APPROACHES FOR TWO-DIMENSIONAL MONITORING AND NUMERICAL MODELING OF DRIP SYSTEMS

Transcription

1 APPROACHES FOR TWO-DIMENSIONAL MONITORING AND NUMERICAL MODELING OF DRIP SYSTEMS By JASON T. ICERMAN A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENGINEERING UNIVERSITY OF FLORIDA

2 2007 Jason T. Icerman 2

3 To my parents, Drs. Joe and Rhoda Icerman. 3

4 ACKNOWLEDGMENTS The only appropriate way to begin a list of those who have helped me to this point is by thanking those who helped me enter this discipline. For giving a lost undergraduate a chance to find his direction and never being short on time to listen, I thank Dr. James Leary. Thanks are also extended to Dr. Wendy Graham for allowing an inquisitive young engineer a chance to get his hands dirty in the field. For their assistance with my research over the past few years, both in the field and out, I thank Danny Burch, Stacia Davis, Kristen Femminella, Paul Lane, Larry Miller, Jonathan Schroder, Mary Shedd, Hannah Snyder, and especially Lincoln Zotarelli. Lincoln while your advice occasionally requires translation, it has proved invaluable time and again. Special thanks go to Dr. Michael Dukes for reasons too numerous for listing here. You have allowed me to study as both an undergraduate and graduate student in my own manner, which anyone reading this document surely knows to be unique. Also Dr. Rafael Muñoz- Carpena deserves thanks for answering my late-night s and helping me decipher the endless world of vadose zone modeling. Finally as this document is dedicated to them, hearty thanks go to my parents. I thank them for the encouragement. I thank them for the unyielding support. And I thank them for the greatest gift I have ever received: their love of learning. 4

5 TABLE OF CONTENTS ACKNOWLEDGMENTS...4 LIST OF TABLES...8 LIST OF FIGURES...9 ABSTRACT...11 CHAPTER 1 RESEARCH BACKGROUND...13 page Rationale...13 Vegetable Production in Florida...13 Drip Irrigation...14 Drip Irrigation Modeling...15 Objectives COMPARISON OF IN-SITU DIELECTRIC PROBE PERFORMANCE IN A RAISED VEGETABLE BED...18 Introduction...18 Materials and Methods...21 Measurement Methods...21 Experiment 1: 2005 In-Season...24 Experiment 2: Non-Planted...26 Experiment 3: 2006 In-Season...26 Equations Used in Analysis...27 Results and Discussion...28 Experiment 1: 2005 In-Season...28 Water content reflectometer precision...28 Soil sample comparisons...30 Experiment 2: Non-Planted...32 Vitel precision...32 Probe to probe comparison...32 Experiment 3: 2006 In-Season...33 Probe to probe comparison...34 Soil sample comparisons...34 Summary and Conclusions WATER ENTRY BOUNDARY CONDITION IMPACTS ON THE CALIBRATION OF HYDRUS-2D TO A SURFACE DRIP IRRIGATION SYSTEM...57 Introduction

6 Materials and Methods...60 Field Experiment...60 Model Description...61 Water Entry Boundary Condition...62 Additional Boundary and Initial Conditions...64 Calibration and Optimization Procedure...65 Prediction Evaluation...66 Results and Discussion...67 Field Results...67 Initial Calibration...68 Full Calibration...70 Summary and Conclusions UNCERTAINTY IMPACTS ON NUMERICAL MODELING OF FERTIGATION...83 Introduction...83 Materials and Methods...85 Measurement Methods...85 Experimental Site...87 Experiment 4: 2006 Post-Season...88 Model Description...88 Initial and Boundary Conditions...89 Calibration and Optimization Procedure...92 Prediction Evaluation...93 Results and Discussion...95 Field Results...95 Soil Hydraulic Parameter Calibration...96 Soil Transport Parameter Calibration...98 Validation Simulations Summary and Conclusions RESEARCH SUMMARY AND FUTURE WORK Research Summary Future Work APPENDIX A SELECT HYDRUS-2D INPUT FILES Initial Calibration of 2DSC Initial Calibration of 3DSC Full Calibration of 2DSC Full Calibration of 3DSC Calibration Bounded by Carsel and Parrish Distributions Calibration Bounded by ROSETTA Distributions Calibration Bounded by Measured Distributions

7 Calibration Bounded by Vanderborght and Vereecken Distributions B POSSIBLE IN-SEASON IMPACTS ON CALIBRATION Introduction Results Summary LIST OF REFERENCES BIOGRAPHICAL SKETCH

8 LIST OF TABLES Table page 2-1 Summary of experiments performed, data collected, means and methods Quantitative comparison of all locations in the timer-based treatment and the sensorbased treatment using the factory sand calibration Quantitative comparison of probe type by location for both the factory and site calibrations of each probe type Results of surface area and influx calculations for the different scenarios simulated in HYDRUS-2D Estimated soil hydraulic parameters for van Genuchten model fit to 11 soil core samples by RETC model Results from initial calibration Results from full calibration Results from soil hydraulic parameter calibrations Reported dispersivity values used in previous HYDRUS-2D fertigation studies

9 LIST OF FIGURES Figure page 2-1 The 2 X 3 matrix formation used in Experiment 1 with labels used in discussion The 2 X 4 matrix formation used in Experiment 2 and Experiment 3 for both probe types with labeling used in discussion Comparison of edge probes within each TIMER treatment matrix Comparison of probes between the TIMER treatment matrices Time-series data from all edge probe locations in the TIMER treatment Comparison of edge probes within each SMS treatment matrix Comparison of probes between the SMS treatment matrices Time-series data from all edge probe locations in the SMS treatment Comparison of WCR SMC and VWC data for the center locations of SMS and TIMER treatments Comparison of WCR SMC and VWC data for the edge locations of SMS and TIMER treatments Comparison and calibration of WCR and gravimetric SMC data Comparison of calibrations for a selected time-series from the TIMER treatment Location comparisons within Vitel probe matrix of the TIMER treatment for Experiment Time-series data from all probe locations in the TIMER treatment Comparison of Vitel to WCR SMC for each location within the bed Relationship of SMC measured in the center of the bed Comparison of the WCR and Vitel probes for each location within the bed during Experiment Residuals for each location presented as Vitel WCR within the TIMER treatment bed during Experiment Comparison of Vitel KNO 3 burden data and soil sample NO 3 -N data Time-series KNO 3 burden for each probe location

10 2-21 Comparison of VWC to the WCR SMC using the 2005 calibration Comparison of gravimetric SMC to WCR using the factory calibration for sand Experiment 2 WCR matrix configuration centered in bed Water entry boundary conditions examined for soil moisture content prediction Averaged soil moisture content from data measured on site Measured soil moisture content from each probe Results of RETC model calibration to soil core data Results of initial calibration soil moisture content predictions Comparison of initial and full calibration soil moisture content predictions Experiment 3 and 4 WCR matrix configuration centered in bed Experiment 4 WCR soil moisture content measurements Experiment 3 Vitel determined nitrate concentrations Root distribution collected during full canopy Parameter Sets 1, 2, and 3 soil hydraulic parameter calibration Nitrate concentration predictions for May 30 fertigation Nitrate concentration predictions for June 6 fertigation Nitrate concentration predictions for June 14 fertigation A-1 Boundary conditions for 2DSC1.0 simulation A-2 Numerical node structure for 2DSC1.0 simulation A-3 Boundary conditions for 3DSC1.0 simulation A-4 Numerical node structure for 3DSC1.0 simulation B-1 Averaged data used in calibrations B-2 Location by location comparison for measured soil moisture content B-3 Results of Experiment 2 calibration and Experiment 4 measured data

11 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Engineering APPROACHES FOR TWO-DIMENSIONAL MONITORING AND NUMERICAL MODELING OF DRIP SYSTEMS Chair: Michael Dukes Cochair: Rafael Muñoz-Carpena Major: Agricultural and Biological Engineering By Jason T. Icerman August 2007 In Florida, intensive bed management systems are commonly used for vegetable production. These systems consist of raised beds for planting covered with plastic mulch, with water and nutrients commonly applied via drip irrigation and fertigation. Currently available dielectric soil moisture sensors provide inexpensive alternatives when compared to Time Domain Reflectometry (TDR) and the labor costs of soil sampling. The CS616 water content reflectometer (WCR) and the Hydra Probe II (Vitel), operating on time-domain and capacitance methods respectively, were installed beneath drip irrigated tomatoes in an intensively managed vegetable production system to examine the monitoring capabilities of each probe through oneto-one and time-series comparisons. The probes were installed in a two-dimensional grid to capture the soil moisture content (SMC) distribution beneath drip irrigation. It was observed during one-to-one comparisons that SMC measured using the factory calibration provided with each probe failed to match volumetric water content (VWC) determined from gravimetric soil samples. However, the longer measurement distance of the WCR probe (150% emitter spacing) allowed for relatively good calibration to VWC data (R 2 = 0.74), while the short measurement distance of the Vitel probe (28.5% emitter spacing) resulted in a poor calibration (R 2 = 0.26). 11

12 Time-series observations were more positive as both probes matched the season-long SMC trends well. And the Vitel probe was observed to match soil water salinity trends during timeseries following two different fertigation events. The HYDRUS-2D program (H2D) has previously been used for drip irrigation management forecasts. A review of the simulations reported in the literature revealed an assortment of techniques for defining the model simulation space. To examine the effectiveness of these techniques, H2D was calibrated to SMC data collected from a non-planted bed section with soil moisture release curve (SMRC) parameters determined from undisturbed soil cores and saturated hydraulic conductivity determined by inverse optimization. The goodness-of-fit indicator (C eff ) was also modified to account for measurement uncertainty (C eff *). Semi-spherical (SC) soil wetting geometries proved superior to their surface-radius counterparts in convergence and simulation time, but nearly identical in SMC prediction. Both the axi-symmetrical and twodimensional SC approaches predicted the SMC data well (C eff > 0.75) and especially well after uncertainty in the SMC and SMRC measurements was considered (C eff * = 1.0). It was also observed that most previous studies of fertigation using H2D used mean values for soil parameter estimation. The determination of appropriate soil hydraulic and transport parameters is essential to accurately simulate distributions beneath fertigation. To account for soil parameter uncertainty, inverse optimization methods were applied for soil hydraulic and transport parameter calibration. Calibration of the soil hydraulic parameters revealed high bubbling pressure (~0.17 cm -1 ) was required to obtain even modest predictions in the bed center (C eff = 0.27). Calibration of soil transport parameters yielded a longitudinal dispersivity of 2.38 cm and a transverse dispersivity of 0.01 cm (C eff = 0.56). As before, accounting for measurement uncertainty improved the results of both calibrations, C eff * = 0.99 and 0.80, respectively. 12

13 CHAPTER 1 RESEARCH BACKGROUND Rationale The economic importance of vegetable production in Florida was the driving force behind the research presented in this document. Funding for this research was provided by the Florida Department of Agriculture and Consumer Services (FDACS) as part of the Integration and Verification of Water Quality and Crop Yield Models for BMP Planning research program. For the purpose of direction, the presentation of research is preceded by a brief introduction of significant water management issues for vegetable production in Florida. Since drip irrigation systems are currently common for vegetable growers in Florida and the focus of this research, general information on drip systems is provided. Concurrently, drip systems exhibit many unique aspects that must be considered for monitoring and modeling efforts. These aspects are also discussed and provide a solid base for fully understanding the research to be presented. Ultimately, specific objectives of the research will be outlined as related to the subsequent chapters within this document. Vegetable Production in Florida Water is a vital resource and is the first limiting nutrient of crop production. Agricultural self-supply accounts for 39% of fresh ground water withdrawals and 62% of fresh surface water withdrawals the highest percentage in either category making agriculture the largest user of freshwater in Florida (Marella, 1999). As the largest consumer of freshwater resources, improved agricultural management practices on a field scale possess the potential for large scale conservation. Vegetables are a major component of Florida agriculture encompassing about 72,000 ha for production and valued at $1.5 billion annually (USDA, 2006). Most of the soils where these 13

14 vegetables are grown are sandy and as such, frequent irrigation and fertigation is required to minimize crop stress and attain maximum production. Currently, over 30% vegetable production in the state occurs in raised beds often covered with plastic mulch otherwise known as intensive bed management systems. Commonly in intensive bed management systems, water is introduced only by irrigation and fertigation via drip emitters. Conversely, resource extraction is limited almost solely to transpiration, as plastic mulch covering the raised bed minimizes the influence of rainfall and soil evaporation (Simonne et al., 2004). Though an established technology still growing in popularity, intensive bed management systems remain understudied and more effective management and measurement techniques continue to be developed all over the globe (Amayreh and Al-Abed, 2005; Vazquez et al., 2006; Zotarelli et al., 2007a). Drip Irrigation Drip irrigation has the potential to enhance the sustainability of high intensity vegetable production by eliminating excess irrigation and reducing chemical leaching. Correct surface placement of drip emitters enables the infiltration process to occur over a small area and promotes three dimensional flows in shapes that have been described as a wet bulb. And most importantly, drip irrigation targets water and nutrient delivery to the root zone, increasing water and nutrient uptake efficiency (Goldberg at al., 1971). Drip irrigation also helps reduce foliar disease incidence compared to overhead sprinkler systems. By maintaining drier plants drip irrigation reduces outbreaks of bacteria and fungal diseases, hence reducing the need for bactericides and fungicides (Hochmuth and Smajstrla, 1998). Also, fertilizers can be prescription-applied during the season based on crop needs. These small, controlled applications of fertilizer not only save fertilizer, but also have the potential to eliminate groundwater pollution caused by leaching from over-irrigation (Schroder, 2006). 14

15 Though resource conservation is the strength of intensive bed management systems, the added cost of drip irrigation places a greater strain on resource management for these systems. Assuming common Florida conditions, annual drip irrigation costs have been estimated at $363 per acre for drip irrigation systems, which is over $100 per acre more than other common irrigation methods such as semi-closed and open-ditch irrigation systems (Pitts et al., 1990). In order to ensure further transition of the vegetable industry to intensive bed management systems, the systems must be proven an economical option. And while drip irrigation and fertigation can be very efficient, mismanagement can lead to over-irrigation and excessive nutrient losses due to leaching. Enhanced monitoring and modeling techniques are necessary to achieve the most effective management cost conserving techniques for intensive bed management systems; moreover, two-dimensional monitoring remains a research need for the assessment of forecasting models (Cote et al., 2003). Drip Irrigation Modeling To accurately predict environmental impacts associated with human practices, a quantitative description of both water and solute movement through the vadose zone is required; furthermore, for drip irrigation it is essential to account for the two-dimensional nature of the system (Muñoz-Carpena et al., 2005b). Lubana and Narda (2001) reviewed modeling approaches specific to drip irrigation and found both over-simplification and over-complexity to have adverse affects in modeling drip irrigation flow dynamics. Feyen et al. (1998) reviewed several models in existence at the time, focusing on the inclusion of both micro- and macro-heterogeneity; it was noted that microheterogeneity on a field scale, such as macropores, could increase the risk of leaching pollutants making such field characteristics vital for accurate modeling to occur. Such a need for dualpermeability models able to handle micro-heterogeneity and simulate chemical transport under 15

16 field conditions has become an outstanding research need for determining the fate of nutrients and other non-point source pollutants (Simunek et al., 2003). Recently the two-dimensional, finite element model HYDRUS-2D, which numerically solves Richards' equation for saturatedunsaturated water flow and the convection-dispersion equation for solute flow, has been applied to multiple drip irrigation systems and proven to be a reliable predictor of soil moisture dynamics (Gardenas et al., 2005). Emitter placement and other characteristics of drip systems allow for different assumptions when modeling the system in HYDRUS-2D, which correspond to boundary conditions representing point source and line source assumptions. A point source boundary, representative of an isolated emitter, creates a quasi-spherical wetted soil region, more or less elongated depending on soil textural characteristics. A line source boundary is applicable when several point sources overlap along an axis placed in the soil surface (i.e. along a crop bed), as may be the case under drip tape. A review of the literature conducted for this study noted there have been three distinct drip system validations of HYDRUS-2D soil moisture predictions using field data (Fernandez-Galvez et al., 2006; Mmolawa and Or, 2003; Skaggs et al., 2004). Two validations were performed on subsurface drip irrigation (SDI) systems, with only Fernandez-Galvez et al. (2006) presenting field data collected beneath surface drip irrigation. Field validation of fertilizer distributions beneath drip systems is even more limited, as only Ajdary et al. (2007) has presented HYDRUS- 2D simulations validated by field measurements (nitrogen fertilizer in the form of urea). In a laboratory setting, Li et al. (2005) measured soil moisture and nitrate-nitrogen in soil cores following fertigation and showed HYDRUS-2D to be an accurate predictor of the system. As can be inferred through this summary of the existing literature, the conclusions of Cote et al. (2003) 16

17 remain valid. Two-dimensional field monitoring of drip irrigation and fertigation systems remains a research need to both strengthen the literature data set and further validate twodimensional model predictions, especially fertigation systems. Objectives Examine the ability of current low cost technologies to measure two-dimensional distributions beneath drip irrigation in a raised vegetable bed Calibrate in-situ soil moisture sensors to volumetric moisture obtained from gravimetric sampling on a one-to-one basis Examine the ability of in-situ measurements to track time-series of property changes following irrigation and fertigation events in a raised vegetable bed Reproduce measured two-dimensional soil moisture distributions using HYDRUS-2D Examine the effect of different water entry boundary conditions Reproduce measured two-dimensional nitrate distributions following fertigation events using HYDRUS-2D Examine the effect of uncertainty in soil hydraulic and transport parameter estimation on fertigation predictions Direct future research efforts in the field 17

18 CHAPTER 2 COMPARISON OF IN-SITU DIELECTRIC PROBE PERFORMANCE IN A RAISED VEGETABLE BED Introduction Vegetables are a major component of Florida agriculture encompassing about 72,000 ha for production and valued at $1.5 billion annually (USDA, 2006). Most of the soils where these vegetables are grown are sands, with frequent irrigation and fertigation required to minimize crop stress and attain maximum production. Water and nutrient delivery to these systems is commonly provided by drip irrigation. Although drip irrigation and fertigation can be very efficient, delivering water and nutrients to the crop root zone (Goldberg et al., 1971), mismanagement can lead to over-irrigation and excessive nutrient losses due to leaching. Also much of the vegetable production in the state occurs on raised beds covered with plastic mulch. Plastic mulch minimizes the influence of evaporation and rainfall in the system (Simonne et al., 2004) isolating irrigation effects and making these systems ideal for experimental monitoring of distributions beneath drip irrigation. To date, two-dimensional monitoring of soil moisture content (SMC) and nutrient distributions for the assessment of drip system effectiveness as well as forecasting model predictions remains a research need (Cote et al., 2003). Traditionally SMC is determined by gravimetric soil sampling, often reported as volumetric moisture content (VWC) after multiplying by bulk density. While VWC is easily calculated from gravimetric data, soil sampling is labor intensive and physically destructive. Any system disturbance is compounded when lateral distributions are desired, especially in confined areas such as raised bed systems. Also errors inherent with soil sampling both for collected gravimetric and bulk density samples creates a sizable source for potential error in reported VWC. The combination of labor cost, 18

19 system disturbance, and potential errors makes non-destructive in-situ measurement techniques a preferable alternative. Time domain reflectrometry (TDR) is perhaps the most well known in-situ technique and is widely accepted as one of the most accurate methods. TDR can be used on a variety of soils using only a single calibration equation (Topp et al., 1980). Yet due to the high cost of TDR, multiple alternative in-situ sensors have been developed. Two common alternatives to TDR are the CS616 (Campbell Scientific, Inc., Logan, Utah) water content reflectometer (WCR) and the Hydra Probe (Stevens Water Monitoring Systems, Inc., Portland, Oregon). The WCR probe is a dielectric probe that uses time-domain methods for measuring SMC, meaning the probe response for equal VWC in varying soils will be similar (Campbell Scientific, Inc., 2002). The Hydra Probe is also a dielectric probe, but uses bulk capacitance measurements to calculate soil properties, like SMC. The utilization of capacitance methods means the probe response for equal VWC in varying soils will not be similar, ie. capacitance probes require at minimum soil class specific calibration (Stevens Water Monitoring Systems, Inc., 2007). Hydra Probe success resulted in the recent development of a similar product, the Hydra Probe II (Stevens Water Monitoring Systems, Inc., Portland, Oregon), with both commonly referred to as Vitel probes. The main advantages of the new Vitel probe (Hydra Probe II) relative to the old Vitel probe (Hydra Probe) are a decrease in power usage and an increase in cable length. All future references to the Vitel probe in this document are references to the new Vitel probe. Another feature of the Vitel probe is the ability to measure bulk salinity and soil water salinity, which can be reported as a NaCl burden or KNO 3 burden (g L -1 ). While soil water salinity is not a common plant stressor for vegetable production in Florida, it can be considered a tracer for agricultural systems in sandy soils representative of applied nutrient dynamics due to 19

20 near zero soil water salinity initial conditions, oxidation conditions, and the negatively charged soil matrix. Also when collecting point measurements to outline distributions it is important to bear in mind the collection volume for a specific method. The WCR has an estimated sensing volume of ~900 cm 3 and sensing length of 30 cm (Campbell Scientific, Inc., 2002). Due to the probe length of a WCR, horizontal probe installation is required to capture both vertical and transverse distributions. Plauborg et al. (2005) noted that horizontally installed WCRs exhibited large variation between probe replicates in the field. The authors considered the probe variation to be a product of both poor factory calibration provided by the manufacturer and the natural heterogeneity found in top soil. The study also found better probe correlation at lower SMC, while reporting WCR probes to measure SMC consistently lower than TDR measurements. The Vitel probe differs from the WCR in sensing volume, 40.3 cm 3, and sensing length, 5.7 cm (Stevens Water Monitoring Systems, Inc., 2007). Yet similar to the WCR, the old Vitel probe has been observed to underestimate SMC in sandy soils when compared to VWC obtained from gravimetric samples (Kennedy et al., 2003); however, the opposite has been reported when the old Vitel probe is compared to TDR. Seyfried and Murdock (2004) concluded that while soil specific calibrations of the old Vitel probe increased accuracy relative to TDR across most soil types, for sandy soils the manufacturer provided factory calibration matched TDR values for SMC very well. As has been previously described through examples here and is often the case in monitoring studies, probe results are commonly compared on a one-to-one basis to established methods. Yet one of the known advantages for continuous in-situ monitoring is the ability to track soil properties over extended time-series (Kennedy et al., 2003); therefore, in this study 20

21 another approach will also be employed for probe examination, comparing the probe measurements over event-long and season-long time-series. Time-series comparison inclusions allows for a more descriptive examination of probe performance, as probe SMC measurements may be consistently off +/ cm 3 cm -3 relative to established methods, but able to match the wetting and drying trends following an irrigation event very well. Simply capturing these trends can be very useful for irrigation management and like studies. Similarly, soil water salinity burdens can be examined over an entire fertigation event or the season-long build-up and reduction of fertigation constituents in the soil. These comparisons will be referred to as timeseries comparisons. Considering this, the objective of this study was to analyze the potential for using a time-domain probe (WCR) and a capacitance probe (Vitel) for two-dimensional monitoring beneath drip irrigation by comparing probe SMC measurements to VWC measurements obtained from gravimetric samples and also comparing Vitel probe measured soil water salinity to soil water nitrate-nitrogen (NO 3 -N) obtained from soil samples, in each case using one-to-one and time-series comparisons. Materials and Methods Three distinct experiments were performed at the University of Florida, Plant Science Research and Education Unit located near Citra, Florida. Buster (1979) classified the soil at the research site as a Tavares sand and Candler sand. These soils contains >97% sand-sized particles and have a field capacity of (cm 3 cm -3 ) in the upper 100 cm of the profile (Carlisle et al., 1978). The experiments are summarized in Table 2-1, with further discussion to follow. Measurement Methods SMC was measured in-situ by dielectric probes, the WCR and Vitel. The WCR probe uses time-domain methods for SMC measurement and consists of two 30 cm long stainless steel rods connected to a printed circuit board. The probe rods can be inserted from the surface or as in the 21

22 case of this study, the probe can be buried at any orientation to the surface. The differentiallydriven probe rods form a transmission line with a wave propagation velocity that is dependent on the dielectric permittivity of the medium surrounding the rods. Since water has a dielectric permittivity significantly larger than other soil constituents, the resulting oscillation frequency is dependent upon the average SMC of the medium surrounding the rods (Campbell Scientific, Inc., 2002). The Vitel probe uses bulk capacitance measurements to calculate SMC, by making a high frequency (50 MHz) complex dielectric constant measurement. The probe head contains the necessary electronics to generate the 50 MHz stimulus and generate voltages that reflect the soil's electrical properties. The three outer and one center tine form the sensing volume of soil. The capacitive part of the response is most indicative of SMC, while the conductive part reflects bulk salinity. Through the use of appropriate calibration curves that are related to soil type, the dielectric constant measurement can be directly related to SMC (Stevens Water Monitoring Systems, Inc., 2007). For SMC comparison, VWC was determined using collected gravimetric samples. Soil samples were collected by a 5 cm diameter soil auger. Each reported soil sample location yielded two depths of composite samples: 0-15 and cm. All collected samples were immediately placed on ice and refrigerated until analyzed. A 20 g subsample was used to determine the gravimetric water content for each composite sample, which in turn was used to calculate (multiplying by the bulk density) the VWC. In order to determine VWC from collected gravimetric data, bulk density measurements were also collected. The field bulk density was estimated by the average bulk density of 11 soil samples collected by an undisturbed core sampler. Soil cores measured 5.4 cm in diameter and 22

23 6.0 cm in height and samples were collected between a depth of 0-6 and 6-12 cm below the bed surface at various places in the field. The samples were saturated to measure wet weight and oven dried to measure dry weight. The average measured bulk density in the field beds was 1.26 g cm -3 with a 6.92% standard deviation. Through this method, it is assumed that bulk density is constant with depth. To track applied fertigation, the soil water salinity burden (KNO 3 g L -1 ) was measured by the Vitel probes. However, the fertigation applied during the study was not KNO 3, but consisted of Ca(NO 3 ) 2, KCl, and Mg(SO 4 ) compounds. Previous studies have used electrical conductivity (EC) measured by capacitance probes to track individual ions such as Br, Cl, and NO 3 (Muñoz- Carpena et al., 2005a), but the KNO 3 burden measured by the Vitel probe is reported here since it is included with the manufacturer provided program and requires no extra work on part of the end user. This is inline with the study objective of testing the probe s effectives as a monitoring tool, not necessarily electrical quantities measured by the probe; therefore, all reported KNO 3 burdens in this study are actually representative of all applied fertigation compounds. It should also be noted that the measured KNO 3 burden is subject to calculation errors as well as measurement errors, since the conversion from bulk burden to soil water burden used was simply a division by SMC (Stevens Water Monitoring Systems, Inc., 2007) and previous studies have shown the relationship between bulk burden and soil water burden to be much more complex (Muñoz-Carpena et al., 2005a). For soil water salinity comparison, soil water NO 3 -N was determined from collected soil samples. These samples were collected in the center of the bed with one sample uniformly mixed from the 0-30 cm depth. The samples were collected -1, 1, 3, and 7 days following a given fertigation event. For NO 3 -N analysis a 10 g subsample was extracted and 50 ml of 2 M KCl 23

24 was added to the subsample. The resulting mixture was filtered by gravity using Fisherbrand filter paper within one day of soil sampling (Mulvaney, 1996). Soil solution extracts were stored at -18 deg C. They were analyzed for NO 3 -N using an air-segmentedautomated spectrophotometer (Flow Solution IV, OI Analytical, College Station, Texas) coupled with a Cd reduction approach similar to the work of Zotarelli et al. (2007a). Experiment 1: 2005 In-Season Between April 12 and June 27, 2005 SMC distributions were monitored under surface drip irrigated tomatoes (Lycopersicon esculentum, FL 47 ). The tomatoes were planted in raised beds covered with black plastic mulch. The two treatments of interest were a timer-based irrigation scheme (TIMER) and a soil moisture sensor-based irrigation scheme (SMS). Both treatments received the University of Florida, Institute of Food and Agricultural Sciences (IFAS) recommended seasonal fertilizer amount of 208 kg ha -1 nitrogen (N) applied as calcium nitrate in weekly fertigation events (Maynard et al., 2003) and were replicated four times within the field. Each treatment contained two surface drip lines (Turbulent Twin Wall, 20 cm emitter spacing, 0.25 mm thickness, 3.72 L min -1 at 69 kpa, Chapin Watermatics, Inc., Watertown, New York), one for irrigation and one for fertigation. The surface drip lines were laid side by side in the center of the bed. Transplants were approximately 45 days old at transplanting on April 7, 2005 and transplanted in a single row approximately 10 cm from the bed center with 45 cm spacing for a plant population of 11,960 plants ha -1. The SMS treatment was set near effective field capacity (~ cm 3 cm -3 ) and was controlled by a Quantified Irrigation Controller (QIC) developed at the University of Florida (Muñoz-Carpena et al., 2006). The QIC device uses a 20 cm long ECH 2 O probe (Decagon Devices, Inc., Pullman, Washington) that was inserted vertically into one representative bed replicate and controlled all replicates to monitor SMC. The QIC was queried every minute at five 24

25 selected time windows during the day, if during any query the ECH 2 O probe returned a SMC below field capacity the QIC allowed irrigation. Conversely, the QIC bypassed irrigation events if the SMC was above field capacity (Dukes and Muñoz-Carpena, 2006). At the beginning of the season, the application windows for the QIC were 12 minutes long beginning at 0812, 1012, 1212, 1412, and 1612 hours and the period of application for the TIMER treatment was 0600 to 0700 hours. Starting May 26, the time windows for the QIC were 24 minutes long beginning at 0824, 1024, 1224, 1424, and 1624 hours and the period of application for the TIMER treatment was 0600 to 0800 hours. During the establishment phase SMC in the beds was maintained at or above field capacity with daily irrigation events to ensure even establishment of all plots. Irrigation treatments were implemented 18 days after transplanting. SMC was measured on an hourly basis by WCRs with the manufacturer provided factory calibration for sand used in data collection. Four matrices each containing six WCRs were installed, two in the SMS treatment and two the TIMER treatment. A 40 cm long section of the entire bed width was removed from both installation locations, centered under an emitter. The section provided enough space for WCR installation parallel to the surface. After installation the section was repacked with the original soil. For each treatment, one WCR matrix was located on the north face of the removed section and one on the south face. The matrices were configured in a 2 X 3 formation (Vertical X Transverse), with a top row buried at 8 cm and a bottom row at 23 cm below the surface. The three columns were spaced 23 cm apart with the center column located in the bed center (Figure 2-1). The purpose of the matrix configuration was to capture the wet-bulb shape of SMC redistribution under the emitter. Soil samples, analyzed for VWC, were collected five times to validate the WCR SMC measurements: April 27; May 11; May 25; June 8; and June 22. Samples were collected from 25

26 each treatment and from all four replicates, compared to the single installation location of the probes. Similar to the probe matrix, soil samples were collected from three points laterally across the bed. A center location and two other locations near the edge, approximately 23 cm from the bed center in each direction. Experiment 2: Non-Planted From October 10 to October 23, 2006 SMC was measured from the same field as Experiment 1 with similar irrigation treatments. For the entire period, the application windows for the QIC were 24 minutes long beginning at 0824, 1024, 1224, 1424, and 1624 hours and the application window for the TIMER treatment was 0600 to 0800 hours, daily. As before, a 40 cm section was removed from one replicate of each treatment; however, for this experiment two changes to the probe matrices occurred. First, each treatment had one WCR probe matrix and one Vitel probe matrix for monitoring. The second change was the matrix size. The 2 X 3 matrix was replaced by a 2 X 4 matrix for each probe type (Figure 2-2). The probes were located 8 cm and 23 cm away from the bed center laterally in each direction and the depths were again 8 cm and 23 cm. The bed sections monitored in this experiment were not planted nor fertigated. Experiment 3: 2006 In-Season From April 14 to July 5, 2006 SMC was measured from the same field as Experiment 1 and Experiment 2, with irrigation treatments identical to Experiment 2. The 2 X 4 matrix configuration and installation method used in Experiment 2 was also used for this experiment (Figure 2-2). The field setup, fertigation, and crop were identical to Experiment 1, with transplanting occurring on April 10, Also similar to Experiment 1, soil samples analyzed for VWC, were collected five times during the season to validate WCR and Vitel SMC measurements: April 27; May 9; May 23; 26

27 June 6; and June 26. The same approach as Experiment 1 was used as samples were collected from each treatment of interest and from all four replicates. To account for the change in probe matrix locations, soil samples were collected from four points laterally across the bed. Two locations 8 cm from the bed center in each direction and the other two locations near the edge, approximately 23 cm from the bed center in each direction. A second group of samples was also collected to capture fertigation distributions. Again, these samples were collected in the center of the bed with one sample uniformly mixed from the 0-30 cm depth. The samples were collected - 1, 1, 3, and 7 days following two unique fertigation events that occurred on May 23 and June 13. Equations Used in Analysis In a sandy soil Plauborg et al. (2005) calibrated horizontally installed WCR probes to TDR measured SMC. Their reported linear relationship was rearranged to yield Equation 2-1. WCR 0.01 TDR = (cm 3 cm -3 ) (2-1) 0.59 All data unless otherwise noted was collected using the manufacturer provided factory calibration for sand for each probe and is presented using this method. Since TDR was not available at the field site to perform a site specific calibration, the Plauborg calibration is presented as an alternative for horizontally installed WCR probes in sandy soils to the manufacturer provided factory calibration and eventual VWC calibration obtained during the study. To compare time-series of probe SMC measurements and established methods the Nash- Sutcliffe (1970) efficiency coefficient (C eff ) was used. The range of C eff lies between 1.0 (perfect fit) and. When C eff is lower than zero the mean value of the measured time-series would have been a better predictor than the probe (Nash and Sutcliffe, 1970). C eff is the reported goodness- 27

28 of-fit indicator in this study because it has been previously reported as a better indicator for timeseries compared to other indicators based on squared residuals (Legates and McCabe, 1999). Results and Discussion Experiment 1: 2005 In-Season Water content reflectometer precision To examine the probe precision, WCR measurements are compared by location. The edge locations are compared both within the matrix (NW8 to NE8, NW23 to NE23, SW8 to SE8, and SW23 to SE23) and between matrices (NW8 to SW8, NW23 to SW23, NE8 to SE8, and NE23 to SE23), while the center locations are compared only between matrices for each treatment (TIMER and SMS) individually (NC8 to SC8 and NC23 to SC23). It is important to establish the probe precision in relation to the monitoring locations before embarking on comparisons between measurement methods. Any significant lack in probe precision would speak to heterogeneity present within the system, making a comparison of methods difficult and likely fruitless. The TIMER treatment results are presented first with SMC reported using the factory sand calibration. Examination of Figure 2-3 reveals reasonable correlation for field data across between the edge locations in each face of the TIMER treatment. And Figure 2-4 shows no consistent bias toward either face, except for in the center of the bed where the center locations appear consistently wetter in the north face. While the measured SMC at the edge locations fall close, but away from the 1:1 line in Figure 2-3, 2-4, and 2-5 displays the time-series of the edge locations and reveals the probe replicates to be very similar. This is especially true once the cm 3 cm -3 SMC probe accuracy is considered (Campbell Scientific, Inc., 2002) as each probes deviation from the average SMC is under 0.01 cm 3 cm -3 for the 8 cm depth and under 0.02 cm 3 cm -3 for the 23 cm depth. It was 28

29 also observed that the spread of the data increases for the center locations as SMC increases. The relation of spread to SMC is in agreement with results reported by Plauborg et al. (2005) as their study revealed more precision for measurement replications at lower SMC. In fact all observations made from Figures 2-3, 2-4, and 2-5 are strengthened when the field setup is considered in addition to the probe accuracy. The double drip line setup prevents the irrigation emitter, or fertigation emitter, or both from being located directly in the center of the bed. While either emitter would be located no further than 2-3 cm from the bed center, the distance is enough to observe a consistent variation in probe moisture. In all, like locations were observed to be within reasonable agreement so that the east and west locations within each face as well as the north and south face of each matrix can be considered replicates for future comparisons. A quick examination of the SMS treatment figures reveals similar precision results when compared to the TIMER treatment results, except at the 23 cm edge locations. No physical explanation exists for the variation observed between probe replicates at 23 cm edge location. When both Figure 2-6 and 2-7 are considered, it is observed that the variation is mostly due to one probe (SW23), which is confirmed when by the time-series presented in Figure 2-8. Ignoring the SW23 location, the north and south matrices can again be considered replicates. And, the previously reported negative relationship between SMC and precision is also visible for the center locations (Figure 2-7). Table 2-2 shows the problem with one-to-one comparisons for field monitoring. Results in Table 2-2 are poor, with C eff values often below 0. Only when the entire time-series is considered (Figure 2-5 and 2-8) do the probes measurements appear to be location replicates. Concurrently considering the entire time-series along with the probe accuracy reported by the manufacturer 29

30 allows for the replicate assumption by location, where as solely one-to-one comparisons preclude such assumptions. Soil sample comparisons In general the VWC obtained from gravimetric samples was more variable than WCR SMC and SMS treatment results were more variable than TIMER treatment results with standard deviations of the data as follows: TIMER WCR, cm 3 cm -3 ; TIMER VWC, cm 3 cm -3 ; SMS WCR, cm 3 cm -3 ; SMS VWC, cm 3 cm -3. The difference in variation between the WCR SMC and VWC is largely a result of their measurement volumes and monitoring location. WCR SMC represents an average over the 900 cm 3 volume, which corresponds to a 30 cm length within the bed. The VWC data represents an average over the cm 3 auger volume, which encompasses only 5 cm of bed length. The shorter length included in the average places more importance on the measurement location relative to a drip emitter, which were spaced 20 cm apart. Recall the reported VWC is an average across the four sampling locations in the field, which are at a similar normal distance from an emitter, but could be from multiple radial distances. This is because soil samples were collected relative to the drip tape and plant location, but without regard for emitter location. Averaging samples alleviates some error relative to WCR SMC since the WCR measurement length is 1.5 X emitter spacing, effectively an average of the entire spacing. Figure 2-9 and 2-10 present WCR SMC and VWC determined from gravimetric data for both treatments. A general trend of WCRs returning lower SMC values than soil samples is seen in the figures, similar to the observations made by Plauborg et al. (2005) when comparing WCR to TDR SMC. That being said, some potential errors in the gravimetric data exist. Beyond the previously reported bulk density uncertainty, the subsample size used for gravimetric soil 30

31 moisture was relatively small (20 g) and could have induced further errors. Though in general, soil sampling occurred too infrequently to make observations on a time-series scale. In traditional one-to-one style, the WCR SMC measurements were calibrated to VWC obtained from gravimetric soil sample results. Both the TIMER and SMS treatment data was included in the calibration. Since the soil samples could not be collected instantaneously and are instead spread on a timeline of collection for each collection date, the WCR readings were accordingly averaged from 1000 hours to 1400 hours for both the north and south locations, for each treatment. Gravimetric data was averaged across the four replicates for each treatment for VWC determination. The resultant calibration equation displayed in Figure 2-11 was transformed to match Equation 2-1 (Equation 2-2). If we consider the equations to be of the general form Y = (X A) / B, it can be seen that though the sample variability results in a relatively low R 2 value of 0.74, Equation 2-1 and Equation 2-2 return similar A parameters, but different B parameters. WCR 0.03 VWC = (cm 3 cm -3 ) (2-2) 0.69 The R 2 value can be explained by the uncertainty in collecting WCR SMC and VWC data. Interestingly, the factory and Plauborg calibrations proved similar in their ability to match collected VWC for this study site. The factory calibration appears to underestimate SMC, while the Plauborg calibration overestimates SMC (Figure 2-12). Regardless of calibration, the WCR SMC collection frequency was an inherent problem for both treatments, but is more obvious in the presented SMS treatment results. With the maximum application period for the SMS treatment set at 24 min and occurring up to five times daily, several states of redistribution are captured randomly each hour in the data set. This is less of a problem for the TIMER treatment, since water application occurs in hour blocks at the same time 31

32 each day. The consistent timing and hourly application allows for a reasonable reproduction of the entire redistribution process. To obtain further accuracy, the measurement frequency was increased to 15 min intervals for the remaining experiments. Experiment 2: Non-Planted Vitel precision First the Vitel probe precision was examined similar to the examination of the WCR probe. For Figure 2-13 the west bias seen in the Vitel probe matrix was likely a by-product of the irrigation drip emitter being off-center intensified by the smaller measurement volume of the Vitel probe. Still the agreement is reasonable for field data and the Vitel probe can also be considered location replicates. Probe to probe comparison In order to examine the accuracy of SMC measured by Vitel probes in field conditions, a comparison to measured WCR SMC was performed for each probe location. Only the TIMER treatment is presented, with WCR SMC reported using the site calibration (Equation 2-2). Since a time-series comparison was previously observed to be a more descriptive comparison, a time-series comparison of TIMER treatment SMC measured by the WCR and Vitel probe is presented prior to a one-to-one comparison (Figure 2-14). It is seen that the Vitel probes located in the center of the bed to consistently measure higher SMC than the WCR probes. No consistent relationship between the edge probe locations can be discerned, especially compared to the center of the bed. These observations are confirmed by the one-to-one comparisons (Figure 2-15). While no general trend can be determined accurately location by location (Vitel E8 to WCR E8, etc.), a very good relationship between the Vitel and WCR probes can be developed if the locations are averaged symmetrically about the drip tape and only the 32