Catch can vs. soil moisture-based water audits

By Chase Straw, Gerald Henry, PhD & Stephen Richwine

The 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 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 on each field as catch cans at 120 locations where the PS6000 collected soil moisture data. 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:



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 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.

It is evident from these maps that there are large amounts of variability (i.e. differences) across this field for both soil moisture and catch can data. Detailed soil moisture and catch can patterns clearly indicate deficiencies in uniformity and efficiency down to individual irrigation heads. Several conclusions can be derived from examining these maps; however, we will only discuss a few. In general, similar distribution patterns exist between the two maps, except for a few areas around some of the irrigation heads. The northern part of the field appears to have the lowest soil moisture values, while the southern part of the field has the highest. Similar conclusions can be drawn from the map of catch can data. However, the northern part of the field appears to be receiving higher levels of water, although the soil moisture map does not reflect that it is infiltrating the soil. The contrasting results in the northern portion of the field indicate that although the area is receiving water, there is some other factor (i.e. soil compaction, localized dry spot, etc.) influencing infiltration into the soil. High soil moisture and catch can values in the southern portion of this field may be due to poor irrigation system design (the last row has an extra irrigation head). The dry area in the central part of the field is likely due to reduced efficiency of the heads in that area.

Further analysis indicates a DU of 61% for soil moisture and a DU of 58% for catch can data. Therefore, this irrigation system is performing at just above poor. The correlation coefficient for the soil moisture and catch can data was 0.45. A positive value was expected, since typically the more irrigation water an area receives the high the soil moisture; however, the strength of the relationship was moderate. This further demonstrates that other factors may be influencing water infiltration.

Field 2 (sand capped)

Unlike Field 1, there are no circular soil moisture or catch can distribution patterns around any of the irrigation heads in the map of Field 2. Low water holding capacity of the sandy soil may have impacted the soil moisture of Field 2, because less runoff and quicker water infiltration is typical for coarse textured soils. Examination of the catch can map reveals that the irrigation system is performing well and distributing water evenly. The design of this system appears to be sufficient, but comparison of the two maps shows conflicting distributions.

Soil moisture is highest in the northern part of the field, specifically in the northeast corner. The catch can map, although somewhat uniform, depicts higher values in the center and southern portion of the field. This might lead one to believe that the slope of the field is responsible for the differences, which is partially correct. The field is crowned, but predominantly directed toward the northern half. Although there is no subsurface drainage within the field, there are drains located around the perimeter. Unfortunately, the drains on the east side of the field were installed behind a track and field runway. This lengthy concrete pad acts as a dam and causes water to puddle in that portion of the field.

The DU for soil moisture and catch can data was 71 and 73%, respectively. Therefore, the irrigation system is performing at an acceptable level. The correlation coefficient for soil moisture and catch can data was 0.34. A positive value was again expected; however, the strength of the relationship was still moderate. This is likely due to the concrete runway blocking drains on the east side of the field and the presence of a 5-inch sand cap on top of a clay subgrade that allows for lateral water movement along the clay layer.


The objective of the case studies was to introduce a new, novel approach to examining irrigation efficiency, the soil moisture based audit, and compare it to the standard catch can audit. Results and interpretation differed substantially between our examples. This was expected due to the variation between sites with respect to soil type, irrigation system design, field construction, and other plant and soil properties that were not measured. The moderate correlation between soil moisture and catch can data suggests that additional agronomic conditions may be affecting irrigation distribution and efficiency. For this reason, further data collection and site analysis (i.e. surface hardness, localized dry spot, etc.) may be warranted.

The current method of irrigation system evaluation, which relies solely on DU values to interpret uniformity, may not be sufficient. DU values for soil moisture and catch can data were nearly identical for each field example. These results could be deceiving and lead sports turf managers to believe the distributions to be similar. However, spatial maps were able to reveal differences through a more detailed analysis of the distribution, which could not be concluded from DU values alone. Technology used to create spatial maps in turfgrass is becoming more prevalent, but unfortunately adoption by sports turf managers has been slow. Future research, development, and awareness of GPS technologies and capabilities should be considered to aide in the management of sports fields.


The authors would like to thank Troy Carson and Kathy Rice, The Toro Company, for technical support with the Precision Sense 6000.

Chase Straw is a graduate research assistant, University of Georgia; Dr. Gerald Henry is an Athletic Association Endowed Professor, University of Georgia. Stephen Richwine is with the Oconee County School District, Watkinsville, GA.



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