SportsTurf provides current, practical and technical content on issues relevant to sports turf managers, including facilities managers. Most readers are athletic field managers from the professional level through parks and recreation, universities.
Issue link: https://read.dmtmag.com/i/678624
IRRIGATION & DRAINAGE 34 SportsTurf | June 2016 www.sportsturfonline.com on each field as catch cans at 120 locations where the PS6000 collected soil moisture data (see Figure 1; each black dot represents a sample location). Three catch can audits were conducted on separate days and averaged to account for wind and random fluctuations in irrigation system performance. Spatial maps were created from the 120 sample points in order to compare soil moisture and catch can distributions across each field. Additionally, two other statistics were calculated for further comparison, the lower quartile distribution uniformity and a correlation coefficient. The lower quartile distribution uniformity is one of the most common methods used to determine distribution uniformity of catch can data. It can also be used to measure the distribution uniformity of moisture in the soil profile. The calculation for distribution uniformity (DU) is: Average in lower quartile of data Overall average of data X 100 DU= A DU of 100% would indicate a perfectly uniform distribution. In general, a DU value greater the 70% is considered acceptable and less than 55% is deemed poor. The correlation coefficient (Pearson's r) in this case study represents a measure of dependence between the soil moisture and catch can data (i.e. the direction and strength of their relationship). Pearson's r is between -1 and 1, where a negative value indicates a negative relationship (e.g. when soil moisture goes up the amount of water in the catch cans go down) and a positive value indicates a positive relationship (e.g. when soil moisture goes up the amount of water in the catch cans goes up). The closer r is to -1 or 1, the stronger the negative or positive relationship, respectively. FIELD 1 (SANDY LOAM) The soil moisture and catch can distribution maps for Field 1 are displayed in Figure 2. The soil moisture map represents percent volumetric water content in the soil profile, while the catch can map reveals the amount of water (in ml) that was caught by the catch cans in response to this irrigation system. The white dots represent irrigation heads. T he catch can audit is a common method to determine irrigation distribution uniformity and efficiency of sports fields. In the case of catch can audits, efficiency is defined by how evenly distributed irrigation water is applied across a field. However, overall efficiency is best determined by how well irrigation water infiltrates the soil profile and becomes available to turfgrass roots. Soil type, soil compaction, surface hardness, slope, drainage, and environmental conditions all impact water infiltration and availability. Therefore, solely determining the distribution uniformity of your irrigation system may not tell you whether it is "efficient." Soil moisture sensors have become common tools employed by turf managers to measure volumetric water content of the soil profile (in the upper 3 to 6 inches). Many sampling devices are now equipped with GPS, which permits the user to map the spatial distribution of soil moisture across a field. Soil moisture-based water audits can be conducted in conjunction with catch can audits to provide insight into water fate (i.e. infiltration, runoff, etc.) following application. This article discusses two case studies where catch can and soil moisture- based irrigation audits were compared using current methods of analysis and mapping technology. [Editor's note: For more information on how to conduct and analyze a standard catch can irrigation audit visit the link from STMA.org in the references.] SITE DESCRIPTIONS AND DATA COLLECTION Irrigation audits were conducted on two high school American football fields in Georgia. Field 1 had a sandy loam soil in the top 5 inches (72/16/12% sand/silt/clay, respectively) and field 2 was sand capped in the top 5 inches (94/4/2% sand/silt/clay, respectively) with clay beneath. Neither field had subsurface drainage systems. Soil moisture data were collected and georeferenced (i.e. latitude and longitude coordinates) with the Toro Precision Sense 6000 (PS6000), a mobile multi-sensor sampling device. Soil moisture was measured at both sites one day following a typical irrigation event. Measurements were recorded during a dry period (no natural rainfall); therefore, soil moisture distributions were indicative of the irrigation systems. Petri dishes (2.75 inch diameter and 0.5 inch depth) were used ■ BY CHASE STRAW, GERALD HENRY, PHD & STEPHEN RICHWINE CATCH CAN VS. SOIL MOISTURE-BASED WATER AUDITS ON SPORTS FIELDS