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Reducing water consumption for residential turfgrass with adaptive irrigation controllers Fazackerley, Scott Ronald 2010-12-31

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Reducing Water Consumption forResidential Turfgrass with AdaptiveIrrigation ControllersbyScott Ronald FazackerleyB.Sc.(Hons), The University of British Columbia, 2008A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe College of Graduate Studies(Interdisciplinary Studies)[Computer Science]THE UNIVERSITY OF BRITISH COLUMBIA(Okanagan)April 2010a169 Scott Ronald Fazackerley 2010AbstractIt has been estimated that 50-75% of residential water use is for irriga-tion [Deg07]. Current domestic systems are poor at adapting irrigation tomeet demand, primarily due to incomplete information for system opera-tors who rely either on visual inspection or periodic irrigation programs.This results in over-watering and fertilizer and soil leaching. This thesisdescribes a complete wireless sensor and irrigation control system that re-duces water consumption for residential turfgrass irrigation. Presented isa proof-of-concept system that demonstrates potential bene ts. The ap-proach couples easy-to-deploy wireless soil moisture sensor nodes with anadaptive irrigation controller that waters to meet demand without user in-put. Watering events are dynamically scheduled in response to changes insoil water and adapt to unplanned additions and variable water  ow. Theadaptive irrigation controller was compared against a standard irrigationcontrol program. Experimental results demonstrate signi cant water sav-ings over using a preset watering program. Adaptive watering amounts arecompared against actual crop water demand and found to meet the needsof the turfgrass without over-watering. The result is a system that requiresless user intervention, lowers water consumption, and adapts to changingclimatic conditions while maintaining a healthy turfgrass.iiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . ixDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1 Soil Water . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.2 Soil Moisture Sensing Technology . . . . . . . . . . . . . . . 112.2.1 Water Content Sensors . . . . . . . . . . . . . . . . . 122.2.2 Water Potential Sensors . . . . . . . . . . . . . . . . . 152.2.3 Summary of In-Situ Sensing Technologies . . . . . . . 212.3 Existing Systems . . . . . . . . . . . . . . . . . . . . . . . . . 242.3.1 Commercial Systems . . . . . . . . . . . . . . . . . . 252.3.2 Research Systems . . . . . . . . . . . . . . . . . . . . 272.4 Irrigation Scheduling Strategies . . . . . . . . . . . . . . . . 282.5 Wireless Sensor Networks . . . . . . . . . . . . . . . . . . . . 312.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343 Adaptive Irrigation Controller . . . . . . . . . . . . . . . . . 363.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.1.1 Wireless Sensor Node . . . . . . . . . . . . . . . . . . 373.1.2 Controller Node . . . . . . . . . . . . . . . . . . . . . 423.1.3 Node Communications . . . . . . . . . . . . . . . . . 443.1.4 Engineering and Design Challenges . . . . . . . . . . 45iiiTable of Contents3.2 Irrigation Scheduler and Measurement Processing . . . . . . 463.3 Adaptive Watering Model . . . . . . . . . . . . . . . . . . . . 463.4 Penalty Function . . . . . . . . . . . . . . . . . . . . . . . . . 524 Experimental Program and Results . . . . . . . . . . . . . . 554.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 554.1.1 Site Description . . . . . . . . . . . . . . . . . . . . . 554.1.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . 594.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 594.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 614.4 Conclusion of Experimental Program . . . . . . . . . . . . . 625 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . 65Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68AppendicesA Water Content Sensors . . . . . . . . . . . . . . . . . . . . . . 78A.1 Neutron Probe Sensor Overview . . . . . . . . . . . . . . . . 78A.1.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . 80A.1.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . 80A.2 Gamma Ray Sensor . . . . . . . . . . . . . . . . . . . . . . . 81A.2.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . 82A.2.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . 82A.3 Time Domain Re ectometry Sensors . . . . . . . . . . . . . . 83A.3.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . 84A.3.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . 84A.4 Other Dielectric Sensors . . . . . . . . . . . . . . . . . . . . . 84A.4.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . 87A.4.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . 88B Water Potential Sensors . . . . . . . . . . . . . . . . . . . . . 90B.1 Thermocouple Psychrometer Sensor . . . . . . . . . . . . . . 90B.1.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . 92B.1.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . 92B.2 Electrical Resistance Block Sensors . . . . . . . . . . . . . . 93B.2.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . 94B.2.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . 96B.3 Tensiometer Sensor Overview . . . . . . . . . . . . . . . . . . 97ivTable of ContentsB.3.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . 97B.3.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . 97B.4 Heat Dissipation Sensor Overview . . . . . . . . . . . . . . . 98B.4.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . 98B.4.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . 98vList of Tables2.1 Functional and Cost Parameters for Volumetric MeasurementTechniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.2 Functional and Cost Parameters for Soil Water Potential Mea-surement Techniques . . . . . . . . . . . . . . . . . . . . . . . 232.3 Various Sensor Architectures. . . . . . . . . . . . . . . . . . . 333.1 Wireless Sensor Node Design Phases . . . . . . . . . . . . . . 383.2 Node Power Requirements for a One Minute Cycle Time . . . 424.1 Adaptive Watering Program Results . . . . . . . . . . . . . . 60viList of Figures2.1 Water Potentials in a Plant . . . . . . . . . . . . . . . . . . . 62.2 Energy Levels in the Soil-Plant-Atmosphere Continuum . . . 62.3 Important Features of the Soil Water Characteristic Curve . . 72.4 Soil Water Characteristic Curves for Sandy, Silty and ClayeySoils as a Function of Available Pore Space. . . . . . . . . . . 82.5 Field Capacity and Permanent Wilting Point on a Soil WaterCharacteristic Curve . . . . . . . . . . . . . . . . . . . . . . . 92.6 Plant Available Water for Di erent Soil Types . . . . . . . . 102.7 Wetting Front Movement During and After Irrigation . . . . 112.8 The Components of a Time Domain Re ectometer . . . . . . 142.9 Thermocouple Psychrometer . . . . . . . . . . . . . . . . . . 172.10 The Tensiometer . . . . . . . . . . . . . . . . . . . . . . . . . 192.11 A Typical Irrigation System . . . . . . . . . . . . . . . . . . . 242.12 Hunter Industries Mini-click Rain Sensor . . . . . . . . . . . . 262.13 Sample Water Budget Worksheet . . . . . . . . . . . . . . . . 292.14 Evaporation Pan used to Calculate Daily ET . . . . . . . . . 292.15 General Crop Water Production Curve . . . . . . . . . . . . . 302.16 Wireless Sensor Architecture . . . . . . . . . . . . . . . . . . 313.1 Adaptive Irrigation System Overview . . . . . . . . . . . . . . 373.2 UBCO Mote Model: Front . . . . . . . . . . . . . . . . . . . . 393.3 UBCO Mote Model: Back . . . . . . . . . . . . . . . . . . . . 403.4 UBCO Mote: Internal Circuit Board . . . . . . . . . . . . . . 413.5 UBCO Mote: Complete Sensor Node in Enclosure . . . . . . 413.6 Controller Node with Solenoid Interface . . . . . . . . . . . . 433.7 Controller Interface . . . . . . . . . . . . . . . . . . . . . . . . 433.8 Transport Dataframe Interactions with XBee 802.15.4 RadioAPI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443.9 Scheduling and Measurement Algorithms . . . . . . . . . . . 463.10 Adaptive Irrigation Algorithm . . . . . . . . . . . . . . . . . . 473.11 Water Contents and Matric Suctions for a Guelph Loam Soil 53viiList of Figures4.1 Daily Maximum and Minimum Temperatures from the FarmwestBelgo Reporting Station . . . . . . . . . . . . . . . . . . . . . 564.2 Daily and Cumulative ET Values from the Farmwest BelgoReporting Station . . . . . . . . . . . . . . . . . . . . . . . . 574.3 Overview of Test Site . . . . . . . . . . . . . . . . . . . . . . 574.4 Control Values and Flow Meters . . . . . . . . . . . . . . . . 584.5 Cumulative Water Depths for Adaptive and Control Programs 604.6 Number of Days Between Watering Events for the Controland Adaptive Irrigation Zones . . . . . . . . . . . . . . . . . . 614.7 Recorded Evapotranspiration During Growing Season . . . . 624.8 Water Additions for Adaptive Watering Program . . . . . . . 634.9 Seasonal Changes in Soil Moisture . . . . . . . . . . . . . . . 64A.1 Neutron Probe Placed in Soil . . . . . . . . . . . . . . . . . . 79A.2 ADR Probe . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87A.3 Phase Transmission Probe . . . . . . . . . . . . . . . . . . . . 88A.4 Time Domain Transmission Probe . . . . . . . . . . . . . . . 89B.1 Gypsum Resistance Block . . . . . . . . . . . . . . . . . . . . 94B.2 Watermark Granular Matrix Sensor . . . . . . . . . . . . . . 95viiiAcknowledgementsI would like to acknowledge and thank Dr. Ramon Lawrence for his ongoingmotivation, support, vision and for the opportunity to study and learn underhis supervision. The value of Dr. Lawrence’s ongoing guidance, feedbackand friendship is immeasurable. His belief in you as a person only makesyou want to excel to a higher level. Without his passion for research andbelief in a vision, this work would not have been possible.I would like to thank my wife Amy, for her ongoing support, patienceand perseverance. Most of all, I am forever grateful for her belief in meand the vision. I would also like to thank my family and friends for theencouragement and support in my choosing to take this path.I would also like to thank Dr. Craig Nichol and Dr. David Scott fortheir tutelage, guidance and direction leading to a better understanding ofthe complexities of soil water and the challenges in measuring it. I wouldalso like to acknowledge Dr. Nichol’s contribution for providing valuable labspace for the construction and testing of our devices.I would like to acknowledge the continuing support of the Natural Sci-ences and Engineering Research Council of Canada (NSERC) which hascontributed to the success of this work.ixDedicationFor Liam, my son.xChapter 1IntroductionYou may never know whatresults come of your action, butif you do nothing there will beno result.Mahatma Gandhi (1869-1948)Water is valuable for all life and its consumption increases as communi-ties grow in size. According to Environment Canada’s 2007 Municipal WaterUse Report, the average residential water user in Canada consumed 329 litresof water per person per day in 2004. Residential British Columbian’s con-sumed considerably more than the national average; for residential use, theaverage daily consumption was 429 litres. Environment Canada notes thatwhile the 2004 daily average is the second lowest since 1991, Canada still isone of the largest water consumers among the Organization for EconomicCo-operation and Development countries [Can07].In semi-arid regions with large population’s such as British Columbia’sOkanagan Valley, over 75% of municipal water usage is attributed to resi-dential turf grass irrigation [Deg07]. Despite the trend to xeriscape land-scapes and low maintenance yards, there remains a considerable amount ofturfgrass that requires irrigation. Large amounts of water are used for ir-rigating parks, recreational areas, and golf courses. The use of automaticand timed irrigation systems has made it easier to guarantee plants haveadequate water but has also caused increased usage of water, often unnec-essarily. Water is no longer a \free" resource that can be taken for granted.The price of water will increase as its availability is strained due to climatechange, population growth, especially in arid areas, and overall increasingdemand [Can07]. Society must be pro-active in the use and management ofwater resources, especially as it relates to irrigation.Since the cost of water has been relatively low, there has been less e ortin conservation and frugality. In British Columbia, only 29.8% of residenceswere charged using a water metering scheme, with the remainder payinga  at block rate regardless of consumption [Can07] which provides little1Chapter 1. Introductionincentive to curb unnecessary water use. Consider a home owner with anautomatic irrigation system who wants a green lawn. Common practice is towater every second day regardless of climate patterns. Some \sophisticated"systems have shut o systems based on recent rainfall (rainfall sensors) thathave been shown to reduce water consumption signi cantly [CLD08]. Sys-tems that employ soil moisture sensors as a bypass device reduce waterconsumption even more [CLDM08]. However, these systems are costly toinstall especially on an existing lawn when the soil moisture sensors mustbe connected by wires to the controller.The basic question for the home owner is: When and by how much shouldI water the lawn to keep it green and use the least amount of water possi-ble? Without proper data, this question is hard to answer. Home ownerstend to favour over-watering in such conditions. Despite the demonstratedbene ts of soil moisture sensors for residential irrigation, few users employsuch techniques due to the cost and di culty of installation.With current technology, it is possible to build a data collection sys-tem that uses o the shelf soil moisture sensors and wireless sensor nodesto determine soil moisture content. Current technology is inadequate forseveral reasons. First, the cost of scienti c sensors is prohibitive for manyenvironments (especially residential use), and the products are not easilycon gurable or deployed. Many products rely on wires for transfer betweensensors and logger, and existing wireless products are very costly. Recentwork has shown that wireless networks can be used to measure soil mois-ture for large scale agriculture but fail to address how watering decisionsare made using the sensing network [ZYWY09]. Other work has measuredmatric suction potentials in the soil matrix, but failed to introduce a closedloop strategy [MMG+08]. Regardless of how the data are collected, there isa requirement for an overall solution that handles the complete cycle of datacollection, analysis, and automatic irrigation system control. This work hasbuilt an irrigation controller that dynamically schedules irrigation based oninput from wireless soil moisture sensors. This system uses wireless sensornetwork nodes that are signi cantly lower in cost than commercial products.This thesis presents a solution for closed loop control for turfgrass irri-gation. The solution has the potential to realize signi cant water savingswithout the need for user input. The organization of this thesis is as follows.In Chapter 2, an overview of soil water interactions, current sensing tech-nology, a typical residential irrigation system and the limitations of currenttechnology is provided. In Chapter 3, the wireless soil moisture sensor nodesand the adaptive irrigation controller are described. In Chapter 4, a descrip-tion of the experimental evaluation is provided and water usage data are2Chapter 1. Introductioncompared against current methods and actual water demands. The resultsdemonstrate signi cant savings over recommended practices. In Chapter 5,the content of this thesis is summarized and conclusions are drawn from theexperimental results. Appendix A and B continue a detailed discussion ofdi erent water content and matric sensing technologies.3Chapter 2BackgroundThe only source of knowledge isexperience.Albert Einstein (1879-1955)Successful growth of a crop is determined by the tilth of a soil. Thisencompasses the classi cation of a soil by type and the nutrient level inaddition to the energy status and amount of available water in the soilmatrix and climate.De nition 1. Tilth [Hil80b, p. 94] Tilth is commonly used by agronomiststo describe the suitability of a soil’s physical conditions for cultivation interms of the state of aggregation. A soil with good tilth allows free movementof air, water and nutrients making it suitable for plant growth.Air, water and nutrients are inter-related in terms of crop success and ade cit in any of the three is detrimental to the overall growth patterns. An-thropogenic e ects have changed modern agriculture and placed signi cantstress on natural systems. Disregard for proper watering practices can leadto degradation of growing lands through nutrient depletion, salinization, andeutrophication of ground water sources. Civilizations have collapsed as a re-sult of not understanding and managing water resources correctly [Hil80a,p. 98]. Responsible environmental stewardship calls for humanity to betterunderstand and control unnecessary water consumption.Key to measuring and managing this complex relationship is to have anunderstanding of how water moves in the soil matrix and how it can be mea-sured. This section provides a background to soil water and its interactionwith plants. It then continues to examine di erent methods of measuringsoil water in-situ and how soil water can be managed with di erent irrigationpractices. An examination of commercial and research irrigation controllersand a discussion of the current issues and limitations with existing systemsis presented. This section concludes with an examination of the currentresearch state for wireless sensor networks.42.1. Soil Water2.1 Soil WaterIn the analysis of soils speci cally for use in agricultural practices, twounique characteristics are observed and measured with respect to water.First, the total amount of water present in a soil, either by volume or massis referred to as the water content or wetness of a soil and is expressed as . Second, the energy status of water in the soil is referred to as the soilwater potential and expressed as  . Understanding how plants interact withsoil and the water contained within, requires a solid understanding of bothmeasurements.De nition 2. Volumetric Water Content  v [BW04, p. 144] [FX94] isa dimensionless value that is de ned as the volume of water associated witha given volume of dry soil often expressed as m3 H2O per m3 of soil andde nes the amount of water contained within the soil’s pore structure.The value of  v as m3 H2O per m3 of soil can also be expressed as adepth ratio by dividing the value by m2 water per m2 of soil which will givemeters (depth) of H2O per meter (depth) of soil. This value is commonlyused to express the amount of water contributed from irrigation or rainfallin agricultural practice.De nition 3. Soil Water Potential  [BW04, pp. 139-140] is the di er-ence in the energy level of water between sites or conditions and determinesthe rate and direction of  ow. Commonly, the di erence in energy levels isexpressed as the di erence in energy between the soil water and pure waterin a reference state.The total soil water potential is contributed to by several forces suchas the gravitational potential  g, the matric potential  m, and the osmoticpotential  o. Of these three, the one of most interest in this discussion is thematric potential, as it is the primary driving force behind unsaturated water ow [BW04, p. 149]. This movement is critical for plant roots as it is one oftheir main sources of soil water [BW04, p. 142]. The matric potential,  m,results from a combination of adhesion, adsorption and capillarity whichin uence water movement and retention in a soil. As a result of a di erencein  m, water will  ow from areas of high  m to areas of low  m if  g areequal in both areas. The units used to express  m in terms of pressure fora given soil are varied and can be expressed in terms of cm of water, mm ofHg, bar, torr, or pascal.Plants exist as part of the Soil-Plant-Atmosphere Continuum (SPAC)which is a major component of the earth’s hydrological cycle. Water is con-tinuously cycled through di erent parts of the continuum. Plants extract52.1. Soil Waterwater from the soil through their roots which continues through the plant.The water continues to the leaf surface and leaves the plant as water vapourwhich is called transpiration [Hil98, p. 547]. The direction of water move-ment in plants is seen in Figure 2.1 [Hil98, p. 547]. This loss is commonlycombined with evaporative water losses from the soil surface. Together thisloss is called evapotranspiration (ET) [BW02, p. 229]. For plants to be ableto utilize water, an energy gradient must exist between di erent parts of theSPAC as in Figure 2.2 [BW02, p. 229]. The di erence in  between theatmosphere and leaf, the leaf and stem, etc, down to the di erence betweenthe soil and roots allows for plants to uptake soil water. As the energy levelof soil water increases, it becomes harder for plants to utilize it and theywill transpire less [Hil98, p. 561].Figure 2.1 has been removed due to copyright restrictions.It was a diagram of a plant showing the di erent water po-tentials the exist in the soil, roots, stem and leaves. Origi-nal source: Daniel Hillel (1998) Environmental Soil Physics.Academic Press, a division of Harcourt Brace & Company,525 B Street, Suite 1900, San Diego, CA, 92101-4495, p. 547.Figure 2.1: Water Potentials in a Plant.Figure 2.2 has been removed due to copyright restrictions.It was a diagram showing the change is water potentials inthe di erent parts of the soil-plant-atmosphere continuum atdi erent soil water levels. Original source: Nyle C. Bradyand Ray R. Weil (2002) The Nature and Properties of Soils.Thirteenth Edition, Pearson Education, Inc., Upper SaddleRiver, New Jersey, USA, F07458, p. 229.Figure 2.2: Energy Levels in the Soil-Plant-Atmosphere Continuum.In [LR97a], the authors note that water content in an unsaturated soil is afunction of water matric potential, and this relationship can be expressed ona plot of volumetric wetness versus matric suction potential. For a speci csoil, the bi-plot of  v and  m is termed a soil water characteristic curve(swcc). The soil water characteristic curve is very important in irrigationapplications as the relationship expresses how much water is available forplant use at a given energy level.62.1. Soil WaterDe nition 4. Soil Water Characteristic Curve [FM77] is a relation-ship between the volumetric water content  v in an unsaturated soil and thecorresponding matric suction  m at  v.Figure 2.3 [FX94] is an example of a soil water characteristic curve for asilt loam soil. On the curve are highlighted the important values that de nethe curve. The values area136  s : the saturated volumetric wetnessa136  r : the residual volumetric water contentFredlund and Xing [FX94] de ne the air-entry pressure to be the point atwhich air  rst enters the largest pores in the soil matrix. They de ne theresidual volumetric water content as the point at which a large change inmatric suction is required to change the water content. As this is an am-biguous de nition, Fredlund and Xing suggest a tangent line method [FX94]to quantify  r. For a given soil, a wetting (absorption) and drying (depsorp-tion) curve exist as soils typically exhibit hysteresis in terms of water contentwith respect to matric suction. For irrigation practices, only the desorptioncurve is considered.Figure 2.3: Important Features of the Soil Water Characteristic Curve. Re-produced from D.G. Fredlund and Anging Xing. Equations for the Soil-Water Characteristic Curve. Canadian Geotechnical Journal, 31(4):521:532,August 1994. a1692008 NRC or its licensors. Reproduced with permission.Di erent soils have di erent soil water characteristic curves due to thedi erent structure, make up, and pore size distribution. As a result, no72.1. Soil Waterone curve can be used to describe all soils. Figure 2.4 [FX94] demonstratestypical di erences in curves between soil types.Figure 2.4: Soil Water Characteristic Curves for Sandy, Silty and ClayeySoils as a Function of Available Pore Space. Reproduced from D.G. Fred-lund and Anqing Xing. Equations for the Soil-Water Characteristic Curve.Canadian Geotechnical Journal, 31(4):521:532, August 1994.a1692008 NRC orits licensors. Reproduced with permission.In the relationship between  v and  m on the swcc, there are speci cpotential energy levels that are of interest. The two energy levels and asso-ciated water contents that are useful in irrigation practices are termed FieldCapacity and Permanent Wilting Point.De nition 5. Field Capacity [VH31] is the \amount of water held insoil after excess has drained away and the rate of downward movement (ofwater) has materially decreased, which usually takes place in 2-3 days aftera rain or irrigation in pervious soils of uniform structure and texture"Field capacity is used in irrigation practices as it represents a practicalconcept but has no solid de nition in terms of soil physics. Field capacityis traditionally measured through a grab sample of known volume that isfully wetted and then allowed to sit under free drainage for 2 to 3 days untilinternal drainage has stopped [CG08, p. 919] [Hil80a, p. 67] [TRC01] butcan vary depending on soil type. Methods also exist for determining  eldcapacity in-situ [FL10a, GHWZ06].The value of  v at  eld capacity (FC) is commonly associated with ma-tric suction levels ranging from -10 kPa to -33 kPa [MG99] on the swcc.82.1. Soil WaterThis is the point where the  ow of water due to gravity in small pores hassigni cantly decreased, with larger pores being air  lled. It represents themaximum amount of water in a soil that is useful to plants. Water applied toraise the energy level above this point, which is termed gravitational water,will rapidly move through a soil and be of no long term use to plants [BW04,p. 155].The second important energy level is at  m =  1500 kPa and the as-sociated  v. At energy levels beyond this point, plants generally can notovercome the energy di erence required to extract any remaining water fromthe soil matrix. This point is termed the Permanent Wilting Point (PWP).De nition 6. Permanent Wilting Point [BW04, p. 156] is the pointat which soil water is only retained in the micropores, and water below thisenergy level is generally unavailable to plants.The soil water in the range between FC and PWP ( 10 kPa   m   1500 kPa) is termed Plant Available Water (PAW). This water is freelyavailable for plant use [BW04, p. 156]. These points are seen on the swcc inFigure 2.5 [BW04, p. 209]. The points at which a soil is at FC or PWP isvariable with the type of soil as shown in Figure 2.6 [BW04, p. 210]. A swccfor a speci c soil can be used to determine how much water is available fora plant. The amount of plant available water is limited by the volumetricwater contents at FC and at the PWP.Figure 2.5 has been removed due to copyright restrictions.It was a diagram showing the soil water characteristic curveand the position of di erent terms used to describe the sta-tus of soil water. Original source: Nyle C. Brady and RayR. Weil) The Nature and Properties of Soils. ThirteenthEdition, Pearson Education, Inc., Upper Saddle River, NewJersey, USA, 07458, p. 209.Figure 2.5: Field Capacity and Permanent Wilting Point on a Soil WaterCharacteristic Curve.Having strong methods and techniques to measure both  v and  m in-situ without unnecessarily disturbing the soil body leads to a solid under-standing of how much water needs to be supplied to a soil. Correctly address-ing the water needs of a soil allows for proper maintenance and promotion ofgood tilth. Introducing unnecessary water to the soil pro le may contributeto root rot, deep drainage, can potentially wash nutrients out of the soilpro le, in uence water table levels, and may lead to soil salinization.92.1. Soil WaterFigure 2.6 has been removed due to copyright restrictions.It was a diagram showing plant available water for di erenttextural classes of soils. Original source: Nyle C. Brady andRay R. Weil) The Nature and Properties of Soils. ThirteenthEdition, Pearson Education, Inc., Upper Saddle River, NewJersey, USA, 07458, p. 210.Figure 2.6: Plant Available Water for Di erent Soil Types.Note that while plant physiology responds to  , using this value to pre-dict the amount (volume) of water to be applied in irrigation practices isdi cult without a calibrated soil water characteristic curve. For a givensoil,  does not linearly relate to the volumetric water content or wetnessof a soil. As a result, using  alone as a measurement cannot be transferreddirectly to a depth of water. On the other hand,  can be used to determinethe amount of water to be applied without the need of a calibrated soil watercharacteristic curve. As expressed in De nition 4, volumetric water contentis a dimensionless value that can express a depth of water per meter depthof soil. As a result, the amount of water available in a given area of soil isdetermined asdepthof water = depthof soil   v(m3waterm3soil )area(m2) : (2.1)While  can be used independently for irrigation practices, knowing  ( )can help to precisely determine turn on and turn o points for irrigation. Byknowing the water content levels at which a soil reaches  eld capacity (FC)and the permanent wilting point (PWP), a more informed decision can bemade.Under normal irrigation, water will enter the soil and a sharp boundarywill form between the wet and dry soil. This boundary is called a WettingFront [BW02, p. 202]. While water near the surface moves primarily inresponse to gravity, water at the wetting front is in uenced by both gravityand matric suction potentials. Figure 2.7 shows the movement of a wettingfront through a soil pro le during and after an irrigation event. As timeproceeds from t1 to t4, the water content will increase in response to themovement of water in the soil pro le as the wetting front passes. Afterirrigation ceases, the wetting front will continue to move down in the pro leand the water content will stabilize at a water content determined by thematric suction. In Figure 2.7 (ii), the water content in the rooting zone will102.2. Soil Moisture Sensing Technologycontinue to decrease as the plants respire and extract water from the soil.Water content levels near the surface will also drop as water is lost due toevaporation and a drying front proceeds in a downward direction.Figure 2.7: Wetting Front Movement During and After Irrigation.Due to the non-linear nature of water movement through soil [LR97b],the rate at which water can move through a soil can vary over several ordersof magnitude as the soil moisture levels change. As a result it may takemany hours for the wetting front to move through the soil in reaction to theadded water from irrigation. The rate at which water can enter a soil pro leis a ected by cracks and macro pores leading to preferential  ow [BW02,p. 197]. Macro pores are a common feature in natural soils that are theresult of worm movement, root channels or soil cracking. The macro poresallow water to enter a small area of soil faster than the regular pulse ofwater is moving into the soil body as a whole [BW02, p. 198]. It hasbeen observed that matric suction and the corresponding volumetric watercontents respond to preferential  ow [BB91]. As time proceeds, water thathas entered through the preferential  ow channels will be drawn into thesoil matrix as the energy levels at the channel boundaries equilibrate withthe energy levels of the internal soil water. As a result, the volumetric watercontent will also change.2.2 Soil Moisture Sensing TechnologyA wide variety of measuring technologies are currently used to measuresoil moisture and the method used depends on the type of measurementdesired. Generally, measurements are divided into methods that measure112.2. Soil Moisture Sensing Technologythe amount of water (in %, weight or volume per volume or mass of soil)and measurements that determine the energy status of water in the soilmatrix which does not depend on the volume of soil.Water content measurements can be divided into two classes: direct andindirect measurements. In direct measurements,  v is a direct measurementof the amount of water contained in the soil. Gravimetric analysis is anexample of a direct measurement, as the technique measures the volume ofwater contained in the sample and expresses the water content as a per-centage to the mass or volume of the sample. Indirect methods estimate v based on a characteristic of the soil that is in uenced by changes in theamount of water in the sample. Soil type and physical properties in uencehow a measurement is made and the type of sensors required [MC04].2.2.1 Water Content SensorsCommon in-situ volumetric methods can be divided into groups based onthe principles of operation. Most available commercial sensors suitable forin-situ use fall into one of two groups; nuclear methods or dielectric methods.Nuclear methods include neutron absorption and gamma attenuation, bothof which use the absorbed or attenuated radiation by the water present inthe soil to determine water content. These methods are not suitable for non-specialist use. Details on nuclear methods can be found in Appendix A.1 andA.2. Dielectric methods include time domain re ectometry, capacitive sens-ing, frequency domain re ectometry, amplitude re ectometry, time domaintransmission and phase transmission. These methods measure the dielectricconstant or impedance for a soil and infer  v based on a known relationshipbetween  v and the dielectric constant. These methods work based on theprinciple that the dielectric constant of dry minerals is approximately 3 to4 and the dielectric of water is approximately 80, a 20 fold increase; thus, inwet soils the dielectric measured is predominately in uenced by the presenceof water in the soil. It should be noted that there are other methods thatcan be used in the laboratory to measure  v but they are not suitable forin-situ measurements.Time Domain Re ectometryTime domain re ectometry (TDR) has been used over the last 30 years asa method to measure the dielectric constant of a soil. It was  rst appliedby Davis and Chudobiak in 1975 [DC75] and has been used under a broadrange of conditions and scales, becoming a standard method of water content122.2. Soil Moisture Sensing Technologymeasurement. A detailed discussion on TDR can be found in Appendix A.3.TDR is principally an electromagnetic method where a series of short risetime pulses or waves are initiated and propagated through a soil body guidedby a transmission medium known as a wave guide. As the wave propagatesthrough the soil, the velocity and amplitude of the wave is in uenced bythe soil surrounding the wave guides. The observed change in the wave isrelated to the amount of moisture in the soil in addition to the soil’s electricalconductivity [ZX94].In [MC04], the author summarizes the measurement characteristics forthe TDR method as follows: the usable range of the TDR method is 5%  v  50% for uncalibrated soils. This range can be extended to 5%  v  saturation, when correctly calibrated to a speci c soil. With soilspeci c calibration the accuracy of the device is  1%. The sensing volumeof the device is about 3 cm in radius along the length of the waveguide.Figure 2.8 [Nob01] shows the di erent components of a TDR. The sensorconsists of a pair of parallel metal rods that form a balanced transmissionpair and are coupled to a pulse generator and signal receiver. The lengthand spacing of the rods is dependent on the measurement application [CG08,p. 941]. Most devices now use three rod probe con guration [ZWJ89]. Therods are inserted into the soil where the rods act as waveguides for theelectromagnetic pulse and the soil surrounding the rods forms the dielectricmedium [Hil98, p. 140]. In addition to the receiver and parallel rods, the sys-tem includes a timing circuit, a pulse generator and recording device. Mostcommercial devices incorporate all components into a single unit [CG08, p.941].It is particularly useful for in-situ measurements [CG08, p. 940]. Itcan be coupled directly to a computer for automated data collection. Com-mercial application speci c devices have also been developed and o er theability to multiplex numerous probes in addition to o ering automated anal-ysis [CG08, p. 941]. While it is not a direct measurement technique, themoisture content of a soil can be related to the dielectric constant of asoil [Str00, p. 180] and does not require knowing additional soil parameterssuch as bulk density [CG08, p. 941]. Additional advantages and disadvan-tages are discussed in Appendix A.3.Other Dielectric MethodsOther designs of dielectric probes infer the volumetric water content in thesoil by attempting to measure the dielectric constant for the soil of interest.Alternatively, a parameter in uenced by the dielectric constant, such as the132.2. Soil Moisture Sensing TechnologyFigure 2.8: The Components of a Time Domain Re ectometer. Reprintedfrom Computers and Electronics in Agriculture, 31(3), K. Noborio, Measure-ment of Soil Water Content and Electrical Conductivity by Time DomainRe ectometry: a Review, 213-237, Copyright (2001), with permission fromElseviersoil impedance, can also be measured. In this way, these methods attempt tomeasure the same electrical characteristics as TDR. In industrial literature,these probes are often grouped together and classi ed as capacitive typeprobes, such as the EC-5 probe sold by Decagon Devices from PullmanWashington [Dec08] or amplitude domain re ectometry (ADR), such as theTheta Probe sold by Delta-T Devices1. This category also includes probesthat are based on phase transmission (PT) and time domain transmission(TDT). This grouping may lead to confusion, as the probes appear to workin a similar method and fashion to TDR as they all rely on the measurementof a dielectric, but unlike true TDR which is a full spectrum method, thesemethods typically rely on the use of a single frequency. A detailed discussionon ADR, PT, and TDT can be found in Appendix A.4.Similar to TDR, the measurement is done using a high frequency energypulse moving through a waveguide. The dielectric constant of the soil canthen be related to the volumetric moisture content of the soil [ZX94]  v asin Equation (A.4) for most mineral soils. The range of frequencies used1http://www.delta-t.co.uk142.2. Soil Moisture Sensing Technologywith the devices is obscured by the di erent manufactures and the exactmethods are often not available. The cost of these devices are generally muchlower than TDR and they are designed to be easily coupled to dataloggingequipment, making them suitable for large scale deployment. One of themost signi cant advantages for the dielectric type probes is the responsivenature of the device. Sampling for a given site can be concluded in a veryshort time frame as the reading is almost instantaneous.In [MC04], the author summarizes the measurement characteristics fornon-TDR dielectric methods: the usable range of the capacitive, FDR, andADR methods is from 0% to saturation with accuracies of the devices being 1% if used with soil speci c calibration. For particular commercial capac-itive and FDR devices, the measurement volume is a sphere approximately4 cm in radius surrounding the device. For particular commercial ADR de-vices, the sensing volume of the device is a cylinder about 3 cm in radiusaround the length of the waveguide. For PT and TDT methods, the usablerange is 5%   v  50%. With soil speci c calibration, PT is accurate to 1% and TDT is accurate to  5%. The sensing volume for speci c com-mercially available PT devices is a cylinder of 15 to 19 litres. The sensingvolume of TDT is a cylinder of 0.75 to 6 litres around the waveguides witha radius of 5 cm.Capacitive, FDR and ADR sensors can experience problems with regardto placement in the soil. If a sensor is placed in close proximity to air pocketsor an air gap is produced around the probe, possibly due to movement or set-tling of the soil pro le, the sensor may not produce reliable readings. Somedevices report increased temperature sensitivity when the probe is placednear the surface due to the impact of temperature on the dielectric  [Cam].Additionally, the sensing volume of the sensor is relatively small due to thedesign of the sensors; if a large volume of soil is to be measured, multipleprobes may be required [MC04]. Additional advantages and disadvantagesare discussed in Appendix A. Water Potential SensorsCommon water potential methods to measure  m are based on tensiometrictechniques that estimate  m through capillary and adsorption e ects of thesoil. They can also be divided into direct and indirect methods. All deviceshave a porous material that must contact the soil in such a fashion that watermovement is not inhibited. Water migrates in and out of the porous materialdepending on the energy status of the soil; a dry soil will draw moisture outfrom the porous material and conversely, the porous material will draw water152.2. Soil Moisture Sensing Technologyin from a wet soil until an equilibrium condition exists between the porousmaterial and the soil. The device measures the potential of the water in theporous material to determine  m. This group includes the direct method oftensiometers and the indirect methods of resistance blocks, heat dissipationsensors and thermocouple psychrometers [MCYLO02].Thermocouple PsychrometerThermocouple psychrometry is one of the most widely accepted methodsfor measuring the matric potential  m of water in soils due to its wideoperational range and accuracy [SKM99, p. 92] but is mostly restricted tolab use. A detailed discussion on thermocouple psychrometry can be foundin Appendix B.1.A thermocouple psychrometer measures the relative humidity of themoisture in soil pores in-situ. The relative humidity is related to the poten-tial energy of the equilibrium partial pressure of water vapour in the porespace. If the soil is in thermal equilibrium with the sensor, the potentialof the vapour in the sensor is in equilibrium with the matric and osmoticpotentials in the sample [Hil98, p. 164]. The relation between the partialpressure of water vapour in the pore space and the total potential energy ofpure liquid water at the same temperature can be expressed as [Car93, p.561] m = RTVm ln ees (2.2)where R is the universal gas constant (Jmol 1K 1), T is the temperatureof the probe (in K), Vm is the volume of the sample space, e is the partialpressure of the water vapour in the sample, and es is the saturation partialpressure at temperature T. It is noted that the dimensionless relation ofe=es is de ned to be the measurement of relative humidity [CG08, p. 975].If the thermocouple psychrometer is used alone to measure  m, it willnot provide complete coverage over the range of potentials monitored inagricultural soils due to is poor performance at energy levels where poresmay be completely  lled with water. The range of operation is generallyaccepted to be  50 kPa     3000 kPa. The accuracy of the device ifused with soil speci c calibration is  20 kPa [MC04]. Figure 2.9 [MC04]shows the typical size and construction of a thermocouple psychrometer.The measuring volume of the device is a sphere surrounding the porous cup,6.5 cm in radius. The device is often coupled with other energy measuringdevices, such as a tensiometer which o ers coverage from 0 kPa  m  80kPa [CG08, p. 974].162.2. Soil Moisture Sensing TechnologyFigure 2.9: Thermocouple Psychrometer. Reprinted from R. Muoz-Carpena, Field Devices for Monitoring Soil Water Content (Bulletin343). Gainesville: University of Florida Institute of Food and Agri-cultural Sciences. Copyright (2004). Retrieved January 2010, fromhttp://edis.ifas.u .edu/ae266.As with other technologies for measuring  m, the thermocouple psy-chrometer is slow to react to changes in matric potential. This is due to thetime required for the vapour pressure inside the sensing bulb to equilibratewith the vapour pressure of water in the soil. Additionally, the device has arelatively small sensing volume. The cost of the sensor also presents a bar-rier to entry as specialized equipment is required to drive the sensor whichmay limit its suitability for large scale deployment [MC04]. The most strictrequirement with this device is the requirement for thermal equilibrium be-tween the soil and all parts of the sensor. This requirement typically limitsits use to the lab. Additional advantages and disadvantages are discussedin Appendix B.1.Electrical Resistance BlocksThe electrical resistance block is a resistive type sensor that is a commonlyused method for in-situ estimation of the matric potential m. They are rela-tively inexpensive devices that can provide a continuous estimate, especiallyin dry soils where  m  50 kPa. Blocks are typically manufactured from172.2. Soil Moisture Sensing Technologya hydrophilic porous material such as gypsum,  berglass, or nylon [CG08,p. 972] with a known soil water characteristic curve (Section 2.1). Whenthe block is buried at depth in the soil of interest, water  ow will take placein the block until  m is the same between the block and the soil. Withthis method,  m is inferred from the electrical resistance of the block anddetermined from a resistance versus matric potential calibration curve. Twodistinct types are commonly used in commercial applications: the gypsumelectrical resistance block and the granular matrix sensor (GMS). A detaileddiscussion on electrical resistance blocks and GMS devices can be found inAppendix B.2.The usable range of the gypsum electrical resistance block is  30 kPa  m  2000 kPa. With soil speci c calibration the accuracy of the deviceis purported to be  1 kPa. The sensing volume of the device is a spheregreater than 10 cm in radius around the block. For the GMS, the usablerange is  10 kPa   m   2000 kPa. Similar accuracy to the gypsumelectrical resistance block can be achieved with calibration. The sensingvolume is a sphere approximately 2 cm in radius around the sensor [MC04].Problems exist for the naive user with this device. In [SB92], the GMSsensor was shown to have a non-reproducible response for given soils. Asa result, calibration is not repeatable and di erent between soil types. De-vices also required individual calibration and have a large temperature de-pendence. In [MKW92], the authors note that the equilibration time is un-reasonable at matric suctions levels below 60 kPa as well as being plaguedby hysteresis e ects. In [SDM07], the authors note that for landscape andturfgrass applications, the ranges of matric suction that are of interest areoutside the usable range for the GMS sensor. Additional advantages anddisadvantages are discussed in Appendix B.2.TensiometerThe tensiometer is a device that measures  m directly for a given soil. It is acommonly used method in agriculture for measuring matric potential in soilscience [Hil98, pp. 163-164] for the range of 0  m  80 kPa. The deviceallows for continuous in-situ measurement of the matric suction potentialof a soil. The tensiometer is ideal for long term placement in soils becauseof its ability to track changes in the soil water energy status over time. Assoil moisture is decreased through drainage and plant uptake or increaseddue to irrigation, rainfall or change in the water table, the tensiometer willrespond accordingly [Hil98, p. 162]. A detailed discussion on tensiometerdevices can be found in Appendix B.3.182.2. Soil Moisture Sensing TechnologyFigure 2.10 [MC04] shows a schematic of a tensiometer. The accuracyof the device depends on the quality of the gauge or pressure transducercoupled to the device. With proper equipment and device calibration, theaccuracy of the device is  1 kPa. The sensing volume of the device is asphere greater than 10 cm in radius around the porous cup [MC04].Figure 2.10: The Tensiometer. Reprinted from R. Muoz-Carpena, FieldDevices for Monitoring Soil Water Content(Bulletin 343). Gainesville: Uni-versity of Florida Institute of Food and Agricultural Sciences. Copyright(2004). Retrieved January 2010, from http://edis.ifas.u .edu/ae266.The tensiometer has numerous advantages over other devices that mea-sure matric potential. The devices are low in cost, complexity, and canbe easily automated, making them ideally suited for sites requiring a largenumber of measurements [CG08, p. 968]. The device requires minimal main-tenance which mostly consists of ensuring that the supply tube is completely lled with water.The most prominent disadvantage is the operational range of the devicebeing 0 kPa  m  80 kPa. This range represents only a small portion ofthe total soil water that may be available to plants [ZX94]. It is still suitablefor applications where maintaining low matric potentials are favourable forplant growth [Hil98, p. 163]. In general, the lower limit for good growthfor most crops is beyond this range. If a tensiometer is solely relied onto determine scheduling for irrigation, it may lead to over watering of the192.2. Soil Moisture Sensing Technologysoil [ZX94]. Problems can arise from improper installation and maintenanceand entrained air can slow the response time of the device. Additionaladvantages and disadvantages are discussed in Appendix B.3.Heat DissipationThe  rst designs of heat dissipation sensors measured the rate at whichheat dissipates in a given soil were proposed in the 1930’s by Shaw andBaver [Str00, p. 187]. The rate of heat dissipation is related to the thermalconductivity of the soil which in turn relates to  v. A soil speci c cali-bration is then used to derive  m. The direct measurement relies upon a xed and known swcc for the soil being measured. Second generation de-signs [PHR71][PRH71] utilize a ceramic block in hydraulic contact with thesoil. The block has a known and  xed swcc. As a result,  v in the blockcan be related to  m in the block which will equilibrate to the  m of thesoil. A detailed discussion on the heat dissipation sensor can be found inAppendix B.4.The sensor consists of the sensing device and set of specialized electronicsto control the heating of the sensor and to make measurements. With  rstgeneration devices, the soil is directly heated. Second generation devices arenow more commonly used, where a ceramic block is placed in contact withthe soil and heated. Hillel notes that several investigations have found thatthe relationship between the rate of heat dissipation and  m is linear [Hil98,p. 166]. Regardless of the advantages, the heat dissipation sensor is widelyused due to problems during installation. The development and maintenanceof good thermal contact between the soil of interest and the sensor is criticalfor proper operation [Str00, p. 187].The usable range of the heat dissipation is at most  10 kPa     1000 kPa for uncalibrated soils. Readings can be extended to  3000 kPawith lower accuracy. Overall, with soil speci c calibration the accuracy ofthe device is 7% of the absolute deviation [MC04].The complexity of the control circuit places this sensor at a disadvan-tage. The heat dissipation sensor’s control electronics are sophisticated com-pared to a standard datalogger or controller as the system needs to be ableto control the heating and sampling of the sensor. The sensor cannot bedirectly interfaced to a standard controller without adding additional hard-ware and cost. Additional advantages and disadvantages are discussed inAppendix B.4.202.2. Soil Moisture Sensing Technology2.2.3 Summary of In-Situ Sensing TechnologiesThe choice of the correct sensor to use for automated in-situ applicationsis not a straightforward process. Many facets must be considered such assoil type, texture, solar exposure, and what parameters and ranges are tobe measured. Additionally, many are not suitable for a naive user. Fora large scale deployment over many hectares, it would not be uncommonfor a variety of soil textures to be present in the pro le. Cost must alsobe considered, not only for the device but for the cost associated with thedegree of accuracy required, maintenance schedules and di culty of use. Tohelp with decision making in regard to sensor selection, a summary of thedi erent decision parameters for each sensor has been compiled with thevolumetric sensor data in Table 2.1 [MC04, ZX94] and soil water potentialsensor data in Table 2.2 [MC04, ZX94, MCYLO02].Each sensor presents its owns pros and cons for use with no one methodclearly being better than the others. No one sensor can cover the completerange of  m representing the zone of plant available water (Section 2.1)with reasonable accuracy. A combination of sensors will often be required ifconcessions in data quality do not want to be made. For  v measurements,most sensing techniques will cover the desired range and a single type ofsensor for a given soil may su ce. The decision will be driven by the costand suitability of the sensor for a given soil. The correct choice depends onthe level of accuracy required. While many of the in-situ sensors may not besuitable for detailed hydrological studies, many are suitable for the coarsegrain measurements required in irrigation.212.2.SoilMoistureSensingTechnologyT able 2.1: F unctional and Cos t P arameters for V olumetric Measuremen t T ec hniques [MC04, ZX94]TDR Capacitanceand FDRADR PT TDTUsable range 5% to 50% or5% to satura-tion with cali-bration0% to satura-tion0% to satura-tion5% to 50% 5% to 50%Accuracy(calibrated) 1%  1%  1%  1%  5%T ypical Mea-suremen t v ol-umecylinder 3cm in radiusaround thelength ofw a v eguidessphere 4 cm inradiuscylinder 3 cmin radius15 to 19 litrecylinder0.75 to 6 litrecylinderResp onsetimeinstan t to 30 s instan t instan t instan t instan tProblematicsoilsorganic, dense ,saline or highcla y soilssaline saline saline organic, d e n s e,saline or highcla y soilsCost(USD) a36 400- a36 23,000 a36 100- a36 3,500 a36 500- a36 700 a36 200- a36 400 a36 400- a36 1,300222.2.SoilMoistureSensingTechnologyT able 2.2: F unctional and Cost P arameters for Soil W ater P oten tial Measuremen t T ec hniqu e s [MC04, ZX94,MCYLO02]T ensiometer Electrical resis-tance blo c kGMS Heat dissipation Thermo couplepsyc hrometryUsable range 0 to -80 kP a -30 to -2000 kP a -10 to -3000 kP a -10 to -1000 kP a -50 to -3000 kP aAccuracy (cal-ibrated) 1 kP a  1 kP a  1 kP a  7%  20 kP aMeasuremen tv olumesphere > 10 cmin radiussphere > 10 cmin radiussphere > 2 cm inradius20 cm cylinder sphere > 10 cmin radiusResp onsetime ainstan t if prop-erly  lled; 2 to 3hrs if air bubblesha v e dev elop ed2 to 3 hrs 2 to 3 hrs hours to da ys < 3 min.Problematicsoilssandy or coarsesoilssandy or coarsesoils; 2:1 cla yssandy or coarsesoils; 2:1 cla yscoarse sandy or coarsesoils; 2:1 cla ysSalinit y a ects no > 6 dS/m > 6 dS/m no y es (ce r am i c cuponly)Main tenancerequiredy es y es: blo c ksdissolv e grad-ually o v er aseason c hangingthe sw cc andcalibrationlo w (only if de-vice dries out)no y es (high saltsoils)Cost U S D a36 75- a36 250 a36 400- a36 700 a36 200- a36 500 a36 300- a36 500 a36 500- a36 1,000a All matric suction sensing devices see increased equilibration times at high matric suctions232.3. Existing Systems2.3 Existing SystemsExtensive work has been conducted in the development of irrigation con-trollers and approaches. While there is a wide selection of commercial sys-tems available utilizing a variety of di erent technologies, active researchpersists into improved control, measurement and practice. Previous re-search has seen numerous implementations of systems to collect and monitordata [MMG+08, CHY05, KEI08, PE08, ZYWY09]. These systems use a va-riety of methods but fail to o er closed loop control. While a large amountof hydrological information is collected, they fail to address the key issue ofautomation. As a result, a user is still required to interpret the data at aknowledge level above a typical home user.Figure 2.11 shows a schematic representation of a typical residentialirrigation system. A residential landscape is divided into di erent irrigationzones. A zone is an area that is watered at the same time. Zones are createdbased on di erent irrigation needs (lawn, shrubs,  ower and garden beds).The number of sprinkler heads that can be run simultaneously is limitedby the available water pressure and  ow rate. Each zone has an associatedvalve that when opened supplies water to the zone. An irrigation controlleris a device that allows the user to control when each zone is watered andthe frequency of application.Figure 2.11: A Typical Irrigation System.242.3. Existing Systems2.3.1 Commercial SystemsA range of controllers are available for both residential and commercialapplications with the price extending from hundreds to thousands of dol-lars [OTRCG07]. Low-end controllers have simple scheduling mechanismsthat allow a user to specify one or more watering programs which include astart time for the program and the amount of time for each zone that watershould be applied. More costly controllers allow for more programs. The is-sue is that the user now must decide how much water to apply to each zone.Typically, users decide to water zones on a daily or two day interval oftenbased on local regulations and recommendations. Users rarely adjust theprograms unless the e ects of insu cient watering are visually noticeable,even though the amount of water required varies signi cantly throughoutthe season based on temperature and precipitation [dGN01].Previous work has shown that water use e ciency can be improvedthrough the addition of add-on sensors (such as rainfall) to existing irriga-tion controllers or through the use of more sophisticated controllers [CLD08,CLDM08, SDM07, GVB+08, OD08]. The types of control systems that areused in common practice can be classi ed as rainfall shuto sensors (RS),soil moisture sensors (SMS) and evapotranspiration controllers (ET) [OD08].A rainfall sensor dramatically reduces the amount of water consumed inareas with high rainfall [CLD08]. The rainfall sensor will override an existingprogram if the sensor indicates that su cient rainfall has occurred such thatirrigation is not required. Figure 2.12 [CLD08] shows a typical rainfall sensorused as a bypass switch for commercial controllers. A similar approach hasbeen used for soil moisture sensors that measure the amount of water contentin the rooting zone. A single SMS is used with a standard controller. If thereading falls below a preset soil water content threshold, the controller willthen execute the preset watering program [CLDM08, OTRCG07]. Thesesystems still rely on a user de ned program and will only bypass the programif the water content is su cient. The weakness of these approaches is thatthey only block an existing program from occurring, they do not createtheir own irrigation programs based on the data collected. Additionally,these systems require an extensive cable network to connect zone sensorsback to the controller.Another weakness with the SMS approach is how it responds to watermovement in the soil. If a soil moisture sensor is used as a bypass, dueto the slow movement of water in soils by the time the wetting front hasreached the sensor, too much water may have accumulated above the sensor.The water levels will continue to rise even after the watering has ceased and252.3. Existing SystemsFigure 2.12 has been removed due to copyright restrictions.It was a diagram of a Hunter Industries rain sensor showingthe adjustment points. Original source: Bernard Cardenas-Lailhacar and Michael D. Dukes (2008), Expanding DiskRain Sensor Performance and Potential Irrigation WaterSavings, Journal of Irrigation and Drainage Engineering.134(2):67-73Figure 2.12: Hunter Industries Mini-click Rain Sensor. The trigger point isadjusted with (a) and the drying rate is adjusted with (b).potentially continue to move to deep drainage and be wasted. Conversely, ifthe moisture sensor intersects a macro pore in the soil, the sensor may read aprematurely high water content. If preferential  ow enters the area aroundthe soil moisture sensor it will cause the system to turn o prematurelyleading to an abnormally low level of water being applied.On-demand controllers improve on the basic SMS controller approach.These systems are extremely expensive, typically use wired SMS, and arecurrently only available in large scale commercial systems. These con-trollers o er the best overall performance in terms of turf quality and ef- ciency [GVB+08]. With on-demand controllers, two set points are usedfor water content. The user de nes a lower and upper water content setpoint. The controllers will water when the water content in the soil reachesthe lower point and will terminate watering once the water content reachesthe upper limit [OTRCG07]. As water moves through soil at a very slow,on-demand controllers use a soak time on the order of 10 minutes, inter-spersed through watering events as an attempt to allow the wetting front toreach the sensor. This method may not actually capture the wetting frontcorrectly which can lead to under or over watering.An alternate approach is used with ET controllers that attempt to esti-mate the plant water demand based on local weather patterns and schedulebased on the water requirements. This information is provided as a serviceto users for speci c locations and conditions. These controllers require amethod of accessing daily evapotranspiration models [OTRCG07, OD08].With ET systems, the accuracy of the watering calculations depends onthe quality of the input data. If used in an area with a large variance interms of local climate, the performance of the unit may lead to less thanoptimal results. As the daily ET is only an estimate, the actual crop ETwill di er from the estimate due to daily environmental changes. Extensive262.3. Existing Systemstesting in North Carolina has shown that ET systems produced no savingsin water due to overestimation of evapotranspiration demands [GVB+08].2.3.2 Research SystemsAttempts have been made to use wireless sensor technologies in irrigationand agricultural applications [KEI08, PE08, ZYWY09, MMG+08]. In[PE08], the authors present a complete wireless sensor network that is aseries of dataloggers linked over a radio backbone. The data are recordedfor analysis, but no real time control is implemented. The system encoun-tered ongoing problems due to the very high power costs for transmission.In [ZYWY09], the authors present a theoretical treatment of wireless tech-nologies for irrigation control systems. The work suggests using ZigBeetechnology (Section 2.5) strictly as a wire replacement. Each wireless sta-tion was used to control remote valves for watering or collecting informationfrom weather stations. No closed loop control is used. In [KEI08], the au-thors use Bluetooth technologies as serial line replacements for commercialdata logging solutions, which limits the size, range, and expandability ofthe network. The system monitors and gathers soil parameters useful forirrigation, but user intervention is required to make irrigation decisions.In [MMG+08], the authors present a wireless sensor system for measur-ing the water energy status of soils. The system uses granular matric suction(GMS) sensors to determine the moisture status of soils. Systems based onthis technology have a short  eld life due to degeneration of the sensor andrequire complicated calibrations. Additionally, the device only measuresmatric suction. Matric suction alone is not a directly usable value in irri-gation practices as the depth of water to be applied cannot be determinedwithout a soil water characteristic curve. The system fails to recognize theshortcomings of the sensor technology in terms of the practical suitabilityfor agriculture.Problems have been discussed with systems based on the GMS (Sec-tion 2.2.2) which lead to this type of system being impractical for the naiveuser [CG08, Phe98, SB92] and are expanded on in Appendix B.2. As a re-sult, while [MMG+08] presents a feasible wireless architecture, the sensingtechnology makes it infeasible for practical implementation. Overall, pre-vious work has only considered component replacement and data gatheringmethods, but has failed to consider closed loop irrigation control.For systems based on matric suctions to be practically useful in closedloop irrigation, this value must be related to  , the volumetric water contentof the soil which can then be directly related to a watering depth. The272.4. Irrigation Scheduling Strategiesrelationship between water content and matric suction is non-linear anddi ers strongly between soil types [LR97a]. Water content can be usedto directly calculate irrigation needs whereas the matric suction cannot beused alone without extensive sampling and modelling of a soil. While thisallows for the construction of a soil water characteristic curve, it is notpractical for residential use due to the complicated nature of establishingthe relationship [Hil56].2.4 Irrigation Scheduling StrategiesDi erent strategies exist in agricultural practices for determining how muchwater is to be applied as well as when or how frequently to irrigate. Homeowners may blindly irrigate a  xed amount of water regardless of the timeof season which represents a far from optimal strategy. Crop water demandis not uniform during the growing season [dGN01] and can vary on a day today basis as well as being impacted by geographic variation between sites;hence using a  xed depth and interval strategy leads to excess usage. As aresult, any small, incremental improvement to irrigation strategies will makea positive di erence to current watering practices.Common strategies that exist are based on a running water budget,estimated evapotranspitative demand or a water de cit methodology. Arunning water budget uses a tally system where the user attempts to trackthe amount of available water in the soil matrix [Nyv04]. Starting at a knownwater content, which typically is a soil that has been saturated and allowedto drain to  eld capacity, a user estimates changes in water content bytallying additions of water from irrigation and precipitation and subtractinglosses from run-o , drainage and daily ET. The ET must be estimated anddepends on the type of crop and time of season. From the inputs to themodel, the user estimates what the soil moisture will be on day i based onwhat happened on day i-1. Once the estimated soil moisture reaches a pointthat is typically 50% of PAW, a depth of water can be calculated that isrequired to be added back to the soil pro le. The water budget method,while not very accurate and subject to cumulative errors, has proven tobe a robust solution [Jon04]. A sample of a water budget table is seen inFigure 2.13 [Nyv04].More sophisticated users will water directly in response to the daily esti-mated ET for the crop. The strategy attempts to estimate the plant waterdemand based on local weather patterns, historical ET and schedule basedon the water requirements. This information is provided as a service to282.4. Irrigation Scheduling StrategiesFigure 2.13 has been removed due to copyright restrictions.It was a diagram showing a schematic representation and ex-ample of using a water budget to schedule irrigation. Orig-inal source: Janine Nyvall (2004) Factsheet No. 577.100-3. Sprinkler Irrigation Scheduling Using a Water BudgetMethod. Resource Management Branch, B.C. Ministry ofAgriculture, Foods and Fisheries, p. 6.Figure 2.13: Sample Water Budget Worksheet.users for speci c locations and conditions, and controllers require a methodof accessing daily evapotranspiration models [OTRCG07, OD08]. The dailyestimate ET is provided as a service in many areas2. It can be calculatedusing an evaporation pan (Figure 2.14) [Hil80b, p. 637] were the depth ofwater that is lost each day is tracked due to wind, humidity and temper-ature and then converted to a crop ET [Hil98, pp. 636-638]. ET can alsobe determined using a mathematical model based on meteorological data3.The ET is provided as a depth of water used by the crop. The user can re-spond to the daily demand by adding the same depth of water back into thesoil pro le. While ET strategies have shown promise in some applicationswith suitable water savings [MDM09, SDM07, HDM07] it has been shownto overestimate in some situations [HDM07] and underestimate in othersdue to variability in local meteorology. These variations lead to unsuitableturfgrass quality [MDM09]. Hillel notes that ET for crops generally responddi erently than values from pan ET estimates [Hil98, p. 561]. Thus, thesuccess of the strategy is based on the quality of the estimated ET. Dailyand seasonal variation from estimates impact the amount of water delivered.Figure 2.14 has been removed due to copyright restrictions.It was a diagram showing a schematic representation of per-son  lling an evaporation pan. Original source: Daniel Hillel(1980) Applications of Soil Physics. Academic Press Inc, 111Fifth Avenue, New York, New York, 10003, p. 637.Figure 2.14: Evaporation Pan used to Calculate Daily ET2In British Columbia, http://www.farmwest.com/ provides daily ET estimates.3Farmwest uses a modi ed Penman Monteith equation based on temperature data only.http://www.farmwest.com/index.cfm?method=pages.showPage&pageid=230292.4. Irrigation Scheduling StrategiesA regulated de cit irrigation (RDI) strategy attempts to maximize cropyield while minimizing the cost of the production including water [FS07,Eng90, GR09] and is gaining popularity in agricultural areas where water isscarce or costly. With RDI, less water is applied to the crop than is required.This causes the crop to remove water from the soil matrix to make up the dif-ference between what has been applied and the calculated ET requirement.Managed correctly, this can actually lower ET demand through inducedchanges in plant physiology [FS07] but notes that the application of excesswater will not increase the yield. The relationship in Figure 2.15 [GR09]is called the crop water production function and demonstrates the link be-tween crop yield and total relative ET. Using the CWP for a speci c cropthe amount of water to be applied can be determined based on how muchstress the crop can withstand while maximizing yield with minimum costs.This results in an overall reduction in the required amount of water. Forturfgrass, it has been shown that if managed correctly, induced stress canimprove the health and durability of the crop [QFU97, BLM+06].Figure 2.15: General Crop Water Production Curve. Reprinted from Agri-cultural Water Management, 96(9), Sam Geerts and Dirk Raes, De cit Ir-rigation as an On-farm Strategy to Maximize Crop Water Productivity indry Areas, 1275-1284., Copyright (2009), with permission from Elsevier.302.5. Wireless Sensor Networks2.5 Wireless Sensor NetworksSensors networks have been an area of research interest  nding application inmilitary, environmental, agricultural and industrial applications [ASSC02,CES04, RM04]. Not to be confused with an ad-hoc network which o ershigh level functionality, wireless sensor networks typically have a greaterdevice density, lower cost and complexity, and di erent communicationparadigms [ASSC02]. Wireless sensor devices and the network formed bysuch, consist of an embedded processor (MCU), power management, sens-ing system, and a communications link [CES04, VCdSdM03] [Kri05, pp.4-5]. Wireless sensor devices can utilize internal measurement systems orcan utilize external sensors. Figure 2.16 [VCdSdM03] highlights the di er-ent components and how they interact. Communications between devicesis accomplished using radio frequency RF modulation techniques [Kri05, p.3] [BCDV09, VCdSdM03]. Each device, commonly known as a node, istasked with processing information that it senses or measures, and commu-nicates the observations back to a common collection point known as thesink [ASSC02, BCDV09, CES04]. This information is transfered by sendingit over the communications link and may be accomplished via a single hopor as a series of relayed messages between nodes (multihop).Figure 2.16 has been removed due to copyright restrictions.It was a diagram showing a schematic representation of thedi erent parts of a wireless sensor. Original source: M.A.M.Vieira, C.N. Coelho Jr., D.C. da Silva Jr., and J.M. da Mata,(2003), Survey on Wireless Sensor Network Devices, In Pro-ceedings of Emerging Technologies and Factory Automation(ETFA ’03), 2003. . volume 1, pp. 537-544.Figure 2.16: Wireless Sensor Architecture.In the design of wireless sensor networks, the energy characteristics andcost of the device present research challenges [VCdSdM03]. The MCU ina node is typically a small 8-bit device [VCdSdM03, ASSC02] with a smallamount of local memory. Devices are chosen to be low in cost and energye cient. The cost of a network is critical to its success as deployments of sen-sors may be dense and disposable such as in forest  re monitoring [ASSC02].Adding to the cost is the labour in maintaining the source of power. A userfaces increased labour costs for replacing batteries on a regular basis dueto the large number of devices in a deployment. As a result, nodes must312.5. Wireless Sensor Networksbe able to last for an extended period of time without battery replacement.To extend the life of a node, power control algorithms are critical to itssuccess [VCdSdM03].Nodes sample or measure the real world through an analogue to digitalconverter (ADC). A sensor converts a physical phenomena to an analogueelectrical signal which is then interpreted by the ADC [VCdSdM03]. TheADC digitizes this signal so it can be manipulated by the MCU. A digitizedsignal is sent over the communications link to the sink. Numerous radiofrequency methods are available with di erent transmission power, distanceand deployment issues. Bluetooth, IEEE 802.11, UWB among other mod-ulation techniques have been explored in research [BCDV09, VCdSdM03],but IEEE 802.15.4 [IEE07] is emerging as the dominant choice [BCDV09].It is speci cally designed for use with low power and low complexity devicesthat can tolerate low data rates [BCDV09, VCdSdM03] but natively onlysupports single hop messaging. Limited multihop routing and clustering hasbeen added to IEEE 802.15.4 by the ZigBee Alliance4 through the develop-ment of a proprietary standard. Unfortunately, IEEE 802.15.4 and ZigBeeare used interchangeably with confusion. In practice they are two di er-ent standards with ZigBee building on the IEEE 802.15.4 standard. WhileIEEE 802.15.4 only de nes the physical and MAC layers, ZigBee de nes anetwork layer [BPC+07].Extensive work has been done to produce low power sensor nodes re-sulting in the development of di erent research platforms with some be-coming commercially available. The devices that have emerged as pop-ular research platforms are the TelosB, Mica2, MicaZ, iMote5 and BTN-ode6 [DTV09, PSC05, VCdSdM03]. Table 2.3 [BPC+07] summarizes keyattributes for popular platforms. Each platform has its own unique designfeatures and performance attributes, but provide little more than a wirelessgateway [PSC05] with local memory and limited on-board sensors. Theseplatforms require the construction and addition of a peripheral interfacecard to support the additional functionality beyond the limited o ering oftemperature and light sensors.4www.ZigBee.org5Available from Crossbow Technologies at www.xbow.com.6http://www.btnode.ethz.ch/322.5.WirelessSensorNetworksT able 2.3: V arious Sensor Arc hitectures. Reprin ted from Computer Comm unic ations, 30(7), P aolo Baron ti,Prashan t Pillai, Vince W.C. Cho ok, Stefano Chessa, Alb erto Gotta and Y. F un Hu, Wireless Sensor Net w orks:A Surv ey on the State of the Art and the 802.15.4 and ZigBee Standards, 1655-1695., Cop yrigh t (2007), withp ermission from Elsevier.332.6. SummaryThe programming of these devices has not seen a convergence due tothe need for application speci c features. At a research level, TinyOS7 hasevolved as the most commonly used open source sensor operating systemwhich is currently supported by the University of California, Berkeley. It isa single process operating system that supports multihop routing [DTV09,PSC05], but may not be suitable for all applications as programming of theoperating system is complex and error prone [BPC+07]. There were morethat 37 sensor operating system under development in 2009 each with uniquestrengths and weaknesses [DTV09]. As a result, the needs of a given speci capplication may not be met by current operating system o erings leadingto the development of custom applications.2.6 SummaryThe goal of this work is to produce a low cost, easy to use residential irri-gation system based on a wireless sensor network that is easy to use for anaive user. The system should operate in a self su cient fashion monitoringthe status of soil water and make appropriate watering decisions withoutthe need for ongoing maintenance.While numerous water content and matric suction sensors are presented,few are suitable for use. All of the matric suction sensors require routinemaintenance or periodic calibration that make them unattractive choicesfor home users. For an irrigation controller to estimate the depth of waterrequired by a given turfgrass using a matric suction sensor, a soil watercharacteristic curve must exist for the speci c soil. Building a curve isbeyond the scope of the average home owner.Using a water content sensor presents the best strategy as water contentcan be linked to irrigation duration and volume. For water content sensors,both gamma ray and neutron scattering methods are infeasible for use dueto cost, di cultly of use and health and safety restrictions. All the dielectricmethods would be suitable for use except for the cost factor. As a result, alow cost dielectric sensor is the most suitable choice.In the examination of di erent irrigation controllers, no one system ex-amined could provide all the necessary functionality nor meet the desiredprice and performance point. Commercial controllers do not support highlycustomized numerical watering programs. Common wireless sensor nodesthat have been used in research also fail to address the goals of this work.The wireless sensor nodes examined do have the ability to support di erent7http://www.tinyos.net/342.6. Summaryprogramming but all require customization to support the di erent typesof sensors,  ow counters and solenoid valves required. To meet the perfor-mance and cost constraints of the application, a custom wireless sensor nodewas designed. The node is generalizable as it can be used for both sensingand control applications. It can easily interface with various sensors andintercommunicate over a wireless link.With the customized platform, di erent irrigation strategies can be eas-ily implemented with custom programming. Of the three di erent modelspresented a water budget model was selected. This model readily supportsthe introduction of sensor data without the need for complicated crop yieldmodels or daily ET model updates. The water budget model coupled witha soil moisture sensor allows for a system to run independent from user in-put and without the need for ongoing maintenance. A minimum number ofsoil water parameters are needed as inputs to allow for the model to func-tion. Section 3 presents a control strategy that couples a Decagon DevicesEC-5 dielectric soil moisture sensor, with low cost wireless sensor nodes toaddresses the design goals of this thesis.35Chapter 3Adaptive IrrigationControllerScience is what we understandwell enough to explain to acomputer. Art is everything elsewe do.Donald Knuth (1928- )The goal is to build an irrigation system that o ers signi cant savingsover current technologies without the need for excessive user input or main-tenance. The approach uses soil moisture sensors to determine the waterrequirement for a soil and schedules events based on the water requirement.This results in a system that reduces water consumption by only wateringwhen the soil conditions require it. Unlike other systems, this technologyadapts to changes in  ow, application e ciency, and crop water demands.It is simple to use as it requires no user intervention.The system is schematically represented in Figure 3.1. A dielectric sensorwas selected based on the size, cost and performance requirements. Unlikecurrent SMS or on-demand controllers, the system just does not water untilthe water content reaches an upper threshold. Instead, the control methodis based on a water budget [Jon04, Nyv04, ZX94] model using a penaltyfunction based on a De cit Irrigation [FS07] strategy to calculate the amountof water required per zone to re-establish water content levels without overwatering. Based on the amount of water required by the soil, the systemdynamically schedules the required event. The system o ers advantages overcurrent ET, SMS or traditional water budget systems.3.1 HardwareThe system consists of three components: a controller node responsible forcontrolling and scheduling irrigation events, sensing nodes that are respon-363.1. HardwareFigure 3.1: Adaptive Irrigation System Overview.sible for measuring and reporting soil moisture readings, and a soil moisturesensor. In each zone of interest, a wireless soil moisture sensor is placed inthe rooting zone of the plants. This soil moisture sensor collects readings ata regular interval and relays the readings back to the controller. The detailsof the sensing node are expanded upon in Section Wireless Sensor NodeThe goal was just not to build a better node with lower power consump-tion, but to design a system that was complete for use in environmentalmonitoring applications. The design is required to meet the criteria:a136 be able to support a wide array of common environmental sensorswithout the need for additional hardwarea136 have low power consumptiona136 have low power non-volatile memorya136 have a low construction price point.Each sensor node is about 5 cm x 7.5 cm x 10 cm. The node is encasedin water-resistant packaging and slightly buried under the soil. To evaluatethese criteria, an iterative design process was followed. Table 3.1.1 outlines373.1. HardwareTable 3.1: Wireless Sensor Node Design PhasesDesign PhasesPhase Date DeliverablesProof ofConceptMay to September2008Initial evaluation of hardware andconstruction of prototype nodeDesign Re-viewSeptember 2008to December 2008Reviewed design challenges fromproof of concept modelPrototypeDesignJanuary 2009 toMarch 2009Researched updated componentsand designed node hardwarePrototypeConstruc-tion, Vali-dation andTestingMarch 2009 toApril 2009Constructed and tested nodeagainst speci cationFirmwareReviewApril 2009 to June2009Reviewed and updated  rmwareto support new functionalitySystem De-ploymentJune 2009 to Oc-tober 2009Operational  eld testingthe major phases of the process followed. Cost and power consumption wereprinciple factors in design choices. All designs were built from scratch oncustom fabricated boards. The initial prototype was based on the AtmelMega32 processor with 32K of EEPROM memory, two pulse counters andan eight port 10-bit analogue to digital converter. The initial prototypeencountered problems with data storage space, interfacing with the wateringsolenoid values and  ow meters as well as with water penetration into thehardware enclosure.During the review phase, failure modes of the prototype were isolatedand analyzed. Using the feedback from the proof of concept phase, a pro-totype design was completed to address these failures. It was determinedthat a lower power processor was a requirement along with expanded datastorage. A model of the prototype (Figures 3.2 and 3.3) was constructedto check for component placement and physical interferences. Figures 3.4and 3.5 show the internal sensing board and completed node. The slightlyburied nodes do not use the optional external antenna. The  nal prototypeconstructed contains an IEEE 802.15.4 wireless radio and an Atmel 644pmicrocontroller. The Atmel Mega644p processor was chosen because of its383.1. Hardwarelow power consumption and suitable program space. Atmel Serial NOR ash was chosen for data storage due to the low power consumption andsmall size. The wireless radio is a Digi International XBee radio which iscompliant with the 802.15.4 standard [IEE07].Figure 3.2: UBCO Mote Model: Front.Each node also contains an eight port 12-bit analogue to digital con-verter, an eight port output driver, and three pulse counters. This allowsfor a sensor node to interact with the environment and conduct numerousfunctions. Each sensing node can connect up to eight analogue sensing de-vices. In this application, soil moisture sensors are used and report theirstatus to the controller node eliminating the need for extensive wiring.The soil moisture is monitored by an EC-58 low cost, soil moisture sen-sor (Decagon Devices) providing 0.1% resolution in water content [Dec08].The EC-5 is a dielectric soil moisture sensor that is grouped with othernon-TDR dielectric sensors (Section 2.2.1) and is purported to use capaci-tance/frequency domain technology. The EC-5 was selected as it met boththe cost and performance requirements. The manufacturer claims that thedevice has an uncalibrated accuracy of  3%. The EC-5 was used uncali-brated. The node is powered by three AA batteries. Based on power models8Technical speci cations available at http://www.decagon.com/.393.1. HardwareFigure 3.3: UBCO Mote Model: Back.for the sensor node, nodes will last more than the length of a growing seasonon one set of batteries.Many sensors require tightly regulated power. With the EC-5 sensor, ifthe input voltage drifts, the output signal will also be impacted. To addressthis issue, each sensing node includes a high accuracy low voltage dropoutregulator that is dedicated to providing power to the sensing system. Thesensing node has the ability to monitor its own power consumption andvoltage levels during sampling. This allows for the device to recognize a lowvoltage condition during a sampling event that may compromise the qualityof the data. This feature is not used for this application.To improve the quality of the sensing system, the power system for theanalogue sampling, radio and microprocessor are independent and isolated.The sensing node has the ability to turn o the radio to eliminate any spuri-ous sources of interference when measuring analogue voltages. The sensingnode also has the ability to conduct on board temperature measurementswhich are required for some sensors.Each wireless sensing node performs monitoring on a  xed schedule.The node wakes from sleep at a minute interval, performs a reading from403.1. HardwareFigure 3.4: UBCO Mote: Internal Circuit Board.Figure 3.5: UBCO Mote: Complete Sensor Node in Enclosure.the soil moisture sensor(s) and stores the results locally. The results are thenwrapped in the valid IEEE 802.15.4 data packet and queued for transmission.The sensor node transmits results over the wireless link (radio layer) at apreset interval. For each sampling interval, the modelled power consumptionis presented in Table 3.2 for nodes operating in a star topology where eachnode transmits directly to the sink. This includes the power budget for both413.1. HardwareTable 3.2: Node Power Requirements for a One Minute Cycle TimePhase Current Est. Time Duty ChargeDraw (mA) (s) Cycle (mC)Sleep 0.275 59.919 99.8% 16.477Idle 10.7 0.01 0.0167% 0.107Sample 20.7 0.02 0.0333% 0.414Radio Idle 70.7 0.04969 0.0828% 3.513Transmit 65.7 0.001398 0.00233% 0.091853the wireless sensor node and the soil moisture sensor. For this application,the payload transmitted consists of 21 bytes. The values are represented asunits of charge required per cycle.Based on the estimated power  gures, the total charge requirement is20.603 mC per cycle. With data being transmitted over the wireless linkonce every minute, the total power draw for one complete cycle is 0.343 mA.Assuming a 90% e ciency to account for internal losses, the node is capableof operating continuously for over 10 months when powered from three AAwith a capacity of 2880 mAh. The large sleep current is due to the peripheralsupport.3.1.2 Controller NodeThe controller uses the same sensor node hardware but has the additionaltask of controlling the irrigation. The pulse counters are used to measure ow during irrigation events. Figure 3.6 shows the controller node. A daugh-ter board interfaces with the node to provide a suitable interface for thesolenoid valves as well as a switching power supply to convert 24 VAC tosuitable DC voltage levels. The controller node and the valve system ispowered by 24 VAC.The controller has a user interface for setting acceptable irrigation timesand controlling the valves for each zone. A sample of the interface is seen inFigure 3.7. The controller analyzes the data provided by the sensor nodesand determines how much (long) to water and schedules an irrigation eventusing the adaptive watering model (Section 3.3). All data can be extractedto a PC for analysis. The main controller monitors water  ow through a ow meter that is attached to a pulse counter input. The controller has areal-time clock that it uses to control watering start and end times. As eachprogram event is determined, it is added to a watering queue. Since onlyone zone can be watered at a time, a program to be executed is placed on423.1. HardwareFigure 3.6: Controller Node with Solenoid Interface.Figure 3.7: Controller Interface.the queue until it is available to be serviced.The cost of each completed node including the controller is less thana36100 in small quantities excluding the cost of the soil moisture sensor. Thisis a signi cant reduction in cost as the lowest cost controller that is able toperform on-demand watering is approximately a363000 [OTRCG07].433.1. Hardware3.1.3 Node CommunicationsTo abstract and facilitate communications with the radio layer from theapplication running on the sensor node, a transport API was constructedto interface with the XBee 802.15.4 radio hardware [Dig08]. The transportframe supports a 16-bit global source and 16-bit global destination addressand a maximum 96 byte application payload. The frame structure is seenin Figure 3.8. The transport layer runs on a separate periodic thread fromthe main application in the sensor node. This allows the transport layerto run independently from the application allowing for easy re-purposing ofthe sensor node without having to rebuild the transport layer. A geographicrouting protocol [AKK04] is implemented at the transport layer for usewith the sensor nodes but was not required for this application. With thisrouting protocol, nodes try to route all packets toward the sink by selectingan outgoing link that minimizes the distance to the sink.Figure 3.8: Transport Dataframe Interactions with XBee 802.15.4 RadioAPI.The current network topology is a star topology with the controller atthe centre of the network. The controller has continuous power whereasthe sensor nodes are battery powered. The sensor nodes wake up at a setinterval and transmit their readings to the controller (sink). The controllermaintains a database of sensor readings used for its calculations. With thestar topology, routing is trivial as every sensor node is within reach of thesink, but due to the implementation of the routing protocol in the transportlayer, the sensor network can be easily extended for multi-hop routing.443.1. Hardware3.1.4 Engineering and Design ChallengesDuring the development cycle for the hardware platform, numerous chal-lenges were encountered. During the  rst test season, as a prototyping sys-tem was used to develop the controller numerous problems were encounteredwith loose and intermittent connections. This was due to the environmentthat the development board was being used in. The unit was susceptibleto corrosion as electrical traces were not protected. This issue was ad-dress through the use of improved enclosures and production quality circuitboards.The initial prototype was designed using a microprocessor with 32k ofprogram space. As the size and complexity of the program increased thisbecame insu cient. To address this issue, a processor with larger codespace was selected that maintained the same power e ciency pro le. Theinitial prototype also su ered from memory corruption due to cross talkfrom other electrical systems. This also caused unexpected and unplannedwatering events. This was addressed through better electrical shielding andan improved solenoid switching circuit. On the  nal design, e orts weretaken to isolate data and control lines as well as shielding local sources ofhigh frequency noise such as the radio and real time clock.Challenges were also faced with memory corruption and lack of storage.In the initial prototype, a multi-chip memory solution was implemented weredata had to be split across multiple devices. This solution provided limitedmemory storage and slow access times. In the  nal design, this memory wasreplaced with a single  ash chip memory solution. This helped to reduce theamount of physical space required by the memory and eliminated problemswith data splitting across multiple devices.The single largest challenge in the development of the sensor networkwas the lack of a real time debugging environment. Unlike developmentof software on a desktop computer where debugging infrastructure is of-ten available, when developing complex projects on an embedded systemhigh level debugging is not available. As a result, strict software engineer-ing design principles were applied to manage and understand the availableresources of the device.453.2. Irrigation Scheduler and Measurement Processing3.2 Irrigation Scheduler and MeasurementProcessingThe adaptive irrigation controller uses an event driven queue to schedule wa-tering events and process measurements. Figure 3.9 provides and overviewof the scheduling and measurement systems.Figure 3.9: Scheduling and Measurement Algorithms.The measurement processing system waits for measurements to be re-ceived from remote devices or the local hardware. When a measurement isavailable for processing, the system will store the data in local storage foruse by the adaptive irrigation algorithm (Section 3.3).The scheduling system dynamically schedules events as needed for wa-tering. The system only services one event at a time. Events indicate thezone to water and the length of time required. For each event serviced thecontroller records the time at which the event started and how long it wa-tered for. Total  ow and calculated application e ciency (Section 3.3) arealso recorded.To facilitate comparison between the adaptive irrigation controller anda standard irrigation system, the controller used for the testing phase wasdesigned to also concurrently runs a time based scheduler. When a wateringevent is required as determined by the time based program it is enqueuedalong with other adaptive events. This prevents events from clashing, al-lowing for both programming strategies to run side by side.3.3 Adaptive Watering ModelThe employed strategy is to build a robust system that can respond tochanges in long term soil moisture. Figure 3.10 provides an overview of theadaptive irrigation algorithm.463.3. Adaptive Watering ModelFigure 3.10: Adaptive Irrigation Algorithm.In order to schedule an event, the controller must determine how muchwater is required and at what point it is required. The approach utilizes awater budget strategy to calculate watering amounts [Nyv04] with a penaltyfunction based on a de cit irrigation philosophy [FS07]. Water is only ap-plied according to the available water budget; the amount of water providedto the soil pro le is less than or equal to the maximum amount the pro le canhold before water starts to move to deep drainage. When water is applied atrates lower than what is required by the crop, water will be extracted fromthe soil matrix. The algorithm also blocks events from being enqueued if agiven irrigation zone is in a daytime blackout period. A daytime blackoutperiod prevents watering events during speci c user selectable time periods.The controller also supports blackout periods where watering may not beallowed due to time of day or day watering restrictions.Building on a water budget methodology which uses estimated ET de-mand, drainage losses and soil moisture levels, the model is simpli ed byonly considering additions through irrigation and losses from root uptakebut uses real time soil moisture measurements. Estimated demand fromET is not considered. Soil moisture is continually monitored through sensornodes and updated for each zone in the controller. Unlike other on-demand473.3. Adaptive Watering Modelsystems that use SMS to determine when to stop watering, the system usessensors to determine how much water must be applied. To determine theamount applied, consider the following parameters for a speci c soil:a136 Field Capacity is the maximum amount of water expressed as  FC thata soil can retain. Any excess water moves to deep drainage in a littleas a few hours to 2 to 3 days. It is the upper bound on water levelsfor irrigation calculations.a136 Permanent Wilting Point is the point at which roots can no longerextract water from the soil matrix and is expressed  PWP: It is thelower bound of water levels for irrigation calculations.Both of these parameters are soil dependent and are inputs to the controller.In the model, the turn on water content point  TO is de ned as TO =  FC + PWP2 (3.1)which sets the turn on point to be the point when half of available soil waterstorage has been consumed. This point was selected as the turn on point dueto the shape of the soil water characteristic curve. As the volumetric watercontent drops below this point, the required amount of energy to in uencea step change in water content increases.In an irrigation system, the amount of water in the soil can be expressedas m3 water per m3 soil. This can be reduced to a water with Equation(2.1). This value can then be used to determine how much water is in thecolumn of soil at our soil moisture sensor if we know the depth of the sensor.For example, if the sensor is placed at 0.1 m and the water content is at 0.26m water per m of soil, the depth of water isdepthof water = zsensor  current (3.2)= 0:1 msoil  0:26 mwatermsoil (3.3)= 0:026 m (3.4)where zsensor is the depth of the sensor and  current is the current watercontent measurement. Thus, the amount of water in the soil is 2.6 cmbetween the surface and the sensor. To calculate how much water is to beapplied, the current depth of water is subtracted from the depth at  eldcapacity asrequireddepthof water = zsensor ( FC  current): (3.5)483.3. Adaptive Watering ModelThe resulting value will be the maximum amount of water that can be storedin the column of soil above the sensor. By applying the di erence, the soilwater content will rise toward the  eld capacity value but not overshoot andlead to waste. For example if  FC = 0.35, the required depth of water asfrom Equation (3.5) is:requireddepthof water = zsensor ( FC  current) (3.6)= 0:1 (0:35 0:26) m (3.7)= 0:009 m: (3.8)Once the depth of water required has been determined by the controllerthrough communications with the sensing nodes, the controller calculateshow much water is required by the zone. In order to accomplish this thecontroller requires as an input the area of each zone. The controller thencomputes the volume of water required by analyzing past delivery perfor-mance from historical  ow meter data from each zone. Knowing the  owrate for each zone, the area of the zone and the depth of water required, thesystem calculates the length of time required to deliver the correct amountof water and enters this event into the scheduler.In irrigation, system losses must be considered. Sprinkler type, dropsize,  ow rate and pressure, and canopy cover a ect delivery of water intothe soil pro le [dG09, Nyv04, Pho07]. The losses are grouped and termedas Application e ciency (Ae) which relates percentage of irrigation waterthat is successfully delivered into the rooting zone relative to the amountof water applied. This must be considered when delivering water into thepro le. In order to deliver the correct amount of water into the soil pro le,more water must be applied than is needed to compensate for the losses. Todetermine the amount of applied water, the desired amount is divided bythe application e ciency. Spray heads typically have an Ae of 76% [dG09]but this does not consider other loss sources. This value is used as a startingpoint and dynamically updated during the adaptive watering program. Forexample, if the required depth of water is 0.009 m then the actual amountrequired to be delivered isactualdepth = requireddepthof waterAe(3.9)= 0:009 m0:76 (3.10)= 0:0118 m: (3.11)In addition to losses due to Ae, applications can loose water due to evapo-ration and soil surface interactions [Nyv04]. As a result, all applications will493.3. Adaptive Watering Modelhave this loss that is labelled as Eirr. A typical value for this loss is 0.005 m.A more sophisticated model could dynamically model the evaporative lossbut for the purposes of this algorithm it is treated as a constant. The actualamount of applied water must include a 0.005 m depth to compensate forthis loss. Continuing with the previous example, the actual amount of waterrequired is nowactualdepth = requireddepthof waterAe+Eirr= 0:009 m0:76 + 0:005 m (3.12)= 0:0168 m: (3.13)If it is assumed that each zone contains 4 heads with a drop rate of 2 litresper minute, the entire zone consumes 8 litres per minute which is equivalentto 0.008 m3 per minute (based on  ow meter measurements). For a zonethat is 9 m2, the system calculates the depth of water per minute asapplicationrate = volumearea (3.14)= 0:008 m3=min9 m2 (3.15)= 0:00089 meters=minute: (3.16)As the application rate is now known, the length of time that the wateringevent must run is determined by dividing the required depth of water by theapplication rate aswateringtime = requireddepthof waterapplicationtime (3.17)= 0:0168 m0:00089 m=min (3.18)= 18:88 minutes (3.19)The complete model for calculating the required watering time is expressedas:time(min) = [Eirr + ( FC  current) zsensorAe] AQ (3.20)where  FC is soil moisture level at  eld capacity,  is the current moisturestatus of the soil, zsensor is the sensor depth, A is the area of the zone, andQ is the previous  ow rate. Thus using Equation (3.20) the controller willgenerate a watering event for this zone for a period of 18.88 minutes. If an503.3. Adaptive Watering Modelevent is generated during a blackout period it will be postponed and recal-culated when the blackout time expires. During this event, the controllerwill monitor and update  ow rates. Along with the requested event, a re-distribution time is entered. This time prevents the system from creating anew watering event until the added water has been allowed to redistributecorrectly through the soil pro le. While redistribution time can range fromtens of minutes to days, the adaptive irrigation algorithm uses a 6 hour post-irrigation blackout time. This is used to compensate for any water that mayhave entered through macro pores. During the post-irrigation blackout time,low energy water the may be in macro pores and collect around the sensorwill be drawn into the soil matrix until it is in equilibrium with the soilwater. This will compensate for anomalously high or low readings that maybe encountered immediately after irrigation. Once the post-irrigation black-out has expired, the system will then re-enter the measurement phase andwait for the soil moisture level to drop below  TO before scheduling the nextevent. Due to daytime blackout periods and changes in Ae, the amount ofwater required between events will change.Once the redistribution blackout time expires, the controller determineshow much water was delivered into the pro le compared to the estimatedamount. The controller uses the di erences between the estimated and ac-tual soil water content values to update Ae for each watering event to ac-curately characterize losses in the system. The new application e ciency iscalculated as:Aenew = ( end  start) zsensorActualDepthWater Eirr(3.21)With this method, the actual water content should never exceed  eld ca-pacity under normal irrigation. This is due to evaporation of water duringwatering and interception of water by the turf canopy. As a result, by pre-venting the water content from rising above the  eld capacity point, excesswaste water is minimized. This o ers a signi cant advantage over the on-demand method as the system never has to react to water being applied asit uses a strictly prescriptive application method.The adaptive irrigation algorithm and sensing method is more resistantto the presence of macro pores in the soil structure than current on-demandsystems. Water can in ltrate into the soil through macro pores and collectaround a sensor at a level higher than in the soil body. This artefact willcause current on-demand systems to read erroneously while the adaptiveirrigation controller presents a more robust solution. This is due to the factthat the algorithm calculates the amount of water to be delivered, waters,and then waits for internal redistribution to occur before measuring the513.4. Penalty Functionupdated water content. Any water that may have  owed into macro poresor collected around the sensor during the irrigation period will be drawn intothe soil matrix during the redistribution time allowing for a more accuratereading of actual soil moisture after irrigation.3.4 Penalty FunctionThe penalty function is a built in consequence of the application e ciency. Itis imposed by limiting the amount of water delivered to the soil in responseto an unscheduled addition of water that is co-incident with a wateringevent. This is accomplished through an adjustment in Ae. With unsched-uled additions of water,  may rise above  eld capacity for periods of time.Unscheduled additions that are not co-incident with an irrigation event orblackout period are not considered for penalty. With unscheduled additions,both  and  will increase allowing for turfgrass to be exposed to longer pe-riods of low energy water. Plants allowed to draw on low energy water forextended periods of time may be more susceptible to crop stress when waterlevels decrease [GR09].To address this a ect, the model introduces a slight stress into the waterevents after the unexpected addition of water, resulting in hardening ofthe crop. When an unexpected addition occurs coincident with irrigation,the system (Section 3.3) interprets the event as being overly e cient. Inreaction to this ‘over-watering’, the algorithm will recalculate a new Ae viaEquation (3.21) that may be higher than what is normally observed by thealgorithm. The system will then apply less water during the next wateringcycle as a result of the new Ae. This allows the turfgrass to be held in aslight de cit state which has been shown to increase crop health [JWV+03,BLM+06]. As subsequent watering events occur without the unexpectedaddition of water, Ae returns to the previous value.To better understand the penalty function, consider the following ex-ample modelled on a Guelph Loam soil. Figure 3.11 shows the changes inmatric suction and water content during a series of watering events. Thematric suction values are shown to demonstrate what is happening in thesoil matrix in terms of water energy status. In phase (a), normal water-ing is occurring. Watering starts at the turn on point of 23% VWC andraises the water content of the soil pro le to 28% with the matric suctionchanging from -100 kPa to -30 kPa. In phase (b), an unexpected additionof water occurs raising the water content to 35% with a corresponding dropin matric suction to -10 kPa. As a result, the adaptive irrigation controller523.4. Penalty FunctionFigure 3.11: Water Contents and Matric Suctions for a Guelph Loam Soil.will impose a penalty during the next watering event by changing Ae. Thenext watering event only delivers enough water to raise the water content to25.5% during phase (c). This results in the matric suction being held below-60 kPa for an extended period of time which introduces slight stress in theturfgrass to compensate for the low energy water in phase (b).The calculation of the new application e ciency is accomplished withEquation (3.21). Continuing with this example, if  TO = 23% and  FC =28% this system would request a watering event of 13 minutes assumingthe same  ow and area parameters as in Section 3.3. After irrigation iscomplete, if  = 35% the new Ae is calculated asAenew = (0:35 0:23) 0:10:0116 0:005 (3.22)= 0:0120:0066 (3.23)= 118% (3.24)assuming that the initial Ae = 76%. Due to the expected addition of water,the system calculates the watering event to be 118% e cient. During thenext watering event, the system will deliver less water to the turfgrass. Thenew application e ciency will impact the next watering event when the533.4. Penalty Functionsystem calculates the time as in Equation (3.20). The length of watering istime(s) = [0:005 + (0:28 0:23) 0:11:18 ] 90:008 (3.25)= [0:005 + (0:0051:18 ] 1;125 (3.26)= 10:39 minutes: (3.27)Assuming that the initial watering time was 13 minutes, this results in areduction in applied water of 21.9%.The algorithm presented therefore includes automatic adaptation to co-incident rainfall without the need for separate rain sensing hardware orseparate calculations. The penalty stress induced is speci c for turfgrassand may not be suitable for other crop types.54Chapter 4Experimental Program andResultsNobody climbs mountains forscienti c reasons. Science is usedto raise money for theexpeditions, but you really climbfor the hell of it.Edmund Hillary (1919-2008)The experimental setup of the test irrigation plot and methods used forevaluation of the adaptive irrigation controller are presented in this section.Results are then presented from the season of testing. The hypothesis wasthat the system would only deliver the required amount of water withoutimpacting the health and quality of the turfgrass, and signi cantly improvewatering e ciency compared to conventional industrial and research irriga-tion systems and approaches.4.1 Experimental SetupThis section outlines the location, climatic variables and controller con gu-ration for the test site.4.1.1 Site DescriptionAn irrigation test site was constructed in the Belgo area of East Kelowna.The experimental area is a semi-arid environment. Climate data and calcu-lated ET were available during the test period from a nearby agriculturalweather station9. Figure 4.1 shows the maximum and minimum daily tem-peratures during the test period. The average maximum daily temperaturewas 33.14a137. Figure 4.2 shows the calculated daily and cumulative ET values9Farmwest Belgo recording station554.1. Experimental Setupduring the test period. Long periods of little or no precipitation were ex-perienced presenting extended periods of high water demand. The site alsoexperienced record setting single rainfall events during the month of August.During the two month period, only 6 days of rain were recorded with only3 days producing more than 5 mm of precipitation. This translates to only0.0029 m of e ective precipitation over the test period.Figure 4.1: Daily Maximum and Minimum Temperatures from the FarmwestBelgo Reporting Station.Figure 4.3 shows the experimental setup. The test area consisted of two3 meter x 3 meter plots of turfgrass. One plot is a control plot which waswatered using a  xed daily application of water. One plot is controlled bythe adaptive irrigation model. The controller was placed above the groundin close proximity to the valve control box. Each zone was monitored by a ow meter to determine water consumption (Figure 4.4). The test systemused spray heads which typically have an Ae of 76% [dG09]. This typicalvalue does not consider losses from other sources. Each zone used a singleuncalibrated EC-5 sensor placed at a depth of 10 cm.564.1. Experimental SetupFigure 4.2: Daily and Cumulative ET Values from the Farmwest BelgoReporting Station.Figure 4.3: Overview of Test Site.574.1. Experimental SetupFigure 4.4: Control Values and Flow Meters.584.2. Results4.1.2 MethodsThe adaptive irrigation controller was compared against a recommended ir-rigation program and the actual water depth demand as estimated by ET.The adaptive irrigation controller also maintained a standard timing pro-gram for normal watering which was used to water the control zone. Nodaily watering restrictions were in place from the local watering authority;thus a daily watering program was used. The amount of water applied tothe control zone was based on recommended watering patterns from [dG09].The recommendations are based on the estimated demand using historicaldata. The depth of water applied to the control zone took into considerationits actual e ciency. The adaptive irrigation algorithm parameters of  FCand  PWP were determined as recommended by the British Columbia Min-istry of Agriculture, Food and Fisheries10. The system recorded wateringinformation, soil moisture, and  ow data for each zone. Results for waterusage and soil moisture were stored in the controller using the on boardmemory. Growing data was collected from July 10, 2009 to September 1,2009 during the hottest summer months. Data was collected, comparing thewater consumption of a conventional zone using a recommended wateringprogram versus water consumption from the adaptive watering program.Total applied water for each zone was tracked. During the shoulder season,which extended from the end of the trial period to October 15, the systemcontinued to operate. During the test period, the depth of water applied asdetermined by the adaptive program was compared to the actual measuredET demand at the agricultural weather reporting station.The quality of the turf was graded using the subjective National Tur-fgrass Evaluation Procedure [NTE98] which considers both functional andaesthetic qualities and rates the turfgrass on a scale of 1 (worst) to 9 (best)with a rating of 5 being considered suitable quality [HDM07].4.2 ResultsThe adaptive watering program delivered 54% less water with no noticeablee ect on visual appearance within and between the test plots during the testperiod. The adaptive system delivered a total depth of 0.324 m of waterwhereas the control zone delivered a total depth of 0.719 m of water. Thecumulative depths of applied water for the adaptive and control programsare in Figure 4.5. The adaptive irrigation controller watered a total of 1310http://www.agf.gov.bc.ca/resmgmt/publist/600Series/619000-1.pdf594.2. Resultsdays during the two month period. The estimated evaporative loss wascalculated by multiplying the number of watering events by a  xed loss of0.005 m per irrigation event. The results are summarized in Table 4.1.Table 4.1: Adaptive Watering Program Results.Adaptive ControlTotal Volume (litres) 2915.4 6471.6Total depth (meters) 0.324 0.719Est. Evap. loss (meters) 0.065 0.265E . Daily Avg. (meters) 0.00498 0.00873Figure 4.5: Cumulative Water Depths for Adaptive and Control Programs.During the summer test period, the actual ET demand was 0.254 mof water11. The adaptive watering program delivered an e ective depth of0.2589 m of water; a di erence of only 2% which can be considered to bestatistically insigni cant due to micro-climate variations between the testsite and the weather reporting station. During the shoulder season, thedaily average ET decreased to 2.7 mm per day from an average of 4.7 mmper day during the growing period. Figure 4.6 shows how the adaptive11Actual ET demand at the Farmwest Belgo recording station604.3. Discussionirrigation controller automatically adjusted its watering patterns (frequencyof events) to address the change in ET. No discernible di erence in turfgrassquality was noticed between the control and test plots, with both being ofhigh quality (7 to 8) based on the National Turfgrass Evaluation Procedure.Figure 4.6: Number of Days Between Watering Events for the Control andAdaptive Irrigation Zones.4.3 DiscussionThe adaptive irrigation controller contributed to water savings by only wa-tering a total of 13 days during the two month period (growing season). Thisschedule e ectively reduced losses through evaporation. As growing condi-tions changed at the end of the season, Figure 4.7 shows how ET demandalso decreases. Also shown is the total ET for the season in addition to therunning total for water applied by the adaptive irrigation program. On av-erage for the entire test period, the total depth of water applied follows thegeneral trend of cumulative ET until the end of the growing season (startof September).In response to decreased water demand from the crop due to changes inET, the adaptive irrigation controller responded by watering less frequently.614.4. Conclusion of Experimental ProgramFigure 4.7: Recorded Evapotranspiration During Growing Season.Figure 4.6 shows the trend in days between watering events for both thecontrol and adaptive irrigation zones. The control zone continues to waterevery day regardless of the decrease in ET. The adaptive zone responded tochanges in ET by watering less frequently.Of particular note is how the adaptive program responded to rainfallevents. Figure 4.8 shows how the soil moisture and watering events area ected by unscheduled additions of water through rainfall. It can be seenthat after a rainfall event, watering events did not take place for several daysas the turfgrass was able to extract existing water from the soil.4.4 Conclusion of Experimental ProgramFigure 4.9 shows the changes in soil moisture levels for both zones during thecourse of the experiment. The adaptive irrigation program operated in ane cient fashion. With no unexpected additions of water from precipitation,the system routinely delivered the required amount of water to bring thesoil to the target water content.During the shoulder season, the adaptive irrigation program demon-strated improvements over conventional controllers. It responded to changesin ET while the control program over-watered. This re-enforces the need624.4. Conclusion of Experimental ProgramFigure 4.8: Water Additions for Adaptive Watering Program.for users to be aware of changes in growing conditions in an e ort to reducewater consumption. If a user was to continue with an unmodi ed daily wa-tering program as used as a comparison in the experiment, the system woulddeliver an additional 0.546 meters of water into the soil pro le. The adaptivecontroller only delivered 0.047 meters of water during the same time period.This amount is less than the total ET demand during the same period. As aresult of the decreased ET demand, the changes in soil moisture over time inthe adaptive control zone decreases. The crop requires less water from soilstorage. As zone 1 continues to water unchanged, the water levels remainsabove  eld capacity leading to waste water moving to deep drainage.The hypothesis is supported by the fact that the system delivered lesswater than the control program over the test period. Moreover, in compar-ison to ET modelling, it delivered the correct amount of water required bythe turfgrass without the need for the ET infrastructure.634.4. Conclusion of Experimental ProgramFigure 4.9: Seasonal Changes in Soil Moisture.64Chapter 5Discussion and ConclusionThe scientist is not a person whogives the right answers, he is onewho asks the right questions.Claude L evi-Strauss (1908-2009)This thesis began by providing a general background on soil water (Sec-tion 2.1) and the two unique characteristics used to describe the state ofwater in the soil matrix. Soil water can be expressed by volume as wa-ter content or by energy level as matric suction. The relationship betweenwater content and matric suction is described using a soil water character-istic curve. Each soil has a unique soil water characteristic curve on whichvalues for  eld capacity and permanent wilting point can be expressed. Un-derstanding the  eld capacity and permanent wilting point for a soil arevaluable for determining irrigation requirements and can be used to deter-mine the amount of water that is available in the soil matrix for plants.Although they can be easily measured with commercial sensing technology(Section 2.2), current watering systems do not consider these parameterswhen watering and fail to o er users low cost and easy to use solution.Section 2.3 presents an overview of existing commercial and research ir-rigation controllers. Many controllers are available but generally o er nodynamic control. More costly controllers may o er by-pass functionalityusing soils moisture sensors or rain sensors but generally rely on preset irri-gation schedules. Other systems utilize subscription data to estimate waterdemand. Research systems present the use of wireless sensor technologiesto monitor soil moisture in terms of  and  but o er no closed loop irri-gation strategies where watering decisions are dynamically based on sensorreadings. Irrigation decisions must still be made by the user. Three irri-gation strategies commonly used in agriculture are presented (Section 2.4).Manual water budgeting, ET demand, and de cit irrigation all present theopportunity for increased water savings, but are complex in nature and re-quire ongoing user intervention rendering them impractical for the naiveuser. They also require access to environmental and crop information that65Chapter 5. Discussion and Conclusionmay not be practically available. The technology presented in this thesisaddresses both of these concerns. This work presents a system that signif-icantly reduces potential water waste through closed loop control withoutthe need for user interaction.As current platforms and software o erings were not suitable, the adap-tive irrigation controller and algorithms (Section 3) were built using cus-tom designed wireless sensor nodes,  ow meters and dielectric soil moisturesensors. The custom built node, speci cally for use in environmental appli-cations, monitors soil moisture and communicates the results to a centralcontroller using the IEEE 802.15.4 protocol. The novel use of wireless sensorsin this system signi cantly reduces the di cultly and cost of installation.The cost of the constructed system is signi cantly less than commercialcontrollers that contain less functionally. The controller uses an adaptiveirrigation algorithm to track moisture levels in the soil and determines whenand how long to water. Application e ciency and  ow are tracked andused to adjust watering patterns without user intervention. The systemalso utilizes a penalty function to handle unscheduled additions of water.Empirical results (Section 4) show that the adaptive irrigation strategycan lead to improved watering habits over a preset periodic function whichis typically used by most residential users. This comparison assumes a worstcase user that does not adjust their program to address seasonal or dailychanges. For a more accurate comparison, the total depth of water appliedby the adaptive irrigation controller was compared to the actual ET demand.It was found that the adaptive irrigation controller performed well, deliveringonly the amount of water required by the crop without user intervention.The system also successfully responded to daily changes in weather and neverover watered. The system o ered signi cant improvements over currentsystems during the shoulder season when ET levels decreased. Withoutcontinual user intervention, a typical system would deliver excess water. Atthe end of the season, the turfgrass was evaluated and found to be of goodquality based on an industry standard evaluation.E cient residential water use is a key part of a responsible water manage-ment strategy. Water consumption can be signi cantly reduced in turfgrassirrigation by using soil moisture sensing technology. This work presents anovel approach to controlling turfgrass irrigation that can improve waterconsumption without the need for continual user input. It has been shownthat a viable system can be constructed at a low cost that o ers signi cantsavings as the system delivers water amounts that correlate with actualplant ET. The ease of use due to the lack of programming, as well as theconsiderably lower cost compared to comparable products, makes this sys-66Chapter 5. Discussion and Conclusiontem attractive for acceptance by residential users. The results of this workhave been accepted for presentation and publication at IEEE Sensors Ap-plication Symposium (SAS2010) in Limerick, Ireland [FL10b]. Future workwill examine the use of multiple sensors to manage large scale irrigationzones as well as examining the suitability of this system to measure otherparameters in agricultural settings. Investigation will also be conducted onthe use of multiple devices across heterogeneous soils. In conclusion, by us-ing an adaptive irrigation strategy, signi cant water savings can be realizedfor the typical residential user.67Bibliography[aJDA80] G.C. Topp annd J.L. Davis and A.P. Annan. ElectromagneticDetermination of Soil Water Content: Measurements in Coax-ial Transmission Lines. Water Resources Research, 16:574582,1980. ! pages 83[AKK04] J.N. Al-Karaki and A.E. Kamal. Routing Techniques in Wire-less Sensor Networks: A Survey. IEEE Wireless Communica-tions, 11(6):6{28, Dec. 2004. ! pages 44[ASSC02] I.F. Akyildiz, Weilian Su, Y. Sankarasubramaniam, andE. Cayirci. A Survey on Sensor Networks. CommunicationsMagazine, IEEE, 40(8):102{114, Aug 2002. ! pages 31[BB91] H. W. G. Booltink and J. Bouma. Physical and MorphologicalCharacterization of Bypass Flow in a Well-Structured ClaySoil. Soil Sci Soc Am J, 55(5):1249{1254, 1991. ! pages 11[BCDV09] Chiara Buratti, Andrea Conti, Davide Dardari, and RobertoVerdone. An Overview on Wireless Sensor Networks Technol-ogy and Evolution. Sensors, 9(9):6869{6896, 2009. ! pages31, 32[BLM+06] C.M. Baldwin, H. Liu, L.B. McCarty, W.L. Bauerle, and J.E.Toler. Response of Six Bermudagrass Cultivars to Di erentIrrigation Intervals. HortTechnology, 16(3):466{470, 2006. !pages 30, 52[Boy95] John S. Boyer. Measuring the Water Status of Plants andSoils. Academic Press, a division of Harcourt Brace & Com-pany, 525 B Street, Suite 1900, San Diego, CA, 92101-4495,USA, 1995. ! pages 91, 92[BPC+07] Paolo Baronti, Prashant Pillai, Vince W.C. Chook, StefanoChessa, Alberto Gotta, and Y. Fun Hu. Wireless Sensor Net-works: A Survey on the State of the Art and the 802.15.4 and68BibliographyZigBee Standards. Computer Communications, 30(7):1655 {1695, 2007. ! pages 32, 34[BW02] Nyle C. Brady and Ray R. Weil. The Nature and Propertiesof Soils. Pearson Education, Inc., Upper Saddle River, NewJersey, USA, 07458, thirteenth edition, 2002. ! pages 6, 10,11[BW04] Nyle C. Brady and Ray R. Weil. Elements of the Nature andProperties of Soils. Pearson Education, Inc., Upper SaddleRiver, New Jersey, USA, 07458, second edition, 2004.!pages5, 9, 92, 95[Cam] Colin S. Campbell. Response of ECH20 Soil Moisture Sensorto Temperature Variation. Technical report, Decagon Devices,Inc. ! pages 15[Can07] Environment Canada. 2007 Municipal Water Use Report.Technical report, April 2007. ! pages 1[Car93] M.R. Carter. Soil Sampling and Methods of Analysis. LewisPublishers, 1993. ! pages 16, 91[CES04] David Culler, Deborah Estrin, and Mani Srivastava. GuestEditors’ Introduction: Overview of Sensor Networks. Com-puter, 37:41{49, 2004. ! pages 31[CG08] M.R. Carter and E.G. Gregorich. Soil Sampling and Methodsof Analysis. CRC Press, Taylor & Francis Group, 6000 BrokenSound Parkway, NW, Suite 300, Boca Raton, FL, 3487-2742,USA, second edition, 2008. ! pages 8, 13, 16, 18, 19, 27, 83,84, 85, 87, 90, 92, 93, 94, 95, 96, 97, 98[CHY05] Camlio Cornejo, Dorota Z. Haman, and Thomas H. Yeager.Evaluation of Soil Moisture Sensors, and Their use to ControlIrrigation Systems for Containers in the Nursery Industry. InProceedings of the ASAE Annual Meeting 2005. ASCE, July2005. ! pages 24[CLD08] Bernard Cardenas-Lailhacar and Michael D. Dukes. Expand-ing disk rain sensor performance and potential irrigation wa-ter savings. Journal of Irrigation and Drainage Engineering,134(1):67{73, 2008. ! pages 2, 2569Bibliography[CLDM08] Bernard Cardenas-Lailhacar, Michael D. Dukes, and GradyMiller. Sensor-Based Automation of Irrigation on Bermuda-grass during Wet Weather Conditions. Journal of Irrigationand Drainage Engineering, 134(1):120{128, 2008. ! pages 2,25[DC75] J. L. Davis and W. J. Chudobiak. In situ meter for measuringrelative permittivity of soils. pages 75{79, 1975. In GSC Paper75-1A. ! pages 12[DD85] G. C. Davis and J. L. Davis. Time-domain re ectometer(TDR) and its Application to Irrigation Scheduling. Advancesin Irrigation, 3, 1985. ! pages 83[Dec08] Decagon devices inc, 2008. Available:http://www.decagon.com/. ! pages 14, 39[Deg07] Don Degen. Water Sustainability: City of Kelowna WaterUtility. Presentation for Okanagan Water Stewardship Coun-cil, June 2007. ! pages ii, 1[dG09] Ted Van der Gulik. Landscape Sprinkler Irrigation SchedulingCalculator Users Guide. Technical report, Irrigation IndustryAssociation of British Columbia, February 2009. !pages 49,56, 59[dGN01] Ted Van der Gulik and Janine Nyvall. Crop Coe cients ForUse In Irrigation Scheduling. Technical Report Factsheet No.577.100-5, Resource Management Branch, B.C. Ministry ofAgriculture, Foods and Fisheries, October 2001. ! pages 25,28[DGS08] British Columbia Ministry of Forests David G. Simpson, Re-seach Scientist Physiology Stand Development Modelling. In-terview, 2008. Interview with David G. Simpson on in-situwater measurement techniques. ! pages 78[Dig08] Digi International Inc. Product Manual v1.xCx- 802.15.4 Protocol, September 2008. Available:http://ftp1.digi.com/support/documentation/90000982 A.pdf. ! pages 4470Bibliography[DTV09] A. K. Dwivedi, M. K. Tiwari, and O. P. Vyas. OperatingSystems for Tiny Networked Sensors: A Survey. InternationalJournal of Recent Trends in Engineering (IJRTE), Issue onComputer Science, 1(2):152{157, May 2009. ! pages 32, 34[Eng90] Marshall English. De cit Irrigation. I: Analytical Framework.Journal of Irrigation and Drainage Engineering, 116(3):399{412, 1990. ! pages 30[FL10a] S. Fazackerley and R. Lawrence. Automatic In-Situ Determi-nation of Field Capacity using Soil Moisture Sensors. Journalof Irrigation and Drainage Engineering, 2010. Submitted. !pages 8[FL10b] Scott Fazackerley and Ramon Lawrence. Reducing turfgrasswater consumption using sensor nodes and an adaptive irri-gation controller. In Sensors Applications Symposium (SAS),2010 IEEE, pages 90{94, feb. 2010. ! pages 67[FM77] Delwyn G. Fredlund and Norbert R. Morgenstern. Stress statevariables for unsaturated soils. Journal of the GeotechnicalEngineering Division, 103(5):447{466, May 1977. ! pages 7[FS07] Elias Fereres and Maria Auxiliadora Soriano. De cit Irrigationfor Reducing Agricultural Water Use. J. Exp. Bot., 58(2):147{159, 2007. ! pages 30, 36, 47[FX94] D. G. Fredlund and Anging Xing. Equations for the soil-water characteristic curve. Canadian Geotechnical Journal,31(4):521{532, August 1994. ! pages 5, 7, 8[GHWZ06] Allan Morris Goldberg, Larry Kent Hopkins, Larry Wawkes,and James Zimmerman. United States Patent No. 7133749 :Method and Apparatus for Optimizing Soil Moisture, Novem-ber 2006. ! pages 8[GM96] G. D. Gaskin and J. D. Miller. Measurement of soil water con-tent using simpli ed impedance measuring technique. Journalof Agricultural Engineering Research, 63:153{160, 1996. !pages 86[GR09] Sam Geerts and Dirk Raes. De cit irrigation as an on-farmstrategy to maximize crop water productivity in dry areas.71BibliographyAgricultural Water Management, 96(9):1275 { 1284, 2009. !pages 30, 52[GVB+08] Garry L. Grabow, Arjun Vasanth, Dan Bowman, Rod-ney L. Hu man, and Grady L. Miller. Evaluation ofEvapotranspiration-Based and Soil-Moisture-Based IrrigationControl in Turf. In Proceedings of the World Environmen-tal and Water Resources Congress 2008, volume 316, pages117{117. ASCE, 2008. ! pages 25, 26, 27[HDM07] Melissa B. Haley, Michael D. Dukes, and Grady L. Miller.Residential Irrigation Water Use in Central Florida. Journalof Irrigation and Drainage Engineering, 133(5):427{434, 2007.! pages 29, 59[Hil56] J. W. Hilf. An Investigation of Pore-water Pressure in Com-pacted Cohesive Soils. Tech. memo. no. 654, Denver, CO,1956. PhD thesis. ! pages 28[Hil80a] Daniel Hillel. Applications of Soil Physics. Academic PressInc, 111 Fifth Avenue, New York, New York, 10003, 1980. !pages 4, 8[Hil80b] Daniel Hillel. Fundamentals of Soil Physics. Academic PressInc, 111 Fifth Avenue, New York, New York, 10003, 1980. !pages 4, 29[Hil98] Daniel Hillel. Environmental Soil Physics. Academic Press,a division of Harcourt Brace & Company, 525 B Street, Suite1900, San Diego, CA, 92101-4495, USA, 1990, 1998. ! pages6, 13, 16, 18, 19, 20, 29, 78, 79, 81, 82, 83, 84, 90, 92, 97, 98,99[IEE07] IEEE Computer Society. IEEE Std. 802.15.4-2007, August2007. Available: http://standards.ieee.org/getieee802/download/802.15.4a-2007.pdf. ! pages 32, 39[Jon04] Hamlyn G. Jones. Irrigation scheduling: Advantages and Pit-falls of Plant-based Methods. J. Exp. Bot., 55(407):2427{2436,2004. ! pages 28, 36[JWV+03] J. E. Jordan, R. H. White, D. M. Vietor, T. C. Hale, J. C.Thomas, and M. C. Engelke. E ect of Irrigation Frequency72Bibliographyon Turf Quality, Shoot Density, and Root Length Density ofFive Bentgrass Cultivars. Crop Sci, 43(1):282{287, 2003. !pages 52[KEI08] Yunseop Kim, R.G. Evans, and W.M. Iversen. Remote Sens-ing and Control of an Irrigation System Using a DistributedWireless Sensor Network. IEEE Transactions on Instrumenta-tion and Measurement, 57(7):1379{1387, July 2008. ! pages24, 27[Kri05] Bhaskar Krishnamachari. Networking Wireless Sensors. Cam-bridge Univseriry Press, 2005. ! pages 31[LR97a] E. C. Leong and H. Rahardjo. A Review on Soil-WaterCharacteristic Curve Equations. Journal of Geotechnical andGeoenvironmental Engineering, 123(12):1106{1117, December1997. ! pages 6, 28[LR97b] E. C. Leong and H. Rahardjo. Permeability Functions forUnsaturated Soils. Journal of Geotechnical and Geoenviron-mental Engineering, 123(12):1118{1126, December 1997. !pages 11[MC04] Rafael Muoz-Carpena. Field Devices for Monitoring Soil Wa-ter Content. Bulletin 343, Gainesville: University of FloridaInstitute of Food and Agricultural Sciences, July 2004. !pages 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 78, 79, 84, 86,87, 88, 92, 94, 95, 96, 98[MCYLO02] Rafael Muoz-Carpena, Yuncong-Li, and Teresa Olczyk. Al-ternatives of Low Cost Soil Moisture Monitoring Devices forVegetable Production in South Miami-Dade County. Bulletin333, Gainesville: University of Florida Institute of Food andAgricultural Sciences, October 2002. ! pages 16, 21, 23[MDM09] M.S. McCready, M.D. Dukes, and G.L. Miller. Water Con-servation Potential of Smart Irrigation Controllers on St. Au-gustinegrass. Agricultural Water Management, 96(11):1623 {1632, 2009. ! pages 29[MG99] Phillip D. Meyer and Glendon W. Gee. Flux-Based Estima-tion of Field Capacity. Journal of Geotechnical and Geoenvi-ronmental Engineering, 125(7):595{599, 1999. ! pages 873Bibliography[MISN98] Nakashima M, M. Inoue, K. Sawada, and C. Nicholl. Measure-ment of Soil Water Content by Amplitude Domain Re ectom-etry (ADR) Methods and its Calibration. Chikaisui Gakkaishi(Bulletin of the Japanese Association of Groundwater Hydrol-ogy), 40:509{519, 1998. ! pages 86[MKW92] I. R. McCann, D. C. Kincaid, and D. Wang. OperationalCharacteristics of the Watermark Model 200 Soil Water Po-tential Sensor for Irrigation Management. Applied Engineeringin Agriculture, 8(5):603{609, 1992. ! pages 18, 96[MMG+08] John McCulloch, Paul McCarthy, Siddeswara Mayura Guru,Wei Peng, Daniel Hugo, and Andrew Terhorst. Wireless Sen-sor Network Deployment for Water Use E ciency in Irriga-tion. In REALWSN ’08: Proceedings of the workshop on Real-world wireless sensor networks, pages 46{50, New York, NY,USA, 2008. ACM. ! pages 2, 24, 27[Nob01] K. Noborio. Measurement of soil water content and electricalconductivity by time domain re ectometry: a review. Com-puters and Electronics in Agriculture, 31(3):213 { 237, 2001.! pages 13[NT72] H. H. Neumann and G. W. Thurtell. A Peltier Cooled Ther-mocouple of Dewpoint Hygrometer for In-Situ Measurementof Water Potential. Psychrometry in Water Relations Reseach,pages 103{112, 1972. ! pages 91[NTE98] NTEP Turfgrass Evaluation Workshop. NTEP TurfgrassEvaluation Workbook, Beltsville, MD, October 1998. !pages59[Nyv04] Janine Nyvall. Sprinkler Irrigation Scheduling Using a Wa-ter Budget Method. Technical report, Resource ManagementBranch, B.C. Ministry of Agriculture, Foods and Fisheries,May 2004. ! pages 28, 36, 47, 49[OD08] Tom Olmsted and Michael D. Dukes. Irrigation. Technicalreport, Conserve Florida Water Clearinghouse, University ofFlorida, 2008. ! pages 25, 26, 29[OTRCG07] Southern California Area O ce, Water Resources Plan-ning Technical Resources Center, and Operations Support74BibliographyGroup. Weather and Soil Moisture Based Landscape Irriga-tion Scheduling Devices. Technical report, U.S. Departmentof the Interior, Bureau of Reclamation, August 2007. !pages25, 26, 29, 43[PE08] F.J. Pierce and T.V. Elliott. Regional and on-farm WirelessSensor Networks for Agricultural Systems in Eastern Wash-ington. Computers and Electronics in Agriculture, 61(1):32 {43, 2008. ! pages 24, 27[Phe98] C. J. Phene. Soil Water Relations. Sensors and Techniquesfor Irrigation Management, pages 127{138, 1998. !pages 27,96[Pho07] A. Phocaides. Handbook on Pressurized Irrigation Techniques.Food and Agriculture Organization of the United Nations,Rome, Italy, second edition, 2007. ! pages 49[PHR71] C. J. Phene, G. J. Ho man, and S. L. Rawlins. Measuring SoilMatric Potential in situ by Sensing Heat Dissipation within aPorous Body: I. Theory and Sensor Construction. Soil SciSoc Am J, 35(1):27{33, 1971. ! pages 20[PRH71] C. J. Phene, S. L. Rawlins, and G. J. Ho man. Measuring SoilMatric Potential in situ by Sensing Heat Dissipation within aPorous Body: II. Experimental Results. Soil Sci Soc Am J,35(2):225{229, 1971. ! pages 20[PSC05] J. Polastre, R. Szewczyk, and D. Culler. Telos: enabling ultra-low power wireless research. In Information Processing in Sen-sor Networks, 2005. IPSN 2005. Fourth International Sympo-sium on, pages 364{369, April 2005. ! pages 32, 34[QFU97] Y. L. Qian, J. D. Fry, and W. S. Upham. Rooting and DroughtAvoidance of Warm-Season Turfgrasses and Tall Fescue inKansas. Crop Sci, 37(3):905{910, 1997. ! pages 30[RGE+98] D. A. Robinson, C. M. K. Gardner, J. Evans, J. D. Cooper,M. G. Hodnett, and J. P. Bell. The Dielectric Calibrationof Capacitive Probes for Soil Hydrology using an OscillationFrequency Response Mode. Hydro. Earth Sys. Sci, 2:83{92,1998. ! pages 8575Bibliography[RM04] K. Romer and F. Mattern. The Design Space of WirelessSensor Networks. Wireless Communications, IEEE, 11(6):54{61, Dec. 2004. ! pages 31[SB92] Egbert J. A. Spaans and John M. Baker. Calibration of Wa-termark Soil Moisture Sensors for Soil Matric Potential andTemperature. Plant and Soil, 143(2):213{217, June 1992. !pages 18, 27, 96[SDM07] Mary Shedd, Michael D. Dukes, and Grady L. Miller. Evalua-tion of Evapotranspiration and Soil Moisture-Based IrrigationControl on Turfgrass. In World Environmental and WaterResources Congress 2007, volume 243, pages 238{238. ASCE,2007. ! pages 18, 25, 29, 96[SKM99] John S. Selker, C. Kent Keller, and James T. McCord. VadoseZone Processes. CRC Press LLC, 1999. ! pages 16, 82, 91[SP02] J. L. Starr and I. C. Paltineau. Capacitance Devices. SoilScience Society of America, Madison, WI, USA, 2002. !pages 85[Str00] Ian Strangeways. Measuring the Natural Environment. ThePress Sybducate of the University of Cambridge, The PittBuilding, Trumpington Street Cambridge, United Kingdom,2000. ! pages 13, 20, 78, 79, 80, 81, 83, 97[TRC01] J. Townend, M. J. Reeve, and A. Carter. Water Release Char-acteristics. Soils and Environmental Analysis: Physical Meth-ods, pages 95{140, 2001. ! pages 8[VCdSdM03] M.A.M. Vieira, Jr. Coelho, C.N., Jr. da Silva, D.C., and J.M.da Mata. Survey on Wireless Sensor Network Devices. InEmerging Technologies and Factory Automation, 2003. Pro-ceedings. ETFA ’03. IEEE Conference, volume 1, pages 537{544, Sept. 2003. ! pages 31, 32[VH31] F. J. Veihmeyer and A. H. Hendrickson. The Moisture Equiv-alent as a Measure of the Field Capacity of Soils. Soil Science,32(2):181{193, 1931. ! pages 8[ZWJ89] S. J. Zeglin, I. White, and D. R. Jenkins. Improved  eldprobes for soil water content and electrical conductivity mea-76surements using time domain re ectometry. J. Water Resour.Res., 25(11):2367{2376, 1989. ! pages 13[ZX94] Fedro S. Zazueta and Jiannong Xin. Soil Moisture Sensors.Bulletin 292, Fl. Coop. Extension Serv. IFAS, University ofFlorida, April 1994. !pages 13, 14, 19, 20, 21, 22, 23, 36, 78,80, 82, 84, 94, 96, 97, 98[ZYWY09] Yiming Zhou, Xianglong Yang, Liren Wang, and Yibin Ying.A Wireless Design of Low-Cost Irrigation System Using Zig-Bee Technology. In International Conference on NetworksSecurity, Wireless Communications and Trusted Computing(NSWCTC ’09), pages 572{575, Los Alamitos, CA, USA,2009. IEEE Computer Society. ! pages 2, 24, 2777Appendix AWater Content SensorsThe following section provides a more detailed explanation on the opera-tion, usage, advantages and disadvantages for soil moisture sensors that aresuitable for in-situ use.A.1 Neutron Probe Sensor OverviewSince the 1950s, neutron scattering has been widely used as a method of es-timating volumetric water content. It gained wide acceptance as a techniquethat produces reliable results, is e cient [Hil98, ZX94] and is generally thebest method for the measurement of water content [Str00]. Even thoughthe use of neutron scattering produces good results, its practical use has de-cayed in industry due to health and regulatory issues [DGS08]. The methodhas many advantages over other methods. After initial installation, the de-vice produces rapid and repeatable measurements without destruction ofthe sample.Figure A.1 [MC04] shows a photo of a neutron probe installation. Theneutron probe operates by emitting fast neutrons from a radioactive sourcethat has been installed in the soil via a permanently installed access tubeto the required depth. The device access tube can be installed so that theprobe can be lowered to allow sampling near the surface to a depth of manymeters [Str00]. The fast neutrons are slowed down or thermalized to slowneutrons by collisions with hydrogen atoms in the soil that are associatedwith water molecules. The water content of a given soil is proportional tothe number of neutrons that are thermalized [ZX94] and can be calculatedasNw=Ns = y (A.1)where Nw is the slow neutron count rate from the sample soil, Ns is thecount rate in water or a standard absorber, which is calibrated to includethe absorption due to the mechanical components of the probe, and y isa constant of proportionality [Hil98, pp. 135-136]. This method presentsnumerous advantages as it can measure a large amount of soil volume as well78A.1. Neutron Probe Sensor OverviewFigure A.1: Neutron Probe Placed in Soil. Reprinted from R. Muoz-Carpena, Field Devices for Monitoring Soil Water Content(Bulletin343). Gainesville: University of Florida Institute of Food and Agri-cultural Sciences. Copyright (2004). Retrieved January 2010, fromhttp://edis.ifas.u .edu/ae266.as being able to scan and build a soil moisture pro le from measurementsover several depths.The neutron probe consists of two separate units. The  rst componentis the source of fast neutrons, commonly a radioactive source of americium-beryllium coupled with a detector for slow neutrons. The second componentis a scaler or rate meter that records the rate at which the neutrons arescattered and attenuated [Str00, p. 168] [Hil98, p. 134]. In addition to thesensing unit, a bore hole must be drilled to accept the probe. Access tubesare required to be installed in the sampling bore hole to prevent the collapseof the bore hole, through which the probe is lowered.The usable range of the neutron scattering device is 0%   v  60%.With soil speci c calibration the accuracy of the device is  0.5%. Thesensing volume of the device is a sphere surrounding the radioactive sourceof 15 to 40 cm [MC04] and varies with water content.79A.1. Neutron Probe Sensor OverviewA.1.1 AdvantagesThe neutron probe sensor can measure water in any phase. Unlike someother sensing technology, the measurement is directly related to the volu-metric content of the water in the soil and is not signi cantly impacted bychanges in temperature, pressure, and salinity. Additionally, as with mostin-situ measurement techniques, the technique is non destructive and has arelatively fast responsive time between 1 to 2 minutes for coarse measure-ments. The time period can be extended in order to achieve better results.The technique is also suitable for automation and due to the method ofinstallation, lends itself to pro le measurement [Str00, p. 168] [ZX94] butis not practical for unattended monitoring due to the radioactive nature ofthe device.A.1.2 DisadvantagesWhile the neutron probe can produce good repeatable results, the costand health risks associated with this method makes it impractical for mostusers [ZX94]. Transporting and procurement of the radioactive materialspresent signi cant safety issues due to radiation hazards [Str00, p. 174]. Aswith most sensing technologies, the probe must be calibrated to produceaccurate results. Theoretical calibration can be done for a probe based on adetailed chemical analysis of the soil, speci cally for elements in addition tohydrogen that may scatter neutrons; namely cadmium, boron and chlorine.For most applications this is impractical as the cost of such analysis is pro-hibitive and needs to be completed for each individual test hole. Laboratorycalibration can be completed for the device using a soil sample from the tar-get site. For this to be e ective, an undisturbed soil core from the test sitemust be extracted. This core can weigh several tonnes as the test core isrequired to be at least 1.5 meters in diameter and 1.5 meters in depth. Inpractice, this is only useful with sand, gravels and silts [Str00, p. 171].With this sensing technology, the volume of soil sampled around theprobe depends on the design of the sampling apparatus. With the neutronprobe, the sphere over which the device samples is dependent on the radioac-tive source, but typically presents a 15 cm sphere in wet soils and 30 cm indry soils; this not only limits the resolution at which samples can be takendue to overlap in spheres, but limits how close a sample can be taken to thesurface of the sample soil. Sampling within 20 cm of the surface can allowthe neutron sampling sphere to extend into the atmosphere. This preventsthe device from being used for requirements of sampling close to the surface,80A.2. Gamma Ray Sensorsuch as would be required for shallow rooting crops. Additionally, samplingclose to the surface presents a health risk due to potential human exposureto the neutron radiation [Str00, pp. 172-173].A.2 Gamma Ray SensorGamma attenuation is a nuclear method that measures the adsorption ofgamma rays by the water molecules present in a soil body. The methodhas been used in the  eld since the late 50’s with commercial equipment,speci cally a double-probe gamma ray device.The device measures the water content of a soil between a radioactivesource and detection unit over a  xed distance. As radiation passes throughthe soil body, the radiation will interact with the soil particles and formsecondary radiation that is not related to the water content. The rays thatpass directly through the soil are called primary rays and are the only raysof interest; secondary rays present unwanted noise to the system [Hil98, p.138].The intensity of radiation absorbed depends on the bulk density of thesoil (Db) and the water content. Generally, it is assumed that the dry bulkdensity is  xed; thus, the intensity level only changes with water content.The water content as mass of water per unit volume of soil can be expressedas [Hil98, pp. 137-138] m = ln(NwNd ) wx (A.2)where Nw=Nd is the count ratio comparing wet and dry soils that is measuredby the detection system,  w is the mass attenuation coe cient for water andx is the path length. The value of  v can then be calculated from Equation(A.2).The double-probe gamma attenuation sensor consists of two units; onebeing the source of gamma rays and the second being the detector. The twounits are installed through parallel access ports drilled into the soil body ofinterest. The area that is measured is the volume between the two accessports. The source of gamma rays is typically Cesium-137. The detectionunit contains a scintillation crystal, that illuminates when hit with gammarays. The scintillation crystal is coupled to a photo multiplier tube and apre-ampli er which converts the number of light pulses to electrical pulses.The signal is ampli ed to a level that can be measured with a scalar or otherrecording device. Additionally, coupled to the detection unit is a pulse height81A.2. Gamma Ray Sensoranalyzer which is used to discriminate the interactions between primary andsecondary rays, thus removing unwanted noise [Hil98, p. 138].In general, gamma attenuation o ers improved spatial resolution overother methods and is suitable for in-situ testing as it is a non-destructivemethod but faces signi cant challenges when used for the determination ofwater content [Hil98, p.138].A.2.1 AdvantagesDue to the geometry of the sensor, it has signi cantly improved resolutionover neutron scattering. Spatial resolutions less than 2.5 cm in thickness ispossible with response times of under 1 minute [ZX94]. This technology alsohas the ability to detect di erences in water  ow between di erent layers inthe soil pro le [Hil98, p. 138]. Due to the good spatial resolution, the devicecan also measure accurately near the surface. The device can be automatedto record soil  v as a function of depth, as well as having the ability to beleft in the  eld for long term recordingA.2.2 DisadvantagesCost may make the sensor impractical for use. As gamma rays are alsoabsorbed by the soil, changes in the soil bulk density in the soil pro lecan cause errors in measurement. This is partially due to the nature ofthe calculation of Equation (A.2) which requires a known count for dry soil.This value changes as Db changes and this error source is ampli ed in highlystrati ed soils [ZX94]. Thus, soil and site speci c calibration is required.Improper installation of the device can present problems with accuratereadings. Access tubes must be parallel to produce accurate readings as thedistance between access tubes (x) in Equation (A.2) is required to determine v. The actual distance between the access will change over the pro leleading to errors in calculations if access tubes are not parallel. The deviceis also sensitive to changes in temperature, but this can be mitigated withproper  eld calibration [Hil98, p. 138].As the device is a nuclear method, signi cant health risks are present dueto the transportation and use of a radioactive substance and risk of humanexposure. Special permits may be required to use and transport the devicewhich further contributes to the cost associated with the device [SKM99, p.94] and make it impractical for unattended operation.82A.3. Time Domain Re ectometry SensorsA.3 Time Domain Re ectometry SensorsThe TDR sensor consists of a pair of parallel metal rods that form a balancedtransmission pair and are coupled to a signal receiver. The length andspacing of the rods is dependent on the measurement application [CG08, p.941]. The rods are inserted into the soil where the rods act as waveguidesfor the electromagnetic pulse and the soil surrounding the rods forms thedielectric medium [Hil98, p. 140]. In addition to the receiver and parallelrods, the system includes a timing circuit, a pulse generator and recordingdevice. Most commercial devices incorporate all components into a singleunit [CG08, p. 941]. Alternate sensor designs are being presented in industrywith three or more waveguides which may work to eliminate the need for abalanced transmission line [Str00, pp. 180-181].To take a sample reading, a signal is generated with the signal generatorand guided along the parallel rods, where the signal re ects o the end ofthe rods and returns to the signal receiver. The timing circuit measuresthe time interval between the transmission and reception of the signal. Asshown by Topp and Davis [DD85], by knowing the length of the transmissionrods and the time for the pulse return, the relative dielectric constant of thesoil can be computed as: r = (ct=2L)2 (A.3)where c is the speed of light, L is the length of the transmission rods, and tis the two way travel time of the pulse within the exposed waveguides. Toppet al [aJDA80] further related the volumetric water content of a soils to itsdielectric constant as: v = 5:3 10 2 + 2:9 10 2 r 5:5 10 4 2r + 4:3 10 6 3r (A.4)Within typical agricultural soils,  v is only weakly dependent on soil type,bulk density, temperature and electrical conductivity of pore water [Hil98,p. 141]. The TDR method will fail at high soil water salinity levels (eg. >8 dS=m) leading to incorrect readings.From a technology perspective, the hardware to measure soil moisturewith this technique is not speci c to soil science. The most commonly useddevice is a Tektronix Model 1502 B or C portable cable tester which isreadily available with a large existing knowledge base.The pulse rise time transmitted through the waveguides is typically 200picoseconds and during one sampling event, an average of 1000 readings isrequired to build up a moisture reading.83A.4. Other Dielectric SensorsA.3.1 AdvantagesOne of the principle advantages to TDR is that the measurement of watercontent is relatively independent of soil texture and temperature [ZX94],thus making the TDR probe a desirable technology for measurement invarious soil bodies.For most environmental and soil applications, TDR can provide suitablyaccurate results without the need for calibration. For uncalibrated soils,accuracy can be within  2 %; with calibration for speci c soils, accuracycan be further improved. The design and placement of probes can be mod-i ed by the user without impacting the results allowing for any site speci crequirements [CG08, p. 940].A.3.2 DisadvantagesThe largest disadvantage to TDR is the cost of the equipment [ZX94] asa single unit can cost thousands of dollars, this technology may not bepractical for budget conscious projects that are dealing with large scaledeployment. Additionally, while the TDR is suitable for automated datacollection, leaving equipment in the  eld for extended unattended periodsis undesirable due to the potential loss in capital equipment due to theft orenvironmental degradation.As the soil moisture reading is a ected by the volume of soil betweenthe waveguides, a potential source of error can arise from air gaps in thezone of in uence. Air gaps may occur as soil shrinks upon drying duringthe period of installation, leading to inconsistent measurements [Hil98, p.141]. Another disadvantage of TDR is the sample measurement time. Themeasurement time for some TDR devices has been purported to be in theorder of 28 seconds [ZX94]. If measurements in response to rapid changesin soil moisture are required, the device will not be able to sample at thecorrect speed leading to a loss of data. Finally, the TDR method is notrecommended for use in organic soils, high salt content soils, or soils high inclay [MC04] as the signal quality becomes increasingly uninterpretable.A.4 Other Dielectric SensorsRegardless of the method of measurement, the general dielectric probe con-sists of two or more electrodes surrounded by soil and support electronics.This soil cell forms part of an electric circuit, in which the content of waterin the soils a ects the dielectric constant for the cell. Depending on the84A.4. Other Dielectric Sensorsmethod of measurement, the function of the soil cell is di erent and willbe discussed in subsequent sections. Regardless of the design, all groupsgenerally su er from the same issues in implementation.Depending on the manufacturer of speci c probes, devices may includea display device to allow the operator to read and record the soil moisture.Others may not include a read out device, but can be interfaced to a datalogging device.Capacitive ProbesFor a strictly capacitive probe, the soil cell formed with the probes is coupledto an inductor (L), forming a tuned circuit. The circuit has a characteris-tic ringing frequency that is dependent on the choice of L, the value ofinductance, and C, the capacitive value of the soil [CG08, p. 953]. Thecapacitance of the soil is dependent on the con guration of the probe aswell as  , which is determined in part by the amount of water in the soilsample. The capacitance of a cell is expressed asC = g2 ra (A.5)where  ra is the dielectric constant for the given soil and g2 is a calculatedgeometrical constant for the cell [SP02]. In the construction of the probe, asthe diectric ranges are known for soils over a given water content, the valueof the inductor is chosen such that the ringing frequency will be between 100and 150 Mhz. Over this range the e ects from dissolved ions in the soil isminimized. The resonant frequency can be expressed as a function of watercontent as [CG08, p. 953]f( v) = 12 pLC (A.6)By combining Equation (A.5) and Equation (A.6), a direct relationship be-tween the dielectric constant of the soil under question and the frequencycan be developed as [RGE+98]f( v) = 12 pLg2p ra= g3p ra(A.7)where g3 combines all design factors [CG08, p. 953]. In order to relatethe frequency to water content, the sensor must be calibrated for a givensoil type. A linear relation can be derived from Equation (A.7) making itrelatively straight forward to evaluate [CG08, p. 953].85A.4. Other Dielectric SensorsFrequency Domain Re ectometerThe construction and theory of operation of the FDR is very similar to thecapacitive probe. The primary di erence between the two designs, is thatunlike the capacitive device that is allowed to stabilize to its natural reso-nant frequency, the FDR uses an oscillator to sweep over a given frequencyrange. The supporting electronics look for the frequency with the greatestamplitude which is the resonant frequency for the soil. This frequency isthen related to water content as done previously [MC04].Amplitude Domain Re ectometerThe amplitude domain re ectometer (ADR) is very similar in design toFDR and TDR methods. The probe has a similar design consisting of par-allel rods that act as waveguides that are inserted into the soil of interest.The device transmits a sinusoidal electromagnetic wave along the waveg-uide. When the electromagnetic wave encounters a change in impedance,which is in part caused by a change in  due to the presence of water insoil, part of the wave is re ected back along the waveguide. The re ectedwave and transmitted wave interact forming a standing wave of which theamplitude is measured [MC04]. The amplitude of the standing wave canbe directly related to the impedance of the soil [MISN98, GM96]. Themeasured impedance, Z, is inversely related to  , the dielectric of the soil,as [GM96]Z = 60p ln r2r1 (A.8)where r1;r2 are geometrical parameters for the probe. A photo of an ADRprobe is seen in Figure A.2 [MC04].Phase TransmissionThe phase transmission (PT) sensor consists of two waveguide electrodesformed into two concentric circles, such that each end of the electrodescan be interfaced to the sensor electronics. The sensor transmits a  xedfrequency sinusoidal electromagnetic wave along the waveguides. As thewave traverses the sensor, the velocity of propagation of the wave may beslowed due to moisture in the soil surrounding the waveguides. This changein velocity causes a phase shift in the wave which can be measured at theend of the waveguides and compared to the original wave injected into thesystem. The degree of phase shift can be directly related to  v [MC04]. Aphoto of a PT probe is seen in Figure A.3 [MC04].86A.4. Other Dielectric SensorsFigure A.2: ADR Probe. Reprinted from R. Muoz-Carpena, Field Devicesfor Monitoring Soil Water Content(Bulletin 343). Gainesville: Universityof Florida Institute of Food and Agricultural Sciences. Copyright (2004).Retrieved January 2010, from http://edis.ifas.u .edu/ae266.Time Domain TransmissionOf all dielectric methods, the time domain transmission (TDT) sensor is themost similar to TDR. As with TDR, the transmission waveguides form anopen circuit in the soil; with TDT, the waveguides are electrically connectedat both ends, similar in fashion to the electrode design of a PT sensor. Thesensor measures the time for a one way electromagnetic pulse to travel alongthe length of the waveguide [MC04]. The determination of  v with respectto  are as given in Equations (A.3) and (A.4). TDT also has the same soilrestrictions encountered with TDR, in terms of use. A photo of a TDT isseen in Figure A.4 [MC04].A.4.1 AdvantagesDielectric sensors relatively easy to calibrate and calibration may not berequired if soil bulk electrical conductivity is < 0:02 S/m [CG08, p. 955].Devices will generally be accurate to  1%. For applications where only arelative change of soil moisture is required, these devices are suitable as the87A.4. Other Dielectric SensorsFigure A.3: Phase Transmission Probe. Reprinted from R. Muoz-Carpena,Field Devices for Monitoring Soil Water Content(Bulletin 343). Gainesville:University of Florida Institute of Food and Agricultural Sciences. Copyright(2004). Retrieved January 2010, from http://edis.ifas.u .edu/ae266.output of the sensor can be linearized from Equation (A.7); thus a relativechange in  v will correspond to a linear change in the output voltage ofthe sensor. Accuracy for ADR without calibration is noted to be  5%, inaddition to being less sensitive to temperature variation when compared tothe other dielectric methods. It is also noted as being the least invasivesensor in terms of soil disturbance due to the waveguide design [MC04].Of the dielectric sensors, the capacitive, FDR and ADR sensors may workin highly saline soils where other techniques such as TDR will not [MC04].One of the unique attributes of PT and TDT compared to the other sensors,is that some commercial probes can measure a signi cant volume of soil dueto the con guration of the waveguides.A.4.2 DisadvantagesFor the dielectric methods of PT and TDT that are strongly related to TDR,the author [MC04] notes the following problems with TDT and PT. Whenattempting to measure soil moisture levels in soils with high saline levels,88A.4. Other Dielectric SensorsFigure A.4: Time Domain Transmission Probe. Reprinted from R.Muoz-Carpena, Field Devices for Monitoring Soil Water Content(Bulletin343). Gainesville: University of Florida Institute of Food and Agri-cultural Sciences. Copyright (2004). Retrieved January 2010, fromhttp://edis.ifas.u .edu/ae266.PT will be sensitive to saline levels above 3 dS/m. Additional problems arealso noted with PT and TDT over the other dielectric methods in regards toin-situ installation. As the size of the sensing volume is signi cantly largerthan with other methods, soils may be heavily disturbed during installationleading to the need for the devices to be permanently installed at a givenlocation. Additionally, both PT and TDT experience decreased precisiondue to deformation of the pulse as it travels the length of the waveguide.TDT also has restrictions similar to TDR with respect to the types of soilsin which it can be used.89Appendix BWater Potential SensorsThe following section provides a more detailed explanation on the operation,usage, advantages and disadvantages for water potential sensors that aresuitable for in-situ use.B.1 Thermocouple Psychrometer SensorThe thermocouple psychrometer sensor consists of a small porous cup, typ-ically 1 cm in length and width. The cup is commonly manufactured out ofbrass, stainless steel or ceramic such that it will allow for water vapour topass through its surface. This will allow the vapour pressure of the waterinside the cup to be in equilibrium with the vapour pressure of water outsidethe cup. The cup typically is closed with a Te on plug. The sensor wirespass through the plug. The actual sensing surface is comprised of a ther-mocouple junction of two  ne dissimilar metal wires such as chromel andconstantan, in combination with a reference junction that is typically madeof copper [CG08, p. 974].The materials used to form the thermocouple junction demonstrate twounique properties that enable this sensor to work. First, if a temperaturegradient is applied across the junction, a voltage potential is created acrossthe junction. Secondly, if a voltage gradient is applied across the junction,a temperature gradient is produced across the junction; the direction ofcurrent  ow through the junction dictates the direction of heating. Thiswill cause one side of the junction to heat, while the other side will cool.This is called the Peltier E ect, with the junction being referred to as aPeltier junction. Exploiting this e ect allows for the measurement of theenergy potential of the water vapour [Hil98, p. 165]. Using this principle,two di erent styles of thermocouple psychrometers are commonly used tomeasure  m.90B.1. Thermocouple Psychrometer SensorPeltier PsychrometersUsing the con guration described previously, a single measurement can betaken by placing a voltage across the Peltier junction such that the temper-ature of the sensor starts to decrease. The rate of cooling of the junction ismonitored, looking for the dew point of the air in the sensor. As the tem-perature passes through the dew point, water will condense on the junctionsurface which changes the rate of cooling due to the thermal capacity of thewater on the junction surface. The temperature decrease can be read as avoltage across the junction and is typically 0.5  V MPa 1 [Car93, p. 564].By comparing the dew point temperature to the temperature of the refer-ence junction, which measures the temperature of the surrounding soil andporous cup, the relative humidity can be calculated. Using Equation (2.2),the energy status of the soil with respect to water can be calculated [SKM99,p. 92]. A similar technique can be employed without use of the referencejunction, where the junction is cooled to the dew point. With this method,the Peltier junction’s output is placed through an ampli er such that thepotential across the junction can be measured. By measuring the tempera-ture change across the junction with respect to time, the rate of evaporationcan be determined which directly correlates to the vapour pressure of waterin the soil air. The measured evaporation rate is then compared against acalibration curve of vapour pressures and known water potentials [Boy95,pp. 59-60].Dew Point HygrometersMany thermocouple psychrometers have the ability to provide a continuouswater potential status by operating in a fashion referred to as a dew pointhygrometer [NT72]. With this method, the Peltier junction is cooled tothe dew point of the air such that water condenses on the junction. Aswater condenses on the junction, the thermal mass increases and the rate ofcooling changes. The system responds by increasing the drive to the Peltierjunction through a series of pulses to keep the junction at the dew point.The measured dew point is then compared against a calibration curve todetermine  m [Boy95, p. 59]. In this mode of operation, the device typicallyhas a larger output signal sensitivity of 0.75  V MPa 1, which represents a50% increase in signal sensitivity.91B.1. Thermocouple Psychrometer SensorB.1.1 AdvantagesThermocouple psychrometers have numerous advantages that complementthe other technologies for measuring  m. Both methods have good sensi-tivity, typically in the range of 50 kPa, which is useful for soils with highmatric potentials where the tensiometer will not function [Hil98, p. 166].The readings are also considered to be scienti cally rigorous and have a verywide operating range of 50 kPa to -10,000 kPa [BW04, p. 145]. It should benoted that this method performs best at levels below -200 kPa. This makesthe sensor ideal for measuring potential in dry soils. Additionally, the sensoris suitable for automation [MC04].B.1.2 DisadvantagesFrom an implementation and use perspective, the thermocouple psychrome-ter presents some signi cant issues for a user that is unaware of the challengesassociated with this technique. The sensor itself is exceptionally sensitive totemperature gradients, both in the sensor cup and the wire leads from thesensor itself. Wires must be protected from thermal gradients as this canlead to skewed readings. Further, the sensor itself can not be used reliablyin the upper 15 to 30 cm of a soil pro le due to large thermal gradients thatmay exist in this area. This problem arises from thermal gradients beingpresent between the reference and sensing junction; a di erence of 0.001a137 translates to an error of 10 kPa. Newer designs utilizing materials withhigh thermal conductivities have reduced measurement errors due to thermalgradients [CG08, p. 975]. Carter [CG08, p. 975] argues that devices oper-ating in the dew point mode of operation may be less susceptible to ambienttemperatures changes whereas Boyer [Boy95, p. 59] argues that the devicecalibration is still susceptible to changes in ambient temperatures. Regard-less of this, while thermal gradients may present a problem with sampling,standard practices are well documented for placement of in-situ devices tominimize the e ects of thermal gradients [CG08, p. 976].Carter [CG08, p. 976] notes that of all devices used to measure waterpotential, the thermocouple pyschrometer is the device most susceptible tofailure and damage due to lack of proper maintenance in addition to im-proper use and has identi ed the following issues. The sensing junction andporous cup are very sensitive to e ects from corrosion, contamination withsalts, and in ltration of fungi. Contamination of the porous cup can leadto slowing of the response time due to a delay in the equilibrium condition.Further, this e ect may invalidate the calibration of the device due to exis-92B.2. Electrical Resistance Block Sensorstence of non-uniform vapour concentrations. Contamination on the sensingjunction may further lead to inconsistent evaporation of liquids, contribut-ing to errors in the dew point calculation. The junction is also susceptibleto corrosion by dissolved salts, especially in saline and sodic soils. This issueshould not deter use of the sensor as they can be properly managed withregular routine maintenance, but may present problems for long term in-situuse [CG08, p. 976].These issues can be managed by implementing routine maintenance pro-cedures that focus on cleaning the porous cup surrounding the sensor inaddition to removing any accumulated salts and debris that may have builtup on the thermocouple junction. Unfortunately, the maintenance procedurefor the device can take many hours and can not be reasonably completedin the  eld. The cleaning procedure involves soaking the sensor for manyhours in water or using multiple washes with various solvents to remove anycontaminates, followed by an extensive drying cycle. In an e ort to dealwith some of the contamination issues, manufacturers o er sensors madefrom di erent materials ranging from brass to stainless steel. While thestainless steel sensor cup has a higher cost, it presents signi cantly reducedmaintenance needs due to the ability of stainless steel to resist corrosion.While signi cantly lower in cost, sensor cups made from brass present con-siderably higher maintenance requirements [CG08, p. 976]. Use of thissensor and choice of components should involve a cost-bene t analysis ifbeing considered for large scale deployment.B.2 Electrical Resistance Block SensorsTwo di erent types of electrical resistance block sensors are commonly usedfor in-situ use; the Gypsum Electrical Resistance Block and the GranularMatrix sensor.Gypsum Resistance BlocksThe sensor consists of two components; the sensing block containing twoelectrodes embedded within the block and a device to excite the electrodesand read the potential. The water in the block saturates with respect togypsum and has a constants conductivity. The bulk electrical conductiv-ity of the block is determined by placing an alternating current across theelectrodes. Knowing that the water conductivity is constants, the bulk con-ductivity can be related to the water content in the block. A calibrationcurve is produced for the resistance of each block with respect to  m for93B.2. Electrical Resistance Block Sensorseach soil under investigation [ZX94]. The trend for electrical resistanceblocks is that the conductance is normally zero when dry and increases asthe block wets [CG08, p. 972]. A photo of a gypsum resistance block is seenin Figure B.1 [MC04].Figure B.1: Gypsum Resistance Block. Reprinted from R. Muoz-Carpena,Field Devices for Monitoring Soil Water Content(Bulletin 343). Gainesville:University of Florida Institute of Food and Agricultural Sciences. Copyright(2004). Retrieved January 2010, from http://edis.ifas.u .edu/ae266.Granular Matrix SensorThe sensor is very similar in operation to the gypsum resistance block buto ers some  eld life improvements. The principles of operation are the sameas with the basic sensor but measurements are made through electrodesembedded in a granular quartz matrix which is surrounded by a syntheticmembrane encased inside a stainless steel sleeve. A small amount of gypsumis also included inside the sensor to help bu er against saliently e ects andmaintain constant soil water conductivity [MC04]. A photo of a granularmatrix sensor is seen in Figure B.2 [MC04].B.2.1 AdvantagesThe major advantage to the electrical resistance block is the cost of thesensor itself [ZX94]. As it is a very low cost sensor, it is possible to place94B.2. Electrical Resistance Block SensorsFigure B.2: Watermark Granular Matrix Sensor. Reprinted from R.Muoz-Carpena, Field Devices for Monitoring Soil Water Content(Bulletin343). Gainesville: University of Florida Institute of Food and Agri-cultural Sciences. Copyright (2004). Retrieved January 2010, fromhttp://edis.ifas.u .edu/ae266.numerous devices throughout an area of interest, allowing for a cost e ective,large scale deployment that does not require maintenance on a regular basis.Various materials are readily available from a large selection of suppliers andare easily attached to data loggers for automated recording. Devices can beprogrammed to read out direct water potential readings [CG08, p. 973].Additionally, devices also can operate in saline soils up to 6 dS/m.Gypsum resistance blocks are able to read over a wide range of availablesoil water and are generally used from -100 kPa  m   1500 kPa [BW04,p. 145]. The GMS sensor o ers a slightly improved range from -30 kPa  m -2000 kPa which may make it suitable for regulated-de cit irrigationin addition to having an improved in-situ lifespan over the gypsum resistanceblock [MC04].95B.2. Electrical Resistance Block SensorsB.2.2 DisadvantagesUnfortunately, the disadvantages for the electrical resistance methods largelyout weight the advantages to the device. Both devices generally have poorresolution, making them impractical for use in research applications [MC04].In-situ use may be hampered due to degeneration of blocks made fromgypsum leading to frequent replacement. Calibration is a challenge withthis type of device as well; each individual block requires a unique pro le tobe developed for each speci c soil being measured [CG08, p. 973] [SB92].Due to the degradation of the blocks with time, standard practice dictatesthat the devices should be re-calibrated every three months in addition tolimiting the lifespan of the device [CG08, p. 973] [Phe98]. Degradationis due to clay deposition and gypsum dissolution within the sensor. Thelifespan is strongly in uenced by soil type and water in ltration rates forthe given application. Gypsum electrical resistance sensors are generally notwell suited for sandy or coarse soils, and soils containing 2:1 clays. The GMSo ers some improvement against this problem over the gypsum resistanceblock through an improved resistance against clay deposition and gypsumdissolution [MC04].From a measurement perspective, the devices experience signi cant hys-teresis, in addition to having a non-linear response to changes in  m withrespect to time [MKW92]. This possibly increases the di culty of use in the eld for quick measurements as a device transfer curve is required to convertthe voltage measured to  m. Additional challenges are introduced with thedevice when replacement is required in the  eld. A new device cannot justsimply be installed, but must be  rst primed; that is the electrical resis-tance block must be pre-soaked in a slurry made from the target soil for 24hours [CG08, p. 973]. The device has a very slow response time of up to 2to 3 hours before a reading stabilizes or re ects the change of water energystate in the soil surrounding the sensor for a single measurement [ZX94].The device also has a degree of temperature sensitivity that contributes toa decrease in accuracy [SDM07]; this can be compensated for by measuringthe temperature of the sensing device during sampling [MC04]. The GMStype compared to the gypsum electrical resistance sensor, exhibits additionalin-situ problems. Unlike the gypsum resistance block sensor, if the GMS isallowed to dry it will not recover as the soil re-wets. In order for the deviceto recover, it must be removed and re-saturated in a slurry made from thetarget soil before being reinstalled [MC04].96B.3. Tensiometer Sensor OverviewB.3 Tensiometer Sensor OverviewThe design of a tensiometer is straightforward, consisting of a porous cupwhich is typically made from ceramic or porous metal attached to a hollowtube  lled with de-aired water. Also attached to the tube is a manome-ter, suction gauge, or pressure transducer which records the suction poten-tial [CG08, p. 969]. In normal operation, the porous cup is saturated withwater and placed in contact with the soil that is to be measured. De-airedwater is placed in the device which is at atmospheric pressure. When the cupcomes into hydraulic contact with the soil, water will migrate through theporous cup such that the suction potential will equilibrate as it draws outwater from the device, creating a negative pressure potential. This pressurepotential can then be read on the manometer or via the pressure transducer.The porous cup is permeable to both water and solutes such that thesolutes in the soil freely di use into the cup so that the water in the deviceand the soil water have the same concentration of solutes. This allows thedevice to be insensitive to osmotic pressure   [Hil98, pp. 162-163] [CG08,p. 969].B.3.1 AdvantagesThe tensiometer works well in the saturated range of soils over the range of0 kPa   m  80 kPa. It is well suited for automation as the transducercan be coupled to a data logger and can operate over a long period of timewith proper maintenance. The tensiometer can also work with  uids otherthan water, such as ethylene glycol, making them useful for measurementsin frozen soils [ZX94].B.3.2 DisadvantagesThe response time of tensiometers can be quite slow compared to other tech-nologies. As the vacuum increases in the device, air bubbles are drawn outof the  uid in the tube or from the porous cup [Str00, p. 190]. This can leadto slowing of the tensiometer response due to the increased compressibilityof the  uid. The response time of the device to changes in  m can be inthe order of 2 to 3 hours. The device is also sensitive to placement andinstallation; if the device is forced into a soil or against rocks, the porouscup can be damaged rendering the device useless. Additionally, if the soilaround the measurement point on the surface is disturbed, it may presentan in ltration pathway around the tube to the porous cup allowing water to97B.4. Heat Dissipation Sensor Overviewin ltrate down to the sensing surface during irrigation or rain fall events. Ifthis is the case, the device will read erroneously [ZX94]. Erroneous readingcan also be caused by lack of maintenance of the sensing device; progressivevaporization will eventually empty the device of water leading to the even-tual failure of the device [CG08, pp. 969-970]. Another source of erroneousreadings can be attributed to temperature gradients developing in the devicebetween various parts. This e ect can be nulli ed if the device is properlyshaded from direct sunlight [Hil98, p. 163].B.4 Heat Dissipation Sensor OverviewThe sensing device consists of a heating device and a thermal sensor embed-ded in a porous ceramic block which is placed in direct contact with the soilof interest. To determine  m for a given soil, the temperature of the blockin the soil is taken and then a heating pulse is applied to the block. Thetemperature is then measured at some  xed time interval. The measuredchange in temperature of the block is proportional to  v. A calibration of v and  m for a given device allows  m to be measured as a function ofthermal conductivity [Hil98, p. 166]. The sensing device must be placed indirect contact with the soil and allowed to come into thermal equilibriumwith the soil before taking any measurement [MC04].B.4.1 AdvantagesThe device is also well suited to work in all soil types as it is not a ected byionic concentrations due to its principle of operation; thermal conductivityof a soil is not related to the salinity. With specialized electronics includedwith the sensor or coupled to a datalogger that can control the sensor, itis well suited for in-situ application as no maintenance is required for thedevice. The device can also be used to collect data in a continuous fashion.B.4.2 DisadvantagesThe heat dissipation sensor may not function correctly in soils with largehydraulic conductivities such as sand or coarse soils. This is due to theslow reaction time of the sensor coming into thermal equilibrium with thesurrounding soil; for some soils, water may drain faster than the equilibriumconditions with the sensor can be reestablished. This will cause erroneousreadings [MC04] as the value of  m being measured is not correct. From anoperations perspective, the heat dissipation sensor will only be useful with98B.4. Heat Dissipation Sensor Overviewcareful calibration and may su er from hysteresis [Hil98, p. 166]. Addition-ally, unlike other devices that have an absolute error, the heat dissipationsensor’s error is a percentage of the total reading; larger readings will havelarger errors, thus making the device unfavorable for values of  m  1000kPa.99


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