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How Many Catch Can Measurements Are Required to Evaluate the Hydraulic Performance of Drip Irrigation Systems?
Israa M. Witwit1, Hadi A. Al-agele1,2,*, Chad W. Higgins2
Published in Applied Engineering in Agriculture 41(3): 353-359 (doi: 10.13031/aea.16202). Copyright 2025 American Society of Agricultural and Biological Engineers.
1 Department of Soil and Water Resource, College of Agriculture, Al-Qasim Green University, Al-Qasim District 964, Babylon, Iraq.
2 Department of Biological and Ecological Engineering, College of Agricultural Science, Oregon State University, Corvallis, Oregon, USA.
* Correspondence: hadi.abdulameer@agre.uoqasim.edu.iq, alageleh@oregonstate.edu
The authors have paid for open access for this article. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License https://creative commons.org/licenses/by-nc-nd/4.0/
Submitted for review on 30 September 2024 as manuscript number NRES 16202; approved for publication as a Research Article by Associate Editor Dr. Isaya Kisekka and Community Editor Dr. Kati Migliaccio of the Natural Resources & Environmental Systems Community of ASABE on 25 March 2025.
Citation: Witwit, I. M. T., Al-agele, H., & Higgins, C. (2025). How many catch can measurements are required to evaluate the hydraulic performance of drip irrigation systems? Appl. Eng. Agric., 41(3), 353-359. https://doi.org/10.13031/aea.16202
Highlights
Abstract.The hydraulic performance of drip irrigation systems is directly linked to water application efficiency, water consumption, and energy consumption. An accurate assessment of this hydraulic performance is needed to understand the system’s function and efficiency. However, direct measurement of drip irrigation system performance with catch cans is often labor-intensive and time-consuming. This study aims to find the minimum viable effort (fewest catch cans) required to evaluate drip irrigation system hydraulic performance by performing a series of extensive tests and down sampling systematically. Measurements from these tests are first compared with an established hydraulic model. Then they are used to determine the emitter flow rate, standard deviation, coefficient of manufacturer variation, emission uniformity, uniformity coefficient, and water distribution coefficient, respectively. The dripline length and distance from the pump are additional independent variables considered and were found to be not significant.
Randomized down sampling of the catch can data shows that system performance is more likely to be overestimated if too few catch cans are deployed. Further, the uniformity coefficient converged to the correct classification with 64 cans, while the coefficient of variation converged to the correct classification with 256 cans.
Drip irrigation systems are designed to provide uniform water and fertilizer distribution to reduce water wastage and improve plant growth and agricultural production. Lower irrigation system uniformity causes increased water losses and water wastage (Oker et al., 2020), decreased water and nutrient distribution (Guan et al., 2013), and reduced water sustainability and production (Zhao et al., 2012; Al-Agele et al., 2021a, 2021b). In addition, poor irrigation system efficiency will increase nutrient and pumping costs.
Presently, agricultural water consumption accounts for approximately 87% of the total consumption of water resources on a global scale (Villalobos and Fereres 2016). Climate change is projected to increase this water usage by increasing temperatures and lowering rainfall in agricultural areas, impacting agricultural production (El-Rawy et al., 2023; Vijai et al., 2023). This high consumption rate necessitates an awareness of agricultural water management and sustainable irrigation technology with high water application efficiencies . The anticipated increase in water shortages for agriculture creates an additional urgency in increasing water uniformity and crop distribution (Soltani and Mellah 2023; Kumar et al., 2024). Improvement in the uniform water application to root zones reduces water losses, increasing irrigation water use efficiency (Sinha,.
Water application efficiency (uniformity and water distribution) depends on the irrigation system’s characteristics (Pereira, 1999; Ascough and Kiker 2002; Oker et al., 2020). Efficient water supply to plants depends on irrigation system methods and technologies (Ascough and Kiker 2002).
The non-uniformity in water application and fertigation may have multiple underlying causes. Variations in manufacturing can impact the emitter’s flow (Li et al., 2007; Fan et al., 2017; Sinha, 2021). Pressure differences can lead to emitter flow variations within the drip irrigation system as a whole (Christiansen, 1942; Wu et al., 1979; Camp et al., 1997). Low water quality or poor filtration can lead to clogged emitters or reduced emitter flow rates in the micro-irrigation system, which reduces water uniformity for water supply and nutrients to the plant (Burt, 2004; Solé-Torres et al., 2021). Taken together, these factors mean that the uniformity and efficiency of an irrigation system can change and degrade over time, and irrigators may need to monitor uniformity to identify degradations and maintain a high efficiency.
However, this monitoring can be costly from a labor perspective because it is typically done manually with many catch cans in the field. This study aims to find the minimum effort required to measure water uniformity and distribution in the drip irrigation system design by using a catch can test. How many emitters must be measured to express the fundamental water application criteria for the drip irrigation system? How can the measurement be made easy for the growers to perform in the drip irrigation design?
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| Figure 1. The field experimental layout design for a drip irrigation system. The layout is designed to contain multiple pipe lengths and distances from the pump. |
Materials and Methods
Experimental Design
A field experiment was performed in an agrivoltaic system field at the Al-Sayahiya region, north of Babylon province, Iraq. The field experiment dimensions were 40 × 40 m (1600 m2) (length × width). A drip irrigation system (described below) was installed on the clay loam soil texture, and black density was 1.5 g/cm3 in the arid and semi-arid regions.
A diagram of the system design is shown in figure 1. The main and sub-main drip pipe diameters were 0.05 m for both lengths (20 and 40 m). A drip tube from Universal Drip Irrigation Pipe Manufacturing was used. The emitter flow was 4 L/h, and the spacing between emitters was 20 cm. Drip connectors were used to connect the sub-main pipe and drip tube. The experiment had three treatments with three replicates; each replicate had three plots for both lengths. Each replicate's dimensions in length and plot were 3 × 20 m, 3 × 40 m, and 3 × 6 m (18 m2), respectively. The individual plot contained three lines of drip tube. In addition, the electrical water pump (SHIMGE) was used to apply the water to the field with a discharge rate of 600 L/min and model SHFm 5 AM, and a filter was used to prevent clogging the emitters.
Irrigation System Design Classification Evaluation
An extensive catch-can test was performed. The first set of measurements was a high-resolution sampling of the irrigation lines nearest to and farthest from the pump. This included the 20 m length tubes and the 40 m length tubes (see fig. 1). A total of 1000 catch cans were randomly placed for this measurement. The second set of measurements was distributed randomly throughout the entire irrigation system. Here, 886 cans were used and monitored. For all measurements, three experimental replicates were performed. Thus, about 1800 catch cans were used for each of the three replicates. The cans are set up underneath an emitter, and the system runs for 5 minutes for each replicate. See figure 2 for photographs of the setup in the field.
One possible critique of this approach is that the catch cans themselves suspend the drip line, and the emitters are not in contact with the soil. This could lead to changes in the flow rate; therefore, an additional methodological check was performed. Twenty-six catch cans were deployed in the lab. Thirteen were arranged as outlined above, and thirteen were filled with soil such that the drip emitter exit was in contact with the soil. The irrigation system was operated for 4 min. Then, the water added to each catch can was determined by the change in weight. The data collected were then analyzed statistically with a two-tailed t-test, and we found no significant difference between the open catch cans and the soil-filled catch cans at the p < 0.05 level.
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| Figure 2. The catch can test field measurement. Catch cans were placed on the soil surface, and the dripline ran above the cans. Sections of the line were populated with catch cans such that each drip emitter was paired with an individual catch can. |
Average Emitter Discharge Rate (qa)
The average emitter discharge rate, qa(m3/s), can be written as:
(1)
where
qi = the flow rate of the emitter i(L/h)
n = the total number of emitters.
The Standard Deviation of the Emitter Flow Rate (Sq)
The standard deviation of emitter flow rate (L/h), Sq, (ASABE Standards, 1999), can be written as:
(2)
Coefficient of Variation of Emitter Flow (Cv)
The drip irrigation system uniformity classification evaluates the flow’s emitter variability depending on the coefficient variation (ASABE Standards, 1999) as shown in table 1. Cv can be written as:
(3)
| Table 1. Drip irrigation system uniformity classification depends on the coefficient of variation (ASABE Standards, 2008R). | ||
| Emitter Type | Cv Range | Classification |
| Point-source | <0.05 | Excellent |
| 0.05-0.07 | Average | |
| 0.07-0.11 | Marginal | |
| 0.11-0.15 | Poor | |
| >0.15 | Unacceptable | |
| Line source | <0.10 | Good |
| 0.10-0.20 | Average | |
| >0.20 | Marginal to unacceptable | |
Emission Uniformity (EU)
Emission uniformity is measured under the trickle irrigation system (ASABE Standards, 2008R), and emission uniformity classification is presented in (table 2). EU can be found at:
(4)
where qm is the average flow rate of the emitters in the lowest quartile.
| Table 2. Emission uniformity classification recommendation. | |
| Emission Uniformity (%) | Classification |
| Above 90 | Excellent |
| 90-80 | Good |
| 80-70 | Fair |
| less than 70 | Poor |
Uniformity Coefficient (UC)
The uniformity of water application (UC) is considered a key factor in drip irrigation systems which can be classified as uniformity as shown in table 3. Christiansen’s UC (%) evaluates the mean deviation, which is represented in ASABE standards as ASABE (Schneider, 2000):
(5)
| Table 3. Drip irrigation system uniformity classification depends on the uniformity coefficient (ASABE Standards, 1999). | |
| Uniformity Coefficient (CU) (%) | Classification |
| Above 90 | Excellent |
| 90-80 | Good |
| 80-70 | Fair |
| 70-60 | Poor |
| Below 60 | Unacceptable |
Distribution Uniformity (DU)
The low quarter distribution uniformity (DU) (Merriam and Keller, 1978) can be written as:
(6)
Statistical analyses were performed with two tailed- t-tests and ANOVA tests with a p<0.05 significance criterion.
Conditional Analysis
The entire dataset described above was aggregated and randomly down-sampled. That is, subsets of the data were randomly drawn from the entire dataset using Excel’s rand() function, and these subsets were used to recompute the hydraulic performance metrics outlined above. Each random draw was replicated 20 times to create an ensemble. Subsets of size 2^n were taken for all integer n between 2 and 10.
Result and Discussion
Firstly, we checked our data against the hydraulic model (Hathoot et al., 1993). This model predicts that the ratio, qi / qa, diminishes with distance from the drip line entry. That is, there is a negative slope in qi / qa, and that slope is between -0.002 and -0.02 near the inlet of the drip line. The catch can data are consistent with the model’s predictions (fig. 3) with a slope of -0.0078. However, a statistical test on the slope of the regression line revealed that this slope was not significant at the p<0.5 level in our data (measured p = 0.12). That is, the measured slope is indistinguishable from 0, meaning that the water was applied uniformly on average across the entire experiment.
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| Figure 3. flow rates relative to the average flow rate for all catch cans within the experiment as a function of distance from the entry point of the drip line. |
The catch can test results contrasting the first and last lines for the drip irrigation system classification are presented in table 4. The emitter flow rate, the standard deviation flow rate, the coefficient of variation of emitter flow, Emission uniformity, uniformity coefficient, and distribution uniformity are range (2.52-3.01 L/h), (0.26-0.34 L/h), (0.10-0.13), (83%-87%), (92%-93%) and (85%-89%) respectively.
| Table 4. Drip irrigation system design parameters for the first and last lines show a relatively small change in EU, UC, and DU. | |||||||
| Distance (m) | Line Number | qa (L/h) | Sq (L/h) | CV | EU (%) | UC (%) | DU (%) |
| 20 | First line (near the pump) | 3.01 | 0.34 | 0.11 | 83 | 92 | 85 |
| 40 | First line (near the pump) | 2.52 | 0.26 | 0.10 | 88 | 93 | 89 |
| 20 | Last line (far from the pump) | 2.81 | 0.32 | 0.12 | 87 | 92 | 89 |
| 40 | Last line (far from the pump) | 2.65 | 0.34 | 0.13 | 87 | 92 | 88 |
The drip irrigation system uniformity classification was measured to evaluate the irrigation design. The results showed the uniformity classification for the drip irrigation system design depending on the catch can test (table 5) The coefficient variation results showed a range between 0.10 and 0.13, and the classification results showed marginal and poor for the distances, first and last line. The uniformity coefficient, emission uniformity, and water distribution results showed that the drip irrigation system classification is excellent and good, respectively.
| Table 5. Field drip irrigation system design uniformity classification depends on uniformity coefficient, water distribution, and emission uniformity. | ||||||||
| Distance (m) | Line Number | CV | Classification | UC (%) | Classification | EU (%) | UC (%) | Classification |
| 20 | first line | 0.11 | marginal | 92 | Excellent | 83 | 85 | Good |
| 40 | first line | 0.10 | marginal | 93 | Excellent | 88 | 89 | Good |
| 20 | last line | 0.12 | poor | 92 | Excellent | 87 | 89 | Good |
| 40 | last line | 0.13 | poor | 92 | Excellent | 87 | 88 | Good |
The catch can test results for the individual lines show the drip irrigation system design parameters including the discharge water flow, standard deviation, coefficient variation, emission uniformity, uniformity coefficient, and water distribution in 20 and 40 m (table 6). The emitter water flow, standard deviation, coefficient variation, emission uniformity, uniformity coefficient, and water distribution performance in the drip irrigation system are range 2.41-3.00 (L/h); 0.00-0.47 (L/h); 0.00-0.18; 71%-90%; 88%-96% and 72%-91%, respectively, in 20 m distance.
In addition, 40 m distance results find the emitter water flow, standard deviation, coefficient variation, emission uniformity, uniformity coefficient, and water distribution performance in the drip irrigation system are range 2.27-3.92 (L/h); 0.00-0.54 (L/h); 0.00- 0.21; 35%-93%; 89%-97% and 36%-94%, respectively.
The coefficient variation classification indicated the excellent to unacceptable performance of the drip irrigation system for both distances (fig. 4). The uniformity coefficient of the system classification showed good to excellent for all the lines in each distance. Also, the emission coefficient showed poor to excellent performance in all lines (fig. 4). These results agreed with (Sinha, 2021).
The data’s down sampling (fig. 5) revealed that sample size (the number of collected catch cans) had an impact on the likelihood that the irrigation system’s performance was classified correctly. The uniformity coefficient appears to be more robust, with 64 samples needed to arrive at the correct classification (classified as Good 80-90) in all 20 randomized subsamples. In contrast, the Coefficient of Variation had subsample outliers that would misclassify the system (<0.2 marginal to acceptable) in all but the N=256 subsamples. The CV and the UC both over-characterized the system’s performance (e.g., as excellent) even in the mean and median of the ensembles when the subsample size was too small (N<8). The implication of these data is that if insufficient catch cans are deployed, the measured performance of the system could likely overestimate the actual performance.
| Table 6. The drip irrigation system design parameters for all measured lines, including both distances and the random check, can be tested in each line. | ||||||||
| Distance (m) | Line Number | Cans Number | qa (L/h) | Sq (L/h) | CV | EU (%) | UC (%) | DU (%) |
| 20 | 1 | 70 | 3.00 | 0.33 | 0.11 | 71 | 93 | 72 |
| 2 | 49 | 2.70 | 0.28 | 0.10 | 78 | 94 | 80 | |
| 3 | 59 | 2.94 | 0.32 | 0.11 | 74 | 92 | 75 | |
| 4 | 20 | 2.61 | 0.21 | 0.08 | 89 | 96 | 91 | |
| 5 | 40 | 2.65 | 0.00 | 0.00 | 90 | 94 | 90 | |
| 6 | 10 | 2.42 | 0.00 | 0.00 | 76 | 90 | 76 | |
| 7 | 85 | 2.41 | 0.37 | 0.15 | 72 | 92 | 74 | |
| 8 | 29 | 2.49 | 0.43 | 0.17 | 84 | 91 | 87 | |
| 9 | 79 | 2.56 | 0.47 | 0.18 | 77 | 88 | 79 | |
| 40 | 1 | 20 | 2.56 | 0.54 | 0.21 | 66 | 92 | 70 |
| 2 | 60 | 2.51 | 0.42 | 0.17 | 81 | 92 | 83 | |
| 3 | 10 | 2.67 | 0.13 | 0.05 | 93 | 96 | 94 | |
| 4 | 49 | 2.53 | 0.32 | 0.13 | 35 | 92 | 36 | |
| 5 | 89 | 2.85 | 0.00 | 0.00 | 85 | 97 | 85 | |
| 6 | 40 | 2.67 | 0.38 | 0.14 | 63 | 92 | 65 | |
| 7 | 68 | 2.27 | 0.35 | 0.16 | 84 | 92 | 86 | |
| 8 | 79 | 2.57 | 0.40 | 0.16 | 75 | 89 | 77 | |
| 9 | 30 | 3.92 | 0.21 | 0.05 | 92 | 96 | 94 | |
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| Figure 4. Field drip irrigation system design uniformity classification depending on a- variation coefficient, b- uniformity coefficient, and emission uniformity. |
The underlying cause of the difference between the lines is unknown but many factors can contribute. These factors include the water level in the river during the measurement time, electric power variation affecting water discharge, filter of water clogging, friction loss, clogging problems, poor handling, less care, and lack of fittings and laterals supply, and soil slope variation affect water supply to plant by duplicate water flow from the other emitters cumulative in one emitter because the slope variation. Growers must care about each part when installing a drip irrigation system. All the results showed no significant difference between the first and last lines for both distance and all the lines. That leads to measuring limited numbers of catch cans tested in one or two lines instead of all the fields to save time and reduce labor costs.
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| Figure 5. Box and whisker plots of the (a) UC and (b) CV as calculated from randomized subsamples. |
Conclusion
The uniformity of drip irrigation systems impacts the irrigation management of the system. If a nonuniformity is unknown, then there is a risk that field areas do not receive adequate levels of irrigation and nutrients. If a nonuniformity is known, then the operator may choose to overapply water and nutrients across most of the field to achieve adequate irrigation everywhere. Of course, if the nonuniformity can be localized, maintenance or repair can be targeted to rectify the problem. The issue addressed in this paper is that performing a uniformity test of a drip irrigation system can be costly from a labor and time perspective. Catch can measurements are often performed manually. We have sought to find the minimum labor (i.e., minimum manual measurements) needed for an accurate assessment.
The data gathered and ensuing analysis indicate that 64 samples were sufficient for the UC and 256 for the CV. There is a likelihood that the system performance is mischaracterized if the number of catch cans is smaller. Further, we found that the performance metrics are more likely to be overestimated when the number of catch cans is small (lower than 16). This suggests that performing a uniformity test with a small (<16) number of catch cans would lead system operators to conclude that the system is more efficient than it is, potentially leading to the underapplication of water and nutrients within localized areas of a field.
There is no significant difference in the hydraulic performance of the drip irrigation system design in the first and last lines between 20 m and 40 m, and each line's result depends on the catch can test. These results encourage growers to evaluate their drip irrigation system where it is convenient for them from a labor perspective and not concentrate on the distance from the water source. Taken collectively, the results presented here have the potential to reduce work by using fewer catch cans in any part of the field.
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