ greater than 80 isgood; the DU k| is based on the average of allcatch-can samples and the average of the lowest 25% of readings• run-time modifier (RTM), which is used toadjust timing in an irrigation zone to allow
adequate water over the whole site where therun-time modifier increases as DU. decreaseslq• a densogram that visually shows the wettest anddriest areas within the test area and is valuable
for making corrective measures such as changing nozzles to obtain greater uniformity (4).

New soil-moisture-based water audit
The new soil-moisture-based water audit
has been formulated as a decision-support system,
which adds value to the data by combining sitedata acquisition, traditional and geostatisticalanalysis, and GIS map displays into a practicaldecision-making package for the end-user (5). For
data acquisition, the Toro Precision Sense 6000
determines soil moisture (%VWC) and the otherparameters during a dry period when the irrigation system uniformity of water application would

Values (%)

Soil moisture (VWC)
Standard deviation (SD)

<10
10-15
15-20
20-25
25-30
30-35
35-40
40-45
45-50

> 2 SD less1.5-2.5 SD less0.5-1.5 SD less
Mean +/- 0.5 SD
0.5-1.5 SD more
1.5-2.5 SD more
>2.5 SD more

>50

Figure 1. GIS maps of fairway 1 of a golf course presented as soil volumetric water content (VWC) (left) and in standard deviation format (right), which highlights thedriest (pink) and wettest (blue) areas. Tables 2 and 3 and Figures 2 and 3 contain information on irrigation heads 4 and 8. Note the four-fold difference in soil volumetricwater content from <15.1 % to >59.4% VWC (see Table 1).

Soil moisture metrics for fairways
Fairway

Min

Mean

Max

Range

SD

CV

Moisture
uniformity

1

15.08

33.67

59.37

44.3

7.27

21.6%

72%

2

7.24

27.2

66.72

59.48

8.62

31.7%

64%

3

12.92

33.93

57.61

44.68

7.47

22%

71%

5

12.25

31.89

54.62

42.37

7.94

24.9%

70%

7

7.8

28.58

52.92

45.12

10.83

37.9%

51%

8

12.72

31.66

45.3

32.58

6.02

19%

76%

9

15.9

33.86

51.87

35.97

6.23

18.4%

76%

10

12.87

33.92

57.79

44.92

7.82

23.1%

70%

11

10.02

37.94

66,05

56.03

9.25

24.4%

67%

12

9,27

33.62

80.98

71.71

12.53

37.3%

62%

14

12.63

37.48

69.98

57.35

11.7

31.2%

60%

16

11.34

37.53

64.85

53.51

7.34

19.6%

75%

17

13.33

43.85

91.08

77.74

7.56

17.2%

79%

18

14.36

38.9

92.85

78.5

9.37

24.1%

70%

Table 1. Soil moisture (%volumetric water content [VWC]) metrics for each fairway area. Standard
deviation (SD) and coefficient of variability (CV) are statistical measures of soil VWC variability,
where low values of each are best. SM-DU is a measure of soil moisture distribution uniformity
across the fairway, where SM-DU (based on lower quartile) is normally somewhat higher than lower
quartile catch-can distribution of uniformity results. An SM-DU above 85% is good. The irrigation
system on this site is an older one with poor distribution because of irrigation head spacing, headand nozzle issues. Note the data for fairway 1, which is discussed in Tables 2 and 3 and Figures 1,
2 and 3.

78 GCM August 2011

be exhibited. The audit based on the volumetricwater content of the soil considers any factor influencing soil-moisture distribution (irrigation system design, system performance, wind distortion,
runoff, high evapotranspiration [ET] areas, soil
texture, etc.), and the entire area is mapped. Mapping of fairways is especially important for maximizing water-use efficiency, but greens and theirsurrounds and tees can also be mapped as individual units. In this paper we emphasize fairways.
A proprietary software program based on ArcGIS allows traditional and geostatistical analysesof spatial variability of measured parameters (6,7).
Analysis of spatial data, presentations of maps andquantitative metrics are applied at three spatial
levels: across the whole fairway, green/surrounds
or tee; within the influence zone of each irrigation
head; and analysis of head-to-head spacing related
to system performance. GIS map and data presentation and transmittal to the end user is assisted by
Google Earth as well as a written report.
Whole-area spatial maps and analysis
When spatially mapping soil and plant attributes on a golf course, it is imperative to first have a
GPS-tagged map of golf course features — boundaries of fairways, greens, tees, bunkers, water features, topography, etc. Such maps can be enteredinto an irrigation control system. If a golf course
does not already have this GPS information, then it
can be obtained as part of the water audit.

% VWC metrics for fairway 1 irrigation heads
Head ID
No.

Min

Mean

Max

Range

SD

CV

Moisture
uniformity

Table 2. Soil moisture (%volumetric water content [VWC])
on the area of influence or impact for each irrigation head).

metrics for each irrigation head on fairway 1 (the data are based

5

24.75

34.86

43.28

18.52

4.72

13.6%

81%

6

23.58

32.85

53.1

29.53

7.16

21.8%

78%

4

28.66

38.06

46.79

18.13

2.81

7.4%

90%

7

19.06

34.82

48.17

29.11

5.57

16%

78%

soil-moisture distribution uniformity across the area of influence

3

23.23

36.15

48.41

25.18

5.57

15,4%

81%

of the irrigation head, where SM-DU (based on lower quartile)

8

18.11

30.11

50.56

32,45

7.11

23.6%

71%

2

20.48

36.33

51.45

30.97

8

22%

70%

9

18.71

32.79

47.57

28.86

5.87

17.9%

76%

distribution caused by problems with irrigation head spacing

1

15.79

36.73

59.37

43.59

10.34

28.1%

61%

and irrigation heads and nozzles. Irrigation head 4 has very good

10

15.63

30.24

43.47

27.85

6.96

23%

72%

SM-DU, whereas head 8 exhibits a lower SM-DU as illustrated

313

16.85

29.98

41.31

24.46

4.63

15.5%

80%

11

15.08

24.92

31.76

16.68

3.6

14.5%

80%

314

21.42

37.33

48.37

26.95

6.78

18.2%

72%

12

18.57

31.76

40.62

22.05

5.27

16.6%

77%

13

21.46

34.66

39.81

18.35

5.02

14.5%

78%

Standard deviation (SD) and coefficient of variability (CV) are
statistical measures of soil volumetric water content variability, where low values of each are best. SM-DU is a measure ofis normally somewhat higher than lower quartile catch-can
distribution uniformity results. An SM-DU above 85% is good.
The irrigation system on this site is an older one with poorby water patterns in Figure 1. See Figure 2 and Table 3 for more
information on head 8.

%VWC metrics for head 8
Head 8 impact

Moisture uniformity: 71.4%
Mean

CV

Soil moisture

30.1%

23.6%

Compaction

226.5 psi

11.3%

-0.86

Turf vigor

0.867 NDVI

3.1%

0.77

area statistics

Soil moisturecorrelation

Table 3. Soil moisture (%VWC) metrics for the area of influence of head 8 (see map) on fairway 1.
Coefficient of variability (CV) is a statistical measure of soil volumetric water content (VWC) variability, where a low value is best. These data appear as a pop-up on the spatial map for fairway 1
and provide information similar to that in Table 2 as well as information that relates soil volumetricwater content values to penetrometer resistance (COMP) values and turf vigor (NDVI). Because of
the considerable spatial variability in soil volumetric water content in this irrigation head's area ofinfluence, penetrometer resistance increases as soil volumetric water content declines, and NDVI
increases as soil volumetric water content increases. See Figure 2 for soil water distribution awayfrom head 8 by quadrant.
Maximum directional variation: 2 0 . 9 %Figure 2. Average volumetric water content at
different distances from head 8 for each quadrant
and for an average of all quadrants (see Table
2 for additional information provided for eachirrigation head).

Distance (in feet) from sprinkler

and V W C tot is the averageof all soil measure&ments. The SM-DU value normally is similar
to traditional catch-can distribution uniformity(DUj) but somewhat higher (10).
Additional fairway maps to aid in irrigation
decisions or assessing system performance are:• the lower-quartile soil volumetric water content (driest) sample sites — very useful indetermining whether the low volumetric watercontent is caused by irrigation water distribution or soil texture changes• the highest quartile soil volumetric water content (wettest)• the number of heads that cover each area on a
fairway• topography contours, slope and aspect.

Figure 3. Assessment of distance between adjacent irrigation
heads on fairway 1. Any heads that are more than 92.6 feet apart(which is 1 standard deviation above the mean spacing for the
fairway) are red-flagged for closer observation as to whether they
are contributing to soil volumetric water content issues. 1 SD
would identify distances between heads that are within the highest 16% of all spacings on a course. However, on this fairway,even the mean distance between irrigation heads is large, basedon the heads, nozzles and operational pressure used. For thisfairway, the mean distance is 85.0 feet (25.9 meters); the minimum distance is 60 feet (18.2 meters); the maximum distance is116.9 feet (35.6 meters); the standard deviation is 7.6 feet (2.3
meters); and the coefficient of variability is 9%.

Soil volumetric water content and other parameters are presented in both raw data and standard
deviation (SD) GIS maps. The GIS maps are very
useful for identifying areas with the lowest (driest) and highest (wettest) soil volumetric watercontent values (Figure 1). Descriptive metrics
presented for each fairway area aid in evaluating
spatial distribution of soil volumetric water content and irrigation system performance (Table 1).
Descriptive statistics include measures of central
tendency (mean) and data variability (minimum,
maximum, range, standard deviation, and coefficient of variability or CV). Also, moisture uniformity (soil-moisture distribution uniformity,
SM-DU) is reported based on the lower quarter ofsoil volumetric water content data for a fairway or
irrigation-head area of influence, where
SM-DU = 1 0 0 ( V W C l q / V W C J
and VWCj is the average of the lowest quarterof soil volumetric water content measurements,

80 GCM August 2011

Individual irrigation heads
With close-grid mapping, the area of impactof each irrigation head can be characterized using
the same metrics as for a whole fairway (Table2), as well as correlation of soil volumetric watercontent with plant NDVI and soil penetrometer resistance (Table 3, Figure 2). Standards canbe assigned that would red-flag a problem head,
where the SM-DU is especially useful for identifying heads with poor uniformity of distribution.
This is valuable considering the number of irrigation heads on a golf course. In Table 2, for example, irrigation head 8 exhibits a low SM-DU valueof 71% with soil moisture ranging from 18.1% to50.56% volumetric water content, and the highstandard deviation and coefficient of variation
values all illustrate considerable variability in soilvolumetric water content within the area of head
impact. Soil volumetric water content variability
at different distances from the head can be evaluated by quadrant and an average of quadrants to
aid in understanding distribution problems at thesingle-head level (Figure 2).

GIS programs allow distances between adjacent irrigation heads to be determined for a fairway
and surrounds (Figure 3). Distances that exceed
1 standard deviation distance of the average distance between irrigation heads are red-flagged for
closer inspection. The standard deviation distancewould represent about 16% of all the distances
between heads. Information about the distances
between irrigation heads combined with data andmaps of fairways and individual irrigation-head
impact areas can determine whether there are justa few head-spacing problems or whether heads are
consistently spaced too wide for adequate coveragewith existing heads and nozzles. In the latter case,

The research says
A mobile multiple-sensor unit
for conducting a soil-moisture-based
water audit on large areas of turf, suchas fairways, brings precision turfgrassmanagement to golf courses.
The soil-moisture-based water
audit combines site data acquisition,
traditional and geostatistical analysis
and GIS displays.
Soil volumetric water content iscombined with GIS maps to identify thedriest and wettest areas of an area suchas a fairway.
- > Each irrigation head is evaluatedbased on soil water content and distribution patterns in the area of influence
for each head, which allows individual
head problems to be identified.
Distances between irrigation
heads are recorded for a fairway, andexcessive distances are highlighted
to call attention to problem irrigation
heads and spacing.
The focus of the soil-moisturebased water audit is to correct deficiencies in the irrigation system, but NDVI
and salinity data can also be helpful tosuperintendents.

theoretical distribution from different heads andnozzle choices can determine replacement options
or whether the system will be inadequate regardless of reasonable changes. Flagged heads are fieldevaluated for possible problems and fixes.

5. Cline, V., K. Rice, T. Carson and R.N. Carrow. 2011. Intensive, new water-audit approach and decision-support system. ASA Abstracts, American Society of Agronomy meeting, Oct. 16-19, 2011, San Antonio, Texas.

Other features
T h e new soil-moisture-based water audit
focuses on water-use efficiency by assessing andcorrecting issues related to irrigation systemdesign, maintenance and scheduling, but theN D V I and salinity data obtained during spatial
m a p p i n g under drier conditions also are very
valuable. Relations between soil volumetric watercontent and plant N D V I or soil salinity can be
explored because the decision support system
report contains this information. Soil moisturestrongly influences turfgrass performance, salinity spatial distribution (when saline irrigationwater is used), and salinity leaching efficiency.

7. Esri. 2004. ArcGIS 9: Using ArcGIS spatial analyst. Esri,
Redlands, Calif.

In m o d e r n turfgrass m a n a g e m e n t , integrationof cutting-edge science technologies and concepts
is i m p o r t a n t to maintain efficiency of resource
inputs, including water. T h e new soil-moisturebased water audit is an i m p o r t a n t step toward
improved site-assessment methodology to support
sustainable golf course management.
Funding
The Center for Advanced Turfgrass Technology of The Toro
Co. provided research funds for the University of Georgia research.
Acknowledgments
We wish to acknowledge Kathy Rice of the Center for
Advanced Turfgrass Technology for her expertise in developmentof the Decision Support System software package.
Literature cited
1. Carrow, R.N., R.R. Duncan and C. Waltz. 2009. BMPs andwater-use efficiency and conservation plan for golf courses:
Template and guidelines. Developed for Golf Course Superintendents Association of America by University of Georgia, www.
commodities.caes.uga.edu/turfgrass/georgiaturf/Water/
Articles/BMPs_Water_Cons_09.pdf (verified June 21,2011).
2. Carrow, R.N., J. Krum, I. Flitcroft and V. Cline. 2010. Precision turfgrass management: Challenges and field applications for mapping turfgrass soil and stress. Precision Agriculture 11(2):115-134.
3. Carrow, R.N., J. Krum and C. Hartwiger, 2009. Precision
turfgrass management: a new concept for efficient applications of inputs. USGA Turfgrass and Environmental Research
Online 8(13):1-12. http://usgatero.msu.edu/v08/n13.pdf(verified June 21,2011).
4. Center for Irrigation Technology. 2009. Web-site and SPACEProTM program, http://cit.cati.csufresno.edu/ (verified June

21,2011).

82 GCM August 2011

6. Esri. 2004. ArcGIS 9: Using ArcGIS geostatistical analyst.
Esri, Redlands, Calif.

8. Flitcroft, l„ J. Krum, R. Carrow, etal. 2010. Spatial mappingof penetrometer resistance on turfgrass soils for site-specific cultivation. 10th International Conference on Precision
Agriculture Proceedings, July 18-21, 2010, Hyatt Regency
Tech Center, Denver, Colo, www.icpaonline.org/finalpdf/
abstract_202.pdf (verified June 21,2011).
9. Irrigation Association. 2003. Certified golf irrigation auditor.
Irrigation Association, Falls Church, Va.
10. Kieffer, D.L., and T.S. 0'Conner. 2007. Managing soil moisture on golf greens using a portable wave reflectometer.
Proceedings 28th International Irrigation Show, Dec. 9-11,
2007, San Diego, Calif. [CD-ROM] The Irrigation Association, Falls Church, Va.
11. McBratney, A , B. Whelan and T. Ancev, 2005. Future directions of precision agriculture. Precision Agriculture 6:7-23.
12. Sadler, E.J., C.R. Camp and R.G. Evans. 2007. New andfuture technology, p. 609-626. In: B.A. Stewart and D.R.
Nielsen, eds. Irrigation of agricultural crops, 2nd ed. Agronomy Monograph No. 30. Crop Science Society of America,
Madison, Wis.
13. Vogt, M.D. 2011. You can't manage what you don't measure.
Golf Course Management 79(6): 57-60.

GCM
Robert N. Carrow (rcarrow@uga.edu) is a professor in thedepartment of crop and soil science at the Georgia Experiment
Station, University of Georgia, Griffin, 6a., and Van Cline ismanager of turf agronomics and research at The Toro Co.,
Bloomington, Minn.