Irrig Sci (2014) 32:193–203 DOI 10.1007/s00271-013-0421-1
ORIGINAL PAPER
Evaluating irrigation applied and nitrogen leached using different smart irrigation technologies on bahiagrass (Paspalum notatum) Nicole A. Dobbs • Kati W. Migliaccio • Yuncong Li • Michael D. Dukes • Kelly T. Morgan
Received: 6 November 2012 / Accepted: 19 November 2013 / Published online: 8 December 2013 Ó Springer-Verlag Berlin Heidelberg 2013
Abstract Irrigation technologies [i.e., automatic timer, automatic timer with rain sensor, automatic timer with soil water sensor (SWS), and evapotranspiration (ET) controller] were compared in a bahiagrass plot study by measuring irrigation applied, water volumes drained, and NO3–N and NH4–N leached. All irrigation technologies were scheduled to irrigate on Sunday and Thursday. Three different irrigation depths were evaluated with the automatic timer: 15, 19, and 32 mm. SWS treatment allowed scheduled irrigation if soil water content was estimated to be below 70 % of water holding capacity, while the ET treatment allowed scheduled irrigation if soil water content was estimated to be below 50 % of plant available water. The rain sensor, SWS, and ET controller treatments applied significantly less water (p \ 0.05) than the automatic timer treatment (which irrigates on specific days and times without regard to system conditions), reducing water by 17–49, 64–75, and 66–70 %, respectively. NO3–N and NH4–N were only significantly different after the second fertilizer Communicated by J. Knox. N. A. Dobbs North Carolina State University, D. S. Weaver Labs, Campus Box 7625, Raleigh, NC 27695, USA K. W. Migliaccio (&) Y. Li University of Florida, 18905 SW 280th St, Homestead, FL 33030, USA e-mail:
[email protected] M. D. Dukes University of Florida, 1741 Museum Road, PO Box 110570, Gainesville, FL 32611-0570, USA K. T. Morgan University of Florida, 2686 SR 29 N, Immokalee, FL 34142, USA
application, which coincided with the 32 mm per event irrigation rate for the automatic timer treatment. Under these conditions, the automatic timer treatment had significantly greater NO3–N and NH4–N leachate than other treatments due to greater occurrence of soil water content exceeding water holding capacity, which resulted in drainage. Findings suggest that water can be saved using rain sensors, SWSs, or ET controllers and that leachate NO3–N and NH4–N can be reduced using rain sensors, SWSs, or ET controllers.
Introduction Despite US regulations to protect water bodies from point and nonpoint sources of pollution, excessive nutrients continue to degrade water quality nationwide as evident by USEPA (2009), which reported nutrient impairment in 62,171 km of streams; 790,111 ha of lakes, ponds, and reservoirs; and 271,175 ha of estuaries. One source of nutrients often linked to nonpoint source pollution is fertilizers. Fertilizers are used in urban environments to maintain esthetic qualities of turf and landscape plants. Turfgrass in urban landscapes may be a cool-season species (e.g., bluegrass, fescues, ryegrasses) or a warm-season species (e.g., bahiagrass, Bermudagrass, St. Augustinegrass, bahiagrass) depending on the climate. Management practices may vary depending on the turf type and environment; however, a general N fertilizer recommendation for warm-season species is 0.0049 kg N/m2 (1.0 lb N/ 1,000 ft2) per growing month (USNA 2006). Nutrients, such as N, are applied to meet plant requirements. N is a necessary nutrient as it is used in photosynthesis and plant growth. However, excessive water from rain or irrigation can result in nutrient transport by leachate or runoff
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(Barton and Colmer 2006). As nutrients enter receiving waters, their concentrations may exceed natural capacity and lead to increased eutrophication or pollution. Nutrient leaching and runoff from turfgrass and landscapes may be considered more critical than nutrient losses in agricultural areas due to the proximity of urban landscapes to drinking water supplies (Waller 2007). Nitrogen (N) is a common nutrient in fertilizers and has been shown to be susceptible to leaching and runoff due to its solubility in water, particularly the nitrate (NO3-) form because of low soil sorption of anions. Nitrogen leaching is considered a primary means of N loss in humid climates and under irrigated conditions (Havlin et al. 2005). Certain forms of N, such as NO3- and ammonium (NH4?), are also commonly limiting nutrients in aquatic systems and a potential health hazard if consumed at a concentration over 10 mg/L for NO3–N (USEPA 2012). Nutrient leaching may occur unnecessarily due to overirrigation in turf systems (Snyder et al. 1984; Barton and Colmer 2006; Barton et al. 2006). The occurrence of overirrigation-induced N leaching has likely increased due to the growing popularity of automated irrigation systems. These systems may apply 47 % more water than nonautomatic techniques due to the ‘‘set and forget’’ mentality associated with their automation (Mayer et al. 1999), thus resulting in N leaching when irrigation exceeds soil water holding capacity. The increase in irrigation observed with the automated systems is linked to their functionality where irrigation events are programmed by the user with little regard to the weather, actual plant water needs, or current soil water status. With more than 40 million ha of turfgrass reported in the USA (Milesi et al. 2005) and the use of automatic irrigation systems growing, there is a need to assess their contribution to N leaching and provide better irrigation methods. Studies indicate that residential properties can reduce water volumes applied using soil water-based or weatherbased irrigation scheduling devices (Dukes and Haley 2009; Davis and Dukes 2010; McCready and Migliaccio 2011). Others have shown that soil water sensing devices can reduce over-irrigation and thereby minimize nutrient leaching compared to conventional automatic irrigation (Augustin and Snyder 1984; Snyder et al. 1984; Pathan et al. 2007). The need to optimize irrigation to reduce the occurrence of N leaching in turf was also reported by Barton and Colmer (2006) and Barton et al. (2006). It follows that the other weather-based technologies [e.g., evapotranspiration (ET) based] should also reduce nutrient leaching. Three irrigation technologies are commercially available and may be used to improve irrigation efficiency in landscapes. The simplest is the rain sensor. Rain sensors are external devices that can be added to an automatic
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irrigation timer to control irrigation application by preventing a scheduled irrigation event if a certain depth of rainfall has occurred, as detected by the device. A more complex device is the soil water sensor (SWS). SWSs provide an indirect estimate of the soil water content in situ. Sensors are installed in the root zone of the irrigated plant and calibrated to determine the soil water holding capacity. Scheduled irrigation will be prevented or applied depending on the percentage of soil water holding capacity estimated by the SWS (Mun˜oz-Carpena et al. 2005; Evett 2007). Lastly, ET-based controllers may be used to schedule irrigation. These devices use weather data to determine ET and thus use a soil water balance or other historical weather-based means to estimate irrigation runtimes. The soil water balance approach estimates water balance using real-time ET, rainfall, and irrigation applied. Historical based ET controllers use a pre-programmed ET curve and minimal real-time data to shift this curve for estimating irrigation demand (Dukes 2012). Theoretically, the ET-based and the SWS-based irrigation scheduling methods should result in similar irrigation depths being applied as the ET-based system estimates irrigation demand based on an assessment of real-time soil water losses (depending on the unit, this may also include a water balance) using weather data and the SWS-based system uses a sensor to measurement real-time soil water content. The ET-based and SWS-based irrigation technologies also include a user input option that sets a threshold of water stress that must be met before irrigation is allowed; differences in selected thresholds would influence the similarity in the irrigation depths applied by the technologies. We combined irrigation technologies that have successfully been applied to improve irrigation efficiency with monitoring equipment to compare the N leaching and water losses by typical automated irrigation systems to that of the technology-based irrigation systems. Leachate, or N-containing drainage water, is expected to be reduced by the use of irrigation technologies such as rain sensors, SWS, and ET controllers as compared to a time-based irrigation schedule as they modify irrigation events to consider some real-time, site-specific measurement with the objective of minimizing irrigation contributions to drainage and runoff. Our study compared applied water quantities, leachate quantities, and leachate N loads and concentrations for four irrigation treatments (i.e., automatic timer, automatic timer with rain sensor, automatic timer with SWS, and ET controller) using a bahiagrass plot study in south Florida.
Materials and methods A field study was conducted at the University of Florida Institute of Food Agricultural Sciences (UF IFAS) Tropical
Irrig Sci (2014) 32:193–203 Table 1 Average monthly values for temperature (Temp), precipitation, and reference evapotranspiration (ETo) from Florida Automated Weather Network (FAWN) data at the study site
195
Month
Average for 1998–Sep 2012 Temp (°C)
Precipitation (mm)
Study period (Oct 2011–Sep 2012) ETo (mm)
Temp (°C)
Precipitation (mm)
ETo (mm)
Jan
18
35
62
18
2
53
Feb
19
53
76
21
69
79
Mar
21
51
105
22
51
99
Apr
23
62
126
23
203
110
May
25
128
139
25
226
126
Jun
27
219
127
27
202
122
Jul
27
179
134
27
166
134
Aug
27
281
120
28
376
137
Sep
27
234
102
27
251
110
Oct
25
160
91
25
300
79
Nov Dec
22 20
55 36
67 57
22 21
44 12
64 55
Research and Education Center (TREC; latitude: 25°300 2400 N longitude: 80°290 5700 N) in Homestead, FL, USA. The study was conducted over a 1-year time span starting in October 2011 and ending in September 2012. The experiment area consisted of 16 square plots, each 20.9 m2 with 0.61 m buffers between plots. Each plot had four quarter-circle pop-up irrigation heads with matched precipitation (MP) rotator nozzles (Hunter Industries, Inc., San Marcos, CA, USA) with an application rate of 130 mm h-1, one in each corner. Water volumes applied were monitored with DLJ multi-jet water meters (Daniel L. Jerman Co., Hackensack, NJ, USA) and recorded manually after each irrigation event for each treatment replicate (i.e., total of 16 water meters). The experiment consisted of four treatments (T1–T4: automatic timer, automatic timer with rain sensor, automatic timer with SWS, and ET controller, respectively) arranged in a randomized block design. Each treatment was replicated four times for water application; leachate was collected from three of the four replicates. Lysimeters were installed to collect and measure leachate that had moved below the root zone and would not be available for plant use. Lysimeters were designed to capture a 38-mm rainfall event. The lysimeter consisted of a water collection chamber made from 38-mm-diameter PVC cut 58.4 cm in length connected to a standard PVC bathroom collection drain with a diameter of 17.1 cm. Two plastic tubes were connected to the bottom of the water chamber to collect water and allow air entry. Screening was attached on top of the collection drain to prevent debris from entering the lysimeter. Sand was placed on top of the screen to prevent finer particles from entering the water holding tube. Lysimeters were buried below the root zone (approximately 15 cm root zone depth) in 12 plots, with three replicates for each treatment.
Average monthly weather parameters were obtained from the Florida Automated Weather Network (FAWN) Homestead station located 1 km from the study site (Table 1). Average monthly values for the study period are also shown. Temperature and reference ET (ETo) values were similar, while precipitation was greater for the study period as compared to the period of record. All plots received the same maintenance during the study following Trenholm and Unruh (2005). Plots were regularly mowed, typically every 7–10 days during rainy season (May–October) and every 12–15 days during dry season (November–April). No pesticides were applied during the study period. Irrigation treatments All treatments were programmed to irrigate biweekly on Sunday and Thursday per Miami-Dade County, Florida urban irrigation restrictions. Each treatment was irrigated separately with the first irrigation start time beginning at 2:00 am. Treatment 1 (T1), or automatic timer, consisted of irrigation controlled by a digital timer (Hunter Pro-C Conventional 9 Zone Outdoor Model, Hunter Industries, Inc., San Marcos, CA, USA). Runtimes were modified to include a range of irrigation depths (Table 2). The automatic timer treatment irrigated on selected days and times as programmed without regard to soil water content or weather conditions. Irrigation events were scheduled to occur once a day on Sundays and Thursdays. The rates in Table 2 are all lower than the estimated water holding capacity (30 cm root depth; 0.12 cm/cm water holding capacity). However, it is unlikely that a soil water deficit of 32 mm would be achieved between irrigation events, while
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Irrig Sci (2014) 32:193–203
Table 2 Irrigation depths with dates of application for automatic timer treatment (T1)
Table 3 Evapotranspiration (ET) controller input parameters used for treatment 4
Start date
End date
Irrigation depth (mm)
Parameter
Site-specific input
October 1, 2011
December 16, 2011
15a
ZIP code
33031
a
Soil type
Loamy sand
December 17, 2011
January 18, 2012
19
January 19, 2012
March 7, 2012
32
Sprinkler type
Rotary nozzle
March 8, 2012
September 30, 2012
19a
Slope
0–2 %
Plant type
Grass lawn ? warm-season variety
Shade factor
Full sun
Plant maturity
Established
a
Within UF IFAS-recommended rates (Trenholm and Unruh 2005)
15–19 mm deficits could occur as historical ETo ranges from 2 to 5 mm per day. Treatment 2 (T2) consisted of an automatic timer with a rain sensor. The rain sensor functionality was based on internal water-absorbing expansion disks (Toro, Bloomington, MN, USA). The rain sensor treatment was controlled by a separate timer, as this technology will either apply the scheduled amount or bypass irrigation if the rainfall threshold has occurred, as detected by the sensor. Bypass irrigation refers to when a scheduled irrigation event is ‘bypassed’ or not occurring due to additional information received from the system (in this case from the rain sensor). Initially, the rainfall threshold was manually set as 12 mm ( September 22 through November 8, 2011). The original rainfall threshold value (12 mm) was selected based on the available settings of the sensor (3, 6, 12, 19, or 25 mm) and the idea that a 12-mm rainfall event would closely represent the irrigation depth at which the experiment started. Based on early study observations, this setting was not resulting in the desired outcome. The rain sensor was later (November 9, 2011) changed and set at 3 mm to increase its potential to interrupt scheduled irrigation events. There were also two runtimes for T2: 1.0 h corresponding to 13 mm of irrigation per event (October 2011–January 22, 2012) and 1.5 h corresponding to 19 mm of irrigation per event (23 January through September 2012). Treatment 3 (T3) used an automatic timer with an SWS (Baseline WaterTec S100, Baseline 2009) to manage irrigation with an irrigation runtime similar to T1. The SWS was set up using the manufacturer-programmed percentage (i.e., 70 %) of FC for bypassing scheduled irrigation events. The sensor was 17.2 cm in length and measured an estimated soil volume of 282 cm3 (Baseline 2011). The SWS devices were installed in four plots. In each of the four plots, one sensor was buried in a corner opposite of the lysimeter, 5–8 cm below the ground surface according to the Baseline WaterTec S100 installation manual. The sensor was installed horizontally to prevent pooling on the wide surface area of the sensor (Baseline 2009). Each sensor had a digital controller wired into the automatic irrigation controller and also connected to the buried
123
sensor. Automatic calibration was performed by saturating the soil surrounding the sensor and using the 24-h automatic calibration feature on the controller. The Baseline SWS device uses time domain transmission (TDT) technology and determines sufficient soil water content based on field capacity and a manufacturer-programmed threshold setting. Treatment 4 (T4) consisted of irrigation controlled by an ET controller (Rain Bird ESP-SMT, Rain Bird Inc., Tucson, AZ). The ET controller was programmed by inputting site-specific parameters (Table 3). The ET controller initiated irrigation based on an accumulated water requirement, as determined by the record of real-time weather data, including ETo and effective rainfall. Effective rainfall was determined using soil type input and data from the onsite tipping bucket, which measured rainfall amount and intensity. Crop ET was calculated by an undisclosed equation based on Penman–Monteith using real-time temperature and historical wind speed and relative humidity data for the designated ZIP code. Irrigation occurred as scheduled when the management-allowable depletion (MAD) of 50 % was reached. MAD values are used to prevent soil water content from being too low resulting in water stress for the plants. MAD values are typically soil type specific (Rain Bird 2013). Thus, when the accumulated water requirement (effective rainfall minus ET) exceeded 50 % of plant available water, irrigation was allowed as scheduled. The soil type selected in the ET controller was loamy sand which corresponds to a plant available water value of 0.08 cm/cm. The controller default crop coefficient (Kc) values were from the Irrigation Association (Table 4; Irrigation Association 2008). Kc values were multiplied by reference ET values to obtain crop-specific ET. Detailed information on Kc values and their application can be found in Allen et al. (1998). Runtimes and weather data were recorded manually from the ET controller after each irrigation event (although a digital record of the last 30 days was available). The SWS treatment (T3) and the ET treatment (T4) used different methods for determining an irrigation threshold or
Irrig Sci (2014) 32:193–203 Table 4 Crop coefficient (Kc) values used by the Rain Bird ESP-SMT ET controller for warm-season turfgrass (Irrigation Association 2008)
197
Kc Jan
0.52
Feb
0.64
Mar
0.70
Apr
0.73
May
0.73
Jun
0.71
Jul
0.69
Aug
0.67
Sep
0.64
Oct
0.60
Nov
0.57
Dec
0.53
an amount of water depletion where irrigation is allowed by the technology. For the SWS-based method, a 70 % threshold of soil water holding capacity was assumed based on calibration set points observed when using the procedure outlined in Baseline (2011). This corresponds to 70 % of 0.12 cm/cm (Saxton and Rawls 2006) or 0.084 cm/cm. The 50 % MAD or 50 % plant available water corresponds to 50 % of 0.08 cm/cm (as defined by the manufacturer) or 0.04 cm/cm. However, the portion of water that is not available to plants must be added to this value to compare it to the SWS threshold. This addition represents the wilting point water content or 0.05 cm/cm (Saxton and Rawls 2006), resulting in a total value of 0.09 cm/cm. The difference in these two irrigation-triggering values (i.e., 0.08 and 0.09 cm/cm) is small and likely not observable in the measured irrigation depths. Uniformity testing Irrigation application uniformity was tested according to the American Society of Agricultural Engineers (ASAE) standard, American National Standards Institute (ANSI)/ ASAE S436.1 (ASAE 2003), prior to the beginning of the experiment. Uniformity tests were performed during the evening and early morning hours to avoid evaporative losses. Twenty-five catchments (GladTM 947 mL disposable containers) were placed in an evenly spaced grid pattern in each plot. The irrigation runtime was 2 h. The wind speed was recorded every 15 min with a handheld digital anemometer to ensure that wind speed did not exceed 5 m s-1, which may affect the results. At the end of the test period, collected water volumes were measured with a graduated cylinder. The opening diameter of the catchments was 13.3 cm. Distribution uniformity (DU) was calculated as (Merriam and Keller 1978; Baum et al. 2005):
DUlq ¼
DUlq DUtot
ð1Þ
where numerator represents the mean of the lower quarter of the distribution and the denominator represents the mean of the total distribution. Wet checks were conducted monthly to test the system and ensure spray patterns and spray heads were operating correctly. Wet checks consisted of irrigating each plot for a short amount of time and observing the spray pattern; problems (such as clogged or misaligned heads) could be easily identified and fixed. Fertilizer application All 16 plots were fertilized according to UF IFAS recommendation for slow-release N fertilizer, 4.8 g N m-2 (1 lb N per 1,000 ft2) (Trenholm and Unruh 2005). LESCOÒ Professional Turf 26–2–11 (N–P–K) fertilizer was used with 26 % total N, 1.15 % ammonical N, and 24.85 % urea N (LESCO, Cleveland, OH, USA). Given this composition, the total amount applied to the 16-plot study area was 8.8 kg (19.3 lbs). The urea N was 6.5 % slowly available urea N from sulfur-coated urea. Other components included 11 % soluble potash, 4 % sulfur, 3 % iron total, 2 % phosphate, 0.8 % manganese total, 0.5 % magnesium total, 0.2 % zinc total, and 0.1 % copper total. The maximum chloride was 8.25 %. Fertilizer was applied using a ScottsÒ Standard broadcast spreader by pushing the spreader at a constant pace in a serpentine pattern at the setting of 5.5, as recommended on the LESCO fertilizer bag. The setting of 5.5 corresponds to the type of material being applied with each material specifying the proper setting that results in optimum distribution. Changing this setting results in physically changing the size of the opening through which material passes or the rate of application of the material. Fertilizer applications occurred on September 7, 2011, January 18, 2012, and September 7, 2012. Soil and tissue samples were collected from each plot during three sampling events: August 2011, April 2012, and September 24, 2012. The tissue and soil samples were analyzed for total N and C using the elemental analyzer Vario MAX CNS (Elementar Analysensysteme, Hanau, Germany). This automatic instrument uses catalytic tube combustion under oxygen supply and high temperatures. One sample was collected per treatment replicate on each date for a total of 16 soil and 16 tissue samples per sampling event. Nitrogen analysis Lysimeter leachate was collected and volumes recorded following each scheduled irrigation event in 250-mL plastic bottles using a small peristaltic pump (TAT pumps,
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Logan, OH, USA). Samples were returned to the laboratory and filtered with Whatman 42 filter paper into 20-mL vials. Samples were analyzed for NO3–N and NH4–N concentrations using a SEAL AQ2 discrete analyzer (SEAL Analytical, Inc., Mequon, WI, USA). Nitrate was determined spectrophotometrically by USEPA method 353.2 (USEPA 1993a). Ammonia and NH4–N were determined spectrophotometrically by USEPA method 350.1 Rev. 2.0 (USEPA 1993b). The detection limits of NO3–N and NH4– N were 0.021 and 0.046 mg/L, respectively. For samples less than the detection limits, the sample concentration was assigned as half of the detection limit (i.e., the minimal NO3–N and NH4–N concentrations were 0.01 and 0.023 mg/L, respectively). Initial (before fertilizer application), mid-project, and final soil and tissue samples were collected and analyzed for total N and C using the elemental analyzer Vario MAX CNS (Elementar Analysensysteme, Hanau, Germany). This automatic instrument uses catalytic tube combustion under oxygen supply and high temperatures. The different components being measured are separated using specific adsorption columns and assessed using a thermal conductivity detector. Turfgrass quality evaluation Turfgrass quality was evaluated in December 2011, February 2012, and October 2012 according to the National Turfgrass Evaluation Program guidelines for the following characteristics: genetic color, turfgrass density, percent living ground cover, and texture. Quality was based on a visual rating scale of one (worst)–nine (best). For genetic color, or inherent genotype color, one is light green and nine is dark green. Turfgrass density is a visual estimate of living turfgrass per unit area. Percent living ground cover is an estimate of surface area that is covered by the originally planted species. Turfgrass texture is an estimate of leaf width, with one being coarse and nine being fine (Morris and Shearman 1998). Data analysis Irrigation water volumes applied and drained per plot were measured and converted to depths. Data were analyzed for significant differences (p B 0.05) between treatments for water applied, drainage depths, NO3–N and NH4–N concentrations, and NO3–N and NH4–N loads by performing either a one-way analysis of variance (ANOVA) (parametric; normally distributed data) or Kruskal–Wallis oneway ANOVA on ranks (nonparametric; non-normally distributed data). Multiple pairwise comparisons were performed using Tukey’s test to identify significant differences (p B 0.05). Data analyses for water quantities applied and drained were divided into four time periods
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according to the application rates per event of the automatic timer treatment (Table 1). Data analyses for N concentrations and loads were divided into two periods: after the first fertilizer application (September 22, 2011–January 18, 2012) and after the second application (January 19– February 16, 2012).
Results and discussion Irrigation The uniformity test resulted in all plots scoring between 0.42 and 0.60 of the DUlq value (Eq. 1). These values were within the average range reported by Baum et al. (2005) for Florida systems (i.e., 0.38–0.71). The Irrigation Association has published guidance with values \0.40 resulting in a ‘failure’ of the uniformity test (IA 2005). Thus, our values were not below this failure point. However, they also typically were not classified as ‘‘good’’ by the same guidance (i.e., 0.60–0.69). Irrigation data collected failed the normality test (i.e., the data did not have a normal distribution). The automatic timer treatment (T1) was significantly greater than all other treatments for all irrigation rates considered (Tables 3, 5). Table 5 Comparison of irrigation treatment medians and means of water depths applied for four time-based application rates Rate 1 15
Rate 2 19
Rate 3 32
Rate 4 19
T1 Median (mm)
11a
17a
29a
19a
Mean (mm)
10
17
29
19
Cumulative (mm)
237
155
404
1,148
T2 Median (mm)
9b
11b
16b
13b
Mean (mm)
9
11
15
17
Cumulative (mm)
197
98
206
785
Median (mm)
0c
11bc
11c
0c
Mean (mm) Cumulative (mm)
4 85
7 62
9 102
5 287
T3
T4 Median (mm)
4c
5c
6c
7c
Mean (mm)
4
5
6
6
Cumulative (mm)
81
49
84
350
355
0
71
1,474
Rainfall
T1 is automatic timer, T2 is automatic timer with rain sensor, T3 is soil water sensor, and T4 is evapotranspiration controller. Rainfall data are from Florida Automated Weather Network located near study site. Treatment median depths with different letters were significantly different (p \ 0.05) by column (application rate) using Tukey’s test
Irrig Sci (2014) 32:193–203
The automatic timer with rain sensor, the SWS, and the ET controller, respectively, applied 17, 64, and 66 % less water than T1 for rate 1 (15 mm); 32, 73, and 69 % less water than T1 for rates 2 and 4 (19 mm) combined; and 49, 75, and 79 % less water than T1 for rate 3 (32 mm). The automatic timer with rain sensor (T2) was significantly different from the SWS and ET controller treatments for all irrigation rates except the period during rate 2 (19 mm). The rain sensor triggered a bypassed irrigation event 20 times (out of 104 irrigation events) during the study period. In addition, when rainfall exceeded field capacity prior to a scheduled irrigation event, SWS and ET controller treatments applied no irrigation for 90 % of the events. The SWS-based irrigation treatment (T3) bypassed irrigation 60 times on average per plot during the study period. The sensors performed well during the life of the study, and results show that cumulative irrigations for these four plots were 530, 530, 490, and 520 mm, indicating low variability between the sensors. The SWS treatment indicated additional water savings as compared to the automatic timer with rain sensor, with significant differences for all rates except during irrigation rate 2 (19 mm). Significant differences were not found for rate 2, which occurred during the dry season when these two technologies would be less likely to bypass irrigation as evident by the 0.0 mm of rainfall during this period (Table 5). The ET controller (T4) irrigated at all scheduled times; however, the irrigation amount was always lower than 15 mm (the lowest irrigation rate in the study). The ET treatment indicated additional water savings as compared to the automatic timer with rain sensor (T2) with significant differences for all irrigation rates. This was expected as T2 used a simple expanding disk rain sensor to bypass irrigation without considering ET, while the ET controller measured actual and effective rainfall and used a real-time ET estimate to schedule irrigation. No significant differences in irrigation depths were found between T3 and T4 for the study period. Cumulative reference ET determined by the ET controller was 25 cm greater than that estimated by the Homestead FAWN station located within 1 km of the study site. This difference is likely due to the methodology used by each device to calculate reference ET with the controllers using an undisclosed equation and FAWN using the Penman method. In addition, the FAWN calculation uses all real-time data, while the controller is limited to real-time temperature data and historic data for other measurements. Comparison of ETo estimation methods in literature has shown ratios of different daily ETo estimation equations as great as 1.4 (Itenfisu et al. 2003). Comparing the ratio of daily ETo from the controller to FAWN for the study period was 1.3 and therefore within the range previously reported. The difference between the two estimation methods was, however, systematic. The amount of
199 Table 6 Water savings of three technology-based irrigation systems compared to time-based irrigation Water savings (%) Results Rain sensor
Soil water sensor
Evapotranspiration controller
17–49
64–75
66–79
a
References
Similar studies 7–30
McCready et al. (2009)
13–24
Cardenas-Lailhacar et al. (2010)
14*
Haley and Dukes (2012)
19
Haley and Dukes (2007)
34
Cardenas-Lailhacar et al. (2008)
11–53
McCready et al. (2009)
16–83
Cardenas-Lailhacar et al. (2010)
25
Pathan et al. (2007)
40
Horst and Peterson (1990)
42–95
Augustin and Snyder (1984)
42–95
Snyder et al. (1984)
65
Haley and Dukes (2012)
69–92
Cardenas-Lailhacar et al. (2008)
73
Qualls et al. (2001)
25–63
McCready et al. (2009)
20–60
Davis et al. (2007)
* Not statistically significant a
Range of average savings for three different time-based application rates as measured in this study
real-time data available for the ET controller is limited to reduce costs of the system; this likely results in the difference in predicted ETo. The greater ETo predicted by the controller is not likely to result in adverse effects to the crop, while lower prediction could potentially lead to plant water stress. The documented savings by T2 through T4 (Results column; Table 6) as compared to the automatic timer treatment were similar to findings reported by others (similar studies column; Table 6). Our study was different from previous research as our rain sensor treatment savings were never \17 %, while others reported savings as low as 7 %. Alternatively, some researchers reported savings of 95 % for SWS technologies, while our greatest water saving was only 75 %. The water savings results of the ET controller were relatively high compared to other studies (Table 6), and still the turfgrass quality was maintained
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throughout the study. The greater savings may be due to the relatively wet year, in which this study was conducted. Annual average rainfall rate for the FAWN weather station located near the study site is 1,490 mm, while rainfall during our study was 1,900 mm. Nutrient leachate Lysimeters were checked after each irrigation event for leachate. However, leaching is only possible when the soil water content exceeds soil water holding capacity. During the first period of the study when irrigation rates were \32 mm, they were generally empty except after rainfall events. This was likely due to the lower irrigation rates (15 and 19 mm), which were at least 40 % lower than soil water holding capacity (i.e., 36 mm). However, when irrigation was increased for T1 (i.e., 32 mm per event; January 19), leachate was collected from T1 after every irrigation event. Leachate was collected for T1 consistently after this increase (and not in other treatments), indicating that the previous rates of irrigation were not sufficient to result in drainage (Fig. 1). The 32-mm irrigation rate for T1 from January 19, 2012 to February 16, 2012, resulted in significantly greater drainage depths for T1 as compared to all other treatments with a mean and median of 11 and 8 mm, respectively. T2, T3, and T4 combined had a mean and median of 3 and 0 mm, respectively, for the same period. Drainage water from the lysimeters was only collected from October 2011 to February 16, 2012, and thus did not span the entire study due to findings that suggested the lysimeters were not operating as intended. Results showed that drainage quantities were smaller than expected. For example, after a 144-mm rainfall event (October 8), only 21–28 mm was collected from the lysimeters. The water holding capacity of our soils is approximately 0.08 cm/cm, and thus, a value of near lysimeter full volume was expected (i.e., 38 mm). This occurred consistently throughout the experiment, suggesting that the lysimeters
Fig. 1 Drainage and rainfall depths collected by treatment mean (n = 3; error bars represent standard deviation) where T1–T4 are automatic timer, automatic timer with rain sensor, automatic timer with soil water sensor, and evapotranspiration controller, respectively
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were not collecting a representative volume of drainage flow. We hypothesized this to be due to preferential flow and/or infiltration rates of the lysimeter. During the lysimeter installation, the top layer of soil was removed and replaced, so normal drainage patterns were disturbed and preferential flow pathways may have been created, which drain outside of the lysimeter catchment area. In addition, Booltink and Bouma (1991) reported that a portion of applied water is usually intercepted in discontinuous macropores (internal catchments) within the soil. Preferential flow may occur in, and even be enhanced by, unsaturated conditions, which corresponds to when most differences occurred between observed and simulated values. There are several examples of this occurrence, as cited by Nimmo (2012). Installation of the lysimeters and associated soil disturbance may also have resulted in textural difference in the soil that would influence drainage water movement into the lysimeter. Future lysimeter designs should minimize preferential flow impacts on collected water volumes in both dry and wet conditions as well as accommodate for a higher infiltration rate into the lysimeter. Although lysimeters did not collect entire volumes of drainage, we contend that the concentrations were representative of leachate as the mechanical limitations of the lysimeters would not impact these measurements. Leachate water quality samples collected were analyzed, and results for NO3–N and NH4–N were evaluated statistically, considering concentrations and loads (Table 7). Data were found to be non-normally distributed. Leachate data collected after the first fertilizer event (October 2011– January 18, 2012) for NO3–N and NH4–N were not significantly different between the treatments. Some differences were identified after the second fertilizer treatment, where the automatic timer (T1) was significantly different than all other treatment median concentrations and loads for both NO3–N and NH4–N. It should be noted that the higher irrigation rate (i.e., 32 mm per event) also coincided with the second fertilizer period. Thus, results suggest that irrigation rates [32 mm per event would significantly increase N leaching from urban turfgrass in this environment. Note that root depth was approximately 15 cm and so soil water holding capacity (considering loamy sand at 0.12 cm/cm) was 1.8 cm total. Box plots for the four data sets (NO3–N and NH4–N, after first fertilization and after the second fertilization) indicate that while values were typically low, some high concentrations did occur (Fig. 2). While the N leaching load is uncertain due to issues identified with the lysimeters, the concentrations are considered representative of the system. The greatest N concentration values in leachate were not associated with the largest rainfall events, but rather with smaller rainfall events. This is likely due to dilution of the N, which occurs with greater volumes of water.
Irrig Sci (2014) 32:193–203
201
NO3–N (1)
NO3–Na (2)
NH4–N (1)
NH4–Na (2)
mg/ L
mg
mg/L
mg
mg/ L
mg
mg/L
mg
Median Mean
0.00 0.35
0.00 0.08
0.30a 2.29
0.02a 0.41
0.00 0.66
0.00 0.03
0.02a 1.43
0.01a 0.19
SD
2.04
0.47
4.08
0.86
1.86
0.08
2.23
0.38
Our results suggest that leachate only occurred with rainfall events and irrigation rates greater than recommended rates. This implies that nutrient leaching in our system was driven more by rainfall than the irrigation technology used as long as automated irrigation systems were set within recommended rates. The study also showed that the watersaving technologies, such as rain sensors, SWSs, and ET controllers, significantly reduce irrigation-related leaching when automatic controllers were set to irrigation 32 mm per event. Other studies have also indicated that SWS-based irrigation reduces nutrient leaching (Augustin and Snyder 1984; Snyder et al. 1984; Pathan et al. 2007).
0.00
0.00
0.00b
0.00b
0.00
0.00
0.00b
0.00b
Turfgrass quality
Table 7 Comparison of nitrate (NO3–N) and ammonium (NH4–N) lysimeter concentrations and loads for periods following two fertilizer applications [noted as 1 (n = 84) and 2 (n = 27)] by treatment: automatic timer (T1), automatic timer with rain sensor (T2), automatic timer with soil water sensor (T3), and ET controller (T4)
T1
T2 Median Mean
0.45
0.12
0.60
0.07
0.08
0.01
0.01
0.00
SD
2.72
0.70
3.02
0.34
0.54
0.07
0.01
0.00
Median
0.00
0.00
0.00b
0.00b
0.00
0.00
0.00b
0.00b
Mean
0.07
0.03
0.23
0.13
0.37
0.07
0.01
0.00
SD
0.38
0.17
1.17
0.64
1.21
0.28
0.01
0.00
Median
0.00
0.00
0.00b
0.00b
0.00
0.00
0.00b
0.00b
Mean
0.49
0.17
0.01
0.00
0.09
0.01
0.01
0.00
SD
2.39
0.97
0.02
0.00
0.65
0.05
0.01
0.00
T3
T4
a
Treatment median depths with different letters were significantly different (p \ 0.05) by column (application rate) based on Tukey’s test. n = sample size
Results from the three turfgrass evaluations conducted in December 2011, February 2012, and October 2012 indicated that the overall quality of the turfgrass for each treatment either remained the same or slightly decreased over the duration of the experiment with values ranging from 5 to 6. No significant differences were observed between treatments for turfgrass visual quality. Tissue and soil nutrients The results of the initial (before fertilization; August 2011), mid-project (April 2012), and final (September 2012)
Fig. 2 Box plots of concentration data after the first fertilization (a, c; October–17 January 2012) and after the second fertilization (b, d; 18 January 2012–16 February 2012). The box represent the 25th and 75th %, the whiskers represent the 10th and 90th %, and the points represent values outside of the 90th %
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Table 8 Total percent nitrogen and carbon in the turfgrass tissue and soil for three sampling events Total % N
Total % C
Initial
Midproject
Final
Initial
Midproject
Final
Mean
1.9
2.2
2.2
44.9
42.7
45.5
Median
2.0
2.1
2.2
44.9
42.6
45.5
Mean
0.2
0.2
0.3
5.3
5.6
6.2
Median
0.2
0.3
0.3
5.7
5.5
6.3
Tissue
Soil
turfgrass tissue and soil total percent N and total percent C showed no differences between the treatments. The mean and median values are reported in Table 8.
Conclusion Water volumes applied, water volumes drained, and NO3–N and NH4–N loads and concentrations leached were measured in a plot study consisting of four irrigation treatments: automatic timer, automatic timer with rain sensor, automatic timer with SWS, and ET controller. Results showed that the automatic timer treatment applied significantly more water than other treatments, with savings for the rain sensor, SWS, and ET controller treatments being 17–49, 64–75, and 66–70 %, respectively. Irrigation rates applied were not significantly different for the SWS and ET irrigation treatments for all irrigation rates. However, all technologies applied significantly less irrigation than the automatic timer treatment. The greater savings for the SWS and ET controller treatments could be attributed to their real-time measurements, and their soil water-based approach where the SWS measured soil water content and the ET controller calculated a soil water balance. Drainage was collected primarily after rainfall events for irrigation rates \32 mm. When irrigation was increased for the automatic timer treatment to 32 mm per event, drainage was collected from this treatment following every irrigation event. Lysimeter water volumes were less than expected, probably due to preferential flow and/or infiltration rates of the lysimeter. NO3–N and NH4–N concentrations and loads for the automatic timer treatment were significantly greater than all other treatments during the 32 mm per event irrigation rate and after the second fertilizer event. Turfgrass visual quality, soil N and C composition, and turfgrass N and C composition did not significantly change from the beginning to the end of the study. Results suggest that leaching mainly coincided with rainfall events and irrigation rates greater than
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recommended rates. This implies that nutrient leaching in our system was driven more by rainfall than by the irrigation technology used as long as automated irrigation systems were set within recommended rates. The study also showed that the water-saving technologies, such as rain sensors, SWSs, and ET controllers, significantly reduce irrigation-related leaching when automatic controllers were set to irrigate 32 mm per event. Based on these findings, irrigation scheduling using the technologies tested or irrigation using recommended rates for Florida (13–19 mm) would minimize N leaching from irrigation events. While we were able to identify water-saving irrigation technologies and verify a significant increase in N leaching for the 32 mm irrigation rate per event treatment, we recognize that more research is needed. Future studies should focus on the development and evaluation of a lysimeter design to more accurately collect drainage water. In addition, our study only considered one turf grass type, bahiagrass; evaluation of nutrient leaching from other common grasses (e.g., St. Augustinegrass, Bermudagrass) would be useful. Acknowledgments We thank the University of Florida Institute of Food and Agricultural Sciences (UF IFAS) Tropical Research and Education Center, Hunter Industries, Carlos Victoria, ValleyCrest, Tina Dispenza, Michael Guiterrez, Jie Fan, Rick Lusher, FAWN, Miami Dade County Water and Sewer.
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