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Evapotranspiration-Based Irrigation Controllers in Florida

Permanent Link: http://ufdc.ufl.edu/UFE0041310/00001

Material Information

Title: Evapotranspiration-Based Irrigation Controllers in Florida
Physical Description: 1 online resource (201 p.)
Language: english
Creator: Rutland, Daniel
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: asce, controllers, evapotranspiration, florida, hargreaves, intellisense, irrigation, monteith, pause, penman, rain, reference, sl1600, standardized, switch, toro, weather, weathermatic
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre: Agricultural and Biological Engineering thesis, M.E.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Evapotranspiration-based irrigation controllers, also known as weather-based irrigation controllers, use evapotranspiration (ET) to schedule irrigation. The objective of this research was to determine if ET irrigation controllers can save water in Florida. The primary goals of this study were to A) evaluate two brands of evapotranspiration controllers and their ability to determine an irrigation schedule compared to a timed schedule with homeowner recommend settings while monitoring turfgrass quality, B) determine the amount of water that can be saved using rain features of the Toro Intelli-sense ET controller, and C) determine the accuracy of ETo values used by two brands of ET controllers compared to ETo calculated using the standardized ASCE Penman-Monteith ETo equation with data collected from an onsite weather station. Videos detailing ET controllers were produced and are available through YouTube. These videos were created to detail how ET controllers operate and provide instructions for proper programming. This research was conducted at the University of Florida Gulf Coast Research and Education Center (GCREC), Wimauma, Florida on twenty existing plots of established St. Augustinegrass (Stenotaphrum secundatum Floratam ) bordered on one side by mixed ornamentals. The turfgrass and mixed ornamentals portion of each plot measured 60 m2 and 33 m2 respectively. Five treatments were established with four replications each in a completely randomized block design. Treatment descriptions and designations were as follows: WM Weathermatic SL1600 with SLW15 weather monitor, TORO WRS Toro Intelli-sense with Hunter Mini-Clik rain sensor set at 6 mm and 100% usable rainfall, TORO WORS Toro Intelli-sense with no rain sensor and 100% usable rainfall, TRS Rain Bird Timer using the UF-IFAS recommended irrigation schedule with a Hunter Mini-Clik rain sensor set at 6 mm, RTRS Rain Bird Timer using 60% of TRS with a Hunter Mini-Clik rain sensor set at 6 mm. All controllers applied less cumulative seasonal irrigation than the theoretical time without rain sensor treatment. Period irrigation totals showed that the addition of a rain sensor set at a 6 mm threshold to a properly maintained and programmed irrigation timer saved 8% to 50% of irrigation compared to a timed treatment without a rain sensor (WORS). The Weathermatic controller (WM) applied an average of 42% less irrigation than the time WORS treatment for the entire study period. However, it frequently applied more weekly irrigation than the reduced time treatment (TRSR). The Toro controller with a rain sensor (TORO WRS) applied an average of 66% less irrigation than the time WORS treatment while the Toro controller without a rain sensor (TORO WORS) applied 57% less than the time WORS treatment. When working properly, the attachment of a rain switch set at a 6 mm threshold to a Toro Intelli-sense ET irrigation controller saved more water than the rain pause events sent out by the weather service. Additionally, because of the spatial variability of rainfall in Florida, it is likely that weather stations could miss rainfall events that happen in the immediate area of the controller. The Weathermatic SL1600 controllers consistently overestimated daily ETo throughout the study period. Overestimation ranged from 9-15% compared to onsite calculated ETo, which contributed to over irrigation. The most severe periods of overestimation were in the summer seasons. The Toro Intelli-sense controllers estimated ETo similar to calculated onsite ETo in cumulative comparison with only 1-3% cumulative difference over 41 months which was not statistically significant. However, the Toro controllers overestimated daily ETo during the summer seasons and underestimated ETo during the winter seasons. Overall ETo estimation performance sent to the Toro Intelli-sense controllers was closer to calculated ETo using the ASCE ETo equation than the Weathermatic SL1600 controllers.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Daniel Rutland.
Thesis: Thesis (M.E.)--University of Florida, 2009.
Local: Adviser: Dukes, Michael D.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041310:00001

Permanent Link: http://ufdc.ufl.edu/UFE0041310/00001

Material Information

Title: Evapotranspiration-Based Irrigation Controllers in Florida
Physical Description: 1 online resource (201 p.)
Language: english
Creator: Rutland, Daniel
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2009

Subjects

Subjects / Keywords: asce, controllers, evapotranspiration, florida, hargreaves, intellisense, irrigation, monteith, pause, penman, rain, reference, sl1600, standardized, switch, toro, weather, weathermatic
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre: Agricultural and Biological Engineering thesis, M.E.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: Evapotranspiration-based irrigation controllers, also known as weather-based irrigation controllers, use evapotranspiration (ET) to schedule irrigation. The objective of this research was to determine if ET irrigation controllers can save water in Florida. The primary goals of this study were to A) evaluate two brands of evapotranspiration controllers and their ability to determine an irrigation schedule compared to a timed schedule with homeowner recommend settings while monitoring turfgrass quality, B) determine the amount of water that can be saved using rain features of the Toro Intelli-sense ET controller, and C) determine the accuracy of ETo values used by two brands of ET controllers compared to ETo calculated using the standardized ASCE Penman-Monteith ETo equation with data collected from an onsite weather station. Videos detailing ET controllers were produced and are available through YouTube. These videos were created to detail how ET controllers operate and provide instructions for proper programming. This research was conducted at the University of Florida Gulf Coast Research and Education Center (GCREC), Wimauma, Florida on twenty existing plots of established St. Augustinegrass (Stenotaphrum secundatum Floratam ) bordered on one side by mixed ornamentals. The turfgrass and mixed ornamentals portion of each plot measured 60 m2 and 33 m2 respectively. Five treatments were established with four replications each in a completely randomized block design. Treatment descriptions and designations were as follows: WM Weathermatic SL1600 with SLW15 weather monitor, TORO WRS Toro Intelli-sense with Hunter Mini-Clik rain sensor set at 6 mm and 100% usable rainfall, TORO WORS Toro Intelli-sense with no rain sensor and 100% usable rainfall, TRS Rain Bird Timer using the UF-IFAS recommended irrigation schedule with a Hunter Mini-Clik rain sensor set at 6 mm, RTRS Rain Bird Timer using 60% of TRS with a Hunter Mini-Clik rain sensor set at 6 mm. All controllers applied less cumulative seasonal irrigation than the theoretical time without rain sensor treatment. Period irrigation totals showed that the addition of a rain sensor set at a 6 mm threshold to a properly maintained and programmed irrigation timer saved 8% to 50% of irrigation compared to a timed treatment without a rain sensor (WORS). The Weathermatic controller (WM) applied an average of 42% less irrigation than the time WORS treatment for the entire study period. However, it frequently applied more weekly irrigation than the reduced time treatment (TRSR). The Toro controller with a rain sensor (TORO WRS) applied an average of 66% less irrigation than the time WORS treatment while the Toro controller without a rain sensor (TORO WORS) applied 57% less than the time WORS treatment. When working properly, the attachment of a rain switch set at a 6 mm threshold to a Toro Intelli-sense ET irrigation controller saved more water than the rain pause events sent out by the weather service. Additionally, because of the spatial variability of rainfall in Florida, it is likely that weather stations could miss rainfall events that happen in the immediate area of the controller. The Weathermatic SL1600 controllers consistently overestimated daily ETo throughout the study period. Overestimation ranged from 9-15% compared to onsite calculated ETo, which contributed to over irrigation. The most severe periods of overestimation were in the summer seasons. The Toro Intelli-sense controllers estimated ETo similar to calculated onsite ETo in cumulative comparison with only 1-3% cumulative difference over 41 months which was not statistically significant. However, the Toro controllers overestimated daily ETo during the summer seasons and underestimated ETo during the winter seasons. Overall ETo estimation performance sent to the Toro Intelli-sense controllers was closer to calculated ETo using the ASCE ETo equation than the Weathermatic SL1600 controllers.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Daniel Rutland.
Thesis: Thesis (M.E.)--University of Florida, 2009.
Local: Adviser: Dukes, Michael D.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2010-12-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2009
System ID: UFE0041310:00001


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1 EVAPOTRANSPIRATION -BASED IRRIGATION CONTROLLERS IN FLORIDA By DANIEL C. RUTLAND A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ENGINEERING UNIVERSITY OF FLORIDA 2009

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2 2009 Daniel C Rutland

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3 To parents, family friends and monkeys For Pepa

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4 ACKNOWLEDGMENTS I thank everyone who has helped to make my academic career a success. My family has been a very importa nt source of strength and encouragement, and I cannot thank them enough. I would like to thank my soon to be wife, Heidi, for your support and love. I would like to thank my committee members for their support and time. Dr. Amy L. Shober lent me her ti me on many occasions to discuss the details of my project and thesis. Dr. Jason K. Kruse taught me about the finer aspects of turfgrass and landscape management in Florida. Dr. Kati W. Migliaccio was a voice of reason and encouragement in the beginning d ays of my project. And finally, Dr. Michael D. Dukes challenged me to be the best I could be in all aspects of my life. I would like to thank Stacia Davis for mentoring me through it all. Her support and knowledge made this experience as painless as poss ible. I would like to thank Gitta Shurberg for her hard work and dedication to the project. Her constant supervision and attention to detail averted many disasters. Sometimes pulling grass out of grass can get tedious, and wi thout the Farm Girls that de vil grass would have taken over. I cannot thank them enough. I also thank Daniel Preston and Larry Miller for their electrical expertise and hard work in the field.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ...................................................................................................... 4 LIST OF TABLES ................................................................................................................ 8 LIST OF FIGURES ............................................................................................................ 10 LIST OF ABBREVIATIONS .............................................................................................. 13 ABSTRACT ........................................................................................................................ 14 CHAPTER 1 INTRODUCTION ........................................................................................................ 17 Evapotranspiration ...................................................................................................... 19 Evapotranspiration Data Quality Analysis .................................................................. 25 Evapotranspiration Irrigation Controllers ................................................................... 28 Historical Based Contro llers ................................................................................ 28 Stand Alone Controllers ....................................................................................... 28 Signal Based Controllers ..................................................................................... 29 Evapotranspiration Controllers in this Study ....................................................... 29 Previous Research ..................................................................................................... 31 2 EVALUATION OF A SIGNAL BASED AND ONSITE EVAPOTRANSPIRATION IRRIGATION CO NTROLLER ..................................................................................... 37 Introduction ................................................................................................................. 37 Materials and Methods ............................................................................................... 41 Results and Discussi on .............................................................................................. 48 Summer 2008 ....................................................................................................... 48 Fall 2008 ............................................................................................................... 49 Winter 2008-09 ..................................................................................................... 50 Spring 2009 .......................................................................................................... 51 Summer 2009 ....................................................................................................... 52 Conclusions ................................................................................................................ 54 3 PERFORMANCE OF TORO INTELLI -SENSE EVAPOTRANSPIRATION CONTROLLER RAIN DELAY FEATURES ................................................................ 71 Introduction ................................................................................................................. 71 Materials and Met hods ............................................................................................... 73 Results and Discussion .............................................................................................. 75 Summer 2008 ....................................................................................................... 77 Fall 2008 ............................................................................................................... 78

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6 Winter 2008-09 ..................................................................................................... 78 Spring 2009 .......................................................................................................... 79 Summer 2009 ....................................................................................................... 79 Conclusions ................................................................................................................ 80 4 EVALUATION OF REFERENCE EVAPOTRANSPIRATION ESTIMATION BY EVAPOTRANSPIRATION CONTROLLERS IN A HUMID CLIMATE ....................... 91 Introduction ................................................................................................................. 91 Materials and Methods ............................................................................................... 93 Results and Discussion .............................................................................................. 96 Onsite Weather Data Quality Analysis ................................................................ 96 Controller ETo Analysis ........................................................................................ 98 Seasonal Controller ETo Analysis ..................................................................... 101 Conclusions .............................................................................................................. 102 5 EDUCATIONAL VIDEOS FOR EVAPOTRANSPIRATION IRRIGATION CONTROLLERS ....................................................................................................... 121 Intr oduction ............................................................................................................... 121 Methods and Materials ............................................................................................. 121 Results ...................................................................................................................... 122 Conclusions .............................................................................................................. 122 6 CONCLUSIONS ........................................................................................................ 124 Water Applied and Turf Quality ................................................................................ 124 Rain Features of the Toro Intelli -sense Evapotranspiration Irrigation Controller ... 126 Controller Reference Evapotranspiration Comparison ............................................ 127 APPENDIX A STATISTI CAL ANALYSIS AND DATA FOR CHAPTER 2 ...................................... 128 One week Comparison ............................................................................................. 128 Program .............................................................................................................. 128 Data .................................................................................................................... 128 Two week Irrigation Summation and Turf Quality Comparison .............................. 163 Program .............................................................................................................. 163 Data .................................................................................................................... 163 B STATISTICAL ANALYSIS AND DATA FOR CHAPTER 3 ...................................... 170 Program .............................................................................................................. 170 Data .................................................................................................................... 170

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7 WORKS CITED ............................................................................................................... 197 BIOGRAPHICAL SKETCH .............................................................................................. 201

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8 LIST OF TABLES Table page 2 -1 Controller and treatment settings for the study period .......................................... 57 2 -2 Scheduled irrigation run times and depth (each event, 2 d/wk) for the ti me treatment (TRS) and the reduced time treatment (TRSR) for all seasons. .......... 57 2 -3 Average weekly irrigation application during summer 2008 (1 July 31 August) and savings as compared to the theor etical time based treatment without rain sensor ................................................................................................. 58 2 -4 Average 2 week irrigation application compared to turf quality during summer 2008 (1 July 31 August) ...................................................................................... 58 2 -5 Average weekly irrigation application during fall 2008 (1 September 30 November) and savings as compared to the theoretical time based treatment without rain sensor ................................................................................................. 59 2 -6 Average 2 week irrigation application compared to turf quality during fall 2008 (1 September 30 November) .............................................................................. 59 2 -7 Average weekly irrigation application during winter 2008 -09 (1 Decembe r 28 February) and savings as compared to the theoretical time based treatment without rain sensor ................................................................................ 60 2 -8 Average 2 week irrigation application compared to turf quality during winter 200809 (1 December 28 February) ................................................................... 60 2 -9 Average weekly irrigation application during spring 2009 (1 March 31 May) and savings as compared to the theoretical time based treatment without rain sensor ..................................................................................................................... 61 2 -10 Average 2 week irrigation application compared to turf quality during spring 2009 (1 March 31 May) ....................................................................................... 61 2 -11 Average weekly irrig ation application during summer 2009 (1 June 31 August) and savings as compared to the theoretical time based treatment without rain sensor ................................................................................................. 62 2 -12 Average 2 week irrigation application compared to turf quality during summer 2009 (1 June 31 August) ..................................................................................... 62 3 -1 Toro Intelli -sense controller and treatment settings for the study period ............. 82 3 -2 Average weekly irrigation application during summer 2008 (1 July 31 August) and days of irrigation delay. ..................................................................... 82

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9 3 -3 Average weekly irrigation application during fall 2008 (1 September 30 November) and days of irrigation delay. ................................................................ 82 3 -4 Average weekly irrigation application during winter 2008 -09 (1 December 28 February) and days of irrigation delay. ............................................................. 83 3 -5 Average weekly irrigation application during spring 2009 (1 March 31 May), and days of irrigation delay. ................................................................................... 83 3 -6 Average weekly irrigation application during summer 2009 (1 June 31 May) and days of irrigation delay. ................................................................................... 83 4 -1 Total recorded ETo for controllers and calculated ETo using the ASCE Penman Monteith ET equation as well as percent differ ences between controller ETo and ASCE ETo for the study period (1 June, 2006 31 August, 2009). .................................................................................................................... 103 4 -2 Average daily and total ETo and percent difference between controllers and onsite calculat ed ETo using the PenmanMonteith ET Equation for the study period (1 June 2006 31 August, 2009). Note that Toro B (TB) was not functional until 15 May, 2008. .............................................................................. 103 4 -3 Average and Total ETo and percent difference between controllers and onsite calculated ETo using the PenmanMonteith ET equation for the period between 15 May, 2008 and 31 August, 2009. ..................................................... 103 4 -4 Combined seasonal data of average daily ETo calculated by the controllers and onsite ETo calculated using the PenmanMonteith ET equation for the study period (1 June, 2006 31 August, 2009). ................................................. 104

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10 LIST OF FIGURES Figure page 2 -1 Sample NTEP scale turfgrass ratings (19 scale) ................................................. 63 2 -2 Monthly and cumulative historical (19792009) and observed rainfall for the duration of the study period (1 July, 2008 31 August, 2009) ............................. 64 2 -3 Monthly and cumulative historical (19792009) and observed rainfall days for the duration of the study period (1 July, 2008 31 August, 2009) ....................... 65 2 -4 Cumulative and daily irrigation application and rainfall for summer 2008 (1 July 31 August) .................................................................................................... 66 2 -5 Cumulative and daily irrigation application and rainfall for fall 2008 (1 September 30 November) ................................................................................... 67 2 -6 Cumulative and daily irrigation application and rainfall for winter 2008 -09 (1 December 28 February) ....................................................................................... 6 8 2 -7 Cumulative and daily irrigation application and rainfall for spring 2009 (1 March 31 May) ..................................................................................................... 69 2 -8 Cumulative and daily irrigation app lication and rainfall for summer 2009 (1 June 31 August) ................................................................................................... 70 3 -1 Monthly and cumulative historical (1979 2009) and observed rainfall for the duration of the study period (1 July, 2008 31 August, 2009). ............................ 84 3 -2 Monthly and cumulative historical (19792009) and observed rainfall frequency of events at or above 6mm for the durations of the study period (1 July, 2008 31 August, 2009). .............................................................................. 85 3 -3 Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for summer 2008 (1 July, 2008 31 August, 2008) ........ 86 3 -4 Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for fall 2008 (1 September, 2008 30 November, 2008) ....................................................................................................................... 87 3 -5 Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for winter 2008-09 (1 December, 2008 28 February, 2009) ....................................................................................................................... 88 3 -6 Cumulative daily irr igation application, cumulative days of rain delay, type of rain delay, and rainfall for spring 2009 (1 March, 2009 31 May, 2009) ............ 89

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11 3 -7 Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for summer 2009 (1 June, 2009 31 August, 2009) ....... 90 4 -1 Daily incoming solar radiation (Rs) data from the onsite FAWN weather station quality anal ysis. ........................................................................................ 105 4 -2 Minimum daily temperature collected from the onsite FAWN weather station compared to calculated daily minimum dew point temperature for the study period (1 June, 2006 31 August, 2009). ............................................................ 106 4 -3 Minimum and maximum daily relative humidity over the course of the study period (1 June, 2006 31 August, 2009) collected from the onsite FAWN weather station. .................................................................................................... 107 4 -4 Daily average wind speed at 2 m height over the course of the study period (1 June, 2006 31 August, 2009) collected from the onsite FAWN weather station. .................................................................................................................. 108 4 -5 Cumulative controller calculated ETo and onsite ETo calculated using the ASCE Penman Monteith ET Equation between 14 October, 2006 and 31 August, 2009 ........................................................................................................ 109 4 -6 Cumulative control ler calculated ETo and onsite ETo calculated using the ASCE Penman Monteith Standardized ET Equation between 18 May, 2008 and 31 August, 2009 ............................................................................................ 110 4 -7 Weathermatic A (WMA) daily controller calcula ted ETo vs. daily onsite ETo calculated using the ASCE PenmanMonteith ET equation for the study period (1 June, 2006 31 August, 2009). ............................................................ 111 4 -8 Weathermatic B (WMB) daily controller calculated ETo vs. daily onsite ETo calculated using the ASCE PenmanMonteith ET equation for the study period (1 June, 2006 31 August, 2009). ............................................................ 112 4 -9 Toro A (TA) daily controller calculated ETo vs. daily onsite ETo calculated using the ASCE PenmanMonteith ET equation for the study period (1 June, 2006 31 August, 2009). ..................................................................................... 113 4 -10 Toro B (TB) daily controller calculated ETo vs. daily onsite ETo calculated using the ASCE PenmanMonteith ET equation for the installation period (15 May, 2008 31 August, 2009). ............................................................................ 114 4 -11 Weathermatic A (WMA) and Weathermatic B (WMB) daily ETo calculation co mpared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the fall periods (1 September 30 November). .............................. 115

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12 4 -12 Weathermatic A (WMA) and Weathermatic B (WMB) daily ETo calcu lation compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the spring periods (1 March 31 May). ........................................... 116 4 -13 Weathermatic A (WMA) and Weathermatic B (WMB) daily ETo cal culation compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the summer periods (1 June 31 August). .................................... 116 4 -14 Weathermatic A (WMA) and Weathermatic B (WMB) daily ETo calculation compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the winter periods (1 December 28 February). ............................ 117 4 -15 Toro A (TA) and Toro B (TB) daily ETo calcula tion compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the fall periods (1 September 30 November) ........................................................................... 117 4 -16 Toro A (TA) and Toro B (TB) daily ETo calculation com pared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the spring periods (1 March 31 May). ................................................................................. 118 4 -17 Toro A (TA) and Toro B (TB) daily ETo calculation compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the summer periods (1 June 31 August). .............................................................................. 119 4 -18 Toro A (TA) and Toro B (TB) daily ETo calculation compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the winter periods (1 December 28 February). .................................................................. 120

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13 LIST OF ABBREVIATIONS ASCE American Society of Civil Engineers ET Evapotranspiration ETc Crop evapotranspiration ETo Refer ence evapotranspiration FAO Food and Agriculture Organization of the United Nations FAWN Florida Automated Weather Network GCREC Gulf Coast Research and Education Center GLM General linear model Kc Crop coefficient NOAA National Oceanic and Atmospheric Adm inistration NTEP National turfgrass evaluation procedures UF University of Florida UF -IFAS University of Florida Institute of Food and Agricultural Sciences USCB United States Census Bureau WORS Without rain sensor WRS With rain sensor

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14 Abstract of Th esis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Engineering EVAPOTRANSPIRATION -BASED IRRIGATION CONTROLLERS IN FLORIDA By Daniel C. Rutland December 2009 Chair: Michael D. Dukes Major: Agricultural and Biological Engineering Evapotranspirationbased irrigation controllers, also known as weather -based irrigation controllers, use evapotranspiration (ET) to schedule irrigation. The objective of this r esearch was to determine if ET irrigation controllers can save water in Florida. The primary goals of this study were to A) evaluate two brands of evapotranspiration controllers and their ability to determine an irrigation schedule compared to a timed sch edule with homeowner recommend settings while monitoring turfgrass quality, B) determine the amount of water that can be saved using rain features of the Toro Intelli sense ET controller, and C) determine the accuracy of ETo values used by two brands of ET controllers compared to ETo calculated using the standardized ASCE PenmanMonteith ETo equation with data collected from an onsite weather station. Videos detailing ET controllers were produced and are available through YouTube. These videos were created to detail how ET controllers operate and provide instructions for proper programming. This research was conducted at the University of Florida Gulf Coast Research and Education Center (GCREC), Wimauma, Florida on t wenty existing plots of established St. Augustinegrass ( Stenotaphrum secundatum Floratam) bordered on one side by

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15 mixed ornamentals. The turfgrass and mixed ornamentals portion of each plot measured 60 m2 and 33 m2 respectively Five treatments were established with four replications each in a completely randomized block design. Treatment descriptions and designations were as follows: WM Weathermatic SL1600 with SLW15 weather monitor, TORO WRS Toro Intelli -sense with Hunter Mini -Clik rain sensor set at 6 mm and 100% usable rainfall, TORO WORS Toro Intelli -se nse with no rain sensor and 100% usable rainfall, TRS Rain Bird Timer using the UF -IFAS recommended irrigation schedule with a Hunter Mini Clik rain sensor set at 6 mm, RTRS Rain Bird Timer using 60% of TRS with a Hunter Mini -Cli k rain sensor set at 6 mm. All controllers applied less cumulative seasonal irrigation than the theoretical time without rain sensor treatment. Period irrigation totals showed that the addition of a rain sensor set at a 6 mm threshold to a properly maint ained and programmed irrigation timer saved 8% to 50% of irrigation compared to a timed treatment without a rain sensor (WORS). The Weathermatic controller (WM) applied an average of 42% less irrigation than the time WORS treatment for the entire study pe riod. However, it frequently applied more weekly irrigation than the reduced time treatment (TRSR). The Toro controller with a rain sensor (TORO WRS) applied an average of 66% less irrigation than the time WORS treatment while the Toro controller without a rain sensor (TORO WORS) applied 57% less than the time WORS treatment. When working properly, the attachment of a rain switch set at a 6 mm threshold to a Toro Intelli -sense ET irrigation controller saved more water than the rain pause events sent out b y the weather service. Additionally, because of the spatial variability of rainfall

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16 in Florida, it is likely that weather stations could miss rainfall events that happen in the immediate area of the controller. The Weathermatic SL1600 controllers consistently overestimated daily ETo throughout the study period. Overestimation ranged from 9 -15% compared to onsite calculated ETo, which contributed to over irrigation. The most severe periods of overestimation were in the summer seasons. The Toro Intelli -se nse controllers estimated ETo similar to calculated onsite ETo in cumulative comparison with only 1 -3% cumulative difference over 41 months which was not statistically significant. However, the Toro controllers overestimated daily ETo during the summer seasons and underestimated ETo during the winter seasons. Overall ETo estimation performance sent to the Toro Intelli -sense controllers was closer to calculated ETo using the ASCE ETo equation than the Weathermatic SL1600 controllers.

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17 CHAPTER 1 INTRODUCTION Florida receives an average of 1,400 mm of rainfall a year, compared to the national average of 762 mm (Carriker 2000) However, the hydrologic cycle in Florida is variable with regards to region, season and year. Areas in the northwest and southeast pa rts of the state receive an average of 1,626 mm of rainfall a year (Hughes 1977). Seasonally, Florida receives 70% of its total rainfall in the summer (Carriker 2001). During the warm season (June-September) average rainfall is highest in south Florida, while in the cold season (December March) average rainfall is highest in the panhandle of the state (Fernald and Purdum 1998). Potential evapotranspiration (ETp) averages from 991 mm/yr in the panhandle to 1,346 mm/yr in Key West (Fernald and Purdum 1998) Availability and demand of water fluctuates based on location, population, and regional weather conditions. Public supply demand in the United States has increased from 53 billion L /d in 1950 to 163 billion L /d in 2000 (USGS 2004a). Florida is one of the principle consumers of groundwater, accounting for an average of 31 billion L/d of freshwater withdrawals (USGS 2004b). Total estimated Floridian population in 2000 was 15.98 million, which represented a 32% change from 12.93 million in 1990 (USCB 20 01). In 2000, Florida domestic (residential) per capita water use was estimated to be 401 L /d Floridian, residential water use accounts for 30% of total water withdrawals with 25% to 75% estimated as being used outdoors (USGS 2004b). Research in central Florida found that 64% of total household water use was applied in the landscape (Haley et al. 2007).

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18 T he environmental horticulture industry, or green industry, which is made up of business es that produce, distribute and provide service for ornamental plants, landscape and garden supplies, is one of the fastest growin g segments of the agricultural economy in the United States The green industries have an estimated economic impact of $148 billion and support 1.96 million jobs for the United States e co n omy. Florida contributes $10 billion and 148 ,000 jobs to this figure, second only to California (Hall et al. 2005) N on economic impacts such as energy savings for building heating and cooling, reduction of atmospheric carbon dioxide, improved air qualit y, reduction of storm water runoff, and aesthetic benefits are contributed by the industry (Hall et al. 2005). Specifically, the lawn care industry in Florida provides 25,000 jobs and has an economic impact of $1.3 million. This sector of the green indust ry provides landscape maintenance services for businesses and homeowners (Haydu et al. 2006). Turfgrass offers substantial functional, recreational, and aesthetic benefits. Functional benefits of healthy turfgrass include runoff reduction, increased groundwater recharge and surface water quality, heat dissipation, noise and glare reduction, and increased air quality (Beard and Green 1994). The adverse affects of reduction of turfgrass can be seen in history when China removed all turfgrasses and trees f rom public places during the Cultural Revolution of the 1960s. As a result air pollution, health problems, and air temperatures increased (Carrow 2005). In Florida, irrigation of turfgrass is required to sustain acceptable turfgrass quality and maintain its beneficial value. Total turfgrass area in the United States was estimated to be 16.4 million ha by Milesi et al. (2006), which makes it the largest irrigated crop in the country. Florida is

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19 second only to Texas for estimated turfgrass area at 1.2 mi llion ha. The amount of water required to irrigate 100% of the turfgrass area across the country at a rate of 25.4 mm/wk would use an average of 73,560 Mm3/yr. Milesi et al (2006) also estimates that if only evapotranspiration losses were replaced by irr igation that total irrigation water needs would be reduced to 11,070 Mm3/yr. The strength and reliability o f the economic progress in the green industries could have significant impacts on water conservation if harnessed appropriately. There could be su bstantial water savings w ith the incorporation of new irrigation s mart technologies. I mproved irrigation practices encompass improved scheduling where real time monitoring of weather conditions is imperative to irrigation application (Carrow 2005) One of the emerging technologies that will help accomplish this is weather based i rrigati on controllers, also known as evapotranspiration (ET) irrigation controllers. Evapotranspiration Evaporation is the vaporization of liquid water by the addition of direct and latent energies from the surrounding environment. Atmospheric pressure and relative humidity reduce the rate at which evaporation occurs by opposing water vapor pressure at the water surface. Additional factors that affect evaporation are solar radi ation, temperature and wind speed (Allen et al. 1998). Transpiration is the biological mechanism by which plants remove liquid water from the soil, transport it to intercellular cavities at the surface of the plant, and allow it to vaporize. This proces s allows for the transport of nutrients and minerals throughout the plant. Most vaporization takes place in the stomata of the plant leaves, which regulate the rate of transpiration. Environmental factors that affect transpiration are solar radiation, temperature, relative humidity and wind speed. (Allen et al. 1998)

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20 Evapotranspiration (ET) is the combination of ev aporation from soil and plant surfaces and transpiration from vegetation. Environmental parameters that affect ET are short wave radiation, l ong wave radiation, latent heat of vaporization, vapor pressure, temperature, and wind (Allen et al. 1998; Clark et al. 1993). Two methods of expressing ET are potential evapotranspiration (ETp) and reference crop evapotranspiration (ETo). Potential ev apotranspiration is the rate at which water is removed from the soil and plant surfaces assuming wet soil conditions. Reference crop evapotranspiration is the combination of the rate at which water is removed from the soil and plant surfaces and the rate at which a reference crop transpires (Jensen et al. 1990). In comparison, the two terms are similar, but the ETo provides an clear and concise definition of what is to be expected of the vegetation being measured (Irmak and Haman 2003) The complexity of ET has resulted in the development of many different methods of measurement and calculation. Various methods of determining ET include soil water depletion, tanks and lysimeters, water balance, energy balance, mass transfer, Eddy correlation, and combinat ion of energy balance and heat and mass transfer (Jensen et al. 1990). There are four different methods of estimation: radiation based, temperature based, evaporation based, and combination (Jensen et al. 1990). Radiation based calculations function usi ng energy balance coefficients. Temperature based methods of calculation use air temperature to determine ET. Evaporation based methods use correlation factors in conjunction with evaporation estimation methods. Combination methods join energy balance and heat and mass transfer methods to estimate ET

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21 (Jensen et al. 1990). Two methods are relevant to this research: temperature based and combination-based. The PenmanMonteith equation is a combination equation that has become the standardized method of calculating ETo more commonly known as the ASCE PenmanMonteith standardized ETo equation. It was developed in cooperation with the American Society of Civil Engineers (ASCE) Environmental and Water Resources Institute (EWRI) at the request of the Irriga tion Association (IA). The task of the cooperative group was to produce an equation that could be accepted by the scientific community (Allen et al. 2005). Equations and method of calculation for daily time steps are as follows: ETref= 0 408 ( Rn-G ) + C nT + 273 u2 ( es-ea ) + ( 1 + C d u 2 ) (1 -1) Where: = 2503 exp 17 27 T T + 237 3 ( T + 237 3 ) 2 (1 -2) R n = R ns R nl (1 3) Ra= 24 Gscdr [ ssin sin + cos cos sin s ] (1 -4) R so = 0 7 5 + 2 10 5 z R a (1 -5) R ns =( 1 ) R s (1 6) Rnl= fcd 0 34 -0 14 ( ea ) 0 5 TK max 4 + TK min 4 2 (1 -7) dr= 1 + 0 033 cos 2 365 J (1 -8) fcd= 1 35 R s R so -0 35 (1 -9) = 0 409 sin 2 365 J -1 39 (1 -10 )

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22 s= cos-1 [ tan tan ] (1 -11) = 0 665 10 3 P (1 -12) P= 101 3 293 -0 0065 z 293 5 26 (1 -13) u2= uz 4 87 ln ( 67 8 w 5 42 ) (1 -14) es= e 0 Tmax+ e 0 Tmin 2 (1 -15) ea= e0 ( Tmin ) RHmin100 + e0 ( Tmax ) RHmax100 2 (1 -16) e0 ( T ) = 0 6108 exp 17. 27 T T + 237 3 (1 -17) ETref = reference evapotranspiration, mm d1 -temperature curve, kPa C1 T = mean daily air temperature at 2 m, C Rn = net radiation at the crop surface, MJ m2 d1 Rns = net short wave radiation, MJ m2 d1 Rnl = net outgoing longwave radiation, MJ m2 d1 Ra = extraterrestrial radiation, MJ m2 d1 Gsc = solar constant, 4.92 MJ m2 h1 dr = inverse relative distance factor fo r the earth-sun s = sunset hour angle, radians Rso = clear -sky solar radiation, MJ m2 d1 Rs = incoming solar radiation, MJ m2 d1 -Boltzmann constant, 4 901 109 MJ K4 m2 d1 fcd = cloudiness function, 0.05 cd ea = actual vapor pressure, kPa J = Julian day, d 1 P = atmospheric pressure at elevation, kPa z = elevation above mean sea level, m u2 = wind speed at 2 m above ground surface, m s1 uz = measured win d speed at zw above ground surface, m s1 zw = height of wind measurement above ground surface, m es = saturation vapor pressure, kPa e0(T) = saturation vapor pressure function, kPa

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23 Energy is taken into account by the estimation of net radiation [ Rn (MJ m2 d1)] from total extraterrestrial radiation [Ra (MJ m2 d1)], daylight percentage [fcd, Rso (MJ m2 d11)]. Factors that affect the mass balance of the equation due to their effect on transpiration include humidity [es (kPa), ea (kPa)] and wind speed [u2 (m s1)] (Jensen et al. 1990). Thus, this calculation method requires measurements of air temperature, relative humidity, solar radiation, and wind speed. Accurate calculation assumes measured weather data were taken in an open area with low growing, well watered vegetation. Clipped grass is the preferable vegetation to surroun d the weather station (Allen et al. 2005). In comparison to 14 different methods of ETo calculation using Florida weather data, the ASCE PenmanMonteith equation was found to be the most accurate, robust, and least bias method to calculate ETo for many different climates (Jacobs and Satti 2001). Research comparing ET equations across 49 different locations representing different climates in the United States found that the ASCE standardized Penman Monteith equation agreed the best with the full form of the ASCE PenmanMonteith equation across all climates (Itenfisu et al. 2003). The Hargreaves ET equation is a temperaturebased method that was used in this research. Derivation of this equation was completed using eight years of lysimeter data under c ool -season Alta fescue grass ( Festuca arundinacea) in Davis, California (Hargreaves and Samani 1985). Solar radiation data are often not available due to the high cost of the instrumentation. Solar radiation can be expressed as a function of latitude and day of the year. Therefore, air temperature is the only input to the equation (Jensen et al. 1990). The Hargreaves equation is defined as the following: ET0= 0 0023 Ra TD0 5 ( T + 17. 8 ) (1 -18)

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24 where: Ra= 24 Gscdr [ ssin sin + cos cos sin s ] (1 -19) = 0 409 sin 2 365 J -1 39 (1 -20) dr= 1 + 0 033 cos 2 365 J (1 -21) s= cos-1 [ tan tan ] (1 -22) Ra = extraterrestrial radiation, MJ m2 d1 Gsc = solar constant, 4.92 MJ m2 h1 TD = difference between mean monthly maximum and minimum temperatures, C T = mean air temperature, C dr = inverse relative distance factor for the earth-sun s = sunset hour angle, radians Ra can also be determined from tables if calculations cannot be completed (Jensen et al.1990). The simplified nature of the Hargreaves equation and development within a non-humid region lends itself to application limitations. Hargreaves was found to overestimate ETo in humid regions in comparison to the ASCE PenmanMonteith standardized ETo equation (Trajkovic 2007). Additional comparisons made to similar ETo equations in humid regions using the ASCE Penman -Monteith standardized ETo equation as a standard of measure found that Hargreaves consistently overestimated ETo, especially in the summer months (Kisekka 2009). However, on a yearly basis, the Hargreaves equation is well correlated with the ASCE ETo equation in humid conditions (Jacobs and Satti 2001). Research comparing ET equations across 49 different locations representing different climates in the United States found that the Hargreaves equation preformed the worst when compared to the full form of the ASCE PenmanMonteith equation (Itenfisu et al. 2003).

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25 M odifications of ETo by crop coefficients can be applied to vegetation that does not meet the specific requirements of ETo (Irmak and Haman 2003) Conversion of ETo to crop evapotranspiration (ETc) requires the use of crop coefficients (Kc). Reference evapotranspiration is multiplied by a Kc value corresponding to the type of vegetation and its stage of growth. Crop coefficients corresponding to alfalfa reference crop cannot be used to calculate ETc with ETo determined using coefficients or equations for a grass reference crop (Jensen et al. 1990). Tables for crop coefficients can be found in Jensen et al. (1990) and Allen et al. (1998). Evapotranspiration Da ta Quality Analysis Meteorological data collected from a weather station must be screened before use in an ET equation. Failure to analyze data with respect to expected values and physical limitations could lead to poor quality and accuracy of calculated ET (Allen et al 2005). The upper and lower limits for measured solar radiation (Rs) are generally defined as clear sky solar radiation (Rso) and 0.2 of extraterrestrial radiation (Ra), respectively. On days of clear sky, Rs and Rso are usually equal. However, because Rso is a theoretical estimation, Rs may still fall below the theoretical value due to increased air turbidity, haziness, high altitude clouds, and afternoon clouds. Clear sky solar radiation estimated in the ASCE ETo method (Equation 11) is calculated as a function of height above sea level, which is a constant value. A more accurate method for comparison, detailed below, evaluates the effects of sun angle and water vapor on absorption of short wave radiation by separating beam and dif fuse radiation components (Allen et al. 2005).

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26 R so = ( K B + K D ) R a (1 23) where: = 0 98 exp 0 00146 sin 24 0 075 sin 24 0 4 (1 -24) = 0 35 0 36 0 15 (1 25) = 0 1 8 + 0 82 < 0 15 (1 26) P= 101 3 293 -0 0065 z 293 5 26 (1 -27) sin 24= sin 0 85+ 0 3 sin 2 365 J -1 39 -0 42 2 (1 -28) W = 0 14 P e a + 2 1 (1 29) Rso = clear -sky solar radiation, MJ m2 d1 KB = clearness index for direct beam radiation KD = transmissivity index for diffuse radiation Ra = extraterrestrial radiation, MJ m2 d1 P = atmospheric pressure at elevation, kPa Kt = turbidity coefficient, 0 < Kt t = 1.0 for clean air Kt air 24 = weighted average sun angle during daylight hours, radians W = precipitable water in the atmosphere, mm 24 = average angle of the sun above the horizon, radians z = elevation above mean sea level, m J = Julian day, d ea = actual vapor pressure, kPa Relative humidity (RH) should fall within the bounds of 30% to 100% for humid regions. In humid climates, RH should generally reach 100% on a daily basis. Values below 30% are not common but are physically possible, while v alues above 100% are not. Thus, only consistent deviation from the estimated bounds should be cause for concern. Additional attention should be applied to RH data if maximum RH (RHmax) consistently stays below 80% for a large portion of the growing seaso n. In addition to equipment malfunction, cloudiness and wind flow at night can reduce RHmax. Proper sensor maintenance and calibration should prevent inaccurate RH values (Allen et al. 2005).

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27 Dew point temperature (Tdew) will come close to reaching mi nimum temperatures (Tmin) for the day in humid regions. Calculation of Tdew can be completed from measured values of relative humidity (RH) and compared to Tmin with the following equation (Allen et al. 2005). Tdew= 116 91 + 237 3 ln ( e a ) 16 78 ln ( e a ) (1 -30) where: Tdew = dew point temperature, C ea = actual vapor pressure, kPa Exceptions to correlation of Tdew and Tmin will occur during changes in air mass due, high winds, and cloudiness at night. Any errors in RH data collection will affect the calculated value of Tdew and its relationship to Tmin (Allen et al. 2005). When in the correct location for measurement of ETo, temperature data are the most reliable of the measured weather parameters. Howev er, consistent temperature measurements above and below historical highs and lows are cause for concern. Quality assessment can be accomplished by plotting the recorded average temperature for a 24 hour period versus the average of the maximum and minimum temperatures for the same 24 hour period. Deviation of the two values should not exceed 2C. Differences of 3C or higher can be caused by rainfall, air mass changes, and abnormal air currents (Allen et al. 2005). Accurate quality evaluation of wind s peed requires the use of multiple sensors. Wind speed values consistently below 1.0 m s1 are cause for concern. High vegetation, solid fences, or physical structures could block wind speed measurements. Symptoms of malfunctioning anemometers are periods of excessively low wind speed

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28 or high gust values. Mean and maximum wind speeds will be similar in failing anemometers (Allen et al. 2005). Evapotranspiration Irrigation Controllers Evapotranspiration irrigation controllers schedule irrigation by esti mating water lost from the soil due to ET. Available controllers on the market vary in calculation methods, controller settings and data collection methods Evapotranspiration controllers use all or a combination of landscape and irrigation settings, such as sprinkler type, precipitation rate, efficiency, soil type, plant type, root depth, grade, and shade to calculate irrigation run time. Controllers are either additions to existing timers or inclusive units that replace existing timers. Generally ET controllers can be categorized by data collection methods: historical, standalone, and signal based controllers. Historical Based Controllers Historical based ET controllers use historical weather information, such as historical monthly ET and historic al solar radiation from the general region to calculate reference evapotranspiration (ETo). This method of ET calculation does not account for real time weather unless the controller is equipped with additional sensors to modify historical ETo values base d on site specific parameters. Controllers without these sensors could under or over irrigate in dry or wet periods. Stand Alone Controllers Stand alone controllers collect weather data using on site sensors located within or near the irrigated landscape. Measurements taken by sensors can include temperature, rainfall, humidity and solar radiation (USBR 2007). These controllers use methods such as the Hargreaves and modified Penman -Monteith equations to calculate

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29 ETo. Examples of this type of control ler are made by Weathermatic, Inc. (Dallas, TX) and Hunter Industries (San Marco CA). Signal Based Controllers Signal based controllers receive ETo information from a signal from an offsite weather service via radio, telephone, cable, cellular, web, or p ager technology (USBR 2007). Weather services gather data from regional weather stations to calculate ETo. Most controllers have the ability for the addition of rain shut off devices to prevent irrigation during precipitation events. Often, the availab ility of the weather service provides for enhanced features related to scheduling and water conservation. Some signal based controllers come with a rain pause service that overrides irrigation based on the services recommended dry out time. These signal based controllers allow for the addition of a rain switch to incorporate immediate shut off features. A subscription to a weather service is required to receive updates. Typical weather service fees range from $4 to $15 per month (Riley 2005). Examples of these controllers are made by AccuWater, Inc. (Austin, TX), Hydropoint Data Systems Inc. (Petaluma, CA), Irrisoft Inc. (Logan, UT), and the Toro Company (Bloomington, MN). Evapotranspiration Controllers in this Study The Toro Intelli -sense is a signal based evapotranspiration controller. Daily ETo values are calculated by Hydropoints WeatherTRAK system using the ASCE Penman Monteith standardized reference evapotranspiration equation (Hydropoint 2003) These values are then sent t o the controller by the use of pager technology. Adjustable settings within the controller include usable rainfall, sprinkler type, precipitation rates, sprinkler efficiency, soil type, plant type, root depth, grade, and shade. Crop coefficients can be modified manually and rain pause features can also be enabled or

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30 disabled. Irrigation schedules can be entered manually or can be set to allow the controller to irrigate when needed. The Toro Intelli -sense controller attempts to irrigate based on a daily theoretical water balance. The controller estimates the amount of water in the soil and irrigates only when soil water depletion has reached the set value for the zone. Settings input by the user are used by the controller to determine schedule and irrigation run times. Irrigation frequency and depth fluctuate based on regional weather conditions. According to Hydropoint, ETo values are calculated to the resolution of 1 km2, a microzone for 90% of the United States. Weather data are gathered from public database s such as the National Oceanic and Atmospheric Administrations National Weather Service (NOAA -NWS) to collect weather information for regional areas. In addition, other sources such as cities, states, water districts and private organizations help to provide g reater coverage resolution. Weather data collected from these weather stations then undergo quality control where both human and automated system verifications are performed. Data are used with the ASCE method to calculate ET. Once daily ETo is calcula ted for a microzone, the values are transmitted to the controller (Hydropoint 2003) In addition to daily ETo values, Hydropoint sends rainfall information to controller called a rain pause. The rain pause feature pauses irrigation for a length of time determined by the weather service. When rainfall is recorded by the weather network, the weather service determines a set number of days that the controller should not irrigate based on depth, intensity, and duration of the rainfall event or events and e vapotranspiration.

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31 The Weathermatic SL1600 is a stand a lone controller that uses the Hargreaves Evapotranspiration equation to calculate ETo. Required inputs include latitude, sprinkler type, precipitation rate, plant type, and soil type. All of these features can be modified from the preset values according to user needs The controller can be run independent of the weather station so that definite irrigation times can be set. The weather station measures temperature and has a built in rain shut off dev ice. W eather stations are available in wired and wireless versions (Weathermatic 2009) The Weathermatic SL1600 irrigates based on soil water depletion from the time of the last irrigation event. This means if irrigation is scheduled for every day of the week the controller will irrigate every day i n very small amounts (Davis 2008). Thus, irrigation frequency is definite and does not fluctuate. However, irrigation run times will fluctuate based on estimated ET. Previous Research Bamezai (2004) compar ed the Hydropoint WeatherTRAK and Water2saves irrigation scheduler in large landscapes such as homeowners associations, school s and parks in 2002 and 2003. The WeatherTRAK controller was a standalone device and the Water2save controller was a signal interrupt device between the existing timer and the irrigation valves. Data were collected for a year after retrofits of existing irrigation systems. It was determined that the WeatherTRAK cont roller reduced irrigation by 17% while Water2saves controller reduced irrigation by 28 % These numbers can be deceptive because of the variations in landscape and previous homeowner maintenance practices. Due to this variability, the study compared total water savings to estimated potential water savings The potent ial water savings was determined by estimating water use by the home prior to controller installation. The report found that

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32 while the WeatherTRAK controller saved less total water, it saved 95 % of its total potential water savings, while the Water2save c ontroller saved 71% of its total potential water savings. The c ombined sav ings of all tested controllers was 27%, which represented 78% of the total potential water savings. The Metropolitan Water District of Southern California (2004) compared three di fferent weather based irrigation controllers to evaluate their functionality with respect to water conservation. The three controllers that were tested were the AquaConserve, Hydropoint, and WeatherSet. The AquaConserve and WeatherSet controllers are ons ite ET controllers, while the Hydropoint is a signal based ET controller. The controllers were set up with three simulated landscapes by way of a bench test. In a bench test, controllers are not attached to an irrigation system, but are monitored for irr igation run times on simulated landscapes. Treatments are as follows: 1) cool season turf in loamy s oils with no slope and full sun; 2) ornamentals in sandy soils with no slope and partial shade; 3) trees in clay soils with 20 degree slope and full sun. The Hydropoint displayed some deficit irrigation in the summer months, but generally was able to maintain the soil water balance in all three simulated landscapes Aqua conserve had more deficit irrigation, but was generally able to maintain the soil wate r balance across all three landscapes The WeatherSet controller applied water in excess, exceeding the soil water balance in all landscapes which would have resulted in significant water loss. In regards to soil water depletion, the Hydropoint controller was able to maintain acceptable moisture levels in all simulated landscapes except in the tree/clay scenario. Irrigation resulted in a high level of moisture that would be deemed harmful to the plant. The AquaConserve controller experienced the same pr oblem as

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33 the Hydropoint controller except for a moderate amount of deficit irrigation in the summer months for annual flower s. The WeatherSet controller displayed adequate moisture content for turf grass deficit irrigation on ornamentals, and higher moist ure than would be accept able in the trees Berg et al. (2001) researched the potential water savings of single family homes with the installation of weather based controllers in Irvine California. An ET controller was compared to historical outdoor water use from each household that participated in the study. In addition, a postcard with irrigation timer suggestions was sent to another group of households without an ET controller as an alternative method of outdoor water use reduction. All participants w ere in the top 23% of water users in the area. B ecause the participants were volunteers, they were likely more naturally prone to water conservation. Historical records were taken 2 years prior to installation, and the research period was 1 year. Evapot ranspiration controller based irrigation compared to previous irrigation methods controllers saved 16% of outdoor water applied. This percentage equates to 85% of the total water saving potential, while the alternative postcard treatment was only able to achieve 30% of the total water potential savings It was concluded that if the ET contr ollers could meet at least a 24% savings in outdoor water use, that the top third of water users in the Irvine Ranch Water District could potentially save 750,000 m3/yr Aquacraft (2003) conducted an analysis of the WeatherTRAK system In addition, controller irrigation was contrasted with historical irrigation. The report was commissioned to determine the reliability of the controllers functionality and the accuracy of its ET calculations with regard to irrigation depth. T he study occurred

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34 during a significant drought. Of the seven home sites in the study, only one irrigated above ET. The cause of this error was determined to be a percent application adjustment made by the homeowner. Three of the homes irrigated more than th eir historical application, three irrigated less than their historical application, and one irrigated the same as their historical application. The seven homes saved an average of 132,000 L/yr of water. Of the seven, four participants saved an average of 242,000 L/yr per home. Pittenger et al (2004) completed a study which used a bench test to compare four ET controllers with respect to their ability to conserve water, complexity and maintenance. The four different controllers were AquaConserve, Calsense ET1 (with electronic ET gauge input ), WeatherSet, and WeatherTRAK. Five landscapes were simulated to compare the controllers. Virtual landscapes were programmed as follows: cool season t urfgrass (T1), trees/shrubs (T2), annual flowers (T3), mixed high water use plants (T4), and mixed low water use plants (T5). Landscapes T 4 and T 5 were used only on the WeatherTRAK controller. The study found that the AquaConserve controller was the simplest and easiest controller to use, while the Calsense ET1 was the most complex. The WeatherSet was easy to use but visually intimidating. The WeatherTRAK controller was found to have the most flexible options and parameters. With respect to theoretical irrigation application, the AquaConserve controller over irrigated turfgrass, the Calsense controller had a technical failure, the WeatherSet controller severely under -irrigated turfgrass and the WeatherTRAK provided accurate irrigation for turfgrass.

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35 Shedd (2008) compared ET controller performance to that of a regular homeowner time based irrigation schedule without a rain sensor. ET controllers used in the study were the Toro Intelli -sense and the Rainbird ET Manager. Each controller irrigated four i ndividually monitored turfgrass plots. The study found that the Toro controller saved approximately 62% of irrigated water while the ET Manager saved roughly 50% of irrigated water in comparison to the time based schedule without a rain sensor. Both cont rollers saved water while maintaining acceptable turfgrass quality when working properly. Davis et al. (2009) compared ET controller performance to that of a regular homeowner time based irrigation schedule without a rain sensor. ET controllers used in the study were Weathermatic SL1600, Toro Intelli -sense and ET Water Smart Controller. Results showed that ET controllers reduced irrigation 35% to 42% compared to a time schedule without a rain sensor. A reduced time irrigation schedule (60% of the time schedule) coupled with a rain sensor saved 53% when compared to the timed treatment without a rain sensor. ET controllers saved roughly double the amount of water saved by a rain sensor alone. Differences in water application did not result in turfgrass quality differences. The study concluded that while regular adjustment of time clocks could result in water savings that ET controllers reduced irrigation without the need to make timer adjustments throughout the year. Cardenas -Lailhacar and Dukes (2008) investigated the possible savings that could be achieved by a n expanding disk rain sensor. Two sensors were used for comparison in the test, the Hunter Mini -Click (MC) and wireless Rain-Click (WL). The MC devices were set at 3, 13, and 25 mm. The test was run between March 25th and December 31st

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36 of 2005. This time period was a wet season with 62% of the days having rainfall. It was shown that the MCs responded close to their set points with some variability. Total potential water savings was determin ed to be 818 mm. Actual theoretical water savings were 363 (44%), 245 (30%), 142 (17%), and 25 (3%) mm for the WL, 3MC, 13 MC, and 25MC treatments, respectively.

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37 CHAPTER 2 EVALUATION OF A SIGNAL BASED AN D ONSITE EVAPOTRANSPIRATION IRRIGATION CONTROL LER Introduction Florida receives an average of 1,400 mm of rainfall a year, compared to the 762 mm for the national average (Carriker 2000) However, the hydrologic cycle in Florida is variable with regards to region, season and year. Areas in the north west and southeast parts of the state receive an average of 1,626 mm of rainfall a year (Hughes 1977). Seasonally, Florida receives 70 % of its total rainfall in the summer (Carriker 2001). During the warm season (June-September) average rainfall is highe st in south Florida, while in the cold season (December March) average rainfall is highest in the panhandle of the state (Fernald and Purdum 1998). Potential evapotranspiration (ETp) averages from 991 mm/yr in the panhandle to 1,346 mm/yr in Key West (Fer nald and Purdum 1998). Availability and demand of water fluctuates based on location, population, and regional weather conditions. Public supply demand for water in the United States has increased from 53 billion L /d in 1950 to 163 billion L /d in 2000 ( USGS 2004a). Florida is one of the principle consumers of groundwater, accounting for an average of 31 billion L/d of freshwater withdrawals (USGS 2004b). Total estimated population in 2000 was 15.98 million, which represented a 32% change from 12.93 mil lion in 1990 (USCB 2001). In 2000, Florida domestic (residential) per capita water use was estimated to be 401 L /d Residential water use accounts for 30% of the total water withdrawals with 25% to 75% of which is estimated as being used outdoors (USGS 20 04b) Research in central Florida found that 64% of total household water use was used in the landscape (Haley et al. 2007).

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38 T he environmental horticulture industry, or green industry, which is made up of business es that produce, distribute and provide service for ornamental plants, landscape and garden supplies, is one of the fastest growin g segments of the agricultural economy in the United States The green industries have an estimated economic impact of $148 billion and support 1.96 million jobs for the United States e co nomy. Florida contributes $10 billion and 148 ,000 jobs to this figure, second only to California (Hall et al. 2005) N on economic impacts such as energy savings for building heating and cooling, reduction of atmospheric carbon dioxi de, improved air quality, reduction of storm water runoff, and aesthetic benefits are contributed by the industry (Hall et al. 2005). Specifically, the lawn care industry in Florida provides 25,000 jobs and has an economic impact of $1.3 million. This sec tor of the green industry provides landscape maintenance services for businesses and homeowners (Haydu et al. 2006). Turfgrass offers substantial functional, recreational, and aesthetic benefits. Functional benefits of healthy turfgrass include runoff r eduction, increased groundwater recharge and surface water quality, heat dissipation, noise and glare reduction, and increased air quality (Beard and Green 1994). The adverse affects of reduction of turfgrass can be seen in history when China removed all turfgrasses and trees from public places during the Cultural Revolution of the 1960s. As a result air pollution, health problems, and air temperatures increased (Carrow 2005). In Florida, irrigation of turfgrass is required to sustain acceptable turfgras s quality and maintain its beneficial value. Total turfgrass area in the United States was estimated to be 16.4 million ha by Milesi et al. (2006), which makes it the largest irrigated crop in the country. Florida is

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39 second only to Texas for estimated t urfgrass area at 1.2 million ha. The study estimates that water required to irrigate 100% of the turfgrass area across the country at a rate of 25.4 mm/wk would use an average of 73,560 Mm3 of water. The study also estimates that if only evapotranspiration losses were replaced by irrigation that total irrigation would be reduced to 11,070 Mm3. The strength and reliability o f the economic progress in the green industries could have significant impacts on the water conservation arena if harnessed appropriately. There could be substantial water savings w ith the adoption of new irrigation s mart technologies. I mproved irrigation practices encompass optimized scheduling where real time monitoring of weather conditions is imperative f o r irrigation application (Carrow 2005) One of the emerging technologies that will help accomplish this is weather based i rrigati on controllers, also known as evapotranspiration (ET) irrigation controllers. Evapotranspiration irrigation controllers schedule irrigation by estimating water lost from the soil due to evapotranspiration. Available controllers on the market have different calculation methods, controller settings and data collection methods Evapotranspiration controllers use all or a combination of landscape and ir rigation settings, such as sprinkler type, precipitation rate, efficiency, soil type, plant type, root depth, grade, and shade to calculate irrigation run time. Most controllers replace existing timers, however some controllers are addons to existing tim ers. Generally ET controllers can be categorized by data collection methods: historical, standalone, and signal based controllers. Historical based ET controllers use historical weather information, such as historical monthly ET and historical solar r adiation from the general region to calculate

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40 reference evapotranspiration (ETo). This method of ET calculation does not account for real time weather unless the controller is equipped with additional sensors to modify historical ETo values based on site specific parameters. Controllers without these sensors could under or over irrigate in dry or wet periods. Stand alone controllers collect weather data using on site sensors located within or near the irrigated landscape. Measurements taken by sensors c an include temperature, rainfall, humidity and solar radiation (USBR 2007). These controllers use methods such as the Hargreaves and modified PenmanMonteith equations to calculate ETo. Examples of this type of controller are made by Weathermatic, Inc. ( Dallas, TX) and Hunter Industries (San Marco CA). Signal based controllers receive ETo information from an offsite weather service via radio, telephone, cable, cellular, web, or pager technology (USBR 2007). Weather services gather data from regional weather stations to calculate ETo. Often, the availability of the weather service provides for enhanced features related to scheduling and water saving. Most of these controllers will also have the ability to add rain sensors to supplement weather service updates. A subscription to a weather service is required to receive updates. Typical weather service fees range from $4 to $15 per month (Riley 2005). Examples of these controllers are made by AccuWater, Inc. (Austin, TX), Hydropoint Data Systems Inc. (Petaluma, CA), Irrisoft Inc. (Logan, UT), and the Toro Company (Bloomington, MN). Based on previous research, ET controllers have the ability to save water in comparison to typical homeowner irrigation practices in a humid climate without affecting turf grass quality. Davis et al (2009) completed a study in Florida that found

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41 ET controllers reduced turfgrass irrigation by 35% to 42% compared to timer irrigation without a rain sensor. In a study with homeowners in California, Berg et al. (2001) found that ET controllers saved 24% of outdoor water applied. The objective of this study is to further quantify and compare the amount of irrigation applied by two different brands of ET controllers compared to a timed schedule with homeowner recommended setting s while monitoring turfgrass quality. Materials and Methods This research was conducted at the University of Florida Gulf Coast Research and Education Center (GCREC), Wimauma, Florida on t wenty existing plots of established St. Augustinegrass ( Stenotaphr um secundatum Floratam) bordered on one side by mixed ornamentals. The turfgrass and mixed ornamentals portion of each plot measured 60 m2 and 33 m2 respectively. All 20 plots were bordered by a 15 cm tall black metal barrier Each plot was separated by a buffer zone of 3 m covered with a plastic weed barrier on all sides. Each plot received a separate irrigation line for turfgrass and mixed ornamentals. Hunter (San Marcos, CA) SRV solenoid valves and Elster AMCO Water (Ocala, FL) V100 flow meters w ere used in combination to operate and monitor the irrigation system. Digital collection of the flow meter data was accomplished by wiring each flow meter to one of five Campbell Scientific (Logan, UT) SDM SW8A switch closure input modules that were monit ored by a Campbell Scientific CR 10x datalogger. Each reading carried a resolution of 18.9 L and was totaled hourly by the datalogger. Manual readings were taken weekly to ensure accuracy of the digital data. The ET irrigation controllers evaluated in this study were the Weathermatic (Dallas, TX) SL1600 coupled with the SLW10 weather monitor and the Toro

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42 (Bloomington, MN) Intelli -sense. The Weathermatic SL1600 is an on -site ET controller that requires the use of a sensor (SLW10 ) that is installed in the surrounding landscape. The sensor gathers temperature and rainfall data for the controller. The controller then calculates ETo using the Hargreaves e quation (Weathermatic 2009) Reference evapotranspiration is converted to crop evapotranspiration (E Tc) using plant type settings from the controller. ETc is then used to calculate water deficit depth from the last irrigation application. Irrigation frequency is determined by the user by setting predetermined irrigation days. As a result, scheduling i rrigation on consecutive days will cause the controller to irrigate in small amounts (i.e. ETc from the previous day). Required settings for proper irrigation application calculation include soil type, plant type, sprinkler type and regional location. Th e Toro Intelli -sense is a signal based controller that receives its signal from Hydropoint Data Systems (Riverside, CA). Hydropoints WeatherTRAK system uses the ASCE Standardized Reference Evapotranspiration Equation to calculate ETo (Hydropoint Data Sy stems, Inc. 2003). ETo is used to calculate ETc using plant type settings entered into the controller for each zone. ETc is recorded daily and summed from the last irrigation event. Once soil water depletion reaches the set point for the entered plant type of the zone, the controller will schedule an irrigation event and application depth. While the controller can automate irrigation frequency based on irrigation need, i rrigation frequency can also be predetermined by setting specific irrigation days or schedules. Required settings for proper irrigation application calculation include sprinkler type, application rate, soil type, plant type, root depth, slope, and shade.

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43 Rain Bird R13 -18 (Glendora, CA) r otary n ozzles were used for the turfgrass irrig ation system on all plots. The Rain Bird 1806 15 cm pop up was used to house the spray heads. Each plot contained four half circle nozzles (R13 -18H) located at the midpoint of the borders and one full circle nozzle (R13 -18F) located in the center. All h eads have a manufacturer documented application rate of 15 mm/hr at pressures of 206 to 380 kPa. Microirrigation spray heads from Maxijet (Dundee, FL) were installed in the ornamental section of the plots. A 138 kPa pressure regulator was installed for t he microirrigation in each plot. Pressure at the manifold was held at approximately 380 kPa. Five treatments were established with four replications each in a complet ely randomized block design. Treatment descriptions and designations were as follows: WM Weathermatic SL1600 with SLW15 weather monitor, TORO WRS Toro Intelli sense with Hunter Mini -Clik rain sensor set at 6 mm and 100% usable r ainfall, TORO WORS Toro Intelli -sense with no rain sensor and 100% usable r ainfall, TRS Rain Bird Timer using the UF -IFAS recommended irrigation schedule (Dukes and Haman 2002) with a Hunter Mini -Clik rain sensor set at 6 mm RTRS Rain Bird Timer using 60% of TRS with a Hunter Mini -Clik rain sensor set at 6 mm (Table 21 and Table 2-2) A theoretical time based treatment without a rain sensor (Time WORS) was created to determine water savings created by the attached rain shut off device. Time WORS was created by substituting bypassed irrigation events by the TRS treatment. V alues pertinent to the operat ion of the controllers were manually collected Monday through Friday For the Weathermatic, collected values consist of maximum and minimum temperatures, irrigation deficit, estimated run time for the next irrigation

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44 event, and total run time for the reco rded period. Temperature values were used to calculate ETo using the Hargreaves equation (see Chapter 1 for equation description). For the Toro Intelli -sense, collected values consist ed of daily ETo, weekly ETo, time of signal reception, percent water depletion, and status of the rain pause and rain switch features. The Toro Intelli -sense rain pause feature works in addition to the rain switch and bypasses irrigation in a similar manner. When rainfall is recorded by the weather network, the weather ser vice determines a set number of days that the controller should not irrigate based on depth, intensity, and duration of the rainfall event or events and evapotranspiration. The number of days of rain pause is then sent to the controller wirelessly along w ith the ETo signal. Information regarding the rain pause feature in the controller manual is sparse and as a result, exact calculation methods for rain pause lengths are not known. Historical rainfall was calculated using a 30 year average. Precipitation data were collected from a weather station in Parrish, Florida, operated by the National Oceanic and Atmospheric Administration (NOAA) approximately 20 km south west of the project site ( 2737'N / 8221'W). The Florida Automated Weather Network (FAWN) m aintains a weather station that is located approximately 125 m from the project site. Weather data were collected via the FAWN online database for calculation of ETo and comparison to collected evapotranspiration values from the controllers (Florida Autom ated Weather Network 2009) Weather parameters that were collected include the following: temperature, relative humidity, solar radiation, wind speed and rainfall. All weather parameters were

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45 reported in 15 minute intervals Data quality analysis was c ompleted using the ASCE methodology (Allen et al 2005). There were a total of five periods of data collection; summer 2008: 25 June 2008 to 31 August 2008; fall 2008: 1 September 2008 to 30 November 2008; winter 200809 : 1 December 2008 to 28 February 2009; spring 2009: 1 March 2009 to 31 May 2009; summer 2009: 1 June 2009 to 31 August 2009. The Weathermatic controller (WM) was limited to a 3 day a week schedule ( Monday, Wednesday, and Saturday ) to reduce application of low irrigation depths and reduce higher irrigation frequencie s (Davis 2008). Treatments TORO WRS and TORO WORS were allowed to irrigate any day of the week and at any frequency automatically determined by the controller. Treatments T RS and T RSR were limited to a 2 day a week schedule, Mo nday and Thursday, according to typical day of the week restrictions in Florida at the time of installation. T ORO WRS and T ORO WORS had useable rainfall settings adjusted to 100% (Table 21). Turfgrass quality was rated using the National Turfgrass Eval uation Program (NTEP) Turfgrass Evaluation Guidelines (Morris and Shearman 2009). Evaluations were performed approximately every 4 weeks for the duration of the study. Extraneous factors that a ffected turfgrass health such as pests, disease, improper application of fertilizers, etc were noted during evaluation. Photos of each plot were taken for an additional record of turfgrass quality. Each plot was evaluated on a 1 to 9 scale, 1 being dead or no turfgrass, 5 being minimally acceptable turfgrass, an d 9 representing perfection (Figure 2 1) Evaluations focused on the center of the plots where good irrigation coverage existed. Regular maintenance of the turf including mowing,

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46 weeding, fertilization and pesticide application was done according to curr ent UF -IFAS recommendations ( Buss and Unrah 2006; Elliot and S imone 2008a; Elliot and Simone 2008b). The medium fertilization schedule for central Florida outlined by Sartain (2007) was applied. Mowing height was set at 8 mm and was completed every 514 days with a rotary mower (Trenholm et. al 2001). Irrigation was totaled weekly for comparison among treatments. Two week irrigation totals were used to quantify the effects of irrigation applications on turf quality ratings. Statistical analysis was comp leted with the use of SAS statistical software (SAS Institute, Inc. Cary, NC). The General Linear Model (GLM) was used assuming a 95% confidence interval. Least square means separation was conducted using Tukeys procedure for treatment comparison. All data were confirmed to be normal before conducting comparative analysis. During the winter season, before treatments began a fungal infection identified as Take all root rot ( Gaeumannomyces graminis var. Graminis ) spread throughout the turfgrass plots. T here was no discernable relationship between affected plots, severity of infection, and/or treatments. To help prevent subsequent blooms of Take all root rot a fungicide regimen was applied during the first two seasons of treatment. HeritageTM (active in gredient: Azoxystrobin: methyl 50%) was applied on 9 July 2008 and 12 September 2008 at a rate of 118 ml of product per 93 m2. Cleary 3336 plusTM (active ingredient: Thiophanate methy l 19.4%) was applied on 19 August 2008 at a rate of 118 ml of product pe r 93 m2. These treatments prevented reoccurrence of Takeall in all affected plots.

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47 Plot 9 (TRSR) was infested with chinch bugs in October 2008. As a result, AttainTM (active ingredient: Bifenthrin 7.9%) was applied to the turf across the entire proje ct site. Chinch bug population was not mitigated before unrecoverable damage was done to the plot. As a result, turf quality ratings for the plot were removed from the study starting in October 2008 until project end. In April 2009 another chinch bug in festation was noticed in plots 16 (TORO WRS) and 19 (TORO WRS) but was not caught in time to prevent permanent damage to the plots because frost damage to the plots masked symptoms of an infestation. AttainTM was applied twice to the entire project with adequate results. By July 2009 another infestation was spreading in plots 16 (TORO WRS) and 19 (TORO WRS) as well as plots 10 (TORO WORS), 12 (TORO WORS) and 17 (TORO WORS). Bifenthirin Pro Lawn GranulesTM (active ingredient: Bifenthrin 0.2%) was applied on 10 July 2009 with no success at removal of the pest. As a result, MeritTM 0.5G (active ingredient: Imidacloprid 0.5%) was applied on 31 July 2009 with success. There was no correlation between water application and pest infestation. Thus, all affected turfgrass ratings were removed from statistical analysis. Irrigation to the project was turned off twice from 16 January 2009 to 24 January 2009 and 29 January 2009 to 6 February 2009 due to hard freezes. The irrigation manifold was covered with tarps for insulation during these periods. Despite efforts to protect the system, flow meters to plots 20 and 19 froze and were replaced 27 January 2009. The broken flow meters still allowed water to flow through the system; however, they did not record data. Missing values for these flow meters were estimated using digital and manual readings from replications within the affected treatments.

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48 Results and Discussion Every month in the 14 month study period was dryer than the historical average except for two months: July 2008 (9% higher than historical rainfall ) and May 2009 (156% higher than historical rainfall). All seasons received less than historical average rainfall. Rainfall between July 2008 and August 2009 totaled 1175 mm which is 35 % less than the historical rainfall value of 1806 mm, and 16% less than the Florida average of 1400 mm/yr (Figure 22 ). There were 100 days of rainfall during the study period, which is below the 30 year historical average of 131 days (Figure 23). Summer 2008 This pe riod received the highest amount of rainfall at 419 mm, compared to the historical average of 429 mm, despite being the shortest period at 62 days (Figure 22). There were 26 days of rainfall compared to the historical average of 31 days (Figure 23). Th ere were differences among treatments (P<0.0001), while there were no differences among replications (P=0.5496) for average weekly water application in this season (Table 2 3). Based on the theoretical time WORS treatment, not using a rain sensor would have applied the most water, 43.7 mm/wk (P<0.0001), followed by timed treatment with a rain sensor (TRS) at 21.7 mm/wk. The Weathermatic controller (WM) applied a similar amount of water, 16.5 mm/wk, to the timed treatment (TRS) (P=0.0606) as well as the reduced time based treatment (TRSR) at 12.6 mm/wk (P=0.2240). The Toro controllers (TORO WRS and TORO WORS) also applied a similar amount of weekly irrigation to the reduced time based treatment (TRS) (P=0.2102), 8.6 to 9.8 mm/wk. Use of a rain sensor on t he Toro controller did not result in significantly less weekly water applied for the period (Table 2-3). The Weathermatic controller (WM) applied 62% less water than the timed WORS treatment, while Toro

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49 controllers (TORO WRS and TORO WORS) applied 73% to 8 3% less than the timed WORS treatment (Table 23) (Figure 2-4). Water savings for the Toro controllers in this season were high compared to 25% savings found by Shedd (2008) and 59% savings found by Davis et al. (2009) for the summer of 2007. In addition to being completed in different locations in Florida, Davis et al. (2009) used a rain sensor attached to the Toro controller while Shedd (2008) did not which contributed to the lower water savings. Higher savings of the Toro controllers during this season is likely due to differing weather conditions affecting ETo as cumulative rainfall was similar to Davis et al. (2009). There were no differences in turf quality for all treatments (P=0.2229) and replications (P=0.1047) for this season. All treatments re ceived turf quality rating above minimally acceptable levels, and ranged between 6.8 and 7.2 on the NTEP scale. There was no correlation between water applied and turf quality across all treatments (Table 24) which was similar to results found by Shedd ( 2008) and Davis et al. (2009). Fall 2008 There was 93 mm of rain over 10 days during the entire 91 day period (Figure 22), which was less than the historical average of 334 mm over 24 days (Figure 23). Weekly water application was different between t reatments (P<0.0001) but similar between replications (P=0.8773). The lack of a rain sensor on the timed WORS treatment, 29.6 mm/wk, resulted in application of more water (P<0.0001) than the timed treatment (TRS) with a rain sensor, at 26.2 mm/wk. The Weathermatic controller (WM) applied less water, 17.5 mm/wk, (P<0.0001) than the timed treatment (TRS) but more water (P<0.0001) than the reduced timed treatment (TRSR). The Toro controllers both applied approximately 10 mm/wk and the addition of the rain se nsor (TORO WRS) did not significantly reduce water application (P=0.9873). The Weathermatic controller

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50 (WM) applied 34% less than the timed WORS treatment, while the Toro controllers (TORO WRS and TORO WORS) applied 61% 64% less than the timed WORS treatment (Table 25) (Figure 2-5). Water savings for the Weathermatic and Toro controllers were similar to savings seen in research completed by Davis et al. (2009). Savings were also similar to results found by Shedd (2008) for fall seasons. These similar ities are likely due to low cumulative and frequency of rainfall in the fall seasons. Turf quality ratings were not different between treatments (P=0.1870) or replications (P=0.3712) despite differences in 2 week irrigation application between treatments (P<0.0001). Turf quality remained above minimally acceptable levels and ranged from 6.1 to 6.5 on the NTEP scale. There was no correlation between water applied and turf quality across all treatments (Table 2-6). The start of this season was afflicted by a lightning strike that destroyed much of the digital equipment used for monitoring irrigation. As a result, manually collected weekly irrigation totals were added to the cumulative data from 2 September 2008 to 12 September 2008 for the appropriate t reatments. Timed based treatments were estimated by monthly irrigation depths in correlation to manual irrigation readings. Individual irrigation events could not be estimated for the ET controller treatments (Figure 25). Winter 200809 This season r eceived the least amount of total rainfall at 86 mm, which is expected for the winter season based on historical averages (Figure 2-2). Rainfall frequency, 8 days, was less than half the 30 year historical average of 17 days (Figure 2 -3). Weekly water ap plication was different between treatments (P<0.0001) but similar between replications (P=0.7869). The timed WORS treatment and timed treatment

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51 (TRS) applied a similar (P=0.3439) amount of water 19.1 and 17.4 mm/wk, respectively. The addition of a rain s ensor to timed treatment (TRS) did not significantly reduce weekly irrigation application for this season. The Weathermatic controller (WM) applied less water, 12.3 mm/wk, (P<0.0001) than the timed treatment (TRS) but more water (P=0.0233) than the reduce d timed treatment (TRSR). The Toro controller without a rain sensor (TORO WORS) applied less water (P<0.0001) than the reduced timed treatment (TRSR) at 7.3 mm/wk. The addition of a rain sensor to the Toro controller (TORO WRS) significantly reduced wate r application (P=0.0420) at 5.6 mm/wk (Table 2 -7). The Weathermatic controller (WM) applied 33% less water than the theoretical timed WORS treatment, while the Toro controllers applied 64% 66% less than the timed WORS treatment for this period (Figure 26). Water savings for the ET controllers were similar to results found by Davis et al. (2009) for the winter 2006-07 season. Regular turf quality ratings were not taken after 9 January 2009 due to winter dormancy, leaving only one turf quality rating f or each plot for the winter season which showed no difference between treatments (P=0.1097) and replications (P=0.8459). Turf quality ratings were above minimally acceptable and fell between the range of 5.6 and 6.4 for the NTEP scale. There was no correlation between water applied and turfgrass quality across all treatments for this period (Table 2-8). Spring 2009 Rainfall in this season totaled 193 mm, with no rainfall in April and 161 mm of rainfall occurring in the last two weeks of May (Figure 2 2). There were 12 days of rainfall during this season, while the historical average is 16 days (Figure 2-3). Irrigation applied was different between treatments (P<0.0001) but not between replications (P=0.7869). Not using a rain sensor resulted in the highest irrigation application, 27.1

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52 mm/wk (Timed WORS), followed by the timed treatment (TRS) with a rain sensor which applied 22.7 mm/wk (Table 2 9). The Weathermatic controller (WM) and the Toro controller without a rain sensor (TORO WORS) applied similar (P=0.4589) amounts of irrigation at 19.2 and 18.4 mm/wk, respectively. The addition of a rain sensor to the Toro controller (TORO WRS) significantly reduced irrigation (P<0.0001) at 15.3 mm/wk. The Weathermatic controller (WM) applied 25% less water than the timed WORS treatment while the Toro controllers (TORO WRS and TORO WORS) applied 30% 40% less water than the timed WORS treatment for this period (Table 29) (Figure 2-7). Water savings for the ET controllers were close to results reported by Davi s et al. (2009), where total water application by all controllers stayed within the TRS and TRSR treatments. Turf quality for this season was not different between treatments (P=0.5932) or within replications (P=0.7664). Turf quality was above minimally acceptable for all treatments and ranged from 6.3 to 6.7 on the NTEP scale. There was no correlation between water applied and turfgrass quality across all treatments for this period (Table 2 -10). Reduction of irrigation application in the month of Apri l was a result of failing irrigation heads. The heads themselves were applying less water even at adequate pressures. Installation of new irrigation heads was completed on 20 April 2009. As a result, irrigation application returned to normal levels (Fig ure 27). Summer 2009 While this period did not receive the least amount of precipitation, a difference of 249 mm between cumulative recorded rainfall and cumulative historical rainfall shows the greatest deficit to historical rainfall out of the 5 seas ons (Figure 22). There were 39

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53 days of rainfall during this period, while the historical average is 43 days (Figure 2 -3). Applied weekly irrigation was different between treatments (P<0.0001) but not between replications (P=0.8173). Not using a rain sensor (Timed WORS) would have caused the greatest amount of irrigation, 33.8 mm/wk, followed by the timed treatment with a rain sensor (TRS) which irrigated 18.4 mm/wk. The Weathermatic controller (WM) and Toro controller without rain sensor (TORO WORS) applied a similar (P=0.9118) amount of water, 14.514.9 mm/wk, and both applied less (P<0.0001) than the timed treatment (TRS) and more (P<0.0001) than the reduced timed treatment (TRSR). Use of a rain sensor on the Toro controller (TORO WRS) significantly reduced (P<0.0001) applied irrigation. The Weathermatic controller (WM) applied 57% less weekly irrigation than the timed WORS treatment, while the Toro controllers applied 55% to 74% less irrigation than the timed WORS treatment (Table 2-11) (Figure 2-8) Water savings for the Toro controllers in this season were high compared to 25% savings found by Shedd (2008) for summer 2007 and lower compared to 59% savings found by Davis et al. (2009) for the summer of 2007. In addition to being completed in different locations in Florida, Davis et al. (2009) used a rain sensor attached to the Toro controller while Shedd (2008) did not which contributed to the lower water savings. Similar savings seen to Davis et al. (2009) were likely due to the incorporation of a rain sensor and high rainfall frequency during this season despite differing cumulative rainfall amounts. This period was the only one to have a difference in turf quality ratings (P=0.0125); however there were no differences between replications (P=0. 1813). Turf quality between time based treatments TRS and TRSR were not different (P=0.7965). This is also the only season where 2 week water application before turf quality ratings was

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54 similar (Table 2 -12). Thus, turf quality differences were not correlated to water application. These differences can be attributed to pest infestation and/or disease. Conclusions All treatments applied less seasonally average cumulative irrigation than the theoretical time without rain sensor treatment. Period irrigat ion totals show that the addition of a rain sensor set at a 6 mm threshold to a properly maintained and programmed irrigation timer saved 8% to 50% of irrigation compared to a timed treatment without a rain sensor (WORS). Irrigation totals for the reduced time treatment (TRSR) in comparison to the time WORS treatment show that the addition of a rain sensor set at a 6 mm threshold in conjunction with a reduction in applied irrigation saved 45% to 75% of irrigation without compromising turf quality. Savings were seen despite dry conditions throughout most of the treatment period. Despite seasonally reduced irrigation totals, all periods except for summer 2009 did not show differences in turf quality. While summer 2009 was the driest of the 5 seasons, turf quality ratings were maintained between 6.3 and 7.4 for plots not affected by chinch bug infestations. Average 2 week water application was not correlated with pest infestations. Differences in turf quality are likely to have been caused by pests and/or disease. The Weathermatic controller (WM) applied an average of 42% less irrigation than the time WORS treatment for the entire study period. However, it frequently applied more weekly irrigation than the reduced time treatment (TRSR). The Weathermati c controller irrigated more frequently and in smaller depths compared to all other treatments due to set application frequency and deficit replacement irrigation scheduling. These results are different from r esearch completed by Davis (2008) that

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55 found we ekly water application by Weathermatic controllers to be similar to a reduced time based treatment when the Weathermatic controller was allowed to irrigate every day. Allowing the controller to irrigate every day caused the controller to irrigate using ve ry short run times. This study limited irrigation frequency to 3 d/wk which caused longer irrigation run times. As a result, when compared to results found by Davis (2008), weekly irrigation application increased as weekly irrigation frequency decreased for the Weathermatic controller. For this study, the Toro controller with a rain sensor (TORO WRS) applied an average of 66 % less irrigation than the time WORS treatment while the Toro controller without a rain sensor (TORO WORS) applied 57 % less than the time WORS treatment. Average savings for the Toro controllers were higher for this study period than similar research completed by Davis et al. (2009), but were similar to savings found by Shedd (2008). Average weekly water applications for the Toro controllers were similar during the summer 2008 period where both cumulative rainfall and rainfall frequency were similar to historical averages. The similarity between Toro controllers during average cumulative rainfall and rainfall frequency is due to either the adequacy of the rain pause feature or inadequacy of the rain switch. However, because the rain switch was not in an isolated treatment in this study, the cause is indistinguishable. Chapter 3 isolates each of the rain features to make this comp arison. Overall, the Toro controllers consistently saved water when compared to the time WORS treatment, saving 56% 66% of water for the entire project period. This research suggests that a signal based controller can save more water than an on-site controller. However, the Weathermatic controller uses an equation known to

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56 overestimate ETo in humid climates which contributed to reduced water savings compared to the Toro controller. It is possible that an onsite ET controller that does not use an equati on that overestimates ETo could perform as well or better than a Toro Intelli -sense controller.

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57 Table 2 1 Controller and treatment settings for the study period WM TORO WRS TORO WORS Application Rate 15.4 mm/h 15.4 mm/h 15.4 mm/h Root Depth NA 30.5 cm 30.5 cm Plant Type Warm season turf Warm season turf Warm season turf Soil Type Sand Sand Sand Shade NA Sunny All Day Sunny All Day Slope 0 0 0 Usable Rainfall NA 100% 100% Efficiency 80% 80% 80% Rain Switch Yes Yes No Zip Code 33598 NA NA Days Mon,Wed,Sat Every day Every day 1 This value was changed to 25% for summer 2009 Table 2 2 Scheduled irrigation run times and depth (each event, 2 d/wk) for the time treatment (TRS) and the reduced time treatment (TRSR) for all seasons Month TRS T RSR 3 Time (min) 1 Depth (mm) 2 Time (min) Depth (mm) January 29 8 18 5 February 31 8 19 5 March 44 11 26 7 April 47 12 28 7 May 43 11 26 7 June 39 10 24 6 July 61 16 37 9 August 67 17 40 10 September 40 10 24 6 October 41 11 25 6 November 42 11 25 6 December 37 9 22 6 Total 521 134 314 80 1 Irrigation schedule based on 100 % historical evapotranspiration replacement. 2 Irrigation depth applied during each individual irrigation event. 3 All values relating to irrigation application are 60% of TRS.

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58 Table 2 3. Average weekly irrigation application during summer 2008 (1 July 31 August) and savings as compared to the theoretical time based treatment without rain sensor Treatment Controller/Schedule Average water application (mm/wk) Savings compared to time WORS WM Weathermatic 16.5 bc 62% TORO WRS 1 Toro with rain sensor 8.6 a 80% TORO WORS 2 Toro without rain sensor 9.5 a 73% TRS Time 21.7 c 50% TRSR 60% Time 12.6 ab 71% Time WORS 3 43.7 d -* Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3The time WORS treatment is the theoretical irrigation applied without the us e of a rain sensor derived from the TRS treatment Table 2 4. Average 2 week irrigation application compared to turf quality during summer 2008 (1 July 31 August) Treatment Controller/Schedule Average 2 week water application (mm) Turfgrass quality3 WM W eathermatic 42 b 6.8 a TORO WRS 1 Toro with rain sensor 16 a 7.2 a TORO WORS 2 Toro without rain sensor 23 ab 7.2 a TRS Time 40 ab 6.8 a TRSR 60% Time 23 ab 7.0 a Numbers with different letters in columns indicate difference at the 95% confidence leve l using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3 Turf quality was completed used the NTEP scale from 1 to 9 where 1 was unacceptable turf quality, 5 was minimally acceptab le turf quality, and 9 was perfect turf quality

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59 Table 2 5 Average weekly irrigation application during fall 2008 ( 1 September 30 November ) and savings as compared to the theoretical time based treatment without rain sensor Treatment Controller /Schedule Average water application (mm/wk) Savings compared to time WORS WM Weathermatic 17.5 c 34% TORO WRS 1 Toro with rain sensor 10.2 a 64% TORO WORS 2 Toro without rain sensor 10.4 a 61% TRS Time 26.2 d 11% TRSR 60% Time 15.8 b 46% Time WORS 3 29 .6 e -* Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3The time WORS treatment is the theoretical irrigation applied without the use of a rain sensor derived from the TRS treatment Table 2 6 Average 2 week irrigation application compared to turf quality during fall 2008 (1 September 30 November ) Treatment Controller/Schedule Avera ge 2 week water application (mm) Turfgrass quality3 WM Weathermatic 39 b 6.5 a TORO WRS 1 Toro with rain sensor 20 a 6.4 a TORO WORS 2 Toro without rain sensor 24 a 6.5 a TRS Time 48 b 6.1 a TRSR 60% Time 30 a 6.5 a Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3 Turf quality was completed used the NTEP scale from 1 to 9 where 1 w as unacceptable turf quality, 5 was minimally acceptable turf quality, and 9 was perfect turf quality

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60 Table 2 7 Average weekly irrigation application during winter 2008 -09 (1 December 28 February ) and savings as compared to the theoretical tim e based treatment without rain sensor Treatment Controller/Schedule Average water application (mm/wk) Savings compared to time WORS WM Weathermatic 12.3 d 33% TORO WRS 1 Toro with rain sensor 5.6 a 66% TORO WORS 2 Toro without rain sensor 7.3 b 64% TRS T ime 17.4 e 8% TRSR 60% Time 10.4 c 45% Time WORS 3 19.1 e -* Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3The time WORS treatment is the theoretical irrigation applied without the use of a rain sensor derived from the TRS treatment Table 2 8 Average 2 week irrigation application compared to turf quality during winter 200809 (1 December 28 February ) Treatment Controller/Schedule Average 2 week water application (mm) Turfgrass quality3 WM Weathermatic 38 b 5.6 a TORO WRS 1 Toro with rain sensor 17 a 6.1 a TORO WORS 2 Toro without rain sensor 18 a 6.1 a TRS Time 60 c 6.1 a TRS R 60% Time 36 b 6.4 a Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3 Turf qual ity was completed used the NTEP scale from 1 to 9 where 1 was unacceptable turf quality, 5 was minimally acceptable turf quality, and 9 was perfect turf quality

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61 Table 2 9 Average weekly irrig ation application during spring 2009 ( 1 March 31 May ) and savings as compared to the theoretical time based treatment without rain sensor Treatment Controller/Schedule Average water application (mm/wk) Savings compared to time WORS WM Weathermatic 19.2 c 25% TORO WRS 1 Toro with rain sensor 15.3 b 43% TOR O WORS 2 Toro without rain sensor 18.4 c 30% TRS Time 22.7 d 13% TRSR 60% Time 13.3 a 49% Time WORS 3 27.1 e -* Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3The time WORS treatment is the theoretical irrigation applied without the use of a rain sensor derived from the TRS treatment Table 2 10 Average 2 week irrigation application compared to turf quality during spring 2009 (1 March 31 May ) Treatment Controller/Schedule Average 2 week water application (mm) Turfgrass quality3 WM Weathermatic 41 ab 6.7 a TORO WRS 1 Toro with rain sensor 31 a 6.3 a TORO WORS 2 Toro without rain s ensor 42 ab 6.3 a TRS Time 56 b 6.5 a TRSR 60% Time 32 ab 6.6 a Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatme nt with no rain sensor attached. 3 Turf quality was completed used the NTEP scale from 1 to 9 where 1 was unacceptable turf quality, 5 was minimally acceptable turf quality, and 9 was perfect turf quality

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62 Table 2 11 Average weekly irrigation application during summer 2009 (1 June 31 August ) and savings as compared to the theoretical time based treatment without rain sensor Treatment Controller/Schedule Average water application (mm/wk) Savings compared to time WORS WM Weathermatic 14.9 c 57% TORO WRS 1 Toro with rain sensor 9.2 a 74% TORO WORS 2 Toro without rain sensor 14.5 c 55% TRS Time 18.4 d 48% TRSR 60% Time 10.9 b 68% Time WORS 3 33.8 e -* Numbers with different letters in columns indicate difference at the 95% confidence level usi ng Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3The time WORS treatment is the theoretical irrigation applied without the use of a rain sensor derived from the TRS treatment Ta ble 2 12 Average 2 week irrigation application compared to turf quality during summer 2009 ( 1 June 31 August ) Treatment Controller/Schedule Average 2 week water application (mm) Turfgrass quality3 WM Weathermatic 35 a 7.1 ab TORO WRS 1 Toro with rain s ensor 27 a 6.3 c TORO WORS 2 Toro without rain sensor 30 a 6.5 bc TRS Time 42 a 7.4 a TRSR 60% Time 25 a 6.5 ab Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Toro treatment with the rain sensor attached. 2 Toro treatment with no rain sensor attached. 3 Turf quality was completed used the NTEP scale from 1 to 9 where 1 was unacceptable turf quality, 5 was minimally acceptable turf quality, and 9 was perfect turf quality

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63 Figure 21. Sample NTEP scale turfgrass ratings (19 scale) where A) 1 nearly dead turf B) 5 minimally acceptable turf C) 9 excellent quality of turf A B C

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64 Date (2008-2009) Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Monthly Rainfall (mm) 0 100 200 300 400 500 Total Rainfall (mm) 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Observed Historical Observed Sum Historical Sum 224 205 195 224 17 203 37 73 38 58 34 57 38 71 13 70 31 84 0 65 162 63 123 203 112 205 224 1806 1175 147 Figure 22. Monthly and cumulativ e historical (19792009) and observed rainfall for the duration of the study period (1 July, 2008 31 August, 2009)

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65 Date (2008-2009) Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Monthly Rainfall Frequency (d) 0 10 20 30 Cumulative Frequency (d) 0 20 40 60 80 100 120 140 Observed Historical Observed Sum Historical Sum 15 16 11 15 6 13 6 6 3 5 2 5 4 6 2 6 4 6 0 5 8 5 11 12 10 16 15 131 100 18 Figure 23. Monthly and cumulative historical (19792009) and observed rainfall days for the duration of the study per iod (1 July, 2008 31 August, 2009)

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66 Total Irrigation (mm) 0 100 200 300 400 500 Date (2008) 6/30 7/14 7/28 8/11 8/25 Daily Rainfall (mm) 0 20 40 60 80 100 Daily Irrigation Events (mm) 0 10 20 30 40 50 Rainfall WM, Weathermatic TORO WRS TORO WORS TRS, Timed TRSR, Timed*0.6 Timed WORS Figure 24. Cumulative and daily irrigation application and rainfall for summer 2008 (1 July 31 August)

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67 Total Irrigation (mm) 0 100 200 300 400 500 Date (2008) 9/1 9/15 9/29 10/13 10/27 11/10 11/24 Daily Rainfall (mm) 0 20 40 60 80 Daily Irrigation Events (mm) 0 10 20 30 40 50 Rainfall WM, Weathermatic TORO WRS TORO WORS TRS, Timed TRSR, Timed*0.6 Timed WORS Figure 2 5. Cumulative and daily irr igation application and rainfall for fall 2008 (1 September 30 November )

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68 Total Irrigation (mm) 0 50 100 150 200 250 300 Date (2008-2009) 12/1 12/15 12/29 1/ 12 1/26 2/9 2/23 Daily Rainfall (mm) 0 20 40 60 80 Daily Irrigation Events (mm) 0 10 20 30 40 50 Rainfall WM, Weathermatic TORO WRS TORO WORS TRS, Timed TRSR, Timed*0.6 Timed WORS Figure 2-6. Cumulative and daily irrigation ap plication and rainfall for winter 2008-09 (1 December 28 February)

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69 Total Irrigation (mm) 0 100 200 300 400 Date (2009) 3/2 3/16 3/30 4/13 4/27 5/11 5/25 Daily Rainfall (mm) 0 20 40 60 80 100 120 Daily Irrigation Events (mm) 0 10 20 30 40 50 Rainfall WM, Weathermatic TORO WRS TORO WORS TRS, Timed TRSR, Timed*0.6 Timed WORS Figure 27. Cumulative and daily irrigation application and rainfall for spring 2009 (1 March 31 May )

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70 Total Irrigation (mm) 0 100 200 300 400 500 Date (2009) 6/1 6/15 6/29 7/13 7/27 8/10 8/24 Daily Rainfall (mm) 0 20 40 60 80 100 120 Daily Irrigation Events (mm) 0 10 20 30 40 50 Rainfall WM, Weathermatic TORO WRS TORO WORS TRS, Timed TRSR, Timed*0.6 Timed WORS Figure 28. Cumulative and daily irrigation applicat ion and rainfall for summer 2009 (1 June 31 August )

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71 CHAPTER 3 PERFORMANCE OF TORO INTELLI -SENSE EVAPOTRANSPIRATION CONTROLLER RAIN DELA Y FEATURES Introduction Florida receives an average of 1,400 mm of rainfall a year, compared to the 762 mm for the national average (Carriker 2000) However, the hydrolo gic cycle in Florida is variable with regards to region, season and year. Areas in the northwest and southeast parts of the state receive an average of 1,626 mm of rainfall a year (Hughes 1977). Seasonally, Florida receives 70% of its total rainfall in t he summer (Carriker 2001). During the warm season (June-September) average rainfall is highest in south Florida, while in the cold season (December March) average rainfall is highest in the panhandle of the state (Fernald and Purdum 1998). Many evapotrans piration (ET) irrigation controllers have the capability to use rain features such as rain shut off devices to bypass or delay irrigation during and after rain events. Cardenas -Lailhacar and Dukes (2008) investigated the possible savings that could be achieved by a rain sensor. Two sensors were used for comparison in the test, the Hunter Mini -Click (MC) and wireless Rain-Click (WL). The MC was set at 3, 13, and 25 mm, while the WL had no threshold setting. It was shown that the MCs responded close to the ir set points with some variability. Total potential water savings was determined to be 818 mm between 25 March and 31 December 2005. Theoretical water savings were 363 (44%), 245 (30%), 142 (17%), and 25 (3%) mm for the WL, 3MC, 13 -MC, and 25MC treatm ents respectively. Shedd (2008) compared ET controller performance to that of a regular homeowner time based irrigation schedule without a rain sensor. Evapotranspiration controllers used in the study were: Toro Intelli -sense ( Bloomington, MN ) and Rainb ird ET Manager

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72 (Glendora, CA) Both controllers are signal based controllers, however the ET Manager controls irrigation by bypassing a regular irrigation timer. The Toro Intelli -sense controller did not use a rain sensor while, the ET Manger used a tipping bucket rain gauge to measure rainfall. Despite low rainfall amounts compared to historical averages, both ET controllers saved water compared to an irrigation system without an ET controller. Davis et al. (2009) compared ET controller performance to that of a regular homeowner time based irrigation schedule without a rain sensor. ET controllers used in the study were: Weathermatic SL1600 (Dallas, TX) Toro Intelli -sense and ET Water Smart Controller ( Corte Madera, CA ). The Weathermatic SL1600 has a rain sensor incorporated into its weather monitor, and a rain switch was attached to the Toro Intelli sense for rainfall measurement. Even during dry conditions, overall the ET controllers saved an average of 43% with respect to timed irrigation system w ith no rain sensor. While savings are seen from rain shut off devices with respect to time based controllers, ET controllers may have variable irrigation frequencies and depths. Based on previous research, the rain delay features of the Toro Intelli -sense should enhance the water saving capabilities of the controller. The quantified effects of rain switch devices in conjunction with an ET controller such as the Toro Intelli -sense have yet to be determined. Research being conducted with homeowners in Fl orida using the Toro Intelli -sense controller heightens the need for better understanding of its incorporated rainfall features. It is the goal of this study to determine the amount of water that can be saved using rain features of the Toro Intelli -sense ET controller.

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73 Materials and Methods The Toro Intelli -sense is a signal based controller that receives its signal from Hydropoint Data Systems (Riverside, CA). Hydropoints WeatherTRAK system uses the ASCE Standardized Reference Evapotranspiration Equation to calculate reference evapotranspiration ( ETo) (Hydropoint Data Systems, Inc. 2003). Reference evapotranspiration is used to calculate crop evapotranspiration (ETc) using plant type settings entered into the controller for each zone. Crop evapotranspiration is recorded daily and summed from the last irrigation event. Once soil water depletion reaches the set point for the entered plant type of the irrigation zone, the controller will schedule an irrigation event and application depth. While the co ntroller can automate irrigation frequency based on irrigation need, i rrigation frequency can also be predetermined by setting specific irrigation days or schedules. Required settings for proper irrigation application calculation include sprinkler type, a pplication rate, soil type, plant type, root depth, slope, and shade. Two physical irrigation treatments and two virtual treatments were established with four replications each in a complet ely randomized block design. Treatment descriptions and designati ons were as follows: TN Toro Intelli -sense with no rain sensor and 0% usable rainfall TRS Toro Intelli -sense with Hunter Mini -Clik rain sensor set at a 6 mm threshold and 0% Usable Rainfall, TRP Toro Intelli -sense with no rain sensor and 100% Usabl e Rainfall TRP -RS Toro Intelli -sense with Hunter Mini Clik rain sensor set at a 6 mm threshold a nd 100% Usable Rainfall (Table 3 -1) In Chapter 2, because no treatment existed to isolate the rain switch or compare results to a controller with no rain f eatures active, the effects of the rain features of the Toro controller could not be fully quantified. T wo virtual treatments were created, TRP and TN as supplement s to

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74 compare and contrast the effects of the Toro Intelli -sense controller rain features. Each of the virtual treatments irrigated with the existing onsite micro irrigation system as a means to quantify applied irrigation. Direct comparison of treatments required the use of a correction factor for the treatments that used micro irrigation. A relationshi p between irrigation types (Eq. 31 ) was developed to properly adjust the application rate: MIv= MA SI SA MIaSI (Eq. 3-1 ) Where: MIv = Virtual Sprinkler Irrigation Application Rate (mm h1) MIa = Actual Micro Irrigation Application Rate (mm h1) MA = Micro Irrigation Application Area (m3) SI = Spray Head Application Rate (mm h1) SA = Spray Head Application Area (m3) The corrected application rate was entered into the controller directly for the appropriat e treatments, which caused the controller to irrigate for longer periods of time. These longer irrigation cycles caused the micro-irrigation system to apply irrigation depths approximately equal to amounts that would have been applied with the spray head irrigation system. The Toro Intelli -sense rain pause feature works in addition to the rain switch and delays irrigation in a similar manner. When rainfall is recorded by the weather network, the weather service determines a set number of days that the c ontroller should not irrigate based on depth, intensity, and duration of the rainfall event or events and evapotranspiration. The number of days of rain pause is then sent to the controller wirelessly along with the ETo signal. Information regarding the rain pause feature in the controller manual is sparse and as a result, exact calculation methods for rain pause lengths are not known.

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75 Historical rainfall was calculated using a 30 year average. Precipitation data were collected from a weather station in Parrish, Florida, operated by the National Oceanic and Atmospheric Administration (NOAA) approximately 20 km south west of the project site ( 2737'N / 8221'W). There were a total of five periods of data collection; summer 2008: 25 June 2008 to 31 August 2008; fall 2008: 1 September 2008 to 30 November 2008; winter 200809 : 1 December 2008 to 28 February 2009; spring 2009: 1 March 2009 to 31 May 2009; summer 2009: 1 June 2009 to 31 August 2009. All treatments were allowed to irrigate any day of the week and at any frequency automatically determined by the controller. The treatments using the rain pause feature had usable rainfall settings adjusted to 100%, while TRS and TN had the usable rainfall settings adjusted to 0% (Table 3-1). For the season summ er 2009, the usable rainfall settings for TRS and TN were changed to 25% (Table 3 -1). Irrigation was totaled weekly for comparison among treatments. Delay days were quantified by number of recorded days with a rain pause or rain switch event active on th e controller. Statistical analysis was completed with the use of SAS statistical software (SAS Institute, Inc. Cary, NC). The General Linear Model (GLM) was used assuming a 95% confidence interval. Least square means separation was conducted using Tukey s procedure for pairwise comparison. All data were confirmed to be normal before conducting comparative analysis. Results and Discussion Every month in the 14 month study period was dryer than the historical average except for two months: July 2008 (9% higher than historical rainfall ) and May 2009 (156% higher than historical rainfall). All seasons received less than historical average

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76 rainfall. Rainfall between July 2008 and August 2009 totaled 1175 mm which is 35 % less than the historical rainfall value of 1806 mm (Figure 3 1 ). There were 51 days of rainfall to taling 6 mm or more leaving 88% of the 4 27 days without rainfall above 6 mm. Upon examination of the soil water depletion values collected from the controller after and during rain events i t was determined that, when in fully automated mode, the Toro Intelli -sense controller does not bypass irrigation in the same manner as a time clock and rain sensor combination. The Toro Intelli -sense cannot bypass irrigation events because it does not hav e a set schedule. Instead, it delays irrigation by not updating the soil water balance within the controller when the rain pause or rain switch is active. It was also found that the rain pause feature changed duration from the initial rain pause duration sent to the controller. Changes to the rain pause duration are likely due to reevaluation of rainfall and ETo by the weather service. In addition, the controller prioritizes the rain pause feature over that of the rain switch. If both features are activ e, the controller will only show the use of the rain pause event, even though zones that are not using the rain pause event are using the rain switch for delay. Thus, the recording of a rain switch event during a rain pause event was impossible. Treatments TRP -RS and TRP received 75 and 72 rain pauses, respectively, from the weather service over the course of the project period. Treatments TRP -RS and TRS were delayed by the switch 39 times over the course of the project period. Total number of days delayed by treatment TRP -RS was 114. Treatments TRS and TN were created to compare and contrast the effects of a rain switch while not using the rain pause feature of the controller. However, during spring 2009 it was determined that TRS was not bypassing i rrigation during rain switch

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77 events. It was concluded through investigation of the manual that if the usable rainfall setting is entered as 0% the controller will not use the rain shut off device as a means of irrigation bypass during or after rainfall ev ents. To counter this effect, treatments TRS and TN were set to 25% (values increment by 25%) for summer 2009 (Table 3-1). The new setting allowed rain switch events to occur while using the minimum amount of the rain pause feature. Summer 2008 This pe riod received the most amount of rainfall at 419 mm, compared to the historical average of 429 mm, despite being the shortest period at 62 days (Figure 31). There were 14 days of rainfall totaling 6 mm or more, compared to the historical average of 18 days (Figure 32). Average water application for this season was different between treatments (P<0.0001) but replications within the treatments were not (P=0.9335). Treatments TN and TRS did not include rain delays in the determination of irrigation frequency by the controller due to the 0% usable rainfall setting. Thus, both treatments acted similarly (P=0.2198) applying 23.8 and 19.5 mm of irrigation, respectively. The rain pause feature saved water compared to treatments not using the rain pause featur e (P=0.0129) applying 8.6 to 12.9 mm of average weekly irrigation. The addition of a rain sensor did not save a significant amount of irrigation for this period (P=0.2252) (Table 32). These results were not similar to research conducted by Cardenas -Lail hacar and Dukes (2008) that found Mini -Clik rain sensors to save 30% and 17% when set at 3 mm and 13 mm thresholds, respectively. This difference may have been due to lower total days of rainfall within this study compared to Cardenas Lailhacar and Dukes (2008).

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78 Fall 2008 There was 93 mm of rain during the entire 91 day period, which was less than the historical average of 334 mm (Figure 3-1). There were 5 days of rainfall totaling 6 mm or more which was less than half the historical average of 12 days. Water application was not different between Toro treatments (P=0.9776). The use of rain features did not save water during this period. All treatments applied between 10.2 mm and 11.0 mm of irrigation (Table 33). Despite low rainfall, treatments TRP -R S and TRP recorded rain pause events; however these events did not save significant amounts of water compared to treatments TRS and TN, which did not use rainfall features (Figure 34). Additionally, the reduction in ETo during the fall season lowered irr igation frequency and reduced the disparity between irrigation delay periods. The start of this season was afflicted by a lightning strike that destroyed much of the digital equipment used for monitoring irrigation. As a result, manually collected week ly irrigation totals were added to the cumulative data from 2 September 2008 to 12 September 2008 for the appropriate treatments. Winter 200809 This season received the least amount of total rainfall at 86 mm, which is expected for the winter season bas ed on historical averages (Figure 3-1). There were 5 days of rainfall totaling 6 mm or more compared to the historical average of 8 days. Weekly water application was not different between Toro treatments (P=0.3379). The use of rain features did not sav e a significant amount of irrigation during this period. All treatments applied between 5.6 mm and 8.7 mm of irrigation (Table 3 -4). As in the fall 2008 season, rain pause and rain switch events that were recorded failed to save water due to lower irrigation frequency and rainfall (Figure 35).

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79 Irrigation to the project was turned off twice from 16 January 2009 to 24 January 2009 and 29 January 2009 to 6 February 2009 due to hard freezes. The irrigation manifold was covered with tarps for insulation during these periods. Despite efforts to protect the system, flow meters to plots 20 and 19 froze and were replaced 27 January 2009. The broken flow meters still allowed water to flow through the system; however, they did not record data. Missing values f or these flow meters were estimated using digital and manual readings from replications within the affected treatments. Spring 2009 Rainfall in this season totaled 193 mm, with no rainfall in April and 161 mm of rainfall occurring in the last two weeks of May (Figure 3 1). There were 6 days of rainfall totaling 6 mm or more compared to the historical average of 8 days. Treatments TN and TRS did not include rain delays in the determination of irrigation frequency by the controller due to the 0% usable rain fall setting. Thus, both treatments preformed similarly (P=0.7577) applying 23.8 and 21.9 mm of irrigation, respectively. Treatment TRP -RS applied 15.3 mm of irrigation, and the addition of a rain sensor did not save a significant amount of water (P=0.27 58) when compared to the TRP treatment which applied 18.4 mm. However, the combination of the rain pause and the rain sensor saved more water compared to treatments not using rain delay features of the controller (P<0.0001) (Table 35). Summer 2009 Whil e this period did not receive the least amount of precipitation, a difference of 249 mm between cumulative recorded rainfall and cumulative historical rainfall shows the greatest deficit to historical rainfall out of the 5 seasons (Figure 31). There were 21 days of recorded rainfall of 6 mm or greater, the most out of all seasons during the

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80 study period, compared to the historical average of 25 days. Weekly irrigation between treatments was different (P<0.0001) but replications within treatments were not (P=0.9892). In this season, Treatment TRP -RS applied 9.3 mm of irrigation and the addition of a rain sensor saved significantly more irrigation than using the rain pause feature alone (P=0.0177), which applied 15.4 mm. These water savings by the rain sw itch are similar to savings seen by Cardenas -Lailhacar and Dukes (2008), and are likely due to higher rainfall frequency during this season. Treatments using the rain pause feature applied 9.3 (TRP -RS) and 15.4 (TRP) mm of irrigation and did not save sign ificantly more water than treatments not using the rain pause feature (P=0.9915; P=0.1129), which applied 8.5 (TRS) and 20.1 mm (TN) of irrigation (Table 3-6). This season saw the greatest rainfall frequency out of all of the seasons. Higher rainfall fre quency increased the number of days irrigation was delayed for treatments with rain features enabled compared to the treatment with no rain features enabled (Figure 3-7). Conclusions Fall 2008 and winter 2008-09 did have significant rainfall events in en ough frequency to affect irrigation application between treatments. However, during the two seasons without treatment differences, the frequency of irrigation is less due to reduced ET rates. Reduction in irrigation frequency reduces the chances of irrig ation delay. While there were still rainfall events significant enough to cause rain features to delay irrigation, water application was not hindered due to lower ET rates and in turn lower irrigation frequency. Fall 2008 and winter 2008-09 had less fre quent and cumulative rainfall 6 mm or more than the historical average and both periods resulted in similar weekly water application between treatments. The spring 2009 period had less rainfall frequency but

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8 1 had similar total rainfall to cumulative histor ical averages, which showed that a rain sensor in combination with the rain pause feature can reduce irrigation on a Toro controller during historically average periods of cumulative rainfall. Cumulative rainfall for the summer 2009 period was less than t he historical average; however, rainfall frequency was near the historical average, which shows that the addition of a rain sensor significantly improved water savings during times of average rainfall frequency while treatments using the rain pause feature did not. One of the advantages of an ET controller is its ability to remove homeowner error from irrigation of the home lawn. Rain sensors require maintenance and proper installation to function correctly. While the weather service attempts to remove t hese errors by removing homeowners from regular responsibilities associated with the home lawn irrigation system, it does not save significant amounts of water. When working properly, the attached rain shut off device set at a 6 mm threshold saved more water than the rain pause events sent out by the weather service. Additionally, because of the spatial variability of rainfall in Florida, it is likely that weather stations could miss rainfall events that happen in the immediate area of the controller. Ba sed on the savings seen in this study, it is recommended that a rain sensor be installed on a Toro Intelli -sense ET irrigation controller.

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82 Table 3 1 Toro Intelli -sense c ontroller and treatment s ettings for the study period TN TRP TRS TRP RS Descrip tion no rain features rain pause only rain switch only rain pause and rain switch Application Rate 15.4 mm/h 15.4 mm/h 15.4 mm/h 15.4 mm/h Root Depth 30.5 cm 30.5 cm 30.5 cm 30.5 cm Plant Type Warm season turf Warm season turf Warm season turf Warm se ason turf Soil Type Sand Sand Sand Sand Shade Sunny All Day Sunny All Day Sunny All Day Sunny All Day Slope 0 0 0 0 Usable Rainfall 0% 1 100% 0% 1 100% Efficiency 80% 80% 80% 80% Rain Switch No No Yes Yes Zip Code NA NA NA NA Days Every day Ever y day Every day Every day 1 Value was changed to 25% for summer 2009. Table 3 -2. Average weekly irrigation application during summer 2008 (1 July 31 August) and days of irrigation delay. Treatment Average Irrigation (mm/wk) Delayed Irrigation (d) TN 23.8 b 0 TRP 12.9 a 21 TRS 19.5 b 9 TRP RS 8.6 a 30 Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. Table 3 3 Average weekly irrigation application during fall 2008 (1 S eptember 30 November) and days of irrigation delay. Treatment Average Irrigation (mm/wk) Delayed Irrigation (d) TN 11.0 a 0 TRP 10.4 a 9 TRS 10.4 a 0 TRP RS 10.2 a 8 Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison.

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83 Table 3 4 Average weekly irrigation application during winter 200809 (1 December 28 February ) and days of irrigation delay. Treatment Average Irrigation (mm/wk) Delayed Irrigation (d) TN 8.7 a 0 TRP 7.3 a 14 TRS 7.6 a 1 TRP RS 5.6 a 14 Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. Table 3 5 Average weekly irrigation application during spring 2009 (1 March 31 May ), and days of irrigation delay. Treatment Average Irrigation (mm/wk) Delayed Irrigation (d) TN 23.8 c 0 TRP 18.4 ab 15 TRS 21.9 bc 7 TRP RS 15.3 a 22 Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. Table 3 6 Average weekly irrigation application during summer 2009 (1 June 31 May ) and days of irrigation delay. Treatment Average Irrigation (mm/wk) Delayed Irrigation (d) TN 20.1b 0 TRP 15.4 b 13 TRS 8.5 a 18 TRP RS 9.3 a 32 Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison.

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84 Date (2008-2009) Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Monthly Rainfall (mm) 0 100 200 300 400 500 Total Rainfall (mm) 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Observed Historical Observed Sum Historical Sum 224 205 195 224 17 203 37 73 38 58 34 57 38 71 13 70 31 84 0 65 162 63 123 203 112 205 224 1806 1175 147 Figure 31. Monthly and cumulative historical (1979 2009) and observed rainfall for the duration of the study period (1 July, 2008 31 August, 2009).

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85 Date (2008-2009) Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Monthly Rainfall Frequency (d) 0 5 10 15 20 Cumulative Frequency (d) 0 20 40 60 80 Observed Historical Observed Sum Historical Sum 7 9 7 9 1 7 2 3 2 2 1 2 3 3 1 3 1 3 0 2 5 3 7 7 5 9 9 71 51 9 Figure 32. Monthly and cumulative historical (19792009) and observed rainfall frequency of events at or above 6mm for the durations of the stud y period (1 July, 2008 31 August, 2009).

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86 Date (2008) Total Irrigation (mm) 0 100 200 300 Daily Rainfall (mm) 0 20 40 60 80 100 120 140 TRP-RS TRP TRS TN Rainfall Date (2008) 6/30 7/14 7/28 8/11 8/25 Rain Delay (d) 0 5 10 15 20 25 30 35 RP Events RS Events Figure 33. Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for summer 2008 (1 July, 2008 31 August, 2008). Treatment descriptions: TRP -RS rain pause and rain sensor, TRP rain sensor only, TRS rain sensor only, TN no features.

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87 Date (2008) Total Irrigation (mm) 0 50 100 150 200 250 Daily Rainfall (mm) 0 10 20 30 40 TRP-RS TRP TRS TN Rainfall Date (2008) 9/01 9/15 9/29 10/13 10/27 11/10 11/24 Rain Delay (d) 0 2 4 6 8 10 RP Events RS Events Figure 34. Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for fall 2008 ( 1 September, 2008 30 November, 2008). Treatment descriptions: TRP -RS rain pause and rain sensor, TRP rain sensor only, TRS rain sensor only, TN no features.

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88 Date (2008-09) Total Irrigation (mm) 0 50 100 150 200 Daily Rainfall (mm) 0 10 20 30 40 50 TRP-RS TRP TRS TN Rainfall Date (2008-09) 12/01 12/15 12/29 1/12 1/26 2/09 2/23 Rain Delay (d) 0 2 4 6 8 10 12 14 16 RP Events RS Events Figure 35. Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for winter 2008-09 (1 December, 2008 28 February, 2009). Treatment descriptions: TRP -RS rain pause and rain sensor, TRP rain sensor only, TRS rain sensor only, TN no features.

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89 Date (2009) Total Irrigation (mm) 0 100 200 300 400 Daily Rainfall (mm) 0 20 40 60 80 100 TRP-RS TRP TRS TN Rainfall Date (2009) 3/02 3/16 3/30 4/13 4/27 5/11 5/25 Rain Delay (d) 0 5 10 15 20 25 RP Events RS Events Figure 36. Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for spring 2009 (1 March, 2009 31 May, 2009). Treatment descriptions: TRP -RS rain pause and rain s ensor, TRP rain sensor only, TRS rain sensor only, TN no features.

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90 Date (2009) Total Irrigation (mm) 0 100 200 300 400 Daily Rainfall (mm) 0 20 40 60 80 100 TRP-RS TRP TRS TN Rainfall Date (2009) 6/01 6/15 6/29 7/13 7/27 8/10 8/24 Rain Delay (d) 0 5 10 15 20 25 30 35 RP Events RS Events Figure 37. Cumulative daily irrigation application, cumulative days of rain delay, type of rain delay, and rainfall for summer 2009 (1 June, 2009 31 August, 2009). Treatment descriptions: TRP -RS rain pause and rain sensor, TRP rain sensor only, TRS rain sensor only, TN no features.

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91 CHAPTER 4 EVALUATION OF REFERENCE EVAPOTRANSPIRATI ON ESTIMATION BY EVAPOTRANSPIRATION C ONTROLLERS IN A HUMID CLIMATE Introduction Evapotranspiration (ET) based irrigation controllers schedule irrigation by estimating water lost from the soil due to evapotranspiration. Evapotranspiration calculation methods and weather parameter collection vary among controllers. Generally ET controllers can be classified by weather collection methods: historical, stand alone, and signal based controllers. This chapter will focus on two controllers, the Weathermatic SL1600 and Toro Intelli -sense, which are classified as s tand alone and signal based controllers, respectively. Stand alone controllers collect weather data using on site sensors located within or near the irrigated landscape. Measurements taken by sensors can include temperature, rainfall, humidity and solar radiation (USBR 2007). These controllers use methods such as the Hargreaves (Hargreaves and Samani 1985) and modified Penman Monteith (Jensen et al. 1990) equations to calculate reference evapotranspiration (ETo). Signal based controllers receive ETo i nformation from an offsite weather service via radio, telephone, cable, cellular, web, or pager technology (USBR 2007). Weather services gather data from regional weather stations to calculate ETo. Often, the availability of the weather service provides for enhanced features related to scheduling and water saving such as the Toro Intelli -sense rain pause feature. A subscription to a weather service is required to receive updates. While real time on site data collection could help improve data quality, limited data parameters collected or simplified ETo estimation methods from an onsite sensor could

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92 potentially have an impact on estimated ETo values for stand alone controllers. Conversely, collection of data from offsite weather stations that are not r epresentative of the local conditions could lead to inaccurate ETo estimations for local irrigation sites, however availability of additional weather parameters could lead to a more accurate calculation of ETo for signal based controllers if weather condit ions are similar between locations (USBR 2007). In a 15 month study conducted in Gainesville, Florida, Davis (2008) found that Toro Intelli -sense ET controllers estimate ETo similar to ETo calculated with the ASCE Penman Monteith ET equation using weather data from an offsite weather station. However, the Toro controllers were found to overestimate ETo by 15% when compared to ETo calculated using on site weather data. The Weathermatic SL1600 overestimated ETo by 24% compared to onsite ETo calculations throughout the entire study period. These results were consistent with research completed by Trajkovic (2007) that found the Hargreaves equation to overestimate ETo in humid climates when compared to the ASCE Penman Monteith standardized ETo equation. In another study conducted in Florida over an 18 month period, Shedd (2008) found that a Toro Intelli -sense ET controller overestimated ETo compared to ETo calculated with the ASCE Penman-Monteith ET equation using on site weather data. An abrupt increase in recorded ETo from the controller mid way through the study suggested that either the weather service changed weather stations for data collection or the selected weather station used by the signal provider was malfunctioning. Evaluation of the Toro Intelli-sense irrigation controller in North Carolina by Grabrow et al. (2008) found that the controller overestimated ETo by 30% from May to

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93 September of 2007 compared to an independent onsite estimate. This over estimation caused the controller to over irri gate. The objective of this study was to determine the accuracy of ETo values used by two brands of ET controllers compared to ETo calculated using the standardized ASCE Penman Monteith ETo equation with data collected from an onsite weather station. Materials and Methods This research was carried out at the University of Florida Gulf Coast Research and Education Center (GCREC), Wimauma, Florida. Evaluation of the controllers started 1 June 2006 and ended 31 August 2009. The ET irrigation controllers being evaluated in this study were the Weathermatic, Inc. (Dallas, TX) SL1600 coupled with the SLW15 weather monitor and the Toro Company (Bloomington, MN) Intelli -sense. The Weathermatic SL1600 is an on-site ET controller that requires the use of a sensor (SLW15) that is installed in the surrounding landscape. The sensor gathers temperature and rainfall data for the controller. The controller then calculates ETo using the Hargreaves e quation (Weathermatic 2009) ETo is converted to crop evapotranspiration (ETc) using plant type settings from the controller. ETc is then used to calculate depth of water deficit from the last irrigation application. Irrigation frequency is determined by the user by setting predetermined irrigation days. As a result, schedu ling irrigation on consecutive days will cause the controller to irrigate in small amounts (i.e. ETc from the previous day). Required settings for proper irrigation application calculation include soil type, plant type, sprinkler type and regional location. The Toro Intelli -sense is a signal based controller that receives its signal from Hydropoint Data Systems (Riverside, CA). Hydropoints WeatherTRAK system uses the ASCE Standardized Reference Evapotranspiration Equation to calculate ETo

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94 (Hydropoint Data Systems, Inc. 2003). ETo is used to calculate ETc using plant type settings (see Chapter 1) entered into the controller for each zone. ETc is calculated daily and summed from the last irrigation event. Once soil water depletion reaches the set po int for the entered plant type of the zone, the controller will schedule an irrigation event and application depth. While the controller can automate irrigation frequency based on irrigation need, i rrigation frequency can also be predetermined by setting specific irrigation days or schedules. Required settings for proper irrigation application calculation include sprinkler type, application rate, soil type, plant type, root depth, slope, and shade. V alues pertinent to the operation of the controllers we re manually collected Monday through Friday For the Weathermatic, collected values consist of maximum and minimum temperatures, irrigation deficit, estimated run time for the next irrigation event, and total run time for the recorded period. Temperature values were used to calculate ETo using the Hargreaves equation (Hargreaves and Samani 1985). For the Toro Intelli -sense, collected values consist ed of daily ETo, weekly ETo, and time of signal reception There were two Weathermatic SL1600 controllers a nd two Toro Intelli sense controllers installed for the study. These controllers were installed at different times during the study: Weathermatic A (WMA): 1 June 2006; Weathermatic B (WMB): 13 October 2006; Toro A (TA): 11 August 2006; Toro B (TB): 15 May 2008. Data collection from the four irrigation controllers was completed on a regular basis; however, values collected were not always viable due to human collection error, sensor failure, and communication failure. Data that were missing or incorrect were

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95 removed. Therefore, comparisons were only made for a controller when both controller data and weather data for independent ETo calculation were available. The Florida Automated Weather Network (FAWN) maintains a weather station that is located approximately 125 m from the project site. Weather data was collected from the FAWN online database (Florida Automated Weather Network 2009) for calculation of ETo and comparison to irrigation controller evapotranspiration. Weather parameters that were collec ted include the following: temperature, relative humidity, solar radiation, wind speed and rainfall. All weather parameters were collected in 15 minute intervals Data quality analysis was completed using the ASCE methodology (Allen et al 2005) detaile d in Chapter 1. Davis (2008) established that these controllers using the same settings and in the same location act similarly, thus treatment replications were not used. Daily ETo was compared between treatments for statistical analysis. Statistical analysis was completed with the use of SAS statistical software (SAS Institute, Inc. Cary, NC). The General Linear Model (GLM) was used assuming a 95% confidence interval. Least square means separation was conducted using Tukeys procedure for pair wise com parison. All data was confirmed to be normal before conducting comparative analysis. Values for ETo were summed for each controller on days where values were available for the Weathermatic A (WMA), Weathermatic B (WMB), and Toro A (TA) controllers and A SCE calculated ETo for the entire study period. Values for ETo were also summed for all controllers after the installation of Toro B (TB) on 15 May 2008. Seasonal analysis of controller estimated ETo compared to onsite estimation of ETo was

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96 completed to determine the performance of Weathermatic and Toro controllers over varying levels of ETo throughout the year. All data were grouped by season and analyzed accordingly. Results and Discussion Onsite Weather Data Quality Analysis Weather data quality analy sis was conducted using ASCE Standardized Reference Evapotranspiration methodology on weather data collected from the onsite weather station operated by the Florida Automated Weather Network (FAWN). Meteorological data collected from a weather station mus t be screened before use in an ET equation. Failure to analyze data with respect to expected values and physical limitations could lead to poor quality and accuracy of calculated ET (Allen et al 2005). Measured solar radiation was analyzed by determini ng upper and lower limits for measured solar radiation (Rs) which are generally defined as clear sky solar radiation (Rso) and 0.2 of extraterrestrial radiation (Ra), respectively. On days of clear sky, Rs and Rso are usually equal. However, because Rso is a theoretical estimation, Rs may still fall below the theoretical value due to increased air turbidity, haziness, high altitude clouds, and afternoon clouds (Allen et al. 1998). An accurate method for comparison, detailed below, evaluates the effects o f sun angle and water vapor on absorption of short wave radiation by separating beam and diffuse radiation components (Allen et al. 2005). R so = ( K B + K D ) R a (1 23) where: = 0 98 exp 0 00146 sin 24 0 075 sin 24 0 4 (1 -24) = 0 35 0 36 0 15 (1 25)

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97 = 0 18 + 0 82 < 0 15 (1 26) P= 101 3 293 -0 00 65 z 293 5 26 (1 -27) sin 24= sin 0 85+ 0 3 sin 2 365 J -1 39 -0 42 2 (1 -28) W = 0 14 P e a + 2 1 (1 29) Rso = clear -sky solar radiation, MJ m2 d1 KB = clearness index for direct beam radiation KD = transmissivity index for diff use radiation Ra = extraterrestrial radiation, MJ m2 d1 P = atmospheric pressure at elevation, kPa Kt = turbidity coefficient, 0 < Kt t = 1.0 for clean air Kt 24 = weighted average sun angle during daylight hours, radians W = precipitable water in the atmosphere, mm 24 = average angle of the sun above the horizon, radians z = elevation above mean sea level, m J = Julian day, d ea = actual vapor pressure, kPa Rs is generally lower than Rso during the summer months due to higher humidity and increased cloud cover (Figure 4-1). On average, Rs values recorded by the weather station were within the defined upper and lower bounds. Recorded Rs was never consistently above Rso by more than 3%. Reference evapotranspiration values that contained Rs readings under the Ra lower limit were removed from analysis (Figure 41). Dew point temperatures (Tdew) calculated from average daily vapor pressure consistently came close to reaching daily minimum temperatures (Tmin) (Figure 42). Tdew values were well correlated to Tmin (r=0.969). Exceptions to correlation of Tdew and Tmin are likely due to changes in air mass, high winds, and cloudiness at night (Allen et al. 1998). Maximum and minimum values for relativ e humidity generally fell between 30% and 100%, which is expected of data collected in humid environments (Allen et al. 1998;

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98 Figure 43 ). There were no collected values above 100%, however there were values collected below 30%. Values below 30% are phys ically possible and because minimum values were not consistently below this threshold. There were 42 recorded minimum relative humidity values below 30% compared 1237 recorded values for the entire study period (Figure 43). Average wind speed was analyzed to determine the reliability of the sensor and look for extraneous values. There was no period of consistent values below 1 m s1 or grossly high which was used to assess the reliability of the wind speed data ( Figure 44 ). A reduction in average wind speed was seen during the summer months which averaged 1.92 m s1, while higher wind speeds were seen in the winter and spring months which averaged 2.49 and 2.72 m s1, respectively. Controller ETo Analysis Cumulative ETo collected from the controllers was compared to onsite ASCE calculated ETo individually for days when data were available for the controller and onsite ETo. The Weathermatic controllers overestimated ETo by 14 -15% for the entire study period (Table 4 1). These results are supported b y research completed by Trajkovic (2007) that found the Hargreaves equation to overestimate ETo by 15 to 29% compared to the ASCE PenmanMonteith ETo equation in humid regions. Jensen et al. (1990) suggested that this overestimation was due to the reliance on temperature by the Hargreaves method which does not account for the lag period of temperature behind solar radiation levels. The Toro controllers received ETo values similar to the ASCE calculated ETo for the study period (Table 41). The use of the ASCE PenmanMonteith ETo equation by the Toro Intelli -sense controllers improved overall accuracy of ETo calculations, within 1% to 3% compared to onsite calculated ETo. Davis (2008) found

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99 that when using the nearest publicly available weather station, To ro controllers overestimated ETo by 4%. The weather service may have been accessing the FAWN onsite weather station for data collection and calculation of ETo, thus resulting in the favorable controller comparison to onsite ETo. The Weathermatic control lers overestimated ETo by an average of 206 mm (11%) over the entire study period (Figure 4 -5). Average daily ETo values calculated by the Weathermatic controllers were similar (P=0.0819) between the controllers but were greater than onsite ETo calculated using the ASCE PenmanMonteith equation (P<0.0001) (Table 42). In this comparison, the Weathermatic controllers overestimated ETo even when compared to another type of ET controller. The Toro controller (TA) had cumulative ETo similar to the onsite ETo calculation, overestimating with less than 3% difference (Table 41). Average daily ETo from the Toro controller (4.14 mm/d) was similar to average ETo (4.08 mm/d) calculated onsite (P=0.6873; Table 42). The two different brands of controllers had diff erent cumulative ETo values (P<0.0001) for the study period when comparing available data (Table 4 -2). The Toro Intelli -sense resulted in a more accurate cumulative ETo than the Weathermatic SL1600. This difference could be due to equations used for calc ulation of ETo or weather data source. Similar results on this site were found by Davis (2008) which found the Weathermatic SL1600 to overestimate ETo by 7% and the Toro Intelli -sense to estimate ETo within 1% of calculated ETo using the ASCE PenmanMonteith Standardized ETo equation. Values for ETo were summed for all controllers after the installation of Toro B (TB) on 15 May 2008 (Figure 4-6). The Weathermatic controllers overestimated cumulative ETo by 9 12% compared to cumulative onsite calculate d ETo (Table 4 -3). Average daily

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100 ETo was overestimated by the Weathermatic controllers compared to average daily onsite ETo (P<0.0001) but was similar between controllers (P=0.6575). The Toro controllers underestimated cumulative ETo by 1 -3% compared to cumulative onsite calculated ETo (Table 4-3) which was similar to average daily onsite ETo (TA: P=0.9658; TB: P=0.7811). Each brand of controller calculated similar values when compared within brand, which is similar to results found by Davis (2008) in a study comparing ETo values between controllers. The Weathermatic SL1600 controllers overestimated ETo while the Toro Intelli -sense controllers received ETo values similar to calculated onsite ETo. Individual controller ETo was compared to calculated onsite ASCE ETo. Weathermatic A (WMA) was found to consistently overestimate ETo compared to onsite calculated ETo while maintaining a linear relationship between recorded and calculated ETo values (Figure 47). Weathermatic B (WMB) was also was found to consistently overestimate ETo compared to onsite calculated ETo while maintaining a linear relationship between recorded and calculated ETo values (Figure 48). Toro A (TA) received ETo closely to calculated onsite ETo for the study period while maintaini ng a linear relationship between recorded and calculated ETo values (Figure 4-9). Toro B (TB) also received ETo closely to calculated onsite ETo for the study period while maintaining a linear relationship between recorded and calculated ETo values (Figur e 410). These individual controller comparisons enhance the previous cumulative comparisons by showing that controller ETo calculation for all of the tested controllers does not deviate from a linear relationship as onsite ETo changes.

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101 Seasonal Contr oller ETo Analysis The Weathermatic SL1600 controllers overestimated daily ETo in every season compared to calculated daily onsite ETo (P<0.0001) (Table 4 -4). The fall and spring seasons resulted in moderate ETo overestimation (Figure 411; Figure 412). The summer seasons had the most severe overestimations (Figure 413) while the winter seasons were the least severe overestimation (Figure 4-14). The Toro controllers received daily ETo similar to calculated onsite daily ETo for the fall (P=0.9979) (Figur e 415) and spring seasons (P=0.2933) (Table 44; Figure 4 -16). However, the Toro controllers overestimated daily ETo in the summer seasons (P<0.0001) (Figure 417) and underestimated daily ETo in the winter seasons (P<0.0001) (Figure 4 -18). The Weatherm atic controllers overestimation of seasonal daily ETo is consistent with overall cumulative ETo analysis. The lack of robust data in the Hargreaves equation limits its ability to accurately calculate ETo in humid climates. Jensen et al. (1990) notes tha t all temperature based methods, such as the Hargreaves equation, should use regional calibration to produce accurate results. While the Toro controllers accurately measured daily ETo in the fall and spring seasons, they overestimated daily ETo in the sum mer seasons and underestimated daily ETo in the winter seasons. Previous research by Grabow (2007) was completed only during the warm season, and found that the Toro Intelli -sense overestimated ETo by 30%. While cumulative daily data for the entire stud y period shows that the Toro controllers perform well, at a higher resolution their accuracy diminishes in colder and warmer periods of the year. ETo values directly affected water application for the Toro controllers. Chapter 2 notes that during the wint er season, irrigation application for the Toro Intelli -sense was below (39%) a timer schedule based on historical ET replacement that was reduced by 40%, however, overestimation of ETo

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102 during the summer seasons did not have a measureable effect on applied irrigation due to interference by rain shut of devices during periods of rainfall. Conclusions The Weathermatic SL1600 controllers consistently overestimated daily ETo throughout the study period. Overestimation ranged from 9 -15% compared to onsite calc ulated ETo. The most severe periods of overestimation were in the summer seasons. These overestimations are most likely are attributed to cloud conditions during the summer and the inability of the Hargreaves equation to account for changes in solar radi ation. There was not a significant ETo difference between the two Weathermatic controllers, which is consistent with research completed by Davis (2008). The Toro Intelli -sense controllers received ETo similar to calculated onsite ETo in cumulative comparison with only 1-3% cumulative difference over 41 months which was not statistically significant. However, the Toro controllers overestimated daily ETo during the summer seasons and underestimated ETo during the winter seasons. Overall ETo estimation per formance sent to the Toro Intelli -sense controllers was closer to calculated ETo using the ASCE ETo equation than the Weathermatic SL1600 controllers. As a result, the Toro Intelli -sense ET irrigation controller is more equipped to handle humid environments such as Florida than the Weathermatic SL1600 ET irrigation controller.

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103 Table 4-1. Total recorded ETo for controllers and calculated ETo using the ASCE Penman-Monteith ET equation as well as percent differences between controller ETo and ASCE ETo for the study period (1 June, 2006 31 August, 2009). Treatment Total ETo (mm) ASCE Method ETo (mm) Difference (%) WMA 1 4787 4203 14 WMB 2 4057 3538 15 TA 3 2531 2464 3 TB 4 922 935 -1 Differences in ASCE Method ETo totals are a result missing values in the data sets of each treatment 1Weathermatic controller A 2Weathermatic controller B 3Toro controller A 4Toro controller B Table 4-2. Average daily and total ETo and percent difference between controllers and onsite calculated ETo using the Penman-Monteith ET Equation for the study period (1 June 2006 31 August, 2009). Note that Toro B (TB) was not functional until 15 May, 2008. Treatment Average ETo (mm/d) Total ETo (mm) Difference (%) WMA 1 4.50 b 2052 9 WMB 2 4.63 b 2101 12 TA 3 4.14 a 1892 1 FAWN 4 4.08 a 1871 -* Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Weathermatic controller A 2Weathermatic controller B 3 Toro controller A 4ETo calculated using the onsit e FAWN weather station Table 4-3. Average and Total ETo and percent difference between controllers and onsite calculated ETo using the Penman-Monteith ET equation for the period between 15 May, 2008 and 31 August, 2009. Treatment Average ETo (mm/d) Total ETo (mm) Difference (%) WMA 1 4.88 b 836 9 WMB 2 5.01 b 859 12 TA 3 4.46 a 758 -1 TB 4 4.41 a 749 -3 FAWN 5 4.52 a 769 -* Numbers with different letters in columns indicate difference at the 95% confidence level using Tukeys pairwise comparison. 1Weathermatic controller A 2Weathermatic controller B 3 Toro controller A 4Toro controller B 5ETo calculated using the onsite FAWN weather station

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104 Table 4 4. Combined seasonal data of average daily ETo calculated by the controllers and onsite ETo calculated using the PenmanMonteith ET equation for the study period (1 June, 2006 31 August, 2009). Treatment Average daily ET o by season(mm/d) Fall 1 Spring 2 Summer 3 Winter 4 Weathermatic 4.02 b 5.40 b 6.05 c 2.99 c Toro 3.71 a 5.06 a 5.42 b 2.38 a FAWN 3.71 a 4.91 a 4.79 a 2.77 b Numbers with different letters in columns indicate different at the 95% confidence level using Tukeys pairwise comparison.1Fall periods are between 1 September and 30 November 2Spring periods are between 1 March and 31 May 3Summe r periods are between 1 June and 31 August 4Winter periods are between 1 December and 28 February

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105 Figure 41. Daily incoming solar radiation (Rs) data from the onsite FAWN weather station quality analysis where clear sky solar radiation (Rso) and 20% o f extraterrestrial radiation (Ra) are the upper and lower bounds of data acceptability for the study period (1 June, 2006 31 August, 2009). All comparisons are made using appendix D of Allen et al. (2005). 0 5 10 15 20 25 30 35 Solar Radiation (MJm2d1)Date (2006-2009) 0.75*Ra 0.2*Ra Rs Rso

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106 Figure 42. Minimum daily temperature colle cted from the onsite FAWN weather station compared to calculated daily minimum dew point temperature for the study period (1 June, 2006 31 August, 2009). R = 0.938 5 0 5 10 15 20 25 30 10 5 0 5 10 15 20 25 30Minimum Temperature (oC)Dew Point Temperature (oC)

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107 Figure 43. Minimum and maximum daily relative humidity over the course of the study period (1 Ju ne, 2006 31 August, 2009) collected from the onsite FAWN weather station. 0 20 40 60 80 100 120 Relative Humidty (%)Date (2006-2009) RH Max RH Min

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108 Figure 44. Daily average wind speed at 2 m height over the course of the study period (1 June, 2006 31 August, 2009) collected from the onsite FAWN weather station. 0 1 2 3 4 5 6 7 8 9 Wind speed (m s1)Date (2006-2009)

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109 F igure 45. Cumulative controller calculated ETo and onsite ETo calculated using the ASCE Penman Monteith ET Equation between 14 October, 2006 and 31 August, 2009 for Weathermatic A (WMA), Weathermatic B (WMB), Toro A (TA), and ETo calculated using the onsi te FAWN weather station. 0 500 1000 1500 2000 2500 Cumulative ETo (mm)Date (20062009) WM A WM B TA FAWN 2052 (WMA) 1892 (TA) 2101 ( WM B) 1871 (FAWN)

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110 Figure 46. Cumulative controller calculated ETo and onsite ETo calculated using the ASCE Penman Monteith Standardized ET Equation between 18 May, 2008 and 31 August, 2009 for Weathermatic A (WMA), Weathermatic B (WMB), T oro A (TA), Toro B (TB), and ETo calculated using the onsite FAWN weather station. 0 100 200 300 400 500 600 700 800 900 1000 Cumulative ETo (mm)Date (2008-2009) WM A WM B TA TB FAWN 769(FAWN) 749(TB) 758(TA) 834 ( WM A) 859 ( WM B)

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111 Figure 47. Weathermatic A (WMA) daily controller calculated ETo vs. daily onsite ETo calculated using the ASCE PenmanMonteith ET equation for the study period (1 June, 2006 31 August, 2009). y = 0.9548x + 0.7464 R = 0.6543 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8Controller ETo(mm d1)ASCE -EWRI ETo(mm d1) WM A 1 to 1 Line

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112 Figure 48. Weathermatic B (WMB ) daily controller calculated ETo vs. daily onsite ETo calculated using the ASCE PenmanMonteith ET equation for the study period (1 June, 2006 31 August, 2009). y = 1.0118x + 0.5125 R = 0.7043 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8Controller ETo(mm d1)ASCE -EWRI ETo(mm d1) WM B 1 to 1 Line

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113 Figure 49 Toro A (T A) daily controller calculated ETo vs. daily onsite ETo calculated using the ASCE PenmanMonteith ET equation for the study period (1 June, 2006 31 August, 2009). y = 1.0535x 0.1296 R = 0.5957 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8Controller ETo(mm d1)ASCE -EWRI ETo(mm d1) Toro A 1 to 1 Line

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114 Figure 410 Toro B (TB) daily controller calculated ETo vs. daily onsite ETo calculated using th e ASCE PenmanMonteith ET equation for the installation period ( 15 May, 2008 31 August, 2009). y = 1.03x 0.1881 R = 0.6171 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8Controller ETo(mm d1)ASCE EWRI ETo(mm d1) Toro B 1 to 1 Line

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115 Figure 4-11. Weathermatic A (WMA) and Weat hermatic B (WMB) daily ETo calculation compared to onsite ETo calculated using the ASCE Penman-Monteith ET equation for the fall periods (1 September 30 November). Figure 4-12. Weathermatic A (WMA) and Weat hermatic B (WMB) daily ETo calculation compared to onsite ETo calculated using the ASCE Penman-Monteith ET equation for the spring peri ods (1 March 31 May). y = 0.7732x + 1.239 R = 0.4968 0 1 2 3 4 5 6 7 8 012345678Controller ETo(mm d-1)ASCE-EWRI ETo(mm d-1) WM A 1 to 1 Line y = 0.7005x + 2.0604 R = 0.413 0 1 2 3 4 5 6 7 8 9 012345678ETo from controller (mm d-1)ASCE-EWRI ETo(mm d-1) WM A 1 to 1 Line

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116 Figure 4-13. Weathermatic A (WMA) and Weat hermatic B (WMB) daily ETo calculation compared to onsite ETo calculated using the ASCE Penman-Monteith ET equation for the summer periods (1 June 31 August). Figure 4-14. Weathermatic A (WMA) and Weat hermatic B (WMB) daily ETo calculation compared to onsite ETo calculated using the ASCE Penman-Monteith ET equation for the winter periods (1 December 28 February). y = 0.4801x + 3.7444 R = 0.2382 0 1 2 3 4 5 6 7 8 9 012345678Controller ETo(mm d-1)ASCE-EWRI ETo(mm d-1) WM A 1 to 1 Line y = 0.5438x + 1.5088 R = 0.3234 0 1 2 3 4 5 6 7 8 012345678Controller ETo(mm d-1)ASCE-EWRI ETo(mm d-1) WM A 1 to 1 Line

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117 Figure 4-15. Toro A (TA) and Toro B (TB) daily ETo calculation compared to onsite ETo calculated using the ASCE Penman-Mont eith ET equation for the fall periods (1 September 30 November) y = 0.9648x -0.1763 R = 0.54780 1 2 3 4 5 6 7 8 012345678Controller ETo(mm d-1)ASCE-EWRI ETo(mm d-1) Toro A 1 to 1 Line

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118 Figure 416 Toro A (T A) and Toro B (T B) daily ETo calculation compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the spr ing periods (1 March 31 May). y = 0.7808x + 1.3771 R = 0.3397 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8Controller ETo(mm d1)ASCE -EWRI ETo(mm d1) Toro A 1 to 1 Line

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119 Figure 417 Toro A (T A) and Toro B ( T B) daily ETo calculation compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the summer periods (1 June 31 August) y = 0.5533x + 2.5208 R = 0.2105 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8Controller ETo(mm d1)ASCE -EWRI ETo(mm d1) Toro A 1 to 1 Line

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120 Figure 418 Toro A (T A) and To ro B ( T B) daily ETo calculation compared to onsite ETo calculated using the ASCE PenmanMonteith ET equation for the winter periods (1 Dece mber 28 February ). y = 0.8873x 0.229 R = 0.548 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8Controller ETo(mm d1)ASCE -EWRI ETo(mm d1) Toro A 1 to 1 Line

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121 CHAPTER 5 EDUCATIONAL VIDEOS F OR EVAPOTRANSPIRATIO N IRRIGATION CONTROLLERS Introduction With t echnological advancements in landscape irrigation equipment and increasing availability of this equipment at a lower cost, it is important that educational materials concerning these new products are made available to encourage appropriate use. Water use in the home lawn has potential to be reduced with implementation of new smart irrigation technologies. The complicated nature of these new technologies demand the availability of educational materials to teach concepts and procedures needed to effectively strengthen their water saving potential. Multimedia resources are an effective way to communicate with a large target audience and offer flexibility of access. The availability of access to educational videos by way of the internet presents an avenue t o reach a wider audience with a diverse set of teaching materials that can be accessed easily. The use of video is an excellent way to demonstrate clear concepts and highlight important information. The creation of videos for homeowners detailing the ope ration and programming of evapotranspiration (ET) controllers can effectively convey appropriate information to operate an ET controller. Methods and Materials Videos were created detailing general concepts and programming of two brands of ET controllers. The two brands of controllers were the Weathermatic ( Dallas, TX) SL1600 and the Toro (Bloomington, MN) Intelli -sense. Recording was completed with a 1080p high definition camera. Video and sound was uploaded, edited, and exported using Final Cut Pro (Apple Inc., Cupertino, CA). For public availability, final versions of

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122 these videos were uploaded to YouTube. The steps to complete a video for YouTube are as follows: 1. Complete outline of video a. description of need b. how the technology works c. how the technology is implemented d. dialogue 2. Filming a. Find desirable locations for filming that do not have loud noise of excessive ambient noise b. Ensure weather conditions are correct: no wind, correct lighting, no rain, etc. c. Set up camera and sound equipment and complete functionality tests d. Film video with multiple takes and angles to ensure adequate transitioning 3. Editing a. Upload video clips from camera to computer b. Import video into video editing software c. Crop and cut video clips into appropriate lengths d. Stitch together video timeline e. Gather and create graphics needed for presentation f. Insert graphics, text, and pictures g. Insert audio and video transitions h. Filter audio to ensure clear sound 4. Upload to YouTube a. Export video using appropriate codecs for video formats required by YouTube b. Upload video to YouTube c. Embed links in videos for logical progression through series Results All videos are linked below: ET Controller Instructional Video: http://www.youtube.com/watch?v=YV68o4HnEr0 Toro Intelli-sense Programming Part 1: http://www.youtube.com/watch?v=xF1wZrQxfx0 Toro Intelli-sense Programming Part 2: http://www.youtube.com/watch?v=FjG9cdd_NXw Weathermatic SL1600 Programming: http://www.youtube.com/watch?v=HuL2YWsCeqk The ET controller instructional video out lines introductory concepts involving weather based irrigation controll ers that are detailed in Chapt er 1. Topics such as ET, ET controllers, and ET controller brands are discussed.

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123 The Weathermatic SL1600 programming video and the Toro Intelli-sense programming parts 1 and 2 are a set of videos that walk a user through the process of programming two different types of ET cont roller. Explanations of the adjustable settings within the controller are first introduc ed. These settings include sprinkler type, precipitation rates, sprinkler efficiency, so il type, plant type, root depth, grade, and shade. The user is then prompted to det ermine the settings for each zone of their irrigation system. Afterwards, initial setup of irrigat ion scheduling and settings are demonstrated in a step by step process using the settings that were determined by the user for each zone of the irrigation system. Conclusions Conveyance of information through multimedia outlets allows for educational materials such as these to reach a wider au dience. It also ensures that the correct information reaches the end user. These videos will be used as part of a set of tools to accomplish the overall goal of disseminati on of information and promotion of smart irrigation technology. Instilling the educatio nal foundations of these technologies could help foster a new wave of water savings in the home landscape.

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124 CHAPTER 6 CONCLUSIONS The objective of this research was to determine if evapotranspiration (ET ) irrigation controllers can save water in Florida. The primary goals of this study were to A) evaluate two brands of evapotranspiration controllers and their ability to determine an irrigation schedule compared to a timed schedule with homeowner recommend settings while monitoring turfgrass quality, B) determine the amount of water that can be saved using rain features of the Toro Intelli -sense ET controller, and C) determine the accuracy of ETo values used by two brands of ET controllers compared to ETo calculated using the standardized ASCE PenmanMonteith ETo equation with data collected from an onsite weather station. Videos detailing ET controllers were produced and are available through YouTube. These videos were created to detail how ET controller s operate and provide instructions for proper programming. Water Applied and Turf Quality All controllers applied less cumulative seasonal irrigation than the theoretical time without rain sensor treatment. Period irrigation totals showed that the addition of a rain sensor set at a 6 mm threshold to a properly maintained and programmed irrigation timer saved 8% to 50% of irrigation compared to a timed treatment without a rain sensor (WORS). Irrigation totals for the reduced time treatment (TRSR) in compari son to the time WORS treatment show that the addition of a rain sensor set at a 6 mm threshold in conjunction with a reduction in applied irrigation saved 45% to 75% of irrigation without compromising turf quality. Savings were seen despite dry conditions throughout most of the treatment period.

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125 Despite seasonally reduced irrigation totals, all periods except for summer 2009 did not show differences in turf quality. While summer 2009 was the driest of the 5 seasons, turf quality ratings were maintained between 6.3 and 7.4 for plots not affected by chinch bug infestations. Furthermore, average 2 week water application was not correlated with pest infestations. Differences in turf quality are likely to have been caused by random effects such as pests, an d/or disease. The Weathermatic controller (WM) applied an average of 42% less irrigation than the time WORS treatment for the entire study period. However, it frequently applied more weekly irrigation than the reduced time treatment (TRSR). The Weather matic controller irrigated more frequently and in smaller depths compared to all other treatments due to set application frequency and deficit replacement irrigation scheduling. This study limited irrigation frequency for this controller to 3 d/wk which c aused longer irrigation run times. As a result, when compared to results found by Davis (200 8 ), weekly irrigation application increased as weekly irrigation frequency decreased for the Weathermatic controller. For this study, the Toro controller with a rain sensor (TORO WRS) applied an average of 66% less irrigation than the time WORS treatment while the Toro controller without a rain sensor (TORO WORS) applied 57% less than the time WORS treatment, which shows rain sensor water savings that are inconsis tent with previous studies. Average weekly water applications for the Toro controllers were similar during the summer 2008 period where both cumulative rainfall and rainfall frequency were similar to historical averages. The similarity between Toro contr ollers during average cumulative rainfall and rainfall frequency is due to either the adequacy of the rain pause

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126 feature or inadequacy of the rain switch. However, because the rain switch was not in an isolated treatment in this study, the cause is indist inguishable. Rain Features of the Toro Intellisense Evapotranspiration Irrigation Controller Fall 2008 and winter 2008-09 did have significant rainfall events in enough frequency to affect irrigation application between treatments. However, during thes e two seasons the irrigation frequency was reduced due to seasonally lower ET rates. Reduction in irrigation frequency (due to decreased ET rates) reduces the chances of irrigation delay. While there were still rainfall events significant enough to cause rain features to delay irrigation, water application was not significantly hindered due to seasonally reduced ET rates and in turn lower irrigation frequency. Fall 2008 and winter 2008-09 had less frequent and less cumulative rainfall 6 mm or more than the historical average and both periods resulted in similar weekly water application among treatments. The spring 2009 period had less frequent rainfall but had similar total rainfall to cumulative historical averages. In spring 2009, a rain sensor in combination with the rain pause feature can reduced irrigation on a Toro controller during historically average periods of cumulative rainfall. Cumulative rainfall for the summer 2009 period was less than the historical average; however, rainfall frequency was near the historical average. In summer 2009 the addition of a rain sensor significantly improved water savings during times of average rainfall frequency while treatments using the rain pause feature did not. When working properly, the attached rain s hut off device set at a 6 mm threshold saved more water than the rain pause events sent out by the weather service. Additionally, because of the spatial variability of rainfall in Florida, it is likely that weather stations could miss rainfall events that happen in the immediate area of the controller.

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127 However, one of the advantages of an ET controller is its ability to remove homeowner error from irrigation of the home lawn. Rain sensors require maintenance and proper installation to function correctly. The weather service attempts to remove these errors by removing homeowners from regular responsibilities associated with the home lawn irrigation system. Controller Reference Evapotranspiration Comparison The Weathermatic SL1600 controllers consistentl y overestimated daily ETo throughout the study period. Overestimation ranged from 9 -15% compared to onsite calculated ETo, which contributed to over irrigation. The most severe periods of overestimation were in the summer seasons. These overestimations are most likely attributed to cloudy weather during the summer and the inability of the Hargreaves equation to account for changes in solar radiation. There was not a significant ETo difference between the two Weathermatic controllers. The Toro Intelli -s ense controllers estimated ETo similar to calculated onsite ETo in cumulative comparison with only 1-3% cumulative difference over 41 months which was not statistically significant. However, the Toro controllers overestimated daily ETo during the summer s easons and underestimated ETo during the winter seasons. Overall ETo estimation performance sent to the Toro Intelli -sense controllers was closer to calculated ETo using the ASCE ETo equation than the Weathermatic SL1600 controllers and may have been due t o the use of the publically available weather data fr om the onsite weather station.

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128 APPENDIX A STATISTICAL ANALYSIS AND DATA FOR CHAPTER 2 One week Comparison Program proc sort data = work. week1_twors; by season tmt rep; proc glm data = work.week1_twors; by season; class tmt rep season; model wkly_irr=tmt rep; lsmeans tmt / pdiff =all; run; Data Season Month Week TMT REP PLOT BLK WKLY_IRR JunAug 08 Jul 1 1 1 4 4 19.1171 JunAug 08 Jul 2 1 1 4 4 7.52148 JunAug 08 Jul 3 1 1 4 4 28.20555 JunAug 08 Jul 4 1 1 4 4 17.55012 JunAug 08 Aug 5 1 1 4 4 7.208085 JunAug 08 Aug 6 1 1 4 4 20.37068 JunAug 08 Aug 7 1 1 4 4 5.01432 JunAug 08 Aug 8 1 1 4 4 20.37068 JunAug 08 Aug 9 1 1 4 4 9.715245 Sep Nov 08 Sep 10 1 1 4 4 0 Sep Nov 08 Sep 11 1 1 4 4 13.16259 Sep Nov 08 Sep 12 1 1 4 4 24.13142 Sep Nov 08 Sep 13 1 1 4 4 22.87784 Sep Nov 08 Oct 14 1 1 4 4 23.19123 Sep Nov 08 Oct 15 1 1 4 4 21.31086 Sep Nov 08 Oct 16 1 1 4 4 20.37068 Sep Nov 08 Oct 17 1 1 4 4 17.23673 Sep Nov 08 Oct 18 1 1 4 4 15.66975 Sep Nov 08 Nov 19 1 1 4 4 15.98315 Sep Nov 08 Nov 20 1 1 4 4 18.49031 Sep Nov 08 Nov 21 1 1 4 4 15.04296 Sep Nov 08 Nov 22 1 1 4 4 16.60994 Dec Feb 0809 Dec 23 1 1 4 4 14.72957 Dec Feb 0809 Dec 24 1 1 4 4 9.276492 Dec Feb 0809 Dec 25 1 1 4 4 15.98315

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129 Dec Feb 0809 Dec 26 1 1 4 4 15.98315 Dec Feb 0809 Jan 27 1 1 4 4 15.66975 Dec Feb 0809 Jan 28 1 1 4 4 11.28222 Dec Feb 0809 Jan 29 1 1 4 4 0 Dec Feb 0809 Jan 30 1 1 4 4 16.29654 Dec Feb 0809 Feb 31 1 1 4 4 0 Dec Feb 0809 Feb 32 1 1 4 4 9.40185 Dec Feb 0809 Feb 33 1 1 4 4 11.28222 Dec Feb 0809 Feb 34 1 1 4 4 10.96883 Mar May 09 Mar 35 1 1 4 4 16.92333 Mar May 09 Mar 36 1 1 4 4 20.68407 Mar May 09 Mar 37 1 1 4 4 22.25105 Mar May 09 Mar 38 1 1 4 4 20.37068 Mar May 09 Mar 39 1 1 4 4 15.04296 Mar May 09 Apr 40 1 1 4 4 20.37068 Mar May 09 Apr 41 1 1 4 4 16.92333 Mar May 09 Apr 42 1 1 4 4 17.23673 Mar May 09 Apr 43 1 1 4 4 32.90648 Mar May 09 May 44 1 1 4 4 31.96629 Mar May 09 May 45 1 1 4 4 31.96629 Mar May 09 May 46 1 1 4 4 4.700925 Mar May 09 May 47 1 1 4 4 5.01432 Mar May 09 May 48 1 1 4 4 0 JunAug 09 Jun 49 1 1 4 4 13.78938 JunAug 09 Jun 50 1 1 4 4 32.27969 JunAug 09 Jun 51 1 1 4 4 4.38753 JunAug 09 Jun 52 1 1 4 4 0 JunAug 09 Jul 53 1 1 4 4 9.40185 JunAug 09 Jul 54 1 1 4 4 7. 208085 JunAug 09 Jul 55 1 1 4 4 27.89216 JunAug 09 Jul 56 1 1 4 4 29.77253 JunAug 09 Aug 57 1 1 4 4 29.45913 JunAug 09 Aug 58 1 1 4 4 15.98315 JunAug 09 Aug 59 1 1 4 4 7.834875 JunAug 09 Aug 60 1 1 4 4 12.22241

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130 JunAug 09 Aug 61 1 1 4 4 0 JunAug 08 Jul 1 1 2 6 2 20.37068 JunAug 08 Jul 2 1 2 6 2 8.14827 JunAug 08 Jul 3 1 2 6 2 31.02611 JunAug 08 Jul 4 1 2 6 2 18.8037 JunAug 08 Aug 5 1 2 6 2 7.52148 JunAug 08 Aug 6 1 2 6 2 22.25105 JunAug 08 Aug 7 1 2 6 2 13.78938 JunAug 08 Aug 8 1 2 6 2 22.25105 JunAug 08 Aug 9 1 2 6 2 8.14827 Sep Nov 08 Sep 10 1 2 6 2 0 Sep Nov 08 Sep 11 1 2 6 2 13.16259 Sep Nov 08 Sep 12 1 2 6 2 25.385 Sep Nov 08 Sep 13 1 2 6 2 25.0716 Sep Nov 08 Oct 14 1 2 6 2 22.56444 Sep Nov 08 Oct 15 1 2 6 2 23.50463 Sep Nov 08 Oct 16 1 2 6 2 22.56444 Sep Nov 08 Oct 17 1 2 6 2 18.8037 Sep Nov 08 Oct 18 1 2 6 2 17.23673 Sep Nov 08 Nov 19 1 2 6 2 11.90901 Sep Nov 08 Nov 20 1 2 6 2 17.23673 Sep Nov 08 Nov 21 1 2 6 2 15.04296 Sep Nov 08 Nov 22 1 2 6 2 16.60994 Dec Feb 0809 Dec 23 1 2 6 2 14.72957 Dec Feb 0809 Dec 24 1 2 6 2 9.40185 Dec Feb 0809 Dec 25 1 2 6 2 15.98315 Dec Feb 0809 Dec 26 1 2 6 2 20.68407 Dec Feb 0809 Jan 27 1 2 6 2 15.66975 Dec Feb 0809 Jan 28 1 2 6 2 11.28222 Dec Feb 0809 Jan 29 1 2 6 2 0 D ec Feb 0809 Jan 30 1 2 6 2 18.8037 Dec Feb 0809 Feb 31 1 2 6 2 0 Dec Feb 0809 Feb 32 1 2 6 2 13.16259 Dec Feb 0809 Feb 33 1 2 6 2 15.04296

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131 Dec Feb 0809 Feb 34 1 2 6 2 14.41617 Mar May 09 Mar 35 1 2 6 2 20.37068 Mar May 09 Mar 36 1 2 6 2 26.01179 Mar May 09 Mar 37 1 2 6 2 23.81802 Mar May 09 Mar 38 1 2 6 2 21.31086 Mar May 09 Mar 39 1 2 6 2 16.29654 Mar May 09 Apr 40 1 2 6 2 21.93765 Mar May 09 Apr 41 1 2 6 2 16.29654 Mar May 09 Apr 42 1 2 6 2 18.49031 Mar May 09 Apr 43 1 2 6 2 33.53327 Mar May 09 May 44 1 2 6 2 32.27969 Mar May 09 May 45 1 2 6 2 32.59308 Mar May 09 May 46 1 2 6 2 4.700925 Mar May 09 May 47 1 2 6 2 4.700925 Mar May 09 May 48 1 2 6 2 0 JunAug 09 Jun 49 1 2 6 2 14.41617 JunAug 09 Jun 50 1 2 6 2 32.27969 JunAug 09 Jun 51 1 2 6 2 4.700925 JunAug 09 Jun 52 1 2 6 2 0 JunAug 09 Jul 53 1 2 6 2 9.088455 JunAug 09 Jul 54 1 2 6 2 7.208085 JunAug 09 Jul 55 1 2 6 2 28.20555 JunAug 09 Jul 56 1 2 6 2 30.39932 JunAug 09 Aug 57 1 2 6 2 30.08592 JunAug 09 Aug 58 1 2 6 2 16. 29654 JunAug 09 Aug 59 1 2 6 2 7.52148 JunAug 09 Aug 60 1 2 6 2 12.5358 JunAug 09 Aug 61 1 2 6 2 0 JunAug 08 Jul 1 1 3 11 3 21.62426 JunAug 08 Jul 2 1 3 11 3 8.14827 JunAug 08 Jul 3 1 3 11 3 31.02611 JunAug 08 Jul 4 1 3 11 3 19.43049 JunAug 08 Aug 5 1 3 11 3 7.834875 JunAug 08 Aug 6 1 3 11 3 22.56444 JunAug 08 Aug 7 1 3 11 3 14.10278 JunAug 08 Aug 8 1 3 11 3 22.56444 JunAug 08 Aug 9 1 3 11 3 8.77506 Sep Nov 08 Sep 10 1 3 11 3 0 Sep Nov 08 Sep 11 1 3 11 3 13.47599 Sep Nov 08 Sep 12 1 3 11 3 25.385 Sep Nov 08 Sep 13 1 3 11 3 25.385 Sep Nov 08 Oct 14 1 3 11 3 23.50463

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132 Sep Nov 08 Oct 15 1 3 11 3 23.50463 Sep Nov 08 Oct 16 1 3 11 3 22.56444 Sep Nov 08 Oct 17 1 3 11 3 19.43049 Sep Nov 08 Oct 18 1 3 11 3 17.55012 Sep Nov 08 Nov 19 1 3 11 3 11.90901 Sep Nov 08 Nov 20 1 3 11 3 17.86352 Sep Nov 08 Nov 21 1 3 11 3 15.35636 Sep Nov 08 Nov 22 1 3 11 3 16.60994 Dec Feb 0809 Dec 23 1 3 11 3 14.41617 Dec Feb 0809 Dec 24 1 3 11 3 9.715245 Dec Feb 0809 Dec 25 1 3 11 3 15.98315 Dec Feb 0 809 Dec 26 1 3 11 3 20.99747 Dec Feb 0809 Jan 27 1 3 11 3 16.29654 Dec Feb 0809 Jan 28 1 3 11 3 11.59562 Dec Feb 0809 Jan 29 1 3 11 3 0 Dec Feb 0809 Jan 30 1 3 11 3 15.98315 Dec Feb 0809 Feb 31 1 3 11 3 0 Dec Feb 0809 Feb 32 1 3 11 3 11.90901 Dec Fe b 0809 Feb 33 1 3 11 3 15.66975 Dec Feb 0809 Feb 34 1 3 11 3 15.66975 Mar May 09 Mar 35 1 3 11 3 17.23673 Mar May 09 Mar 36 1 3 11 3 22.56444 Mar May 09 Mar 37 1 3 11 3 24.44481 Mar May 09 Mar 38 1 3 11 3 22.25105 Mar May 09 Mar 39 1 3 11 3 16.60994 Mar May 09 Apr 40 1 3 11 3 23.19123 Mar May 09 Apr 41 1 3 11 3 16.92333 Mar May 09 Apr 42 1 3 11 3 20.05728 Mar May 09 Apr 43 1 3 11 3 34.47345 Mar May 09 May 44 1 3 11 3 33.21987 Mar May 09 May 45 1 3 11 3 33.84666 Mar May 09 May 46 1 3 11 3 4.7009 25 Mar May 09 May 47 1 3 11 3 5.01432

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133 Mar May 09 May 48 1 3 11 3 0 JunAug 09 Jun 49 1 3 11 3 14.10278 JunAug 09 Jun 50 1 3 11 3 33.21987 JunAug 09 Jun 51 1 3 11 3 4.700925 JunAug 09 Jun 52 1 3 11 3 0 JunAug 09 Jul 53 1 3 11 3 10.02864 JunAug 09 Jul 54 1 3 11 3 7.52148 JunAug 09 Jul 55 1 3 11 3 29.45913 JunAug 09 Jul 56 1 3 11 3 31.02611 JunAug 09 Aug 57 1 3 11 3 31.02611 JunAug 09 Aug 58 1 3 11 3 16.60994 JunAug 09 Aug 59 1 3 11 3 8.77506 JunAug 09 Aug 60 1 3 11 3 12.8492 JunAug 08 Jul 1 1 4 13 1 20.05728 JunAug 08 Jul 2 1 4 13 1 7.834875 JunAug 08 Jul 3 1 4 13 1 30.39932 JunAug 08 Jul 4 1 4 13 1 19.1171 JunAug 08 Aug 5 1 4 13 1 7.52148 JunAug 08 Aug 6 1 4 13 1 21.93765 JunAug 08 Aug 7 1 4 13 1 14.10278 JunAug 08 Aug 8 1 4 13 1 21.93765 JunAug 08 Aug 9 1 4 13 1 8.14827 Sep Nov 08 Sep 10 1 4 13 1 0 Sep Nov 08 Sep 11 1 4 13 1 12.8492 Sep Nov 08 Sep 12 1 4 13 1 25.69839 Sep Nov 08 Sep 13 1 4 13 1 24.75821 Sep Nov 08 Oct 14 1 4 13 1 22.87784 Sep Nov 08 Oct 15 1 4 13 1 23.19123 Sep Nov 08 Oct 16 1 4 13 1 22.25105 Sep Nov 08 Oct 17 1 4 13 1 19.1171 Sep Nov 08 Oct 18 1 4 13 1 17.23673 Sep Nov 08 Nov 19 1 4 13 1 11.59562 Sep Nov 08 Nov 20 1 4 13 1 17.55012 Sep Nov 08 Nov 21 1 4 13 1 15.04296 Sep Nov 08 Nov 22 1 4 13 1 16.60994 Dec Feb 0809 Dec 23 1 4 13 1 14.41617 Dec Feb 0809 Dec 24 1 4 13 1 9.40185 Dec Feb 0809 Dec 25 1 4 13 1 15.98315 Dec Feb 0809 Dec 26 1 4 13 1 20.68407

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134 Dec Feb 0809 Jan 27 1 4 13 1 15.66975 Dec Feb 0809 Jan 28 1 4 13 1 11.28222 Dec Fe b 0809 Jan 29 1 4 13 1 0 Dec Feb 0809 Jan 30 1 4 13 1 19.1171 Dec Feb 0809 Feb 31 1 4 13 1 0 Dec Feb 0809 Feb 32 1 4 13 1 16.29654 Dec Feb 0809 Feb 33 1 4 13 1 20.68407 Dec Feb 0809 Feb 34 1 4 13 1 20.37068 Mar May 09 Mar 35 1 4 13 1 20.37068 Mar Ma y 09 Mar 36 1 4 13 1 26.32518 Mar May 09 Mar 37 1 4 13 1 23.81802 Mar May 09 Mar 38 1 4 13 1 21.62426 Mar May 09 Mar 39 1 4 13 1 16.29654 Mar May 09 Apr 40 1 4 13 1 22.25105 Mar May 09 Apr 41 1 4 13 1 16.92333 Mar May 09 Apr 42 1 4 13 1 17.55012 Mar May 09 Apr 43 1 4 13 1 33.84666 Mar May 09 May 44 1 4 13 1 32.27969 Mar May 09 May 45 1 4 13 1 32.90648 Mar May 09 May 46 1 4 13 1 4.700925 Mar May 09 May 47 1 4 13 1 5.01432 Mar May 09 May 48 1 4 13 1 0 JunAug 09 Jun 49 1 4 13 1 14.10278 JunAug 09 Jun 50 1 4 13 1 32.59308 JunAug 09 Jun 51 1 4 13 1 4.700925 JunAug 09 Jun 52 1 4 13 1 0 Jun Aug 09 Jul 53 1 4 13 1 9.40185 JunAug 09 Jul 54 1 4 13 1 7.208085 JunAug 09 Jul 55 1 4 13 1 28.51895 JunAug 09 Jul 56 1 4 13 1 30.39932 JunAug 09 Au g 57 1 4 13 1 30.08592 JunAug 09 Aug 58 1 4 13 1 16.29654 JunAug 09 Aug 59 1 4 13 1 8.14827 JunAug 09 Aug 60 1 4 13 1 12.5358 JunAug 09 Aug 61 1 4 13 1 0 JunAug 08 Jul 1 2 1 1 1 0

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135 JunAug 08 Jul 2 2 1 1 1 16.92333 JunAug 08 Jul 3 2 1 1 1 17.86 352 JunAug 08 Jul 4 2 1 1 1 14.10278 JunAug 08 Aug 5 2 1 1 1 0 JunAug 08 Aug 6 2 1 1 1 10.34204 JunAug 08 Aug 7 2 1 1 1 0 JunAug 08 Aug 8 2 1 1 1 0 JunAug 08 Aug 9 2 1 1 1 16.92333 Sep Nov 08 Sep 10 2 1 1 1 0 Sep Nov 08 Sep 11 2 1 1 1 0 Sep Nov 08 Sep 12 2 1 1 1 8.77506 Sep Nov 08 Sep 13 2 1 1 1 30.08592 Sep Nov 08 Oct 14 2 1 1 1 15.04296 Sep Nov 08 Oct 15 2 1 1 1 15.04296 Sep Nov 08 Oct 16 2 1 1 1 15.04296 Sep Nov 08 Oct 17 2 1 1 1 0 Sep Nov 08 Oct 18 2 1 1 1 13.78938 Sep Nov 08 Nov 1 9 2 1 1 1 16.60994 Sep Nov 08 Nov 20 2 1 1 1 0 Sep Nov 08 Nov 21 2 1 1 1 16.60994 Sep Nov 08 Nov 22 2 1 1 1 0 Dec Feb 0809 Dec 23 2 1 1 1 0 Dec Feb 0809 Dec 24 2 1 1 1 16.92333 Dec Feb 0809 Dec 25 2 1 1 1 0 Dec Feb 0809 Dec 26 2 1 1 1 0 Dec Feb 080 9 Jan 27 2 1 1 1 16.60994 Dec Feb 0809 Jan 28 2 1 1 1 0 Dec Feb 0809 Jan 29 2 1 1 1 0 Dec Feb 0809 Jan 30 2 1 1 1 16.60994 Dec Feb 0809 Feb 31 2 1 1 1 0 Dec Feb 0809 Feb 32 2 1 1 1 0 Dec Feb 0809 Feb 33 2 1 1 1 15.04296 Dec Feb 0809 Feb 34 2 1 1 1 0

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136 Mar May 09 Mar 35 2 1 1 1 15.35636 Mar May 09 Mar 36 2 1 1 1 15.98315 Mar May 09 Mar 37 2 1 1 1 27.89216 Mar May 09 Mar 38 2 1 1 1 0 Mar May 09 Mar 39 2 1 1 1 15.04296 Mar May 09 Apr 40 2 1 1 1 16.29654 Mar May 09 Apr 41 2 1 1 1 17.23673 Mar May 0 9 Apr 42 2 1 1 1 7.52148 Mar May 09 Apr 43 2 1 1 1 29.77253 Mar May 09 May 44 2 1 1 1 29.45913 Mar May 09 May 45 2 1 1 1 21.31086 Mar May 09 May 46 2 1 1 1 0 Mar May 09 May 47 2 1 1 1 0 Mar May 09 May 48 2 1 1 1 17.55012 JunAug 09 Jun 49 2 1 1 1 0 JunAug 09 Jun 50 2 1 1 1 34.16006 JunAug 09 Jun 51 2 1 1 1 19.74389 JunAug 09 Jun 52 2 1 1 1 0 JunAug 09 Jul 53 2 1 1 1 0 JunAug 09 Jul 54 2 1 1 1 14.10278 JunAug 09 Jul 55 2 1 1 1 0 JunAug 09 Jul 56 2 1 1 1 15.98315 JunAug 09 Aug 57 2 1 1 1 20.37068 JunAug 09 Aug 58 2 1 1 1 0 JunAug 09 Aug 59 2 1 1 1 0 JunAug 09 Aug 60 2 1 1 1 9.715245 JunAug 09 Aug 61 2 1 1 1 0 JunAug 08 Jul 1 2 2 14 2 0 JunAug 08 Jul 2 2 2 14 2 17.23673 JunAug 08 Jul 3 2 2 14 2 18.8037 JunAug 08 Jul 4 2 2 14 2 14.41617 JunAug 08 Aug 5 2 2 14 2 0 JunAug 08 Aug 6 2 2 14 2 10.65543 JunAug 08 Aug 7 2 2 14 2 0 JunAug 08 Aug 8 2 2 14 2 0 JunAug 08 Aug 9 2 2 14 2 17.55012 Sep Nov 08 Sep 10 2 2 14 2 0 Sep Nov 08 Sep 11 2 2 14 2 0 Sep Nov 08 Sep 12 2 2 14 2 8.77506 Sep Nov 08 Sep 13 2 2 14 2 30.71271 Sep Nov 08 Oct 14 2 2 14 2 15.04296 Sep Nov 08 Oct 15 2 2 14 2 15.04296 Sep Nov 08 Oct 16 2 2 14 2 15.04296

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137 Sep Nov 08 Oct 17 2 2 14 2 0 Sep Nov 08 Oct 18 2 2 14 2 13.78938 Sep Nov 08 Nov 19 2 2 14 2 17.23673 Sep Nov 08 Nov 20 2 2 14 2 0 Sep Nov 08 Nov 21 2 2 14 2 17.55012 Sep Nov 08 Nov 22 2 2 14 2 0 Dec Feb 0809 Dec 23 2 2 14 2 0 Dec Feb 0809 Dec 24 2 2 14 2 17.23673 Dec Feb 0809 Dec 25 2 2 14 2 0 Dec Feb 0809 Dec 26 2 2 14 2 0 Dec Feb 0809 J an 27 2 2 14 2 17.23673 Dec Feb 0809 Jan 28 2 2 14 2 0 Dec Feb 0809 Jan 29 2 2 14 2 0 Dec Feb 0809 Jan 30 2 2 14 2 16.92333 Dec Feb 0809 Feb 31 2 2 14 2 0 Dec Feb 0809 Feb 32 2 2 14 2 0 Dec Feb 0809 Feb 33 2 2 14 2 15.35636 Dec Feb 0809 Feb 34 2 2 1 4 2 0 Mar May 09 Mar 35 2 2 14 2 13.47599 Mar May 09 Mar 36 2 2 14 2 13.47599 Mar May 09 Mar 37 2 2 14 2 25.69839 Mar May 09 Mar 38 2 2 14 2 0 Mar May 09 Mar 39 2 2 14 2 13.47599 Mar May 09 Apr 40 2 2 14 2 15.98315 Mar May 09 Apr 41 2 2 14 2 17.5501 2 Mar May 09 Apr 42 2 2 14 2 8.461665 Mar May 09 Apr 43 2 2 14 2 30.39932 Mar May 09 May 44 2 2 14 2 31.3395 Mar May 09 May 45 2 2 14 2 22.56444 Mar May 09 May 46 2 2 14 2 0 Mar May 09 May 47 2 2 14 2 0 Mar May 09 May 48 2 2 14 2 18.17691 JunAug 0 9 Jun 49 2 2 14 2 0

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138 JunAug 09 Jun 50 2 2 14 2 36.04043 JunAug 09 Jun 51 2 2 14 2 20.37068 JunAug 09 Jun 52 2 2 14 2 0 JunAug 09 Jul 53 2 2 14 2 0 JunAug 09 Jul 54 2 2 14 2 15.35636 JunAug 09 Jul 55 2 2 14 2 0 JunAug 09 Jul 56 2 2 14 2 16.9233 3 JunAug 09 Aug 57 2 2 14 2 21.31086 JunAug 09 Aug 58 2 2 14 2 0 JunAug 09 Aug 59 2 2 14 2 0 JunAug 09 Aug 60 2 2 14 2 10.34204 JunAug 09 Aug 61 2 2 14 2 0 JunAug 08 Jul 1 2 3 16 4 0 JunAug 08 Jul 2 2 3 16 4 17.23673 JunAug 08 Jul 3 2 3 16 4 17.86352 JunAug 08 Jul 4 2 3 16 4 13.78938 JunAug 08 Aug 5 2 3 16 4 0 JunAug 08 Aug 6 2 3 16 4 10.96883 JunAug 08 Aug 7 2 3 16 4 0 JunAug 08 Aug 8 2 3 16 4 0 JunAug 08 Aug 9 2 3 16 4 17.23673 Sep Nov 08 Sep 10 2 3 16 4 0 Sep Nov 08 Sep 11 2 3 16 4 0 Sep Nov 08 Sep 12 2 3 16 4 11.28222 Sep Nov 08 Sep 13 2 3 16 4 29.77253 Sep Nov 08 Oct 14 2 3 16 4 15.04296 Sep Nov 08 Oct 15 2 3 16 4 15.04296 Sep Nov 08 Oct 16 2 3 16 4 15.04296 Sep Nov 08 Oct 17 2 3 16 4 0 Sep Nov 08 Oct 18 2 3 16 4 13. 78938 Sep Nov 08 Nov 19 2 3 16 4 16.92333 Sep Nov 08 Nov 20 2 3 16 4 0 Sep Nov 08 Nov 21 2 3 16 4 17.23673 Sep Nov 08 Nov 22 2 3 16 4 0 Dec Feb 0809 Dec 23 2 3 16 4 0 Dec Feb 0809 Dec 24 2 3 16 4 17.23673 Dec Feb 0809 Dec 25 2 3 16 4 0 Dec Feb 0809 Dec 26 2 3 16 4 0 Dec Feb 0809 Jan 27 2 3 16 4 17.23673

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139 Dec Feb 0809 Jan 28 2 3 16 4 0 Dec Feb 0809 Jan 29 2 3 16 4 0 Dec Feb 0809 Jan 30 2 3 16 4 16.92333 Dec Feb 0809 Feb 31 2 3 16 4 0 Dec Feb 0809 Feb 32 2 3 16 4 0 Dec Feb 0809 Feb 33 2 3 16 4 1 7.55012 Dec Feb 0809 Feb 34 2 3 16 4 0 Mar May 09 Mar 35 2 3 16 4 15.66975 Mar May 09 Mar 36 2 3 16 4 15.66975 Mar May 09 Mar 37 2 3 16 4 22.56444 Mar May 09 Mar 38 2 3 16 4 0 Mar May 09 Mar 39 2 3 16 4 15.04296 Mar May 09 Apr 40 2 3 16 4 16.29654 Mar May 09 Apr 41 2 3 16 4 17.23673 Mar May 09 Apr 42 2 3 16 4 7.52148 Mar May 09 Apr 43 2 3 16 4 31.02611 Mar May 09 May 44 2 3 16 4 31.3395 Mar May 09 May 45 2 3 16 4 22.87784 Mar May 09 May 46 2 3 16 4 0 Mar May 09 May 47 2 3 16 4 0 Mar May 09 Ma y 48 2 3 16 4 18.17691 JunAug 09 Jun 49 2 3 16 4 0 JunAug 09 Jun 50 2 3 16 4 36.66722 JunAug 09 Jun 51 2 3 16 4 20.99747 JunAug 09 Jun 52 2 3 16 4 0 JunAug 09 Jul 53 2 3 16 4 0 JunAug 09 Jul 54 2 3 16 4 15.04296 JunAug 09 Jul 55 2 3 16 4 0 J unAug 09 Jul 56 2 3 16 4 17.23673 JunAug 09 Aug 57 2 3 16 4 21.62426 JunAug 09 Aug 58 2 3 16 4 0 JunAug 09 Aug 59 2 3 16 4 0 JunAug 09 Aug 60 2 3 16 4 10.65543 JunAug 09 Aug 61 2 3 16 4 0 JunAug 08 Jul 1 2 4 19 3 0 JunAug 08 Jul 2 2 4 19 3 1 7.55012 JunAug 08 Jul 3 2 4 19 3 18.8037

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140 JunAug 08 Jul 4 2 4 19 3 14.72957 JunAug 08 Aug 5 2 4 19 3 0 JunAug 08 Aug 6 2 4 19 3 10.65543 JunAug 08 Aug 7 2 4 19 3 0 JunAug 08 Aug 8 2 4 19 3 0 JunAug 08 Aug 9 2 4 19 3 17.55012 Sep Nov 08 Sep 10 2 4 19 3 0 Sep Nov 08 Sep 11 2 4 19 3 0 Sep Nov 08 Sep 12 2 4 19 3 9.40185 Sep Nov 08 Sep 13 2 4 19 3 30.71271 Sep Nov 08 Oct 14 2 4 19 3 15.04296 Sep Nov 08 Oct 15 2 4 19 3 15.04296 Sep Nov 08 Oct 16 2 4 19 3 15.04296 Sep Nov 08 Oct 17 2 4 19 3 0 Sep Nov 08 Oct 18 2 4 19 3 13.78938 Sep Nov 08 Nov 19 2 4 19 3 17.23673 Sep Nov 08 Nov 20 2 4 19 3 0 Sep Nov 08 Nov 21 2 4 19 3 17.55012 Sep Nov 08 Nov 22 2 4 19 3 0 Dec Feb 0809 Dec 23 2 4 19 3 0 Dec Feb 0809 Dec 24 2 4 19 3 17.55012 Dec Feb 0809 Dec 25 2 4 19 3 0 Dec Feb 0809 Dec 26 2 4 19 3 0 Dec Feb 0809 Jan 27 2 4 19 3 15.98315 Dec Feb 0809 Jan 28 2 4 19 3 0 Dec Feb 0809 Jan 29 2 4 19 3 0 Dec Feb 0809 Jan 30 2 4 19 3 17.23673 Dec Feb 0809 Feb 31 2 4 19 3 0 Dec Feb 0809 Feb 32 2 4 19 3 0 Dec Feb 0809 Feb 33 2 4 19 3 18.17691 Dec Feb 0809 Feb 34 2 4 19 3 0 Mar May 09 Mar 35 2 4 19 3 17.23673 Mar May 09 Mar 36 2 4 19 3 17.86352

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141 Mar May 09 Mar 37 2 4 19 3 29.14574 Mar May 09 Mar 38 2 4 19 3 0 Mar May 09 Mar 39 2 4 19 3 15.98315 Mar Ma y 09 Apr 40 2 4 19 3 16.29654 Mar May 09 Apr 41 2 4 19 3 16.92333 Mar May 09 Apr 42 2 4 19 3 7.208085 Mar May 09 Apr 43 2 4 19 3 30.08592 Mar May 09 May 44 2 4 19 3 30.71271 Mar May 09 May 45 2 4 19 3 22.25105 Mar May 09 May 46 2 4 19 3 0 Mar May 09 May 47 2 4 19 3 0 Mar May 09 May 48 2 4 19 3 17.86352 JunAug 09 Jun 49 2 4 19 3 0 JunAug 09 Jun 50 2 4 19 3 35.72703 JunAug 09 Jun 51 2 4 19 3 20.37068 JunAug 09 Jun 52 2 4 19 3 0 JunAug 09 Jul 53 2 4 19 3 0 JunAug 09 Jul 54 2 4 19 3 15.04296 JunAug 09 Jul 55 2 4 19 3 0 JunAug 09 Jul 56 2 4 19 3 16.92333 JunAug 09 Aug 57 2 4 19 3 21.31086 JunAug 09 Aug 58 2 4 19 3 0 JunAug 09 Aug 59 2 4 19 3 0 JunAug 09 Aug 60 2 4 19 3 10.34204 JunAug 09 Aug 61 2 4 19 3 0 JunAug 08 Jul 1 3 1 7 3 2.50716 JunAug 08 Jul 2 3 1 7 3 14.72957 JunAug 08 Jul 3 3 1 7 3 31.02611 JunAug 08 Jul 4 3 1 7 3 13.78938 JunAug 08 Aug 5 3 1 7 3 52.33697 JunAug 08 Aug 6 3 1 7 3 0 JunAug 08 Aug 7 3 1 7 3 14.72957 JunAug 08 Aug 8 3 1 7 3 15.66975 JunAug 08 Aug 9 3 1 7 3 0 Sep Nov 08 Sep 10 3 1 7 3 0 Sep Nov 08 Sep 11 3 1 7 3 0 Sep Nov 08 Sep 12 3 1 7 3 27.26537 Sep Nov 08 Sep 13 3 1 7 3 16.29654 Sep Nov 08 Oct 14 3 1 7 3 18.17691 Sep Nov 08 Oct 15 3 1 7 3 0 Sep Nov 08 Oct 16 3 1 7 3 17.86352 Sep Nov 08 Oct 17 3 1 7 3 0 Sep Nov 08 Oct 18 3 1 7 3 17.86352

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142 Sep Nov 08 Nov 19 3 1 7 3 17.23673 Sep Nov 08 Nov 20 3 1 7 3 0 Sep Nov 08 Nov 21 3 1 7 3 18.49031 Sep Nov 08 Nov 22 3 1 7 3 0 Dec Feb 0809 Dec 23 3 1 7 3 0 Dec Feb 0809 Dec 24 3 1 7 3 0 Dec Feb 0809 Dec 25 3 1 7 3 18.17691 Dec Feb 0809 Dec 26 3 1 7 3 0 Dec Feb 0809 Jan 27 3 1 7 3 17.86352 Dec Feb 0809 Jan 28 3 1 7 3 0 Dec Feb 0809 Jan 29 3 1 7 3 0 Dec Feb 0809 Jan 30 3 1 7 3 16.60994 Dec Feb 0809 Feb 31 3 1 7 3 0 Dec Feb 0809 Feb 32 3 1 7 3 0 Dec Feb 0809 Feb 33 3 1 7 3 16.29654 Dec Feb 0809 Feb 34 3 1 7 3 16.29654 Mar May 09 Mar 35 3 1 7 3 0 Mar May 09 Mar 36 3 1 7 3 32.90648 Mar May 09 Mar 37 3 1 7 3 17.23673 Mar May 09 Mar 38 3 1 7 3 14.72957 Mar May 09 Mar 39 3 1 7 3 15.3563 6 Mar May 09 Apr 40 3 1 7 3 15.66975 Mar May 09 Apr 41 3 1 7 3 20.68407 Mar May 09 Apr 42 3 1 7 3 16.92333 Mar May 09 Apr 43 3 1 7 3 18.8037 Mar May 09 May 44 3 1 7 3 35.41364 Mar May 09 May 45 3 1 7 3 27.89216 Mar May 09 May 46 3 1 7 3 16.60994 Ma r May 09 May 47 3 1 7 3 15.66975 Mar May 09 May 48 3 1 7 3 15.04296 JunAug 09 Jul 53 3 1 7 3 18.8037 JunAug 09 Jul 54 3 1 7 3 0 JunAug 09 Jul 55 3 1 7 3 15.98315

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143 JunAug 09 Jul 56 3 1 7 3 34.16006 JunAug 09 Aug 57 3 1 7 3 15.98315 JunAug 09 Aug 58 3 1 7 3 16.60994 JunAug 09 Aug 59 3 1 7 3 21.93765 JunAug 09 Aug 60 3 1 7 3 10.02864 JunAug 09 Aug 61 3 1 7 3 0 JunAug 08 Jun 3 3 2 10 2 0 JunAug 08 Jun 2 3 2 10 2 0 JunAug 08 Jun 1 3 2 10 2 0 JunAug 08 Jun 0 3 2 10 2 0 JunAug 08 Jul 1 3 2 10 2 1.88037 JunAug 08 Jul 2 3 2 10 2 14.72957 JunAug 08 Jul 3 3 2 10 2 31.02611 JunAug 08 Jul 4 3 2 10 2 14.10278 JunAug 08 Aug 5 3 2 10 2 12.22241 JunAug 08 Aug 6 3 2 10 2 0 JunAug 08 Aug 7 3 2 10 2 14.72957 JunAug 08 Aug 8 3 2 10 2 15.98315 JunAug 08 Aug 9 3 2 10 2 0 Sep Nov 08 Sep 10 3 2 10 2 0 Sep Nov 08 Sep 11 3 2 10 2 0 Sep Nov 08 Sep 12 3 2 10 2 27.57876 Sep Nov 08 Sep 13 3 2 10 2 16.29654 Sep Nov 08 Oct 14 3 2 10 2 18.17691 Sep Nov 08 Oct 15 3 2 10 2 0 Sep Nov 08 Oct 1 6 3 2 10 2 18.17691 Sep Nov 08 Oct 17 3 2 10 2 0 Sep Nov 08 Oct 18 3 2 10 2 18.17691 Sep Nov 08 Nov 19 3 2 10 2 16.60994 Sep Nov 08 Nov 20 3 2 10 2 0 Sep Nov 08 Nov 21 3 2 10 2 18.49031 Sep Nov 08 Nov 22 3 2 10 2 0 Dec Feb 0809 Dec 23 3 2 10 2 0 De c Feb 0809 Dec 24 3 2 10 2 0 Dec Feb 0809 Dec 25 3 2 10 2 18.49031 Dec Feb 0809 Dec 26 3 2 10 2 0 Dec Feb 0809 Jan 27 3 2 10 2 17.55012 Dec Feb 0809 Jan 28 3 2 10 2 0

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144 Dec Feb 0809 Jan 29 3 2 10 2 0 Dec Feb 0809 Jan 30 3 2 10 2 17.86352 Dec Feb 0809 Feb 31 3 2 10 2 0 Dec Feb 0809 Feb 32 3 2 10 2 0 Dec Feb 0809 Feb 33 3 2 10 2 16.60994 Dec Feb 0809 Feb 34 3 2 10 2 16.29654 Mar May 09 Mar 35 3 2 10 2 0 Mar May 09 Mar 36 3 2 10 2 31.96629 Mar May 09 Mar 37 3 2 10 2 16.29654 Mar May 09 Mar 38 3 2 1 0 2 13.78938 Mar May 09 Mar 39 3 2 10 2 15.35636 Mar May 09 Apr 40 3 2 10 2 15.04296 Mar May 09 Apr 41 3 2 10 2 18.8037 Mar May 09 Apr 42 3 2 10 2 16.92333 Mar May 09 Apr 43 3 2 10 2 17.86352 Mar May 09 May 44 3 2 10 2 34.47345 Mar May 09 May 45 3 2 10 2 26.01179 Mar May 09 May 46 3 2 10 2 16.29654 Mar May 09 May 47 3 2 10 2 15.04296 Mar May 09 May 48 3 2 10 2 14.10278 JunAug 09 Jul 53 3 2 10 2 18.17691 JunAug 09 Jul 54 3 2 10 2 0 JunAug 09 Jul 55 3 2 10 2 15.04296 JunAug 09 Jul 56 3 2 10 2 31.3395 JunAug 09 Aug 57 3 2 10 2 15.66975 JunAug 09 Aug 58 3 2 10 2 15.66975 JunAug 09 Aug 59 3 2 10 2 18.49031 JunAug 09 Aug 60 3 2 10 2 9.088455 JunAug 09 Aug 61 3 2 10 2 0 JunAug 08 Jun 3 3 3 12 4 0 JunAug 08 Jun 2 3 3 12 4 0 JunAug 08 Jun 1 3 3 12 4 0 JunAug 08 Jun 0 3 3 12 4 0 JunAug 08 Jul 1 3 3 12 4 2.50716 JunAug 08 Jul 2 3 3 12 4 14.41617 JunAug 08 Jul 3 3 3 12 4 32.27969 JunAug 08 Jul 4 3 3 12 4 14.41617 JunAug 08 Aug 5 3 3 12 4 13.16259

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145 JunAug 08 Aug 6 3 3 12 4 0 JunAug 08 Aug 7 3 3 12 4 15.35636 JunAug 08 Aug 8 3 3 12 4 16.60994 JunAug 08 Aug 9 3 3 12 4 0 Sep Nov 08 Sep 10 3 3 12 4 0 Sep Nov 08 Sep 11 3 3 12 4 0 Sep Nov 08 Sep 12 3 3 12 4 27.89216 Sep Nov 08 Sep 13 3 3 12 4 16.29654 Sep Nov 08 Oct 14 3 3 12 4 18.17691 Sep Nov 08 Oct 15 3 3 12 4 0 Sep Nov 08 Oct 16 3 3 12 4 18.8037 Sep Nov 08 Oct 17 3 3 12 4 0 Sep Nov 08 Oct 18 3 3 12 4 18.8037 Sep Nov 08 Nov 19 3 3 12 4 17.55012 Sep Nov 08 Nov 20 3 3 12 4 0 Sep Nov 08 Nov 21 3 3 12 4 18.49031 Sep Nov 08 Nov 22 3 3 12 4 0 Dec Feb 0809 Dec 23 3 3 12 4 0 Dec Feb 0809 Dec 24 3 3 12 4 0 Dec Feb 0809 Dec 25 3 3 12 4 18.49031 Dec Feb 0809 Dec 26 3 3 12 4 0 Dec Feb 0809 Jan 27 3 3 12 4 17.86352 Dec Feb 0809 Jan 28 3 3 12 4 0 Dec Feb 0809 Jan 29 3 3 12 4 0 Dec Feb 0809 Jan 30 3 3 12 4 18.49031 Dec Feb 0809 Feb 31 3 3 12 4 0 Dec Feb 0809 Feb 32 3 3 12 4 0 Dec Feb 0809 Feb 33 3 3 12 4 18.17691 Dec Feb 0809 Feb 34 3 3 12 4 18.17691 Mar May 09 Mar 35 3 3 12 4 0 Mar May 09 Mar 36 3 3 12 4 34.47345 Mar May 09 Mar 37 3 3 12 4 17.55012 Mar May 09 Mar 38 3 3 12 4 14.72957

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146 Mar May 09 Mar 39 3 3 12 4 16.29654 Mar May 09 Apr 40 3 3 12 4 15.35636 Mar May 09 Apr 41 3 3 12 4 19.1171 Mar May 09 Apr 42 3 3 12 4 17.23673 Mar May 09 Apr 43 3 3 12 4 18.49031 Mar May 09 May 44 3 3 12 4 34.47345 Mar May 09 May 45 3 3 12 4 26.01179 Mar May 09 May 46 3 3 12 4 16.29654 Mar May 09 May 47 3 3 12 4 13.78938 Mar May 09 May 48 3 3 12 4 13.16259 JunAug 09 Jun 49 3 3 12 4 14.10278 JunAug 09 Jun 50 3 3 12 4 1 6.29654 JunAug 09 Jun 51 3 3 12 4 14.10278 JunAug 09 Jun 52 3 3 12 4 15.35636 JunAug 09 Jul 53 3 3 12 4 16.29654 JunAug 09 Jul 54 3 3 12 4 0 JunAug 09 Jul 55 3 3 12 4 13.78938 JunAug 09 Jul 56 3 3 12 4 29.45913 JunAug 09 Aug 57 3 3 12 4 14.10 278 JunAug 09 Aug 58 3 3 12 4 14.41617 JunAug 09 Aug 59 3 3 12 4 18.49031 JunAug 09 Aug 60 3 3 12 4 8.461665 JunAug 09 Aug 61 3 3 12 4 0 Jun Aug 08 Jul 1 3 4 17 1 2.50716 JunAug 08 Jul 2 3 4 17 1 15.04296 JunAug 08 Jul 3 3 4 17 1 31.6529 JunAug 08 Jul 4 3 4 17 1 13.78938 JunAug 08 Aug 5 3 4 17 1 13.16259 JunAug 08 Aug 6 3 4 17 1 0 JunAug 08 Aug 7 3 4 17 1 14.72957 JunAug 08 Aug 8 3 4 17 1 15.98315 JunAug 08 Aug 9 3 4 17 1 0 Sep Nov 08 Sep 10 3 4 17 1 0 Sep Nov 08 Sep 11 3 4 17 1 0 Sep Nov 08 Sep 12 3 4 17 1 28.51895 Sep Nov 08 Sep 13 3 4 17 1 16.60994 Sep Nov 08 Oct 14 3 4 17 1 18.49031 Sep Nov 08 Oct 15 3 4 17 1 0 Sep Nov 08 Oct 16 3 4 17 1 17.86352 Sep Nov 08 Oct 17 3 4 17 1 0 Sep Nov 08 Oct 18 3 4 17 1 18.49031 Sep Nov 0 8 Nov 19 3 4 17 1 17.23673 Sep Nov 08 Nov 20 3 4 17 1 0

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147 Sep Nov 08 Nov 21 3 4 17 1 18.49031 Sep Nov 08 Nov 22 3 4 17 1 0 Dec Feb 0809 Dec 23 3 4 17 1 0 Dec Feb 0809 Dec 24 3 4 17 1 0 Dec Feb 0809 Dec 25 3 4 17 1 18.17691 Dec Feb 0809 Dec 26 3 4 17 1 0 Dec Feb 0809 Jan 27 3 4 17 1 18.17691 Dec Feb 0809 Jan 28 3 4 17 1 0 Dec Feb 0809 Jan 29 3 4 17 1 0 Dec Feb 0809 Jan 30 3 4 17 1 17.86352 Dec Feb 0809 Feb 31 3 4 17 1 0 Dec Feb 0809 Feb 32 3 4 17 1 0 Dec Feb 0809 Feb 33 3 4 17 1 17.55012 Dec Feb 0809 Feb 34 3 4 17 1 17.55012 Mar May 09 Mar 35 3 4 17 1 0 Mar May 09 Mar 36 3 4 17 1 33.21987 Mar May 09 Mar 37 3 4 17 1 17.23673 Mar May 09 Mar 38 3 4 17 1 14.72957 Mar May 09 Mar 39 3 4 17 1 15.04296 Mar May 09 Apr 40 3 4 17 1 15.35636 Mar May 0 9 Apr 41 3 4 17 1 20.99747 Mar May 09 Apr 42 3 4 17 1 16.92333 Mar May 09 Apr 43 3 4 17 1 17.86352 Mar May 09 May 44 3 4 17 1 34.16006 Mar May 09 May 45 3 4 17 1 26.95197 Mar May 09 May 46 3 4 17 1 15.66975 Mar May 09 May 47 3 4 17 1 15.35636 Mar Ma y 09 May 48 3 4 17 1 13.78938 JunAug 09 Jun 49 3 4 17 1 15.98315 JunAug 09 Jun 50 3 4 17 1 17.86352 JunAug 09 Jun 51 3 4 17 1 53.90394 JunAug 09 Jun 52 3 4 17 1 13.16259 JunAug 09 Jul 53 3 4 17 1 13.78938

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148 JunAug 09 Jul 54 3 4 17 1 0 JunAug 09 Jul 55 3 4 17 1 11.59562 JunAug 09 Jul 56 3 4 17 1 25.385 JunAug 09 Aug 57 3 4 17 1 11.90901 JunAug 09 Aug 58 3 4 17 1 12.8492 JunAug 09 Aug 59 3 4 17 1 17.55012 JunAug 09 Aug 60 3 4 17 1 7.834875 JunAug 09 Aug 61 3 4 17 1 0 JunAug 08 Jul 1 4 1 2 2 21.62426 JunAug 08 Jul 2 4 1 2 2 20.99747 JunAug 08 Jul 3 4 1 2 2 42.93512 JunAug 08 Jul 4 4 1 2 2 21.31086 JunAug 08 Aug 5 4 1 2 2 21.62426 JunAug 08 Aug 6 4 1 2 2 42.30833 JunAug 08 Aug 7 4 1 2 2 22.87784 JunAug 08 Aug 8 4 1 2 2 0 JunAug 08 Aug 9 4 1 2 2 0 Sep Nov 08 Sep 10 4 1 2 2 29.58449 Sep Nov 08 Sep 11 4 1 2 2 29.58449 Sep Nov 08 Sep 12 4 1 2 2 29.58449 Sep Nov 08 Sep 13 4 1 2 2 29.58449 Sep Nov 08 Oct 14 4 1 2 2 14.79224 Sep Nov 08 Oct 15 4 1 2 2 14.72957 Sep Nov 08 O ct 16 4 1 2 2 30.08592 Sep Nov 08 Oct 17 4 1 2 2 13.47599 Sep Nov 08 Oct 18 4 1 2 2 30.39932 Sep Nov 08 Nov 19 4 1 2 2 24.44481 Sep Nov 08 Nov 20 4 1 2 2 30.71271 Sep Nov 08 Nov 21 4 1 2 2 30.71271 Sep Nov 08 Nov 22 4 1 2 2 29.77253 Dec Feb 0809 Dec 23 4 1 2 2 25.385 Dec Feb 0809 Dec 24 4 1 2 2 25.69839 Dec Feb 0809 Dec 25 4 1 2 2 25.69839 Dec Feb 0809 Dec 26 4 1 2 2 37.9208 Dec Feb 0809 Jan 27 4 1 2 2 26.01179 Dec Feb 0809 Jan 28 4 1 2 2 7.208085 Dec Feb 0809 Jan 29 4 1 2 2 0

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149 Dec Feb 0809 Jan 30 4 1 2 2 0 Dec Feb 0809 Feb 31 4 1 2 2 0 Dec Feb 0809 Feb 32 4 1 2 2 21.93765 Dec Feb 0809 Feb 33 4 1 2 2 21.93765 Dec Feb 0809 Feb 34 4 1 2 2 21.62426 Mar May 09 Mar 35 4 1 2 2 30.39932 Mar May 09 Mar 36 4 1 2 2 31.02611 Mar May 09 Mar 37 4 1 2 2 25.0716 Mar May 09 Mar 38 4 1 2 2 23.81802 Mar May 09 Mar 39 4 1 2 2 28.83234 Mar May 09 Apr 40 4 1 2 2 27.89216 Mar May 09 Apr 41 4 1 2 2 7.52148 Mar May 09 Apr 42 4 1 2 2 16.60994 Mar May 09 Apr 43 4 1 2 2 33.84666 Mar May 09 May 44 4 1 2 2 29.14574 Mar May 09 May 45 4 1 2 2 28.20555 Mar May 09 May 46 4 1 2 2 15.35636 Mar May 09 May 47 4 1 2 2 0 Mar May 09 May 48 4 1 2 2 15.04296 JunAug 09 Jun 49 4 1 2 2 28.51895 JunAug 09 Jun 50 4 1 2 2 27.57876 JunAug 09 Jun 51 4 1 2 2 13.78938 JunA ug 09 Jun 52 4 1 2 2 0 JunAug 09 Jul 53 4 1 2 2 25.69839 JunAug 09 Jul 54 4 1 2 2 19.1171 JunAug 09 Jul 55 4 1 2 2 40.11456 JunAug 09 Jul 56 4 1 2 2 40.42796 JunAug 09 Aug 57 4 1 2 2 19.43049 JunAug 09 Aug 58 4 1 2 2 0 JunAug 09 Aug 59 4 1 2 2 23.81802 JunAug 09 Aug 60 4 1 2 2 0 JunAug 09 Aug 61 4 1 2 2 0 JunAug 08 Jul 1 4 2 5 1 20.99747 JunAug 08 Jul 2 4 2 5 1 20.99747 JunAug 08 Jul 3 4 2 5 1 41.99493 JunAug 08 Jul 4 4 2 5 1 22.25105 JunAug 08 Aug 5 4 2 5 1 22.25105 JunAug 08 Aug 6 4 2 5 1 42.62172 JunAug 08 Aug 7 4 2 5 1 23.81802

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150 JunAug 08 Aug 8 4 2 5 1 0 JunAug 08 Aug 9 4 2 5 1 0 Sep Nov 08 Sep 10 4 2 5 1 29.58449 Sep Nov 08 Sep 11 4 2 5 1 29.58449 Sep Nov 08 Sep 12 4 2 5 1 29.58449 Sep Nov 08 Sep 13 4 2 5 1 29.5844 9 Sep Nov 08 Oct 14 4 2 5 1 14.79224 Sep Nov 08 Oct 15 4 2 5 1 15.98315 Sep Nov 08 Oct 16 4 2 5 1 30.39932 Sep Nov 08 Oct 17 4 2 5 1 13.47599 Sep Nov 08 Oct 18 4 2 5 1 30.71271 Sep Nov 08 Nov 19 4 2 5 1 25.69839 Sep Nov 08 Nov 20 4 2 5 1 30.39932 S ep Nov 08 Nov 21 4 2 5 1 31.3395 Sep Nov 08 Nov 22 4 2 5 1 29.45913 Dec Feb 0809 Dec 23 4 2 5 1 26.32518 Dec Feb 0809 Dec 24 4 2 5 1 26.01179 Dec Feb 0809 Dec 25 4 2 5 1 25.69839 Dec Feb 0809 Dec 26 4 2 5 1 36.98061 Dec Feb 0809 Jan 27 4 2 5 1 24.75821 Dec Feb 0809 Jan 28 4 2 5 1 7.52148 Dec Feb 0809 Jan 29 4 2 5 1 0 Dec Feb 0809 Jan 30 4 2 5 1 0 Dec Feb 0809 Feb 31 4 2 5 1 0 Dec Feb 0809 Feb 32 4 2 5 1 18.8037 Dec Feb 0809 Feb 33 4 2 5 1 16.92333 Dec Feb 0809 Feb 34 4 2 5 1 16.29654 Mar May 0 9 Mar 35 4 2 5 1 31.3395 Mar May 09 Mar 36 4 2 5 1 31.3395 Mar May 09 Mar 37 4 2 5 1 24.44481 Mar May 09 Mar 38 4 2 5 1 24.13142 Mar May 09 Mar 39 4 2 5 1 29.14574 Mar May 09 Apr 40 4 2 5 1 28.83234

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151 Mar May 09 Apr 41 4 2 5 1 5.327715 Mar May 09 Apr 42 4 2 5 1 18.8037 Mar May 09 Apr 43 4 2 5 1 31.6529 Mar May 09 May 44 4 2 5 1 28.20555 Mar May 09 May 45 4 2 5 1 26.63858 Mar May 09 May 46 4 2 5 1 15.04296 Mar May 09 May 47 4 2 5 1 0 Mar May 09 May 48 4 2 5 1 15.35636 JunAug 09 Jun 49 4 2 5 1 28.83234 JunAug 09 Jun 50 4 2 5 1 27.89216 JunAug 09 Jun 51 4 2 5 1 13.78938 JunAug 09 Jun 52 4 2 5 1 0 JunAug 09 Jul 53 4 2 5 1 24.13142 JunAug 09 Jul 54 4 2 5 1 18.17691 JunAug 09 Jul 55 4 2 5 1 38.86098 JunAug 09 Jul 56 4 2 5 1 40.11456 JunAug 09 Aug 57 4 2 5 1 18.17691 JunAug 09 Aug 58 4 2 5 1 0 JunAug 09 Aug 59 4 2 5 1 23.81802 JunAug 09 Aug 60 4 2 5 1 0 JunAug 09 Aug 61 4 2 5 1 0 JunAug 08 Jul 1 4 3 15 3 22.56444 JunAug 08 Jul 2 4 3 15 3 21.62426 JunAug 08 Jul 3 4 3 15 3 44.1887 JunAug 08 Jul 4 4 3 15 3 21.62426 JunAug 08 Aug 5 4 3 15 3 22.56444 JunAug 08 Aug 6 4 3 15 3 42.93512 JunAug 08 Aug 7 4 3 15 3 23.50463 JunAug 08 Aug 8 4 3 15 3 0 JunAug 08 Aug 9 4 3 15 3 0 Sep Nov 08 Sep 10 4 3 15 3 29.58449 Sep Nov 08 Sep 11 4 3 15 3 29.58449 Sep Nov 08 Sep 12 4 3 15 3 29.58449 Sep Nov 08 Sep 13 4 3 15 3 29.58449 Sep Nov 08 Oct 14 4 3 15 3 14.79224 Sep Nov 08 Oct 15 4 3 15 3 15.35636 Sep Nov 08 Oct 16 4 3 15 3 31.3395 Sep Nov 08 Oct 17 4 3 15 3 13.78938 Sep Nov 08 Oct 18 4 3 15 3 31.3395 Sep Nov 08 Nov 19 4 3 15 3 24.75821 Sep Nov 08 Nov 20 4 3 15 3 31.6529 Sep Nov 08 Nov 21 4 3 15 3 32.27969 Sep Nov 08 Nov 22 4 3 15 3 30.71271

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152 Dec Feb 0809 Dec 23 4 3 15 3 26.95197 Dec Feb 0809 Dec 24 4 3 15 3 26.95197 Dec Feb 0809 Dec 25 4 3 15 3 26.95197 Dec Feb 0809 Dec 26 4 3 15 3 38.86098 Dec Feb 0809 Jan 27 4 3 15 3 26.95197 Dec Feb 0809 Jan 28 4 3 15 3 7.52148 Dec Feb 0809 Jan 29 4 3 15 3 0 Dec Feb 0809 Jan 30 4 3 15 3 0 Dec Feb 0809 Feb 31 4 3 15 3 0 Dec Feb 0809 Feb 32 4 3 15 3 21.31086 Dec Feb 0809 Feb 33 4 3 15 3 19.1171 Dec Feb 0809 Feb 34 4 3 15 3 18.49031 Mar May 09 Mar 35 4 3 15 3 27.57876 Mar May 09 Mar 36 4 3 15 3 26.95197 Mar May 09 Mar 37 4 3 15 3 24.75821 Mar May 09 Mar 38 4 3 15 3 23.50463 Mar May 09 Mar 39 4 3 15 3 28.83234 Mar May 09 Apr 40 4 3 15 3 28.20555 Mar May 09 Apr 41 4 3 15 3 7.52148 Mar May 09 Apr 42 4 3 15 3 53.90394 Mar May 09 Apr 43 4 3 15 3 32.59308 Mar May 09 May 44 4 3 15 3 29.14574 Mar May 09 May 45 4 3 15 3 28.20555 Mar May 09 May 46 4 3 15 3 15.35636 Mar May 09 May 47 4 3 15 3 0 Mar May 09 May 48 4 3 15 3 15.35636 JunAug 09 Jun 49 4 3 15 3 29.14574 JunAug 09 Jun 50 4 3 15 3 27.89216 JunAug 09 Jun 51 4 3 15 3 14.10278 JunAug 09 Jun 52 4 3 15 3 0 JunAug 0 9 Jul 53 4 3 15 3 26.01179 JunAug 09 Jul 54 4 3 15 3 19.43049 JunAug 09 Jul 55 4 3 15 3 40.42796

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153 JunAug 09 Jul 56 4 3 15 3 41.05475 JunAug 09 Aug 57 4 3 15 3 19.43049 JunAug 09 Aug 58 4 3 15 3 0 JunAug 09 Aug 59 4 3 15 3 24.13142 JunAug 09 Au g 60 4 3 15 3 0 JunAug 09 Aug 61 4 3 15 3 0 JunAug 08 Jul 1 4 4 20 4 22.56444 JunAug 08 Jul 2 4 4 20 4 21.31086 JunAug 08 Jul 3 4 4 20 4 43.8753 JunAug 08 Jul 4 4 4 20 4 21.31086 JunAug 08 Aug 5 4 4 20 4 21.93765 JunAug 08 Aug 6 4 4 20 4 41.36814 JunAug 08 Aug 7 4 4 20 4 22.87784 JunAug 08 Aug 8 4 4 20 4 0 JunAug 08 Aug 9 4 4 20 4 0 Sep Nov 08 Sep 10 4 4 20 4 29.58449 Sep Nov 08 Sep 11 4 4 20 4 29.58449 Sep Nov 08 Sep 12 4 4 20 4 29.58449 Sep Nov 08 Sep 13 4 4 20 4 29.58449 Sep Nov 08 Oct 14 4 4 20 4 14.79224 Sep Nov 08 Oct 15 4 4 20 4 14.72957 Sep Nov 08 Oct 16 4 4 20 4 30.71271 Sep Nov 08 Oct 17 4 4 20 4 13.47599 Sep Nov 08 Oct 18 4 4 20 4 30.39932 Sep Nov 08 Nov 19 4 4 20 4 24.13142 Sep Nov 08 Nov 20 4 4 20 4 30.71271 Sep N ov 08 Nov 21 4 4 20 4 31.6529 Sep Nov 08 Nov 22 4 4 20 4 29.77253 Dec Feb 0809 Dec 23 4 4 20 4 25.69839 Dec Feb 0809 Dec 24 4 4 20 4 26.01179 Dec Feb 0809 Dec 25 4 4 20 4 26.32518 Dec Feb 0809 Dec 26 4 4 20 4 37.9208 Dec Feb 0809 Jan 27 4 4 20 4 25.69839 Dec Feb 0809 Jan 28 4 4 20 4 7.208085 Dec Feb 0809 Jan 29 4 4 20 4 0 Dec Feb 0809 Jan 30 4 4 20 4 0

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154 Dec Feb 0809 Feb 31 4 4 20 4 0 Dec Feb 0809 Feb 32 4 4 20 4 21.31086 Dec Feb 0809 Feb 33 4 4 20 4 20.05728 Dec Feb 0809 Feb 34 4 4 20 4 19.74389 Mar May 09 Mar 35 4 4 20 4 31.02611 Mar May 09 Mar 36 4 4 20 4 30.71271 Mar May 09 Mar 37 4 4 20 4 24.44481 Mar May 09 Mar 38 4 4 20 4 23.50463 Mar May 09 Mar 39 4 4 20 4 28.83234 Mar May 09 Apr 40 4 4 20 4 28.51895 Mar May 09 Apr 41 4 4 20 4 6.581 295 Mar May 09 Apr 42 4 4 20 4 15.04296 Mar May 09 Apr 43 4 4 20 4 33.53327 Mar May 09 May 44 4 4 20 4 28.51895 Mar May 09 May 45 4 4 20 4 27.26537 Mar May 09 May 46 4 4 20 4 15.35636 Mar May 09 May 47 4 4 20 4 0 Mar May 09 May 48 4 4 20 4 15.35636 JunAug 09 Jun 49 4 4 20 4 29.14574 JunAug 09 Jun 50 4 4 20 4 28.20555 JunAug 09 Jun 51 4 4 20 4 14.10278 JunAug 09 Jun 52 4 4 20 4 0 JunAug 09 Jul 53 4 4 20 4 25.69839 JunAug 09 Jul 54 4 4 20 4 18.8037 JunAug 09 Jul 55 4 4 20 4 40.42796 JunAug 09 Jul 56 4 4 20 4 40.74135 JunAug 09 Aug 57 4 4 20 4 19.1171 JunAug 09 Aug 58 4 4 20 4 0 JunAug 09 Aug 59 4 4 20 4 24.13142 JunAug 09 Aug 60 4 4 20 4 0 JunAug 09 Aug 61 4 4 20 4 0 JunAug 08 Jul 1 5 1 3 3 13.16259 JunAug 08 Jul 2 5 1 3 3 12.8492 JunAug 08 Jul 3 5 1 3 3 26.32518 JunAug 08 Jul 4 5 1 3 3 12.8492 JunAug 08 Aug 5 5 1 3 3 13.16259 JunAug 08 Aug 6 5 1 3 3 26.01179 JunAug 08 Aug 7 5 1 3 3 14.72957 JunAug 08 Aug 8 5 1 3 3 0

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155 JunAug 08 Aug 9 5 1 3 3 0 Sep Nov 08 Sep 10 5 1 3 3 17.55012 Sep Nov 08 Sep 11 5 1 3 3 17.55012 Sep Nov 08 Sep 12 5 1 3 3 18.49031 Sep Nov 08 Sep 13 5 1 3 3 18.49031 Sep Nov 08 Oct 14 5 1 3 3 9.715245 Sep Nov 08 Oct 15 5 1 3 3 9.088455 Sep Nov 08 Oct 16 5 1 3 3 18.49031 Sep Nov 08 Oct 17 5 1 3 3 9.40185 Sep Nov 08 Oct 18 5 1 3 3 18.8037 Sep Nov 08 Nov 19 5 1 3 3 15.35636 Sep Nov 08 Nov 20 5 1 3 3 19.1171 Sep Nov 08 Nov 21 5 1 3 3 19.43049 Sep Nov 08 Nov 22 5 1 3 3 19.43049 Dec Feb 0809 Dec 23 5 1 3 3 16.60994 Dec Feb 0809 Dec 24 5 1 3 3 16.29654 Dec Feb 0809 Dec 25 5 1 3 3 16.60994 Dec Feb 0809 Dec 26 5 1 3 3 24.75821 Dec Feb 0809 Jan 27 5 1 3 3 16.29654 Dec Feb 0809 Jan 28 5 1 3 3 5.954505 Dec Feb 0809 Jan 29 5 1 3 3 0 Dec Feb 0809 Jan 30 5 1 3 3 0 Dec Feb 0809 Feb 31 5 1 3 3 0 Dec Feb 0809 Feb 32 5 1 3 3 13.16259 Dec Feb 0809 Feb 33 5 1 3 3 13.47599 Dec Feb 0809 Feb 34 5 1 3 3 13.78938 Mar May 09 Mar 35 5 1 3 3 19.74389 Mar May 09 Mar 36 5 1 3 3 19.1171 Mar May 09 Mar 37 5 1 3 3 9.40185 Mar May 09 Mar 38 5 1 3 3 17.23673 Mar May 09 Mar 39 5 1 3 3 19.1171 Mar May 09 Apr 40 5 1 3 3 17.55012 Mar May 09 Apr 41 5 1 3 3 4.38753

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156 Mar May 09 Apr 42 5 1 3 3 15.04296 Mar May 09 Apr 43 5 1 3 3 20.37068 Mar May 09 May 44 5 1 3 3 17.23673 Mar May 09 May 45 5 1 3 3 16.29654 Mar M ay 09 May 46 5 1 3 3 9.088455 Mar May 09 May 47 5 1 3 3 0 Mar May 09 May 48 5 1 3 3 9.088455 JunAug 09 Jun 49 5 1 3 3 17.23673 JunAug 09 Jun 50 5 1 3 3 16.92333 JunAug 09 Jun 51 5 1 3 3 8.461665 JunAug 09 Jun 52 5 1 3 3 0 JunAug 09 Jul 53 5 1 3 3 15.35636 JunAug 09 Jul 54 5 1 3 3 12.8492 JunAug 09 Jul 55 5 1 3 3 24.75821 JunAug 09 Jul 56 5 1 3 3 24.13142 JunAug 09 Aug 57 5 1 3 3 13.16259 JunAug 09 Aug 58 5 1 3 3 0 JunAug 09 Aug 59 5 1 3 3 12.5358 JunAug 09 Aug 60 5 1 3 3 0 JunAug 09 Aug 61 5 1 3 3 0 JunAug 08 Jul 1 5 2 8 4 12.8492 JunAug 08 Jul 2 5 2 8 4 12.5358 JunAug 08 Jul 3 5 2 8 4 25.69839 Jun Aug 08 Jul 4 5 2 8 4 12.8492 JunAug 08 Aug 5 5 2 8 4 12.5358 JunAug 08 Aug 6 5 2 8 4 26.01179 JunAug 08 Aug 7 5 2 8 4 14. 10278 JunAug 08 Aug 8 5 2 8 4 0 JunAug 08 Aug 9 5 2 8 4 0 Sep Nov 08 Sep 10 5 2 8 4 17.55012 Sep Nov 08 Sep 11 5 2 8 4 17.55012 Sep Nov 08 Sep 12 5 2 8 4 18.17691 Sep Nov 08 Sep 13 5 2 8 4 17.86352 Sep Nov 08 Oct 14 5 2 8 4 9.40185 Sep Nov 08 Oct 15 5 2 8 4 9.088455 Sep Nov 08 Oct 16 5 2 8 4 17.55012 Sep Nov 08 Oct 17 5 2 8 4 9.40185 Sep Nov 08 Oct 18 5 2 8 4 18.17691 Sep Nov 08 Nov 19 5 2 8 4 15.66975 Sep Nov 08 Nov 20 5 2 8 4 18.8037 Sep Nov 08 Nov 21 5 2 8 4 18.8037 Sep Nov 08 Nov 22 5 2 8 4 18.8037 Dec Feb Dec 23 5 2 8 4 16.29654

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157 0809 Dec Feb 0809 Dec 24 5 2 8 4 15.98315 Dec Feb 0809 Dec 25 5 2 8 4 15.98315 Dec Feb 0809 Dec 26 5 2 8 4 24.13142 Dec Feb 0809 Jan 27 5 2 8 4 15.66975 Dec Feb 0809 Jan 28 5 2 8 4 6.2679 Dec Feb 0809 Jan 29 5 2 8 4 0 Dec Feb 0809 Jan 30 5 2 8 4 0 Dec Feb 0809 Feb 31 5 2 8 4 0 Dec Feb 0809 Feb 32 5 2 8 4 9.715245 Dec Feb 0809 Feb 33 5 2 8 4 9.40185 Dec Feb 0809 Feb 34 5 2 8 4 9.715245 Mar May 09 Mar 35 5 2 8 4 19.1171 Mar May 09 Mar 36 5 2 8 4 19.11 71 Mar May 09 Mar 37 5 2 8 4 9.088455 Mar May 09 Mar 38 5 2 8 4 16.92333 Mar May 09 Mar 39 5 2 8 4 18.8037 Mar May 09 Apr 40 5 2 8 4 17.23673 Mar May 09 Apr 41 5 2 8 4 4.074135 Mar May 09 Apr 42 5 2 8 4 14.72957 Mar May 09 Apr 43 5 2 8 4 19.74389 M ar May 09 May 44 5 2 8 4 16.92333 Mar May 09 May 45 5 2 8 4 15.98315 Mar May 09 May 46 5 2 8 4 8.77506 Mar May 09 May 47 5 2 8 4 0 Mar May 09 May 48 5 2 8 4 8.77506 JunAug 09 Jun 49 5 2 8 4 16.92333 JunAug 09 Jun 50 5 2 8 4 16.29654 JunAug 09 Jun 51 5 2 8 4 8.14827 JunAug 09 Jun 52 5 2 8 4 0 JunAug 09 Jul 53 5 2 8 4 15.04296 JunAug 09 Jul 54 5 2 8 4 12.8492 JunAug 09 Jul 55 5 2 8 4 23.50463 JunAug 09 Jul 56 5 2 8 4 23.19123

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158 JunAug 09 Aug 57 5 2 8 4 12.5358 JunAug 09 Aug 58 5 2 8 4 0 JunAug 09 Aug 59 5 2 8 4 11.90901 JunAug 09 Aug 60 5 2 8 4 0 JunAug 09 Aug 61 5 2 8 4 0 JunAug 08 Jul 1 5 3 9 1 12.22241 JunAug 08 Jul 2 5 3 9 1 11.90901 JunAug 08 Jul 3 5 3 9 1 24.75821 JunAug 08 Jul 4 5 3 9 1 11.28222 JunAug 08 Aug 5 5 3 9 1 11.90901 JunAug 08 Aug 6 5 3 9 1 24.13142 JunAug 08 Aug 7 5 3 9 1 13.16259 JunAug 08 Aug 8 5 3 9 1 0 JunAug 08 Aug 9 5 3 9 1 0 Sep Nov 08 Sep 10 5 3 9 1 17.55012 Sep Nov 08 Sep 11 5 3 9 1 17.55012 Sep Nov 08 Sep 12 5 3 9 1 17.55012 Sep Nov 08 Sep 13 5 3 9 1 16.92333 Sep Nov 08 Oct 14 5 3 9 1 8.77506 Sep Nov 08 Oct 15 5 3 9 1 8.461665 Sep Nov 08 Oct 16 5 3 9 1 16.92333 Sep Nov 08 Oct 17 5 3 9 1 8.461665 Sep Nov 08 Oct 18 5 3 9 1 17.23673 Sep Nov 08 Nov 19 5 3 9 1 14.41617 Sep Nov 08 No v 20 5 3 9 1 17.86352 Sep Nov 08 Nov 21 5 3 9 1 17.55012 Sep Nov 08 Nov 22 5 3 9 1 17.86352 Dec Feb 0809 Dec 23 5 3 9 1 15.35636 Dec Feb 0809 Dec 24 5 3 9 1 15.35636 Dec Feb 0809 Dec 25 5 3 9 1 15.35636 Dec Feb 0809 Dec 26 5 3 9 1 23.19123 Dec Feb 0 809 Jan 27 5 3 9 1 15.04296 Dec Feb 0809 Jan 28 5 3 9 1 5.954505 Dec Feb 0809 Jan 29 5 3 9 1 0 Dec Feb 0809 Jan 30 5 3 9 1 0 Dec Feb Feb 31 5 3 9 1 0

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159 0809 Dec Feb 0809 Feb 32 5 3 9 1 11.59562 Dec Feb 0809 Feb 33 5 3 9 1 11.28222 Dec Feb 0809 Feb 34 5 3 9 1 11.59562 Mar May 09 Mar 35 5 3 9 1 17.55012 Mar May 09 Mar 36 5 3 9 1 17.23673 Mar May 09 Mar 37 5 3 9 1 8.461665 Mar May 09 Mar 38 5 3 9 1 16.92333 Mar May 09 Mar 39 5 3 9 1 16.92333 Mar May 09 Apr 40 5 3 9 1 15.98315 Mar May 09 Apr 41 5 3 9 1 4.074135 Mar May 09 Apr 42 5 3 9 1 14.10278 Mar May 09 Apr 43 5 3 9 1 20.37068 Mar May 09 May 44 5 3 9 1 17.23673 Mar May 09 May 45 5 3 9 1 16.60994 Mar May 09 May 46 5 3 9 1 8.77506 Mar May 09 May 47 5 3 9 1 0 Mar May 09 May 48 5 3 9 1 9.40185 JunAug 09 Jun 49 5 3 9 1 17.55012 JunAug 09 Jun 50 5 3 9 1 16.92333 JunAug 09 Jun 51 5 3 9 1 8.461665 JunAug 09 Jun 52 5 3 9 1 0 JunAug 09 Jul 53 5 3 9 1 15.66975 Jun Aug 09 Jul 54 5 3 9 1 13.16259 JunAug 09 Jul 55 5 3 9 1 24.44481 JunAug 09 Jul 56 5 3 9 1 24.13142 JunAug 09 Aug 57 5 3 9 1 12.8492 JunAug 09 Aug 58 5 3 9 1 0 JunAug 09 Aug 59 5 3 9 1 12.8492 JunAug 09 Aug 60 5 3 9 1 0 JunAug 09 Aug 61 5 3 9 1 0 JunAug 08 Jul 1 5 4 18 2 13.16259 JunAug 08 Jul 2 5 4 18 2 13.16259 J unAug 08 Jul 3 5 4 18 2 26.32518 JunAug 08 Jul 4 5 4 18 2 12.8492 JunAug 08 Aug 5 5 4 18 2 12.8492 JunAug 08 Aug 6 5 4 18 2 16.29654 JunAug 08 Aug 7 5 4 18 2 14.41617 JunAug 08 Aug 8 5 4 18 2 0 JunAug 08 Aug 9 5 4 18 2 0

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160 Sep Nov 08 Sep 10 5 4 18 2 17.55012 Sep Nov 08 Sep 11 5 4 18 2 17.55012 Sep Nov 08 Sep 12 5 4 18 2 18.17691 Sep Nov 08 Sep 13 5 4 18 2 18.49031 Sep Nov 08 Oct 14 5 4 18 2 9.40185 Sep Nov 08 Oct 15 5 4 18 2 8.77506 Sep Nov 08 Oct 16 5 4 18 2 18.49031 Sep Nov 08 Oct 17 5 4 18 2 9.40185 Sep Nov 08 Oct 18 5 4 18 2 18.49031 Sep Nov 08 Nov 19 5 4 18 2 15.66975 Sep Nov 08 Nov 20 5 4 18 2 18.8037 Sep Nov 08 Nov 21 5 4 18 2 19.1171 Sep Nov 08 Nov 22 5 4 18 2 19.1171 Dec Feb 0809 Dec 23 5 4 18 2 16.29654 Dec Feb 0809 Dec 24 5 4 18 2 16.29654 Dec Feb 0809 Dec 25 5 4 18 2 16.29654 Dec Feb 0809 Dec 26 5 4 18 2 24.44481 Dec Feb 0809 Jan 27 5 4 18 2 14.10278 Dec Feb 0809 Jan 28 5 4 18 2 1.88037 Dec Feb 0809 Jan 29 5 4 18 2 0 Dec Feb 0809 Jan 30 5 4 18 2 0 Dec Feb 0809 Feb 31 5 4 18 2 0 Dec Feb 0809 Feb 32 5 4 18 2 3.447345 Dec Feb 0809 Feb 33 5 4 18 2 11.59562 Dec Feb 0809 Feb 34 5 4 18 2 11.90901 Mar May 09 Mar 35 5 4 18 2 15.98315 Mar May 09 Mar 36 5 4 18 2 15.35636 Mar May 09 Mar 37 5 4 18 2 9.40185 Mar May 09 Mar 38 5 4 18 2 17.55012 Mar May 09 Mar 39 5 4 18 2 9.40185 Mar May 09 Apr 40 5 4 18 2 18.17691 Mar May 09 Apr 41 5 4 18 2 4.700925 Mar May 09 Apr 42 5 4 18 2 15.66975

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161 Mar May 09 Apr 43 5 4 18 2 20.37068 Mar May 09 May 44 5 4 18 2 17.23673 Mar May 09 M ay 45 5 4 18 2 16.60994 Mar May 09 May 46 5 4 18 2 8.77506 Mar May 09 May 47 5 4 18 2 0 Mar May 09 May 48 5 4 18 2 8.461665 JunAug 09 Jun 49 5 4 18 2 16.92333 JunAug 09 Jun 50 5 4 18 2 15.66975 JunAug 09 Jun 51 5 4 18 2 8.14827 JunAug 09 Jun 52 5 4 18 2 0 JunAug 09 Jul 53 5 4 18 2 9.088455 JunAug 09 Jul 54 5 4 18 2 13.16259 JunAug 09 Jul 55 5 4 18 2 24.44481 JunAug 09 Jul 56 5 4 18 2 24.13142 JunAug 09 Aug 57 5 4 18 2 12.8492 JunAug 09 Aug 58 5 4 18 2 0 JunAug 09 Aug 59 5 4 18 2 12. 5358 JunAug 09 Aug 60 5 4 18 2 0 JunAug 09 Aug 61 5 4 18 2 0 JunAug 08 Jul 1 6 1 42 24 43.93765 JunAug 08 Jul 2 6 1 42 24 43.23251 JunAug 08 Jul 3 6 1 42 24 43.24851 JunAug 08 Jul 4 6 1 42 24 43.62426 JunAug 08 Aug 5 6 1 42 24 44.09435 JunA ug 08 Aug 6 6 1 42 24 42.30833 JunAug 08 Aug 7 6 1 42 24 45.26958 JunAug 08 Aug 8 6 1 42 24 44 JunAug 08 Aug 9 6 1 42 24 44 Sep Nov 08 Sep 10 6 1 42 24 29.58449 Sep Nov 08 Sep 11 6 1 42 24 29.58449 Sep Nov 08 Sep 12 6 1 42 24 29.58449 Sep Nov 08 Sep 13 6 1 42 24 29.58449 Sep Nov 08 Oct 14 6 1 42 24 29.58448 Sep Nov 08 Oct 15 6 1 42 24 29.9919 Sep Nov 08 Oct 16 6 1 42 24 30.63436 Sep Nov 08 Oct 17 6 1 42 24 28.34657 Sep Nov 08 Oct 18 6 1 42 24 30.71271 Sep Nov 08 Nov 19 6 1 42 24 24.75821 Se p Nov 08 Nov 20 6 1 42 24 30.86941 Sep Nov 08 Nov 21 6 1 42 24 31.4962 Sep Nov 08 Nov 22 6 1 42 24 29.92922 Dec Feb 0809 Dec 23 6 1 42 24 26.09013

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162 Dec Feb 0809 Dec 24 6 1 42 24 26.16848 Dec Feb 0809 Dec 25 6 1 42 24 26.16848 Dec Feb 0809 Dec 26 6 1 4 2 24 37.9208 Dec Feb 0809 Jan 27 6 1 42 24 25.85509 Dec Feb 0809 Jan 28 6 1 42 24 17.36478 Dec Feb 0809 Jan 29 6 1 42 24 0 Dec Feb 0809 Jan 30 6 1 42 24 10 Dec Feb 0809 Feb 31 6 1 42 24 0 Dec Feb 0809 Feb 32 6 1 42 24 20.84077 Dec Feb 0809 Feb 33 6 1 42 24 19.50884 Dec Feb 0809 Feb 34 6 1 42 24 18.88205 Mar May 09 Mar 35 6 1 42 24 30.08592 Mar May 09 Mar 36 6 1 42 24 30.00757 Mar May 09 Mar 37 6 1 42 24 24.67986 Mar May 09 Mar 38 6 1 42 24 23.73967 Mar May 09 Mar 39 6 1 42 24 28.91069 Mar May 09 Apr 40 6 1 42 24 28.36225 Mar May 09 Apr 41 6 1 42 24 13.47599 Mar May 09 Apr 42 6 1 42 24 26.09013 Mar May 09 Apr 43 6 1 42 24 32.90648 Mar May 09 May 44 6 1 42 24 28.75399 Mar May 09 May 45 6 1 42 24 27.57876 Mar May 09 May 46 6 1 42 24 26.71721 Mar May 09 May 47 6 1 42 24 27.728 Mar May 09 May 48 6 1 42 24 30.55607 JunAug 09 Jun 49 6 1 42 24 28.91069 JunAug 09 Jun 50 6 1 42 24 27.89216 JunAug 09 Jun 51 6 1 42 24 27.88608 JunAug 09 Jun 52 6 1 42 24 27.88 JunAug 09 Jul 53 6 1 42 24 25. 385 JunAug 09 Jul 54 6 1 42 24 30.79106 JunAug 09 Jul 55 6 1 42 24 39.95786 JunAug 09 Jul 56 6 1 42 24 40.58465 JunAug 09 Aug 57 6 1 42 24 37.9208

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163 JunAug 09 Aug 58 6 1 42 24 40 JunAug 09 Aug 59 6 1 42 24 45.97472 JunAug 09 Aug 60 6 1 42 24 44 JunAug 09 Aug 61 6 1 42 24 22 Two week Irrigation Summation and Turf Quality Comparison Program proc sort data = work.week2_tq; by season tmt rep; proc glm data = work.week2_tq; by season; class tmt rep season; model wkly_irr2=tmt rep; lsmeans tmt / pdiff =all; run; proc sort data = work.week2_tq; by season tmt rep; proc glm data = work.week2_tq; by season; class tmt rep season; model tq=tmt rep; lsmeans tmt / pdiff =all; run; Data Season Month Date TMT REP PLOT BLK WKLY_IRR2 TQ JunAug 08 Jul 7/30 /2008 1 1 4 4 49.829805 7 JunAug 08 Aug 9/2/2008 1 1 4 4 36.98061 7 Sep Nov 08 Sep 10/2/2008 1 1 4 4 52.65036 8 Sep Nov 08 Oct 11/4/2008 1 1 4 4 36.98061 7 Sep Nov 08 Nov 12/2/2008 1 1 4 4 31.96629 6 Dec Feb 0809 Dec 1/9/2009 1 1 4 4 40.11456 6 Mar May 09 Mar 4/1/2009 1 1 4 4 38.86098 6 Mar May 09 Apr 5/5/2009 1 1 4 4 76.154985 8 Mar May 09 May 6/5/2009 1 1 4 4 10.342035 7 JunAug 09 Jul 7/13/2009 1 1 4 4 10.02864 7 JunAug 09 Jul 8/5/2009 1 1 4 4 62.05221 7 JunAug 08 Jul 7/30/2008 1 2 6 2 49.203015 7 JunAug 08 Aug 9/2/2008 1 2 6 2 35.72703 7 Sep Nov 08 Sep 10/2/2008 1 2 6 2 51.710175 6 Sep Nov 08 Oct 11/4/2008 1 2 6 2 36.040425 6 Sep Nov 08 Nov 12/2/2008 1 2 6 2 31.652895 6 Dec Feb 0809 Dec 1/9/2009 1 2 6 2 38.86098 6

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164 Mar May 09 Mar 4/1/ 2009 1 2 6 2 37.6074 6 Mar May 09 Apr 5/5/2009 1 2 6 2 73.647825 7 Mar May 09 May 6/5/2009 1 2 6 2 10.342035 7 JunAug 09 Jul 7/13/2009 1 2 6 2 9.088455 8 JunAug 09 Jul 8/5/2009 1 2 6 2 60.485235 8 JunAug 08 Jul 7/30/2008 1 3 11 3 45.442275 7 JunA ug 08 Aug 9/2/2008 1 3 11 3 35.10024 7 Sep Nov 08 Sep 10/2/2008 1 3 11 3 49.51641 7 Sep Nov 08 Oct 11/4/2008 1 3 11 3 32.906475 7 Sep Nov 08 Nov 12/2/2008 1 3 11 3 31.652895 6 Dec Feb 0809 Dec 1/9/2009 1 3 11 3 34.160055 6 Mar May 09 Mar 4/1/2009 1 3 11 3 35.413635 6 Mar May 09 Apr 5/5/2009 1 3 11 3 72.08085 6 Mar May 09 May 6/5/2009 1 3 11 3 10.342035 7 JunAug 09 Jul 7/13/2009 1 3 11 3 9.40185 7 JunAug 09 Jul 8/5/2009 1 3 11 3 59.231655 6 JunAug 08 Jul 7/30/2008 1 4 13 1 48.3411788 6 JunAug 08 Aug 9/2/2008 1 4 13 1 35.8837275 7 Sep Nov 08 Sep 10/2/2008 1 4 13 1 51.39678 7 Sep Nov 08 Oct 11/4/2008 1 4 13 1 35.5703325 6 Sep Nov 08 Nov 12/2/2008 1 4 13 1 31.75736 6 Dec Feb 0809 Dec 1/9/2009 1 4 13 1 39.27884 6 Mar May 09 Mar 4/1/2009 1 4 13 1 37.4507025 6 Mar May 09 Apr 5/5/2009 1 4 13 1 73.8828713 7 Mar May 09 May 6/5/2009 1 4 13 1 10.342035 7 JunAug 09 Jul 7/13/2009 1 4 13 1 9.48019875 7 JunAug 09 Jul 8/5/2009 1 4 13 1 60.5635838 6 JunAug 08 Jul 7/30/2008 2 1 1 1 14.2594725 7 JunAug 08 Aug 9/2/2008 2 1 1 1 17.3150738 7 Sep Nov 08 Sep 10/2/2008 2 1 1 1 30.3209663 7 Sep Nov 08 Oct 11/4/2008 2 1 1 1 13.78938 6 Sep Nov 08 Nov 12/2/2008 2 1 1 1 17.236725 6 Dec Feb 0809 Dec 1/9/2009 2 1 1 1 16.7666325 7 Mar May 09 Mar 4/1/2009 2 1 1 1 14.8862625 6 Mar May 09 Apr 5/5/2009 2 1 1 1 61.0336763 6 Mar May 09 May 6/5/2009 2 1 1 1 17.9418638 7 JunAug 09 Jul 7/13/2009 2 1 1 1 14.8862625 6 JunAug 09 Jul 8/5/2009 2 1 1 1 37.920795 7 JunAug 08 Jul 7/30/2008 2 2 14 2 13.78938 6 JunAug 08 Aug 9/2/2008 2 2 14 2 17.236725 7

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165 Sep Nov 08 Sep 10/2/2008 2 2 14 2 29.772525 7 Sep Nov 08 Oct 11/4/2008 2 2 14 2 13.78938 7 Sep Nov 08 Nov 12/2/2008 2 2 14 2 17.236725 6 Dec Feb 0809 Dec 1/9/2009 2 2 14 2 17.236725 6 Mar May 09 Mar 4/1/2009 2 2 14 2 15.04296 6 Mar May 09 Apr 5/5/2009 2 2 14 2 62.365605 6 Mar May 09 May 6/5/2009 2 2 14 2 18.17691 7 JunAug 09 Jul 7/13/2009 2 2 14 2 15.04296 6 JunAug 09 Jul 8/5/2009 2 2 14 2 38.86098 6 JunAug 08 Jul 7/30/2008 2 3 16 4 14.729565 8 JunAug 08 A ug 9/2/2008 2 3 16 4 17.55012 8 Sep Nov 08 Sep 10/2/2008 2 3 16 4 30.71271 7 Sep Nov 08 Oct 11/4/2008 2 3 16 4 13.78938 6 Sep Nov 08 Nov 12/2/2008 2 3 16 4 17.55012 6 Dec Feb 0809 Dec 1/9/2009 2 3 16 4 15.983145 6 Mar May 09 Mar 4/1/2009 2 3 16 4 15.983145 6 Mar May 09 Apr 5/5/2009 2 3 16 4 60.79863 Mar May 09 May 6/5/2009 2 3 16 4 17.863515 JunAug 09 Jul 7/13/2009 2 3 16 4 15.04296 JunAug 09 Jul 8/5/2009 2 3 16 4 38.23419 JunAug 08 Jul 7/30/2008 2 4 19 3 14.2594725 7 JunAug 08 Aug 9/ 2/2008 2 4 19 3 17.3150738 7 Sep Nov 08 Sep 10/2/2008 2 4 19 3 30.3209663 7 Sep Nov 08 Oct 11/4/2008 2 4 19 3 13.78938 6 Sep Nov 08 Nov 12/2/2008 2 4 19 3 17.236725 6 Dec Feb 0809 Dec 1/9/2009 2 4 19 3 16.7666325 6 Mar May 09 Mar 4/1/2009 2 4 19 3 14. 8862625 Mar May 09 Apr 5/5/2009 2 4 19 3 61.0336763 Mar May 09 May 6/5/2009 2 4 19 3 17.9418638 JunAug 09 Jul 7/13/2009 2 4 19 3 14.8862625 JunAug 09 Jul 8/5/2009 2 4 19 3 37.920795 JunAug 08 Jul 7/30/2008 3 1 7 3 29.145735 6 JunAug 08 Aug 9/2/2008 3 1 7 3 15.983145 7 Sep Nov 08 Sep 10/2/2008 3 1 7 3 34.786845 6 Sep Nov 08 Oct 11/4/2008 3 1 7 3 18.17691 6 Sep Nov 08 Nov 12/2/2008 3 1 7 3 18.490305 6 Dec Feb 0809 Dec 1/9/2009 3 1 7 3 17.55012 6 Mar May 09 Mar 4/1/2009 3 1 7 3 29.1457 35 6 Mar May 09 Apr 5/5/2009 3 1 7 3 52.336965 5

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166 Mar May 09 May 6/5/2009 3 1 7 3 45.442275 7 JunAug 09 Jul 7/13/2009 3 1 7 3 18.17691 7 JunAug 09 Jul 8/5/2009 3 1 7 3 47.00925 6 JunAug 08 Jul 7/30/2008 3 2 10 2 30.399315 7 JunAug 08 Aug 9/2/2008 3 2 10 2 16.609935 8 Sep Nov 08 Sep 10/2/2008 3 2 10 2 34.47345 7 Sep Nov 08 Oct 11/4/2008 3 2 10 2 18.8037 7 Sep Nov 08 Nov 12/2/2008 3 2 10 2 18.490305 6 Dec Feb 0809 Dec 1/9/2009 3 2 10 2 17.863515 6 Mar May 09 Mar 4/1/2009 3 2 10 2 31.026105 6 Ma r May 09 Apr 5/5/2009 3 2 10 2 52.963755 7 Mar May 09 May 6/5/2009 3 2 10 2 41.054745 7 JunAug 09 Jul 7/13/2009 3 2 10 2 16.29654 JunAug 09 Jul 8/5/2009 3 2 10 2 43.561905 JunAug 08 Jul 7/30/2008 3 3 12 4 29.145735 8 JunAug 08 Aug 9/2/2008 3 3 12 4 15.983145 8 Sep Nov 08 Sep 10/2/2008 3 3 12 4 34.786845 7 Sep Nov 08 Oct 11/4/2008 3 3 12 4 18.490305 7 Sep Nov 08 Nov 12/2/2008 3 3 12 4 18.490305 7 Dec Feb 0809 Dec 1/9/2009 3 3 12 4 18.17691 7 Mar May 09 Mar 4/1/2009 3 3 12 4 29.772525 6 Mar May 09 Apr 5/5/2009 3 3 12 4 52.02357 6 Mar May 09 May 6/5/2009 3 3 12 4 45.12888 7 JunAug 09 Jul 7/13/2009 3 3 12 4 13.78938 JunAug 09 Jul 8/5/2009 3 3 12 4 37.294005 JunAug 08 Jul 7/30/2008 3 4 17 1 29.04127 7 JunAug 08 Aug 9/2/2008 3 4 17 1 15.87868 7 Sep Nov 08 Sep 10/2/2008 3 4 17 1 34.577915 7 Sep Nov 08 Oct 11/4/2008 3 4 17 1 18.17691 6 Sep Nov 08 Nov 12/2/2008 3 4 17 1 18.490305 6 Dec Feb 0809 Dec 1/9/2009 3 4 17 1 17.863515 6 Mar May 09 Mar 4/1/2009 3 4 17 1 29.66806 6 Mar May 0 9 Apr 5/5/2009 3 4 17 1 52.85929 6 Mar May 09 May 6/5/2009 3 4 17 1 44.7371363 7 JunAug 09 Jul 7/13/2009 3 4 17 1 16.7666325 JunAug 09 Jul 8/5/2009 3 4 17 1 44.50209 JunAug 08 Jul 7/30/2008 4 1 2 2 65.499555 7 JunAug 08 Aug 9/2/2008 4 1 2 2 14.792244 7 Sep Nov 08 Sep 10/2/2008 4 1 2 2 44.376732 7 Sep Nov 08 Oct 11/4/2008 4 1 2 2 41.99493 6

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167 Sep Nov 08 Nov 12/2/2008 4 1 2 2 58.29147 6 Dec Feb 0809 Dec 1/9/2009 4 1 2 2 56.097705 6 Mar May 09 Mar 4/1/2009 4 1 2 2 53.27715 6 Mar May 09 Apr 5/5 /2009 4 1 2 2 75.2148 7 Mar May 09 May 6/5/2009 4 1 2 2 30.399315 7 JunAug 09 Jul 7/13/2009 4 1 2 2 24.131415 8 JunAug 09 Jul 8/5/2009 4 1 2 2 58.29147 8 JunAug 08 Jul 7/30/2008 4 2 5 1 66.43974 6 JunAug 08 Aug 9/2/2008 4 2 5 1 14.792244 7 Sep No v 08 Sep 10/2/2008 4 2 5 1 44.376732 6 Sep Nov 08 Oct 11/4/2008 4 2 5 1 41.36814 6 Sep Nov 08 Nov 12/2/2008 4 2 5 1 60.17184 6 Dec Feb 0809 Dec 1/9/2009 4 2 5 1 60.17184 7 Mar May 09 Mar 4/1/2009 4 2 5 1 52.336965 6 Mar May 09 Apr 5/5/2009 4 2 5 1 104 .987325 6 Mar May 09 May 6/5/2009 4 2 5 1 30.71271 7 JunAug 09 Jul 7/13/2009 4 2 5 1 26.011785 7 JunAug 09 Jul 8/5/2009 4 2 5 1 60.485235 7 JunAug 08 Jul 7/30/2008 4 3 15 3 65.499555 7 JunAug 08 Aug 9/2/2008 4 3 15 3 14.792244 7 Sep Nov 08 Sep 10/2/2008 4 3 15 3 44.376732 6 Sep Nov 08 Oct 11/4/2008 4 3 15 3 40.11456 6 Sep Nov 08 Nov 12/2/2008 4 3 15 3 57.978075 6 Dec Feb 0809 Dec 1/9/2009 4 3 15 3 57.66468 6 Mar May 09 Mar 4/1/2009 4 3 15 3 52.336965 6 Mar May 09 Apr 5/5/2009 4 3 15 3 73.0210 35 7 Mar May 09 May 6/5/2009 4 3 15 3 30.71271 7 JunAug 09 Jul 7/13/2009 4 3 15 3 25.69839 7 JunAug 09 Jul 8/5/2009 4 3 15 3 59.858445 7 JunAug 08 Jul 7/30/2008 4 4 20 4 65.499555 7 JunAug 08 Aug 9/2/2008 4 4 20 4 14.792244 6 Sep Nov 08 Sep 10/2/ 2008 4 4 20 4 44.376732 6 Sep Nov 08 Oct 11/4/2008 4 4 20 4 40.9763963 6 Sep Nov 08 Nov 12/2/2008 4 4 20 4 58.5265163 6 Dec Feb 0809 Dec 1/9/2009 4 4 20 4 58.0564238 6 Mar May 09 Mar 4/1/2009 4 4 20 4 52.65036 6 Mar May 09 Apr 5/5/2009 4 4 20 4 81.9527925 6 Mar May 09 May 6/5/2009 4 4 20 4 30.4776638 7 JunAug 09 Jul 7/13/2009 4 4 20 4 25.384995 8

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168 JunAug 09 Jul 8/5/2009 4 4 20 4 59.6233988 7 JunAug 08 Jul 7/30/2008 5 1 3 3 38.23419 7 JunAug 08 Aug 9/2/2008 5 1 3 3 8.77506 7 Sep Nov 08 Sep 10/2/2008 5 1 3 3 27.265365 6 Sep Nov 08 Oct 11/4/2008 5 1 3 3 24.44481 7 Sep Nov 08 Nov 12/2/2008 5 1 3 3 36.35382 6 Dec Feb 0809 Dec 1/9/2009 5 1 3 3 37.920795 7 Mar May 09 Mar 4/1/2009 5 1 3 3 35.72703 6 Mar May 09 Apr 5/5/2009 5 1 3 3 46.38246 6 Mar May 09 May 6/5/2009 5 1 3 3 17.55012 7 JunAug 09 Jul 7/13/2009 5 1 3 3 15.04296 JunAug 09 Jul 8/5/2009 5 1 3 3 35.72703 JunAug 08 Jul 7/30/2008 5 2 8 4 35.413635 8 JunAug 08 Aug 9/2/2008 5 2 8 4 8.77506 7 Sep Nov 08 Sep 10/2/2008 5 2 8 4 25.69 839 8 Sep Nov 08 Oct 11/4/2008 5 2 8 4 22.877835 8 Sep Nov 08 Nov 12/2/2008 5 2 8 4 34.47345 7 Dec Feb 0809 Dec 1/9/2009 5 2 8 4 36.667215 7 Mar May 09 Mar 4/1/2009 5 2 8 4 33.84666 6 Mar May 09 Apr 5/5/2009 5 2 8 4 46.695855 7 Mar May 09 May 6/5/2009 5 2 8 4 18.490305 8 JunAug 09 Jul 7/13/2009 5 2 8 4 15.66975 7 JunAug 09 Jul 8/5/2009 5 2 8 4 36.98061 7 JunAug 08 Jul 7/30/2008 5 3 9 1 38.86098 6 JunAug 08 Aug 9/2/2008 5 3 9 1 8.77506 7 Sep Nov 08 Sep 10/2/2008 5 3 9 1 27.892155 6 Sep Nov 08 Oct 11/4/2008 5 3 9 1 24.44481 Sep Nov 08 Nov 12/2/2008 5 3 9 1 36.667215 Dec Feb 0809 Dec 1/9/2009 5 3 9 1 32.279685 Mar May 09 Mar 4/1/2009 5 3 9 1 26.95197 Mar May 09 Apr 5/5/2009 5 3 9 1 47.63604 Mar May 09 May 6/5/2009 5 3 9 1 17.236725 JunAug 09 Jul 7/13/2009 5 3 9 1 9.088455 JunAug 09 Jul 8/5/2009 5 3 9 1 36.98061 JunAug 08 Jul 7/30/2008 5 4 18 2 37.920795 7 JunAug 08 Aug 9/2/2008 5 4 18 2 8.77506 7 Sep Nov 08 Sep 10/2/2008 5 4 18 2 27.265365 7 Sep Nov 08 Oct 11/4/2008 5 4 18 2 24.131415 6 Sep Nov 08 Nov 12/2/2008 5 4 18 2 36.1971225 6 Dec Feb Dec 1/9/2009 5 4 18 2 36.4321688 7

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169 0809 Mar May 09 Mar 4/1/2009 5 4 18 2 33.21987 6 Mar May 09 Apr 5/5/2009 5 4 18 2 47.0875988 6 Mar May 09 May 6/5/2009 5 4 18 2 17.863515 7 JunAug 09 Jul 7/13/2009 5 4 18 2 13.78938 7 JunAug 09 Jul 8/5/2009 5 4 18 2 36.7455638 7

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170 APPENDIX B STATISTICAL ANALYSIS AND DATA FOR CHAPTER 3 Program proc sort data = work.chapter3; by season tmt rep; proc glm data = work.chapter3; by season; class tmt rep week month season; model wkly_irr=tmt rep season; lsmeans tmt / pdiff =all; run; Data Season Month Week TMT REP WKLY_IRR JunAug 08 Jul 1 2 1 0 JunAug 08 Jul 2 2 1 16.92333 JunAug 08 Jul 3 2 1 17.86352 JunAug 08 Jul 4 2 1 14.10278 JunAug 08 Aug 5 2 1 0 JunAug 08 Aug 6 2 1 10.34204 JunAug 08 Aug 7 2 1 0 JunAug 08 Aug 8 2 1 0 JunAug 08 Aug 9 2 1 16.92333 Sep Nov 08 Sep 10 2 1 0 Sep Nov 08 Sep 11 2 1 0 Sep Nov 08 Sep 12 2 1 8.77506 Sep Nov 08 Sep 13 2 1 30.08592 Sep Nov 08 Oct 1 4 2 1 15.04296 Sep Nov 08 Oct 15 2 1 15.04296 Sep Nov 08 Oct 16 2 1 15.04296 Sep Nov 08 Oct 17 2 1 0 Sep Nov 08 Oct 18 2 1 13.78938 Sep Nov 08 Nov 19 2 1 16.60994 Sep Nov 08 Nov 20 2 1 0 Sep Nov 08 Nov 21 2 1 16.60994 Sep Nov 08 Nov 22 2 1 0 Dec F eb 0809 Dec 23 2 1 0 Dec Feb 0809 Dec 24 2 1 16.92333 Dec Feb 0809 Dec 25 2 1 0 Dec Feb 0809 Dec 26 2 1 0

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171 Dec Feb 0809 Jan 27 2 1 16.60994 Dec Feb 0809 Jan 28 2 1 0 Dec Feb 0809 Jan 29 2 1 0 Dec Feb 0809 Jan 30 2 1 16.60994 Dec Feb 0809 Feb 31 2 1 0 Dec Feb 0809 Feb 32 2 1 0 Dec Feb 0809 Feb 33 2 1 15.04296 Dec Feb 0809 Feb 34 2 1 0 Mar May 09 Mar 35 2 1 15.35636 Mar May 09 Mar 36 2 1 15.98315 Mar May 09 Mar 37 2 1 27.89216 Mar May 09 Mar 38 2 1 0 Mar May 09 Mar 39 2 1 15.04296 Mar May 09 A pr 40 2 1 16.29654 Mar May 09 Apr 41 2 1 17.23673 Mar May 09 Apr 42 2 1 7.52148 Mar May 09 Apr 43 2 1 29.77253 Mar May 09 May 44 2 1 29.45913 Mar May 09 May 45 2 1 21.31086 Mar May 09 May 46 2 1 0 Mar May 09 May 47 2 1 0 Mar May 09 May 48 2 1 17.55 012 JunAug 09 Jun 49 2 1 0 JunAug 09 Jun 50 2 1 34.16006 JunAug 09 Jun 51 2 1 19.74389 JunAug 09 Jun 52 2 1 0 JunAug 09 Jul 53 2 1 0 JunAug 09 Jul 54 2 1 14.10278 JunAug 09 Jul 55 2 1 0 JunAug 09 Jul 56 2 1 15.98315 JunAug 09 Aug 57 2 1 2 0.37068 JunAug 09 Aug 58 2 1 0 JunAug 09 Aug 59 2 1 0 JunAug 09 Aug 60 2 1 9.715245 JunAug 09 Aug 61 2 1 0 JunAug 08 Jul 1 2 2 0

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172 JunAug 08 Jul 2 2 2 17.23673 JunAug 08 Jul 3 2 2 18.8037 JunAug 08 Jul 4 2 2 14.41617 JunAug 08 Aug 5 2 2 0 JunAug 08 Aug 6 2 2 10.65543 JunAug 08 Aug 7 2 2 0 JunAug 08 Aug 8 2 2 0 JunAug 08 Aug 9 2 2 17.55012 Sep Nov 08 Sep 10 2 2 0 Sep Nov 08 Sep 11 2 2 0 Sep Nov 08 Sep 12 2 2 8.77506 Sep Nov 08 Sep 13 2 2 30.71271 Sep Nov 08 Oct 14 2 2 15.04296 S ep Nov 08 Oct 15 2 2 15.04296 Sep Nov 08 Oct 16 2 2 15.04296 Sep Nov 08 Oct 17 2 2 0 Sep Nov 08 Oct 18 2 2 13.78938 Sep Nov 08 Nov 19 2 2 17.23673 Sep Nov 08 Nov 20 2 2 0 Sep Nov 08 Nov 21 2 2 17.55012 Sep Nov 08 Nov 22 2 2 0 Dec Feb 0809 Dec 23 2 2 0 Dec Feb 0809 Dec 24 2 2 17.23673 Dec Feb 0809 Dec 25 2 2 0 Dec Feb 0809 Dec 26 2 2 0 Dec Feb 0809 Jan 27 2 2 17.23673 Dec Feb 0809 Jan 28 2 2 0 Dec Feb 0809 Jan 29 2 2 0 Dec Feb 0809 Jan 30 2 2 16.92333 Dec Feb 0809 Feb 31 2 2 0 Dec Feb 0809 F eb 32 2 2 0 Dec Feb 0809 Feb 33 2 2 15.35636 Dec Feb 0809 Feb 34 2 2 0

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173 Mar May 09 Mar 35 2 2 13.47599 Mar May 09 Mar 36 2 2 13.47599 Mar May 09 Mar 37 2 2 25.69839 Mar May 09 Mar 38 2 2 0 Mar May 09 Mar 39 2 2 13.47599 Mar May 09 Apr 40 2 2 15.9831 5 Mar May 09 Apr 41 2 2 17.55012 Mar May 09 Apr 42 2 2 8.461665 Mar May 09 Apr 43 2 2 30.39932 Mar May 09 May 44 2 2 31.3395 Mar May 09 May 45 2 2 22.56444 Mar May 09 May 46 2 2 0 Mar May 09 May 47 2 2 0 Mar May 09 May 48 2 2 18.17691 JunAug 09 J un 49 2 2 0 JunAug 09 Jun 50 2 2 36.04043 JunAug 09 Jun 51 2 2 20.37068 JunAug 09 Jun 52 2 2 0 JunAug 09 Jul 53 2 2 0 JunAug 09 Jul 54 2 2 15.35636 JunAug 09 Jul 55 2 2 0 JunAug 09 Jul 56 2 2 16.92333 JunAug 09 Aug 57 2 2 21.31086 JunAug 09 Aug 58 2 2 0 JunAug 09 Aug 59 2 2 0 JunAug 09 Aug 60 2 2 10.34204 JunAug 09 Aug 61 2 2 0 JunAug 08 Jul 1 2 3 0 JunAug 08 Jul 2 2 3 17.23673 JunAug 08 Jul 3 2 3 17.86352 JunAug 08 Jul 4 2 3 13.78938 JunAug 08 Aug 5 2 3 0 JunAug 08 Aug 6 2 3 10.96883 JunAug 08 Aug 7 2 3 0 JunAug 08 Aug 8 2 3 0 JunAug 08 Aug 9 2 3 17.23673 Sep Nov 08 Sep 10 2 3 0 Sep Nov 08 Sep 11 2 3 0 Sep Nov 08 Sep 12 2 3 11.28222 Sep Nov 08 Sep 13 2 3 29.77253 Sep Nov 08 Oct 14 2 3 15.04296 Sep Nov 08 Oct 1 5 2 3 15.04296 Sep Nov 08 Oct 16 2 3 15.04296

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174 Sep Nov 08 Oct 17 2 3 0 Sep Nov 08 Oct 18 2 3 13.78938 Sep Nov 08 Nov 19 2 3 16.92333 Sep Nov 08 Nov 20 2 3 0 Sep Nov 08 Nov 21 2 3 17.23673 Sep Nov 08 Nov 22 2 3 0 Dec Feb 0809 Dec 23 2 3 0 Dec Feb 08 09 Dec 24 2 3 17.23673 Dec Feb 0809 Dec 25 2 3 0 Dec Feb 0809 Dec 26 2 3 0 Dec Feb 0809 Jan 27 2 3 17.23673 Dec Feb 0809 Jan 28 2 3 0 Dec Feb 0809 Jan 29 2 3 0 Dec Feb 0809 Jan 30 2 3 16.92333 Dec Feb 0809 Feb 31 2 3 0 Dec Feb 0809 Feb 32 2 3 0 De c Feb 0809 Feb 33 2 3 17.55012 Dec Feb 0809 Feb 34 2 3 0 Mar May 09 Mar 35 2 3 15.66975 Mar May 09 Mar 36 2 3 15.66975 Mar May 09 Mar 37 2 3 22.56444 Mar May 09 Mar 38 2 3 0 Mar May 09 Mar 39 2 3 15.04296 Mar May 09 Apr 40 2 3 16.29654 Mar May 09 A pr 41 2 3 17.23673 Mar May 09 Apr 42 2 3 7.52148 Mar May 09 Apr 43 2 3 31.02611 Mar May 09 May 44 2 3 31.3395 Mar May 09 May 45 2 3 22.87784 Mar May 09 May 46 2 3 0 Mar May 09 May 47 2 3 0 Mar May 09 May 48 2 3 18.17691 JunAug 09 Jun 49 2 3 0

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175 JunAug 09 Jun 50 2 3 36.66722 JunAug 09 Jun 51 2 3 20.99747 JunAug 09 Jun 52 2 3 0 JunAug 09 Jul 53 2 3 0 JunAug 09 Jul 54 2 3 15.04296 JunAug 09 Jul 55 2 3 0 JunAug 09 Jul 56 2 3 17.23673 JunAug 09 Aug 57 2 3 21.62426 JunAug 09 Aug 58 2 3 0 JunAug 09 Aug 59 2 3 0 JunAug 09 Aug 60 2 3 10.65543 JunAug 08 Jul 1 2 4 0 JunAug 08 Jul 2 2 4 17.55012 JunAug 08 Jul 3 2 4 18.8037 JunAug 08 Jul 4 2 4 14.72957 JunAug 08 Aug 5 2 4 0 JunAug 08 Aug 6 2 4 10.65543 JunAug 08 Aug 7 2 4 0 JunAug 08 Aug 8 2 4 0 JunAug 08 Aug 9 2 4 17.55012 Sep Nov 08 Sep 10 2 4 0 Sep Nov 08 Sep 11 2 4 0 Sep Nov 08 Sep 12 2 4 9.40185 Sep Nov 08 Sep 13 2 4 30.71271 Sep Nov 08 Oct 14 2 4 15.04296 Sep Nov 08 Oct 15 2 4 15.04296 Sep Nov 08 Oct 16 2 4 15.04 296 Sep Nov 08 Oct 17 2 4 0 Sep Nov 08 Oct 18 2 4 13.78938 Sep Nov 08 Nov 19 2 4 17.23673 Sep Nov 08 Nov 20 2 4 0 Sep Nov 08 Nov 21 2 4 17.55012 Sep Nov 08 Nov 22 2 4 0 Dec Feb 0809 Dec 23 2 4 0 Dec Feb 0809 Dec 24 2 4 17.55012 Dec Feb 0809 Dec 25 2 4 0 Dec Feb 0809 Dec 26 2 4 0 Dec Feb 0809 Jan 27 2 4 15.98315 Dec Feb Jan 28 2 4 0

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176 0809 Dec Feb 0809 Jan 29 2 4 0 Dec Feb 0809 Jan 30 2 4 17.23673 Dec Feb 0809 Feb 31 2 4 0 Dec Feb 0809 Feb 32 2 4 0 Dec Feb 0809 Feb 33 2 4 18.17691 Dec Feb 080 9 Feb 34 2 4 0 Mar May 09 Mar 35 2 4 17.23673 Mar May 09 Mar 36 2 4 17.86352 Mar May 09 Mar 37 2 4 29.14574 Mar May 09 Mar 38 2 4 0 Mar May 09 Mar 39 2 4 15.98315 Mar May 09 Apr 40 2 4 16.29654 Mar May 09 Apr 41 2 4 16.92333 Mar May 09 Apr 42 2 4 7 .208085 Mar May 09 Apr 43 2 4 30.08592 Mar May 09 May 44 2 4 30.71271 Mar May 09 May 45 2 4 22.25105 Mar May 09 May 46 2 4 0 Mar May 09 May 47 2 4 0 Mar May 09 May 48 2 4 17.86352 JunAug 09 Jun 49 2 4 0 JunAug 09 Jun 50 2 4 35.72703 JunAug 09 J un 51 2 4 20.37068 JunAug 09 Jun 52 2 4 0 JunAug 09 Jul 53 2 4 0 JunAug 09 Jul 54 2 4 15.04296 JunAug 09 Jul 55 2 4 0 JunAug 09 Jul 56 2 4 16.92333 JunAug 09 Aug 57 2 4 21.31086 JunAug 09 Aug 58 2 4 0 JunAug 09 Aug 59 2 4 0 JunAug 09 Aug 60 2 4 10.34204 JunAug 09 Aug 61 2 4 0 JunAug 08 Jul 1 6 1 15.35636 JunAug 08 Jul 2 6 1 14.72957 JunAug 08 Jul 3 6 1 30.71271 JunAug 08 Jul 4 6 1 27.57876

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177 JunAug 08 Aug 5 6 1 13.47599 JunAug 08 Aug 6 6 1 29.14574 JunAug 08 Aug 7 6 1 15.6697 5 JunAug 08 Aug 8 6 1 14.72957 JunAug 08 Aug 9 6 1 14.41617 Sep Nov 08 Sep 10 6 1 0 Sep Nov 08 Sep 11 6 1 6.581295 Sep Nov 08 Sep 12 6 1 13.47599 Sep Nov 08 Sep 13 6 1 21.62426 Sep Nov 08 Oct 14 6 1 15.35636 Sep Nov 08 Oct 15 6 1 14.72957 Sep No v 08 Oct 16 6 1 0 Sep Nov 08 Oct 17 6 1 15.04296 Sep Nov 08 Oct 18 6 1 14.10278 Sep Nov 08 Nov 19 6 1 16.60994 Sep Nov 08 Nov 20 6 1 0 Sep Nov 08 Nov 21 6 1 15.98315 Sep Nov 08 Nov 22 6 1 0 Dec Feb 0809 Dec 23 6 1 16.60994 Dec Feb 0809 Dec 24 6 1 0 Dec Feb 0809 Dec 25 6 1 0 Dec Feb 0809 Dec 26 6 1 15.66975 Dec Feb 0809 Jan 27 6 1 0 Dec Feb 0809 Jan 28 6 1 15.04296 Dec Feb 0809 Jan 29 6 1 0 Dec Feb 0809 Jan 30 6 1 0 Dec Feb 0809 Feb 31 6 1 15.35636 Dec Feb 0809 Feb 32 6 1 6.89469 Dec Feb 080 9 Feb 33 6 1 8.14827 Dec Feb 0809 Feb 34 6 1 10.65543 Mar May 09 Mar 35 6 1 14.72957 Mar May 09 Mar 36 6 1 17.23673 Mar May 09 Mar 37 6 1 21.62426

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178 Mar May 09 Mar 38 6 1 22.56444 Mar May 09 Mar 39 6 1 24.13142 Mar May 09 Apr 40 6 1 14.41617 Mar May 09 Apr 41 6 1 18.17691 Mar May 09 Apr 42 6 1 23.50463 Mar May 09 Apr 43 6 1 14.41617 Mar May 09 May 44 6 1 30.71271 Mar May 09 May 45 6 1 27.89216 Mar May 09 May 46 6 1 24.75821 Mar May 09 May 47 6 1 29.77253 Mar May 09 May 48 6 1 15.66975 JunAug 09 Jun 49 6 1 15.35636 JunAug 09 Jun 50 6 1 15.66975 JunAug 09 Jun 51 6 1 29.14574 JunAug 09 Jun 52 6 1 0 JunAug 09 Jul 53 6 1 0 JunAug 09 Jul 54 6 1 0 JunAug 09 Jul 55 6 1 0 JunAug 09 Jul 56 6 1 13.16259 JunAug 09 Aug 57 6 1 13.78938 JunAug 09 Aug 58 6 1 7.52148 JunAug 09 Aug 59 6 1 0 JunAug 09 Aug 60 6 1 13.16259 JunAug 09 Aug 61 6 1 0 JunAug 08 Jul 1 6 2 16.29654 JunAug 08 Jul 2 6 2 15.04296 JunAug 08 Jul 3 6 2 30.08592 JunAug 08 Jul 4 6 2 28.20555 JunAug 08 Aug 5 6 2 14.10278 JunAug 08 Aug 6 6 2 30.08592 JunAug 08 Aug 7 6 2 16.29654 JunAug 08 Aug 8 6 2 15.35636 JunAug 08 Aug 9 6 2 14.72957 Sep Nov 08 Sep 10 6 2 0 Sep Nov 08 Sep 11 6 2 7.208085 Sep Nov 08 Sep 12 6 2 13.78938 Sep Nov 08 Sep 13 6 2 22.56444 Sep Nov 08 Oct 14 6 2 15.98315 Sep Nov 08 Oct 15 6 2 15.35636 Sep Nov 08 Oct 16 6 2 0 Sep Nov 08 Oct 17 6 2 15.66975 Sep Nov 08 Oct 18 6 2 14.41617 Sep Nov 08 Nov 19 6 2 17.23673

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179 Sep Nov 08 Nov 20 6 2 0 Sep Nov 08 Nov 21 6 2 16.29654 Sep Nov 08 Nov 22 6 2 0 Dec Feb 0809 Dec 23 6 2 16.92333 Dec Feb 0809 Dec 24 6 2 0 Dec Feb 0809 Dec 25 6 2 0 Dec Feb 0809 Dec 26 6 2 16.29654 Dec Feb 0809 Jan 27 6 2 0 Dec Feb 0809 Jan 28 6 2 15.35636 Dec Feb 0809 Jan 29 6 2 0 Dec Feb 0809 Jan 30 6 2 0 Dec Feb 080 9 Feb 31 6 2 16.60994 Dec Feb 0809 Feb 32 6 2 7.834875 Dec Feb 0809 Feb 33 6 2 8.77506 Dec Feb 0809 Feb 34 6 2 13.16259 Mar May 09 Mar 35 6 2 15.04296 Mar May 09 Mar 36 6 2 17.55012 Mar May 09 Mar 37 6 2 23.19123 Mar May 09 Mar 38 6 2 25.69839 Mar May 09 Mar 39 6 2 27.26537 Mar May 09 Apr 40 6 2 15.35636 Mar May 09 Apr 41 6 2 20.68407 Mar May 09 Apr 42 6 2 25.69839 Mar May 09 Apr 43 6 2 15.66975 Mar May 09 May 44 6 2 32.90648 Mar May 09 May 45 6 2 30.39932 Mar May 09 May 46 6 2 26.63858 Mar May 09 May 47 6 2 32.27969 Mar May 09 May 48 6 2 16.60994 JunAug 09 Jun 49 6 2 16.60994 JunAug 09 Jun 50 6 2 16.60994 JunAug 09 Jun 51 6 2 31.6529 JunAug 09 Jun 52 6 2 0

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180 JunAug 09 Jul 53 6 2 0 JunAug 09 Jul 54 6 2 0 JunAug 09 Jul 55 6 2 0 J unAug 09 Jul 56 6 2 14.41617 JunAug 09 Aug 57 6 2 13.78938 JunAug 09 Aug 58 6 2 7.834875 JunAug 09 Aug 59 6 2 0 JunAug 09 Aug 60 6 2 13.47599 JunAug 09 Aug 61 6 2 0 JunAug 08 Jul 1 6 3 15.66975 JunAug 08 Jul 2 6 3 15.04296 JunAug 08 Jul 3 6 3 31.02611 JunAug 08 Jul 4 6 3 28.20555 JunAug 08 Aug 5 6 3 13.47599 JunAug 08 Aug 6 6 3 26.01179 JunAug 08 Aug 7 6 3 14.72957 JunAug 08 Aug 8 6 3 14.10278 JunAug 08 Aug 9 6 3 13.16259 Sep Nov 08 Sep 10 6 3 0 Sep Nov 08 Sep 11 6 3 6.89469 Sep Nov 08 Sep 12 6 3 13.78938 Sep Nov 08 Sep 13 6 3 21.93765 Sep Nov 08 Oct 14 6 3 15.66975 Sep Nov 08 Oct 15 6 3 15.04296 Sep Nov 08 Oct 16 6 3 0 Sep Nov 08 Oct 17 6 3 15.35636 Sep Nov 08 Oct 18 6 3 14.41617 Sep Nov 08 Nov 19 6 3 16.92333 Sep Nov 08 Nov 20 6 3 0 Sep Nov 08 Nov 21 6 3 16.29654 Sep Nov 08 Nov 22 6 3 0 Dec Feb 0809 Dec 23 6 3 16.60994 Dec Feb 0809 Dec 24 6 3 0 Dec Feb 0809 Dec 25 6 3 0 Dec Feb 0809 Dec 26 6 3 15.66975 Dec Feb 0809 Jan 27 6 3 0 Dec Feb 0809 Jan 28 6 3 15.04296 Dec Feb Jan 29 6 3 0

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181 0809 Dec Feb 0809 Jan 30 6 3 0 Dec Feb 0809 Feb 31 6 3 15.66975 Dec Feb 0809 Feb 32 6 3 7.834875 Dec Feb 0809 Feb 33 6 3 8.77506 Dec Feb 0809 Feb 34 6 3 12.22241 Mar May 09 Mar 35 6 3 15.04296 Mar May 09 Mar 36 6 3 16.92333 M ar May 09 Mar 37 6 3 21.62426 Mar May 09 Mar 38 6 3 23.50463 Mar May 09 Mar 39 6 3 24.75821 Mar May 09 Apr 40 6 3 14.72957 Mar May 09 Apr 41 6 3 19.43049 Mar May 09 Apr 42 6 3 24.44481 Mar May 09 Apr 43 6 3 15.04296 Mar May 09 May 44 6 3 31.3395 Ma r May 09 May 45 6 3 28.83234 Mar May 09 May 46 6 3 25.385 Mar May 09 May 47 6 3 30.71271 Mar May 09 May 48 6 3 15.98315 JunAug 09 Jun 49 6 3 15.35636 JunAug 09 Jun 50 6 3 15.98315 JunAug 09 Jun 51 6 3 30.08592 JunAug 09 Jun 52 6 3 0 JunAug 09 Jul 53 6 3 0 JunAug 09 Jul 54 6 3 0 JunAug 09 Jul 55 6 3 0 JunAug 09 Jul 56 6 3 13.78938 JunAug 09 Aug 57 6 3 13.78938 Jun Aug 09 Aug 58 6 3 7.834875 JunAug 09 Aug 59 6 3 0 JunAug 09 Aug 60 6 3 13.47599 JunAug 09 Aug 61 6 3 0 JunAug 08 Jul 1 6 4 14.72957 JunAug 08 Jul 2 6 4 15.04296 JunAug 08 Jul 3 6 4 30.39932 JunAug 08 Jul 4 6 4 27.57876 JunAug 08 Aug 5 6 4 13.47599 JunAug 08 Aug 6 6 4 28.83234

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182 JunAug 08 Aug 7 6 4 15.66975 JunAug 08 Aug 8 6 4 14.72957 JunAug 08 Aug 9 6 4 1 4.10278 Sep Nov 08 Sep 10 6 4 0 Sep Nov 08 Sep 11 6 4 6.581295 Sep Nov 08 Sep 12 6 4 13.47599 Sep Nov 08 Sep 13 6 4 21.62426 Sep Nov 08 Oct 14 6 4 15.04296 Sep Nov 08 Oct 15 6 4 14.41617 Sep Nov 08 Oct 16 6 4 0 Sep Nov 08 Oct 17 6 4 15.04296 Sep N ov 08 Oct 18 6 4 13.47599 Sep Nov 08 Nov 19 6 4 16.29654 Sep Nov 08 Nov 20 6 4 0 Sep Nov 08 Nov 21 6 4 15.66975 Sep Nov 08 Nov 22 6 4 0 Dec Feb 0809 Dec 23 6 4 16.29654 Dec Feb 0809 Dec 24 6 4 0 Dec Feb 0809 Dec 25 6 4 0 Dec Feb 0809 Dec 26 6 4 15. 35636 Dec Feb 0809 Jan 27 6 4 0 Dec Feb 0809 Jan 28 6 4 14.72957 Dec Feb 0809 Jan 29 6 4 0 Dec Feb 0809 Jan 30 6 4 0 Dec Feb 0809 Feb 31 6 4 14.41617 Dec Feb 0809 Feb 32 6 4 6.89469 Dec Feb 0809 Feb 33 6 4 7.834875 Dec Feb 0809 Feb 34 6 4 12.22241 Mar May 09 Mar 35 6 4 14.72957 Mar May 09 Mar 36 6 4 17.23673 Mar May 09 Mar 37 6 4 21.31086 Mar May 09 Mar 38 6 4 22.87784 Mar May 09 Mar 39 6 4 24.44481

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183 Mar May 09 Apr 40 6 4 14.41617 Mar May 09 Apr 41 6 4 18.8037 Mar May 09 Apr 42 6 4 23.81802 Mar May 09 Apr 43 6 4 14.72957 Mar May 09 May 44 6 4 30.71271 Mar May 09 May 45 6 4 27.89216 Mar May 09 May 46 6 4 19.1171 Mar May 09 May 47 6 4 30.08592 Mar May 09 May 48 6 4 15.66975 JunAug 09 Jun 49 6 4 15.35636 JunAug 09 Jun 50 6 4 15.66975 J unAug 09 Jun 51 6 4 29.45913 JunAug 09 Jun 52 6 4 0 JunAug 09 Jul 53 6 4 0 JunAug 09 Jul 54 6 4 0 JunAug 09 Jul 55 6 4 0 JunAug 09 Jul 56 6 4 13.78938 JunAug 09 Aug 57 6 4 13.78938 JunAug 09 Aug 58 6 4 7.834875 JunAug 09 Aug 59 6 4 0 JunAug 09 Aug 60 6 4 13.16259 JunAug 09 Aug 61 6 4 0 JunAug 08 Jul 1 3 1 2.50716 JunAug 08 Jul 2 3 1 14.72957 JunAug 08 Jul 3 3 1 31.02611 JunAug 08 Jul 4 3 1 13.78938 JunAug 08 Aug 5 3 1 52.33697 JunAug 08 Aug 6 3 1 0 JunAug 08 Aug 7 3 1 14.72957 JunAug 08 Aug 8 3 1 15.66975 JunAug 08 Aug 9 3 1 0 Sep Nov 08 Sep 10 3 1 0 Sep Nov 08 Sep 11 3 1 0 Sep Nov 08 Sep 12 3 1 27.26537 Sep Nov 08 Sep 13 3 1 16.29654 Sep Nov 08 Oct 14 3 1 18.17691 Sep Nov 08 Oct 15 3 1 0 Sep Nov 08 Oct 16 3 1 17.86352 Sep Nov 08 Oct 17 3 1 0 Sep Nov 08 Oct 18 3 1 17.86352 Sep Nov 08 Nov 19 3 1 17.23673 Sep Nov 08 Nov 20 3 1 0 Sep Nov 08 Nov 21 3 1 18.49031

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184 Sep Nov 08 Nov 22 3 1 0 Dec Feb 0809 Dec 23 3 1 0 Dec Feb 0809 Dec 24 3 1 0 Dec Feb 0809 Dec 25 3 1 18.17691 Dec Feb 0809 Dec 26 3 1 0 Dec Feb 0809 Jan 27 3 1 17.86352 Dec Feb 0809 Jan 28 3 1 0 Dec Feb 0809 Jan 29 3 1 0 Dec Feb 0809 Jan 30 3 1 16.60994 Dec Feb 0809 Feb 31 3 1 0 Dec Feb 0809 Feb 32 3 1 0 Dec Feb 0809 Feb 33 3 1 16.29654 Dec Feb 0809 Feb 34 3 1 16.29654 Mar May 09 Mar 35 3 1 0 Mar May 09 Mar 36 3 1 32.90648 Mar May 09 Mar 37 3 1 17.23673 Mar May 09 Mar 38 3 1 14.72957 Mar May 09 Mar 39 3 1 15.35636 Mar May 09 Apr 40 3 1 15.66975 Mar May 09 Apr 41 3 1 20.68407 Mar May 09 Ap r 42 3 1 16.92333 Mar May 09 Apr 43 3 1 18.8037 Mar May 09 May 44 3 1 35.41364 Mar May 09 May 45 3 1 27.89216 Mar May 09 May 46 3 1 16.60994 Mar May 09 May 47 3 1 15.66975 Mar May 09 May 48 3 1 15.04296 JunAug 09 Jul 53 3 1 16.60994 JunAug 09 Jul 54 3 1 18.49031 JunAug 09 Jul 55 3 1 16.29654 JunAug 09 Jul 56 3 1 17.86352 JunAug 09 Aug 57 3 1 18.8037 JunAug 09 Aug 58 3 1 0

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185 JunAug 09 Aug 59 3 1 15.98315 JunAug 09 Aug 60 3 1 34.16006 JunAug 09 Aug 61 3 1 15.98315 JunAug 08 Jul 1 3 2 1 .88037 JunAug 08 Jul 2 3 2 14.72957 JunAug 08 Jul 3 3 2 31.02611 JunAug 08 Jul 4 3 2 14.10278 JunAug 08 Aug 5 3 2 12.22241 JunAug 08 Aug 6 3 2 0 JunAug 08 Aug 7 3 2 14.72957 JunAug 08 Aug 8 3 2 15.98315 JunAug 08 Aug 9 3 2 0 Sep Nov 08 Sep 10 3 2 0 Sep Nov 08 Sep 11 3 2 0 Sep Nov 08 Sep 12 3 2 27.57876 Sep Nov 08 Sep 13 3 2 16.29654 Sep Nov 08 Oct 14 3 2 18.17691 Sep Nov 08 Oct 15 3 2 0 Sep Nov 08 Oct 16 3 2 18.17691 Sep Nov 08 Oct 17 3 2 0 Sep Nov 08 Oct 18 3 2 18.17691 Sep Nov 08 Nov 19 3 2 16.60994 Sep Nov 08 Nov 20 3 2 0 Sep Nov 08 Nov 21 3 2 18.49031 Sep Nov 08 Nov 22 3 2 0 Dec Feb 0809 Dec 23 3 2 0 Dec Feb 0809 Dec 24 3 2 0 Dec Feb 0809 Dec 25 3 2 18.49031 Dec Feb 0809 Dec 26 3 2 0 Dec Feb 0809 Jan 27 3 2 17.55012 Dec Feb 0809 Jan 28 3 2 0 Dec Feb 0809 Jan 29 3 2 0 Dec Feb 0809 Jan 30 3 2 17.86352 Dec Feb 0809 Feb 31 3 2 0 Dec Feb 0809 Feb 32 3 2 0

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186 Dec Feb 0809 Feb 33 3 2 16.60994 Dec Feb 0809 Feb 34 3 2 16.29654 Mar May 09 Mar 35 3 2 0 Mar May 09 Mar 36 3 2 31.96629 Mar May 09 Mar 37 3 2 16.29654 Mar May 09 Mar 38 3 2 13.78938 Mar May 09 Mar 39 3 2 15.35636 Mar May 09 Apr 40 3 2 15.04296 Mar May 09 Apr 41 3 2 18.8037 Mar May 09 Apr 42 3 2 16.92333 Mar May 09 Apr 43 3 2 17.86352 Mar May 09 May 44 3 2 34. 47345 Mar May 09 May 45 3 2 26.01179 Mar May 09 May 46 3 2 16.29654 Mar May 09 May 47 3 2 15.04296 Mar May 09 May 48 3 2 14.10278 JunAug 09 Jul 53 3 2 16.29654 JunAug 09 Jul 54 3 2 17.86352 JunAug 09 Jul 55 3 2 15.04296 JunAug 09 Jul 56 3 2 16. 92333 JunAug 09 Aug 57 3 2 18.17691 JunAug 09 Aug 58 3 2 0 JunAug 09 Aug 59 3 2 15.04296 JunAug 09 Aug 60 3 2 31.3395 JunAug 09 Aug 61 3 2 15.66975 JunAug 08 Jul 1 3 3 2.50716 JunAug 08 Jul 2 3 3 14.41617 JunAug 08 Jul 3 3 3 32.27969 JunA ug 08 Jul 4 3 3 14.41617 JunAug 08 Aug 5 3 3 13.16259 JunAug 08 Aug 6 3 3 0 JunAug 08 Aug 7 3 3 15.35636 JunAug 08 Aug 8 3 3 16.60994 JunAug 08 Aug 9 3 3 0 Sep Nov 08 Sep 10 3 3 0 Sep Nov 08 Sep 11 3 3 0 Sep Nov 08 Sep 12 3 3 27.89216 Sep Nov 08 Sep 13 3 3 16.29654 Sep Nov 08 Oct 14 3 3 18.17691 Sep Nov 08 Oct 15 3 3 0 Sep Nov 08 Oct 16 3 3 18.8037

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187 Sep Nov 08 Oct 17 3 3 0 Sep Nov 08 Oct 18 3 3 18.8037 Sep Nov 08 Nov 19 3 3 17.55012 Sep Nov 08 Nov 20 3 3 0 Sep Nov 08 Nov 21 3 3 18.49031 Sep Nov 08 Nov 22 3 3 0 Dec Feb 0809 Dec 23 3 3 0 Dec Feb 0809 Dec 24 3 3 0 Dec Feb 0809 Dec 25 3 3 18.49031 Dec Feb 0809 Dec 26 3 3 0 Dec Feb 0809 Jan 27 3 3 17.86352 Dec Feb 0809 Jan 28 3 3 0 Dec Feb 0809 Jan 29 3 3 0 Dec Feb 0809 Jan 30 3 3 18.49031 Dec Feb 0809 Feb 31 3 3 0 Dec Feb 0809 Feb 32 3 3 0 Dec Feb 0809 Feb 33 3 3 18.17691 Dec Feb 0809 Feb 34 3 3 18.17691 Mar May 09 Mar 35 3 3 0 Mar May 09 Mar 36 3 3 34.47345 Mar May 09 Mar 37 3 3 17.55012 Mar May 09 Mar 38 3 3 14.72957 Mar M ay 09 Mar 39 3 3 16.29654 Mar May 09 Apr 40 3 3 15.35636 Mar May 09 Apr 41 3 3 19.1171 Mar May 09 Apr 42 3 3 17.23673 Mar May 09 Apr 43 3 3 18.49031 Mar May 09 May 44 3 3 34.47345 Mar May 09 May 45 3 3 26.01179 Mar May 09 May 46 3 3 16.29654 Mar Ma y 09 May 47 3 3 13.78938 Mar May 09 May 48 3 3 13.16259 JunAug 09 Jun 49 3 3 14.10278

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188 JunAug 09 Jun 50 3 3 16.29654 JunAug 09 Jun 51 3 3 14.10278 JunAug 09 Jun 52 3 3 15.35636 JunAug 09 Jul 53 3 3 16.29654 JunAug 09 Jul 54 3 3 0 JunAug 09 Ju l 55 3 3 13.78938 JunAug 09 Jul 56 3 3 29.45913 JunAug 09 Aug 57 3 3 14.10278 JunAug 09 Aug 58 3 3 14.41617 JunAug 09 Aug 59 3 3 18.49031 JunAug 09 Aug 60 3 3 8.461665 JunAug 09 Aug 61 3 3 0 JunAug 08 Jul 1 3 4 2.50716 JunAug 08 Jul 2 3 4 1 5.04296 JunAug 08 Jul 3 3 4 31.6529 JunAug 08 Jul 4 3 4 13.78938 JunAug 08 Aug 5 3 4 13.16259 JunAug 08 Aug 6 3 4 0 JunAug 08 Aug 7 3 4 14.72957 JunAug 08 Aug 8 3 4 15.98315 JunAug 08 Aug 9 3 4 0 Sep Nov 08 Sep 10 3 4 0 Sep Nov 08 Sep 11 3 4 0 Sep Nov 08 Sep 12 3 4 28.51895 Sep Nov 08 Sep 13 3 4 16.60994 Sep Nov 08 Oct 14 3 4 18.49031 Sep Nov 08 Oct 15 3 4 0 Sep Nov 08 Oct 16 3 4 17.86352 Sep Nov 08 Oct 17 3 4 0 Sep Nov 08 Oct 18 3 4 18.49031 Sep Nov 08 Nov 19 3 4 17.23673 Sep Nov 0 8 Nov 20 3 4 0 Sep Nov 08 Nov 21 3 4 18.49031 Sep Nov 08 Nov 22 3 4 0 Dec Feb 0809 Dec 23 3 4 0 Dec Feb 0809 Dec 24 3 4 0 Dec Feb 0809 Dec 25 3 4 18.17691 Dec Feb 0809 Dec 26 3 4 0 Dec Feb 0809 Jan 27 3 4 18.17691

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189 Dec Feb 0809 Jan 28 3 4 0 Dec Feb 0809 Jan 29 3 4 0 Dec Feb 0809 Jan 30 3 4 17.86352 Dec Feb 0809 Feb 31 3 4 0 Dec Feb 0809 Feb 32 3 4 0 Dec Feb 0809 Feb 33 3 4 17.55012 Dec Feb 0809 Feb 34 3 4 17.55012 Mar May 09 Mar 35 3 4 0 Mar May 09 Mar 36 3 4 33.21987 Mar May 09 Mar 37 3 4 1 7.23673 Mar May 09 Mar 38 3 4 14.72957 Mar May 09 Mar 39 3 4 15.04296 Mar May 09 Apr 40 3 4 15.35636 Mar May 09 Apr 41 3 4 20.99747 Mar May 09 Apr 42 3 4 16.92333 Mar May 09 Apr 43 3 4 17.86352 Mar May 09 May 44 3 4 34.16006 Mar May 09 May 45 3 4 2 6.95197 Mar May 09 May 46 3 4 15.66975 Mar May 09 May 47 3 4 15.35636 Mar May 09 May 48 3 4 13.78938 JunAug 09 Jun 49 3 4 15.98315 JunAug 09 Jun 50 3 4 17.86352 JunAug 09 Jun 51 3 4 53.90394 JunAug 09 Jun 52 3 4 13.16259 JunAug 09 Jul 53 3 4 1 3.78938 JunAug 09 Jul 54 3 4 0 JunAug 09 Jul 55 3 4 11.59562 JunAug 09 Jul 56 3 4 25.385 JunAug 09 Aug 57 3 4 11.90901 JunAug 09 Aug 58 3 4 12.8492 JunAug 09 Aug 59 3 4 17.55012 JunAug 09 Aug 60 3 4 7.834875 JunAug 09 Aug 61 3 4 0 JunAug 08 Jul 1 7 1 16.92333 JunAug 08 Jul 2 7 1 31.6529 JunAug 08 Jul 3 7 1 29.77253

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190 JunAug 08 Jul 4 7 1 18.17691 JunAug 08 Aug 5 7 1 34.16006 JunAug 08 Aug 6 7 1 32.90648 JunAug 08 Aug 7 7 1 15.35636 JunAug 08 Aug 8 7 1 17.23673 JunAug 08 Aug 9 7 1 18.8037 Sep Nov 08 Sep 10 7 1 0 Sep Nov 08 Sep 11 7 1 0 Sep Nov 08 Sep 12 7 1 12.22241 Sep Nov 08 Sep 13 7 1 33.21987 Sep Nov 08 Oct 14 7 1 14.10278 Sep Nov 08 Oct 15 7 1 17.23673 Sep Nov 08 Oct 16 7 1 16.29654 Sep Nov 08 Oct 17 7 1 0 Sep Nov 08 Oct 18 7 1 15.66975 Sep Nov 08 Nov 19 7 1 17.55012 Sep Nov 08 Nov 20 7 1 0 Sep Nov 08 Nov 21 7 1 16.92333 Sep Nov 08 Nov 22 7 1 0 Dec Feb 0809 Dec 23 7 1 17.55012 Dec Feb 0809 Dec 24 7 1 0 Dec Feb 0809 Dec 25 7 1 0 Dec Feb 0809 Dec 26 7 1 17.86352 Dec Feb 0809 Jan 27 7 1 17.86352 Dec Feb 0809 Jan 28 7 1 0 Dec Feb 0809 Jan 29 7 1 0 Dec Feb 0809 Jan 30 7 1 17.55012 Dec Feb 0809 Feb 31 7 1 0 Dec Feb 0809 Feb 32 7 1 17.55012 Dec Feb 0809 Feb 33 7 1 0 Dec Feb 0809 Feb 34 7 1 16.92333 Mar May 09 Mar 35 7 1 16.92333 Mar May 09 Mar 36 7 1 31.02611

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191 Mar May 09 Mar 37 7 1 31.02611 Mar May 09 Mar 38 7 1 14.72957 Mar May 09 Mar 39 7 1 31.3395 Mar May 09 Apr 40 7 1 17.23673 Mar May 09 Apr 41 7 1 25.385 Mar May 09 Apr 42 7 1 10.65543 Mar May 09 Apr 43 7 1 30.08592 Mar May 09 May 44 7 1 31.6529 Mar May 09 May 45 7 1 15.98315 Mar May 09 May 46 7 1 15.35636 Mar May 09 May 47 7 1 31.96629 Mar May 09 May 48 7 1 28.51895 JunAug 09 Jun 49 7 1 13.47599 JunAug 09 Jun 50 7 1 15.04296 JunAug 09 J un 51 7 1 31.3395 JunAug 09 Jun 52 7 1 15.98315 JunAug 09 Jul 53 7 1 15.35636 JunAug 09 Jul 54 7 1 15.66975 JunAug 09 Jul 55 7 1 31.96629 JunAug 09 Jul 56 7 1 13.78938 JunAug 09 Aug 57 7 1 26.32518 JunAug 09 Aug 58 7 1 26.95197 JunAug 09 Au g 59 7 1 14.41617 JunAug 09 Aug 60 7 1 27.89216 JunAug 09 Aug 61 7 1 12.5358 JunAug 08 Jul 1 7 2 18.17691 JunAug 08 Jul 2 7 2 34.47345 JunAug 08 Jul 3 7 2 32.27969 JunAug 08 Jul 4 7 2 17.86352 JunAug 08 Aug 5 7 2 34.47345 JunAug 08 Aug 6 7 2 32.90648 JunAug 08 Aug 7 7 2 15.66975 JunAug 08 Aug 8 7 2 17.23673 JunAug 08 Aug 9 7 2 18.8037 Sep Nov 08 Sep 10 7 2 0 Sep Nov 08 Sep 11 7 2 0 Sep Nov 08 Sep 12 7 2 12.5358 Sep Nov 08 Sep 13 7 2 34.16006 Sep Nov 08 Oct 14 7 2 14.41617 Sep Nov 08 Oct 15 7 2 17.86352 Sep Nov 08 Oct 16 7 2 16.60994 Sep Nov 08 Oct 17 7 2 0 Sep Nov 08 Oct 18 7 2 16.29654

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192 Sep Nov 08 Nov 19 7 2 17.86352 Sep Nov 08 Nov 20 7 2 0 Sep Nov 08 Nov 21 7 2 17.55012 Sep Nov 08 Nov 22 7 2 0 Dec Feb 0809 Dec 23 7 2 17.86352 Dec Feb 0809 Dec 24 7 2 0 Dec Feb 0809 Dec 25 7 2 0 Dec Feb 0809 Dec 26 7 2 18.17691 Dec Feb 0809 Jan 27 7 2 18.17691 Dec Feb 0809 Jan 28 7 2 0 Dec Feb 0809 Jan 29 7 2 0 Dec Feb 0809 Jan 30 7 2 16.92333 Dec Feb 0809 Feb 31 7 2 0 Dec Feb 0809 Feb 32 7 2 17.55012 Dec Feb 0809 Feb 33 7 2 0 Dec Feb 0809 Feb 34 7 2 16.92333 Mar May 09 Mar 35 7 2 18.17691 Mar May 09 Mar 36 7 2 31.96629 Mar May 09 Mar 37 7 2 30.71271 Mar May 09 Mar 38 7 2 14.72957 Mar May 09 Mar 39 7 2 31.3395 Mar May 09 Apr 40 7 2 17.23673 Mar May 09 Apr 41 7 2 26.32518 Mar May 09 Apr 42 7 2 10.96883 Mar May 09 Apr 43 7 2 30.39932 Mar May 09 May 44 7 2 32.27969 Mar May 09 May 45 7 2 15.98315 Mar May 09 May 46 7 2 15.66975 Mar May 09 May 47 7 2 32.59308 Mar May 09 May 48 7 2 28.83234 JunAug 09 Jun 49 7 2 13.78938 JunAug 09 Jun 50 7 2 15.04296 JunAug 09 Jun 51 7 2 31.3395

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193 JunAug 09 Jun 52 7 2 15.66975 JunAug 09 Jul 53 7 2 15.98315 JunAug 09 Jul 54 7 2 16.29654 JunAug 09 Jul 55 7 2 33.21987 JunAug 09 Jul 5 6 7 2 13.78938 JunAug 09 Aug 57 7 2 26.95197 JunAug 09 Aug 58 7 2 26.95197 JunAug 09 Aug 59 7 2 14.41617 JunAug 09 Aug 60 7 2 27.89216 JunAug 09 Aug 61 7 2 12.5358 JunAug 08 Jul 1 7 3 17.55012 JunAug 08 Jul 2 7 3 32.27969 JunAug 08 Jul 3 7 3 30.71271 JunAug 08 Jul 4 7 3 16.92333 JunAug 08 Aug 5 7 3 32.90648 JunAug 08 Aug 6 7 3 31.3395 JunAug 08 Aug 7 7 3 15.35636 JunAug 08 Aug 8 7 3 16.29654 JunAug 08 Aug 9 7 3 18.17691 Sep Nov 08 Sep 10 7 3 0 Sep Nov 08 Sep 11 7 3 0 Sep Nov 0 8 Sep 12 7 3 12.5358 Sep Nov 08 Sep 13 7 3 32.27969 Sep Nov 08 Oct 14 7 3 12.8492 Sep Nov 08 Oct 15 7 3 16.60994 Sep Nov 08 Oct 16 7 3 15.66975 Sep Nov 08 Oct 17 7 3 0 Sep Nov 08 Oct 18 7 3 15.04296 Sep Nov 08 Nov 19 7 3 17.23673 Sep Nov 08 Nov 20 7 3 0 Sep Nov 08 Nov 21 7 3 16.60994 Sep Nov 08 Nov 22 7 3 0 Dec Feb 0809 Dec 23 7 3 16.92333 Dec Feb 0809 Dec 24 7 3 0 Dec Feb 0809 Dec 25 7 3 0 Dec Feb 0809 Dec 26 7 3 17.23673 Dec Feb 0809 Jan 27 7 3 17.23673 Dec Feb 0809 Jan 28 7 3 0

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194 Dec Feb 0 809 Jan 29 7 3 0 Dec Feb 0809 Jan 30 7 3 17.23673 Dec Feb 0809 Feb 31 7 3 0 Dec Feb 0809 Feb 32 7 3 17.86352 Dec Feb 0809 Feb 33 7 3 0 Dec Feb 0809 Feb 34 7 3 17.55012 Mar May 09 Mar 35 7 3 15.66975 Mar May 09 Mar 36 7 3 30.71271 Mar May 09 Mar 37 7 3 30.71271 Mar May 09 Mar 38 7 3 15.04296 Mar May 09 Mar 39 7 3 31.3395 Mar May 09 Apr 40 7 3 17.23673 Mar May 09 Apr 41 7 3 24.13142 Mar May 09 Apr 42 7 3 10.34204 Mar May 09 Apr 43 7 3 30.08592 Mar May 09 May 44 7 3 31.6529 Mar May 09 May 45 7 3 15.98315 Mar May 09 May 46 7 3 15.66975 Mar May 09 May 47 7 3 32.59308 Mar May 09 May 48 7 3 28.83234 JunAug 09 Jun 49 7 3 13.78938 JunAug 09 Jun 50 7 3 15.04296 JunAug 09 Jun 51 7 3 30.71271 JunAug 09 Jun 52 7 3 15.66975 JunAug 09 Jul 53 7 3 15.98315 JunAug 09 Jul 54 7 3 15.66975 JunAug 09 Jul 55 7 3 31.96629 JunAug 09 Jul 56 7 3 13.47599 JunAug 09 Aug 57 7 3 24.13142 JunAug 09 Aug 58 7 3 26.95197 JunAug 09 Aug 59 7 3 14.41617 JunAug 09 Aug 60 7 3 27.89216 JunAug 09 Aug 61 7 3 12.22241 JunAug 08 Jul 1 7 4 16.29654 JunAug 08 Jul 2 7 4 30.08592 JunAug 08 Jul 3 7 4 27.89216 JunAug 08 Jul 4 7 4 16.92333 JunAug 08 Aug 5 7 4 34.16006

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195 JunAug 08 Aug 6 7 4 31.6529 JunAug 08 Aug 7 7 4 15.35636 JunAug 08 Aug 8 7 4 17.2367 3 JunAug 08 Aug 9 7 4 18.8037 Sep Nov 08 Sep 10 7 4 0 Sep Nov 08 Sep 11 7 4 0 Sep Nov 08 Sep 12 7 4 12.5358 Sep Nov 08 Sep 13 7 4 32.90648 Sep Nov 08 Oct 14 7 4 14.10278 Sep Nov 08 Oct 15 7 4 16.60994 Sep Nov 08 Oct 16 7 4 15.66975 Sep Nov 08 Oct 17 7 4 0 Sep Nov 08 Oct 18 7 4 15.66975 Sep Nov 08 Nov 19 7 4 17.55012 Sep Nov 08 Nov 20 7 4 0 Sep Nov 08 Nov 21 7 4 16.60994 Sep Nov 08 Nov 22 7 4 0 Dec Feb 0809 Dec 23 7 4 16.92333 Dec Feb 0809 Dec 24 7 4 0 Dec Feb 0809 Dec 25 7 4 0 Dec Feb 080 9 Dec 26 7 4 16.92333 Dec Feb 0809 Jan 27 7 4 16.92333 Dec Feb 0809 Jan 28 7 4 0 Dec Feb 0809 Jan 29 7 4 0 Dec Feb 0809 Jan 30 7 4 16.92333 Dec Feb 0809 Feb 31 7 4 0 Dec Feb 0809 Feb 32 7 4 17.55012 Dec Feb 0809 Feb 33 7 4 0 Dec Feb 0809 Feb 34 7 4 16.92333 Mar May 09 Mar 35 7 4 16.60994 Mar May 09 Mar 36 7 4 30.39932 Mar May 09 Mar 37 7 4 30.71271 Mar May 09 Mar 38 7 4 14.10278

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196 Mar May 09 Mar 39 7 4 31.02611 Mar May 09 Apr 40 7 4 16.60994 Mar May 09 Apr 41 7 4 25.69839 Mar May 09 Apr 42 7 4 10.65543 Mar May 09 Apr 43 7 4 29.45913 Mar May 09 May 44 7 4 31.02611 Mar May 09 May 45 7 4 15.98315 Mar May 09 May 46 7 4 15.66975 Mar May 09 May 47 7 4 33.53327 Mar May 09 May 48 7 4 29.45913 JunAug 09 Jun 49 7 4 13.78938 JunAug 09 Jun 50 7 4 15.35636 JunAug 09 Jun 51 7 4 31.96629 JunAug 09 Jun 52 7 4 16.29654 JunAug 09 Jul 53 7 4 15.98315 JunAug 09 Jul 54 7 4 15.35636 JunAug 09 Jul 55 7 4 31.96629 JunAug 09 Jul 56 7 4 13.78938 JunAug 09 Aug 57 7 4 27.26537 JunAug 09 Aug 58 7 4 28.20555 JunAug 09 Aug 59 7 4 14.41617 JunAug 09 Aug 60 7 4 27.26537 JunAug 09 Aug 61 7 4 13.16259

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197 WORKS CITED Allen, R.G., Pereira, L.S., Raes, D., Smith, M. (1998). FAO Irrigation and Drainage Paper No. 56 Crop Evapotranspiration: guidelines for computing crop water requirements. < http://www.kimberly.uidaho.edu/ref et/fao56.pdf > (29 July, 2009). Aquacraft. (2003). Analysis of Operation of WeatherTRAK Controller in Field Conditions During 2002. Boulder, CO. < http://www.irrigation.org/swat/images/boulder.pdf > (16 October, 2009). Bamezai, A. (2004). LADWP Weather Based Irrigation Controller Pilot Study. Western Policy R esearch, Santa Monica, CA. < http://www.weathertrak.com/pdfs/studies/LADWP_pilot_2002_2003.pdf > (16 October, 2009). Beard, J. B. and Green, R. L. (1994). The Role of Turfgra sses in Environmental Protection and their Benefits to Humans. Journal of Environmental Equality Vol. 23. < http://jeq.scijournals.org/cgi/content/abstract/23/3/452.> (4 October, 2009 ). Berg, J., Wiedmann, J., Ash, T., D., P., Marian, M., & Bamezai, A. (2001). Residential Weather -Based Irrigation Scheduling: Evidence from the Irvine "ET Controller" Study. Irvine, CA. < htt p://www.irrigation.org/swat/images/irvine.pdf > (16 October, 2009). Buss E. A. and Unruh J. B. (2006). Insect Management in Your Florida Lawn. CIR427 Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/LH034 > (21 July, 2009). Cardenas -Lailhacar, B., & Dukes, M. D. (2008). Expanding Disk Rain Sensor Performance And Potential Irrigation Water Savings. Journal of Irrigation and Drainage Engineerin g Vol. 131(31). < 10.1061/(ASCE)07339437(2008)134:1(67)> (25 June, 2008). Carriker, R. R. (2001). Florida's Water Resources. WQ101 Institute of Food and Agricultural Science, Univ. of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/WQ101> (16 October, 2009) Carriker, R. R. (2000). Florida's Water: supply, Use, and Public Policy. FE207 Institute of Food and Agricultural Science, Univ. of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/FE207> (16 October, 2009). Carrow, R. N. (2005). Can we maintain turf to customers' satisfaction with less water ? Journal of Agricultural Water Management Vol. 80. <10.1016/j.agwat.2005.07.008> (6 June, 2008).

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198 Clark, G. A., Smajstrla, A. G., & Zazueta, F. S. (1993). Atmospheric Parameters Which Affect Evapotranspiration. CIR822 Institute of Food and Agricultural Science, Univ. of Florida, Gainesville, FL. < http://edis.ifas .ufl.edu/AE037> (16 October, 2009). Davis S. L. (2008). Evapotranspiration-Based Irrigation Controllers Under Dry Conditions in Florida. MS thesis, Univ. of Florida, Gainesville FL. Davis, S.L., Dukes, M.D., Miller, G. L., (2009). Landscape Irrigati on by Evapotranspirationbased Irrigation Controllers Under Dry Conditions in Southwest Florida. Agricultural Water Management. < 10.1016/j.agwat.2009.08.005 > ( 10 Sept, 2009). Duke s, M. D. and Haman, D. Z. (2002). Operation of Residential Irrigation Controllers. CIR1421 Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/AE2 20 > (2 June, 2008). Elliott, M. L and Simone, G. W. (2008a). Takeall Root Rot. SSPLP16 Institute of Food and Agricultural Sciences, Universit y of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/LH079> (21 July, 2009). Elliot, M. L. and Simone, G. W. (2008b). Turfgrass Disease Management. SSPLP14 Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/LH040> (21 July, 2009.) Fernald, E. A. and Purdum, E. D. (1998). Water Resources Atlas of Florida. Institute of Science and Public Affairs of Florida State University, Tallahassee, FL. Grabow, G. L., Vasanth, A., Bowman, D., Huffman, R. L., Miller, G. L. (2007). Evaluation of Evapotranspiration-Based and Soil Moisture -Based Irrigation Control in Turf. Department of Biological and Agricultural Engineerin g, North Carolina State Univ., Raleigh, NC. Florida Automated Weather Network. (2009). Univer sit y of Florida, Gainesville, FL. < http://fawn.ifas.ufl.edu/data/reports/ > (24 July, 2009). Hall, C. R., Hodges, A. W., & Haydu, J. J. (2005). Economic Impacts of the Green Industry in the United States FE566 Institute of Food and Agricultural Science, Univ. of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/FE566> (16 October, 2009). Hargreaves, G. H. and Samani, Z. A. (1985). Reference Crop Evapotra nspiration from Temperature. American Society of Agricultural Engineers 1(2):9699. Haydu, J. J., Hodges, A. W., Hall, C. R. (2006). Economic Impacts of the Turfgrass and Lawncare Industry in the United States. FE632, Institute of Food and Agricul tural Sciences, University of Florida, Gainesville, FL. (4 October, 2009).

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199 Hughes, G.H. (1977). Runoff from Hydrologic Units in Florida. Florida Department of Natural Resources, Bureau of Geology Tallahassee, FL. Hydropoint. (2003). WeatherTRAK ET Everywhere Data Service Technical Overview. Petaluma, CA: Hydropoint Data Systems. < http://www.weathertrak.com/smart ir rigation/research-studies.php> (16 October, 2009). Haley, M. B., Dukes, M. D., Miller, G. L. (2007). Residential Irrigation Water Use in Central Florida. Journal of Irrigation and Drainage Engineering Vol. 133(5). Irmak, S., & Haman, D. Z. (2003). Evapotranspiration: Potential or Reference. ABE343, Institute of Food and Agricultural Science, Univ. of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/AE256 > (16 October, 2009). Itenfisu, D., Elliott, R L., Allen, R. G., Walter, I. A. (2003). Comparison of Reference Evapotranspiration Calculations as Part of the ASCE Standardization Effort. Journal of Irrigation and Drainage Engineering Vol. 129(6). Jacobs, J. M., & Satti, S. R. (2001). Evaluati on of Reference Evapotranspiration Methodologies and AFSIRS Crop Water Use Simulation Model. Department of Civil and Coastal Engineering, Univ. of Florida, Gainesville, FL. < http: //sjr.state.fl.us/technicalreports/pdfs/SP/SJ2001 -SP8.pdf > (16 October, 2009). Jensen, M.E., Burman, R.D., and Allen, R.G. (1990). Evapotranspiration and Irrigation Water Requirements. ASCE Manuals and Reports on Engineering practices No. 70. Ameri can Society of Civil Engineers New York, NY. Kisekka, I. (2009). Evapotranspiration Based Irrigation Scheduling for a Tropical Fruit Orchard in South Florida. MS thesis, Univ. of Florida, Gainesville, FL. < http://trec.ifas.ufl.edu/kwm/research/documents/kisekka_i.pdf > (19 August, 2009.) Metropolitan Water District of Southern California. (2004). Weather Based Controller Bench Test Report. Los Angeles, CA. Milesi, C., Running, S. W., Elvidge, C. D., Dietz, J.B., Tuttle, B. T., Nemani, R. R. (2006). Mapping and Modeling the Biogeochemical Cycling of Turf Grasses in the United States. Environmental Management Vol. 36, No. 3. < http://www.springerlink.com/content/l647rg4540731668/ > (9 October, 2009). Morris, K. N. and Shearman, R.C. (2009). NTEP Turfgrass Evaluation Guidelines. National Turfgrass Evaluation Program, Beltsville, MD. < http://ntep.org/pdf/ratings.pdf > (18 July, 2009.)

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200 Pittenger, D. R., Shaw, D. A., & Richie, W. E. (2004). Evaluation of Water -Sensing Landscape Irrigation Controllers. University of California Cooperative Extens ion, Riverside, CA. < http://ucce.ucdavis.edu/files/filelibrary/5764/21863.pdf > (16 October, 2009). Riley, M. (2005). The Cutting Edge of Residential Smart Irrigation Technology. Ca lifornia Landscaping July/August pp 1926. Sartain, J. B. (2007). General Recommendations for Fertilization of Turfgrasses on Florida Soils. SL21, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/LH014> (21 July, 2009). Shedd, M. L. (2008). Irrigation of St. Augustinegrass with Soil Moisture Sensor and Evapotranspiration Controllers. MS thesis, Univ. of Florida, Gainesville, FL. Trajkovic, S. (2007). Hargreaves versus PenmanMonteith under Humid Conditions. Journal of Irrigation and Drainage Engineering Vol. 133(1). Trenholm, L. E., Unruh, J. B., and Cisar, J. L. (2001). Mowing Your Florida Lawn. ENH10 Institute of Food and Agricult ural Sciences, University of Florida, Gainesville, FL. < http://edis.ifas.ufl.edu/LH028 > (21 July, 2009). United States Bureau of Reclamation [USBR]. (2007). Weat her and Soil Moisture Based Landscape Irrigatio n Scheduling Devices: Technical Review report 2nd Edition. Temecula, CA and Denver, CO. < http://www.usbr.gov/waterconservation/docs/SmartController.pdf > (28 September, 2009) United States Census Bureau [USCB]. (2001). Population Change and Distribution, 1990 to 2000. C2KBR/012. < http://www.census.gov/prod/2001pubs/c2kbr01 2.pdf .> (5 October, 2009). United States Geological Survey [USGS]. (2004a). Estimated use of water in the United States in 2000. USGS, Reston, VA. < http://pubs.usgs.gov/circ/2004/circ1268/pdf/circular1268.pdf .> ( 19 February, 2009). USGS. (2004b). Water Withdrawals, Use, Discharge, and Trends in Florida, 2000. USGS, Reston, VA. < http://pubs.usgs.gov/sir/2004/5151/pdf/20045151.pdf > (16 October, 20 09). Weathermatic. (2009). Technical Specifications: SmartLine SL1600 Series Controllers. Garland, Texas. < http://www.smartline.com/index.cfm?page=Literature,%20Photos,% 20etc > (24 July, 2009).

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201 BIOGRAPHICAL SKETCH Daniel C. Rutland started his education and Sallas Mahone Elem entary school in Valdosta, GA, where he attended kindergarten through fourth grade. He was then moved for the start of the fifth grade year to St. Johns Catholic school w h ich he attended through eighth grade. St. Johns was a small school which only taught roughly 20 children per grade level. Attention to strengths and weaknesses within the classroom of each individual student were strongly emphasized. His strength in mathematics was noticed and strengthened through specialized classes and homework assignments. After graduation from middle school he attended Valdosta High school and placed into honors classes. After moving at the end of his sophomore year, he attended Allen D. Nease High school where he finished all required mathematics classes by the end of his junior year. He then graduated from Bartram High school after moving yet again near the end of his senior year. Mr. Rutland attend ed the University of Florida for his undergraduate degree after applying for early acceptance. He was the first person in his family to attend college directly following high school graduation. His undergraduate experience incorporated many c hallenging projects and concepts He graduated in 2007 with a b achelor s degree in a gricultural and b iological e ngineering. Mr. Rutland enjoyed his undergraduate experience in the D epartment of Agricultural and Biological Engineering enough to seek an op portunity for a graduate degree within the department. Dr. Michael D. Dukes brought him on to research ET based irrigation controllers and their water conservation potential in Florida. He thoroughly appreciated the challenge and experience the Agricultural and Biological Engineering department provided for him during his tenure at UF.