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DATE DUE DATE DUE DATE DUE : ELK—7L4- » _—l Ell—7 “ADM ICIJ _ MSUI eAnAffirmd Action/Equel Olpportunfly net union mm: WATER TABLE MANAGEMENT TO MAXIMIZE THE ECONOMIC EFFICIENCY OF BIOMASS PRODUCTION By Harold Walter Belcher A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Agricultural Engineering 1990 ABSTRACT WATER TABLE MANAGEMENT TO MAXIMIZE THE ECONOMIC EFFICIENCY OF BIOMASS PRODUCTION By Harold Walter Belcher For hundreds of years throughout the world, agricultural producers have used underground drainage pipe systems to improve crop production by removing excess soil water from the soil profile within the root zone (Weaver, 1964). Agricultural producers and scientists have recently shown underground drainage pipe systems can also be used as water table management systems to provide water to crops during rainfall deficit periods. The Objectives of this research are to: 1) quantify water table management operation parameters that influence plant biomass production and 2) develop a model for the efficient design of water table management systems that will allow the systems to be operated for maximum plant biomass production economic efficiency. Through field research it was confirmed that corn and soybean production is sensitive to mean water table depth and water table fluctuation. The field research results suggest the best operation strategy for subirrigating field Harold Walter Belcher crops is: (l) establish a water table depth immediately following seeding, (2) for soybean production maintain that depth until crop maturity and for corn production raise the water periodically for short time periods during the growing season, (3) at crop maturity put the system into the subsurface drainage mode and maintain it in that mode until after harvest and (4) repeat the cycle the next spring. For the second Objective, a mathematical model for determining water table management system design proportions and efficiently transforming those design proportions to system installation requirements was developed and tested. The model establishes the optimum lateral spacing for both the subsurface drainage and subirrigation modes. A steady state saturated groundwater flow formulation is used to determine lateral spacing needed for subsurface drainage and to maintain the water table at design depth during peak evapotranspiration without rainfall during subirrigation. A nonsteady, falling water table analysis is made to adjust the lateral spacing, if needed, to handle precipitation events that occur during subirrigation operation. Copyright by HAROLD WALTER BELCHER 1990 Approved By: Dr. Committee: Dr. T. L. Dr. J. T. Dr. J. R. Dr. R. B. Mr. S. S. G. E. Merva Loudon Ritchie Crum Wallace Davis Harold Walter Belcher (WA/I Agricul% a1 fiEREineering Major Pf fessor Agricultural Engineering Committee Member Crop and Soil Sciences Committee Member Crop and Soil Sciences Committee Member Civil and Envir. Engineering Committee Member USDA Soil Conservation Service Outside Examiner TABLE OF CONTENTS INTRODUCTION . . . . . . . . . Subsurface Drainage . . . Water Table Management . . Research Objectives . . . LITERATURE REVIEW . . . . . . . FIELD STUDIES . . . . . . . . . Methodology . . . . . . . Bannister Site . . . St. Johns Site . . . Meteorological Data . Agronomic Data . . . System Operation Data Ground Water Data . . Observation Well Data Statistical Analyses Results . . . . . . . . . Meteorological Data . Agronomic Data . . . System Operation Data Ground Water Data . . Statistical Analyses Discussion . . . . . . . . vi Analysis 10 12 16 16 '16 20 24 25 26 26 32 36 39 39 45 48 48 56 60 Meteorological Data . . . . Agronomic Data . . . . . . System Operation Data . . . Ground Water Data . . . . . Statistical Analyses . . . One Dependent Variable Regressions . . Two Dependent Variable Regressions . . Corn Yield - Two Dependent Variables . Soybean Yield - Two Dependent Regression Without Outliers Conclusions . . . . . . . . . . WATER TABLE MANAGEMENT SYSTEM DESIGN Methodology . . . . . . . . . . System Components . . . . . System Operation . . . . . SIDESIGN Computer Model . . MODEL FORMAT O O O O O O O 0 O O O O SIRAIN DESCRIPTION . . . . . . . Data Input . . . . . . . . Calculations . . . . . . . Example . . . . . . . . . . SILSPACE DESCRIPTION . . . . . . Data Input . . . . . . . . System Variables . . . . . vii Variables 60 61 63 65 67 67 71 73 78 81 81 87 87 87 88 89 90 90 91 92 93 95 96 96 Soil Variables . . . Initial Calculations Steady State Analysis Transient Analysis . Calculated Crop Yield Results Output . . . Model Evaluation . . Results . . . . . . . Discussion . . . . . SIMAIN DESCRIPTION . . . . SIECON DESCRIPTION . . . . Module Algorithm . . Data Input . . . . . Discussion . . . . . EXAMPLE APPLICATION . . CONCLUSIONS . . . . . . . SILSPACE Module . . . SIECON Module . . . . SIDESIGN Model . . . CONCLUSIONS . . . . . . . . . . APPENDIX A APPENDIX B viii Parameters 97 98 100 104 108 111 111 112 112 115 117 117 120 121 123 124 124 124 125 126 127 129 APPENDIX C APPENDIX D APPENDIX E APPENDIX F APPENDIX G REFERENCES ix .131 .146 .179 .188 .206 .211 APPENDIX APPENDIX APPENDIX APPENDIX APPENDIX APPENDIX APPENDIX 0 '11 F1 U O m > LIST OF APPENDICES BANNISTER SITE SOIL DATA. . . . . . . ST. JOHNS SITE SOIL DATA. . . . . . . WATER TABLE ELEVATION VS. TIME PLOTS. FIELD DATA SCATTER PLOTS. . . . . . . FIELD DATA STATISTICAL SUMMARIES. . . SIDESIGN PROGRAM SOURCE CODE. . . . . WATER TABLE MGMT SYSTEM DESIGN EXAMPLE. 127 129 131 146 179 188 206 LIST OF FIGURES Figure 1. Cross sectional schematic of a water table management system operating in a subirrigation mOde O O O O O O O O I O O O O O O O O O O O O C 0 Figure 2. Bannister site topographic map with contours in meters I C O O O O O I O O O O C O O C O I O 0 0 Figure 3. Bannister site water management zones (A through H), subsurface drainage pipe layout (lateral spacing 6, 12, 18 m) and treatments within each zone (for example A4001). . . . . . . Figure 4. St. Johns site topographic map with contours in meters. . . . . . . . . . . . . . . . . . . . . Figure 5. St. Johns site water management zones (A through E), subsurface drainage pipe layout (lateral spacing 12, 17, 24 m) and treatments within each zone (for example A50Cl). . . . . . . Figure 6. Bannister site well locations. Groups of three within a set of laterals equally spaced are located 1 m from the lateral, midway between the laterals and at the upper end of the water management zone, midway between laterals. . . . . Figure 7. St. Johns site well locations. Groups of two within a set of laterals equally spaced are located at 1 m from the lateral, midway between the laterals and at the upper end of the water management zone, midway between laterals. . . . . Figure 8. Bannister site accumulated rainfall for 1986 and 1987 growing seasons in mm. . . . . . . . . . Figure 9. St. Johns site accumulated rainfall for 1987 and 1988 growing seasons in mm. . . . . . . . . . Figure 10. Bannister site 1986 growing season daily low and high air temperatures in degrees C. . . . . . Figure 11. Bannister site 1987 growing season daily low and high air temperatures in degrees C. . . . . . Figure 12. St. Johns site 1987 growing season daily low and high air temperatures in degrees C. . . . . . Figure 13. St. Johns site 1988 growing season daily low and high air temperatures in degrees C. . . . . . xi 17 19 21 23 27 29 40 40 41 41 42 42 Figure 14. for 1986 and 1987 growing season in dehn Figure 15. St. Johns site accumulate solar irradiance for 1988 growing season in W'h/m . . Figure 16. 2 Bannister site accumulated heat units in Bannister site accumulated solar irradiance degree C-days for 1986 and 1987 growing seasons. Figure 17. St. Johns accumulated heat units in degree C-days for 1987 and 1988 growing seasons. Figure 18. Subsurface drainage/subirrigation lateral spacing design notation. . . . . . . Figure 19. initially in a subirrigation mode. . Figure 20. V8. time following a rainfall event. xii Schematic of assumed water table elevation Schematic showing change in water table (WT) with time (t) following a rainfall event through water table drawdown with the subirrigation system 43 43 44 44 102 108 110 LIST OF TABLES Table 1. Multiplication factor for water table management system lateral spacing as a function of USDA soil classification. . . . . . . . . . . . . Table 2. Bannister site 1986 growing season agronomic data summary. . . . . . . . . . . . . . . . . . . Table 3. Bannister site 1987 growing season agronomic data summary table. . . . . . . . . . . . . . . . Table 4. St. Johns site 1987 growing season agronomic data summary table. . . . . . . . . . . . . . . . Table 5. St. Johns site 1988 growing season agronomic data summary table. . . . . . . . . . . . . . . . Table 6. Bannister site water table management system operation summary. . . . . . . . . . . . . . . . . Table 7. St. Johns site water table management system operation summary. . . . . . . . . . . . . . . . . Table 8. Regression equations for ground water observation wells used at Bannister and St. Johns SiteSI I I I I I I I I I I I I I I I I I I I I I Table 9. Summary of yields and blow tube measured water table depths by zone and lateral spacing. . . . . Table 10. Relative yield results by treatment and observation well data analyses results. . . . . . Table 11. Coefficients of determination (r2) resulting from linear regression analyses of data from the Bannister and St. Johns sites (one dependent variable). . . . . . . . . . . . . . . . . . . . . Table 12. Coefficients of determination (r2) resulting from linear regression analyses of data from the Bannister and St. Johns sites (two dependent variables). . . . . . . . . . . . . . . . . . . . Table 13. Comparison of water table drawdown simulation results to field observation. . . . . . . . . . . xiii 45 46 47 47 49 49 51 54 55 61 63 113 adfi ANEV ASI ASC atimea atimeb awfi awtd cyield jdfi jtimea jtimeb jwfi jwtd MARR LIST OF SYMBOLS water table fluctuation dry stress index for August, mth/h the net annual equivalent monetary value of an economic analysis alternate economic analysis annual system monetary income economic analysis annual system monetary cost percentage of time water table is above the mean water table during month of August, X percentage of time water table is below the mean water table during month of August, X water table fluctuation wet stress index for August, m*h/h mean depth to the water table during month of August, m relative corn yield, % water table fluctuation dry stress index for July, mth/h percentage of time water table is above the mean water table during month of July, % percentage of time water table is below the mean water table during month of July, % water table fluctuation wet stress index for July, mth/h mean depth to the water table during month of July, m economic analysis interest rate that represents the minimum attractive rate of return required by the investor the monetary installed cost of a water table management system used for economic analysis probability that linear regression equation result is due to chance as determined by a single tailed F statistic test for significance xiv r2 sdfi spacing stimea stimeb swfi swtd syield linear regression correlation coefficient squared water table fluctuation dry stress index for the season, mth/h distance between parallel subsurface drain pipes, m percentage of time water table is above the mean water table from start of monitoring period to end of monitoring period, x percentage of time water table is below the mean water table from start of annual monitoring period to end of annual monitoring period, X water table fluctuation wet stress index for the season, mth/h mean depth to the water table from start of annual monitoring period to end of annual monitoring period, m relative soybean yield, X XV INTRODUCTION Many agriculturally productive soils in the United States and the world have a naturally occurring shallow water table that fluctuates during the growing season. Subsurface Drainage Underground subsurface drainage pipe is used to lower the water table. In the United States the subsurface drainage systems are installed at about 1 m depth. The agricultural benefits of removing excess water from the soil profile using below ground drainage pipe systems (i.e. subsurface drainage) are well documented (Pavelis, 1987). Agricultural producers install below ground drainage pipe systems for many reasons: to remove excess soil water, to reduce diseases of crops, livestock and people, to remove excess accumulations of undesirable salts, to reduce erosion and to reduce delays in seeding and harvesting. The soil surface warms earlier in the spring and field operations can be performed earlier without soil structure damage. Over the years, subsurface drainage system variables such as pipe depth, pipe spacing and flow capacity have been determined by one of three methods: past experience in similar soils, drainage equations and computer simulation models. Today, in the United States, the most common method of designing subsurface drainage system variables for a site is to evaluate the site soils and topography and then use design dimensions that have been used in the region for similar soil and topography situations. Generally, the soil at the site is evaluated by combining information received from the site owner with a United States Department of Agriculture Soil Conservation Service (SCS) soil survey map and narrative information for the site. Occasionally this is supplemented with on—site soil investigation (to approximately 1 m) by borings using hand Operated soil augers or test pits excavated with a backhoe. The topography of the site is evaluated by topographic surveying and mapping techniques. Using this information, the system designer establishes design proportions based upon his or her past experience in similar situations and/or information provided by drainage guides for the area. The second most popular procedure for designing subsurface drainage systems is by using drainage equations. These relatively simple equations relate pipe spacing and depth to water table elevation or drainage rate. Drainage equations 3 based upon a fixed water table profile assume steady state conditions. The best known steady state equations were developed by Hooghoudt and Ernst (Van Beers, 1976). Drainage equations that relate design variables to the rate of fall of the water table are commonly called transient method equations. These equations were developed by Glover (Dumm, 1954), Bouwer and van Schilfgaarde (1963) and others. Both type equations, steady state and transient, require a knowledge of the hydraulic conductivity of the soil and depth from the surface to the restricting layer. The steady state equations also require knowledge of the appropriate steady state drainage rate for the crops to be drained and the site location. The transient equations require knowledge of the appropriate rate of water table drawdown for the crops to be drained and the site location. In actual practice the steady state method is used more often than transient analysis. Drainage guides provide recommended drain pipe spacing based upon soil type. Those spacings have been established using a steady state equation. Also, the pipes that deliver the drainage water from the parallel pipes (laterals) to the site outlet are sized for a steady state design drainage rate. Recently, computer programs to simulate subsurface drainage system performance have been developed and been shown to be 4 applicable to the design process. The simulation models vary in complexity, input data requirements and ease of use. Examples of computer simulation models being used for subsurface drainage system design are DRAINMOD (Skaggs, 1978), the SWATRE model (Feddes et al. 1978; Belmans et al. 1983) and the WATRCOM model (Parsons, 1987). DRAINMOD is based on a one dimensional (vertical) water balance within the soil profile and at the soil surface. The SWATRE model is based on solving the Richard’s equation (Richards, 1931) for combined saturated-unsaturated flow in the vertical direction only. For drainage system design, the SWATRE model is linked with other models to predict trafficability, germination, emergence, crop growth and production (van Wijk and Feddes, 1986). The WATRCOM model links a finite element solution of the two-dimensional Boussinesq equation for the saturated zone below the water table with a vertical water balance for the unsaturated zone above the water table. The Boussinesq equation as used in the WATRCOM model is defined by Parsons, 1987. Water Table Management For many crops and soil textures, experience and research has shown a constant 0.8 to 1.2 m depth to the water table is near the optimum for corn production (Goins et al., 1966; Williamson and Kriz, 1970). However, when rainfall during the growing season is less than the volume needed by the crop, the water table falls below the 1.2 m depth and water deficit stress can reduce plant biomass production. This deficiency may be overcome by irrigation; however, the economic return on irrigation system investment via traditional sprinkler type systems is limited due to the fact that relatively high average yields are obtained without irrigation. Skaggs (1978) has shown that underground pipe used for drainage can often be used to provide water to the soil profile during rainfall deficit times at very little increased cost. This practice is called subirrigation and the field system is a water table management system (see Figure 1). A water table management system that combines subirrigation with subsurface drainage potentially provides an ideal root zone soil water regime. The system operating in the subsurface drainage mode drains excess water from the root \\ if -—— IKEBRIZEABLE BARRIER Figure 1. Cross sectional schematic of a water table management system operating in a subirrigation mode. zone following rainfall events. The system operating in the subirrigation mode establishes and maintains a water table near the bottom of the crop root zone from which water moves by capillarity into the root zone thus preventing stress due to a deficit matrix potential. Because capillarity is a function of soil water potential, a function of the soil water content, the plant controls the irrigation rate and timing. Thus, for a constant depth to the water table, the plants schedule the irrigation based upon physiological needs. 7 This reasoning leads to the obvious conclusion that the optimum water table management system for plant biomass production is one in which the water table is: a) maintained near the soil surface from seeding to germination, b) lowered at the optimum root length development rate, to an optimum depth for the crop and c) maintained at that depth until the crop matures. Thus the system for maximum production would have pipe sizes large enough to drain excess water at the maximum rainfall rate and provide subirrigation water at the maximum evapotranspiration rate. In addition the pipe laterals would be spaced so as to allow for saturated flow between the pipe to midway between pipes at maximum rainfall rate and maximum evapotranspiration rate with only a slight water table surface elevation difference. A water table management system is operated in a subsurface drainage mode during tillage and harvest times. This causes the water table to be at or near the pipe depth and thus reduces the potential for soil compaction due to field operations. During the growing season, a properly designed system allows the water table to be maintained at the desired depth for optimum crop production. During this time period, the system will be in a drainage mode during times of excess rainfall and in an irrigation mode when rainfall does not meet the evapotranspiration needs of the crop. 8 At the present time, the design methods used to establish water table management system pipe depth, spacing and flow capacity are established for a specific site by one or a combination of three methods. For the most part the parallel pipe spacings are established by modifying the spacing that would be used at the site for subsurface drainage. The factor most often used is to multiply the recommended drainage spacing by 0.7. The multiplication factor may be adjusted based upon the United States Department of Agricultural (USDA) classification for the soil in the profile as shown in Table 1 (Doty et al., 1986). Table 1. Multiplication factor for water table management system lateral spacing as a function of USDA soil classification. SOIL HYDRAULIC MULTIPLICATION TYPE CONDUCTIVITY FACTOR C-SiL 0 - 0.5 m/d 0 - 0.61 SCL & L 0.5 - 1.5 m/d 0.61 - 0.77 SL 1.5 - 3.0 m/d 0.77 - 0.85 LS 3.0 - 6.0 m/d 0.85 - 0.91 A second method of determining lateral spacing is to calculate the spacing using a modification of a steady state equation developed by Hooghoudt and Ernst (Van Beers, 1976). The third method is to simulate the performance of water table management systems. By varying the system design 9 variables, the simulation model may be used to determine the best combination of those variables. The simulation models for subsurface drainage (DRAINMOD and WATRCOM) have the capability of modeling drainage, controlled drainage and subirrigation. Often the first two design methods are used to establish the initial system proportions for subsequent simulation. The simulation model DRAINMOD is most frequently used for water table management system design. The applicability of the model for that purpose has been documented by Mostaghimi et al. (1985), Evans and Skaggs (1987) and others. Recently, attention has been given to using the simulation models to develop water table management system design dimension guidelines for benchmark soils within a given region (Skaggs and Tabrizi, 1986). It is likely the key element of a water table management system design is to economically control the fluctuation of the water table following rainfall events. For this the system designer must determine the lateral spacing and pipe sizes that will limit yield reduction due to water table rise. The cost of limiting water table fluctuation and thus reduced yield must be balanced against the cost of the system. Thus we need to determine how close should the laterals be spaced to obtain the maximum return 10 on the system cost when a rainfall event occurs while in the subirrigation mode. The computer simulation models available have the capability of assisting with the design for a site on the basis of transient system Operation and economic return on investment. However, because their use requires multiple runs and detailed soil and weather data often not available, application of the models for water table management system design has been limited. Research Objectives The overall goal of this research is to develop a water table management system design process suitable for use by system designers with limited technical training in porous media flow and in computer simulation modeling. The design process should be site specific, should provide realistic output using input data that is readily available, and should be operational on computing systems not exceeding personal computer capability. The specific research objectives are to: 1. Quantify water table management operation parameters that influence plant biomass production. 2. Develop a model for the efficient design of water 11 table management systems that will allow the system to be operated for maximum plant biomass production economic efficiency. To arrive at the design process that follows, it was necessary to quantify the effect on yield of a fluctuating water table. A field study, described subsequently, contributed to that process. The data from the field study were used to establish relationships between corn and soybean yield vs. water table depth and fluctuation. This allowed the formulation of water table management parameters (design water table depth and time limits to return the water table to design depth following rainfall events that caused soil profile saturation) in terms of economic benefit. A computer model was then developed to translate these parameters to system installation requirements. 12 LITERATURE REVIEW The adverse effects of excess soil water on corn and sorghum production has been widely studied and reported: Williamson and van Schilfgaarde (1965), Goins et al. (1966), Ritter and Beer (1969), Lal and Taylor (1969; 1970), DeBoer and Ritter (1970), Williamson and Carreker (1970), Purvis and Williamson (1972), Follett et al. (1974), Chaudhary et al. (1975), Howell and Hiler (1974), Howell et al. (1976), Zolezzi et al. (1978), Benz et al. (1978), Singh and Ghildyal (1980), Fausey et al. (1985) and Fausey and McDonald, Jr. (1985). These studies assume either a flooded condition or a constant depth to the water table during the study period. Generally, the studies confirm that extended flooding reduces grain yield and that reduction is greatest during emergence and early growth stages. Zolezzi et al. (1978) found that flooding of grain sorghum in field lysimeters for three durations during the early productive growth stage reduced yield by 2.5 percent, 12.9 percent and 21.9 percent for 7, 12 and 17 day flooding periods, respectively. Purvis and Williamson (1972) concluded that 12 day old corn is severely injured if flooded for more than one day. Lal and Taylor (1969; 1970) concluded that intermittent flooding early in the growing season reduced yield of corn more than did constant water 13 tables of 0.15 m and 0.30 m depth. Other studies have shown that a period of flooding for 48 to 96 h at the four to six leaf vegetative growth stage retarded the growth of corn hybrids (Singh and Ghildyal, 1980). Fausey and McDonald, Jr. (1985) report that a very short period of flooding (48 h to 96 h) reduced field emergence of both hybrid and inbred cultivars. Constant water table depths giving maximum yields have been reported to be 0.76 m to 0.86 m for corn (Williamson and van Schilfgaarde, 1965). The constant water table studies show that lower water tables with surface irrigation provide better yields than higher water tables when surface water is not applied or applied sparingly (Williamson and Kriz, 1970). Benz et al. (1978) maintained a water table at three depths (between 1 m and 3 m) in a sandy loam soil and applied sprinkler irrigation amounts from 0 (precipitation only) to 1.5 times calculated irrigation requirements. For each of the three years studied, the production of corn grain and total dry weight was highest from the shallow water table (which varied from 1.2 m depth at the start of the growing season to 1.8 m depth at the end of the growing season) and with no irrigation. The corn studies that address a fluctuating water table (Follett et al., 1974; Chaudhary et al., 1975; Howell and 14 Hiler, 1974; Howell et al., 1976; Zolezzi et al., 1978) provide useful information but do not lend themselves to development of algorithms suitable to crop growth simulation modeling of fluctuating water table conditions. For those algorithms, quantitative information of the effect of a fluctuating water table on root and shoot growth is needed. Kanwar et al. (1988) provide quantitative data on the effect of a fluctuating water table on corn yield at five different growth stages. They reported yields were significantly reduced when the sum of the daily values of the amount the water table depth was less than 0.30 m exceeded 0.40 m'days during the first growth stage. The effect of excess water on soybean production has not received much research attention. Williamson and van Schilfgaarde (1965) report constant water table depths from 0.46 m to 0.61 m provide maximum soybean yield. A recent lysimeter study of soybean responses to excess water (VanToai et al., 1987) shows flooding for 10 days at the early vegetative, rapid flowering and early pod filling stages affects the soil oxygen diffusion rate, canopy temperature, photosynthetic rate, leaf water potential, plant height, total leaf area, stem and leaf growth rates and seed yield. Flooding at the rapid flowering and early pod filling stages reduced yield. 15 The mechanisms of yield reduction due to excess soil water have been the subject of many studies. Patwardhan et al. (1988) provided an excellent review of the research and concepts related to aeration requirements of crops in terms of oxygen diffusion rates and oxygen content as affected by excess soil water conditions. Hiler et al. (1971), McCree (1982) and Grable and Siemer (1968) have shown excess soil water within the root zone affects the respiration capability of the roots by limiting the oxygen uptake and carbon dioxide release and that the reduced respiration capability may reduce plant biomass production. In addition, Wesseling (1974) and Miller and Johnson (1964) point out excessive soil water also affects microbial activity, carbon dioxide evolution, nitrification and nitrogen mineralization. VanToai et al. (1988) found a positive correlation between tolerance of corn to flooding and its ability to produce, or conserve, metabolic energy under stress. They also found that the fluctuation between high and low 02 levels was more damaging to germination and seedling growth than a constant low 02 level. 16 FIELD STUDIES The literature review indicates field crop biomass production under artificially drained shallow water table conditions is influenced by the average depth to the water table during the growing season. The literature also suggests the growing season fluctuation of the water table may affect biomass production. However, the study of systems that maintain the growing season water table above pipe depth has largely been limited to computer simulation with very little supporting field research. To quantify the effect of water table depth and fluctuation on field crop yield, field studies were conducted at two sites for two growing seasons to relate water table depth and fluctuation to corn and soybean biomass production. Methgdology The field study sites are privately owned and operated agricultural fields located in the south central area of the lower peninsula of Michigan. Bannister Site: In August 1985, a combination subsurface drainage and 17 (<1 .5. 300 29 1] Es s} I: 3:954 >DREL 3Q4 .5 ,0 o \304 *> 305 ‘5 2 0 '4 <> 4) 30-6 i 30.“ '9 9M? $~93~9/\ Figure 2. Bannister site topographic map with contours in meters. 30. 7 subirrigation system in a privately owned 16.2 ha field near Bannister in Gratiot County Michigan (a part of the S.W. 1/4, N.W. 1/4, Section 34, T.9 N., R.1 W.) was installed. The Bannister site is relatively level with the predominant slope toward the northwest (see Figure 2). The soil is mapped as Lenawee series, however, on-site investigation and laboratory analysis by SCS and Michigan State University l8 (MSU) soil scientists resulted in revising the classification to Ziegenfuss for the entire 16.2 ha. The soil investigation results are given in Appendix A. The Ziegenfuss series consists of deep, poorly drained soils formed in loamy and clayey calcareous glacial till on till - plains and moraines. The surface layer is black silty clay loam 0.15 m deep. The subsoil is dark gray and gray mottled clay 1.15 m thick. The substratum is gray clay and extends to a very dense compacted clay layer at approximately 1.5 m below the surface. Saturated lateral hydraulic conductivity, by the auger hole method, varied from 10 mm/h to 25 mm/h. The dominate saturated lateral hydraulic conductivity for the site was determined to be 17 mm/h. The auger holes used for hydraulic conductivity testing were 0.1 m diameter, 1.5 m depth and bottomed in the dense clay layer determined to be the impermeable barrier. The topography of the site allowed subdivision of the area into eight water table management zones in which the surface elevation variance within a zone did not exceed 0.30 m. The subsurface drainage / subirrigation system consists of 102 mm inside diameter (ID) corrugated plastic tubing laterals discharging into corrugated plastic submains and mains 19 I Lin .J Figure 3. Bannister site water management zones (A through R), subsurface drainage pipe layout (lateral spacing 6, 12, 18 m) and treatments within each zone (for example A40C1). ranging in size from 127 mm through 305 mm ID. The system was installed August 5-9, 1985 by members of the Michigan Land Improvement Contractors Association. The submains and mains were installed by a trenching machine. The laterals were installed by drainage plows. The laterals are at 6, 12 and 18 m spacing as shown by Figure 3. The depths to the 20 inverts of the laterals vary from 1.1 m to 1.4 m below the ground surface. The system, as installed, provides 8 water table management zones (A through H) and a maximum of 32 irregularly shaped treatment plots that vary in size. The surface elevation (from an arbitrary datum) of the water table management zones is from 29.75 m to 30.18 m for zone A, 29.87 m to 30.18 m for zone B, 30.18 m to 30.48 m for zones C and D, 30.48 m to 30.78 m for zones E and H, and 30.78 m to 31.03 m for zones F an G. St. Johns Site: In August 1986, a combination subsurface drainage and subirrigation system was installed in a privately owned 22.2 ha field near St. Johns in Clinton County Michigan (a part of the W. 1/2, S.E. 1/4, Section 30, T.7 N., R.2 W.). The St. Johns site is relatively level with the predominant slope toward the northwest (see Figure 4). The soil in the north half of the site is mapped as Wasepi series and in the south half as Gilford. The on—site investigations and laboratory analysis by SCS and MSU soil scientists resulted in determining the entire research area is Wasepi. The soil investigation results are given in Appendix B. The Wasepi series consists of somewhat poorly drained soils formed in loamy deposits underlain by sand and gravel at 0.5 21 l. ‘ O '0‘- 7 Figure 4. St. Johns site topographic map with contours in meters. m to 1.0 m. The soils are formed in loamy and sandy glaciofluvial deposits on uplands and have a very dark grayish-brown sandy loam surface layer 0.20 m thick and brown sandy loam subsurface layers 0.13 m thick. The subsoil is mottled yellowish-brown very friable loamy sand to mottled brown sandy clay loam. The underlying material 22 is light brownish-grey and fine gravel and extends to a very dense compacted fine sand layer at approximately 6.0 m below the surface. Saturated lateral hydraulic conductivity was determined by the auger hole method with the auger hole extending to 0.9 m during the spring of 1986. The results ranged from 30 mm/h to 70 mm/h. In October 1986 further hydraulic conductivity investigations were made using the velocity head permeameter (Merva, 1987) in five backhoe excavations. The velocity head permeameter results ranged from 20 mm/h to 460 mm/h with the 460 mm/h being located in a gravel layer just below drain pipe depth. The topography of the site allowed subdivision of the St Johns site into five water table management zones, A through E, in which the surface variance within a zone did not exceed 0.30 m. The surface elevation of the zones (from an arbitrary datum) vary from 30.14 m to 30.45 m for zone A, 29.90 m to 30.14 m for zone B, 29.59 m to 29.90 m for Zone C and 29.29 m to 29.59 m for Zones D and E. The subsurface drainage / subirrigation system consists of 102 mm inside diameter (ID) corrugated plastic tubing laterals discharging into corrugated plastic submains and mains ranging in size from 127 mm through 305 mm ID. The system was installed August 11-13, 1986 by members of the Michigan Land Figure 5. St. Johns site water management zones (A through E), subsurface drainage pipe layout (lateral spacing 12, 17, 24 m) and treatments within each zone (for example A5001). Improvement Contractors Association. The submains and mains were installed by a trenching machine. The laterals were installed by drainage plows. The laterals are at 12, 17 and 24 m spacing as shown by Figure 5. The depths to the 24 lateral inverts vary from 1.1 m to 1.4 m below the ground surface. The installed system provides up to 18 irregularly shaped treatment plots that vary in size. Meteorological Data: At the Bannister site during the 1986 and 1987 growing season and at the St. Johns site during the 1988 growing season, the minimum daily meteorological data set defined by the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) program of the United States Agency for International Development (Jones, 1984) was collected throughout the growing season. The data collected consists of the date, total daily solar irradiance, minimum daily air temperature, maximum daily air temperature, mean daily air temperature and total daily precipitation. A pyranometer at each site was used to sense solar irradiance. Air temperature data was measured with a linear thermistor at each site. Daily precipitation was measured with a tipping bucket rain gauge and the hourly precipitation was from a bubbler system rain gauge using the technique reported by Goebel (1986). For the St. Johns site 1987 growing season, the maximum and minimum air temperatures and daily precipitation data collected at the National Weather Service Cooperative 25 Observer Station Index No. 20-7280—9 (Section 9, T.7 N., R.2 W.) were used for subsequent analyses. That station is 6 km NE of the St. Johns site. Agronomic Data: The agronomic data collected at each site included seeding date, emergence date, harvest date, seed cultivar identification, population seeded, population after emergence, nutrients and pesticides applied and crop yield. At the end of each growing season each treatment plot was harvested and the harvested weight measured using a weigh wagon. The harvest moisture content was determined using an electronic moisture meter (Hydroprobe Model 503 DR manufactured by CPN Corp., Pacheco, CA). During the harvest operation, the boundaries of each yield plot were flagged and field measurements made following harvest to determine plot area. The relative yield was calculated by dividing the measured yield (corrected to 15.5% moisture for corn and 13% moisture for soybeans) by the management goal for the crops (12,120 kg/ha for corn and 4,300 kg/ha for soybeans). 26 System Operation Data: A record of the operation of each water table management system was maintained by recording the dates the pumps were started or stopped and recording any change in the setting of water table controls during the growing season. The electrical power required for operation of each system was also recorded. During the 1988 growing season, the rate of irrigation water flow into each water management zone was monitored and recorded. Ground Water Data: To meet the research objectives, it is essential the elevation of the water table be closely monitored for each treatment plot throughout the growing season. The water table is defined as the upper surface of ground water or that level in the soil where the water is at atmospheric pressure (Soil Sci. Soc. Am. 1978). To achieve that capability, observation wells were installed at the approximate locations shown by Figures 6 and 7. For each treatment plot, a well was installed midway between the laterals approximately in the center of the plot and another 1 m from an adjacent lateral. In many of the plots a third observation well was installed midway between the laterals approximately 20 m from the upper end of the plot. 27 Figure 6. Bannister site well locations. Groups of three within a set of laterals equally spaced are located 1 m from the lateral, midway between the laterals and at the upper end of the water management zone, midway between laterals. For the 1986 growing season, all wells were constructed of 1.£5 m length, 19 mm diameter polyvinyl-chloride (PVC) pipe with holes drilled throughout their length. The wells were 28 wrapped with a thin spun fiberglass material to prevent soil movement into the well. The wells were fabricated so that the top 0.40 m could be removed to allow field operations. The wells were installed using a 100 mm diameter bucket soil auger and backfilled with soil from the site. After the 1986 growing season field operations were completed, the PVC wells were replaced with 1.5 m length, 19 mm diameter galvanized steel electrical conduit with holes drilled throughout the length and with a fiberglass material wrap as above. Using galvanized steel greatly assisted in locating the observation wells using a magnetic locator device when the top portion of the wells were removed. The observation wells at the St. Johns site are galvanized steel with dimensions similar to the Bannister site wells. The St. Johns site wells were installed prior to seeding for the 1987 growing season. 'The value of open auger holes for the measurement of water tabde position has been questioned by many researchers (for (Example Hinson et al. 1970; Anonymous, 1978; Bouma et al. 1980). Further, potential errors in water table measurements due to soil inhomogeneity and anisotropy using waiter table wells are discussed by Merva and Fausey (1986). However for structured clays, Armstrong (1983) shows that sniter'table differences between sites can be detected with ccuifidence using open auger hole techniques. Also, earlier 29 work by Merva and Fausey (1984) indicates that the small diameter (19 mm) casing used at the Bannister site is sufficiently responsive to water table fluctuation to 1.1 i ) A) lIILlLU/ze. FLW . . PI .J.._L_LJ.L| . is . . l . . . ._L.L.L.~;1_J_I.L_1 Figure 7. St. Johns site well locations. Groups of two within a set of laterals equally spaced are located at 1 m.from the lateral, midway between the laterals and at the upper end of the water management zone, midway between laterals. ‘pxwovide an accurate hourly measurement of the water table 30 location. At both sites the data acquisition system for the observation wells is a modification of the bubbler system described by Goebel and Merva (1985) and Goebel (1986). The modification consists of adding a switching mechanism to allow the number of wells to be increased from a maximum of 8 to a maximum of 64. All components of the data acquisition system are off-the-shelf items and are relatively inexpensive. The pressure transducers used at Bannister during the 1986 growing season had a range of 0 to 700 mm of water with an accuracy of 0.4 mm. To improve the range of water table rise and fall that could be monitored during the 1987 growing season, the 1986 growing season Bannister site pressure transducers were replaced with pressure transducers having a range of 0 to 1400 mm of water and an accuracy of 0.7 mm. The pressure transducers at the St. Johns site had a range of 0 to 1400 mm and an accuracy of 0.7 mm for the 1987 and 1988 growing seasons. 'The power source for operation of the data acquisition hardware consists of two deep cycle marine type 12 volt batteries. At the start of the 1988 growing season, (”Immercial electrical service was installed at the St. Johns saite. The commercial electrical service was used to charge tflme system 12 volt batteries by a commercial battery 31 charger. Using batteries to power the system allows the system to operate when the commercial electrical service fails or is interrupted. The water table data acquisition system was made operational following seeding and cultivating operations and maintained in an operational mode until near harvest time. For the 1986 growing season, data acquisition at the Bannister site began June 9 and ended October 27. For the 1987 growing season, data acquisition at the Bannister site began July 2 and ended September 16 and at the St. Johns site began July 1 and ended September 18. For 1988, data acquisition at the St. Johns site began June 20 and ended October 27. Operation of the observation well / data acquisition system requires one time installation of the Observation wells and data acquisition components, removal and replacement of observation well tops for each field operation (tillage, seeding, cultivating and harvesting), a one time determination of observation well top elevations, periodic blow tube readings to calibrate the wells and to provide a check on the data acquisition results, and periodic replacement of the nitrogen supply tank and system 'batteries. A 6,500 l nitrogen supply tank lasts zapproximately one month and one of the two 12 v batteries Inust be replaced with a fully charged battery on a 10 to 14 32 day schedule. The output from the data acquisition system consists of well identification, date, time and digital representation of the pressure transducer for each reading. These data were automatically dumped to a cassette tape. The cassettes were replaced approximately weekly. The data on cassettes is transferred to an IBM compatible computer in the office for further transformation and analysis. Observation Well Data Analysis: The observation wells and data acquisition systems at the two sites were used to monitor, on an hourly basis, the water table elevation in each treatment plot. The resultant data were then used to evaluate water table response to precipitation events, water table control changes, subirrigation pump startup and shutdown, crop use effects on 'water table elevation on hourly, daily, weekly, monthly and seasonal time basis for variable lateral spacings and water table management strategies. In addition, hourly water ‘bable elevation is an output variable provided by the (momputer simulation model DRAINMOD (Skaggs, 1978) and thus :is useful for model verification and/or calibration. Ifiar analysis the observation well data were transformed as follows: 33 Each well was identified by a code consisting of 6 characters. The first character is always a ’W' for well. The second character designates the water management zone location (’A’ to ’H’ for Bannister, ’A’ to ’E’ for St. Johns). Character 3 is for lateral spacing (2, 4 or 6 for 6, 12 and 18 m respectively - Bannister; 4, 5 or 8 for 12, 16 and 24 m respectively -St. Johns). The fourth character represents the location of the well within the plot - M’ for Midpoint between laterals, ’L’ for 1 m from Lateral and ’E’ for midpoint between laterals at End of the plot. The last character is a number used to differentiate between wells within a plot that would otherwise have the same designation. The time was transformed from hourzminutezsecond to hour and decimal hour. The date was converted from month, day and year to day of year and decimal fraction of the day. The numeric representations of pressure transducer voltage output were correlated with the blow tube 34 reading elevations for each observation. Only wells with a coefficient of determination (r2) equal to or greater than 0.80 were used for subsequent analysis. For those wells the regression equation was used to convert the observations from a numeric representation of voltage to a water table elevation. The hourly water table elevations were averaged for the months of July and August and for the growing season at each observation well location (jwtd, awtd and swtd). The resulting means were then used to 1) calculate the vertical distance above and below the mean water table elevation for each hourly water table observation at each observation well location and 2) calculate the mean water table depth within the zone by subtracting the mean water table elevation from the average surface elevation of the zone. The hourly vertical distance and time above and below the mean water table elevation was accumulated by day, week, month and growing season for each treatment, each crop and each season for both sites. The accumulated time above and below the Inean water table elevation was then used to calculate the Inercent time the water table was above and below the mean waiter table elevation for each treatment for the months of Jinly and August and the growing season (jtimea, atimea, stimea, jtimeb, atimeb and stimeb). The accumulated time 35 and accumulated distance the water table was above the mean water table elevation was used to calculate a water table fluctuation wet stress index and water table fluctuation dry stress index for July, August and the growing season for each treatment (jwfi,awfi,swfi,jdfi,adfi and sdfi) in accordance with the following equations: xwfi = _x_da * ggta / gtt [1] xdfi : xdb * xtb / gtt [2] where g = ’j’ for July, ’a’ for August and ’s’ for growing season wfi = water table fluctuation wet stress index dfi = water table fluctuation dry stress index d3 = accumulated vertical distance the water table is above the mean water table elevation during July, August or growing season ta 2 accumulated time the water table is above the mean water table elevation during July, August or growing season tt 2 accumulated time the water table is above or below the mean water table elevation during July, August or growing season db = accumulated vertical distance the water table is below the mean water table elevation during July, August or growing season tb = accumulated time the water table is below the mean water table elevation during July, August or season 1%”; water table fluctuation indices quantify both the extent 36 and duration of the water table fluctuation from the mean water table elevation for the water table data available. By including division by tt in the calculation Of wfi and dfi, a comparison of the indices by treatment has meaning even though the period of water table data record may differ slightly between treatments. Statistical Analyses: To investigate relationships between yield, the dependent variable, and the independent water table variables, the following linear regression analyses were performed: I. Y = B0 + B1 * X A. Y = relative yield, % X = mean water table depth for the growing season, m B. Y = relative yield, % X : percent time below the mean water table elevation for the growing season, % C. Y = relative yield, % X = percent time above the mean water table elevation for the growing season, % D. Y 2 relative yield, % X = water table fluctuation dry stress index for the growing season, mth/h E. Y = relative yield, % X 2 water table fluctuation wet stress index for the growing season, mth/h II. Y 2 B0 + Bl * X relative yield, % mean water table depth for Jul , m 3> K: II II + 37 relative yield, % percent time below the mean water table elevation for July, % relative yield, % percent time above the mean water table elevation for July, % relative yield, % water table fluctuation dry stress index for July, m*h/h relative yield, % water table fluctuation wet stress index for July, m*h/h Bl * X relative yield, % B. Y X C. Y X D. Y X E. Y X III. Y : BO AI Y X B. Y X C. Y X D. Y X E. Y X mean water table depth for August, m relative yield, % percent time below the mean water table elevation for August, % relative yield, X percent time above the mean water table elevation for August, % relative yield, % water table fluctuation dry stress index for August, m*h/h relative yield, % water table fluctuation wet stress index for August, m*h/h Recognizing the water table fluctuation effect is likely to be influenced by a combination of distance and time above aJui below mean water table elevation as well as the stage of lilant development, multiple linear regression analyses using tJue forward stepping procedure were made on the following data sets: IV. Y : B0 + B1 3 X1 + B2 * X2 Y X1 X2 Y X1 X2 Y X1 X2 Y X1 X2 Y X1 X2 Y X1 X2 Y X1 X2 Y X1 X2 Y X1 X2 Y X1 X2 Y 38 relative yield, X mean water table depth for the season, m percent time below the mean water table elevation for the season, X relative yield, X mean water table depth for the season, m percent time above the mean water table elgygtion for the season, X relative yield, X mean water table depth for the season, m water table fluctuation dry stress index for the season, m*h/h relative yield, X mean water table depth for the season, m water table fluctuation Egg stress index for the season, m*h/h relative yield, X mean water table depth during Jul , m percent time below the mean water table elevation during July, X relative yield, X mean water table depth during Jul , m percent time above the mean water table elevation during July, X relative yield, X mean water table depth for Jul , m water table fluctuation dgy stress index for July, m*h/h relative yield, X mean water table depth for Jul , m water table fluctuation 3;; stress index fgr July, mth/h relative yield, X mean water table depth during August, m percent time below the mean water table elevation during August, X relative yield, X mean water table depth during Au ust, m percent time above the mean water table elevation during August, X relative yield, X 39 X1 = mean water table depth for Au ust, m X2 = water table fluctuation dgy stress index for August, m*h/h L. Y = relative yield, X X1 = mean water table depth for August, m X2 = water table fluctuation Egg stress index for August, m*h/h TO investigate the effect of water table management system physical proportions, the relative yield data were correlated with water management zone and lateral spacing for each site and each growing season as follows: V. Y = B0 B1 * X + A. Y = relative yield, X X = lateral spacing, m B. Y = relative yield, X X : water management zone Results Meteorological Data: The meteorological data collection efforts for the two sites provided: accumulated daily rainfall data (Figures 8 and 9); daily low, high (Figures 10 through 13) and mean air “temperature; daily integrated solar irradiance (Figures 14 axui 15); and accumulated degree days (Figures 16 and 17). Syuatem failure at St. Johns prevented obtaining good data gfor'the latter part of the 1988 growing season. 800- 700- 6004 1 J 500‘ 400- F—-—- 4 300‘ 1 > 2004 I 100% 4 ACCUMULATED RAINFALL (mm) H 1987 s—a1986 150' 1330 r 250 ' 220 fizio i 2350 ‘ 230 r360 . 320 DAYS FROM START OF CALENDAR YEAR 0 Figure 8. Bannister site accumulated rainfall for 1986 and 1987 growing seasons in mm. 5001 5001 400~ 300* 200- 100~ ACCUMULATED RAINFALL (mm) 0—01988 s—s1987 o~-£'.r,...,...,...,r.., 120 160 200 240 280 320 DAYS FROM START OF CALENDAR YEAR Figure 9. St. Johns site accumulated rainfall for 1987 and 1988 growing seasons in mm. 41 4o- 8 35 30- 0) L Ch 4 0 .0 V 20-1 LLJ l a < ‘ r— ' I § 10" l “J Ii. 0. 1 2 l E o— 0: 2 ‘ e—o DALYHIGHTEWERATURE -10 H DAILY LOWTEMPERATURE 150 iéo 250*T 220 ' 210 ' 280 ' zéo ' 360 ' 320 DAYS FROM START OF 1986 Figure 10. Bannister site 1986 growing season daily low and high air temperatures in degrees C. 4.0-4 8 w I-' g 150-4 a . a) l- l ' ‘O V 20.. LIJ . m I v a 1 g 10- t L” . CL 2 E3 o— O: :f o—o DAILY mo TEMPEMTURE HDAILYLOVITEMPERATURE -10'1TT‘TT'TfT'T‘I‘T—T 160 180 200 220 240 260 280 300 320 DAYS FROM START OF 1987 Figure 11. Bannister site 1987 growing season daily low and high air temperatures in degrees C. 42 10 ‘I H I- LY ‘- TEMPERATURE H DALY LOW TEMPERATURE 1201110'1250‘Iéorzéo'zioTsz'zéoTzéo'soo'flo DAYS FROM START OF 1987 AIR TEMPERATURE (degrees C) -10 Figure 12. St. Johns site 1987 growing season daily low and high air temperatures in degrees C. 404 304 .‘ o—o DAILY HIGH TEMPERATURE H DAILY LOW TEMPERATURE T T ' l ' T ' T ' T T T T j r T ' T T I 120 140 160 180 200 220 240 260 280 300 320 DAYS FROM START OF 1988 AIR TEMPERATURE (degrees C) -10 Figure 13. St. Johns site 1988 growing season daily low and high air temperatures in degrees C. 43 700000 ~ 0 L0 . l—‘A ENE 600000 — €32? ‘ I; g 500000 ~ .ev . LIJ :0 400000 - <1: 5 1 c3__ QC 300000 a m R I-— g I 5 200000 4 3 CC 55 i 08 100000 1 L) I e—o 1987 < O H 1986 r ‘l T 1 V 150 ' 100 200 220 T 240 200 ' 200 300 I 320 DAYS FROM START OF CALENDAR YEAR Figure 14. Bannister site accumulated solar irradiance for 1986 and 1987 growing season in W'h/m . 700000— .3 LLJA ‘ ENE 600000 J (K a} I I— ; 500000 4 EV LIJ 50 400000 4 2 E . Q __ 09 300000 « mé 4 EE :01 200000 « 5 5 ‘ o 8 100000 4 O . < 0 T I T T I I ‘l 1 I I T —‘[ 7 T I I T 1 f 1 120 140 160 180 200 220 240 260 280 300 320 DAYS FROM START OF 1988 Figure 15. St. Johns site acqumulated solar irradiance for 1988 growing season in W'h/m . 44 16004 g3 1400 4 4. I as Lu 1200 ~ LLJ 1 23 1000 1 L” 1 C) Q 800 -1 E 4 :5 600 a g 1 D 400 -1 8 I 200 < .. o—e 1987 O H 1986 135 135‘ this; I 3935' Izisv 'ngs' I 1205 2175 DAYS FROM START OF CALENDAR YEAR Figure 16. Bannister site accumulated heat units in degree C-days for 1986 and 1987 growing seasons. 16001 0) >_ 1400 - < I o L0 1200 a LLJ .1 ES 1000 ~ Lu . Q 4 C) 800 E j 600 — g n D 400 -1 O ‘1 0 200 — < 1 0—0 1988 0 H 1987 135 ' 115155 ' '195' ' '15'15' ' '2i5' ' '2'35 255‘ Y 275 DAYS FROM START OF CALENDAR YEAR Figure 17. St. Johns accumulated heat units in degree C- days for 1987 and 1988 growing seasons. 455 Agronomic Data: The agronomic data for the Bannister site is presented in Tables 2 and 3. Table 2. Bannister site 1986 growing season agronomic data summary. 1986 COHN (Great Lakes 579) 1986 SOYBBANS (Hoytville / Crest Lakes 2634) HT HHHRC’D HT VARIETY BHHRC'D HCNT POPUL’N NCNT POPUL’N ZONH plants/ha ZONE plants/ha A 65460 A Hoyt 871800 CL 2634 551600 8 65460 B Hoyt 844700 CL 2634 738800 C 66760 C Hoyt CL 2634 610700 0 67200 D Hoyt CL 2634 568800 R 60850 R Hoyt 651000 CL 2634 331200 F F Hoyt 572600 CL 2634 325100 H 66340 H Hoyt 562700 CL 2634 402600 Hoyt CL 2634 DATE SHHDED: 05/06/86 DATE OF HHHHCBNCH: 05/12/86 DATE OF HARVEST: 11/10/86 DATE SHHDHD: DATE OF BHRHCBNCR: DATE OF HARVEST: 05/29/86 05/29/86 06/05/86 06/07/86 10/06/86 10/06/86 FERTILIZER: 55 kg/ha Potash 31 L/ha 28$ Nitrogen 37 kg/ha 18-40-0 (starter) 112 kglhn 28$ Nitrogen (sidedress) FERTILIZER: 55 kg/ha Potash 31 L/ha 288 Nitrogen 28 11/62 12.5-11-11 (starter) PESTICIDES: 0.8 L/ht Lasso 0.3 kg/ha Atrax 90 PESTICIDES: 0.8 L/ha Lasso 0.1 kg/ha Sencor Table 3. Bannister site summary table. 465 1987 growing season agronomic data 1987 CORN (Great Lakes 579) NT EHERC’D HCHT POPUL’N ZONE plants/ha A 61240 8 61490 C 58900 D 64080 E 65670 H 66040 DATE SEEDED: 05/08/87 DATE OF EHERCENCE: 05/18/87 DATE OF TASSELINC: 07/09/87 DATE OF HARVEST: FERTILIZER: 55 kg/ha Potash 31 L/ha 281 Nitrogen 37 kg/ha 18-46-0 (starter) 37 kg/ha Anhydrous (sidedress) PESTICIDES: 0.8 L/ha Lasso 0.3 kg/ha Atrax 90 1987 SOYHEANS (Hoytville/Creat Lakes 2634) VT EHERC’D HCHT POPUL’N ZONE plants/ha A 479300 8 486500 C 501600 0 483200 E 411500 DATE SEEDED: 05/23/87 DATE OF EHERCRNCE: DATE OF ELOVEHINC: 07/16/87 DATE OF HARVEST: FERTILIZER: 55 kg/ha Potash PESTICIDES: 0.8 L/ha Lasso 0.1 kg/ha Sencor The agronomic data for the St. Johns site is presented in Tables 4 and 5. 47 St. Johns site 1987 growing season agronomic data 1987 SOYHEANS (Pioneer 9771) 1987 CORN (Pioneer 3475) summary table. Table 4. 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Lu T 00 TDD KERR T T TTTT 05939 113 Sn/unau l m 00 "UN TTTT R7773 0300‘. .AATAAA 356.1163 90 . . b m 9 T00 .AAnAnAu 903333 E . . 48 The corn and soybean yields obtained for the Bannister and St. Johns sites are tabulated by treatment as a part of Table 9. System Operation Data: Operating the water table management systems at each site consisted of starting and stopping the irrigation supply pumps and adjusting the water table control for each water table management zone to set the system in subsurface drainage or subirrigation and to raise and lower the water table within each zone. Tables 6 and 7 are summaries of those operations for each site. The elevations refer to an arbitrary datum of 30.48 m set as a temporary benchmark at both the St. Johns and Bannister sites. Ground Water Data: .For the Bannister site, during the 1986 growing season, 'water table measurements began June 9, 1986 and ended (Matcher 27, 1986. Water table measurements at 55 locations produced 29,965 water elevation observations during that ‘time*period. During the 1987 growing season the water table nmnasurements began July 2, 1987 and continued through September 23, 1987 and resulted in 29,284 water table elxyvation observations. The output from each observation 49 Table 6. Bannister site water table management system operation summary. 1999 anowxno senson: enevnrxon or were (a) one: zone zone zone zone zone A n c 9 e99 OB/Ol Dr. Dr.l Dr.1 Dr.1 Dr.l 99/91 9r.1 29.79 39.99 39.22 39.51 97/99 Dr.l 29.99 29.93 39.97 39.41 99/91 92.1 39.99 29.93 39.97 39.31 99/97 Dr.l 29. 9 3"f3 3°‘f7 39. 1 10/01 Dr. Dr. Dr. Dr. Dr. PUMP SCIBDULB: start 07/04 stop 09/06 1997 oeowxna season: eeevnrlon or WEIR (n) 9279 zone zone zone zone zone zone zone A 9 c n 999 e a 05/01 Dr.} Dr.l Dr.1 Dr.l Dr.l Dr.l Dr.1 99/12 Dr.1 29.17 29.99 29.99 29.99 29.39 29.92 99/19 91.1 29.72 29.79 29.93 39.94 39.49 39.99 99/22 Dr.1 29.79 29.79 29.99 39.99 39.15 39.92 99/29 97.1 29.99 29.79 29.99 39.99 39.43 39.49 97/97 Dr.1 29.99 29.79 29.99 39.99 29.94 99/19 Dr.1 29. 2 29. 9 1 3°°f° 39. 4 39.97 99/29 Dr. Dr. Dr. Dr. Dr. Dr. Dr. PUMP SCHEDULE: start 06/12 stop O7/01 start O7/02 stop 08/20 1 Water table control set for subsurface drainage. Table 7. St» Johns site water' table management system operation summary. 1987 GIOWING SEASON: ELEVATION OF WEIR (I) DATE ZONE ZONE ZONE ZONE ZONE A I c D B 05/26 Dr.1 Dr} Dr} Dr} Dr} 05/27 29. 2 29. 9 29. 6 Dr. 29. 9 08/27 Dr. Dr. Dr. Dr. Dr. PUKP SCHEDULE: start 06/22 stop 08/27 1988 GROWING SEASON: ELEVATION OF WEIR (fl) DATE ZONE ZONE ZONE ZONE ZONE A D c D B 03/28 Dr.1 Dr.1 Dr.1 Dr.1 Dr.1 03/29 29. 2 29. 9 29. 6 Dr. 29. 9 09/15 Dr.f Dr.f Dr.f Dr. Dr.f PUMP SCIEDULE: 1 start 05/24 stop 09/15 Hater table control set for subsurface drainage. 50 well was compared to field measured depths to the water table by regression analyses. The regression coefficient of determination (r2) exceeded 0.80 for 17 observation wells in 1986 and 13 observation wells in 1987 (see Table 8). For subsequent analyses the number of wells was further reduced to one well per treatment. The preferred well was a well located midway between laterals with the highest coefficient of determination (r2). Thus, for Bannister, the number of groundwater observations used for analyses reduced to 9,085 observations from 11 wells for 1986 and 4,001 observations from 7 wells for 1987. At the St. Johns site, 7 of 36 observation wells produced regression coefficients of determination (r2) greater than 0.80. Of these, 6 of 36 observation wells providing 9,085 useful hourly water table elevation observations (beginning July 1, 1987 and ending September 18, 1987), which were used for subsequent analyses. During the 1988 growing season, ‘water table elevation monitoring began June 20, 1988 and «continued until October 27, 1988 producing 14,032 «observations from 7 observation wells all of which were used for’subsequent analyses. Tine observation wells used for subsequent analyses are listed in Table 8. 51 Table 8. Regression equations for ground water observation wells used at Bannister and St. Johns sites. H911 10 IEGBESSION EQUATION r2 I Bannister. 1988 HA4H1 9:28.220.006808! 0.935 3 H8212 y=28.03+.00447tx 0.975 3 H92M2 y=29.00+.002828x 0.999 4 H8411 y=28.99+.00295tx 1.000 3 H9481 y=29.11+.0035181 0.899 3 HI4H2 y=29.019.00278tx 0.982 4 H8011 y=29.17+.004532x 0.983 3 H0211 y=29.25+.003348x 0.955 3 HCOHI y=29.17+.003558x 0.995 3 H0211 y=29.28+.002988x 0.883 4 H9011 y=29.22t.00282tx 0.880 4 HDOHI y=29.38+.00214tx 0.973 3 H8211 y=30.02+.0029482 0.944 5 H92I1 y=29.05t.002348x 0.937 3 H6211 y=29.97+.oozssax 0.933 3 H62I1 Y=29.95+.002708x 0.991 4 HI4K2 y=29.43+.0022021 0.984 4 Bannister. 1987 H9411 y=27.98t.000288: 1.000 4 HC211 y=28.30+.00375tx 0.978 5 HC2H1 y=28.55+.0035521 0.957 5 H0211 y=28.58+.0043481 0.814 5 H0431 y=29.09+.00193tx 0.955 5 H0511 y=29.99+.00309:x 0.838 5 HDOHI y=2s.43+.099159x 0.918 3 HE2H1 y=29.13+.003253x 0.979 3 H8411 y=28.75+.003548x 0.801 5 HE4H1 y=29.11+.00399!x 0.978 5 H8431 y=29.32+.00327tx 0.982 4 HF4H1 y=28.54+.004028x 0.972 5 HI4H2 y=28.78+.004058x 0.997 3 St. Johns. 1987 H9412 y=27.93+.00797tx 0.955 3 HI4H2 y=27.48+.00755lx 1.000 3 H0411 y=27.71+.005148x 0.957 3 HC4M1 y=27.94+.006358x 0.845 0 HC4H2 y=27.94+.0053621 0.845 5 HC4H3 y=28.14t.006428x 0.972 5 H0811 y=28.57+.001448x 0.851 3 St. Johns, 1988 HA511 y=28.89+.005428x 0.909 3 HA5H1 y=28.47+.004543x 0.824 3 HA8H1 y=28.59+.00475tx 0.988 4 H9511 y=28.42+.005158x 0.943 4 HC8H1 7:28.410.00532tx 0.964 3 HC4M3 y=28.21+.004258x 0.849 4 H0811 y=28.54t.0055181 0.968 3 where y = water table elevation x s pressure transducer readout converted to digital r2 a correlation confliclent squared n x number of observations .A summary of the blow tube measured water table elevations and a tabulation of the average relative yield and lateral spacing within each water management zone is provided by 52 Table 9. For spacings within zones with more than a single treatment, the yields shown are the arithmetic average of the yields. For the 1986 season, the mean water table depths in Table 9 are the average of 6 measured elevations during the time period 6/25/86 through 7/30/86. For 1987 the Bannister mean water table depths are from 7 measurements taken between 7/01/87 and 8/24/87. The 1987 St. Johns mean depths are the average of 6 measurements between 7/14/87 and 8/17/87 and for 1988, 10 readings taken from 6/7/88 to 8/15/88. For the 1986 growing season at Bannister the maximum water table depth (growing season, July and August) occurred in zone A, the zone without water table control. The least water table depth occurred in zone B, the zone with the water table control set nearest the soil surface. For 1987 automatically collected observation well data for zone A is not available because for most of the season the water table in zone A was below the lower capability of the instrumentation. For the other zones, the mean water table elevation differed from zone to zone. For zones A and B, the blow tube measured water table depths (for observation inells WA4M1 and WB4M2) show a mean water table depth of 1.38 In and 0.51 m. flflne results of the analyses of the hourly observation well 53 observations for the Bannister and St. Johns sites are provided by Table 10. Table 10 also provides the relative yields measured for each treatment used in the analyses. The Table 10 treatment codes are provided as a part of Figures 3 and 5. The observation well codes were defined previously in the Observation Well Data Analysis part of the Methodology section of FIELD STUDIES. 54 Table 9. Summary of yields and blow tube measured water table depths by zone and lateral spacing. Lateral Corn Soybean Kean Corn Soybean lean Trt. Zone Spacing Yield Yield 71 Depth Yield Yield 71 Depth I 1 I I 1 S I --xx=86-- --xx:87-- Bxx120 A 6 85 65 0.95 86 89 1 4 8xx440 A 12 75 60 0.75 79 80 1.4 BxleO A 18 83 60 0.58 68 85 1 2 8xx820 B 6 86 48 0.53 113 88 0.4 BxxB40 8 12 52 0.54 92 0.60 8xx860 8 18 83 51 93 78 BxxC20 C 6 75 63 0.77 86 79 1 06 BxxC40 C 12 79 60 0.38 79 88 BxxC60 C 18 83 52 0.79 100 93 0 82 8xx020 D 75 63 0.72 86 80 0 83 BxxD40 D 12 79 63 0.68 85 95 0 95 8xx060 0 18 68 58 0.74 78 92 0.9 Bxx820 8 6 77 0.77 71 0.73 Bxx840 8 12 63 0.83 89 0.87 81x860 8 18 77 55 77 92 Bxx840 F 12 78 60 83 74 1 58 BxxGZO G 6 58 0.73 69 8xx820 H 6 77 71 BxxH40 I 12 88 71 80 82 0 8x2860 8 18 84 92 02 --yy=8?-- --yy=83-- Syy140 A 12 72 49 0.89 84 52 l 01 SyyA50 A 17 84 45 0.91 97 71 SyyABO A 24 68 45 0.98 80 53 8yy840 B 12 77 39 1.02 75 65 . SyyBSO 8 17 94 42 0.89 99 68 Syy880 8 24 80 47 0.90 87 78 SyyC40 C 12 79 56 1.05 58 55 Syy080 C 24 72 66 1.16 49 65 Syy040 D 12 81 82 0.92 83 83 Syy080 0 24 84 80 0.86 91 80 0 7 Syy850 8 17 87 72 92 70 treatment and by results 55 In 1d observation well data analyses results. yie e—O“-|n-—1N-—em Relative 'OVNfim'n'v-u—q: l- Table 10. 2 2 2 2 2 2 2 2 a z 2 2 2 2 2 2 2 z 2 2 2 2 2 2 2 s 3 3 .Snuaiz. 28¢ 82‘ 2: 2: 533 S 2 2 2 2 2 2 2 2 E S 2 2 S 3 2 2 2 2 S S 2 2 2 3 2 2; =4 :4 2. 8; 2. 2. :4 24 8; 2; 2; 2. 2. 2; 3; 2; 2. 2. a. z. 2. S. 2. 2. 2. 2; 33 EENFmfifihaafi Egg S 5: 9° N . O d g 0 . . . . . — Nam-”m—neomu—Oem «NI-vamflwaamnaaece‘ —o 2 2 2 2 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 S 2 z 3.2“ £32. 2: S 2 2 z z = a 2 3 z z 3 2 2 2 z 2 2 2 3 2 8 2 s 3 2 2 2 3; =4 2; 2. 3; 2. 2. 2; S; 24 2; 2; .w a; 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. s... 23.932Ep325 BE: :52 1§2 a: may .32 .Egam NN NN N. 2.. s N. 3.... .22 NN .N N. NN.. N. N. .5... .28 NN . .N N... .N N. 2.3 Na... .. N. N. N.. N. .. 3.... a... .N .. NN N... .N N. a... a... NN. N. .. 3. .N N. 2.... as. NN. e.2 .NN .N .. .. N... N. .N s... as. N. .N N. .N. N. .N 2.3 Na... .. .N .. .N.. NN .. .5... a... 2. N. .. .N.. .. N. N5... N8... 2. s... 5.. .N N. N. N... N. 2. N2... NN... NN .N .. N... .N N. a... a... N. .. N. .N. .N a... a... NN N. .N N.. .N .5... as. .N .N N. N... N. N. 3...... a... NN N. N. 3. N. .N :2. as. N. N. .. N... N. .N =5. .28 .N. .9......N N. N. N. .N. .N N. NE... a... N. NN N. N.. . N. as. .st .. NN N. 2. N. =5... as. .2 s = 2. s a .se .22 .N N. N. ... N. N. SN... .22 N. .. .. N... N. N. as. as. N. .. N. a. N. N. as. as. .. N. .. N... .N N. 3.9 .2... .. .. NN N.. N. a... .2... . N. .N N.. N. N. 2N: NsNN NN .. .N .N. s N. a... a... .N. 52.9.. a... as... .8... 3... 33.. 2.... 2.... .N... . as. test: E... 5.... s... N. as. as. .3... e. 8...... .5 N... a... N. a. a... 3.. a... 3...... as so 525. 56 Statistical Analyses: Tables 11 and 12 provide the results of the linear regression analyses performed. The analyses codes (I.A. through V.B.) refer to the linear regression descriptions provided in the Statistical Analyses part of the Methodology section. The data used for the regression analyses are provided by Appendix C (scatter plots) and Appendix D (statistical summaries). The field data produced the following water table depth and fluctuation linear regression equations (with the greatest rz’s and least p statistic values) by site and by crop: a. Ba ’86: cyield = 64.5 + 0.283 atimea (z'=0.442, p=0.015) b. Ba ’87: cyield = 49.8 + 9.47 jwtd + 0.437 jtimea (I'=0.985, p=0.015) c. SJz ’87: cyield = 195 — 126 jwtd + 1.51 jdfi (r =0.997, p=0.005) d. SJz ’88: cyield = 71.9 + 197 adista (r =0.447, p=0.147) e. Ba ’86: syield = 26.9 + 64.2 jwtd - 1.11 jdfi (r =0.825, p=0.002) f. Ba ’87: syield = 98.8 - 9.68 jwtd —2.07 jwfi (r =0.768, p=0.054) g. SJz ’87: syield = 138 - 66.5 awtd (r =0.360, p=0.400) h. SJz ’88: syield = 93.3 - 0.554 atimea (r =0.883, p=0.005) 57 The field data produced the following water table depth and fluctuation linear regression equations (with the greatest rz’s and least p statistic values) by site and by independent variable: _timea: a. Ba ’86: (r =0.442, b. Ba ’87: (r =0.985, o. SJ2 ’87: (r =0.566, d. SJz’88 (r =0.445, e. Ba ’86: (I'=0.618, f. Ba ’87: (r'=0.711, g. SJ2 ’87 (r =0.317, h. SJ2 ’88: (r =0.883, _timeb: a. Ba ’86: (I‘=O.367, b. Ba ’87: (r =0.936, o. SJ2 ’87: (r =0.547, d. SJz ’88: (r =0.384, e. Ba ’86: (r'=0.616, f. Ban ’87: cyield = 64.1 + 0.283 atimea p=0.072) cyield = 49.8 + 9.47 jwtd + 0.437 jtimea p=0.015) cyield = 63.9 + 0.452 jtimea p=0.248) cyield = 149 - 43.7 awtd - 0.359 atimea p=0.413) syield = 34.4 + 39.0 jwtd — 0.033 jtimea p=0.034) syield = 133 + 19.8 awtd - 0.644 atimea p=0.084) syield = 189 - 2.50 stimea p=0.437) syield = 93.3 - 0.554 atimea p=0.005) cyield = 92.9 - 0.296 atimeb p=0.111) cyield = 86.7 + 9.94 jwtd - 0.349 jtimeb p=0.064) cyield = 12 - 0.706 jtimeb p=0.260) cyield = 54.0 + 0.794 atimeb p=0.189) syield = 32.2 p=0.035) + 40.0 jwtd + 0.002 jtimeb syield = 68.8 - 21.2 awtd + 0.707 atimeb (r2=0.634, g. SJz ’87: (r =0.307, h. SJ ’88 atimeb 2 (r =0.867, _wfi: a. Ba ’86: (r =0.203, b. Ba ’87: (r :0.797, c. SJ2 ’87: (r =0.841, d. SJz ’88: (r 20.362, e. Ba ’86: (r :0.729, f. Ba ’87: (r :0.768, g. SJz ’87: (r =0.330, h. SJ2 ’88: (r =0.632, a. Ba ’86: (r =0.238, b. Ba ’87: (r =0.308, o. SJz ’87: (r =0.997, d. SJ2 '88: (r =0.415, e. Ba ’86: (r =0.825, 58 p=0.134) syield = 156 - 1.94 atimeb p=0.445) syield = -17.5 + 43.4 awtd + 0.981 p=0.049) cyield = 83.3 - 0.139 swfi p=0.224) cyield = 76.3 + 1.19 awfi p=0.041) cyield = 148 - 70.2 jwtd + 0.930 jwfi p=0.399) cyield = 77.1 + 0.530 jwfi p=0.206) syield = 35.0 + 45.6 jwtd - 0.812 jwfi p=0.010) syield = 98.8 - 9.68 jwtd - 2.07 jwfi p=0.054) syield = 79.2 - 0.256 swfi p=0.426) syield = 104 - 24.5 awtd - 1.38 awfi p=0.223) cyield : 85.7 - 0.216 sdfi p=0.183) cyield = 78.1 + 0.639 adfi p=0.332) cyield = 195 - 126 jwtd + 1.51 jdfi p=0.051) cyield = 73.9 + 1.98 adfi p=0.167) syield = 26.9 + 64.2 jwtd - 1.11 jdfi p=0.002) 59 Ba '87: syield = 93.1 - 8.11 jwtd - 0.872 jdfi (r =0.619, p=0.145) SJz ’87: syield = 78.6 — 0.278 sdfi (r 20.293, p=0.459) SJz ’88: syield = 81.1 - 9.2 awtd - 0.52 adfi (r =0.032, p=0.952) The field data produced the corn and soybean relative yield (cyield and syield) vs. lateral spacing and water management zone (spacing and zone) regression equations: Ba ’86: cyield = 78.9 — 0.003 spacing (r =0.000, p=0.995) Ba? ’87: cyield = 89.9 - 0.679 spacing (r 20.907, p=0.012) SJ2 ’87: cyield = 76.7 + 0.306 spacing (r =0.068, p=0.740) SJz ’88: cyield = 111 - 1.43 spacing (r =0.254, p=0.308) Ba ’86: syield = 61.3 0.324 spacing ( r =0.059, p=0.500) Ba ’87: syield = 69.0 + 1.17 spacing (r'=0.494, p=0.078) SJz ’87: syield = 30.7 + 2.06 spacing (r =0.340, p=0.417) SJ2 ’88: syield = 75.2 (r =0.059, p=0.643) 0.372 spacing Ba ’86: cyield = 76.6 + 0.695 zone (I‘=0.048, p=0.569) Ba ’87: cyield = 86.7 0.812 zone ( r =0.206, p=0.442) SJ2 ’87: cyield = 79.8 + 0.500 zone (r =0.003, p=0.942) 60 l. SJ2 ’88: cyield = 89.7 - 2.36 zone (r =0.033, p=0.732) m. Ba ’86: syield = 50.5 + 1.97 zone (r =0.407, p=0.047) n. Ba ’87: syield = 84.1 — 0.62 zone (r =0.018, p=0.772) o. SJ2 ’87: syield = 22.5 + 13.0 zone (r 20.252, p=0.498) p. SJ2 ’88: syield = 57.1 + 4.73 zone (r =0.447, p=0.147) Discussion Meteorological Data: As can be seen from Figures 8 through 15, from 1986 through 1988 the growing seasons became progressively hotter and dryer. During 1986 the growing season rainfall was above normal for the area and area irrigation systems saw very little use. The 1987 growing season had much less rainfall beginning before planting until an extreme precipitation event in early September. Area producers with irrigation systems did irrigate in 1987. The 1988 growing season was extremely dry. As can be seen from Figure 9 :practically no rainfall fell during May, June and July. (Irops grown in the area without benefit of irrigation had greatly reduced yields in 1988. Table 11. Coefficients (51 of determination from linear regression analyses of data from the Bannister and St. Johns sites (one dependent variable). (r2) resulting ANAL’S CROP 1.4 corn 1.8. corn I.C. corn I.D corn 1.8. corn 11.4. corn 11.8. corn 11.0. corn 11.0 corn 11.8. corn 111.4. corn 111.8. corn 111.0. corn 111.0. corn 111.8. corn 1.4. soyb’n 1.8. soyb’n 1.0. soyb’n I.D. soyb’n 1.8 soyb’n 11.1. soyb’n 11.8. soyb’n 11.0. soyb’n 11.0. soyb’n 11.8. soyb'n 111.4. soyb'n 111.8. soyb’n 111.0. soyb’n 111.0. soyb’n 111.8. soyb’n v.1. corn v.8. corn V.A. soyb’n v.8. soyb’n BAN’86 BAN’8? SJ’87 83’88 0.319 0.016 0.017 0.332 0.263 0.324 0.001 0.101 0.357 0.362 0.337 0.384 0.092 0.415 0.098 0.000 0.224 0.374 0.000 0.181 0.007 0.422 0.226 0.006 0.008 0.000 0.353 0.883 0.015 0.401 0.254 0.033 0.059 0.447 8808838108 VARIABLES cyield,sutd cyield,stileb cyield,stilea cyield,sdfi cyield,sufi cyield,jutd cyield,jti|eb cyield,jtilea cyield,jdfi cyield,jufi cyield,autd cyield,atileb cyield,atilea cyield,adfi cyield,aufi syield,sutd syield,stileb syield,stilea syield,sdfi syield,sufi syield,jutd syield,jti|eb syield,jti|ea syield,jdfi syield,jufi syield,autd syield,ati|eb syield,atilea ayield,adfi syield,aufi cyield,8pacing cyield,zone syield,spacing syield,zone Agronomic Data: Each water table management zone and pipe lateral spacing treatment at each site was seeded to corn and soybeans. The 62 agronomic decisions were made by the agricultural producers who owned the sites. Those producers also performed all agronomic operations. Except for soybean seeding rate and method, the agronomic practices used at each site were typical for agricultural production in south-central Michigan. The soybean seed was drilled at approximately 150 mm spacing at each site. This is customary for irrigated soybeans in many north central states but not typical for Michigan. Due to the producers not being familiar with soybean seed drilling operations, considerable variation in population rates were observed at each site. Relative yield with yield goal as the denominator was used to compare treatments and for subsequent statistical analysis. Relative yield was chosen so that yield vs. water table variable relationships derived from the field data (from two sites and three growing seasons) are independent of site and climatic variables. The data suggests that none of the treatments can be considered as controls in which Inaximum yield possible is obtained. Therefore, it is felt ;yield goal is a more appropriate datum for calculating :relative yield. Using relative yield with yield goal in the (lenominator also allows the relationships between yield and waiter table parameters to be used in a mathematical model 63 Table 12. Coefficients of determination (r2) resulting from linear regression analyses of data from the Bannister and St. Johns sites (two dependent variables). ANAL’S CROP 848’86 849’87 8J'87 83’88 8808838108 948148088 19.4. corn 0.039 0.806 0.560 0.359 cyield,swtd,sti|eb 19.0. corn 0.031 0.826 0.157 0.409 cyield,sutd,stilea 19.0. corn 0.238 0.117 0.442 0.373 cyield,swtd,sdfi 19.0. corn 0.203 0.599 0.461 0.337 cyield,sutd,sufi 19.8. corn 0.096 0.936 0.567 0.391 cyield,jwtd,jtileb 19.8. corn 0.142 0.985 0.596 0.393 cyield,jwtd,jtilea 19.0. corn 0.017 0.497 0.997 0.393 cyield,jutd,jdfi 19.8. corn 0.064 0.048 0.841 0.394 cyield,jwtd,jwfi 19.1. corn 0.367 0.325 0.555 0.414 cyield,autd,atileb 19.1. corn 0.443 0.512 0.339 0.445 cyield,autd,atilea 19.8. corn 0.087 0.367 0.088 0.423 cyield,autd,adfi 19.0. corn 0.112 0.825 0.014 0.341 cyield,autd,aufi 19.8. soyb’n 0.451 0.292 0.410 0.317 syield,swtd,sti|eb 19.8. soyb’n 0.457 0.318 0.375 0.646 ayield,sutd,stilea 19.0. soyb’n 0.331 0.186 0.329 0.001 syield,sutd,sdfi 19.0. soyb’n 0.381 0.419 0.341 0.431 syield,3wtd,lufi 19.8. soyb’n 0.616 0.337 0.061 0.477 syield,jutd,jtileb 19.8. soyb'n 0.618 0.346 0.059 0.540 syield,jutd,jtilea 19.0. soyb'n 0.825 0.619 0.478 0.007 syield,jutd,jdfi 19.8. soyb’n 0.729 0.768 0.125 0.009 syield,jutd,jufi 19.1. soyb’n 0.039 0.634 0.370 0.867 syield,autd,atileb 19.3. soyb’n 0.030 0.711 0.366 0.884 syield,autd,ati|ea 19.8. soyb’n 0.007 0.083 0.465 0.032 syield,autd,adfi 19.0. soyb’n 0.025 0.104 0.563 0.632 syield,nutd,awfi that can be applied independently of site location and growing season year. System Operation Data: For the growing seasons studied, the water table controls at both sites were set to the desired water table elevation immediately following spring field operations. The initiation of irrigation water pumping to the site was 64 started at the point in time where rainfall was not maintaining or raising the water table. After start of pumping, pumping was continuous until the crop matured near the end of August. The rate of irrigation water supply at the Bannister site was set at all times to cause water discharge at the water management zone outlet. Thus the supply of irrigation water to the Bannister site water table management zones was not limited to the maintenance of the water table depth within the zone. To better study the effect of water table fluctuation on plant biomass production, irrigation pumping was not stopped during or following rainfall events. At the Bannister site, the water table controls were varied during the growing season to cause water table fluctuation (see Table 6). At the St. Johns site the soil profile allows high lateral seepage to occur. This resulted in the need to begin pumping at an early date (6/12/87 and 5/24/88). The high rate of lateral seepage also prohibited holding the water table in one water management zone at a different level than in adjacent zones. The level of the water table was controlled by the lateral seepage and crop use of the water in relation to the pumping rate to the field. An irrigation water pumping capacity of 0.9 L/(tha) was not sufficient to 65 maintain the water table at design depths during the growing season. Thus during most of the growing season the elevation of the water table control weir had no effect on water table depth for any zone except zone D, the drainage only zone. Ground Water Data: At both sites many more observation wells were installed than were used. It was found the observation wells located nearest the center of the water management zone and midway between pipe laterals provided data that best represented the mean water table elevation within the zone. Those wells are identified by the letter ’M’ in the fourth place of the well identification code. At both sites instrumentation breakdowns and water tables above and below the capability of the instrumentation to measure resulted in occasional lapses of water table data. The wells chosen for subsequent analyses are those whose data omissions are relatively infrequent and thus do not influence the analyses results. To relate observation well system output to water table elevation, the digital output for each well was compared to manual measurements of the depth to the water table made during each growing season. Because the output resulted from pressure transducers which 66 provide a linear measurement of the water column at the observation well, the relationship between water table depth or elevation at an observation well vs. digital output is linear. Thus, relating digital output to field measured water table elevation for an observation well results in a calibration equation (the Table 8 regression equation) and a measurement of the functioning of the well (the Table 8 r2). Table 8 presents the results of the regression analyses for the observation wells for the study period at both sites. The wells shown all have a correlation coefficient squared greater than 0.800. Observation wells not shown in Table 8 were not operable or had a correlation coefficient squared less than 0.800. The Table 8 regression equation slope differences are the result of each pressure transducer having a unique slope. The constant value of the regression equations represent the pressure transducer intercept and the elevation of the bubbler exit port within each well. Table 8 shows the regression equation for the same observation well differed from year to year. These differences resulted from changes to the system being made during the winter months. For example between the 1986 and 1987 growing seasons, 7001mn1n0 capacity rated pressure 67 transducers were replaced with transducers with a 1400 mm IMO capacity rating. Other changes that caused year to year changes in the regression equation include changing the bubbler tube exit port elevation and changing the nitrogen tank pressure and bubbling rate. Statistical Analyses: The statistical analyses of the field data were made to evaluate the relationship between water table and relative yield for corn and soybeans for each of two soil types. The relationships explored include both depth and fluctuation of the water table. Tables 9 and 10 provide the yield, water table depth and water table fluctuation data resulting from the field study. Table 11 provides the results of mean square linear regressions of yield vs. water table depth, water table variation and other variables. One Dependent Variable Regressions: From Table 11 it can be seen the following regression variables produced a coefficient of determination greater than 0.500 and a p statistic less than 0.200 for relative yield vs. either a mean water table depth or a water table 68 fluctuation variable: a. soybean yie d vs. mean water table depth during July (Ban ’86, r =0.616, p=0.007, syield=32.3+40.093wtd) b. corn yield vs. % time ?bove mean water table depth for the season (Ban '87, r =0.653, p=0.098, cyieldz69.1+0.3033timea) c. soybean yield vs. % tim above mean water table depth during August (SJ’88, r =0.883, p=0.005, syield=93.3- 0.554atimea) d. corn yield vs. 1 time Pelow mean water table depth for the season (Ban ’87, r =0.563, p=0.144, cyield=98.1- 0.287stimeb e. corn yield vs. wet stress fluctuation index for August (Ban ’87, r2=0.797, p=0.041, cyield=76.3+1.19awfi) f. soybean yie d vs. wet stress fluctuation index for July (Ban ’87, r =0.556, p=0.054, syield=89.9-2.30jwfi) The regression analyses of yield vs. mean water table 2 greater than 0.500. provided only one data set with an r It is not surprising that the data shows few high single variable linear regression coefficients of determination. The literature suggests for corn and soybeans there is an optimum water table depth for yield (Goins et al., 1966 and Williamson and van Schilfgaarde, 1965). Thus the relationship between yield and mean water table depth should not be linear. It has been shown (Williamson and Kriz, 1970; Howell and Hiler, 1974; Zolezzi et al., 1978; Fausey et al., 1985; VanToai et al., 1987) that saturation of corn and soybean 69 roots for varying lengths of time reduces yields. The literature further suggests that the yield reduction is related to the duration and extent of root system saturation (Kanwar et al., 1988). Likewise, root zone water deficient conditions lead to reduced yields with the reduction being related to the duration and degree of the deficiency. Thus it is expected that water table fluctuation does impact yield and that the relationship will be more linear than the yield vs. mean water table depth relationship. The regression analyses of the field data do indicate the water table fluctuation parameters (% time above and below mean water table depth and wet/dry stress fluctuation index) are more linear than the yield vs. mean water table depth relationship. This is evidenced by the fact that of the six single dependent variable regression equations with r2 greater than 0.500 and p statistic less than 0.200, five have water table fluctuation dependent variables (b through f). Examination of the five water fluctuation parameter regression equations shows consistency. An increase in water table fluctuation above the mean water table (b and e) and a decrease in water table fluctuation below mean water table (d) would have resulted in increased corn yield. Thus the signs of the single independent variable corn yield :regression equations with r'2 greater than 0.500 are 70 consistent and indicate corn yields at Bannister in 1987 would have been greater if the water table had risen into the root zone more often and/or for longer duration. The soybean yield regression equations (a, c and f), while consistent, suggest the opposite. The negative coefficient of the % time above mean water table elevation regression equation (c) and the wet stress fluctuation index (f) both indicate the soybean yield at the St. John’s site during 1988 was reduced due to the frequency and/or duration of the water table rising into the root zone during July and August. The regression coefficient for the mean depth to the water table during July, 1988 at the St. Johns site indicates a deeper water table (from less water table rise fluctuation) would have resulted in increased production. The regression equations that best describe the effect of mean water table or water table fluctuation from the mean water table elevation on corn and soybean yield are: a. cyield = 76.3 + 1.19 awfi or the Bannister site during the 1987 growing season (r :0.797, p=0.041) b. syield = 32.3 + 40.9 jwtd for the Bannister site during the 1986 growing season (r =0.616, p=0.007) c. syield = 93.3 - 0.554 atimea for he St. Johns site during the 1988 growing season (r =0.883, p=0.005) However, the single dependent variable regression analyses do not provide conclusive evidence that corn or soybean 71 yield is a function of either mean water table depth or water table fluctuation above and below the mean depth. This is not surprising because both mean water table depth and water table fluctuation parameters were treatment variables and thus neither were held constant. The single variable regression analyses results do indicate that the water table fluctuation parameters have a greater effect on corn and soybean yield than mean water table depth for the study location and time period. Further, the analyses results suggest that during 1987 at the Bannister site, the corn yield was reduced because of deficient soil water and at the St. Johns site in 1988, the soybean yield was reduced because the water table rose above the mean water table depth. Two Dependent Variable Regressions: Table 12 provides the results of least square linear regression analyses for data sets that include both mean water table depth and a water table fluctuation variable. The Table 12 results strongly indicate corn and soybean yield at the sites were influenced by both mean water table depth and water table fluctuation. This is reflected in the r2 values obtained for the regression equations that included mean water table depth and one of the following water table fluctuation variables: % time below mean water 72 table elevation, % time above mean water table elevation, water table fluctuation wet and dry stress indices and percent time above and below the mean water table elevation. From Table 12, those regression variables that produced rz’s greater than 0.500 with p statistics less than 0.200 are: a. corn yield vs. mean water table depth and percent of time below {he mean water table elevation: (Ban’87, r =0.806, p=0.194, cyield=96.7+6.928wtd- 0.4203timeb (Ban’87, r =0.936, p=0.064, cyield=86.7+9.94jwtd- 0.349jtimeb) b. corn yield vs. mean water table depth and dry stress fluctuationzindex: (SJ ’87, r =0.997, p=0.051, cyield=195- 126jwtd+1.513dfi) 0. corn yield vs. mean water table depth and percent of time above he mean water table elevation: (Ban’87, r =0.826, p=0.174, cyield=58.4+5.463wtd+ 0.3908timea (Ban’87, r =0.985, p=0.015, cyield=49.8+9.47jwtd+ 0.437jtimea) d. soybean yield vs. mean water table depth and percent of time below he mean water table elevation: (Ban’86, r =0.616, p=0.035, syield: 32.2+40.0jwtd+ 0.0023timeb2 (Ban’87, r =0.634, p=0.134, syield: 68.8-21.2awtd+ 0.707atimeb (SJ ’88, r =0.867, p=0.049, syield=-17.5+43.4awtd+ 0.981atimeb) e. soybean yield vs. mean water table depth and dry stress fluctuationzindex: (Ban’86, r =0.825, p=0.002, syield=26.9+64.2jwtd- 1.11jdfi) (Ban’87, r =0.619, p=0.145, syield=93.1-8.11jwtd- 0.872jdfi) 73 f. soybean yield vs. mean water table depth and percent of time above he mean water table elevation: (Ban’86, r =0.618, p=0.034, syield=34.4+39.0jwtd- 0.033jtimea (Ban’87, r :0.711, p=0.084, syield=l33 +19.8awtd- 0.644atimea (SJ ’88, r =0.884, p=0.040, syield=94.l-0.72awtd- 0.554atimea) ' g. soybean yield vs. mean water table depth and wet stress fluctuationzindex: (Ban’86, r :0.729, p=0.010, syield=35.0+45.6jwtd- 0.812jwfi) 2 (Ban’87, r :0.768, p=0.054, syield=98.8-9.68jwtd- 2.07jwfi) Corn Yield - Two Dependent Variables: It can be concluded that during the 1987 growing season the corn yield at the Bannister site was strongly influenced by the combination: mean depth to the water table and fluctuation of the water table. The regression equations consistently show the 1987 Bannister corn yield was proportional to the mean depth to the water table and percent of time the water table is above the mean water table elevation (a and c). This suggests maximum yield would result from establishing a water table fairly deep within the soil profile, frequently raising the water table to the surface and immediately returning it to the original water table depth. For the St. Johns site during 1987, the regression results are less conclusive but do indicate corn yield was inversely 74 proportional to the mean water table depth and proportional to the dry stress water table fluctuation index. Thus, to increase yields, the water table would be established at a depth less than the mean water table depth that occurred in 1987 and the water table maintained at or above the mean water table depth. For both Bannister and St. Johns, the regression analyses indicate that corn yield will increase if the water table is not allowed to fall below the water table depth established early in the season. The regression analyses did not produce rz’s greater than 0.500 for corn yield during the 1986 season at the Bannister site nor the 1988 season at the St. Johns site. The yield differences observed at each site and for each season are influenced by both the experimental variables and other factors outside the control of the experiment such as spatial variability of soil properties, rainfall, tillage, planting, harvesting, plant health and hardiness, pesticide control, and fertilizer effectiveness as well as inaccuracies in measurement of the experiment variables. The lack of correlation of the Bannister site 1986 data is attributed to excessive disturbance of the clay soil during the installation of the subirrigation system in July, 1985 which persisted into 1986. This disturbance caused spatial 75 variation of the soil structure and soil properties thus affecting soil water movement, root penetration, fertilizer utilization, etc. The excessive disturbance of the soil during system installation resulted in affecting yield to a greater extent than the experimental variables, mean water table depth and water table fluctuation. For the St. Johns site 1988 growing season, the lowest corn yield of 77% from treatment A80C1 (see Table 10) appears to be an outlier point (and in fact that treatment had a greater infestation of weeds than the other treatments). However, performing the regression analyses without treatment A80C1 data did not result in linear regression equations with r2 greater than 0.500 and p less than 0.200. The regression equations that best describe the effect of mean water table depth and water table fluctuation from the mean water table elevation on corn yield are: cyield = 49.8 + 9.47 jwtd + 0.437 jtimea for 1987 Bannister data and jwtdzfrom 0.73 m to 1.77 m and jtimea from 38 to 68 (r =0.985, p=0.015) cyield = 195 - 126 jwtd + 1.51 jdfi for 1987 St. Johns data an? jwtd from .99 m to 1.72 m and jdfi from 8.8 to 69.1 (r =0.997, p=0.051) Neither the 1986 Bannister site nor the 1988 St. Johns site provided data that related corn yield to mean water table depth and water table fluctuation from the mean water table elevation. 76 The two preceding regression equations for relative corn yield do accurately reflect the site conditions. The high clay content soil profile at the Bannister site allowed the water table to be maintained at a constant, but relatively shallow, depth throughout the growing season. Thus it is not surprising yield is improved by decreasing the percent time the water table fluctuates above the mean water table. The St. Johns site is characterized by difficulty in maintaining the water table at a shallow depth due to high lateral conductivity of the soil profile. The water table elevation achieved early in the season constantly dropped during the growing season. This appears to have resulted in the regression equation for the 1987 growing seasons showing crop yield improvement from decreasing the mean depth to the water table and increasing water table fluctuation. Thus for both sites, the 1987 data shows an increase in water table fluctuation would have resulted in increased corn yield. Obviously, the preceding analyses of the corn yield regression equations suggest that factors other than mean depth and fluctuation of the water table are important to corn grain production. It is assumed for the 1986 and 1988 growing seasons the lack of corn yield correlation is the result of other factors masking the water table depth and 77 fluctuation factors. The analyses do show that site soil factors influence the maintenance of a water table and weather factors, including rainfall volumes and timing, strongly influence the relationship of both mean water table depth and water table fluctuation to corn yield. Further, the data supports the hypothesis that for a site in which a relative constant water table elevation can be maintained at a shallow depth, the subirrigation system should be operated to minimize fluctuation of the water table above the desired constant water table depth. For a site that will not allow the maintenance of a constant water table throughout the growing season, the subirrigation system should be operated to cause the water table to frequently raise for short time durations. In actual practice this means for a constant water table site the system operator would operate the system in a drainage mode following rainfall events that cause a rise in the water table. For a falling water table site the system operator would operate the system in a drainage mode following rainfall events only if the combination of event frequency, duration or volume cause the water table to rise for an extended period of time. The end result is that for a constant water table site, the operator has more control over the yield but much more work is required. For a falling water table site, the operator is more dependent upon rainfall but less work is involved. 78 The data suggests that at a site that has constant water table elevation depth capability, the best operation scenario is to allow the water table, over the season, to recede at a rate less than the rate of corn root elongation. This would allow an irrigation rate less than the rate of evapotranspiration plus deep and lateral seepage and reduce the work involved in operating the system. There is a need to study this concept further. Certainly, the differences in the relative corn yield regression equations (Tables 11 and 12) illustrate the need to consider site, weather and crop factors in a design procedure for water table management systems. Soybean Yield - Two Dependent Variables: The Table 12 regression equations for soybean yield show that for both the 1986 and 1987 growing seasons at the Bannister site the yield was related to the combination of mean water table depth and water table fluctuation parameters. The relative soybean yield regression equations show, generally for the 1986 season at Bannister, the yield was inversely proportional to the percent time above the mean water table elevation in July, the July wet stress water 79 table fluctuation index and the July dry stress water table fluctuation index each along with a direct proportional relationship with the July mean depth to the water table. The 1987 Bannister regression analyses show the water table fluctuation parameters (July percent time above the mean water table and July wet and dry stress water table fluctuation indices) all being inversely proportional to soybean relative yield as was the case for the 1986 Bannister data. However, opposite to the 1986 results, the 1987 Bannister regression analyses show the mean depth to the water table (during July) being inversely proportional to yield. For the 1988 season at St. Johns, the two equations with r2 greater than 0.500 and p less than 0.200 indicate increased soybean yield would have resulted if the mean depth to the water table was less in July with less fluctuation of the water table above the July mean water table elevation. The St. Johns site 1987 growing season did not produce any water table depth/fluctuation regression equations with an r2 greater than 0.500 and a p less than 0.200. Examination of the soybean yield data for that site and year (see Table 10) shows that the treatment C40C1 relative yield of 35 percent may be an outlier (the yield is much lower with the 80 water table parameters being similar to the other treatments). The weed infestation in treatment C40C1 was observed to be much greater than for the remainder of the field. However, because the deletion of treatment C40C1 data reduces the sets of data to three, regression equations with more than a single dependent variable are not meaningful. Thus the analyses suggest for 1987 at the Bannister site and 1987 at the St. Johns site, the soybean yield would have benefited from a higher mean water table elevation and less fluctuation of the water table above and below the mean water table elevation. During 1986 at Bannister a lower mean water table elevation and less water table fluctuation would have improved soybean yield. The soybean yield regression equations with both mean depth to the water table and water table fluctuation above the 2, mean water table elevation with the best r s are: syield = 35.0 + 45.6 jwtd - 0.812 jwfi for 1986 Bannister data with 'wtd from 0.45 m to .72 m and jwfi from 4.3 to 14.5 (r :0.729 p=0.010) syield = 98.8 - 9.68 jwtd - 2.07jwfi for 1987 Bannister data withzjwtd from 0.71 m to 1.77 m and jwfi from 1.6 to 9.0 (r :0.768, p=0.054) syield = 94.1 - 0.72 awtd - 0.554 atimea for 1988 St. Johns data with awfd from 0.89 m to 1.47 m and atimea from 31% to 72% (r =0.884, p=0.040) 81 Regression Without Outliers: A close examination of the Table 10 data reveals that for the 1987 St. Johns data set, the treatment C4001 relative soybean yield may be an outlier as is the 1988 St. Johns data set treatment A80C1 relative corn yield. Performing the regression analyses without St. Johns 1987 treatment C40C1 and St. Johns 1988 treatment A80C1 data produces the following soybean yield regression equations with r2 greater than 0.400 and p less than 0.250: a. syield = 118 — 36.9 jwtd, r220.989, p=0.066, SJ ’87 b. syield = 87.7 - 0.281 sdfi, r2=0.995, p=0.047, SJ ’87 c. syield = 86.7 - 0.241 swfi, r220.964, p=0.122, SJ ’87 d. syield = 83.3 — 0.425 jdfi, r2=0.999, p=0.019, SJ ’87 e. syield = 81.4 - 0.455 jwfi, rz=0.972, p=0.108, SJ ’87 f. syield = 90.6 - 1.48 adfi, rz=0.928, p=0.172, SJ ’87 g. syield = 87.6 — 1.19 awfi, r220.997, p=0.037, SJ ’87 h. syield = 50.2 + .531 jtimeb, rz=0.544, p=0.155, SJ ’88 i. syield = 96.9 - .647 atimea, r =0.678, p=0.087, SJ ’88 The results show the same trends as resulted from the regression analyses using all of the data. The regression analyses for the St. Johns 1987 data without treatment C40C1 2 provides regression equations with r ’s greater than 0.500 for soybean yield that were not obtained previously. Conclusions 1. The diurnal fluctuation of the water tables measured at each site and for each growing season indicates 82 evaporated and transpired soil water was in part being replenished from the water table. Comparing the r2 and p statistics of the spacing and zone regression equations with the regression equations arranged by site and by crop and regression equation arranged by site and dependent variable, it is concluded that water table depth fluctuation parameters are better predictors of corn and soybean yield than lateral spacing or water table management zone for the sites and growing seasons studied. The regression analyses of the field data from the Bannister and St. Johns field sites did not provide an equation for design that relates corn or soybean yield to mean water table depth or water table fluctuation above or below mean water table elevation consistent for both sites and/or all three years. However, the analyses did provide insight into the relative importance of those water table parameters in situations where the agricultural producer has the opportunity to provide a measure of water table control. Examination of the field data scatter plots (Appendix D) coupled with the results of the single independent 83 variable regression analyses indicate water table fluctuation parameters have a greater effect on corn and soybean yield than does mean depth to the water table. Two out of three years soybean yields at the field sites were more affected by the fluctuating water table parameters than were corn yields at the field sites. For the research sites and study period, generally the water table fluctuation parameter regression coefficients were positive for fluctuation above the mean water table for corn yield and negative for soybean yield. Those coefficients were negative for fluctuation below the mean water table for corn yield and positive for soybean yield. The water table fluctuation wet/dry stress index appears to be a valid procedure for quantifying the combined effect of time and accumulated distance the water table is above/below the mean water table elevation. However, the data did not show that the water stress fluctuation wet/dry stress index offers more prediction accuracy than the percent of time above/below the mean water table elevation parameter. 10. 84 There is a need to continue studies of water table depth and fluctuation effects on corn and soybean production and other crops under a variety of soil and climatic conditions. For continued work, greater control of the water table within a treatment and other crop production variables other than water table depth and fluctuation is needed. Also, the treatments should be located randomly with three or more replications. The data suggests that at a site that has constant water table elevation depth capability, the best operation scenario is to allow the water table, over the season, to recede at a rate less than the rate of corn root elongation. This would allow an irrigation rate less than the rate of evapotranspiration plus deep and lateral seepage and reduce the work involved in operating the system. There is a need to study this concept further. The effect of water table depth and fluctuation on corn and soybean yield needs to be considered in the design process for water table management systems. The field data regression analyses results suggest the critical design parameter is to return the water table to the design depth following frequent water table raises for corn production and following rainfall events for 11. 85 soybean production. To account for both the frequency and extent of water table rise, the system design criteria should utilize the water table fluctuation wet stress index. The study did not produce design criteria in which relative corn and soybean yield is dependent upon only water table depth and/or fluctuation parameters. Thus the objective of quantifying water table management operation parameters that influence plant biomass production was not realized. A single mathematical model relating relative yield to water table depth and water table fluctuation independent of field location, soil profile and growing season could not be derived from the field data. The study does suggest that for corn production at the Bannister and St. Johns sites and with a mean water table depth of 0.5 m the regression equation: Y : B0 + B1 3 X [3] can be used for water table management system design with: Y = relative corn yield in % X = jwfi in m*h/h BO : 77 0.5 <= Bl <= 1.2 Likewise, the data suggests that for soybean production 86 at the Bannister and St. Johns sites (also with the mean water table depth at 0.5 m) the regression equation: Y : B0 - B1 * X [3a] would have: Y = relative soybean yield in % X = awfi in m*h/h BO = 92 1.4 (2 Bl (z 2.1 Letting B1 be midway between the range given, for corn production the design equation with the water table fluctuation wet stress index as a variable is: cyield = 77 + 0.85 awfi [4] and for soybean production is: syield = 92 - 1.75 jwfi [4a] both with the mean water table depth at 0.5 m. 87 WATER TABLE MANAGEMENT SYSTEM DESIGN Methodology System Components: A water table management system consists of perforated underground pipe spaced at regular intervals. These pipe are called laterals and are arranged in zones determined by the elevation variance of the soil surface within the zone. The laterals within each zone discharge to an underground collector pipe called a submain. The submain for each zone outlets to an underground pipe called a main. The number and size of the zones, submains and mains is a function of the topography of the site. Water table management systems provide the capability to lower the water table (subsurface drainage mode) or raise and maintain the water table at a given elevation (subirrigation modes). Each zone requires a water table control structure located in the submain immediately downstream of the zone. The water table control structure has the capability to be set to allow free drainage (subsurface drainage mode) or to establish a water table upstream of the structure at a desired elevation (controlled drainage and subirrigation modes). Irrigation intake 88 structures, vertical pipes from the submain to the ground surface, are provided for irrigation water access to the underground system during times when rainfall does not maintain the water table at the desired elevation. The irrigation water is pumped from the source to the field through irrigation water supply pipes. A water table management system thus consists of laterals, submains, mains, water table control structures, irrigation intake structures, irrigation water supply pipes, a pump and a power supply. System Operation: A water table management system has three modes of operation. The subsurface drainage operation mode is used to lower a water table that is above the elevation of the laterals by draining water from the soil profile via the underground pipe system. The controlled drainage operation mode is used to capture rainfall to raise or maintain a water table above the elevation of the laterals. The subirrigation mode is used to raise or maintain a water table above the elevation of the laterals by providing irrigation water to the soil profile via the underground pipe system. 89 The field research suggests system operation must consider both the depth to the water table during the growing season and the fluctuation of the water table. Further, the research suggests that water table fluctuation has a greater effect on yield than does mean water table depth. SIDESIGN Computer Model: An objective of this research was to develop a model for the efficient design of water table management systems that will allow the system to be operated for maximum plant biomass production economic efficiency. That objective was met by developing the computer module SIDESIGN. The present version of the SIDESIGN computer model has the following requirements and attributes: 1. The model is operational on the following minimum system configuration: a. IBM personal computer or compatible with a minimum of 256 k RAM memory and a single floppy disk. b. CGA or higher resolution monitor, monochrome or color. 0. 80 character line printer. 2. Model operation is interactive with the user responding 90 to prompts displayed on the monitor. 3. The model does not require additional software other than the operating system software (MSDOS or PCDOS Version 2.11 or higher). 4. The model is written in the QuickBASIC compiler language (Version 4.5) from Microsoft Corporation, Redmond, Washington, USA. MODEL FORMAT The model is in modular format. The present version of the model has the following modules: * SIRAIN t SILSPACE x SIMAIN x SIECON A detailed description of each module follows. SIRAIN DESCRIPTION The SIRAIN module is used to calculate the design rainfall 91 event to be used for subsequent calculations. The module uses historic growing season rainfall records provided by the model user. The input data may be provided as a text file or by interactive keyboard input. Data Input: The model user inputs the number of years (NumberOerars) to be analyzed, the growing season start date (SeasonStartDate) and end date (SeasonEndDate) and the daily rainfall for each day of the growing season (including 0.0 rainfall days) for each year {rain(y,d)}. The data input can be interactive with the user responding to screen prompts or by the user inputting the name of the data disk file. For diskfile input, the data must follow the following format: Line 1 NumberOerars, SeasonStartDate, SeasonEndDate Line 2 year(n) Line 3 rain(y,d) 9 9 Line 3 is followed by rainfall for each day of year n 92 starting with the SeasonStartDate and ending with the SeasonEndDate. Lines 2 and 3 are repeated for each year of historic data. Calculations: The module uses the input data to calculate and output the 50% probability (2 year recurrence interval) and 10% probability (10 year recurrence interval) daily rainfalls by month and growing season. The module also calculates and outputs the number of rainfall events per month and growing season at the 50% probability level. To calculate the 50% and 10% probability daily rainfalls, the historic daily rainfall data is ranked in decreasing order, excluding 0 rainfall days, and the recurrence interval calculated by the mathematical model (Schwab, et al., 1981): '1‘ = ”*1 [51 n where: T = recurrence interval, yr N = total number of daily rainfall events, unitless n = rank of rainfall events arranged in descending order, unitless 93 All of the rainfall events are ranked by month and by growing season. For the 2 year recurrence interval rainfall, equation 5 requires the rainfall that falls midpoint in each ranking be determined and provided as output. If at any time the number of ranked rainfall events is not odd, the smallest rainfall value is dropped from the ranking. This insures that the 2 year recurrence can always be calculated. The same rankings are used to determine the 10 year recurrence interval rainfall. That ranking of that rainfall is at 10% of the total number of rainfall events for each ranking. Example: A numeric example of the procedure for determining the 2 year recurrence rainfall follows: Assume the historical daily rainfall (in mm) is from 1980 through 1982 and the growing season is June 1 through August 31. The daily rainfall amounts for the days rainfall occurred for those years and months is: June July August 1980 18 3 9 2 12 3 11 4 5 21 1981 5 7 11 9 l 3 12 4 94 1982 2 4 21 1 7 1 14 11 5 6 To obtain the 50% probability rainfall (2 year recurrence interval) by month and growing season the above data is ranked as follows: June: 18, 14, 12, 11, 9, 5, 3, 2, 2, 1 July: 12, 11, 7, 7, 6, 4, 4, 3, 1 August:21, 21, 11, 9, 5, 5, 4, 3, 3, 1 Season:21, 21, 18, 14, 12, 12, 11, 11, 11, 9, 9, 7, 7, 7, 6, 5, 5, 5, 4, 4 For the June and August data, the 1 mm rainfall is dropped from the ranking so as to have an odd number of rainfall events in each of the four rankings. This results in N values for equation 5 equal to 9, 9, 9 and 29 for June, July, August and the season, respectively. Applying equation 5 to the data produces 2 year recurrence values of daily rainfall equal to 9 mm, 6 mm, 5 mm and 6 mm for June, July, August and the season respectively. The model uses a similar procedure to determine and output the number of rainfall occurrences for each month and the growing season. The example follows: For June, 3 events occurred in 1980, 4 in 1981 and 3 in 1982. Likewise, for July 3, 2 and 4 events occurred for 1980, 1981 and 1982 respectively; for August 4, 3 and 3 occurred for 1989, 1981 and 1982 and for the season 10, 9 and 10 events occurred for those same three years. Ranking the number of events by year: June: 4, 3, 3 July: 4, 3, 2 August: 4, 3, 3 Season: 10, 10, 9 Applying equation 5 to the data results in 50% probability for number of rainfall events at 3, 3, 3 95 and 10 events for June, July, August and the season, respectively. The preceding example is to illustrate the procedure only. To determine recurrence interval rainfalls and number of events for design, the period of rainfall records should equal or exceed 20 years. The SIRAIN module has the capability to analyze up to 30 years of daily rainfall data for a 12 month growing season per year. The procedure is a modification of a partial series duration analysis. For further information the reader is referred to Chow, 1964. SILSPACE DESCRIPTION The water table system components that limit control of depth to the water table and rate of fluctuation are the depth, spacing and hydraulic capacity of the laterals, the hydraulic capacity of the submains and mains, the operational capability of the water table control structures and the capacity of the water supply system. The SILSPACE module allows the combined effects of those components on the operation of subirrigation systems to be investigated. The SILSPACE module allows the user to evaluate system design alternatives on system performance in 96 terms of water table depth and water table fluctuation. SILSPACE consists of five sections: data input, initial calculations, steady state analysis, transient analysis and results output. Data Input: User input data describes the soil profile, the design rainfall event, the system components and the system operation. The model uses those data to compute and output the lateral spacing and discharge capacity required for steady state supply of irrigation water and steady state subsurface drainage. Next, the vertical rise of the water table due to infiltration of the design rainfall is calculated. This is followed by transient analysis to determine the time and discharge rates for the water table to return to the levels preceding the rainfall event. A description of the data that must be provided by the model user follows: System Variables: depth to the lateral pipe.............TileDepth diameter of the lateral pipe..........TileDiameter minimum grade of the lateral pipe.....TileGrade length of the lateral pipe............TileLength 97 For Subirrigation: depth to water table at lateral.......siWTdepthLateral depth to water table at midpoint......siWTdepthMidpoint For Subsurface Drainage: depth to water table at lateral.......deTdepthLateral depth to water table at midpoint......deTdepthMidpoint design subirrigation rate.............sirate design subsurface drainage rate.......drrate design storm runoff...................runoff design storm occurrences..............events depth to weir following rainfall......WeirDepth Soil Variables: depth to barrier......................BarrierDepth number of soil layers.................nlayers For Each Soil Layer: layer thickness.......................th saturated hydraulic conductivity......hydc water content at saturation...........sat water content at drained upper limit..dul The input data required, for the most part, is self explanatory and easily obtained. The water content at saturation (sat) is the volumetric soil water content when the soil is saturated. The drained upper limit (dul) is the volumetric water content that results from complete soil water drainage from the soil layer without evaporation or transpiration. The ’sat’ and ’dul’ terms are further described by Ritchie et al., 1986. The model allows data input from a diskfile or interactively. A data input diskfile requires the data to 98 be arranged in the following sequence: Line 1 BarrierDepth, nlayers Line 2 (Note: Line 2 data are provided for each soil layer in sequence beginning with the surface layer.) th, hydc, sat, dul Line 3 TileDepth, TileDiameter, TileGrade, TileLength, siWTdepthMidpoint, siWTdepthLateral, deTdepthMidpoint, deTdepttheral Line 4 sirate, drrate, rainfall, rainfall occurrences, runoff curve number, WeirDepth Initial Calculations: SILSPACE first calculates the infiltration resulting from the user inputted rainfall and runoff curve number using the equation: Infiltration = rainfall (Pl - Runoff (Q) [6] The runoff is calculated by the USDA Soil Conservation Service curve number method (USDA Soil Conservation Service, 1972): 99 _ 2 (P 0.28) [7] where s=-2—5—‘399--254 [81 CN for units of P, Q and S in mm. Next the model calculates and uses in subsequent calculations a weighted value for saturated hydraulic conductivity and the difference in the volumetric water contents at saturation and drained upper limit. The weighted values are calculated by: $[hydcnkthn] n égthn weighted hydc = n Z1sat-du1)n(thln n gthn where n = layer number for layers from weighted sat-dul = the soil surface to 0.6 m below the pipe depth hydc = user inputted values of the saturated lateral hydraulic conductivity, l/t, for each soil layer. 100 sat = user inputted values of the saturated volumetric water content for each layer l/l. dul = user inputted values of the drained upper limit volumetric water content for each layer, l/l. th = user inputted thickness of each layer, 1. Steady State Analysis: SILSPACE calculates the lateral spacing required for subsurface drainage at the design subsurface drainage rate, drrate and subirrigation at the design subirrigation rate, sirate. The lateral spacing algorithm used by the SILSPACE model is to calculate the spacing using a modification of a steady state equation developed by Hooghoudt and by Ernst (Van Beers, 1976). That method is described in detail by Skaggs, 1980. The modified Hooghoudt equations used are: 8-k-De.n+4.k.n2 L2 [111 qd for subsurface drainage and 101 4-k‘H[2-ho+22.H] Do qs = L2 [121 for subirrigation with the terms defined as follows: de ho Do qd C18 The design distance between drainage laterals, l. [ldr and lsi] The effective saturated lateral hydraulic conductivity, l/t. [hydc] The difference in water level as measured over the lateral pipe vs. midway between the lateral pipes, l. [deTdepthMidpoint—deTdepthLateral and siWTdepthMidpoint-siWTdepthLateral] The depth from the center of the lateral pipe to the equivalent impermeable layer, 1. [dem] The distance from the water level over the lateral pipe to the equivalent impermeable layer, 1 [dwtm- dem]. The distance from the water level over the drain to the actual impermeable barrier, l [dwtm-dbm]. The steady state subsurface drainage coefficient, l/t. [drrate] The steady state evapotranspiration rate, l/t. [sirate] 102 de subsurface droMoge ____1 __ ______ ____JL Equivalent Impermeoble Barrier W L subirrigation Figure 18. Subsurface drainage/subirrigation lateral spacing design notation. The equivalent depth, de, to the impermeable layer is introduced to account for losses incurred as water leaves the drain and flows outward during subirrigation mode and for the losses that occur as the flow converges to the drain openings during subsurface drainage. Hooghoudt (Hooghoudt, 1940 and van Schilfgaarde, 1974) evaluated that effect by comparing radial flow near the pipe with flow conforming to the Dupuit-Forchheimer assumptions away from the pipe. Hooghoudt’s solutions were formulated by Moody (1966) as 103 follows: For 0 < d/L < 0.3 de = [13] 14%[9-1n[;%]-a] where d d 2 a = 3.55—1.6L—+2[L—] [14] For d/L > 0.3 de = L'" [151 [8~ln%:-1.15] in which L = Lateral Spacing, l. [ldr and lsi] de = equivalent depth to the barrier, 1. [dem] d = actual depth to the barrier, l. [BarrierDepth] re = drain tube radius, 1. [rem] The effective drain radius is less than the actual drain radius to account for additional loss of hydraulic head due to convergence of the flow lines resulting from flow entering or leaving the pipe through a finite number of perforations. The values used for re are 3.5, 5.1 and 10.0 104 mm for pipe diameters 66, 102 and 127 mm respectively (USDA Soil Conservation Service, 1985). The iterative method of solving for the lateral spacing, L includes calculating the depth to the equivalent impermeable layer. For both drainage and subirrigation the lateral spacing, L, is solved by iteration using the hydraulic conductivities, depth to barrier, depth to tile and depth to water table values provided by the user. The module thus computes two lateral spacings - one for subsurface drainage and the second for subirrigation. The design lateral spacing for further computations is set equal to the lessor of L for drainage and L for subirrigation. The model user has the option of choosing a different design lateral spacing for subsequent calculations. Transient Analysis: For the first step in the transient analysis, the model establishes the maximum flow capacity of a lateral pipe using Manning’s equation and user inputted values for pipe diameter and grade. In terms of model variable names, 105 Mannings equation is: 2 1 . _ 1_ TileArea 3. TileGrade :- . FullPlpeQ — n [TilePerimeter] [ 100 ] TileArea [16] where FullPipeQ = full pipe flow discharge, l3/t n = Manning’s roughness coefficient TileArea = cross-sectional area of the pipe, 12 TilePerimeter = wetted perimeter of the pipe, 1 TileGrade = grade of the pipe, % The FullPipeQ is put in units of l/t by dividing FullPipeQ by the design lateral spacing and the length of the lateral. The user has the option of reducing FullPipeQ if desired. Next the rise in the water table from infiltration of the design runoff is calculated. The initial condition is that the system is in the subirrigation mode with the water table at the user inputted depths at the lateral and midway between laterals. The initial water content is assumed to be at 80% of the drained upper limit water content. The rainfall infiltration is assumed to cause 1) an instantaneous leveling of the water table at a depth equal to the average depth at the lateral and depth midway between laterals and 2) an instantaneous rise in the level water table sufficient 106 to store 100% of the infiltration based upon the weighted saturated - .80 * drained upper limit water contents. Next, the modified Hooghoudt steady state equation is used to calculate the drainage flux (MaxEllipseQ) with the variable m being the difference between the depth to the pipe and the water table depth resulting from the rise in water table because of infiltration. Calculation of the time for drawdown of the water table from the water table made shallower by infiltration to the design water table depth for steady state subirrigation proceeds in two phases. For the first phase, the water table is assumed to vary from approximately horizontal to elliptical. At the first phase conclusion 1/2 the vertical height of the ellipse is equal to the difference in the pipe depth and the water table depth immediately following the rise in the water table due to runoff distribution. The horizontal width of the ellipse is equal to the lateral spacing. The ellipse at the conclusion of the first phase is defined by the curve designated WT Q t3 > 0 in Figure 19. The time for phase 1 is calculated by varying the horizontal width of the water table ellipse curve from 0 to L/2 in 1000 steps, integrating the ellipse curve at each step and calculating the time to 107 drain the volume of soil between steps. The time for drainage between steps is calculated by dividing the volume drained between steps (the area between steps times the difference in soil water content at saturation and soil water content at drained upper limit) by the average of the drainage flux between steps. The drainage flux at each step (q) is calculated using the Hooghoudt equation as previously defined. The calculated drainage flux is not allowed to exceed FullPipeQ nor be less than the user inputted drainage coefficient (drrate) + the user inputted subirrigation rate (sirate). For water table drawdown as shown in Figure 19 the control weir is lowered to drainage lateral depth at time t2 = 0. For phase 2 flow, the elliptical water table is dropped vertically in 30 mm midpoint increments from the midpoint height of the water table at the end of phase 1 to the midpoint depth of the water table in steady state subirrigation mode before the rainfall event. At each incremental drOp, the ellipse curve is integrated and the time to drain the volume of soil within the increment is calculated. The time for drainage between increments is calculated by dividing the volume drained between increments (the area between steps times the difference in soil water content at saturation and soil water content at drained 108 lnitBQl WT (t0) /A§:;VJT @9'0320\\ d‘-’ — —~_~—“-‘~\ ’A-"-'—d—— f“. "‘ x / / I /§—WT @ t4>t3 t3 to t3 +> Phase 1 t3 to t4 +> Phase 2 Figure 19. Schematic showing change in water table (WT) with time (t) following a rainfall event through water table drawdown with the subirrigation system initially in a subirrigation mode. upper limit) by the average of the drainage flux between steps. The drainage flux at each increment is computed by the Hooghoudt equation as previously described. During the phase 1 and 2 calculation cycles, the time is accumulated and the elapsed time, water table depth at midpoint and drainage flux is shown on the monitor at each step. Calculated Crop Yield Parameters: The model uses the rise in water table, number of events and 109 elapsed drawdown time to calculate a crop yield parameter called the wet stress fluctuation index (wfi). The wfi is a parameter that may be used to estimate the mean crop yield that the subirrigation system described by the input data will produce. The wfi quantifies the fluctuation of the water table above the mean water table over the period of time represented by the number of events input by the program user. This provides a water table fluctuation parameter that can be used to estimate yield by comparing the computed wfi to wfi vs. yield relationships obtained through field studies and/or simulation models such as DRAINMOD. The module computation of wfi is based upon a rise and fall of the water table resulting from the user inputted rainfall as follows: The wfi parameter is calculated by the following mathematical equation: % dwfi.twf12-events . = ‘ _. 7 wf1 TotalTime-24 [1 1 where events = number of rainfall events during the time period of interest (unitless) TotalTime 2 Duration of time period of interest (days). 110 :f-Ground Surface \ Water Tabm> /\ FoHowmg a ramFaH event WthQm VTQvg ‘WTSIOPI /’ VIN7 31 l thi 24 hr299) 0L17 SILT 3190 81118 003 8198 001838 91‘ E 9 0 90 ------- 981087 ------- 97 319PLE DEPTH 11081209 L7 .002 .05 L7 LT .002 .02 .05 .10 .25 .5 1 2 5 20 .1- PCT 08 19) (074) .002 -.05 -2 .0002 .002 -.02 -.05 -.10 -.25 -.50 -1 -2 -5 -20 -75 75 980L8 ( ------------- PCT 07 (299 (331) ----------------- ) (- PCT 08 (7599(381) -) SOIL 85814823 0- 23 AP 24.3 47.9 27.8 29.3 18.6 5.7 12.5 7.4 1.6 0.6 2 -- -- 24 2 85814833 23- 38 801 30.3 39.4 30.3 27.5 11.9 6.1 14.3 7.4 1.7 0.8 1 1 -- 26 2 85834843 38- 61 802 28.0 33.6 38.4 22.1 11.5 7.4 17.9 9.7 2.3 1.1 5 -- -- 34 5 85834853 61- 92 803 33.0 36.3 30.7 .7 10.6 6.0 14.6 7.8 1.6 1.3 2 2 -- 28 4 85834863 92-112 001 31.6 36.8 31.6 25.6 11.2 6.4 14.5 7.8 1.6 1.3 2 2 -- 28 4 85P3487S 112-160 002 29.7 39.1 31.2 26.7 12.5 6.1 13.6 7.5 2.4 1.6 2 3 1 30 6 DRPTR 0809 (-81710/0L17-) (BULK 0893177) COLE (-91788 0097897-) 980 0 15 1/3 0989 9901.8 1/10 1/3 15 91101.8 080 818 818 087 SOIL 818 818 818 SOIL (- - - CLAY/9198810007 - RELATIVE 19011973 - - -) (09) PCT (- -0/00- -) 09/09 (- PCT 07 (299 -) 09/04 (000299) 0- 23 1.90 0.61 0.44 1.62 1.76 0.028 23.3 22.2 10.6 0.19 883 913 992 981 23- 38 0.78 0.44 0.40 1.65 1.79 0.027 21.3 20.3 12.2 0.13 883 913 99 2 98 1 38- 61 0.42 0.41 0.39 1.68 1.84 0.030 21.5 20.4 10.9 0.15 88 3 913 993 081 61- 92 0.38 0.35 0.40 1.68 1.85 0.032 21.3 20.6 13.3 0.12 913 883 99 2 08 1 92-112 0.32 0.34 0.42 1.69 1.84 0.028 19.8 19.1 13.3 0.10 913 883 992 08 1 112-160 0.41 0.34 0.42 1.63 1.81 0.034 21.4 21.0 12.5 0.13 91 3 883 99 2 08 1 19881083, 08878 251-1001 PCT 01.47 31 PCT .1-7599 29 19107313: 3: 1LL 09 318980 (2104 81313 9198811007: 8190 07 919881L 88 810L19178 91 9101 99 9889-9101 98 9881410110178 08 00878178 88117198 AMOUNT 6 19087881419178 5 009191178 4 18U90197 3 90088178 2 SHALL 1 78108 9178: 7813 P8009 13 1 718110907 70 788 21808971133 388183. 17 8.43 L833 71119 352 01.17 19 THE CONTROL 3807109 APPENDIX B ST. JOHNS SITE SOIL DATA 130 APPENDIX B CLASSIFIQTICI: mm; (NEE-1M, mm, IBIC W0 81811811118 (- - -70710- - -) (- CLAY -) (- -31LT- -) ( -------- 3190 ------- ) (- 001838 881071093049) -) (>299) 0017 SILT 3190 8198 003 8198 001838 98 8 9 0 90 ------- 981087 ------- 97 319808 08878 8081209 LT .002 .05 07 07 .002 .02 .05 .10 .25 .5 l 2 5 20 .1- PCT 08 90 (09) .002 -.05 -2 .0002 .002 -.02 -.05 -.10 -.25 -.50 - -2 -5 -20 -75 75 98008 < ------------- 807 08 (299 (331) ----------------- > <- 807 08 (75189381) -) 3010 86855483 0- 23 18 10.3 16.2 73.5 9.2 7.0 8.3 27.2 29.4 6.8 1.8 3 3 -- 67 6 86855493 23- 48 871 13.8 19.3 66.9 11.2 8.1 . 29.9 21.1 4.9 2.5 3 3 -- 61 6 86855503 48- 71 872 10.7 13.2 76.1 7.3 5.9 7.4 30.4 28.1 6.8 3.4 4 5 1 72 10 86855513 71-100 80 7.8 11.9 80.3 6.1 5.8 6.3 19.8 37.3 12.8 4.1 5 8 -- 77 13 86855523 100-150 200 1.9 4.1 94.0 1.8 2.3 1.9 26.1 56.3 7.4 2.3 3 6 4 93 13 08878 0809 (-81710/01.AY-) (801.8 DENSITY) 0008 (-91788 00978974 980 0 15 1/3 0989 98008 1/10 1/3 15 98008 080 818 818 087 SOIL 818 818 818 SOIL (- - - CLAY/9198811007 - 88017198 1900973 - - -) (09) 807 (- -0/00- -) 011/09 (- 80708 (299 -) 09/09 (000299) 0- 23 1.88 1.24 0.64 6.6 973 882 912 001 021 23- 48 0.26 0.75 0.49 6.7 97 3 883 913 982 081 48- 71 0.30 0.56 0.46 4.9 97 3 88 3 913 982 081 71-100 0.32 0.50 0.53 4.1 88 3 91 3 97 2 98 2 082 100-150 0.10 0.26 0.47 0.9 88 3 91 2 98 1 971 08 1 9198810 19788888717109: 97 IontIorillinite 88 kaolinite 91 lien 00 chlorine 02 quartz 98 veniculite 08 goethite 01 gibbsite 88017198 8818 3128: 5 Very Large 4 Large 3 Hediul 2 Snell 1 Very Slall 6 90 Peaks APPENDIX C WATER TABLE ELEVATION VS. TIME PLOTS 132 75.0 - 75.0 E 50.0 . 50.0 g 25.0 125.0 I 0.0 -0 0 3“] zone as: 29.75 m to 30.10 m '3“ E 929.5 v ”2.5 5 E [29.4 a {-29.2 d .59 i3 .. " 20.0 E '3 23.0 ’ 202 m 1. -. 210 .. . 2 DAYS FROM START OF 7986 BANNISTER SITE OBSERVATION WELL WMMT Figure C1. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WA4M1 for 1986 growing season at the Bannister site. 75.0 75.0 A E 50.0 50.0 V g 25.0 25.0 0.0 0.0 30.0 zoo: azv: 29.37mto3a10m '3” E 2... :218 V 20.0 r291 Z 2 23,4 P”.4 a 20.2 ~29.2 El 20.0 >29» “1 . Q1 20.: *2“ I‘ 20.0 [20.0 E ”.4 .204 I 3 203 720.2 9 no 23.0 1 .. 1 "2io"'250" 7250' "WHY 28f DAYS FROM START OF 1986 BANNISTER SITE OBSERVATION WELL 9782942 Figure C2. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WB2M2 for 1986 growing season at the Bannister site. 133 70.0 700 A E 33.0 5500 v I g 20.0 ~20» 00 10.0 00.0 200: am 20.07 m 10 30.00 m ’3” E 200 ~20.0 v 20.0 , 20.0 2 I I g 28.4 ' >204 I g 20.2 g »20.2 28.0 40.0 El I ’ 9 2" | I'm | " 20.0 . -20.0 m | 13 20.4 I .20.0 ’ 202 ' {20.2 200 ' 20.0 100 200 FR:0'270 TV '23; "250" ' "W200 MSTART 0F1 BANNISTERY's SITE FROOBSERVATION WSELL 9484942 Figure C3. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WB4M2 for 1986 growing season at the Bannister site. 700 ~7so A E E 500 4.00 v D g 20.0 :200 00 Lao 30.0 zucmnmmmaonam "”5 E m Pm v 30.1 ~30: 3 200 >200 ‘3 g 201 >20.7 20.6 20.5 a 20.0 ”-3 " 20.1 20.4 g 20.0 20.0 < ’ 20.1 20.1 1” - fi. 205 100 1 -. 1001-. -. 210 YSFROM START OF 7986 BANNISTER SITE OBSERVATION WELL WBSLI Figure C4. Water table elevation (m) and raqinfall (mm) vs. time (days) for observation well WB6L1 for 1986 growing season at the Bannister site. 134 75.0- -75.0 ’E‘ E 50.0- -50.0 V g 25_g.| L -25.0 An .—. l L l' l .I l n I I v V f L. an 30.5- ZONE ELEV: 3015 m to 30.00 m 30.5 E 30..» 030,: v 301. >301 5 29.9- - . E: 209 a 29.71 -29.7 _I 1.; 205+ -29.5 1.2 Q‘ 29.3J ~20; '- 29.1. -29.1 E '3 20.01 I209 3 2074 F257 :35— ‘ —205 1 :8 200 210 220 230 208 DA AYS FROM START OF 1986 BANNISTER SITE OBSERVATION WELL WC2L1 Figure C5. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WCZLl for 1986 growing season at the Bannister site. 750 -7s.o A E 500 -5o.0 V g 20.0-l L -23.0 n‘n _L' '- I. l. l' .I l' n I' l 1 I ? - ' 11.00 ”’5‘ zone ELEV: 30.10 m m 30.40 m '3‘" E 30;. ~30; v 30.14 -30.1 5 200 0 5 . . -20. a 20.7- -20.7 CI 20,5. ~2IJ5 3 ‘2 20.3- 40.0 '— 20.“ -20.1 a: E 20.0. -20.0 3 201. >203 205- >205 100 200 210 220 230 210 AYS FROM START OF 1986 D BANNISTER SITE OBSERVATION WELL WCSMI Figure CB. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WC6M1 for 1986 growing season at the Bannister site. 75.0 135 P 75.0 I A I E 50.0 P5017 g 25.0 ~25.D 0.0 -o.o 30.0 zacmuAamusoun I3” A 30.3 >303 \El ”.1 .3111 5' 20.0 >20.0 a 20.7 120.7 d ”.5 1.20.6 a 20.3 >203 g 20.1 >20.1 I 20.0 . 1&- 20.0 ’ 20.1 >20.7 20.0 ff - L200 210 '25 ' DAYS FROM START 0F1986 BANNISTER SITE OBSERVATION WELL WDZL1 v! vvv vvv 1 .. 1 Figure C7. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WDZLl for 1986 growing season at the Bannister site. 700 70.0 E 5°” 30.0 v § 20.0 20.0 0.0 0.0 30.0 mammaemhaoum {”5 E 30.3 I,“ v bJ >30.1 <23 20.0 >200 E 3 20.7 >207 20.0 -20.0 1.1 i 20.3 '1” g 20.1 -20.1 20.0 >200 E . ’ 20.1 120.7 28.3 20 250"B0'”'2i0” DA Y‘S FROM START OF 1986 BANNISTER SITE OBSERVATION WELL WDSMI 1. 1 1 1 Figure C8. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WD6M1 for 1986 growing season at the Bannister site. 136 75.0 E 50.0 50.0 v g 20.0 20.0 0.0 0.0 30.0 20120020300011.0137". ”'5 "E‘ 30.3 30.1 v 30.1 30.1 <23 200 E 20.0 g 201 20.7 20.0 20.5 g 2” 20.3 "’ 20.1 20.1 g 20.0 20.0 4 3 20.1 20.7 200 - - - 20.0 1 . .. 100 200 "210fi"220"'2§0 240" ' '200 DAYS FROM START OF1 1986 BANNISTER SITE OBSERVATION WELL WEZMI Figure C9. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WEZMI for 1986 growing season at the Bannister site. 70.0 >70.o E 000 >509 V I g 20.0 >20» 00 >00 31.0 2010: am: 30.70 111 10 31.03 m '3‘” A 301 T300 ‘5’ 3,. >300 3 30.4 In. 5 ‘ ’ a 30.2 >302 30.0 >300 a 20.0 >202 '— 2“ >200 It: 20.4 Ill-4 < ’ 20.2 >202 20.0 I - - - - - - . e ..... . - W . 20.0 1 . 1 . .. 100 -. T210 220 230 240 200 D AY'S FROM START OF1986 BANNISTER SITE ossmvmon WELL WGZMI Figure 010. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WGZMl for 1986 growing season at the Bannister site. 137 700 >700 A E E 50.0 500 v g 20.0- I l >201) A'n __.I III. L -I .I I - II I I I I A I ".3 3 . - ° 5 20010 51.8“ 30.40 m to 30.70 m I305 WATER TABLE ELEVATION (m) ? 29,5 . >293 20 1 I 29 1 20.0. I I I >200 20 7 I >207 20 5— I ' 20 5 1 170 100 100 200 210 220 - 210 250 200 DAYS FROM START OF 1986 BANNISTER SITE OBSERVATION WELL WH4M2 Figure C11. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WH4M2 for 1986 growing season at the Bannister site. 70.0- >700 ’e‘ E 500- >500 V g 20.0 I 20.0 0,. n I I L I- j. *4 I I I 10.11 : .1 . 293‘ z01¢E1.Ev 30 0111103000111 >200 ’0‘ v 20.1« >207 6 20.1% 5 a 20.34 U 323‘ 20.1< ’— 5 28.04 ,_ ‘3‘ 20.14 20.5- 112 202 22 2 202 DAYS FROM START OF 1987 BANNISTER SITE OBSERVATION WELL WCZMI Figure 012. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WCZMI for 1987 growing season at the Bannister site. ‘5: 138 7&0- -75.0 A E E 50.0- .501) V 30‘ I I I L I; j. *4 . I I a: Z : .1 . 293‘ ONEELEV 30 Bmtomwm 219 A \E/ 29.7< Z 2 2954 g . d 29.3- 3 29... p. a: 25.5» ,‘t’ § up 285d 2 2 DAYS FROM START OF 1967 BANNISTER SITE OBSERVATION WELL WDZL1 Figure 013. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WD2L1 for 1987 growing season at the Bannister site. 75.0- 75.0 A E E 50.0- >501; V g 15.0 I 250 "f‘ ' . . L .- j. *4. . . fi—. 3." 2M ELEV: .ve use.“ 29.9- 3° N '" >219 A E 29.7. >297 z 0 E 20.54 >295 d 29.1— >293 U E" 29.1< >29» :5 c: 29.9. .253 E 3 25.7 257 23: -zu.5 202 212 222 2‘2 DAYS FROM START OF1987 BANNISTER SITE OBSERVATION WELL WDSL‘I Figure 014. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WD6L1 for 1987 growing season at the Bannister site. 139 75.0- >75 0 A E E 50 0 -50D V g 25,0 I ~25!) cc ' . . L w J! ‘*‘?l . . . u ZONE ELEV: sous m to .5073 m 293- >219 ’5‘ v 29.7. . >227 2 1 8 29 5 29 5 3 ' I ‘ d 29.3> ‘ >293 “ I ‘5‘ 29.1 >29 1 "5 i a: 23.9- .233 E I § 23 7 - I 23 7 I 215 5 - I , I .. . 1 2 192 202 212 222 232 2‘2 252 26.2 YSFROM START OF1 BANNISTER SITE OBSERVATION WELL WE2M1 Figure C15. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WEZMI for 1987 growing season at the Bannister site. 75.0- >751) A E E 50.0- 40.0 V g 2311- l >259 -. .l- .- 1,—4. . . i t . .7 30A< ZONEEIVJO‘GmmJOGm >30‘ ’E‘ V 30.2. >302 2 g 300 300 .1 ’ - ( E 29.8- >218 w (a 29.6- >2” .5 5 29M .2“ ,_ § 292. >292 290- >290 1 2 202 212 222 2.12 242 DAYS FROM START OF BANNISTER SITE OBSERVATION WQEgLL WE4M‘I Figure 016. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WE4M1 for 1987 growing season at the Bannister site. OVns- 75.0 - 81.0 - 25.0 ‘ RAIN (mm) 140 L .‘ jI m4 293‘ 29.1 < 29.5 2 29.3 1 ”.14 23.9 1 WATER TABLE amt/mow (m) 2BJ< 23.5- 102 Figure 017. 78.0 - 50.0 - 28.0 ‘ RAIN (mm) v v ZOE ELEV: 30.75m1o31mm M 22 22 DAYS FROM START OF 1987 BANNISTER STTE OBSERVATION WELL WF4M1 -29.7 -29.5 >213 Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WF4M1 for 1987 growing season at the Bannister site. L - j I.-__..I_ - 75.0 - 50.0 -28.0 29.94 29.74 29.5w 29.3‘ WATER TABLE ELEVATION (m) v v v v v v 20& ELEV: 30.46 m 20 50.73 m 202 212 2 2 242 DAYS FROM START 0F1798 BANNSTER SITE OBSERVATION WELL WH4M2 Figure 018. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WH4M2 for 1987 growing season at the Bannister site. 141 E E v 250 E E 29.5- ZONE ELEV: 29.90 m to 30.14 rn WATER TABLE ELEVATION (m) DAYS FROM START OF 1987 ST. JOHNS SITE OBSERVATION WELL WB4M2 Figure 019. Water table elevation (m) and rainfall (mm) vs. {25.0 -0.0 ~29.5 time (days) for observation well WB4M2 for 1987 growing season at the St. Johns site. 50.0- 25.0- RAIN (mm) um Ah ZONE ELEV: 29.59 m to 29.90 m n» 29.! - WATER TABLE ELEVATION (m) 2 242 DAYS FROM START OF 1987 ST. JOHNS SITE OBSERVATION WELL WC4M1 Figure C20. Water table elevation (m) and rainfall (mm) vs. - 50.0 >259 -o.o -29.5 p20.) F211 -za.e 43.7 - 25.5 I-ZBJ » 25.1 .273 >277 time (days) for observation well WC4M1 for 1987 growing season at the St. Johns site. 142 we mo g I E V zu- -2so 2 am Oh 30.01 ZONE ELEV: 29.59 m to 29.90 m '3" WATER TABLE ELEVATION (m) 212 222 2 DAYS FROM START OF 19 ST JOHNS SITE OBSERVATION WSELZ WC4M3 Figure C21. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WC4M3 for 1987 growing season at the St. Johns site. 50.0 -501) E V 25.0 -25.° i 0.0 -O.D 2"" ZONE ELEV: 29.29 m Io 29 59 m '2“ A 293 >295 g 29.1 >211 z 2 < 6 d N _J .2 I! H < 3 2172 212 222 2 242 Dmsnmuswnorum7 ST. JOHNS SITE OBSERVATION WELL wosLI Figure 022. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WD8L1 for 1987 growing season at the St. Johns site. 143 50.0. -ao.o E V 25.0- >25» 75: o.o- -o.o ”'0‘ zoo: an: mummy)» m 30° A E V g 3 2 5 E 3 232 242 YSFROM STARTO 988 ST. JOHNS SITE OBSERVATION WELL WA5M1 Figure 023. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WA5M1 for 1988 growing season at the St. Johns site. 50.0 500 A E v m '25.!) no 410 30.0 ZONE ELEVh 30." m In 30.45 m '5°‘° 29.: .2“ 29.6 >218 29.4 #291 -29.2 ~29.0 >285 WATER TABLE ELEVATION (m) 202 21 2 222 2.32 2‘2 DAYS FROM START OF 1988 ST. JOHNS SITE OBSERVATION WELL WA8M1 Figure 024. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WA5M1 for 1988 growing season at the St. Johns site. . _ .6 W w z I - I . T, . - - a-.. . k l d RAIN (mm) 3 '5’ WATER TABLE ELEVATTON (m) Figure 025. 50.0 - 26.0 ‘ RAIN (mm) 0.0 - 30.0 - 29.. < 29.6 < 29.4-I 29.2 -I WATER TABLE ELEVATION (m) 3 O Figure C26. 144 ZONE HIV: 29.90 m to 30.14 m 202 212 222 232 242 DAYS FROM START OF 1988 ST. JOHNS SITE OBSERVATION WELL WBSLT {$9 I- 25.0 Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WB5L1 for 1988 growing season at the St. Johns site. 20?! ELEV: 29.59 m to 29.90 m 212 2 242 DAYS FROM START OF 1988 ST. JOHNS SITE OBSERVATION WELL WC4M3 50.0 25.0 Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WC4M3 for 1988 growing season at the St. Johns site. 145 50.0 r500 E i V 25.0 r25.0 Z 5 0.0 ~0.0 3°” zoozmnsomtozuom I)” A 29.: an \E/ 29.5 L293 5 E 29.4 -29.4 a 29.2 L29.2 d ~29.o M a #285 < '" ~23: a: E 20.4 3 28.2 28.0 202 212 2 242 262 DAYS FROM START OF 1988 ST. JOHNS SITE OBSERVATION WELL wcam Figure 027. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WCBMI for 1988 growing season at the St. Johns site. 50.0 -50.0 E I v 250 ~25» E I 0.0 ~o.o so.o~ lumMmuzoam "3°” A an L2” 3 29;. ~29; g .... ..... é 3.3 I- E 3 202 212 DAYS FROM START OF 1988 ST. JOHNS SITE OBSERVATION WELL WDBL1 Figure 028. Water table elevation (m) and rainfall (mm) vs. time (days) for observation well WD8L1 for 1988 growing season at the St. Johns site. APPENDIX D FIELD DATA SCATTER PLOTS 147 - A A .. A2 80+ - A3 A - B - A - Bz 60+ B B B - B 32 - B ------ +---------+---------+------ 8.0 12.0 16.0 A = cyield vs. spacing B = syield vs. spacing Figure D1. Scatter plot of relative corn yield (cyield,%,A) and relatvie soybean yield (syield,%,B) vs. spacing of underground pipe system laterals (spacing,m) during the 1986 growing season at the Bannister site. _ A A - A A 80+ - A A A A - B - A - B B 60+ B B B - B B B - B -—--+ --------- + --------- + -------- 0.48 0.60 0.72 A = cyield vs. swtd B = syield vs. swtd Figure D2. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (swtd,m) during the 1986 growing season at the Bannister site. 148 - A A - A A 80+ - A A A A - B - A - B B 60+ B B B - B B B - B ----+ --------- + --------- + -------- 30 45 60 A = cyield vs. stimeb B = syield vs. stimeb Figure D3. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (stimeb,%) during the 1986 growing season at the Bannister site. - A A _. A2 80+ - A A A A - B - A - B B 60+ B B B - Bz B - B ----+ --------- + --------- + -------- 3O 45 60 A = cyield vs. stimea B = syield vs. stimea Figure D4. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (stimea,%) during the 1986 growing season at the Bannister site. 149 - A A _ A A 80+ - A A A A - B - A - B B 60+ B B B - B B B - B —---+ --------- + --------- + -------- 15 30 45 A = cyield vs. sdfi B = syield vs. sdfi Figure D5. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (sdfi,m*hr/hr) for the 1986 growing season at the Bannister site. A = cyield vs. swfi B = syield vs. swfi Figure D6. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wety stress index (swfi,mthr/hr) for the 1986 growing season at the Bannister site. 150 - A A - A A 80+ - AA A A - B - A - B B 60+ Bz B - 82 B - B --—-+ --------- + --------- + -------- 0.45 0.60 O 75 A = cyield vs. jwtd B = syield vs. jwtd Figure D7. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (jwtd,m) during July, 1986 at the Bannister site. - A A .. A2 80+ — A AA A - B - A — BB 60+ B B B - B Bz - B -------- +--—-----—+----—-—-—+---— 40 50 60 A : cyield vs. jtimeb B : syield vs. jtimeb Figure D8. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (jtimeb,%) during July, 1986 at the Bannister site. 151 - A A — A A 80+ - A A A A - B - A — B B 60+ B B B - B B B - B - ------- + --------- + --------- +---- 36 48 60 A : cyield vs. jtimea B : syield vs. jtimea Figure D9. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (jtimea,%) during July, 1986 at the Bannister site. - A A - A A 80+ - AZA A - B - A - B B 60+ B B B - B2 B - B ------ +------—-—+---------+------ 5.0 10.0 15.0 A = cyield vs. jdfi B = syield vs. jdfi Figure D10. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (jdfi,m*hr/hr) for July, 1986 at the Bannister site. 152 — A A _ A A 80+ - A2 A A — B - A _ Bz 60+ B B B - B B B - B ----+ --------- + --------- + -------- 3 5 7.0 10 5 A = cyield vs. jwfi B = syield vs. jwfi Figure D11. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wet stress index (jwfi,m*hr/hr) for July, 1986 at the Bannister site. - A - A A 80+ - A A A A - B - A - B B 60+ BB B - 82 B -------- +---------+---------+---— 0.50 0.75 1.00 A : cyield vs. awtd B = syield vs. awtd Figure D12. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (awtd,m) during August, 1986 an; the Bannister site. 153 - A - A A 80+ - A A A A — B - A — B B 60+ B B B - B B B + --------- + --------- + --------- +-- 36 48 60 72 A = cyield vs. atimeb B : syield vs. atimeb Figure D13. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the meanwwater table depth (atimeb,%) during August, 1986 at the Bannister site. - A - A A 80+ - A A A A - B - A — B B 60+ B B B - B B B --+ --------- + --------- + --------- + 16 32 48 64 A = cyield vs. atimea B = syield vs. atimea Figure D14. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (atimea,%) during August, 1986 at the Bannister site. 154 - A - A A 80+ - A A A A - B - A - B B 60+ B B B - B B B + --------- + --------- + --------- +-- 0.0 2.5 5 0 7.5 A = cyield vs. adfi B = syield vs. adfi Figure D15. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (adfi,m*hr/hr) for August, 1986 at the Bannister site. - A - A A 80+ - A A A A - B - A — B B 60+ B B B - B B B + --------- + --------- + --------- +-- 0.0 4.0 8.0 12.0 A = cyield vs. awfi B : syield vs. awfi Figure D16. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wet stress index (awfi,m*hr/hr) for August, 1986 at the Bannister site. 155 - B - B - A2 84+ A - B — A2 A - A - B 72+ B ------ +—-----—--+-—-----—-+----—- 8.0 12.0 16 0 A = cyield vs. spacing B = syield vs. spacing Figure D17. Scatter plot of relative corn yield (cyield,%,A) and relatvie soybean yield (syield,%,B) vs. spacing of underground pipe system laterals (spacing,m) during the 1987 growing season at the Bannister site. — B - B - A A 84+ A - B - B B A - A - B 72+ B ——+ --------- + --------- + --------- + 0.90 1.20 1.50 1.80 A = cyield vs. swtd B = syield vs. swtd Figure D18. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (swtd,m) during the 1987 growing season at the Bannister site. 156 - B - B — A A 84+ A — B - B B A - A - B 72+ B + --------- + --------- + --------- +-- 42.0 49.0 56.0 63.0 A = cyield vs. stimeb B = syield vs. stimeb Figure D19. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (stimeb,%) during the 1987 growing season at the Bannister site. - B - B - A A 84+ A - B - A B B - A - B 72+ B + --------- + --------- + --------- +-- 35.0 42.0 49.0 56.0 A = cyield vs. stimea B = syield vs. stimea Figure D20. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (stimea,%) during the 1987 growing season at the Bannister site. 157 - B - B - A A 84+ A - B - B A B - A - B 72+ B - ------- + --------- + --------- +---- 20.0 25.0 30.0 A = cyield vs. sdfi B = syield vs. sdfi Figure D21. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (sdfi,m*hr/hr) for the 1987 growing season at the Bannister site. — B - B - A A 84+ A - B - A B B — A - B 72+ B - ------- + --------- + --------- +---- 16.0 24.0 32.0 A = cyield vs. swfi B = syield vs. swfi Figure D22. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wety stress index (swfi,m*hr/hr) for the 1987 growing season at the Bannister site. 158 - B - B - A A 84+ A - B - B B A - A - B 72+ B + --------- + --------- + --------- +-- 0 70 1.05 1.40 1.75 A = cyield vs. jwtd B = syield vs. jwtd Figure D23. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (jwtd,m) during July, 1987 at the Bannister site. - B - B - A A 84+ A - B - B B A - A - B 72+ B - ------- + --------- + --------- +—--- 30 45 60 A = cyield vs. jtimeb B = syield vs. jtimeb Figure D24. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (jtimeb,%) during July, 1987 at the Bannister site. 159 - B - B - A A 84+ A - B — A B B - A - B 72+ B ----+ --------- + --------- + -------- 40 50 60 A = cyield vs. jtimea B = syield vs. jtimea Figure D25. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (jtimea,%) during July, 1987 at the Bannister site. — B - B - A2 84+ A - B - A2 A - A - B 72+ B + --------- + --------- + --------- +—- 0.0 5 0 10.0 15.0 A = cyield vs. jdfi B = syield vs. jdfi Figure D26. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (jdfi,m*hr/hr) for July, 1987 at the Bannister site. 160 - B - B - A A 84+ A - B - B BA - A - B 72+ B ------ +---------+--—------+--—--- 2.5 5.0 7.5 A = cyield vs. jwfi B = syield vs. jwfi Figure D27. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wet stress index (jwfi,m*hr/hr) for July, 1987 at the Bannister site. - B - B - A A 84+ A - B — B B A - A - B 72+ B -—+ --------- + --------- + ————————— + 0.90 1.20 1.50 1.80 A = cyield vs. awtd B = syield vs. awtd Figure D28. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (awtd,m) during August, 1987 an; the Bannister site. 161 - B - B - A A 84+ A - B — B A2 - A - B 72+ B ----+ --------- + --------- + -------- 40 50 60 A = cyield vs. atimeb B = syield vs. atimeb Figure D29. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (atimeb,%) during August, 1987 at the Bannister site. — B - B - A A 84+ A - B - AB B - A - B 72+ B - ------- + --------- + --------- +---— 36 48 60 A = cyield vs. atimea B = syield vs. atimea Figure D30. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (atimea,%) during August, 1987 at the Bannister site. 162 — B - B - A A 84+ A - B - B A B - A - B 72+ B --+ --------- + --------- + --------- + 3.0 6.0 9.0 12.0 A = cyield vs. adfi B = syield vs. adfi Figure D31. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (adfi,m*hr/hr) for August, 1987 at the Bannister site. - B - B - A A 84+ A - B - A B B — A - B 72+ B ----+ --------- + --------- + -------- 2.5 5.0 7.5 A = cyield vs. awfi B = syield vs. awfi Figure D32. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wet stress index (awfi,m*hr/hr) for August, 1987 at the Bannister site. 163 90+ A _ A A _ B B - A 60+ — B - B 30+ --+ --------- + --------- + --------- + 12.0 16.0 20.0 24.0 A = cyield vs. spacing B = syield vs. spacing Figure D33. Scatter plot of relative corn yield (cyield,%,A) and relatvie soybean yield (syield,%,B) vs. spacing of underground pipe system laterals (spacing,m) during the 1987 growing season at the St. Johns site. 90+ A - A A _ B B - A 60+ ‘ 1.3 - B 30+ + --------- + --------- + --------- +-- 1.00 1.10 1.20 1.30 A = cyield vs. swtd B = syield vs. swtd Figure D34. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (swtd,m) during the 1987 growing season at the St. Johns site. 164 90+ A - A A _ B B - A 60+ - B - B 30+ ------ +---------+-----—---+------ 44.0 48.0 52.0 A = cyield vs. stimeb B = syield vs. stimeb Figure D35. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (stimeb,%) during the 1987 growing season at the St. Johns site. 90+ A _ A A - B B - A 60+ - B - B 30+ ------ +---------+---------+------ 45.5 49.0 52.5 A = cyield vs. stimea B = syield vs. stimea Figure D36. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (stimea,%) during the 1987 growing season at the St. Johns site. 165 90+ A - A A - B B - A 60+ - B - B 30+ --+ --------- + --------- + --------- + 30 60 90 120 A = cyield vs. sdfi B = syield vs. sdfi Figure D37. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (sdfi,m*hr/hr) for the 1987 growing season at the St. Johns site. 90+ A - A A - B B - A 60+ - B - B 30+ -------- +----—-—--+-—-—-----+——-— 40 80 120 A = cyield vs. swfi B = syield vs. swfi Figure D38. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wety stress index (swfi,m*hr/hr) for the 1987 growing season at the St. Johns site. 166 90+ A _ A A - B B - A 60+ - B - B 30+ --+ --------- + --------- + --------- + 1.00 1.25 1 50 1.75 A = cyield vs. jwtd B : syield vs. jwtd Figure D39. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (jwtd,m) during July, 1987 at the St. Johns site. 90+ A - A A _ B B - A 60+ - B - B 30+ + --------- + --------- + --------- +-- 49.0 56.0 63.0 70.0 A = cyield vs. jtimeb B = syield vs. jtimeb Figure D40. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (jtimeb,%) during July, 1987 at the St. Johns site. 167 90+ A - A A _ B B - A 60+ - B — B 30+ + --------- + --------- + --------- +—- 20 30 40 50 A = cyield vs. jtimea B = syield vs. jtimea Figure D41. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (jtimea,%) during July, 1987 at the St. Johns site. 90+ A _ A A - B B — A 60+ — B - B 30+ —————— +---—----—+---------+----—— 20 40 60 A = cyield vs. jdfi B = syield vs. jdfi Figure D42. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (jdfi,m*hr/hr) for July, 1987 at the St. Johns site. 168 90+ A _ A A - B B — A 60+ - B - B 30+ + --------- + --------- + --------- +-- 0 20 40 60 A = cyield vs. jwfi B : syield vs. jwfi Figure D43. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wet stress index (jwfi,m*hr/hr) for July, 1987 at the St. Johns site. 90+ A - AA - B B - A 60+ — B - B 30+ ------ +---------+—-—---—-—+------ 1 05 1.20 1 35 A = cyield vs. awtd B = syield vs. awtd Figure D44. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (awtd,m) during August, 1987 at the St. JOhns site. 169 90+ A - A A - B B - A 60+ - B - B 30+ + --------- + --------- + --------- +-- 40 0 45 0 50 0 55.0 A = cyield vs. atimeb B = syield vs. atimeb Figure D45. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (atimeb,%) during August, 1987 at the St. Johns site. 90+ A - A A - B B - A 60+ - B - B 30+ ------ +---------+—--------+------ 30 40 50 A = cyield vs. atimea B = syield vs. atimea Figure D46. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was.above the mean water table depth (atimea,%) during August, 1987 at the St. Johns site. 170 90+ A - A A - B B - A 60+ - B — B 30+ - ------- + --------- + --------- +—--— 7.0 14.0 21.0 A = cyield vs. adfi B = syield vs. adfi Figure D47. Scatter plot of relative oorn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (adfi,m*hr/hr) for August, 1987 at the St. Johns site. 90+ A _ A A - B B - A 60+ - B - B 30+ + --------- + --------- + --------- +-- 0 10 20 30 A = cyield vs. awfi B = syield vs. awfi Figure D48. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wet stress index (awfi,m*hr/hr) for August, 1987 at the St. Johns site. 171 100+ A - A A _ A B 75+ A - B B2 - B - AB 50+ -—+ --------- + --------- + --------- + 12.0 16.0 20.0 24.0 A = cyield vs. spacing B = syield vs. spacing Figure D49. Scatter plot of relative corn yield (cyield,%,A) and relatvie soybean yield (syield,%,B) vs. spacing of underground pipe system laterals (spacing,m) during the 1988 growing season at the St. Johns site. 100+ A - A A - B A 75+ A - B B B - B - B A 50+ -------- +--------—+--——-----+--—- 1.00 1.20 1.40 A = cyield vs. swtd B = syield vs. swtd Figure D50. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (swtd,m) during the 1988 growing season at the St. Johns site. 172 100+ A - A A - B A 75+ A - B B B - B - B A 50+ ------ +--------—+—-----——-+-----— 45 60 75 A = cyield vs. stimeb B = syield vs. stimeb Figure D51. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (stimeb,%) during the 1988 growing season at the St. Johns site. 100+ A - AA — A B 75+ A - B B B — B - A B 50+ + --------- + --------- + --------- +-- 0 20 4O 60 A = cyield vs. stimea B = syield vs. stimea Figure D52. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (stimea,%) during the 1988 growing season at the St. Johns site. 173 100+ A _ A A - BA 75+ A - B B B - B - A B 50+ + --------- + --------- + --------- +-- 0 60 120 180 A = cyield vs. sdfi B = syield vs. sdfi Figure D53. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (sdfi,m*hr/hr) for the 1988 growing season at the St. Johns site. 100+ A _ A A - A B 75+ A - B B B - B _ A B 50+ + --------- + --------- + --------- +-- 0 40 80 120 A = cyield vs. swfi B = syield vs. swfi Figure D54. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wety stress index (swfi,m*hr/hr) for the 1988 growing season at the St. Johns site. 174 100+ A - A A - B A 75+ A — B B B - B - B A 50+ - ------- + ————————— + --------- +---- 1 00 1.20 1.40 A = cyield vs. jwtd B = syield vs. jwtd Figure D55. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (jwtd,m) during July, 1988 at the St. Johns site. 100+ A _ A A - A B 75+ A - B B B - B — B A 50+ -------- +-——--—--—+---——--—-+-—-- 35.0 42.0 49.0 A = cyield vs. jtimeb B = syield vs. jtimeb Figure D56. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (jtimeb,%) during July, 1988 at the St. Johns site. 175 100+ A _ A A - A B 75+ A — B B B - B - A B 50+ ----+ --------- + --------- + -------- 30 45 60 A = cyield vs. jtimea B = syield vs. jtimea Figure D57. Scatter plot of relative corn yield (oyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was above the mean water table depth (jtimea,%) during July, 1988 at the St. Johns site. 100+ A - A A - A B 75+ A - B B B - B - A B 50+ + --------- + --------- + --------- +-- 0.0 8.0 16.0 24.0 A = cyield vs. jdfi B = syield vs. jdfi Figure D58. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (jdfi,m*hr/hr) for July, 1988 at the St. Johns site. 176 100+ A - A A - AB 75+ A - B B B - B - A B 50+ + --------- + --------- + --------- +-- 0 15 30 45 A = cyield vs. jwfi B = syield vs. jwfi Figure D59. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wet stress index (jwfi,m*hr/hr) for July, 1988 at the St. Johns site. 100+ A — A A - B A 75+ A _ BB B — B - B A 50+ - ------- + --------- + --------- +—--- 1.00 1.20 1.40 A = cyield vs. awtd B = syield vs. awtd Figure D60. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. mean depth to the water table (awtd,m) during August, 1988 at the St. JOhns site. 177 100+ A - A A - A B 75+ A - B BB - B - B A 50+ + --------- + --------- + --------- +-- 20 30 40 50 A = cyield vs. atimeb B = syield vs. atimeb Figure D61. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was below the mean water table depth (atimeb,%) during August, 1988 at the St. Johns site. 100+ A - A A - B A 75+ A - B B B - B - A B 50+ —-+ --------- + --------- + --------- + 30 45 60 75 A = cyield vs. atimea B = syield vs. atimea Figure D62. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. time water table was.above the meanwwater table depth (atimea,%) during August, 1988 at the St. Johns site. 178 100+ A — A A - A B 75+ A - B B B - B - A B 50+ + --------- + --------- + --------- +-- 0 0 5.0 10.0 15.0 A = cyield vs. adfi B = syield vs. adfi Figure D63. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation dry stress index (adfi,m*hr/hr) for August, 1988 at the St. Johns site. 100+ A - A A - A B 75+ A — B B B - B — A B 50+ + --------- + --------- + ————————— +-- 0.0 5.0 10 0 15.0 A = cyield vs. awfi B : syield vs. awfi Figure D64. Scatter plot of relative corn yield (cyield,%,A) and relative soybean yield (syield,%,B) vs. the water table fluctuation wet stress index (awfi,m*hr/hr) for August, 1988 at the St. Johns site. APPENDIX E FIELD DATA STATISTICAL SUMMARIES Table 513. Field data used 180 Bannister site growing season. regression analyses from the .—--¢-—-—————---———--—-----————---———-——----—c—---—--.------—--—-—--—--—-——. Row zone 1 1 2 2 3 2 4 2 5 3 6 3 7 4 8 4 8 5 10 7 11 8 now jwtd 1 0.66 2 0.45 3 0.45 4 0.45 5 0.69 6 0.72 7 0.65 8 0.66 9 0.83 10 0.75 11 0.84 now adista 0.237 HOOOQOUOUNH O I O 0 . Ini- cyield 75 86 t 83 75 83 75 68 77 3 88 jtimeb 38 33 35 56 61 56 60 53 63 62 52 sdfi syield 60 68 52 51 63 52 63 58 I 58 71 jdistb 0.106 0.076 0.073 0.033 0.050 0.096 0.061 0.068 0.067 0.082 0.061 swfi for linear spacing swtd 12 0.81 6 0.48 12 0.47 18 0.45 6 0.58 18 0.77 6 0.61 18 0.72 6 0.79 6 0.77 12 0.81 jtimea Jdisia 57 0.070 64 0.038 57 0.045 34 0.052 30 0.102 44 0.114 38 0.066 39 0.066 33 0.126 35 0.146 46 0.068 jdfi jwfi 8.4 2.7 4.3 8.3 4.8 7.8 5.1 3.3 10.6 5.2 14.5 11.8 8.3 5.3 7.2 5.3 14.8 7.9 17.0 9.6 8.6 7.7 s! cor-aunt»...— stimeb 31 29 33 69 58 56 68 61 63 62 52 awtd 1.02 0.66 0.65 0.37 0.95 0.56 0.91 0.90 0.93 0.75 adfi uOv-bhOOiw-INUI N 0106300006!” sdistb 0.607 0.156 0.135 0.086 0.163 0.135 0.071 0.163 0.096 0.119 0.083 atimeb 37 I 39 63 63 57 61 73 52 58 36 awfi “NOOOOQOQIQ 67 68 65 63 61 66 30 58 35 37 63 adistb 0.396 8 0.168 0.169 0.105 0.063 0.069 0.017 0.071 0.011 0.129 0.189 0.066 0.069 0.095 0.202 0.168 0.158 0.115 0.166 0.200 0.099 atimea 62 I 61 56 55 63 38 19 65 13 66 181 Table Elh. Statistical summary of the field data used for analyses from the 1986 Bannister site growing season. regression N N! MEAN MEDIAN TRHEAN STDEV SEMEAN cyield 9 2 78.89 77.00 78.89 6.67 2.16 syield 10 1 57.60 58.00 57.13 7.01 2.22 spacing 11 0 10.91 12.00 10.67 5.26 1.58 swtd 11 0 0.6600 0.7200 0.6667 0.1653 0.0638 861-60 11 0 69.09 52.00 69.22 13.73 6.16 801860 11 0 0.1666 0.1350 0.1233 0.0925 0.0279 861-88 11 0 68.27 63.00 68.11 13.70 6.13 801868 11 0 0.1388 0.1580 0.1399 0.0516 0.0155 jwtd 11 0 0.6500 0.6600 0.6511 0.1632 0.0632 Jtileb 11 0 51.36 56.00 52.11 11.03 3.33 101860 11 0 0.06627 0.06700 0.06556 0.02252 0.00679 561-88 11 0 63.36 39.00 62.56 11.39 3.63 301868 11 0 0.0812 0.0680 0.0788 0.0356 0.0107 8W60 10 1 0.7260 0.8250 0.7338 0.2699 0.0790 861-80 10 1 69.90 67.50 68.75 12.25 3.87 801860 10 1 0.1176 0.0880 0.0966 0.1122 0.0355 861.88 10 1 65.60 50.00 67.37 17.88 5.65 801868 10 1 0.0988 0.0830 0.0876 0.0536 0.0169 8011 11 0 31.55 31.00 31.66 15.98 6.82 8H21 11 0 30.65 25.00 26.67 19.01 5.73 1021 11 0 9.62 8.60 9.16 6.33 1.31 wal 11 0 7.727 7.800 7.667 2.895 0.873 8021 10 1 3.630 3.800 3.675 2.067 0.656 8Wf1 10 1 6.26 6.00 3.71 3.66 1.16 MIN MAX 01 Q3 cyield 68.00 88.00 75.00 86.50 syield 68.00 71.00 51.75 63.00 898010! 6.00 18.00 6.00 18.00 swtd 0.6500 0.8100 0.6800 0.7900 861-80 29.00 68.00 33.00 62.00 801860 0.0710 0.6070 0.0860 0.1560 861-88 30.00 68.00 37.00 65.00 801868 0.0660 0.2020 0.0950 0.1890 5W60 0.6500 0.8600 0.6500 0.7500 361.80 33.00 63.00 38.00 61.00 301860 0.03300 0.10600 0.06800 0.08200 161-08 30.00 66.00 36.00 57.00 301868 0.0380 0.1660 0.0520 0.1160 awtd 0.3700 1.0200 0.6675 0.9350 861.80 36.00 73.00 38.50 58.75 801860 0.0110 0.3960 0.0610 0.1682 861-08 13.00 66.00 33.25 61.25 801868 0.0500 0.2370 0.0697 0.1123 8061 9.00 55.00 19.00 65.00 swfi 16.00 81.00 16.00 38.00 jdfi 6.30 17.00 5.10 16.50 jwfi 3.300 12.700 5.300 9.600 8061 1.000 7.500 1.625 6.900 8Wfi 0.20 12.50 0.75 5.85 Table 82a. Field data used for 182 Bannister site growing season. 80" zone 1 3 2 4 3 4 4 5 5 5 6 6 7 8 ROW jwtd 1 0.98 2 0.73 3 0.82 4 1.01 5 0.71 6 1.77 7 1.09 now adista cyield 86 86 78 2 I 83 80 jtimeb 31 23 65 62 33 60 56 sdfi syield 79 80 92 71 89 76 82 jdisth 0.073 0.129 0.055 0.120 0.052 0.081 0.079 swfi linear spacing swtd 6 1.15 6 0.99 18 1.03 6 0.97 12 0.89 12 1.76 12 1.30 jtimea jdista 60 0.038 68 0.044 47 0.052 38 0.198 47 0.037 38 0.129 45 0.093 dei wai stimeb 62 67 61 51 65 66 57 awtd 1.19 1.06 1.12 0.87 0.95 1.78 1.39 regression analyses sdisth 0.187 0.261 0.126 0.186 0.220 0.101 0.166 atimeb 63 56 62 36 55 67 56 from stimea 57 52 36 48 54 36 42 adisth 0.134 0.180 0.051 0.143 0.174 0.099 0.083 608 0.138 0.237 0.210 0.198 0.186 0.182 0.196 atimea 56 65 31 63 66 32 63 183 Table 820. Statistical summary of the field data used for analyses from the 1987 Bannister site growing season. regression N N! MEAN MEDIAN TRHEAN STDBV SBHBAI 031810 5 2 82.60 83.00 82.60 3.58 1.60 831010 7 0 81.00 80.00 81.00 7.53 2.85 898018! 7 0 10.29 12.00 10.29 6.56 1.71 8W60 7 0 1.156 1.030 1.156 0.298 0.113 861.80 7 0 52.63 51.00 52.63 8.60 3.18 801860 7 0 0.1750 0.1860 0.1750 0.0557 0.0210 861.88 7 0 66.63 68.00 66.63 8.56 3.26 801868 7 0 0.1921 0.1960 0.1921 0.0302 0.0116 jwtd 7 0 1.016 0.980 1.016 0.363 0.137 361.80 7 0 66.00 65.00 66.00 15.36 5.80 301860 7 0 0.0861 0.0790 0.0861 0.0298 0.0113 161.88 7 0 69.00 67.00 69.00 11.17 6.22 101868 7 0 0.0866 0.0520 0.0866 0.0606 0.0229 8W60 7 0 1.191 1.120 1.191 0.310 0.117 861.80 7 0 53.00 56.00 53.00 10.58 6.00 801860 7 0 0.1236 0.1360 0.1236 0.0678 0.0181 861.88 7 0 66.86 66.00 66.86 11.65 6.60 801868 7 0 0.1669 0.1050 0.1669 0.0622 0.0235 8021 7 0 26.16 27.00 26.16 5.27 1.99 8W21 7 0 23.86 26.00 23.86 8.01 3.03 $021 7 0 6.69 2.20 6.69 6.91 1.86 wai 7 0 3.863 3.600 3.863 2.637 0.921 8021 7 0 7.03 5.60 7.03 3.61 1.29 8W21 7 0 5.863 5.600 5.863 2.568 0.971 MIN MAX 91 93 691010 78.00 86.00 79.00 86.00 811010 71.00 92.00 76.00 89.00 808818! 6.00 18.00 6.00 12.00 8H60 0.890 1.760 0.970 1.300 861.80 62.00 66.00 65.00 61.00 801860 0.1010 0.2610 0.1260 0.2200 811.88 36.00 57.00 36.00 56.00 801868 0.1380 0.2370 0.1820 0.2100 Jwtd 0.710 1.770 0.730 1.090 161.80 23.00 62.00 31.00 60.00 301860 0.0520 0.1290 0.0550 0.1200 561.88 38.00 68.00 38.00 60.00 301868 0.0370 0.1980 0.0380 0.1290 8w60 0.870 1.780 0.950 1.390 811.00 36.00 67.00 63.00 62.00 801860 0.0510 0.1800 0.0830 0.1760 861.88 31.00 63.00 32.00 56.00 801868 0.0810 0.2170 0.1020 0.2160 8021 17.00 32.00 22.00 30.00 swfi 12.00 35.00 16.00 30.00 5021 1.20 16.80 1.30 5.80 jwfi 1.600 9.000 2.300 6.000 adfi 3.20 11.10 6.10 11.00 awfi 2.000 9.200 6.300 8.900 —————---___———-———-——------——————-———___—-—----—-———---_-_—----———----—--- Table 83a. Field data used for Johns site growing season. ----------‘----—----n------——----——--------—--‘-‘----—-----_-~-------‘----— jwtd 1.72 1.07 1.15 0.99 adista 0.155 0.051 0.090 0.067 cyield 83 87 71 86 jtimeb 54 61 70 51 sdfi syield 56 35 77 80 jdisth 0.690 0.083 0.128 0.123 swfi 132 72 53 19 184 regression analyses from the 1987 St. linear spacing swtd 12 1.29 12 1.26 12 1.30 26 1.02 Jtimea jdista 46 0.796 36 0.139 23 0.383 49 0.127 jdfi jwfi 69.1 59.9 18.0 10.7 13.9 4.7 8.8 8.6 stimeb 67 66 63 56 awtd 1.00 1.39 1.21 0.99 sdistb 0.390 0.222 0.186 0.063 atimeb 65 55 52 62 stimea 52 56 56 66 adisth 0.185 0.025 0.072 0.051 0.351 0.189 0.168 0.077 atimea 56 27 62 66 185 Table 83h. Statistical summary of the field data used for analyses tron the 1987 St. Johns site growing season. regression -‘---‘-—-----------—--_~---—-----------—---—----——------‘—---------—-————-— N MEAN HEDIAN TRMEAN STDEV SEMEAN cyield 4 81.25 83.50 81.25 7.04 3.52 syield 4 61.5 65.5 61.5 21.1 10.6 spacing 4 15.00 12.00 15.00 6.00 3.00 swtd 4 1.2175 1.2750 1.2175 0.1328 0.0664 stileh 4 47.50 46.50 47.50 4.65 2.33 sdisth 4 0.2153 0.2040 0.2153 0.1349 0.0675 stimea 4 51.00 53.00 51.00 4.76 2.38 sdista 4 0.1912 0.1685 0.1912 0.1161 0.0581 Jwtd 4 1.233 1.110 1.233 0.331 0.166 jtimeb 4 59.00 57.50 59.00 8.45 4.22 Jdisth 4 0.256 0.126 0.256 0.290 0.145 jtimea 4 38.50 41.00 38.50 11.73 5.87 301868 4 0.361 0.261 0.361 0.313 0.156 awtd 4 1.1475 1.1050 1.1475 0.1909 0.0954 atimeb 4 48.50 48.50 48.50 6.03 3.01 adistb 4 0.0833 0.0615 0.0833 0.0705 0.0353 atilea 4 42.25 44.00 42.25 11.32 5.66 adista 4 0.0857 0.0705 0.0857 0.0501 0.0250 sdfi 4 61.5 51.5 61.5 41.2 20.6 swfi 4 69.0 62.5 69.0 47.4 23.7 jdfi 4 27.4 15.9 27.4 28.0 14.0 jwfi 4 21.0 9.6 21.0 26.1 13.0 adfi 4 11.03 8.70 11.03 9.22 4.61 awfi 4 11.30 7.75 11.30 11.75 5.88 MIN MAX 01 03 cyield 71.00 87.00 74.00 86.25 syield 35.0 80.0 39.8 79.2 spacing 12.00 24.00 12.00 21.00 swtd 1.0200 1.3000 1.0800 1.2975 stimeb 43.00 54.00 43.75 52.25 sdistb 0.0630 0.3900 0.0937 0.3480 stimea 44.00 54.00 46.00 54.00 sdista 0.0770 0.3510 0.0948 0.3105 Jwtd 0.990 1.720 1.010 1.577 jtimeb 51.00 70.00 51.75 67.75 jdisth 0.083 0.690 0.093 0.549 Jtinea 23.00 49.00 26.25 48.25 501868 0.127 0.796 0.130 0.693 awtd 0.9900 1.3900 0.9925 1.3450 atimeb 42.00 55.00 42.75 54.25 801860 0.0250 0.1850 0.0315 0.1567 atimea 27.00 54.00 30.75 52.00 adista 0.0470 0.1550 0.0480 0.1388 sdfi 24.0 119.0 28.5 104.5 swfi 19.0 132.0 27.5 117.0 jdfi 8.8 69.1 10.1 56.3 jwfi 4.7 59.9 5.7 47.6 adfi 3.10 23.60 3.65 20.73 awfi 1.50 28.20 2.58 23.58 Table 84a. Field data used for Johns site growing season. now zone 1 1 2 1 3 2 4 3 5 3 6 4 now jwtd 1 0.94 2 1.05 3 0.89 4 1.46 5 1.24 6 1.09 now adista 97 77 99 82 57 93 Jtimeb 31 36 36 62 38 51 syield 71 56 68 71 65 80 301810 0.232 0.106 0.686 0.011 0.023 0.018 swfi 186 spacing 15 26 15 12 26 26 jtimea 69 61 66 27 63 31 linear regression analyses from the 1988 St. 0.90 1.10 0.98 1.65 1.23 1.09 jdista 0.106 0.059 0.253 0.016 0.021 0.029 stimeb 68 39 62 79 62 55 awtd 0.89 1.08 0.92 1.67 1.26 1.09 adistb 0.208 0.199 0.267 0.021 0.026 0.035 atimeb 67 26 68 22 36 51 stimea 32 61 56 7 31 29 adistb 0.086 0.171 0.161 0.019 0.023 0.018 sdista 0.639 0.127 0.200 0.226 0.052 0.067 atimea 35 72 69 61 66 31 Table E60. Statistical analyses fro: the 1988 St. 187 summary of the Johns site growing field data $888011. used for regression cyield syield spacing swtd stimeb sdisth stilea sdista jwtd jtineh Jdisth Jtinea jdista awtd atineb adistb ati-ea adista sdfi swfi dei jwfi adfi awfi cyield syield spacing swtd sti-eb adistb stimea sdista jwtd jtimeb jdisth Jti-ea jdista awtd atileb adistb atilea adista sdfi swfi jdfi iwfi adfi awfi OOOGOOOOOOGGQOOOOUSOGOOGOZ MIN 57.00 56.00 12.00 0.9000 39.00 0.0210 7.00 0.0520 0.8900 31.00 0.0110 27.00 0.0160 0.8900 22.00 0.0180 31.00 0.0100 20.0 3.0 0.80 0.50 0.60 0.70 MEAN 86.17 68.17 19.00 1.1250 57.50 0.1260 36.00 0.1868 1.1117 38.33 0.1657 69.50 0.0807 1.1150 38.00 0.0763 65.33 0.0622 68.3 55.3 7.23 13.38 5.17 6.37 MAX 99.00 80.00 26.00 1.6500 79.00 0.2670 61.00 0.6390 1.6600 51.00 0.6860 69.00 0.2530 1.6700 51.00 0.1710 72.00 0.1380 188.0 125.0 26.20 66.50 13.70 16.10 MEDIAN 87.50 69.50 19.50 1.0950 58.50 0.1170 31.50 0.1635 1.0700 36.00 0.0635 52.00 0.0660 1.0850 60.50 0.0565 62.50 0.0650 63.0 50.5 3.70 5.50 3.55 6.00 91 72.00 62.25 16.25 0.9600 61.25 0.0267 23.50 0.0633 0.9275 33.25 0.0162 30.00 0.0197 0.9125 25.00 0.0187 36.00 0.0160 21.5 8.3 1.25 1.10 1.00 1.15 TRMEAN 86.17 68.17 19.00 1.1250 57.50 0.1260 36.00 0.1868 1.1117 38.33 0.1657 69.50 0.0807 1.1150 38.00 0.0763 65.33 0.0622 68.3 55.3 7.23 13.38 5.17 6.37 93 97.50 73.25 26.00 1.2850 70.75 0.2228 57.25 0.2778 1.2950 66.25 0.2955 66.75 0.1627 1.2975 68.75 0.1685 56.75 0.1222 117.5 101.0 13.25 27.38 9.87 16.10 STDEV 15.85 8.57 5.59 0.1950 15.37 0.1107 19.78 0.1623 0.2101 7.28 0.1868 18.31 0.0908 0.2155 12.38 0.0675 16.56 0.0539 65.2 53.1 8.93 18.01 5.15 6.33 SEMEAN 6.67 3.50 2.28 0.0796 6.28 0.0652 8.07 0.0581 0.0858 2.97 0.0763 7.67 0.0371 0.0880 5.05 0.0276 5.96 0.0220 26.6 21.7 3.65 7.35 2.10 2.58 APPENDIX F SILSPACE PROGRAM SOURCE CODE 189 REM ******¥¥***************************************X**************¥***** REM PROGRAM NAME SILSPACE.BAS REM PROGRAM VERSION Version 1.01 REM PROGRAM AUTHOR H. W. Belcher, MSU REM DATE OF LAST REVISION 09/30/89 REM PROGRAM LANGUAGE TUrbo Basic — version 1.0 REM REM This program is used to design the spacing laterals should be REM installed for subirrigation. REM - DIM hydc(15),dporosity(15),th(15),length(100) $INCLUDE "SIINPUT.INC" $INCLUDE "SILSPACE.INC" $INCLUDE "SITIME.INC" $INCLUDE "SIOUTPUT.INC" $INCLUDE "SISUBR.INC" END m::==:=::::::::=:::::::::::::::::::::::::::::::222::2::::::::::::::::Z::: REM SUBROUTINE NAME SIINPUT.INC REM SUBROUTINE AUTHOR H. Belcher, MSU REM DATE OF LAST REVISION 10/11/89 REM PROGRAM LANGUAGE Turbo Basic - Version 1.0 REM debugoutput1$="off" debugoutput2$="off" CLS PRINTAB(10);"*************************¥***********************************" PRINT PRINT TAB( 10) ; "SILSPACE — A lateral spacing design program for subirrigation" PRINT pRINTAB(10);"*************************************************************" PRINT PRINT PRINT PRINT TAB(28);" H. W. Belcher " PRINT TAB(28);"MICHIGAN STATE UNIVERSITY" PRINT TAB(28);" Version: 1.01 " PRINT TAB(28);" 09/29/89 " LOCATE 24,26 PRINT "PRESS ANY KEY TO CONTINUE? "; WHILE LEN(anykey$):O anykey$=INKEY$ WEND begin: REM input data CLS anykey$zflfl PRINT "WILL INPUT DATA COME FROM DISKFILE (f) OR KEYBOARD (k)? " 190 WHILE LEN(anykey$)=O:anykey$=INKEY$:WEND IF anykey$="F" OR anykey$="f" THEN INPUT "Enter input file name ..... CLS OPEN FileName$ FOR INPUT AS #1 INPUT #1, barrierdepth,nlayers% FOR i%=1 TO nlayers% INPUT #1, th(i%),hydc(i%),sat(i%),dul(i%) n(i%)=sat(i%)—dul(i%) NEXT 1% .... ";FileName$ I N P U T # 1 TileDepth,TileDiameter,TileGrade,TileLength,siWTdepthMidpoint,_ siWTdepthLateral,deTdepthMidpoint,deTdepthLateral INPUT #1, sirate,drrate,rainfall,rcn,WeirDepth CLOSE #1 REM debugloutput IF debugoutput1$="on" THEN LPRINT "barrierdepth = ";barrierdepth LPRINT "nlayers% = ";nlayers% FOR 1%:1 'I‘O nlayers% LPRINT "th = ";th(i%),"hydc = "dul = ";dul(i%) ";hydc(i%),"sat = ";sat(i%),_ NEXT 1% LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT LPRINT "TileDepth : ";TileDepth "TileDiameter = ";TileDiameter "TileGrade = ";TileGrade "TileLength = ";TileLength "siWTdepthMidpoint = ";siWTdepthMidpoint "siWTdepthLateral = ";siWTdepthLateral "deTdepthMidpoint = ";deTdepthMidpoint "deTdepthLateral = ";deTdepthLateral "sirate = ";sirate "drrate = ";drrate "rainfall = ";rainfall "I‘Cl'l : n ;I‘Cfl "weirDepth = ";WeirDepth END IF REM ELSE PRINT "SYSTEM PARAMETERS:" PRINT INPUT INPUT "Enter depth to the lateral pipe (ft)............. ";TileDepth "Enter diameter of the lateral pipe (in).......... ";TileDiameter INPUT INPUT PRINT INPUT " "Enter minimum grade of the lateral pipe (%)...... ";TileGrade "Enter length of the lateral pipe (ft)............ ";TileLength " For Subirrigation: Enter depth to water table at lateral (ft)..... " ;siWI‘depthLateral INPUT " Enter depth to water table at midpoint (ft).... ";siWTdepthMidpoint PRINT " For Subsurface Drainage: 191 INPUT " Enter depth to water table at lateral (ft)..... ";deTdepthLateral INPUT " Enter depth to water table at midpoint (ft).... ";deTdepthMidpoint INPUT "Enter design subirrigation rate (in/day)......... ";sirate INPUT "Enter design subsurface drainage rate (in/day)... ";drrate INPUT "Enter design storm rainfall (in)................. ";rainfall INPUT "Enter SCS runoff curve number......... ........... ";rcn INPUT "Enter depth to weir following rainfall (ft) ...... ";WeirDepth PRINT PRINT PRINT "SOIL PARAMETERS:" PRINT INPUT "Enter Depth to Barrier (ft) ......... ";BarrierDepth INPUT "Enter number of soil layers......... ";nlayers nlayers%=fix(nlayers) totalth:0 PRINT " For Surface Layer Enter layer thickness (in) ...... "; INPUT "",th(1) totalth=totalth+th(1) PRINT " Enter sat. hydr. cond. (in/hr).. "; INPUT "",hydc(1) PRINT " Enter sat. water content ....... . "; INPUT "",sat(1) PRINT " Enter dul water content ......... "; INPUT "",dul(1):n(1)=sat(1)—dul(1) IF nlayers%>1 THEN FOR i% = 2 TO nlayers%-1 PRINT " For Layer ";i%;" Enter layer thickness (in)..... "; INPUT "",th(i%) totalthztotalth+th(i%) PRINT " Enter sat. hydr. cond. (in/hr).. "; INPUT "",hydc(i%) PRINT " Enter sat. water content........ "; INPUT "",sat(i%) PRINT " Enter dul water content......... "; INPUT "",dul(i%):n(i%)=sat(i%)-dul(i%) NEXT i% END IF i%:nlayers IF barrierdepth*12 > totalth THEN th(i%)=barrierdepth¥12-totalth PRINT " For Layer ";i%;" Layer thickness (in) is........."; PRINT th(i%) PRINT " Enter sat. hydr. cond. (in/hr).. "; INPUT "",hydc(i%) PRINT " Enter sat. water content........ "; INPUT "",sat(i%) PRINT " Enter dul water content......... "; INPUT "",dul(i%):n(i%)=sat(i%)—dul(i%) END IF END IF 192 REM calculate model parameters REM Weight k and dporosity for soil layers to 2 feet below tile FOR i% = 1 TO nlayers% dz(i%) = th(i%) k:k+hydc(i%)*th(i%) dporosity:dporosity+n(i%)*th(i%) tdepth:tdepth+th(i%) IF tdepth/12>TileDepth+2 THEN EXIT FOR NEXT 1% kzk/tdepth dporosityzdporosity/tdepth 'REM calculate rainfall infiltration --------------- s=1000/rcn—10 IF rainfall>.2*s THEN runoff=(rainfall-O.2*s)‘2/(rainfall+.8*s) ELSE runoff=0 END IF infiltrationzrainfall-runoff REM —debug2 output ---— IF debugoutput2$="on" THEN LPRINT nlayersabove%,nlayersbelow% LPRINT "layer"," ","thickness","hydc","n" FOR i% = 1 TO nlayers% LPRINT i%,dz(i%),th(i%),; LPRINT USING "##.##";hydc(i%); LPRINT TAB(56);:LPRINT USING "#.##";n(i%) NEXT i% LPRINT TAB(O);:LPRINT USING "##.##";k; LPRINT TAB(lO);:LPRINT USING "#.##";dporosity LPRINT CHR$(12) END IF m:=:===:==:==:=====::==:Z:2:Z:::::::::::::::::22::2222222:22:22 REM SUBROUTINE NAME SILSPACE.INC REM PROGRAM AUTHORS P. Gerrish and H. Belcher REM DATE OF LAST REVISION 10/09/89 REM PROGRAM LANGUAGE Turbo Basic - Version 1.0 REM ------ units$="FPS" diazTileDiameter IF TileDiameter=3 THEN remm=3.5 IF TileDiameter=4 THEN remm=5.1 IF TileDiameter=5 THEN remm=10.0 msdzABS(deTdepthMidpoint—deTdepthLateral) msi:ABS(siWTdepthLateral-siWTdepthMidpoint) dwtsdzdeTdepthMidpoint dwtsizsiWTdepthLateral CLS FOR 11%:1 TO 2 IF ii%=1 THEN dq = drrate m = msd 193 dwt = dwtsd END IF IF ii%=2 THEN dq = sirate m = msi dwt = dwtsi END IF FOR 1 = 100 TO 1 STEP —5 GOSUB CONVERT GOSUB QCALC IF units$ = "FPS" THEN IF (dq—q) < .1 THEN GOSUB FINE GOSUB srlOO EXIT FOR END IF ELSEIF units$ : "CGS" THEN dr = dq IF (dr-r) ( 1 THEN GOSUB MFINE GOSUB srlOO EXIT FOR END IF END IF NEXT 1 NEXT ii% REM find minimum spacing IF 1dr < lsi THEN FinalLSpacing:ldr ELSE FinalLSpacingzlsi END IF PRINT "LATERAL SPACING USED FOR SUBSEQENT CALCULATIONS : "; PRINT USING "###";CINT(FinalLSpacing);:PRINT " ft." LOCATE 24,26:anykey$="" PRINT "PRESS ANY KEY TO CONTINUE? "; WHILE LEN(anykey$)=0:anykey$=INKEY$:WEND REM:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: REM SUBROUTINE NAME SITIME.INC REM SUBROUTINE AUTHOR H. W. Belcher REM DATE OF LAST REVISION 10/13/89 REM PROGRAMMING LANGUAGE Turbo Basic - version 1.0 REM start: REM compute maximum discharge for lateral pipe assuming full pipe flow TileArea=3.1416*(TileDiameter/lZ)“2/4 TilePerimeterz3.1416*TileDiameter/12 FullPipeQ=1.486/.015*(TileArea/TilePerimeter)“(2/3)*(TileGrade/100)“.5*Tile Area FullPipeQCFullPipeQ/FinalLspacing*12*24t60*60/TileLength 194 Lmits$="FPS" IF TileDiameter=3 THEN remm=3.5 IF TileDiameter=4 THEN renm25.1 IF TileDiameter=5 THEN renln=10.0 dz=0.001 dd=1000 ElapsedTime=0 CLS wthain= ( siWRiepthLateral+siWTdepthMidpo int ) /2—( inf i ltration/ 1 2 ) /DPoros i ty* . 8 IF wthain<0 THEN wthain=0 IF WeirDepth>siWTdepthLateral THEN DrainTozsiWFdepthlaterall-dz ELSE DrainTo=WeirDepth END IF REM calculate maximum q for drawdown using hooghoudt equation m2WeirDepth—wthain 1=FinalLspacing GOSUB convertl GOSUB qcalcl IF q>FullPipeQ THEN quullPipeQ Maxqu LOCATE 22,5:PRINT SPACE$(75); LOCATE 23,5:PRINT SPACE$(75); LOCATE 24,5:PRINT SPACE$(75); LOCATE 22,5:PRINT "CALCULATED MAXIMUM DISCHARGE DURING DRAWDOWN IS "; HUNT USING "#.###";MaxQ; :PRINT " IN/DAY" anykey$="":LOCATE 23,5 PRINT "DO YOU WANT TO REDUCE THE MAXIMUM DRAWDOWN DISCHARGE (y/n)? "; WHILE LEN ( anykey$ ) =0 : anykey$=INKEY$ :WEND IF anykey$="y" OR anykey$="Y" THEN LOCATE 24,5:INPUT "ENTER NEW MAXIMUM DRAWDOWN DISCHARGE (IN/DAY)";Ma>\Q END IF CLS REM perform calculations for initial flow (0(ldistsirate+drrate THEN q=sirate+drrate IF q>FullPipeQ THEN q=FullPipeQ time:(oldarea-area)*2*DPorosity/((oldq+q)/2/24/12*(oldl+l)/2) ElapsedTimezElapsedTime+time GOSUB sr1001 oldareazarea oldq=q oldl=l END IF NEXT ldist LOCATE 3,1:PRINT "Phase 1 Elapsed Time: ";zPRINT USING "###";ElapsedTime REM perform calculations for ldist = FinalLSpacing/Z REM by varying z until 2 = DrainTo FOR zzwthain TO DrainTo—dz STEP dz REM calculate drainage q m:WeirDepth—z area = (3.1416*FinalLSpacing/2*(DrainTo-z))/4 leinalLSpacing GOSUB convertl GOSUB qcalcl IF q>MaxQ THEN szaxQ IF q>FullPipeQ THEN q=FullPipeQ time=(oldarea-area)*2*DPorosity/((oldq+Q)/2/24/12*FinalLSpacing) ElapsedTime2ElapsedTime+time GOSUB sr1001 oldareazarea oldq=q NEXT 2 LOCATE 21,5:PRINT "FOR LATERAL SPACING = ";zPRINT USING "###";_ CINT(FinalLspacing);:PRINT " FTR AND MAXIMUM PIPE DISCHARGE = ";:_ PRINT USING "#.###";MaxQ;:PRINT " IN/HR" LOCATE 22,5:PRINT "WATER TABLE DRAWDOWN ELAPSED TIME = ";:_ PRINT USING "####";CINT(ElapsedTime);:PRINT " HRS" anykey$="":LOCATE 23,5:PRINT "DO YOU WANT TO REVISE LATERAL SPACING (y/n)? WHILE LEN(anykey$)=O:anykey$=INKEY$:WEND IF anykey$="y" OR anykey$="Y" THEN LOCATE 24,5:INPUT "ENTER NEW SPACING (ft)";F1nalLSpacing GOTO start END IF REM::::::::::::::::::=:::::::::::::::::::::::::::::::::::::::::::::::::::::: REM SUBROUTINE NAME SISUBR.INC REM SUBROUTINE AUTHOR P. Gerrish and H. Belcher REM DATE OF LAST REVISION 10/06/89 REM PROGRAM LANGUAGE Turbo Basic - Version 1.0 REM erOO: IF 11% = 1 THEN 196 PRINT HWECDREILATERALEH¥KHIK1FOREHHEBDRAINAGE::'3 PRINT USING "###";CINT(1);:PRINT " ft." ldr=l ELSEIF ii% = 2 THEN PRINT'TEDUIRRIIAHEFMI.SPACINGIKXQSUBIRRIGATION 'H PRINT USING "###";CINT(1);:PRINT " ft." lsi=l END IF RETURN CONVERT: "FPS" THEN TileDepth112*0.0254 BarrierDepthX12*0.0254 1*12*0.0254 re remm/IOOO km k*0.0254*24 mm — m*12*0.0254 = dwt*12*0.0254 IF units$ dtm dbm lm 2‘ S ELSE dtm = TileDepth dbm = BarrierDepth lm 1 re remm/IOOO km k mm m : dwt i=3: 5 END IF RETURN QCALC: doverl = (dbm - dtm)/lm a = 3.55 - 1.6*doverl + 2*doverl“2 IF doverl > 0.3 THEN var = lm/re dem = lm*(3.14159)](8*(DOG(var)-1.15)) ELSE dem = (dbm-dtm)/(1+doverl¥((8/3.14159)*LOG((dbm-dtm)/re)-a)) END IF de = dem*100/2.54/12 IF ii% = 1 THEN r = (8*km*mm*dem + 4*kmxmm‘2)/lm“2*1000 ELSEIF ii% = 2 THEN r = 4*kmtmm*(dtm—dwtm+dem)*(Z-mm/(dbm-dwtm))/lm‘2*1000 END IF q = r/25.4 RETURN FINE: 197 IF (dq—q) > 0.0005 THEN WHILE (dq-q) > 0.0005 DECR l, 0.1 GOSUB CONVERT GOSUB QCALC WEND ELSEIF (dq-q) < -0.0005 THEN WHILE (dq-q) < -0.0005 INCR l, 0.1 GOSUB CONVERT GOSUB QCALC WEND END IF RETURN MFINE' IF (dr-r) > 0.0005 THEN WHILE (dr-r) > 0.0005 DECR l, 0.01 GOSUB CONVERT GOSUB QCALC WEND ELSEIF (dr-r) < —0.0005 THEN WHILE (dr-r) < -0.0005 INCR l, 0.01 GOSUB CONVERT GOSUB QCALC WEND END IF RETURN REM sr1001: LOCATE 1,1 PRINT "DISCHARGE RATE 2 "; PRINT USING "#.###";q;:PRINT " in/day"; PRINT " AT TIME 2 "; PRINT USING "####";CINT(ElapsedTime);:PRINT " hr"; PRINT " AT WT DEPTH = "; PRINT USING "##.##";Z;IPRINT " ft" RETURN convertl: IF units$ = "FPS" THEN dtm = TileDepth*12X0.0254 dbm = BarrierDepth¥12*0.0254 lm = 1*12*0.0254 re = remm/IOOO km : k*0.0254*24 mm = m*12*0.0254 ELSE “I 198 dtm = TileDepth dbm = BarrierDepth lm = 1 re 2 remm/lOOO km = k mm = m END IF RETURN qcalcl: doverl = (dbm - dtm)/lm a = 3.55 - 1.6*doverl + 2*doverl“2 IF doverl > 0.3 THEN var = lm/re dem = lm*(3.14159)/(8*(LOG(var)-1.15)) ELSE dem :: (dbm—dtm)/(1+doverl*((8/3.14159)*LOG((dbm-dtm)/re) — 3)) END IF de = dem*100/2.54/12 r = (8*km*mm*dem + 4*kamm“2)/lm“2*1000 q = r/25.4 RETURN REM:::::::::::::::::2::::::::::::::::::::::::::::::::::::::::::::::::::::::: REM SUBROUTINE NAME SIOUTPUT.INC REM SUBROUTINE AUTHOR H. Belcher, MSU REM DATE OF LAST REVISION 10/13/89 REM PROGRAM LANGUAGE Turbo Basic - Version 1.0 REM CLS PRINTAB(10);"*****X************¥***************X*****************t********" PRINT TAB(lO);" S I L S P A C E R E S U L T S PRINT TAB(10);" Michigan State University PRINTAB(10);"*************************************************************" X%=8 PRINT TAB(X%)"FOR:" PRINT TAB(X%);"Depth to the lateral pipe (ft)............. "; PRINT USING "##.##";TileDepth PRINT TAB(X%);"Diameter of the lateral pipe (in) .......... "; PRINT USING "##";TileDiameter ‘PRINT TAB(X%);"Minimum grade of the lateral pipe (%) ...... "; PRINT USING "#.###";TileGrade PRINT TAB(X%);"Length of the lateral pipe (ft)............ "; PRINT USING "####";TileLength PRINT TAB(X%);"For Subirrigation: PRINT TAB(X%);" Depth to water table at lateral (ft) ...... "; PRINT USING "##.##";siWTdepthLateral PRINT TAB(X%);" Depth to water table at midpoint (ft)..... "; 199 PRINT USING "##.##";siWTdepthMidpoint PRINT TAB(X%);"For Subsurface Drainage: PRINT TAB(X%);" Depth to water table at lateral (ft)..... "; PRINT USING "##.##";deTdepthLateral PRINT TAB(X%);" Depth to water table at midpoint (ft).... "; PRINT USING "##.##";deTdepthMidpoint PRINT TAB(X%);"Design subirrigation rate (in/day)......... "; PRINT USING "#.##";sirate PRINT TAB(X%);"Design subsurface drainage rate (in/day)... "; PRINT USING "#.##";drrate PRINT TAB(X%);"Design storm rainfall (in)........ ....... .. "; PRINT USING "##.##";rainfall PRINT TAB(X%);"SCS runoff curve number ..... .......... ..... "; PRINT USING "###";rcn PRINT TAB(X%);"Design weir depth during drawdown (ft)..... "; PRINT USING "#.##";WeirDepth PRINT TAB(X%);"Depth to Barrier (ft)...................... "; PRINT USING "## . #" iBarrierDepth PRINT TAB(X%);"Saturated hydraulic conductivity (in/hr)... "; PRINT USING "#.##";k PRINT TAB(X%);"Saturated - Drained Upper Limit............ "; PRINT USING "#.##";DPbrosity anykey$="":WHILE LEN(anykey$):0:anykey$=INKEY$:WEND CLS PRINT TAB(X%)"RESULTS:" PRINT TAB(X%)"Maximum lateral spacing for subirrigation (ft)... ";. PRINT USING "###";lsi PRINT TAB(X%)"Maximum lateral spacing for subs. drainage (ft).. "; PRINT USING "###";ldr PRINT TAB(X%)"For lateral spacing (ft)......................... "; PRINT USING "###";FinalLspacing PRINT TAB(X%)"Time to return to subirrigation WTD (hr) ..... .... "; PRINT USING "####";ElapsedTime PRINT TAB(X%)"Following precipitation with infiltration (in)... "; PRINT USING "#.##";infiltration PRINT TAB(X%)"Water table depth after precipitation (ft)....... "; PRINT USING "##.##";wthain PRINT TAB(X%)"Maximum discharge for drawdown (in/day).......... "; PRINT USING "#.###";MaxQ; REM LOCATE 23,8 anykey$= n n PRINT "DO YOU WANT A PRINTOUT (Y/n)? "; WHILE LEN(anykey$)=0:anykey$=INKEY$:WEND IF anykey$="y" OR anykey$="Y" THEN L P R I N TAB( 10) ;"**********************************t***************¥******¥***" LPRINTTAB(10);" SILSPACE RESULTS LPRINT TAB(lO);" Michigan State University 200 L P R I N TAB( 10);"**************X*X***********¥*********************¥**********" X%=12 LPRINT LPRINT TAB(X%)"FOR:" LPRINT LPRINT TAB(X%);"Depth to the lateral pipe (ft)............. "; LPRINT USING "## . ##" ;TileDepth LPRINT TAB(X%);"Diameter of the lateral pipe (in) ......... . "; LPRINT USING "##";TileDiameter LPRINT TAB(X%);"Minimum grade of the lateral pipe (%) ...... "; LPRINT USING "#.###";TileGrade LPRINT TAB(X%);"Length of the lateral pipe (ft) ............ "; LPRINT USING "####";TileLength LPRINT TAB(X%);"For Subirrigation: LPRINT TAB(X%);" Depth to water table at lateral (ft)...... "; LPRINT USING "##.##";siWTdepthLateral LPRINT TAB(X%);" Depth to water table at midpoint (ft)..... "; LPRINT USING "##.##";siWTdepthMidpoint LPRINT TAB(X%);"For Subsurface Drainage: LPRINT TAB(X%);" Depth to water table at lateral (ft) ..... "; LPRINT USING "##.##";deTdepthLateral LPRINT TAB(X%);" Depth to water table at midpoint (ft).... "; LPRINT USING "##.##";deTdepthMidpoint LPRINT TAB(X%);"Design subirrigation rate (in/day) ......... "; LPRINT USING "#.##";Sirate LPRINT TAB(X%);"Design subsurface drainage rate (in/day)... "; LPRINT USING "#.##";drrate LPRINT TAB(X%);"Design storm rainfall (in)................. "; LPRINT USING "##.##";rainfall LPRINT TAB(X%);"SCS runoff curve number.................... "; LPRINT USING "###";rcn LPRINT TAB(X%);"Design weir depth during drawdown (ft)..... "; LPRINT USING "#.##";WeirDepth LPRINT TAB(X%);"Depth to Barrier (ft)...................... "; LPRINT USING "##.#";BarrierDepth LPRINT TAB(X%);"Saturated hydraulic conductivity (in/hr)... "; LPRINT USING "#.##";k LPRINT TAB(X%);"Saturated - Drained Upper L1mit............ "; LPRINT USING "#.##";DPorosity LPRINT LPRINT TAB(X%)"RESUL :" LPRINT LPRINT TAB(X%)"Maximum lateral spacing for subirrigation (ft)... "' LPRINT USING "###";lsi LPRINT TAB(X%)"Maximum lateral spacing for subs. drainage (ft).. "' LPRINT USING "###";ldr LPRINT TAB(X%)"For lateral spacing (ft)......................... "° LPRINT USING "###";FinalLspacing LPRINT TAB(X%)"Time to return to subirrigation WTD (hr)......... "; LPRINT USING "####";ElapsedTime LPRINT TAB(X%)"Following precipitation with infiltration (in)... "; 201 LPRINT USING "#.##";infiltration LPRINT TAB(X%)"Water table depth after precipitation (ft). ...... "; LPRINT USING "##.##";wthain LPRINT TAB(X%)"Maximum discharge for drawdown (in/day).......... "; LPRINT USING "#.###";MaxQ FOR i%=1 T0 31:LPRINT:NEXT 1% END IF CLS anykey$: u n PRINT "DO YOU WANT TO QUIT (y/n)? "; WHILE LEN(anykey$)=0:anykey$=INKEY$:WEND IF anykey$="y" OR anykey$="Y" THEN CLS END ELSE CLEAR GOTO begin END IF REM:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: REM PROGRAM NAME SIECON.BAS REM PROGRAM AUTHOR H. Belcher, MSU REM DATE OF LAST REVISION 10/17/89 REM PROGRAM LANGUAGE Turbo Basic - Version 1.0 REM ------------------- CLS P R I N T TAB(3);"*********************X*****************************************X*** ****" PRINT PRINT TAB(3);" SIECXDN: An economic analysis program for water table management " PRINT P R I N T TAB(B);"***************************************************************¥*** #222" PRINT PRINT PRINT PRINT TAB(28);" H. W. Belcher " PRINT TAB(28);"MICHIGAN STATE UNIVERSITY" PRINT TAB(28);" Version: 1.01 " PRINT TAB(28);" 09/29/89 " LOCATE 24,26 PRINT "PRESS ANY KEY TO CONTINUE? "; WHILE LEN(anykey$)=0 anykey$=INKEY$ WEND begin: REM input data—- ——————— CLS anykey$znu PRINT "WILL INPUT DATA COME FROM DISKFILE (f) OR KEYBOARD (k)? " WHILE LEN(anykey$)=0:anykey$=INKEY$:WEND IF anykey$="F" OR anykey$="f" THEN INPUT "Enter input file name ................ ";FileName$ CLS OPEN FileName$ FOR INPUT AS #1 INPUT #1,P,A,L,i,area,YldWO,YldW,ProdCostWO,ProdCostW,Price CLOSE #1 REM ELSE PRINT INPUT "Enter estimated installation cost of the system ($).... ";P$ IF LEN(P$)=0 THEN P=INSTAL ELSE P=VAL(P$) INPUT "Enter estimated cost of maintaining the system ($)..... ";A$ IF LEN(A$)=0 THEN A:ANN ELSE A=VAL(A$) INPUT "Enter expected life of the system (yr)................. ";L$ IF LEN(L$)=O THEN L=LIFE ELSE L=VAL(L$) 203 INPUT "Enter minimum attractive rate of return (%)............ ";i$ IF LEN(1$)=0 THEN iZRATE ELSE 1=VAL(1$) 1m '.FieldSize (ar‘ea).....00.00.00.000...00.00.000.00000099 ";area$ IF LEN(area$)=0 THEN area:SIZE ELSE area:VAL(area$) INPUT "Enter estimated yield w/o system (vol/unit area)....... ";YldWO$ IF LEN(Y1dWO$):O THEN YldWO=YIELD1 ELSE YldWO=VAL(YldWO$) INPUT "Enter estimated yield w/ system (vol/unit area)........ ";YldW$ IF LEN(YldW$)=O THEN Y1dW=YIELD2 ELSE YlszVAL(YldW$) INPUT "Enter production cost w/o system ($/unit area) . . . . . . . . . ";PTodCostWO$ IF LEN(ProdCostWO$)=O THEN ProdCostWO=COST1 ELSE ProdCostWO=VAL(ProdCostWO$) INPUT "Enter production cost w/ system ($/unit area).......... ";ProdCostW$ IF‘LENiProdCostW$)=O THEN ProdCostW:COST2 ELSE ProdCosthVAL(ProdCostW$) INPUT "Enter expected product market price ($/unit vol)....... ";Price$ IF LEN(Price$)=0 THEN PricezVALUE ELSE Price = VAL(Price$) END IF REM INSTAL=P =A LIFE=L RATE=1 SIZEzarea YIELD1=YldWO YIELD2=YLDW COST1=ProdCostWO OOST2=ProdCostW VALUE=Price REPL=O AINSTAL=INSTALH (RATE/100M ( 1+RATE/100)‘(LIFE)/( (1+RATE/100) “ (LIFE)-1)) ANNUAL = AINSTAL+ANN—(REPL*(RATE/100)/( (1+RATE/100)“(LIFE)-1)) INCREASE : SIZE!(YIEIDZXVALLJE-YIELD1*VALUE)-SIZEX (OOSTZ—OOSTl ) RTN=INCREASE/ANNUAL CLS PRINT TAB(S);"**************************¥*****X********************X****" PRINT TAB(8);" S I E C O N R E S U L T S " PRINT TAB(8);" Michigan State University " PRINT TAB(S);"**********************************************************" X%=8 PRINT PRINT TAB(X%)"FOR:" PRINT TAB(X%);"Estimated installation cost of the system ($)..... "; PRINT USING "#######";P PRINT TAB(X%);"Cost of operating and maintaining the system (5).. "; PRINT USING "#######";A PRINT TAB(X%);"Expected life of the system (yr).................. "; 204 PRINT USING "#######";L PRINT TAB(X%);"Minimum attractive rate of return (%)............. "; PRINT USING "####.##";1 PRINT TAB(X%);"Field size (area)................................. "; PRINT USING "#######";area PRINT TAB(X%);"Estimated yield w/o system (vol/unit area)........ "; PRINT USING "#######";YldWO PRINT TAB(X%);"Estimated yield w/ system (vol/unit area)......... "; PRINT USING "#######";YldW PRINT TAB(X%);"Production cost w/o system ($/unit area).......... "; PRINT USING "####.##";ProdCostWO PRINT TAB(X%);"Production cost w/ system ($/unit area)........... "; PRINT USING "####.##";ProdCostW PRINT TAB(X%);"Expected product market price ($/unit vol) ..... ... "; PRINT USING "####.##";price PRINT PRINT TAB(X%);"RESULTS:" PRINT TAB(X%);"TOTAL INSTALLATION COST..........................."; PRINT USING "$$######";INSTAL+.5 PRINT TAB(X%);"TOTAL ANNUAL SYSTEM COST.........................."; PRINT USING "$$######" ;ANNUAL+.5 PRINT TAB(X%);"TOTAL ANNUAL INCREASE IN" PRINT TAB(X%);"INOOME DUE TO SYSTEM.............................."; PRINT USING "$$######";INCREASE+.5 PRINT TAB(X%);"BENEFIT/OOST RATIO................................"; PRINT USING "#####.##";RTN+.005 REM LOCATE 25,8 mykey$=fl H PRINT "DO YOU WANT A PRINTOUT (y/n)? "; WHILE LEN(anykey$)=0:anykey$=INKEY$:WEND IF anykey$="y" OR anykey$="Y" THEN LPRINT L P R I N TAB(IZ);"*****************6*t****************X*********************" LPRINT TAB(IZ);" S I E C O N R E S U L T S LPRINT TAB(12);" Michigan State University L P R I N TAB(IZ);"***********X*********************************X*X**********" X%=12 LPRINT LPRINT TAB(X%)"FOR1" LPRINT TAB(X%);"Estimated installation cost of the system ($)..... LPRINT USING "#######";P ‘ LPRINT TAB(X%);"Cost of operating and maintaining the system ($).. LPRINT USING "#######";A LPRINT TAB(X%);"Expected life of the system (yr)... ............... LPRINT USING "#######";L LPRINT TAB(X%);"Minimum attractive rate of return (%) ............. 205 LPRINT USING "####.##";i LPRINT TAB(X%);"Field size (area) ....... ..... ....... .............. "; LPRINT USING "#######";area LPRINT TAB(X%);"Estimated yield w/o system (vol/unit area)........ "; LPRINT USING "#######";YldWO LPRINT TAB(X%);"Estimated yield w/ system (vol/unit area) ......... "; LPRINT USING "#######";YldW LPRINT TAB(X%);"Production cost w/o system ($/unit area)... ..... .. "; LPRINT USING "####.##";ProdCOStWO LPRINT TAB(X%);"Production cost w/ system ($/unit area).... ..... .. "; LPRINT USING "####.##";ProdCostW LPRINT TAB(X%);"Expected product market price ($/unit vol)........ "; LPRINT USING "####.##";price «- LPRINT LPRINT TAB(X%);"RESULTS:" LPRINT TAB(X%);"TOTAL INSTALLATION COST...................... ..... "; LPRINT USING "$$######";INSTAL+.5 ”” LPRINT TAB(X%);"TOTAL ANNUAL SYSTEM COST........... ..... . ......... "; LPRINT USING "$$######";ANNUAL+.5 LPRINT TAB(X%);"TOTAL ANNUAL INCREASE IN" LPRINT TAB(X%);"INCOME DUE TO SYSTEM.............................."; LPRINT USING "$$######";INCREASE+.5 LPRINT TAB(X%);"BENEFIT/COST RATIO................................"; LPRINT USING "#####.##";RTN+.005 FOR i%=1 T0 42:LPRINT:NEXT 1% END IF CLS anykey$: n u PRINT "DO YOU WANT TO QUIT (y/n)? "; WHILE LEN(anykey$)=O:anykey$=INKEY$:WEND IF anykey$="y" OR anykey$="Y" THEN CLS END ELSE GOTO begin END IF APPENDIX G WATER TABLE MANAGEMENT SYSTEM DESIGN EXAMPLE 207 Design a soybean subirrigation system for 39 acre field located NE of St. Johns, Michigan. Field studies of subirrigated soybean production suggests that soybean yield is related to the depth and fluctuation of the water table by the linear regression equation relative soybean yield = 89.9 - 2.30 * wfi where wfi is the wet stress fluctuation index for June through August. The soil at the site is Ziegenfuss clay loam with a dense clay layer at 5.5 ft depth practically impervious to water. A site investigation indicates the soil above the dense clay layer has the following properties: depth k sat dul (in) (in/h) (in/in) (in/in) 0 - 9 106 .45 030 9 - 15 0.2 .42 .27 15 — 66 0.7 .44 .29 where: depth depth below soil surface, in k = saturated lateral hydraulic conductivity, in/hr sat = volumetric water content at saturation, in/in dul = volumetric water content at drained upper limit, in/in Daily rainfall record near the site are available from a National Weather Service Cooperative observer station in: St. Johns. The SIRAIN output using those records for the June, July and August months follow: MONTH 2 YR 2 YR 10 YR RAIN EVENTS RAIN (in) (in) JUN 0.16 10 0.91 JUL 0.15 8 0.77 AUG 0.20 8 0.96 SEASON 0.16 28 0.83 Next, three subirrigation system design alternatives were investigated using the computer model SILSPACE. For the three alternatives, the lateral spacing to provide steady state subsurface drainage and subirrigation is held constant. The only' parameter varied for each alternative is the maximum discharge during drawdown following the .16 in design rainfall. That discharge was set equal to 0.375 in./day for alternate 1, 208 0.500 in./day for alternate 2 and 0.750 in./day for alternate 3. The input data for the alternates: Depth to the lateral pipe (ft)............. 4.00 Diameter of the lateral pipe (in).......... 4 Minimum grade of the lateral pipe (%)......0.050 Length of the lateral pipe (ft)............ 300 For Subirrigation: Depth to water table at lateral (ft)...... 1.50 Depth to water table at midpoint (ft)..... 2.00 For Subsurface Drainage: Depth to water table at lateral (ft)..... 4.00 Depth to water table at midpoint (ft).... 2.00 Design subirrigation rate (in/day) . . . . . . . . . 0. 30 Design storm rainfall (in)................. 0.16 Design storm occurences.................... 28 SCS runoff curve number.................... 82 Total time (days).......................... 90 Design weir depth during drawdown (ft)..... 4.00 produced the following results: Maximum lateral spacing for subirrigation (ft)... 27, 27, 27 Maximum lateral spacing for subs. drainage (ft).. 40, 34, 27 For lateral spacing (ft)......................... 27, 27, 27 Time to return to subirrigation WTD (hr)......... 27, 36, 42 Following precipitation with infiltration (in)... 0.16, 0.16, 0.16 Water table depth after precipitation (ft)....... 1.46, 1.46, 1.46 Water table depth after drawdown (ft)............ 1.61, 1.61, 1.61 for: Maximum discharge for drawdown (in/day).......... .375, .500, .750 The results of each SILSPACE simulation follow: DESIGN ALTERNATE 1 2 3 EVALUATION PERIOD season season season EVALUATION PERIOD days 90 90 90 RAINFALL in 0.16 0.16 0.16 EVENTS DURING EVAL 28 28 28 DRAWDOWN TIME hr 42 36 27 DEPTH TO HIGH WT ft 1.46 1.46 1.46 DEPTH TO IOWWT ft 1.61 1.61 1.61 DEPTHTOMEANWT m 1.58 1.59 1.59 wfi mthr 1.57 2.68 3.54 209 Substituting the parameters wfi parameter into the Bannister site soybean yield regression equation resulted in: IESHEJAUUEEMTE 1 2 3 syld:89.9-2.30wfi 82% 84% 86% The preceding indicates increasing the subirrigation maximum discharge capacity to .375, .500 and .750 in/day will result in a soybean relative yield increase of 82, 84 and 86% respectively. Assuming a 100% relative yield is 65 bu/ac this translates to a yield of 57, 58, and 59 bu/ac for design alternative 1, 2 and 3 respectively. Thus to maximize yield, the third design alternative is best, ie. design the mains and submains to handle a flow rate of 0.750 inches/day. However, to optimize the economic efficiency of the system to produce soybean yield costs of the alternatives must be considered” iDetail design of enufii alternate resulted 111 the following system quantities and estimated installation costs: COST OF SUBMAINS unit dc:.375 dc:.375 dc:.500 dc:.500 dc:.750 dc:.750 price lf $ lf $ lf $ 5" .79 1188 939 918 725 594 469 6" 1.11 729 809 648 719 324 360 8" 2.14 1080 2311 1107 2369 999 2138 10" 3.37 324 1092 756 2548 12" 4.25 324 1377 TOTAL 2997 4059 2997 4905 2997 6891 COST OF MAINS unit dc:.375 dc:.375 dc:.500 dc:.500 dc:.750 dc:.750 price lf $ 1f $ lf $ 5" .79 6" 1.11 350 389 8" 2.14 350 749 350 749 10" 3.37 400 1348 12" 4.25 15 64 400 1700 15" 5.91 15 89 400 2364 18" 7.25 15 109 TOTAL 765 1800 765 2538 765 3222 210 OEH‘OFIAHEUHS unit dc:.375 dc:.375 dc:.500 dc:.500 dc:.750 dc:.750 price lf 6 If $ lf $ .47 25913 12179 25913 12179 25913 12179 MISC COSTS 13000 13000 13000 GRAND TOTAL 31038 32622 35292 The detailed. design and cost estimate for each alternate indicates: Alternate 1 will cost $31038 and provide a 57 bugac annual yield. Alternate 2 will cost $32622 and provide a 58 buZac annual yield. Alternate 3 will cost $35292 and provide a 59 bulac annual yield. To determine the most economic efficient alternative the computer module SIECON is used to calculate the benefit/cost ratio of each of the three alternatives. Using an estimated annual operating and maintenance expense equal to $500 per year, an expected system life of 20 years, a minimum attractive interest rate of 8%, a 35 bu/ac estimated yield without the system, a without system production cost of $110.00 per acre, a with system production cost of $120.00 per acre and an expected market price of $6.50 per bushel of soybeans, the SIECON economic analysis provides the following results: alt 1 alt 2 alt 3 Annual System Cost REESE. $ESES- S4095 Annual Increase in Income $4124 $4325 $4527 Benefit/Cost Ratio 1.13 1.14 1.11 Thus the analysis indicates the second design alternate is the most economic efficient alternative of the three. 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