EPA-600/2-76-226
September 1976
Environmental Protection Technology Series
IRRIGATION MANAGEMENT AFFECTING
QUALITY AND QUANTITY OF RETURN FLOW
Robert S. Kerr Environmental Research Laboratory
Office of Research and Development
U.S. Environmental Protection Agency
Ada, Oklahoma 74820
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RESEARCH REPORTING SERIES
Research reports of the Office of Research and Development, U.S. Environmental
Protection Agency, have been grouped into five series. These five broad
categories were established to facilitate further development and application of
environmental technology. Elimination of traditional grouping was consciously
planned to foster technology transfer and a maximum interface in related fields.
The five series are:
1. Environmental Health Effects Research
2. Environmental Protection Technology
3. Ecological Research
4. Environmental Monitoring
5. Socioeconomic Environmental Studies
This report has been assigned to the ENVIRONMENTAL PROTECTION
TECHNOLOGY series. This series describes research performed to develop and
demonstrate instrumentation, equipment, and methodology to repair or prevent
environmental degradation from point and non-point sources of pollution. This
work provides the new or improved technology required for the control and
treatment of pollution sources to meet environmental quality standards.
This document is available to the public through the National Technical Informa-
tion Service, Springfield, Virginia 22161.
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EPA-600/2-76-226
September 1976
IRRIGATION MANAGEMENT AFFECTING QUALITY
AND QUANTITY OF RETURN FLOW
by
Lyman S. Willardson
Department of Agricultural and
Irrigation Engineering
and
R. John Hanks
Department of Soil Science
and Biometeorology
Utah State University
Logan, Utah 84322
Grant No. R-802864
Project Officer
James P. Law, Jr.
Source llanagement Branch
Robert S. Kerr Environmental Research Laboratory
Ada, Oklahoma 74820
ROBERT S. KERR ENVIRONMENTAL RESEARCH LABORATORY
OFFICE OF RESEARCH AND DEVELOPMENT
U.S. ENVIRONMENTAL PROTECTION AGENCY
ADA, OKLAHOMA 7482Q
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DISCLAIMER
This report has been reviewed by the Robert S. Kerr Environmental Research
Laboratory, U.S. Environmental Protection Agency, and approved for publication.
Approval does not signify that the contents necessarily reflect the views and
policies of the U.S. Environmental Protection Agency, nor does mention of
trade names or commercial products constitute endorsement or recommendation for
use.
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ABSTRACT
Management practices for control of quality and quantity of return subsurface
flow were studied theoretically, in the laboratory, and full scale in the
field.
Field water management studies using waters of different qualities and
different leaching fractions showed that the soil in the project area has
a high salt buffering capacity. The soil acted either as a source or a
sink for salt depending on the leaching fraction and the quality of water
used for irrigation. Minimum average leaching fractions attainable on a
field scale were found to be controlled by the uniformity of irrigation
water application.
Digital computer models were developed that consider properties of the soil,
plant, water, and environment. One model allows prediction of salt buildup
and the yield response over several years. Salt buildup in the soil
eventually caused a yield decrease. It was necessary to include a source-
sink term in a salt flow model to accurately simulate field data. Source-
sink phenomena observed in the field were confirmed by leaching tests
conducted in the laboratory. Both models are potentially useful for salt
management in the field.
Production functions were developed for dry matter and grain yields of corn
for variable water and salt application. A relation between evapotranspira-
tion and yield indicates that the osmotic effect of salt in the profile
reduces evapotranspiration and, consequently, crop yields.
The response of a given soil-water-plant system to management for the
purposes of controlling quantity and quality of return flow depends on the
physical and chemical character of the soil, as well as the management
factors.
This report was submitted in fulfillment of Grant No. R-802864 by Utah
State University under the partial sponsorship of the U.S. Environmental
Protection Agency. Work was completed in March, 1976.
111
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CONTENTS
Abstract ............ ill
List of Figures .......... vi
List of Tables ........... x
List of Abbreviations and Symbols ....... xiv
Acknowledgments .......... xvi
I, Introduction .......... 1
II. Summary and Conclusions ....... 4
III. Recommendations ......... 8
IV. Methods 10
V. Results and Discussion ....... 31
VI. Publications .......... 145
VII. References .......... 146
VIII. Appendices .......... 148
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LIST OF FIGURES
Page
Number
1 Location map, Hullinger farm, Vernal, Utah . *•*•
2 Farm layout .......••• -*-^
3 Typical plot layout .....•••• ^•->
4 Continuous variable plot layout and instrumentation • • 22
5 Relative yield as related to relative evapotranspiration under
various saline conditions ....••• 29
6 Comparison of measured and computed transpiration where
different sections of the root zone were irrigated with
saline water. T is top, M is middle and B is the bottom
section ........••• 30
7 Relative composite can-catch for sample plot, 1974 • 34
8 Relative composite can-catch for sample plot, 1975 . . 35
9 Pressure - discharge curve for 5/32" (0.40 cm) lo-hi
nozzle as determined from pressure - discharge tests. . . 36
10 Measured and expected average soil water salinity for three
water table depth treatments, 1974 ...... 53
11 Measured and expected average soil water salinity with time
for two water table depth treatments in 1975 .... 54
12 Measured and expected average soil water salinity for three
irrigation water quality treatments ..... 55
13 Measured and expected average soil water salinity with time
for three irrigation water quality treatments ... 56
14 Measured and expected average soil water salinity for three
leaching treatments, 1974, ....... 57
15 Measured and expected average soil water salinity with time
for four leaching fractions ....... 58
16 Average soil solution salinity profiles for low leaching
plots on four dates ........ 59
17 Average soil solution salinity profiles for middle leaching
plots on four dates ....-•••. g^
18 Average soil solution salinity profile for high leaching
plots on four dates ....••••• 62
vi
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LIST OF FIGURES (Continued)
Number Page
19 Average soil solution salinity profiles for low water
quality plots on four dates ....... 63
20 Average soil solution salinity profile for middle water
quality plots on four dates ....... g/
21 Average soil solution salinity profile for high water
quality plots on four dates ....... 65
22 Average soil solution salinity profile for deep water table
on three dates .......... 66
23 Average soil solution salinity profile for middle water
table depth on four dates ....... 67
24 Average soil solution salinity profile for shallow water table
on four dates .......... 68
25 Cumulative lysimeter ET for alfalfa and pan evaporation . 72
26 Approximate 4-day average pan coefficients (ET alfalfa/E
pan) for alfalfa ......... 73
27 Cumulative pan evaporation and potential ET . . . 74
28 Cumulative lysimeter ET and computed ET for alfalfa using
Jensen irrigation scheduling program ..... 75
29 Cumulative lysimeter ET and computed ET using airport
temperatures for alfalfa (Jensen irrigation scheduling
program) ........... 76
30 Cumulative ET for alfalfa from east and west lysimeters . 73
31 Relative sprinkler application rate as a function of
distance from the sprinkler line ...... 86
32 Average volumetric water content in the 1-3 feet (0.3 - 0.9 m)
zone during the growing season, 1974. ..... 87
33 Average volumetric water content in the 1-3 feet (0.3 - 0.9 m)
zone during the growing season, 1974 ..... 87
34 Dry matter and grain yields as influenced by salt and
water levels .......... 89
35 Projected and actual dry matter yields as a function of salt
and irrigation levels ........ 90
36 Average volumetric water content in the 1-3 feet (0.3 -
0.9 m) zone during the growing season, 1974 .... 93
vii
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LIST OF FIGURES (Continued)
Number Page
37 Average volumetric water content in the 1-3 feet (0.3 -
0.9 m) zone during the growing season, 1974 .... 93
38 1975 corn grain yield at Logan related to evapo-
transpiration .......... 104
39 1975 corn dry matter yield at Logan related to
evapotranspiration ......... 105
40 The setup of laboratory leaching experiments 1 and 2 . . 107
41 The location of the grid points relative to the
experimental segments ........ 110
42 Computed and measured EC of the effluent vs. time - Laboratory
trial 1 ........... 113
43 EC computed by the model and by the (4P) vs. time
laboratory trial 1 ......... 114
44 Computed and measured EC of the effluent vs. time . . . 117
45 EC computed by the model and by the (4P) vs. time . . . 118
46 The setup of experiment 3 ....... 120
47 The computed and the measured EC of the effluent vs. the
depth of the effluent - laboratory trial 3 .... 125
48 The computed and the measured EC of the effluent vs. the
depth of the effluent - laboratory trial 3 .... 126
49 Measured and computed salinity vs. depth in the reference
column - laboratory trial 3 . . ... . . . 128
50 Measured and computed salinity vs. depth in the reference
column - laboratory trial 3 ....... 129
51 Measured and simulated comparison using a no source-sink
term (K = 0.0) and a variable source-sink term for the Vernal
Field trial ••••...... 131
52 Measured and simulated comparison using a constant source-sink
term and chemical equilibrium model for the Vernal field
trial 132
53 Relative yield as related to the amount of irrigation and
rain, concentration of the irrigation water (C. ), and
initial soil solution concentration (C. ) for a medium
rooted crop .........
viii
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LIST OF FIGURES (Continued)
Number Page
54 Upward flow and drainage versus irrigation and rain
for deep rooted crops at two water qualities and two
initial soil salinities ....... 135
55 Final soil solution concentration predictions as
influenced by concentration of the irrigation water
(C. ), the amount of irrigation applied and initial
soil solution concentration (C. ) . . . . 137
is
56 Relative yield, final salinity, and upward flow versus
time for the deep rooted crop. Irrigation water
quality: 6.4 meq/1 ........ 138
57 Predicted final soil salinity versus depth for the
medium rooted crop at various times and at three
irrigation water qualities ...... 140
58 Patterns of distribution for uniformity calculations . 141
59 Relative yield and salt outflow versus time for
two coefficients of uniformity ..... 144
Figures found in Appendix A
A-l Sequence of computer operations ..... 150
A-2 Clear day solar radiation for Vernal, Utah,
irrigation season ........ 151
Figures found in Appendix B
B-l Dry matter yields as influenced by salt and
irrigation levels, metric tons/ha ..... 17^
B-2 Dry matter yields in grams/plant as influenced by
salt and irrigation levels ...... 172
B-3 Grain yields metric tons/ha, as influenced by
salinity and water levels ...... 173
ix
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LIST OF TABLES
Number Page
1 Design leaching fractions for water management studies . • 13
2 Osmotic potential of the soil solution as a function of
salt level, desired and obtained (Soil samples taken 6-11-74) 23
3 Cumulative hydraulic lysimeter ET less rain for alfalfa
at Vernal, Utah, May 12 to August 26, 1974 .... 32
4 Cumulative ET, May 12 to September 13, 1975. Calculated
from Class A Pan ......... 33
5 1974 irrigation schedule giving dates and cumulative
depths applied for 27 individual plots used in the
study ........... 37
6 1975 irrigation schedule giving dates and cumulative
depths applied for 18 individual plots used in the
study ........... 38
7 1974 seasonal water balance for the barrel lysimeters . . 39
8 1975 seasonal water balance for barrel lysimeters . 40
9 Average study area and sampling point leaching fractions
for each treatment, 1974 ........ 4^
10 Average study area leaching fraction for each treatment, 1975 42
11 Average depths to water table by date for each water table
treatment, 1974 ......... 43
12 Average depths to water table for each water table
treatment, 1975 ......... 44
13 Matric potential and vertical hydraulic gradient averages
for three leaching fraction treatments in 1974 ... 45
14 Matric potential and vertical hydraulic and gradient
averages for three irrigation water quality treatments
in 1974 46
15 Matric potential and vertical hydraulic gradient averages
for three water table depth treatments in 1974 ... ,7
16 Irrigation water qualities for each irrigation for Ashley
Creek, Naples Drain and South Tributary, 1974 ... 3
17 Irrigation water qualities in 1975 .
48
18 Theoretical EC, in mmhos/cm for equilibrium conditions
using 1974 water quality and leaching fractions ... 50
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LIST OF TABLES (Continued)
Number Pa8e
19 Theoretical expected EC , in mmhos/cm for equilibrium
conditions using actual 1975 qualities and leaching
fractions. .......... 50
20 Theoretical average profile salinity for equilibrium
conditions using 1974 water qualities and leaching
fractions ........... 51
21 Expected change in average soil profile salinity for three
leaching fractions, three water qualities and three depths
to water table, 1974 ... 52
22 Expected change in average soil profile salinity for three
leaching fractions, three water qualities and two depths
to water table,1975 ......... 52
23 Individual ion analyses of selected irrigation water samples
and soil solution extracts ....... 59
24 Comparison of electrical conductivities for three extract
ratios of soil samples from two treatment plots ... 70
25 Sprinkler system performance parameters for single irrigations
using #30 Rainbird sprinklers with 9/64 in. (3.6 mm)
nozzles on 30 ft x 50 ft spacing ...... 79
26 Sprinkler system performance for single applications using
#20 Hilo Rainbird sprinklers with 5/32 in. nozzles in
25 ft x 30 ft spacing ........ 79
27 Sprinkler system performance composited to date for #30
Rainbird sprinkler with 9/64 in. (3.6 mm) nozzle on 30 ft
x 50 ft spacing ......... go
28 Sprinkler system performance composite to date for #20
Hilo Rainbird sprinkler with 5/32 in. nozzle on 25 ft x 30 ft
spacing ........... go
29 Sprinkler system performance with simulated composited alternate
sets for #30 Rainbird sprinkler with 9/64 in. (3.6 mm)
nozzle on 30 ft by 50 ft spacing ...... gl
30 Alfalfa dry matter yields from 1974 and 1975 water
management experiments in Vernal, Utah ..... 34
31 Average dry matter alfalfa yields for three cuttings in
1975 ............. 85
32 Average number of plants per plot row as a function of salt
and irrigation treatments ........ gg
xi
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LIST OF TABLES (Continued)
Number Page
33 Electrical conductivity of the soil solution as
measured by salinity sensors as a function of time
and site at the 30-cm depth ....... 91
34 Irrigation applied at Vernal, Utah, on the corn plots
in 1975 94
35 Influence of salinity level imposed in 1974 on 1975
corn dry matter yields as influenced by water level . . 95
36 Irrigation in cm at Vernal, Utah, on the alfalfa plots
in 1975 96
37 Alfalfa dry matter yields as influenced by salinity
and water levels at Vernal, Utah . ..... 97
38 Summary of alfalfa dry matter yields for two harvests as
affected by salt and water treatments (Vernal, 1975) . . 99
39 Irrigation applied at various dates and distances from
the sprinkler line in Logan in 1975 ..... 100
40 Corn grain yields at Logan in 1975 as influenced by
salinity and water level, average of 2 replicates. . . 101
41 Corn dry matter yields at Logan in 1975 as influenced
by salinity and water level, average of 2 replicates . . 102
42 Soil water depletion as influenced by water and
salinity level at Logan in 1975 ...... 103
43 Results of leaching, trial 1 . . . . . . 108
44 The initial salinity conditions used in trial 1 . . Ill
45 The initial salinity conditions used in trial 2 . . . 115
46 Results of leaching experiment no. 2 ..... 116
47 Results of leaching experiment no. 3A . . . . . 121
48 Results of leaching experiment no. 3B . . . 122
49 Leaching of the reference column 3A ..... 122
XO-l
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LIST OF TABLES (Continued)
Number Page
50 Leaching of the reference column 3B ..... 123
51 Measured and computed EC of the soil solution in
several time points ......... 130
52 Example of uniformity calculations - 5-year sequence
for the shallow rooted crop ....... 143
A-l Experimental coefficients for net radiation equation . . 149
A-2 Area constants for Vernal, Utah ...... 152
A-3 Approximate amounts of available water that can be
depleted from soils by the time the soil moisture
tension reaches the values indicated. Adapted from
Haise and Hagan (1967) ........ 153
A-4 Guides to allowable soil depletion on a deep , medium
textured soil .......... 153
B-l Oven dry matter yield, metric ton dry matter /ha. . . . 165
B-2 Oven dry matter yield, grams dry matter /plant . . . 157
B-3 Grain yield, Kg/ha. .........
B-4 Electrical conductivity of the soil solution as a
function of time and site, 4-probe results in
mmhos/cm @ 25°C ......... 174
B-5 Electrical conductivity of water samples extracted
from ceramic cups, mmhos/cm @ 25 °C . . . . . .
D-l Leaching experiments to compute D . . . . . 188
xiii
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LIST OF ABBREVIATIONS AND SYMBOLS
EC. Electrical conductivity of irrigation water
EC Electrical conductivity
EC Electrical conductivity of drainage water
LF Leaching fraction
ET Evapotransporation
E Maximum potential evapotranspiration
t' Julian Calendar day for maximum evapotranspiration
At Time interval in days
cw Wind coefficient
Kc Crop coefficient
~ Hydraulic gradient
A/I
Z Depth
M Matric suction tensiometer reading
D, Depth of drainage water
aw
D. Depth of irrigation water
C Christiansen's coefficient of uniformity
ED Sum of the absolute values of the deviations of the can-catches from
the average catch.
C Average can-catch
av 6
n Number of catches
PE Potential irrigation efficiency
C, Average low quarter catch
D Average depth applied
3.
Du.. Low quarter distribution uniformity
DC Average depth caught
Du, Low catch distribution uniformity
C- Low catch
C Concentration
t Time
D Combined diffusion and hydrodynamic dispersion
q Water flow
z Depth
R Soil solution concentration for no precipitation or dissolution
K Coefficient of proportion
i Subscript of depth increments
xiv
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j Subscript of time increment
s Solid salt
Da Depth applied
mb millibars
AEG Change in electrical conductivity of the soil water
D Depth of root zone
6- Water content by volume at field capacity
H Lateral line inlet pressure
H Average line pressure
A
R Manifold headless adjustment
EL Change in elevation
IL Downstream end pressure
AH Headloss in line
m
Q. Average sprinkler discharge
CL Discharge of last sprinkler
Dm Distance in meters
EC(4P) Electrical conductivity measured with a 4-probe device
D Depth of effluent
EC ,. Electrical conductivity of the effluent
Dp Apparent diffusion coefficient
U Interstitial velocity
A Transfer coefficient
p Bulk density
6 Water content by volume
v
S Solid salts in milliequivalents per centimeter depth
K Coefficient of Proportion
C. Concentration of the influent water
ir
ECC Computed EC of the effluent
ECM Measured EC of the effluent
Deff Depth of effluent
T Total salinity
s
D Diffusivity at the surface z = 0
qo Water flux at the surface z = 0
C Concentration at the surface z = 0
o
C. Initial salinity
is
D Average absolute deviation from M
M Average depth of an ideal irrigation
xv
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ACKNOWLEDGMENTS
Research of the magnitude and importance of the work reported here is
obviously the work of more than two people. Many have contributed in ways
known and unknown.
Dr. L. G. King initiated the project and outlined the principle direction
of the research. Dr. Vaughn Hunsaker, Utah State University Extension
Service at Vernal, Utah, gave important assistance in many phases of the
work. Graduate students of the Agricultural and Irrigation Engineering
Department and the Department of Soil Science and Biometeorology of Utah
State University have contributed significantly. Those who have worked on
the project are: D. Melamed, R. D. Bliesner, S. Childs, T. Sullivan. Those
working as technicians were E. Smith and J. Wolf.
Dr. J. P. Law, Jr., of the U.S. Environmental Protection Agency, provided
valuable suggestions as well as support for the project. He was helpful
in all phases of the work, including review of the manuscript.
The Agricultural Experiment Station and other departments of Utah State
University contributed to the successful conduct of the work with direct
and indirect support.
xvi
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SECTION I
INTRODUCTION
Irrigation return flow is a large portion of the water influent to the
streams and rivers of the Colorado River Basin. It is well documented
(Utah State University, 1975) that salinity is the most serious water quality
problem in the Basin. Indications of the importance of the problem are
given in the report issued by U.S. Environmental Protection Agency (1971)
entitled "The Mineral Quality Problem in the Colorado River Basin." Further
indications are: the February, 1972 session of the Federal-State Enforcement
Conference on the Colorado River held in Las Vegas; discussions of Colorado
River salinity problems between the Presidents of the United States and
Mexico; the new Western Regional Research Project entitled "Salinity Manage-
ment of the Colorado River Basin" supported by the Agricultural Experiment
Stations of the seven Basin states and the Cooperative State Research Service;
and the National Conference on Managing Irrigated Agriculture to Improve
Water Quality. At the present time, it is estimated that damages due to
salinity amount to $230,000 per mg/1 measured at Imperial Dam (Utah State
University, 1975).
The consensus of present opinion is that salinity control in the Colorado
River Basin may be accomplished in part by improvement of irrigation and
drainage practices. Research work conducted by Utah State University (USU)
under EPA grants WP-01492-01 (N)l, 13030 FDJ, and S-801040 (King and Hanks,
1975) indicated that there was considerable promise for exercising control of
return flow quality by proper irrigation management. The basic premise under-
lying the total research effort in this direction has been that the soil
profile above the water table can be used as a salt storage reservoir.
Then, by proper management of irrigation, this salt may be held indefinitely
or may be released by leaching only when desired. Recent work using green-
house lysimeters at the United States Salinity Laboratory in Riverside,
California, supports the idea of salt storage and indicates that very small
leaching fractions can be used over extended periods of time without
adversely affecting yield of crops (van Schilfgaarde, et al., 1974). Leach-
ing percentages suggested may be as small as 1 to 3 percent. Previous work
at Utah State University, cited above, also suggests that under certain
conditions, salt may be precipitated and stored within the soil profile
indefinitely without significant adverse effects on farming operations.
Inasmuch as the theory and laboratory tests indicate a high probability of
success using irrigation management to control return flow quality and
quantity, field studies were established to assess the feasibility of
managing the soil profile as a salt storage reservoir. The research reported
here gives the results of the field assessment. The experiment was designed
to evaluate the importance of variables such as water table depth, salinity
contents of applied water, and leaching fractions. The degree of control
over irrigation applications necessary for management of the salt storage
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reservoir was evaluated. It was expected that techniques of irrigation
scheduling could be developed to enable such irrigation management on a
large scale. The data base developed extended improvement of mathematical
models that are required to be used for efficient soil profile salinity
management. The data collected and the models developed are to allow
extrapolation of results to other conditions encountered in other irrigated
areas. The research results should provide an improved basis for technical
and economic evaluation of implementation of large-scale irrigation manage-
ment for water quality improvement.
The field facilities for the research were provided by the previous research
grants (King and Hanks, 1975). Additions of necessary sprinkler pipe to
these research facilities were made for this study. Thus the research
benefited from the initial investment to the original field facilities toward
solving the problems of managing irrigation return flow quality.
A secondary benefit derived from the research was the training of scientific
personnel in agricultural water quality management. Not only did personnel
supported directly by the research grant benefit from the research experience,
the facility provided research opportunities for students on traineeships
under the EPA Training Grant #WP-213-05, T90060 with firsthand experience
in dealing with real water quality problems. The experience thus gained
provides trained personnel for entry into the water quality field or for
consultation on water quality problems in the future.
The technique recently developed for determining crop production functions
as influenced by soil-water and fertility levels were included in the
investigation. This technique was used to determine crop production functions
as influenced by soil-water and soil salinity levels. The technique is quite
simple and inexpensive. It yields important information not currently
available. Since crop production data can be converted to economic costs
and returns, this information is basic to an economic evaluation of the
effect of various salinity levels on crop production. Use of the production
function technique was suggested by the very successful experiment developed
to measure crop production functions as influenced by soil-water and
fertility levels conducted over the last two years under the direction of
R. J. Hanks and D. W. James of Utah State University (Bauder, Hanks and
James, 1975). This experiment employed the continuous variable method in
which a large number of water and fertility variables are employed in a
sequence. Adjacent plots are not sufficiently different from their neighbors
that borders are needed. Thus, the size of the plot is decreased from that
required in conventional experiments with a corresponding increase in the
number of variables that can be evaluated several times.
Inasmuch as field research on water management is expensive and time
consuming, it was important to include laboratory tests and computer modeling
components in the study. This balanced approach provides for maximum use-
fulness of the results obtained.
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OBJECTIVES
The general objective of the research project was to develop a guide for farm
management practices related to irrigation and drainage that could provide
a method to exercise control over the quality and quantity of irrigation
return flow. The concept of using the unsaturated zone above the water
table as a salt storage reservoir was to be tested. Control of the mineral
water quality (salinity) of subsurface irrigation return flows was the main
objective of this work.
The specific objectives were:
1. To monitor the movement of water and dissolved salts within the
soil profile above the water table, into the saturated zone, and
into the tile drain effluent, under field conditions.
2. To field test the feasibility of using the unsaturated zone as a
manageable salt reservoir under conditions of different salinity
contents of applied water, different water table depths, and
different leaching fractions.
3. To determine the effect on alfalfa yield of salt storage in the
unsaturated zone as influenced by the variables of Objective 2.
4. To develop suitable irrigation scheduling techniques for effecting
control over the quality and quantity of irrigation return flow.
5. To develop field methods for detection of soil water salinity
changes suitable for evaluating the effectiveness of different
irrigation management practices.
6. To further develop and verify management models (digital computer
models) for describing the movement of water and dissolved salts
in the soil profile to allow for extrapolation of the results
obtained from the research area to other areas.
7. To determine crop production functions of corn and alfalfa as
influenced by irrigation and salinity level.
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SECTION II
SUMMARY AND CONCLUSIONS
IRRIGATION CONTROL FOR LOW LEACHING FRACTIONS
It has been shown that it is possible to control irrigation closely enough
to maintain desired leaching fractions for small areas. However, the degree
of control necessary to accomplish this on a commercial scale may not be
feasible. For instance, this control was possible only by scheduling
irrigations from lysimeter readings, and then only for select small areas.
Also, for the system used on the water management plots, a variation of
only 5 minutes in irrigation time could change a 3 percent leaching percent-
age to 4 percent for a 3-inch application. This is a 33 percent variation
in leaching percentage, with only 1 percent variation in time.
EFFECT ON SALINITY PROFILES
For the particular conditions on the research farm, no significant change
occurred in the salinity profiles for any of the treatments except for the
lowest irrigation water quality treatment. For this treatment, the upper
zones of the root zone became more saline for all leaching fractions. The
expected change in root-zone salinity for each treatment as determined by
a salt balance calculation for the conditions of each treatment was not
achieved. The fact that the actual changes did not compare to the expected
changes is attributed to the composition of the salts in the soil. Both
the irrigation water and the soil solutions were high in calcium carbonate
and calcium sulfate which are relatively insoluble. From the plot leaching
trials, it appears that low leaching fractions cause precipitation of salts.
This leads to the assumption that, at least for short periods of time, soil
water salinity and drainage water salinity are relatively insensitive to
management changes in leaching fractions for this particular soil and
irrigation water.
IRRIGATION SCHEDULING AND MANAGEMENT
The Jensen irrigation scheduling computer program under-predicted ET for
alfalfa by 16 percent when compared to measured ET when using weather data
collected at the weather station in the alfalfa field. When temperatures
from the nearby Vernal airport reporting station were used, the program
still under-predicted ET by 8.6 percent. The program is calibrated for
standard U.S. Weather Bureau reporting stations, thus, the justification
for using Vernal airport data. This 8.6 percent error is considered to be
as accurate as could be expected since the ET actually measured between two
lysimeters varied 11.5 percent. The fact that the predicted ET for the two
weather stations varied by 7.4 percent indicates the problem of choosing a
single weather station location in an area and then extending the information
collected at that station to other areas.
-------
The technique of maintaining an irrigation interval of approximately 10 days
seemed to work well for minimizing upward flow of water from the water table
for the soils and depths to water table encountered in the study.
The irrigation scheduling and management techniques evaluated in this study
may be workable tools for controlling the salinity of the root zone and
thereby the salinity of the return flow only when combined with periodic
field checks. Both soil moisture and soil salinity checks should be made
periodically to evaluate the performance of the scheduling and management
techniques used. Both checks should be done in areas that are expected to
receive the least irrigation water. Determination of this location would
require a detailed system evaluation.
SPRINKLER SYSTEM EVALUATION
The solid set sprinkler irrigation system evaluated had a very high uniformity
and efficiency when evaluated over a full season. As expected, compositing
the uniformity measurements over the season improved both uniformity and
efficiency over that measured for a single irrigation. Alternate setting
of the sprinkler lines every other irrigation also improved performance.
Under the best management conditions, the minimum average leaching percentage
that the irrigation system was capable of accomplishing without under-
irrigating some portion of the field was 10.3 percent. This leaching
fraction must be added to any desired design leaching fraction to get the
average leaching fraction necessary for the field in order to maintain the
desired minimum leaching fraction on the least watered area. For the solid-
set sprinkler system used on the farm, operated on an alternate set basis,
the minimum leaching fraction attainable, without actually under-irrigating
some area, is 10.3 percent. If some leaching fraction is maintained over
all the area, 3 percent for example, then the minimum average leaching
fraction required for the farm would be 13.3 percent.
The irrigation system tested was definitely above average in performance. It
is expected that most systems would not be capable of this level of
uniformity and control.
In general, it is unrealistic to expect to maintain average leaching
percentages of less than 10 to 15 percent on a field scale under sprinkler
irrigation. However, it is possible to maintain leaching percentages lower
than normally attained in irrigated agriculture. If it is assumed that a
previously recommended leaching percentage of 10 percent can now be reduced
to 3 percent as the literature suggests, then this would result in a water
savings equal to the difference in the two leaching percentages. This is
due to the fact that the same conditions apply to the 10 percent leaching
as to the 3 percent leaching. In other words, any minimum leaching fraction
used must be applied to the least watered area, so reducing the minimum
leaching requirement from 10 percent to 3 percent would result in a
reduced average field leaching fraction no matter what the system
uniformity.
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LEACHING MODEL SIMULATION
Using a mathematical model that considers parameters relating the soil,
water, plant and atmospheric system, and using the assumption that there is
a direct relation between dry matter production and transpiration, the
salinity effects due to osmotic potential were determined. The influence of
initial soil salinity on model-predicted crop growth depended on root zone
depth and irrigation management. Predictions made of the localized salt
buildup in the root zone over a period of several years showed that some
water management systems would produce high yields for several years before
salt buildup would begin to decrease yields. Predictions verified that the
influence of irrigation water application uniformity on salt buildup and
yield reduction is significant.
Leaching trials conducted in the field and in the laboratory to study salt
transport phenomena showed that when only mixing and displacement processes
were accounted for, lower than expected salt concentrations of the soil
solution were obtained when the influent was more concentrated than the
original soil solution. When the influent was less concentrated than the
soil solution, the measured concentration of the soil solution was higher
than the expected. From the experimental results, it was concluded that
incorporating the process of sink and source in a mathematical model that
simulates salt movement could improve the predictive capabilities of the
model. In determining the nature and effect of a "source-sink" term
required in the model, several assumptions were made. It was assumed that
the dissolution and precipitation played the main role in the source-sink
processes. It was assumed that the rate of the process was directly
proportional to the difference between the concentration of the surrounding
solution and some concentration for which the rate was zero. And, it was
assumed that the process was independent of the solid salt quantity as long
as it existed in the soil unit.
The model was tested against laboratory data for steady and transient flow
conditions. The model simulated the data reasonably well. The poorest
agreement between the simulated and the measured curves occurred at the
beginning of the leaching process.
The model was also tested against field data and was then integrated into a
water flow model that provided water flow data. Two wetting cycles which
differed in depth of water applied and irrigation water salt concentration
were simulated in different ways. If the source-sink term was ignored,
the model overestimated the salinity in the case where the electrical
conductivity of the irrigation water (EC. ) was greater than the initial
EC of the soil solution. When the EC. was smaller than the initial EC
of the soil solution, the salinity was underestimated.
When a source-sink term with uniform parameters for each profile depth
increment was included, the average salinity of the profile was correct
but the distribution from the average was still poor. When individually
calibrated source-sink coefficients for each soil layer were fitted and
assigned to each layer, a good simulation was achieved.
-------
Thus the addition of a "source-sink" term to the model improved simulation
of salt movement in the soil. The modification of the model has produced
data which suggests that the level of prediction accuracy is approaching
that useful for efficient irrigation management at the field level.
Coefficients for the "source-sink" term can be easily obtained from field
soil samples.
INFLUENCE OF SOIL SALINITY ON YIELD
The influence of salinity levels on the yield of corn showed an approximate
linear decrease as salinity levels increased in studies at Logan and
Vernal, Utah. Studies at Logan showed that the decrease was essentially
due to the osmotic effect of the salt. The yield decreased because there
was less water available for plant uptake and thus less soil water depletion.
The results from Vernal showed the same trends but were confused by water
flow upward from the water table in an unknown amount.
There was no influence of different applied salinity levels on the yield for
alfalfa at Vernal, Utah, in 1975 studies. This was apparently due to
presence of alfalfa roots below the zone of salinized soil. Thus, if plant
roots are in salty and non-salty soil, and there is adequate water in the
non-salty zone, it appears that there will be no yield reduction.
The influences of salinity on yield measured in the field can be accounted
for with the model developed (Childs and Hanks, 1975). The model incorporated
soil, water, plant and atmospheric influences to predict relative crop yield.
Yield predictions assumed a direct relation between dry matter production
and transpiration. The only salinity effect considered was osmotic
potential. The influence of initial soil salinity on yield depended on
crop type and irrigation management. Predictions made of salt buildup showed
that some management systems would produce high yields for several years
before salt accumulations would decrease yields. Predictions also showed
that irrigation system uniformity could influence salt buildup and, therefore,
yields.
-------
SECTION III
RECOMMENDATIONS
1. Studies need to be made to determine the soil and water chemical
properties that cause precipitation of salts to occur. There
apparently are soil and irrigation water quality situations, like those
found on the Vernal farm, where the buffering capacity of the soil
causes leaching to be unnecessary for many years. Salts are apparently
precipitated out, regardless of the leaching fraction, in a way which
leaves the soil solution concentration relatively unchanged. This
process can undoubtedly continue on for many years because it has been
going on under irrigated conditions for many years. Thus it is
apparent, under these conditions, that salts can be stored in the soil
for many years before leaching is needed. Until that time, salts in
the return flow are simply the product of the volume of drainage water
and the soil solution concentration just above the drains. Studies are
needed to determine how widespread the occurrence is in the Colorado
River Basin.
2. Improved mathematical models need to be developed to incorporate the
complex soil chemistry effects controlling source-sink behavior of
soils so that accurate predictions can be made of the long-term effects
of water management for control of the quality of return flow.
3. The use of an average leaching fraction below about 10 percent does not
appear attainable under commercial sprinkler irrigation management
practice. It would seem more practical to irrigate so that no leaching
occurs for several seasons while monitoring the soil salinity status,
and then leach periodically. This procedure would minimize salt flow
to irrigation return flow during periods of low stream flow.
4. The model developed by Childs and Hanks (1975) to predict the influence
of salinity on yield has been verified in the field. It will account
for the osmotic effect of the salt, irrigation variations and water
flow upward from a water table. It is recommended that this type of
model be used to evaluate irrigation management systems as they relate
to irrigation return flow and yield interactions. It is very important
that these evaluations be done over several years' time because salinity
buildup in a single year may not be significant.
5. It is suggested, based on model predictions, that it will be difficult
to attain a low leaching fraction on a field basis where normal variation
in applied irrigation water is encountered. It appears very reasonable
to use the soil profile for storing salts for one to several years and
to periodically leach the root zone.
6. The combined influence of salinity and water stress on yield can be
accounted for by considering the combined osmotic and matric potential
of the soil solution. Yield prediction is difficult where there is a
8
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water table near the root zone that may have an influence on water
availability. When there is a salinity variation in the plant root
zone, it is recommended that root extraction be predicted based on
the least saline zone (or the zone having the highest sum of matric and
osmotic potential). Additional research is needed to determine the
limits of partial root zone salination that can be tolerated by plants.
-------
SECTION IV
METHODS
WATER MANAGEMENT EXPERIMENTS
The field research was conducted on a 53-acre (21 ha.) farm in the Ashley
Valley near Vernal, Utah. The location of the farm relative to the city of
Vernal is shown in Figure 1. The farm was operated by Utah State University
as a research facility.
For the purposes of the water management study, twenty-seven plots were
located in an established alfalfa field, Figure 2. The plots and treatments
were selected to provide three depths to water table, three irrigation
water qualities and three leaching fractions. Locations of other field
experiments related to the interrelationship of soil salinity and water
application are also shown in Figure 2.
Water Table Depths
The locations of the water management plots were such that nine plots lay in
an area where the water table was 5 feet (1.5 m) or more below the surface,
nine plots were where the water table was 7 feet (2.1 m) or more deep and
nine plots were where the water table was 9 feet (2.7 m) or more below the
surface. The locations of the particular areas of these depths to water table
were determined from averages of piezometer data collected previously in 1971,
1972 and 1973.
Irrigation Water Qualities
The three different qualities of irrigation water used were available at the
farm. The best quality water, about 0.8 mmho/cm, was delivered from the
valley irrigation system into a small storage pond. The middle water quality,
about 2 mmho/cm, was available from the Naples Drain. The low quality water,
about 2.7 mmho/cm, was available from the South Tributary to the Naples Drain
(see Figure 2). The actual salinity of the water sources varied with the
area hydrologic conditions. Intermittent flow conditions made it necessary
to construct a small storage reservoir on the South Tributary to the Naples
Drain. Water was released from the reservoir as needed to supply the
irrigation system pump during the required irrigation treatments.
Water was delivered to all the plots by sprinklers. Water for the 0.8 mmho/cm
plots was obtained from the main irrigation supply and was delivered by the
same electric motor driven irrigation pump used to irrigate the non-plot area
of the farm. The water from the Naples Drain and the South Tributary was
10
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O 1/2 I Mile
Figure 1. Location map, Hullinger NORTH
Farm, Vernal, Utah.
11
STUDY
AREA
-------
-d
H
13
S
INSTRUMENT TRAIL
PIEZOMETER LOCA
GATE
LYStMETER
PUMP
DRAIN LINES
SPRINKLER MAIN I
FENCE
GROUND SURFACE
MANHOLES
OBSERVATION HOL
ANEMOMETER
(3 LYSIMETER
© PYRAMOMETER
ACCESS ROADS
HULLINGER FARM
-------
delivered through a separate sprinkler system by a gasoline engine driven
pump located at the junction of the two streams. The pump was so situated
that^the suction line could he transferred from one stream to the other to
obtain the two different low quality waters without moving the pump.
Leaching Fractions
Three leaching fraction treatments were included in the experiment: zero
leaching, and the calculated leaching fractions that would result in
electrical conductivities at the bottom of the root zone of 8 and 25 mmhos/cm.
The irrigation water applications were then determined from evapotranspira-
tion and the desired treatment electrical conductivity of the drainage water
by computing the required different leaching fractions for each water
quality. The first treatment leaching fraction was zero, so the water
applied was equal to evapotranspiration. The second and third treatment
leaching fractions were calculated from an equation of the U.S. Salinity
Laboratory (6) :
EC
~ EC~
dw
where,
ECiw = electrical conductivity of irrigation water
ECdw ~ e^ectrical conductivity of drainage water
LF = leaching fraction.
Table 1 gives the design EC, , EC. and the corresponding leaching fractions
used in the study. 1W
TABLE 1. DESIGN LEACHING FRACTIONS FOR WATER MANAGEMENT STUDIES
_, . Design EC,
Design dw
EC. * 25 mmho/cm 8 mmho/cm
iw
Leaching Fractions
.8
2.0
2.7
0
0
0
.03
.08
.11
.10
.25
.34
Designed for zero leaching, i.e., water application equal to evapo-
transpiration.
13
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Plot Design
The water management plots were identified by a numeral, a letter, and a
numeral. The first numeral indicated the depth to water table, 5, 7, or
9 ft (1.5, 2.1, or 2.7 m). The letter designated the water supply, using A
for Ashley Creek (main water supply), N for Naples Drain, and S for South
Tributary. The second numeral indicated the leaching fraction, 1 for zero
leaching fraction, 2 for the middle leaching fraction and 3 for the high
leaching fraction. For example, the plot for 5 ft (1.5 m) water table,
best water quality, and lowest leaching fraction was designated 5A1.
Each plot was 25 ft x 30 ft (7.6 m x 9.1 m) with an area in the center of
the plot 12 ft x 15 ft (3.7 m x 4.6 m) used for the study area (Figure 3).
The instrumentation was placed along one edge of the study area so the
instruments could be read without disturbing the crop in the study area.
Each plot included the following equipment: (i) a piezometer to measure the
depth to water table, (ii) a 2-in. (5 cm) diameter PVC neutron probe access
tube with a ceramic cup sampler on the end set below the ground water depth
to provide for both measurement of soil moisture content and sampling of the
ground water, (iii) two tensiometers, one at 4 ft (1.2 m) and one at 5 ft
(1.5 m) below the ground surface to determine the direction of vertical
ground water flow, and (iv) soil solution samplers placed at 1 ft (0.3 m)
intervals from 1 1/2 ft (0.46 m) below the ground surface to within 1 1/2
ft (0.46 m) of the expected high water table (5, 7, or 9 ft [1.5, 2.1, or
2.7 m]). Six of the zero leaching plots were equipped with barrel lysimeters
as an added check on leaching fraction estimates.
ET Prediction and Scheduling
For eyapotranspiration prediction and for an irrigation scheduling evaluation
following the first irrigation season, climatological data from the on-farm
weather station were analyzed using a computer program. The results obtained
were compared to the measured evapotranspiration from the two hydraulic
weighing lysimeters located in the field. The lysimeters used were the
hydraulic weighing type described by Hanks and Shawcroft (1965). They were
each 4 ft x 4 ft x 4 ft (1.22 x 1.22 x 1.22 m) fiberglass boxes with neutron
access tubes and ceramic candle drains (see Figure 2 for location). The
on-farm weather station consisted of an Eppley pyranometer connected to
a strip chart recorder and integrator to give instantaneous intensity and
integrated incoming solar radiation, a Class-A Weather Bureau evaporation
pan, anemometers at two meters and pan height, a non-weighing rain gauge,
maximum and minimum thermometers, wet and dry bulb thermometers, and a
hygrothermograph for recording air temperature and relative humidity (see
Figure 2 for location).
The computer program used was a modification of the program developed by
Jensen (1969) and Jensen et al. (1970). The Jensen program was designed for
irrigation scheduling and does not allow for the over-irrigation necessary
to obtain controlled leaching fractions. The Jensen program assumes that
the soil moisture depleted is exactly replaced. In order to extend this
program for use in salinity control, it was necessary to make modifications
14
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TYPICAL PLOT LAYOUT - 7AI
SPRINKLER
SPRINKLER
\
\
\
\
\
\
AREA CUT
FOR YIELD
O
BARREL
LYSIMETER
\
\
\
Neutron Access
o Tube
• Piezometer
• 4' Tensiometer
• 51 Tensiometer
• 18" Sampler
• 3O" Sampler
• 42" Sampler
• 54" Sampler
• 66" Sampler
/
\
SPRINKLER
SCALE
O
5'
Figure 3. Typical plot layout.
15
SPRINKLER
-------
to account for over- and under-irrigation as well as to provide for including
leaching fractions in the calculation of irrigation water required.
Since this study was conducted with sprinkler irrigation only, the program
was changed to have a constant gross depth of water applied per irrigation
as determined by the irrigation programmer. This allowed the frequency of
irrigation to be varied without changing the gross depth applied. In the
computer program, the leaching fraction for each field was determined by the
programmer. To account for actual over- and under-irrigation, it would be
necessary for a farmer to relay to the programmer the actual depth of
irrigation applied. If the field was over irrigated, the leaching fraction
in the program was set to zero until the planned accumulated seasonal
leaching fraction was reached. Then the leaching fraction was returned to
the predetermined scheduling value. Reducing the program leaching fraction,
changed the allowable depletion, allowing the same gross depth to be applied.
This, in effect, lengthened the irrigation interval. If the field was under-
irrigated during any irrigation, the leaching fraction value in the program
was increased to bring the field into balance by the next irrigation. This
change, in effect, decreased the allowable depletion and decreased the
irrigation interval.
For expediency in this comparison study, the disc storage feature of Jensen's
program was taken out. For practical application it would be desirable to
restore the disc storage included in Jensen's original program. The program
used here, and a brief explanation of its operation is given in Appendix A.
Irrigation Practice
Each plot was irrigated by four #20 Hi-Lo Rainbird sprinklers with 5/32 in.
(3.97 mm) nozzles located on each corner of the plot. The nozzle angles
were adjusted to keep the water for one plot from reaching the study area
of the next plot. The study area of each plot received water over its entire
area from all four sprinklers. The sprinklers of the plots for a single
water quality were all connected to the same line and operated simultaneously.
At the base of each sprinkler was a gate valve. To allow for different leach-
ing fractions and different sprinkler pressures, each plot had to be
irrigated for a different length of time. All plots for a given water
quality were started at the same time. As the lower leaching fraction
plots received their water, the valves on those sprinklers were closed and
the system continued to operate until all other plots received the proper
amount of water. A single can-catch area was set up on a sample plot (see
Figure 2 for location), and uniformity data were collected during each
irrigation to determine the relationship between sprinkler pressure and
application rate. Each irrigation was based on the previous can-catch
information and then reevaluated for the can-catch taken during the
irrigation. Irrigation time was controlled to the nearest minute.
The irrigation scheduling for the plots was done by using the hydraulic
weighing lysimeters during the first year and by using pan evaporation the
second year. The allowable soil depletion was kept as near as possible to
16
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3.00 inches (76.2 mm) as indicated by the lysimeters. The non-weighing
barrel lysimeters in the plots, measured with a neutron meter, were used
as a check for under- or over-irrigation.
All plots were initially irrigated in the spring with the regular solid-set
system used on the remainder of the field. At this time, the profile moisture
content was assumed to be at field capacity, and the scheduling program for
the treatment plots began.
System Evaluation
To provide for extension of the experience of the plot irrigations to field
use, a separate can-catch was set up for the solid-set sprinkler irrigation
system used on the farm. The farm system uses #30 WS TNT Rainbird sprinklers
with 9/64 in. (3.57 mm) nozzles on a 30 ft x 50 ft (9.1 x 15.2 m) spacing.
Four adjacent lines were operated at one time. A can-catch with a 5 ft
(1.52 m) grid was set up in a clipped area of the alfalfa field (see Figure 2
for location). The depth of catch, wind movement, line pressure, sprinkler
discharge, and evaporation were measured for each irrigation throughout the
season. From this information, a precise field system evaluation was
possible.
Salinity Monitoring
To monitor the change in salinity of the soil water and relate this change to
the irrigation practice on the plots, salinity monitoring of both the
irrigation water and the soil water was necessary. To effectively monitor
the irrigation water, automatic water samplers were used during each irrigation.
A sampler was mounted adjacent to the pump intake in each water supply. Prior
to each irrigation, the samplers were charged by evacuating the sample bottles
and closing the valves to hold the vacuum. During the irrigation, a clock
tripped the valves at 2-hour intervals to give an even distribution of water
samples over the period. Following each irrigation, the samples were removed
and the electrical conductivity measured. One complete specific ion analysis
was done on each irrigation supply during the season.
Following each irrigation, the soil solution samplers in the plots were
evacuated with a hand pump and the vacuum was held over-night. The following
day the samples in the tubes were collected and the electrical conductivities
measured. The samples were taken at about the same time following each
irrigation so the soils would be approximately the same moisture content at
the time of sampling. As a check on the variation in moisture content,
neutron probe readings were taken in the plots at the time of sampling. Once
at the beginning of the season and once at the end of the season a complete
specific ion analyses was done on the soil solution samples to determine
the change in concentration of specific ions from the irrigation water to
the soil water and from the beginning to the end of the season.
17
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Soil Water Movement
To monitor the direction of vertical soil water movement in the unsaturated
zone, the 4 ft (1.2 m) and 5 ft (1.5 m) tensiometers were read daily. From
the tensiometer readings, the average hydraulic gradient between the two
depths was calculated using the following relationship
AP_ Z2+M2- (Zl +MX)
AZ Z2 - Zi
Where
AP
— = hydraulic gradient
L\b
Zi = depth of shallow tensiometer (cm)
Z2 = depth of deep tensiometer (cm)
M} = matric suction reading of shallow tensiometer (cm of water)
M£ = matric suction reading of deep tensiometer (cm of water)
-r-=- is positive when flow is downward
t\£-i
An attempt was made to maintain a downward gradient during the full season to
prevent salt distribution in the soil profile by upward flow. Quantitative
analysis of soil water flows would have required an accurate determination of
the soil matric potential-hydraulic conductivity relationship for field
conditions. This was not done since an accurate relationship would be very
difficult to obtain under the heterogeneous soil conditions in the study
area.
Piezometer readings were taken every other day to determine fluctuations in
ground water levels throughout the season. These readings were necessary to
determine the relationship between ground water depth and direction of soil
water flow below the bottom of the root zone.
Actual Leaching Fractions
Since the distribution of water over the plot was not uniform, areas of
interest in the plots did not necessarily receive a uniform amount of water.
The two main areas of interest were the barrel lysimeter location and the
soil solution extractor location in each plot. Irrigations on these exact
locations were represented by averaging the water collected in the cans in
the can-catch area which were in the same relative position as were the
areas of interest in the plots. By this method, the seasonal water received
by the lysimeter area and the sampling area of each plot was estimated.
The barrel lysimeters were used to estimate the actual leaching fraction
for the plots the depth of drainage water collected in the lysimeter and
the change in moisture content for each lysimeter was measured for the full
season. By subtracting the depth of drainage and the change in moisture
content (positive being an increase in moisture content) from the depth of
water applied to the lysimeter, the ET for the plot was found. Since the
sampling area was the area of interest for estimating the salt distribution-
leaching fraction relationship, the ET for the plot was subtracted from the
18
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amount of water applied to the sampling area to arrive at the drainage water
for the sampling area. The actual leaching fraction was determined from
the following relationship:
T-C- dw
LF = r—• [3]
Diw
where
LF = leaching fraction
DJ = depth of drainage water
D. = depth of irrigation water
Since every plot did not contain a barrel lysimeter, the average ET for all
plots was taken to be the same as the average of all the barrel lysimeter
plots.
ET Prediction Analysis
For the purpose of measuring and predicting ET, climatological data and
lysimeter data were collected daily throughout the growing season. However,
when comparing lysimeter ET with pan evaporation and predicted ET from the
irrigation scheduling program, cumulative values were used. As the lysimeter
weight changes, the lysimeter moves up and down only slightly. As it moves,
some binding and sticking occurs, resulting in reading fluctuations.. The
cumulative values tend to smooth out the fluctuations, since on a cumulative
basis, a balance is maintained.
Irrigation System Uniformity Analysis
To relate the water application uniformity results obtained on the individual
plots to actual field situations, an analysis of the performance of the
standard sprinkler irrigation system was necessary as previously described.
In order to evaluate the results of this analysis fully, several alternative
procedures were examined .
First, the distribution uniformity of the system for each irrigation was
estimated by Christiansen's coefficient of uniformity
100 < 1
av
where
Cu = uniformity coefficient
ED = sum of the absolute values of the deviations of the catches from
the average catch
19
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C = average catch
av 6
n = number of catches
In addition to Cu, several other distribution calculations were made which
included: potential irrigation efficiency, PE
C
PE = jg=3- x 100 , [5]
a
where
PE = potential efficiency
C1 = average low 1/4 catch
D = average depth applied
and, low 1/4 distribution uniformity, Du..
Cl
Dul = D X 10° ^
where
Du- = low 1/4 distribution uniformity
D = average depth caught
and, minimum distribution uniformity, Du,
Dulc ""D1 X 10° [?]
c
where
Du- = low catch distribution uniformity
C1 = low catch
D = average depth caught
Second, the individual catches were progressively composited over the season
to examine the "averaging" effect of long-term application. These composites
were then evaluated by the same method as the individual tests.
Third, the catch data were theoretically superimposed to simulate an alternate
setting pattern every other irrigation and were then composited. Alternate
setting amounts to placing the sprinkler lines for each alternate irrigation
midway between their position for the previous irrigation. Again, the
evaluation was done as for the individual tests. These three sets of results
were then compared to determine what improvement might be expected by follow-
ing these management alternatives.
20
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Crop Production as Influenced by Soil Salinity
The field work on crop production and soil salinity levels was conducted on
the Hullinger experimental farm near Vernal, Utah. The farm was described in
detail by King and Hanks (1973). A continuous variable plot design (Figure 4)
replicated four times was established early in the spring of 1974. Each
replication measured 50 x 100 feet (15.24 x 30.84 m) and included 10 salt
treatments 10 feet (3.05 m) wide by 50 feet (15.24 m) long and 20 water
treatments each 2.5 feet (0.76 m) wide by 100 feet (30.48 m) long. A single
row of corn constituted a plot for a water treatment. Irrigation was
accomplished approximately every 10 days through a line source sprinkler
system as described by Hanks, Keller and Bauder (1974).
Corn (Utah hybrid 330) was first planted on May 22, but because of poor
germination was replanted on June 13, 1974. The second planting, oriented
about 6 inches (15 cm) to the side of the first, helped to increase the
stand. After the two plantings, the corn was thinned to about 21,800
plants/acre (53,800 plants/ha.).
In 1974, CaCl2 salt was applied with a 10-foot (3.0 m) wide fertilizer
spreader pulled behind a tractor. The quantity of salt applied was
determined by the osmotic potential desired for each treatment (Table 2). The
spreader was calibrated to apply 3.4 pounds per acre (3.9 Kg/ha.) of salt,
the amount required to obtain the osmotic potential of the first salt
application treatment (S2). Each salt level thereafter required only
additional passes by the tractor and spreader to obtain the desired salt
application.
Initial intentions were to apply the salt in four applications and to disc
the ground and irrigate to wet the soil to field capacity to a depth of six
inches (15.2 cm) . This procedure was designed to produce a uniform salinity
in the top two feet (0.6 m) of soil. After the first application of water,
it became obvious that it was impractical to add more water, get the
tractor across the plots without getting stuck, and still complete the salt
application in the time allowed. The following day the wet soil was disced
and a second application of salt applied. The soil was then disced again,
and with the spreader recalibrated for heavier application, the remaining
salt was applied. Only after all the salt had been applied was the remaining
water applied.
The water variable was obtained using Rainbird #30 sprinkler heads with a
three-sixteenths inch (.48 cm) front nozzle and a three-thirty-second inch
(.24 cm) rear nozzle with a 7 degree spreader slit. Two parallel irrigation
lines consisted of 30 foot (9.14 m) sections of 3-inch (7.6 cm) aluminum
irrigation pipe that were placed side-by-side to position a sprinkler every
15 feet (4.57 m) . The high water treatment next to the water line was
designed to receive about 1.5 times eva—transpiration, ET. ET was estimated
from the two hydraulic weighing lysimeters located near the plot area
(Figure 2) that were planted to alfalfc.
To determine the quantity of water being applied, a series of nine funnels
mounted every 6 feet (1.8 m) on 3 inch (7.6 cm) aluminum irrigation pipe were
21
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DECREASING
DECREASING
WATER
WATER
1 1 1 1 1 1 III III lit
RER 4
„-_.__.-...-_„.
. -_ ~ _- _ _ - ___ -.
• • •• •
— — - m
• Z
GC
• • ••[_!
--^
. » 1
0 •
•
_ __..______ __k - _ A. 1 1 1
• • D Z
• • • •
•_• -MM f^
•• D UJ
^ h
-•— Y — , — -f. — .... — __ — _ — -f--i
RER 3 r
m
i i I i i i i i i f i i MI
i i i i i i i i ± i i I i i i 1 i ^
• •
• REP. 1
A "i A -A A A A — A
• • • •
• D • •
_______ _§____«______
DO*
•
D •
•
~ •
• • •
• «D • •• •
°
A A" A — 1C f~~TC~~S~"'f
J REP. 2 •
•
^
1 1 1 1 1 1 1 1 1 | 1 1 1 M 1 1 •
SIO
S 9
S 8
S 7
S 6
S 5
S 4
S 3
S 2
S 1
SIO
S 9
S 8
S 7
S 6
S 5
S 4
S 3
S 2
S 1
b
<
CO
(D
Z
CO
<
LU
IT
O
Z
b
<
00
O
Z
00
<
LU
£
O
Z
17 13 9 5 II 5 9 13 17
IRRIGATION LEVEL IRRIGATION LEVEL
D THERMOCOUPLE PSYCHROMETER
• SALINITY SENSOR AND PSYCHROMETER
• ACCESS TUBE
A FUNNEL PLACEMENT
Figure 4. Continuous variable plot layout and instrumentation.
22
-------
TABLE 2. OSMOTIC POTENTIAL OF THE SOIL SOLUTION AS A FUNCTION OF SALT
LEVEL, DESIRED AND OBTAINED. (SOIL SAMPLES TAKEN 6-11-74)
ca;it Osmotic Potential
level DesiredObtained
(bars) (bars)
Depth-inches
0-6 6-12 12-18 18-24 24-36 36-48
SI
S2
S3
S4
S5
S6
S7
S8
S9
S10
0.0
- 0.5
- 1.0
- 1.5
- 2.0
- 2.5
- 3.0
- 5.0
- 7.0
- 9.0
- 0.8
- 0.8
- 1.3
- 1.4
- 1.1
- 1.4
- 1.9
- 4.8
- 6.3
-11.8
- 0.9
- 1.0
- 2.9
- 2.8
- 2.5
- 3.3
- 3.2
- 6.0
- 5.6
-12.8
- 0.8
- 1.5
- 2.1
- 2.7
- 3.5
- 4.2
- 5.0
- 4.5
- 7.5
- 5.2
- 0.6
- 1.7
- 1.5
- 1.4
- 1.9
- 2.2
- 2.0
- 2.1
- 2.8
- 2.4
- 1.1
- 1.2
- 1.5
- 1.3
- 1.7
- 1.4
- 1.4
- 1.1
- 1.5
- 1.7
- 1.3
- 1.2
- 1.6
- 1.3
- 1.5
- 2.3
- 1.2
- 1.2
- 1.4
- 1.6
used. A 4 inch (10.2 cm) diameter aluminum funnel was fitted snugly into the
nine-sixteenth inch (1.4 cm) hole drilled into the funnel line pipe. To
each funnel was connected a section of polyethylene tubing one-fourth inch
(0.6 cm) inside diameter that ran through the length of the funnel line pipe
and out of the plot area. The end of each tube was then connected to a 500
ml glass jar for collecting the water from each individual funnel. A number
11 rubber stopper with two 3-inch (7.6 cm) pieces of copper tubing mounted
through it provided for air relief and the connection of the tubing to the
jar. A slight slope on the funnel line pipe allowed most of the collected
water to drain into the jars. It was necessary to use a hand vacuum
system to extract all the water from the tubing for final measurement. The
funnel line pipe was fastened to four lengths of three-quarter inch (1.9 cm)
steel pipe driven vertically into the ground. This arrangement allowed the
funnel precipitation collection system to be raised as the corn grew. The
tops of the funnels were kept at the top of the corn. The funnel system
allowed an accurate measure of the water being applied without entering
the plot area. After an initial priming essentially all of the sprinkler
water entering the funnel collection system could be extracted with the hand
vacuum system.
In 1975 corn was again grown on the same plot with no additional salt added to
determine if there were residual effects. Irrigation water was added in the
same way as in 1974.
23
-------
A similar study, applying various amounts of salt and water was also
initiated in Vernal in 1975 on established alfalfa. Because of the
difficulties encountered in applying the dry CaCl2 in 1974, in 1975 salt was
added as a solution with controlled concentrations. The treatments were
established after the first crop of alfalfa was harvested in June. The
number of treatments was reduced to 6 with equally spaced concentrations
ranging from the normal irrigation water check to a solution with EC of
10 mmhos/cm (about -3.6 bars). It was originally intended to put on the
water with a trickle irrigation system but the emitters clogged. A wooden
dike was built to assist in a ponding type application. The infiltration
rate of this soil was very high so the wooden dikes worked quite well. A
line sprinkler water application system was then used during the season to
apply irrigation water to the plots. About 10.2 inches (26 cm) of water
was added to each plot in the salt application treatments which wetted the
soil to about 35 inches (90 cm) depth.
A salinity-water application study was also established on corn at the Utah
State University Greenville farm at Logan, Utah, in 1975. This was done to
obtain a soil situation without a shallow water table that could supply
water to the plant roots. The design of this experiment was similar to the
alfalfa study at Vernal with 6 equal interval salinity treatments. The only
difference between the two new 1975 experiments was that a mixture of salts
was added to the soil at Logan whereas only CaCl^ was used at Vernal. The
mixture of salts used at Logan was chosen to approximate Colorado River water
and was CaCl2 (12 g), NaCl (6.2 g), Na2SC>4 (3.9 g), and MgS04 . 7 H20 (7.8 g).
This mixture in 1 gallon (3785 cc) of water gave the highest treatment salt
level of about 10 mmhos/cm (- 3.6 bars). In Logan about one foot (30 cm)
of water was added to the soil with the salt application in mid-May. The
water was applied using the trickle system. Water application took several
days because of clogging of the emitters.
WATER MANAGEMENT MODEL WITH A SOURCE-SINK TERM
A field type water management model was developed in this study which deals
with the simultaneous movement of water and salt in the soil. Other available
models that consider only mixing and displacement processes during leaching
usually overestimate the change in salinity of the soil solution for non-
leached soils. Experience at several field locations (King and Hanks, 1975)
showed that when water having either a higher or lower salt concentration
than the soil solution was added to the soil, the resultant soil solution
was changed very little. There appears to be a characteristic soil solution
concentration that is unique to each section of the soil profile. The
concentration in the soil solution is relatively unchanged when water of
different concentrations is added. It was concluded that, in general,
soil has a high "buffering capacity" that can be explained by dissolution
and precipitation processes and that these processes would have to be accounted
for in any model. The necessity of using a source-sink term in models of
simultaneous salt and water movement in soil was suggested by Bresler and
Hanks (1969) and by Childs (1975). Attempts to improve the predictions by
including a "source-sink" term that used principles of solubility products
and equilibrium exchange did not yield much improvement (Gupta, 1972). The
24
-------
use of the Gupta (1972) analysis considering primarily precipitation and
solution of gypsum and calcite was insufficient to explain the field results.
Apparently some mixture of impure magnesium and calcium sulfates was also
involved. The chemistry of the soil solution is so undefined that it was
decided that it could be best handled in the model by using a very general
source-sink term. It was also decided that the dynamics of the process
rather than equilibrium states should be accounted for. Combining mass
flow and the diffusive flow and including a sink and source term gives a
defining equation:
f = i[Df ] -i(qO+fn(C, z, t) [8]
where C is solution concentration, D is the combined diffusion and hydro-
dynamic dispersion, q is waterflux, z is depth, t is time and fn(C, z, t)
is a function that represents sink and source due to ion exchange, precipi-
tation and dissolution, adsorption, and changes due to chemical and
biological activities.
In choosing the source-sink term, assumptions were made as follows:
1. The most important source-sink process is dissolution and
precipitation.
2. The ion exchange process is of minor importance because the model
treats the salt movement in terms of total salinity rather than
in terms of the individual ion species; and the ion exchange
capacity is relatively unaffected by salinity levels in this
study.
3. Chemical changes due to biological activities are quantitatively
small and considered negligible.
4. There is a characteristic concentration of the soil solution (R)
at which there is no precipitation and no dissolution. This
concentration is the saturation concentration relative to a given
salt specie in mixtures of salt species. If (C) is greater than
(R), precipitation will occur. If (R) is greater than the
concentration of the soil solution (C), dissolution will occur
as long as excess soluble salt in a solid state exists in the
soil.
5. The specific surface of the solid phase compounds involved as a
source or sink of salt is invariant.
6. The rate of the solution-dissolution process is directly proportional
to the degree of departure from the equilibrium state.
As a result of the above assumptions, the term for fn(C,z,t) of equation [11]
was assumed to be:
fn(C, z, t) = K(R - C) [93
25
-------
Where K is a coefficient of proportion related to the composite soil properties
and salt composition. Substituting this term in equation [9] into the
equation [8] of Warrick et al. (1971) gives:
= _ _ _ + K(R _ c) [10]
3 t 8z (Q, q) 9z ' 9z
where 9 is the volumetric water content (fraction) and K = 0 when there are
no solid phase salts present, i.e., there is no "source."
Note that when R < C the source-sink term is negative indicating precipitation.
When R > C the term is positive, indicating dissolution.
The second order numerical approximation of the terms of [10] for non-equal
space increments, using the Crank-Nicolson method (which reduces the 0(At)
term to 0[(At)2]), (see Carnahan et al., 1967) is as follows:
j+i • 9"
I - CJ^ (-AD) + CJ+1 ( ~— + AB + AD) + C^ (-AB)
i
(-AD) + C (AB + AD - ) + C (-AB)
0-0
, .11
where A =
8 (e|+1 + 0^) • DLXC
DLXB (02 + 0) x 2
D =
DLXA (QJ. 2 + 0r,2) x 2
26
-------
where
D
E =
D
2 . DLXC • DLXA
F =
U4
2 • DLXC • DLXB
Cf 1 (G - H)
(G - H) +
where G =
4 x DLXC
; H =
4 x DLXC
(R -
(2R.-
Using these numerical approximations, the general equation becomes:
c^: (-H - AD - E) + c-T" (
+AB+AD+E+F+G-H)
+ cj£[ (-AB - F + G)
1— i.
At
(AB + F - G) - RP
where:
C is the concentration of the solution
i is the subscript for depth increment z
j is the superscript of time increment t
DLXA = z ~Z±_Y> DLXB =
> DLXC
With specific boundary conditions imposed, n equations could be represented
by a tridiagonal matrix that can be easily solved. The chosen boundary
conditions must be specified. In addition to the computation of the concen-
tration of the soil solution, a computation of the solid salt, s, was
included as follows :
4 ~ K(R ~
At-
[12]
27
-------
The testing of the concept was done as follows:
1. R and K values were found and used to simulate measured data of
breakthrough curves developed in leaching studies in a soil
column.
2. The R and K values obtained were used to predict the salinity
profiles in another separate soil column. The simulation allowed
determination of the concentration of the soil solution, the
soluble salts in the solid phase and the total salts.
3. Simulation of data obtained from a field leaching experiment was
then used to verify the model.
WATER QUALITY EFFECTS ON TRANSPIRATION
Model Prediction of Salinity Effects on Crop
Production Assuming No Source-Sink Terms
The model described by King and Hanks (1975) was tested to evaluate whether
yield prediction is influenced by salinity. Figure 5 shows that an additional
principle assumption made in the model, i.e., that relative dry matter yield
(Y/Ymax) is directly related to relative transpiration (T/Tp), is approxi-
mately correct. The model was used to evaluate crop yields where there were
different salinity levels within the root profile. The model was also used
to evaluate extraction of water from different sections of the root zone
irrigated with saline water. Comparison of the computed data (Figure 6) shows
good agreement with measured data from Lunin and Gallatin (1965) and Bingham
and Garber (1970). In all cases computed transpiration from the saline soil
profile sections was slightly less than that measured. Thus it appears that
the model accounts for the effects of salinity on transpiration (and thus dry
matter production) and will be useful for predicting the effect of salinity
on crop yields.
28
-------
O.8
x
O
E
>• 0.6
£
O
4^
x Lunin and Gallatin (1965)
ABingham and Garber (I97O) A
oShalhevet and Bernstein (1968)
/
0 e "/ °A
/00
/
~~
/ '\
0.0
0.2
0.4
0.6
T/Tp
0.8
1.2
Figure 5. Relative yield as related to relative evapotranspiration under
various saline conditions.
29
-------
z
g
5
{£
Q.
(/)
1
UJ
>
§
UJ
tr
O
DATA FROM BINGHAM AND GARBER (I97O)
C
COMPUTED
MEASURED
ih/
•Jl
TMB TMB TMB TMB
CONTROL TOP MIDDLE BOTTOM
SALINE SALINE SALINE
3
z
g
i
5
cc
TRANSPI
ro
UJ
>
I—
5 i
UJ
a:
O
DATA OF LUNIN AND GALLATIN (1965)
COMPUTED
;
/
£
|
X
/ .MEASURED
T
•:
n
l?i
TMB T
CONTROL
*
I
£
|:|
-
•A
-
V
:|
-i
nn
Jl
I
-
MB TMB T
i
M
TOP MIDDLE TOP
SALINE SALINE
X
X
1
B
AND
MIDDLE
SALINE
Figure 6. Comparison of measured and computed transpiration where different
sections of the root zone were irrigated with saline water. T is
top, M is middle and B is the bottom section.
30
-------
SECTION V
RESULTS AND DISCUSSION
WATER MANAGEMENT AND LEACHING UNDER FIELD CONDITIONS
Plot Irrigation
The plot irrigation was scheduled from the lysimeter information shown in
Table 3- Table 3 gives the cumulative measured hydraulic lysimeter ET less
rainfall in inches for the period May 12, 1974 to August 26, 1974. This was
the period over which the plots were studied. On May 12, the beginning date,
the soil moisture depletion was assumed to be the season reference.
A soil moisture depletion of approximately 3.0 inches (7.6 cm) was allowed
between irrigations. The plots were irrigated so that the 12 ft by 15 ft
(3.6 x 4.6 m) study area in each plot received an average depth of water
determined by:
where
D - depth applied
E? = evapotranspiration for irrigation interval
LF = leaching fraction for the treatment water quality.
Table 4 shows cumulative ET calculated from evaporation pan readings in 1975.
The lysimeters became inoperative over the winter and could not be used in
1975. The computed ET values were therefore used to control irrigation
during the 1975 season.
The average depth of water applied by the sprinkler system each irrigation
was determined from the can-catch area data taken in the sample plot.
Figures 7 and 8 show the relative composite can-catch for the two seasons.
The average depth applied to the study area was taken as the average of the
catch in the twelve interior cans. This average was used to determine the
application rate for each plot by dividing the depth applied by the operation
time and using the pressure-discharge curve in Figure 9 to adjust the water
application time for changes in sprinkler pressure.
Table 5 contains the 1974 plot irrigation schedule giving the average-
accumulated depth applied to the study area of each plot at the end of
each irrigation. The desired depth of application was not necessarily
applied each individual irrigation, but the cumulative depth for the season
was very close to the desired application depth. In other words, the exact
leaching fraction was not reached each irrigation, but the seasonal leaching
fraction was near the desired value.
31
-------
TABLE 3. CUMULATIVE HYDRAULIC LYSIMETER ET LESS RAIN FOR ALFALFA AT
VERNAL, UTAH, MAY 12 TO AUGUST 26, 1974
Date
5/12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
6/01
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Cu. ET Less.. ,
Rain (in.)—
.13
.42
.75
1.01
1.33
1.57
1.82
2.12
2.46
2.63
2.78
3.01
3.20
3.46
3.64
3.85
4.14
4.42
4.87
5.26
5.60
5.87
6.16
6.34
6.98
7.60
7.46
7.46
7.72
7.88
8.00
8.17
8.36
8.60
8.79
8.96
Date
6/17
18
19
20
21
22
23
24
25
26
27
28
29
30
7/01
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
ET Less
Rain (in.)
9.02
9.15
9.36
9.61
9.93
10.19
10.48
10.42
10.84
11.08
11.43
11.83
12.14
12.44
12.90
13.36
13.66
13.94
13.69
14.20
14.36
14.65
14.98
15.29
15.64
16.06
16.34
16.60
16.91
16.97
17.15
17.435
17.880
18.10
18.31
18.57
Date
7/23
24
25
26
27
28
29
30
31
8/01
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
ET Less
Rain (in.)
18.95
18.90
18.90
19.06
19.22
19.36
19.62
19.90
20.01
20.26
20.51
20.68
20.94
21.14
21.36
21.68
22.00
22.20
22.48
22.62
23.04
23.26
23.635
24.12
24.39
25.02
25.00
25.33
26.62
25.92
26.16
26.46
26.70
26.93
27.17
27.37
— 1 inch = 2.54 cm
32
-------
TABLE 4. CUMULATIVE ET, MAY 12 TO SEPTEMBER 13, 1975, CALCULATED FROM
CLASS A PAN
Date
5/12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
6/1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Cum ET
(in.)
.234
.356
.558
.776
.999
1.154
1.242
1.356
1.602
1.675
1.885
2.092
2.234
2.434
2.636
2.797
2.887
3.053
3.167
3.332
3.470
3.904
4.368
4.554
4.776
4.965
5.034
5.132
5.112
5.302
5.554
5.706
5.938
6.166
6.382
6.652
6.692
6.798
6.882
6.884
7.046
7.179
Date
6/23
24
25
26
27
28
29
30
7/1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
8/1
2
3
Cum ET
(in.)
7.328
7.479
7.560
7.782
7.878
8.068
8.146
8.146
8.286
8.640
8.997
9.283
9.667
9.854
10.205
10.447
10.693
10.915
11.203
11.464
11.743
11.948
12.110
12.361
12.571
12.830
13.006
13.246
13.430
13.738
13.975
14.246
14.507
14.784
15.051
15.282
15.449
15.689
15.912
16.184
16.482
16.756
Date
8/4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
9/1
2
3
4
5
6
7
8
9
10
11
12
13
Cum ET
(in.)
17.092
17.317
17.714
18.150
18.513
18.802
19.005
19.178
19.392
19.746
19.915
20.126
20.358
20.599
20.895
21.077
21.163
21.442
21.632
21.879
22.170
22.375
22.525
22.636
22.844
23.008
23.175
23.354
23.492
23.589
23.759
23.837
23.966
24.088
24.204
24.298
24.447
24.541
24.660
24.782
24.902
- 1 inch =2.54 cm.
-------
.702 .756 .850 .777 .6
.775
.837
.795
.716
822
.957
.899
.886
.594 .667 .753 .697 .588
KEY* • CAN LOCATION [] SAMPLING LOCATION
STUDY AREA(S?)BARREL LYSIMETER
i
X SPRINKLER SCALE' I"«5 feet (1.52m)
5'
Figure 7. Relative composite can-catch for sample plot, 1974
34
-------
.495 .549
.820 .863
.942
.891
•
.617
.441
.741
•
.383 .599
8^2 .828
1.07 1.21
.682
9
1.32
I.O7
.949
.733
KEY: *CAN LOCATION SAMPLING LOCATION
BARREL LYSIMETER
STUDY
AREA
X SPRINKLER SCALE: l" = 51(l.52m)
Figure 8. Relative composite can-catch for sample plot, 1975.
35
-------
IOO
8O
6O
a
4O
LU
tr
CO
CO
UJ
a:
10
I I /I I
q = .
1
1
I
2 3 4567
DISCHARGE-q (gpm)
8 9 IO
Figure 9. Pressure-discharge, curve for 5/32" (0.40 cm) lo-hi
nozzle as determined from pressure-discharge tests.
36
-------
TABLE 5. 1974 IRRIGATION SCHEDULE GIVING DATES AND CUMULATIVE DEPTHS
APPLIED FOR 27 INDIVIDUAL PLOTS USED IN THE STUDY
Plot
5A1
7A1
9A1
5N1
7N1
9N1
5S1
7S1
9 SI
5A2
7A2
9A2
5N2
7N2
9N2
5S2
7S2
9S2
5A3
7A3
9A3
5N3
7N3
9N3
5S3
7S3
9S3
-1 inch
6/5 -
6/7
6.80
6.80
5.44
6.12
6.12
5.44
6.12
6.12
5.44
6.90
6.90
5.52
6.60
6.60
5.87
6.79
6.79
6.04
7.47
7.47
5.98
7.64
7.64
6.79
8.19
8.19
7.28
=2.54 cm.
6/22 -
6/24
9.75
10.20
9.03
9.22
9.22
8.84
9.22
9.26
9.15
9.94
10.40
9.20
9.88
9.88
9.49
10.27
10.31
10.39
10.77
11.28
9.96
11.89
11.89
11.30
13.42
13.42
12.17
7/4 -
7/6
Cumulative
13.47
13.42
12.81
13.65
13.69
13.52
13.54
13.65
13.55
13.96
13.87
13.63
14.86
14.92
14.89
14.62
14.83
14.96
15.01
14.91
14.97
17.66
17.71
18.06
18.85
19.64
18.86
Date
7/15 -
7/17
7/31 -
8/2
8/12 -
8/13
8/26 -
8/27
Depth Applied (in.)V
17.02
16.93
17.05
16.94
19.99
17.04
16.92
16.91
16.86
17.51
17.41
17.69
18.44
18.45
18.85
19.08
18.93
18.93
18.87
18.79
19.12
22.59
22.73
22.68
25.78
25.58
25.61
19.72
19.76
19.73
20.05
20.02
19.79
19.87
19.79
19.85
20.35
20.37
20.37
21.52
21.40
21.48
22.34
22.15
22.30
21.91
21.96
21.94
26.42
26.27
26.38
30.19
29.78
30.04
22.56
22.56
22.71
22.50
22.62
22.48
22.50
22.56
22.52
23.26
23.25
23.25
24.50
24.62
24.54
25.28
25.35
25.26
25.03
25.06
25.06
29.99
30.08
29.97
34.11
34.20
34.05
27.19
27.19
26.95
27.11
27.08
27.07
27.13
27.11
27.03
28.03
28.00
27.78
29.45
29.41
29.40
30.49
30.47
30.48
30.30
30.17
29.89
36.15
36.08
36.20
41.12
41.07
41.11
37
-------
TABLE 6. 1975 IRRIGATION SCHEDULE GIVING DATES AND CUMULATIVE DEPTHS
APPLIED FOR 18 INDIVIDUAL PLOTS^-'USED IN THE STUDY
Dates
Plot 7-9 7-21 8-13
8-7 8-14
8-8
2/
Cumulative Depth Applied (in.)—
5A1
7A1
5N1
7N1
5S1
7S1
5A2
7A2
5N2
7N2
5S2
7S2
5A3
7A3
5N3
7N3
5S3
7S3
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
4.03
6.19
6.19
7.97
8.08
5.92
5.97
6.37
6.38
8.11
8.20
6.03
6.07
6.43
6.51
8.39
8.58
6.21
6.21
9.75
9.84
10.45
10.70
8.72
8.83
10.08
10.11
10.63
10.87
8.86
9.02
10.16
10.25
10.91
11.29
9.10
9.26
— No 9 ft. water table depth plots were sampled in 1975
—1 inch = 2.54 cm.
Table 6 shows the plot irrigation schedule for 1975. The cold wet spring
slightly reduced the need for irrigation.
Actual Attained Leaching Fractions
The water balance for the barrel lysimeters in the low leaching treatment
plots is given in Table 7 and Table 8. A zero leaching fraction was attained
for only one of the lysimeters in 1974. Tables 9 and 10 give the average
adjusted leaching fractions attained for the plot-sized study area and for
the sampling point in the plots for the treatments shown. The values in
Tables 7, 8, 9, and 10 were obtained by using the amount of water received
by the sampling point, the amount received by the study area, and the amount
received by the lysimeter, for the respective calculations. The amount
received by the lysimeter was determined for each plot by comparing the
average of the catch in the can just north and the can just south of the
38
-------
TABLE 7. 1974 SEASONAL WATER BALANCE FOR THE BARREL LYSIMETERS
Plot 5A1 5S1 7A1 7 SI 9A1 9S1
Depth
Applied 29.42 29.35
Drainage .30 .28
(in.)±r
Change in
Storage -.45 .60
(in.)-7
*
Net
Leaching [-.15] .88
(in.)-7
ET , 29.57 28.47
(in.)-7
Average Plot ET = 27.24 in.-7
29.42 29.33 29.42 29.25
3.10 1.35 1.95 3.23
0 0 2.40 0
3.10 1.35 4.35 3.23
26.32 27.98 25.07 26.02
Minus indicates under irrigation.
-/I inch = 2.54 cm
lysimeter location in the sample plot to the average can-catch for the plot-
sized study area of the whole sample plot. The amount of water received at
the sampling location is determined for each plot by comparing the average
of the can just east and the can just west of the sampling area of the sample
plot to the average can-catch for the plot-sized study area.
The average leaching fractions attained for the treatments shown in Table 9
were very near to the desired leaching fractions in 1974. The actual leach-
ing fractions for the study areas were slightly lower, while those for the
sampling points were slightly higher than the target values. The 1975 values
in Table 10 were more variable.
Depths to Water Table
Tables 11 and 12 show the average actual depths to water table throughout the
season for all the 5 ft (1.5 m) plots, 7 ft (2.1 m) plots and 9 ft (2.7 m)
plots measured.
39
-------
TABLE 8. 1975 SEASONAL WATER BALANCE FOR BARREL LYSIMETERS
Plot 5A1 5S1 7A1 7S1
Depth
Applied . 10.69 9.66 10.78 9-77
(in. Irrig. & Rain)—
Drainage 00 00
Change in
Storage — 2.10 -2.28 1.80
(in.)!/
Net
Leaching — 2.10 [-2.28] 1.80
(in.)* I/
ET ** 7.56 13.06 7.97
(in.)
I/
Average Plot ET = 9.53 inches^
*Minus indicates under irrigation
** access tube damaged
-1 inch =2.54 cm.
The depths shown in the 9 ft (2.7 m) depth column in Table 11 are not the
true average. The piezometers in plots 9S2, 9A3, 9N3 and 9S3 could not be
driven to the depth of the water table due to rocky conditions. The values
shown for these areas were the end depths of the piezometers; the actual
water table was deeper. The seasonal averages, high and low values, for each
general water table depth are also given in Tables 11 and 12. It may be noted
that the average water table depths were somewhat deeper than design called
for in both years. The Ashley Valley had a short water year in 1974
Consequently, the irrigation water applied in the whole basin was somewhat
lower than normal which in turn reduced the contribution to the ground water
and resulted in a lower than normal water table. The water tables were
higher in 1975 but were still not up to the design level
40
-------
TABLE 9. AVERAGE STUDY AREA AND SAMPLING POINT LEACHING FRACTIONS FOR EACH TREATMENT, 1974
Al Nl SI
Depth
Applied 27.11 27.09 27.09
(in.)!/
ET , 27.24 27.24 27.24
(in.)!/
Leaching* [-.13] l~.15] [.15]
(in.)!/
Leaching* [-.005] [0.006] [.025]
Fraction
Depth
Applied 27.35 27.33 27.33
(in.)!/
ET (in.)!/ 27.24 27.24 27.24
Leaching .11 .09 .09
(in.)!/
Leaching .004 .003 .003
Fraction
Treatment
A2 N2 S2 A3 N3 S3
Study Area
27.93 29.42 30.48 30.12 36.14 41.10
27.24 27.24 27.24 27.24 27.24 27.24
.69 2.18 3.24 2.88 8.90 13.86
.074 .074 .106 .096 .246 .337
Sampling Area
28.17 29.68 30.74 30.38 36.45 41.46
27.24 27.24 27.24 27.24 27.24 27.24
.93 2.44 3.50 3.14 9.21 14.22
.030 .082 .114 .103 .253 .343
*Minus indicates under irrigation
!/l inch = 2.54 cm
-------
TABLE 10. AVERAGE STUDY AREA- LEACHING FRACTION FOR EACH TREATMENT, 1975
Al
Depth
Applied 9 . 80
(in.)l/
ET (in.)-/ 9.53
Leaching
(in.) I/ .27
Leaching
Fraction 0.028
Treatment
Nl SI A2 N2 32
10.57 8.78 10.09 10.75 8.94
9.53 9.53 9.53 9.53 9.53
^>J-
1.04 - .75 .56 1.22 -.59
0.109 -0.079 0.059 0.128 -0.062
A3 N3 S3
10.21 11.10 9.18
9.53 9.53 9.53
.68 1.57 -.35
0.071 0.165 -0.037
**Minus indicates under irrigation
— The sampling area classification was not used in 1975.
-1 inch = 2.54 cm
-------
TABLE LL. AVERAGE DEPTHS TO WATER TABLE BY DATE FOR EACH WATER TABLE
TREATMENT, 1974
Date
Minimum Desired Water Table Depth
5 ft 7 ft 9 ft
(1.5 m) (2.1 m) (2.7 m)
Depth to Water Table (ft)-
5/16
6/05
6/06
6/07
6/10
6/12
6/14
6/17
6/19
6/21
6/24
6/26
6/27
6/28
7/03
7/06
7/08
7/10
7/16
7/18
7/22
7/26
7/30
8/01
8/05
8/07
8/09
8/12
8/14
8/20
8/23
8/27
8/28
8/30
9/04
9/06
9/13
Average
Minimum
Maximum
7.01
6.47
6.43
6.39
6.36
6.50
6.59
6.66
6.58
6.59
6.81
5.99
6.47
6.72
7.33
7.04
6.94
7.30
7.53
7.37
7.07
7.18
7.37
7.40
7.37
7.52
7.50
7.71
7.71
7.87
7.86
7.89
7.88
7.52
7.72
7.90
8.01
7.15
5.99
8.01
8.84
8.46
8.44
8.33
8.35
8.37
8.47
8.52
8.56
8.56
8.67
8.03
8.48
8.63
9.00
8.81
8.72
8.59
9.08
8.99
8.82
8.88
8.87
8.99
9.01
9.12
9.09
9.20
9.22
9.33
9.29
9.27
9.30
9.08
9.26
9.36
9.41
8.85
8.03,
9.41
11.21
11.06
10.94
10.88
10.73
10.78
10.80
10.81
10.86
10.90
10.96
10.99
11.06
11.05
11.17
11.16
11.14
11.16
10.97
10.83
10.73
10.69
10.65
10.60
10.63
10.72
10.77
10.79
10.88
11.02
11.13
11.03
10.96
10.93
11.11
11.30
11.40
10.94
10.60
11.40
— 1 foot = 0.305 m
43
-------
TABLE 12. AVERAGE DEPTHS TO WATER TABLE FOR EACH WATER TABLE TREATMENT,
1975
Minimum Desired Water Table Depth
5 fti/ 1 fti'
Date Depth to Water Table (ft)—
7-18
7-21
7-22
7-28
8-4
8-9
8-18
5.64
4.67
5.22
6.67
6.43
6.70
6.72
8.01
5.98
7.68
8.61
8.48
8.56
8.61
Average 6.01 7.99
Minimum Average
Maximum Average
4.67
6.72
5.98
8.61
- 1 foot = 0.305 m
Vertical Ground Water Movement
Tables 13, 14, and 15 summarize the matric potentials and hydraulic gradients
by leaching treatment, water quality treatment and depth to water table at
the 4.5 ft (1.4 m) depth in 1974. A negative value for hydraulic gradient
indicates upward flow. No attempt was made to quantitatively correlate
these readings to the various treatments. They only indicate direction of
flow and relative matric potential deep in the soil profile.
The experiment was designed to manage irrigation to maintain downward flow
since continuous downward flow makes determination of water and salt balances
easier. The goal was accomplished generally for all treatments. The lower
water table depth for the 1974 season probably helped maintain the downward
gradient. Higher water table conditions may have necessitated a shorter
irrigation interval to prevent some upward movement of water from the
shallower water tables into the root zone between irrigations.
Salinity Monitoring
The measured quality of the irrigation waters applied in both years is shown
in Tables 16 and 17. There was a considerable fluctuation in the quality
of water from the South Tributary. This was due to fluctuations in flow.
44
-------
TABLE 13. MATRIC POTENTIAL AND VERTICAL HYDRAULIC GRADIENT AVERAGES FOR THREE LEACHING FRACTION
TREATMENTS IN 1974
Date
6/10
6/15
6/20
6/25
6/30
7/05
7/10
7/15
7/20
7/25
7/30
8/04
8/09
8/14
8/19
8/24
8/29
9/03
9/08
Low Leaching
Matric Potential Gradient
Mb
106.1
131.4
131.6
119.9
136.3
131.4
118.1
165.8
125.6
129.7
125.7
94.3
110.3
106.6
122.2
114.3
94.2
117.8
141.5
0.79
0.60
0.27
-0.03
-0.13
1.26
1.13
1.44
1.78
0.62
0.51
1.12
0.86
1.51
0.87
1.06
1.49
1.04
0.92
Middle Leaching
Matric Potential Gradient
Mb
83.1
95.2
110.6
107.2
119.2
123.6
108.0
122.0
84.5
112.1
124.3
94.7
113.0
99.9
117.7
93.7
97.3
126.2
162.5
0.82
0.44
0.43
0.84
0.94
0.99
1.55
1.70
1.33
0.78
0.42
0.51
0.93
1.33
0.83
1.02
1.24
1.03
1.23
High Leaching
Matric Potential Gradient
Mb
66.0
83.3
100.6
79.4
90.2
97.6
88.6
110.2
74.9
100.4
113.0
69.4
89.8
72.6
96.4
98.2
80.1
102.5
128.3
0.58
0.88
0.63
1.09
0.54
0.76
0.72
0.88
0.60
0.45
0.34
0.79
0.40
0.47
0.32
1.27
0.83
0.53
0.63
-------
TABLE 14. MATRIC POTENTIAL AND VERTICAL HYDRAULIC AND GRADIENT AVERAGES FOR THREE IRRIGATION
WATER QUALITY TREATMENTS IN 1974
Date
6/10
6/15
6/20
6/25
6/30
7/05
7/10
7/15
7/20
7/25
7/30
8/04
8/09
8/14
8/19
8/24
8/29
9/03
9/08
High Water
Matrlc Potential
Mb
99.6
120.6
120.2
118.8
126.6
158.0
111.1
133.2
111.1
119.1
120.7
91.3
106.0
100.7
111.5
103.4
96.5
114.7
146.0
Quality
Gradient
0.45
0.27
0.14
0.11
0.09
1.47
1.43
2.70
2.03
1.10
0.54
0.62
0.95
1.35
0.87
2.36
1.75
1.14
0.90
Middle Water
Matric Potential
Mb
81.8
96.3
112.8
86.5
114.9
73.1
105.2
136.5
92.3
115.5
127.2
74.9
104.2
82.6
108.0
88.2
92.6
114.7
141.9
Quality
Gradient
1.23
1.20
0.84
0.87
0.66
1.35
0.62
0.60
0.99
0.52
0.59
1.29
0.75
1.11
0.67
0.69
0.93
1.01
0.84
Low Water
Matric Potential
Mb
73.8
93.0
109.8
101.2
104.1
121.6
98.4
128.3
81.6
107.8
115.1
92.2
102.9
95.8
116.9
114.6
82.4
117.1
144.4
Quality
Gradient
0.51
0.45
0.35
0.92
0.59
0.18
1.34
0.73
0.69
0.24
0.15
0.52
0.49
0.84
0.48
0.29
0.88
0.44
1.04
-------
TABLE 15. MATRIC POTENTIAL AND VERTICAL HYDRAULIC GRADIENT AVERAGES FOR THREE WATER TABLE DEPTH
TREATMENTS IN 1974
Date
6/10
6/15
6/20
6/25
6/30
7/05
7/10
7/15
7/20
7/25
7/30
8/04
8/09
8/14
8/19
8/24
8/29
9/03
9/08
5 ft -Water
Matric Potential
Mb
78.7
98.8
96.8
89.7
101.9
95.9
87.2
125.9
86.8
97.0
102.4
64.8
87.2
73.2
96.1
80.6
86.1
106.7
132.8
Table
Gradient
0.38
0.16
-0.37
0.08
0.25
1.41
1.47
1.66
1.86
0.56
0.36
1.07
0.64
1.36
0.63
0.70
0.82
0.62
0.89
7 ft -Water
Matric Potential
Mb
75.7
91.5
104.0
86.1
108.3
111.7
102.2
120.7
102.4
113.2
116.5
85.8
92.8
83.1
94.5
81.7
88.2
109.5
140.9
Table
Gradient
1.24
1.27
0.94
1.23
0.75
1.05
11.5
1.47
0.85
0.61
0.63
1.08
0.98
1.14
0.89
1.63
1.53
1.14
1.27
9 ft -Water
Matric Potential
Mb
100.7
119.6
142.0
130.8
135.4
145.1
125.3
151.4
95.9
132.1
144.1
107.8
133.1
122.8
145.7
143.9
97.3
130.3
158.6
Table
Gradient
0.56
0.46
0.75
0.59
0.34
0.56
0.77
0.90
1.00
0.69
0.28
0.27
0.57
0.80
0.50
1.01
1.21
0.84
0.63
-1 foot = 0.305 m
-------
TABLE 16. IRRIGATION WATER QUALITIES FOR EACH IRRIGATION FOR ASHLEY
CREEK, NAPLES DRAIN AND SOUTH TRIBUTARY, 1974
Electrical Conductivity, mmhos/cm
Irrigation
Number
1
2
3
4
5
6
7
Average
Ashley
Creek
1.05
1.05
1.08
1.02
.63
1.03
1.01
.98
Naples
Drain
2.45
2.82
2.13
1.91
1.70
1.88
2.62
2.22
South
Tributary
1.29
3.15
2.26*
4.38
1.81
4.01
2.72
2.80
*Storage reservoir in operation
TABLE 17. IRRIGATION WATER QUALITIES IN 1975
EC, (mmhos/cm)
Irrigation
Number
1
2
3
Ashley
Creek
.660*
.660
.708
Naples
Drain
.660*
1.718
1.472
South
Tributary
.660*
1.592
1.483
Average
.676
1.283
1.245
*First irrigation used Ashley Creek water for all irrigations,
EC assumed to be .660
48
-------
When farmers upstream were irrigating, surface runoff entered the channel
and improved the water quality. The construction of the storage reservoir
on this stream allowed collection and use of the very low flows which were of
lower quality. This was particularly true in 1975. The South Tributary
of the Naples Drain had a lower average salinity than the Naples Drain. The
storage helped average the quality but some fluctuation still occurred.
Fluctuation in the quality of the Naples Drain was due to the same effect.
The normal stream flow is of higher quality than the surface runoff water.
When runoff water entered the stream, the quality was lowered.
Even though there were fluctuations in the quality of all three irrigation
water supplies, the average for the season was quite close to the expected
values in 1974, with each being of a slightly lower quality than anticipated.
Before examining the actual seasonal change in average profile salinity it is
helpful to reexamine Table 1, showing the design salinity of the drainage
water for each leaching fraction, and to look at the changes in soil profile
salinity expected. The values in Table 1 are the design figures. By using
the actual attained leaching fractions for the sample areas given in Tables
9 and 10 and the average irrigation water quality from Tables 16 and 17,
and by using Equation [3] , the values for the theoretical expected EC, in
Tables 18 and 19 were obtained.
Assuming a straight line salinity profile varying from the conductivity of
the irrigation water at the top to the design EC at the bottom, the expected
average profile salinity for each of the above conditions can be found. These
values are shown in Table 20 and all assume no precipitation or dissolution
of salts. It seems obvious that no plant could continue to extract water
from a soil with the average profile salinity indicated in row 1 of Table 20.
The actual salinity values measured in the field were much lower.
Since all the salinity values in Table 20 are theoretical equilibrium values,
it is not expected that they would be reached in one season. To predict the
expected increase in salinity for 1974, a salt balance for each treatment
condition was made. The expected change in soil water salinity is given
by
D x EC. - D, x EC,
AEC xw i-w dw dw
'sw Dr x 6fc
where
AEC = change in electrical conductivity of the soil water
sw
D. = depth of irrigation water
iw
EC = electrical conductivity of irrigation water
iw
D, = depth of drainage water
EC = electrical conductivity of drainage water
dw
D = depth of root zone
6 = moisture content by volume at field capacity (0.30 for Vernal
f c
soil). 49
-------
TABLE 18. THEORETICAL EC IN MMHOS/CM FOR EQUILIBRIUM CONDITIONS USING
1974 WATER QUALITY AND LEACHING FRACTIONS
Irrigation Water Quality
EC, mmho/cm
Ashley
.98
L.F.
.004
.030
.103
Expected
EC,
dw
mmho/cm
245.0
32.7
9.5
Naples
2.22
L.F. Expected
EC,
dw
mmho/cm
.003 740.0
.082 27.1
.253 11.1
South
Tributary
2.80
L.F. Expected
EC,
dw
mmho/cm
.003 933.3
.114 24.6
.343 8.2
TABLE 19. THEORETICAL EXPECTED EC IN ms/CH FQR EQUILIBRIUM CONDITIONS
USING ACTUAL 1975 QUALITIES AND LEACHING FRACTIONS
Ashley
0.676
L.F. Expected
EC,
dw
mmho/cm
.028 24.1
.059 11.5
.071 9.5
— — - __
Irrigation Water Quality
EC, mmho/cm
Naples
1.283
L.F. Expected
EC,
dw
mmho/cm
.109 11.7
.128 10.0
.165 7.8
South
Tributary
1.245
L.F. Expected
EC,
dw
mmho/cm
-.079^
-.062
-.037
— Negative leaching fraction, i.e.,
50
-------
TABLE 20. THEORETICAL AVERAGE PROFILE SALINITY FOR EQUILIBRIUM CONDITIONS
USING 1974 WATER QUALITIES AND LEACHING FRACTIONS
Irrigation Water Quality Treatment
Leaching High Medium Low
Fraction • ••—•
Average Profile Salinity, mmho/cm
Low
Medium
High
123.0
15.3
5.2
371.0
14.7
5.5
468.0
13.7
5.5
The quality of the drainage water was taken to be the quality of the 42-inch
(1.06 m) depth soil solution extract and the root zone was assumed to be
42 inches (1.06 m) deep. This procedure assumes all salt in the profile is
in solution. The results of these calculations appear in Table 21 for 1974
and in Table 22 for 1975.
Figure 10 gives the measured and expected average soil profile salinity for
the 1974 season for the three water table depth treatments. The figure shows
that the measured changes are much smaller than the expected changes.
Figure 11 shows similar data for 1975. The expected salinity increased from
the application of irrigation water and the measured salinity remained
relatively constant through both seasons. An increase in measured salt
concentration in the soil solution was found as the soil dried following
the last irrigation. All other measurements were taken with the soil near
field capacity. Figures 12, 13, 14, and 15 show similar results for the
three irrigation water quality treatments and three leaching treatments,
respectively. In some cases, the measured soil profile salinity actually
decreased when an increase was expected. In no case did the expected and
measured change in salinity agree. It must be remembered that in all cases
the expected change in average profile salinity assumes no precipitation or
dissolution of salts. The water table depth treatments showed no appreciable
effect on soil profile salinity. In Figure 12 there is a slight separation
in the curves of measured salinity with an improvement in soil profile
salinity where high quality water was used. The low quality water treatment
caused a slight increase in average profile salinity over the season but the
effect was much less than expected. The leaching treatment averages shown
in Figure 14 show a higher average salinity for the low leaching treatment
but only a slight seasonal increase.
To see where the changes in salinity occurred, it is important to look at
the individual treatment salinity profiles at intervals throughout the
season. Figure 16 shows the average soil solution salinity profile at three
51
-------
TABLE 21. EXPECTED CHANGE IN AVERAGE SOIL PROFILE SALINITY FOR THREE
LEACHING FRACTIONS, THREE WATER QUALITIES AND THREE DEPTHS
TO WATER TABLE FOR 1974
Leaching
Fraction
Low
Medium
High
Change in
Salinity
mmho/cm
1+b.27
+4.00
+3.03
Water
Quality
Low
Medium
High
Change in
Salinity
mmho/cm
+5.45
+4.40
+1.81
Water Table
Depth
Low
Medium
High
Change in
Salinity
mmho/cm
+3.97
+3.92
+3.64
+ indicates the expected change in salinity.
TABLE 22. EXPECTED CHANGE IN AVERAGE SOIL PROFILE SALINITY FOR THREE
LEACHING FRACTIONS, THREE WATER QUALITIES AND TWO DEPTHS
TO WATER TABLE, 1975
Leaching
Fraction
Low
Medium
High
Change in
Salinity
mmho / cm
+ .84
+ .86
+ .89
Water
Quality
Low (S)
Medium (N)
High (A)
Change in
Salinity
mmho/cm
+ .89
+1.10
+ .542
Water Table
Depth
I/
I!
Medium (7)
High (5)-7
Change in
Salinity
mmho/cm
+ .84
+ .83
!/
2/
9 foot (2.7 m) water table treatment not included in 1975
— 1 foot = 0.305 meters
+ indicates increased salinity
52
-------
^ 8.0
O
o
-C
E 7O
j:
H
P 6.O
O
D
Q
Z
O
0 5.0
J
<
O
a:
t 40
LJ
1
LJ
3.O
i 1 r i i i i
• SHALLOW WATER TABLE '' ^ _
0 MEDIUM WATER TABLE / x'
A DEEP WATER TABLE -/' ' /
MEASURED x ^ xX
EXPECTED ^x Axxx
/'S'''*
' / /&
X X X*
X* / /
X X ^
— x x —
x* x^xX
x^ ^ O
XX Axx^X
x* x^OX
/ X'x
~ X ^ X
,x xJ/X
* >•"> -^. ft ^
/xx ft _ . ^ __ A-. . d§b_ Q
^ ^ __^fv*^MFa~~~* *»^ •^^^__— yj ** ~~~~_^. ''T
- £Z^^ -
\ III II 1
6/11 6/27 7/9 7/18 8/6 8/14 8/28
DATE
Figure 10. Measured and expected average soil water salinity for three water table depth
treatments, 1974.
-------
Ul
"g 9.O
O
o
JC
c 8.O
^ 7O
H
U
D
Q
O 6'°
O
o
o:
H
o
5.O
4.O
3.O
8-28-74
SHALLOW WATER TABLE
MEDIUM WATER TABLE
MEASURED SALINITY
EXPECTED SALINITY
I
8-9-75
8-28-75
DATE
Figure 11. Measured and expected average soil water salinity with time for two water
table depth treatments in 1975.
-------
Ul
Ul
8.O
I
o
°
6.O
U
Q
O
O
5.0
O
a:
t
UJ
_j
UJ
3.0
T
T
X I
• HIGHEST QUALITY
O MIDDLE QUALITY
A LOWEST QUALITY
MEASURED
EXPECTED
I
I
I I
6/1
6/27 7/9 7/18 8/6 8/14
DATE
8/28
Figure 12. Measured and expected average soil water salinity for three irrigation water
quality treatments.
-------
I2.O
E
o
E
E
H
>
H
O
D
O
z
o
o
o
(T
I-
O
UJ
.J
UJ
10.0
8.0
6.O
4.0
2.O
HIGHEST QUALITY
MEDIUM QUALITY
LOWEST QUALITY
MEASURED SALINITY
EXPECTED SALINITY
I I
8-28-74
7-9-75
DATE
8-14-75 8-29
Figure 13. Measured and expected average soil water salinity with time for
three irrigation water quality treatments.
56
-------
8.O
*
O
JZ
E
E
H
5e.o
U
D
Q
§ 5.0
U
o:
u
iii
_j
ui
• LOW LEACHING
O MIDDLE LEACHING
A HIGH LEACHING
MEASURED
- - EXPECTED
/
6/1
6/27 7/9 7/18 8/6 8/14
DATE
8/28
Figure 14. Measured and expected average soil water salinity for three leaching treatments
1974.
-------
10.O
Ui
00
E
o
\
o
.c
E
E
o
(T
I-
O
LJ
J
111
9.O
8.O
i= 70
o
D
Q
Z
O
o
6.O
5.O
4.O
3.O
LOW LEACHING
MEDIUM LEACHING
HIGH LEACHING
MEASURED SALINITY
EXPECTED SALINITY
I
8-28-74 DATE 8-9-75 8-28-75
Figure 15. Measured and expected average soil water salinity with time for three
leaching fractions.
-------
I
I-
Q_
UJ
Q
8
1O
• 6/11/74
O 7/18/74
A 8/28/74
D 8/28/75
x
O
I
Q.
LJ
Q
I.O
2.O 3.O 4.O 5,O
ELECTRICAL CONDUCTIVITY (mmho/cm)
6.O
Figure 16. Average soil solution salinity profiles for low leaching
plots on four dates.
59
-------
times during the 1974 season and at the end of the. 1975 season for the
averaged low leaching treatments. The profiles are not true average profiles
below 3.5 ft (1.1 m) because the shallower water table plots did not have
samplers at deeper depths to 7.5 ft (2.3 m). The points down to and including
3.5 ft (1.1 m) depths are averages for nine or six plots. The 4.5 (1.4 m) and
5.5 ft (1.7 m) depths are averages for six or three plots and the 6.5 (2.0 m)
and 7.5 (2.3 m) ft depths are averages for 3 plots. The ground water data
are all plotted as the bottom point in the figures, and are averages for nine
or six plots and are plotted at an average water table depth for the season.
These profiles are included to show the average change in salinity over the
season at a given depth rather than to show a true salinity profile. The
higher concentration in the upper profile for 1975 is related to the fact that
the sample was taken two weeks after the last irrigation.
Figures 17 through 24 show essentially the same results as Figure 16 for the
other eight treatments. No significant increase in salinity is noted at any
depth for any treatment with the possible exception of the low irrigation
water quality treatment shown in Figure 19. Figure 19 shows a salt
accumulation high in the profile, but a loss at deeper depths.
It is apparent from the results shown in Figures 10 through 24 that precipi-
tation and dissolution of salts in the soil profile cannot be ignored in
water management for salinity control. It appears that the salt saturation
limit of the soil water varies, but at a given location or depth, any attempt
to increase the salinity of the soil water causes precipitation, and the
concentration of the soil water remains essentially constant, at least for
the period of time involved in this study. The increase in salinity seen in
Figure 19 came at the 1.5 (0.5 m) and 2.5 ft (0.8 m) levels where the more
saline irrigation water was added to an unsaturated zone. The salinity,
however, did not exceed the salinity of the soil at the lower depths. Adding
saline or non-saline water to the profile had little effect on the equilibrium
condition of salt in the profile in two seasons.
Individual Ion Analysis
Table 23 gives the individual ion analysis of one sample of water from each
irrigation supply and one soil solution sample for each of the treatments
listed. Examination of Table 23 shows that most of the cations in the
irrigation water are calcium and magnesium. The majority of the anions are
sulfates. The same is true of the soil solution extracts. Under these
conditions, precipitation would be expected to be important due to the
low solubility of calcium sulfate.
Diluted Extract Analysis
To further support the precipitation premise, soil samples for different depths
from some of the plots were taken. 1:0.4, 1:1 and 1:5 extracts were prepared
for each sample, and the electrical conductivity of the extract measured. If
there were no dissolution of precipitated salts, the conductivity of the 1:1
extracts would be expected to be 5 times the conductivity of the 1:5 extracts.
60
-------
o
• 6/11/74
O 7/18/74
A 8/28/74
D 8/28/74
Figure
2.O 3.O 4.O 5.O 6.
ELECTRICAL CONDUCTIVITY (mmho/cm)
17. Average soil solution salinity profiles for middle
leaching plots on four dates.
61
-------
- 4
I
tL
UJ
Q
8
IO
• 6/11/74
O 7/18/74
A 8/28/74
D 8/28/75
I
H
Q.
LJ
Q
I.O 2.O 3.O 4.O 5.O 6.O
ELECTRICAL CONDUCTIVITY (mmho/cm)
Figure 18. Average soil solution salinity profile for high
leaching plots on four dates.
62
-------
• 6/11/74
O 7/18/74
A 8/28/74
D 8/28/75
O
I
H
Q.
LJ
Q
2.O 3.O 4.O 5.O 6.
ELECTRICAL CONDUCTIVITY (mmho/cm)
Figure 19. Average soil solution salinity profiles for low water
quality plots on four dates.
63
-------
o
- 4
I
H
DL
UJ
Q
8
IO
• 6/11/74
O 7/18/74
A 8/28/74
D 8/28/75
I
O
X
I-
0.
UJ
Q
I.O 2.O 3.O 4.O 5.O 6.O
ELECTRICAL CONDUCTIVITY (mmho/cm)
Figure 20. Average soil solution salinity profile for middle water quality
plots on four dates.
64
-------
J 4
I
0.
UJ
Q
6
8
IO
• 6/11/74
O 7/18/74
A 8/28/74
D 8/28/75
O
I
I-
QL
UJ
Q
I.O 2.O 3.O 4.O 5.O 6.O
ELECTRICAL CONDUCTIVITY (mmho/cm)
Figure 21. Average soil solution salinity profile for high water quality
plots on four-dates.
65
-------
o
I
Q.
UJ
Q 6
8
IO
• 6/11/74
O 7/18/74
A 8/28/74
_L
I
O
I
Q.
UJ
Q
I.O 2.O 3.O 4.O 5.O 6.O
ELECTRICAL CONDUCTIVITY (mmho/cm)
Figure 22. Average soil solution salinity profile for deep water table
on three dates.
66
-------
o
I
I-
0.
LJ
Q
8
IO
• 6/11/74
O 7/18/74
A 8/28/74
D 8/28/75
/
/J
i
I
l
E
I
o.
hi
Q
I.O 2.O 3.O 4.O 5.O 6.O
ELECTRICAL CONDUCTIVITY (mmho/cm)
Figure 23. Average soil solution salinity profile for middle water table
depth on four dates.
67
-------
o
S 4
I
Q_
III
Q
8
IO
6.93
• 6/13/74
O 7/18/74
A 8/28/74
D 8/28/75
I
I
I
Q_
UJ
Q
I.O 2.O 3.O 4.O 5.O 6.O
ELECTRICAL CONDUCTIVITY (mmho/cm)
Figure 24. Average soil solution salinity profile for shallow water table
on four dates.
68
-------
TABLE 23. INDIVIDUAL ION ANALYSES OF SELECTED IRRIGATION WATER SAMPLES
AND SOIL SOLUTION EXTRACTS
Cations
Treatment
Ca
Anions
Mg
Na
K
Cl
SO,
CO.,
Milliequivalents/Liter
HCO,
I.W.A.
I.W.N.
I.W.S.
5A
7A
9A
5N
7N
9N
5S
7S
9S
9.18
12.03
11.23
14.97
14.80
13.27
14.82
14.02
23.40
16.87
16.67
22.90
8.91
13.08
13.90
36.77
25.17
36.05
28.30
32.90
27.47
62.43
43.19
30.11
.87
4.74
6.74
2.44
2.35
7.74
2.39
4.13
5.96
4.39
5.22
5.31
.05
.21
.16
.05
.25
.20
.20
.29
.17
.26
.23
.14
.30
1.00
1.00
.10
.40
2.70
1.00
.80
1.80
.80
.70
1.50
15.00
17.00
26.90
49.90
36.20
47.20
37.90
46.20
42.80
68.00
54.10
45.70
.90
.60
.60
1.00
1.50
1.00
1.20
1.10
1.10
3.20
1.60
1.60
2.20
9.20
3.80
2.40
4.00
4.00
2.10
2.60
7.40
6.50
7.00
5.40
I.W. means irrigation water.
Table 24 gives the results of this experiment on the field soil samples. In
the table, columns 2 and 5 would be the same if there were no dissolution
of precipitated salts. In each case, however, even at the shallow depths,
some precipitated salts are evidently present. The amount of precipitated
salts generally increases with depth as would be expected. There are
apparently three to six times as much salt precipitated in the soil as is
in the soil solution at the saturation water content.
ET Prediction and Irrigation Scheduling
In order to evaluate the ET prediction section of the irrigation
program for management purposes , comparisons were made between pan evaporation
and lysimeter ET, pan evaporation and potential ET from the "rigation
scheduling program, and lysimeter ET and computed ET from the scheduling
program for alfalfa.
69
-------
TABLE 24. COMPARISON OF ELECTRICAL CONDUCTIVITIES FOR THREE EXTRACT
RATIOS OF SOIL SAMPLES FROM TWO TREATMENT PLOTS
Electrical Conductivity mmho/cm
Treatment Depth
in.
5N1 0-3
3-6
6-9
9-12
12-15
15-18
18-21
21-24
24-27
27-30
30-33
33-36
9S2 0-3
3-6
6-9
9-12
12-15
15-18
18-21
21-24
24-27
27-30
30-33
33-36
*
This column adjusts
comparable moisture
— 1 inch = 2.54 cm.
I/ 1:0'4
1.84
1.72
1.90
2.05
3.60
3.95
4.10
4.05
4.50
4.55
5.30
5.65
3.10
1.60
1.40
1.39
1.40
1.60
1.60
2.20
3.15
3.20
3.25
3.35
the conductivity
content of the 1
1:1
1.03
1.00
1.11
1.20
3.00
3.10
3.45
3.35
3.60
3.45
4.30
4.30
1.62
.88
.77
.80
.82
.95
.96
1.25
2.00
2.15
2.49
2.50
of the 1:5
:0.4 extract
1:5
.44
.38
.39
.40
1.00
1.22
1.70
1.52
2.25
1.52
2.50
2.75
.48
.30
.28
.28
.29
.32
.30
.38
.66
.76
.92
1.04
extract
*
(I:5)xl2.5*
5.50
4.75
4.88
4.94
12.50
15.31
21.25
18.94
28.12
18.94
31.25
34.38
6.00
3.75
3.56
3.50
3.62
3.94
3.75
4.75
8.25
9.50
11.50
13.00
to the
,
70
-------
Figure 25 compares the cumulative seasonal lysimeter ET for alfalfa with
cumulative seasonal pan evaporation. Figure 26 gives the approximate pan
coefficients (crop ET divided by pan evaporation) during the season. The
wide scatter in points is due to the fluctuation in lysimeter readings caused
by the sticking problem mentioned under procedures. The curves in Figure 26
are average lines that approximate the true pan coefficients. The high
values that occurred just prior to each cutting appear to be about 1.2. Crop
ET is not normally expected to be higher than pan evaporation for such
extended periods of time.
The location of an evaporation pan has an influence on the amount of water
evaporated from it. In this case, the pan was situated in the middle of a
fully irrigated alfalfa field. Although the alfalfa was clipped in the
standard pan enclosure area, the location is not entirely representative of
normal evaporation pan locations. Since the pan was completely surrounded
by a crop that is transpiring water into a relatively wet atmosphere, it is
expected that the pan evaporation would be somewhat less than that of more
typical installations.
Figure 27 compares pan evaporation to the potential ET calculated by the
irrigation scheduling program. If it is assumed that pan evaporation is equal
to the potential ET for the area, then the program under-predicted potential
ET consistently throughout the season by about 8 percent. The weather
information used in the program was collected in the same location as the
pan evaporation data, so if the assumption that pan evaporation is the same
as potential ET is correct, the calculated potential ET should have
corresponded to pan evaporation. However, the discussion of pan evaporation
and lysimeter ET given above would indicate that the pan evaporation for
this location was somewhat below "potential ET." If this is true, then the
under-prediction of potential ET by the computer program was even greater
than the 8 percent shown.
Figure 28 compares lysimeter ET to the empirical ET calculated by the
scheduling program. The correspondence is even less favorable than that
between pan evaporation and potential ET. At the beginning of each growth
period following harvest, the program predicts ET very closely. As the
plants develop and ET increases, the predicted ET becomes less and less
accurate, with the largest deviation generally occurring just prior to
cutting. This is reasonable after looking at the limits in the computer
program and at the pan coefficients in Figure 26. The maximum pan
coefficient is 1.2 while the program limits the pan coefficient to 1.0.
The net result is an under-prediction of ET of 16 percent.
Since the Jensen irrigation scheduling program was originally calibrated for
standard Weather Bureau reporting stations, it was decided that a comparison
should be made between the weather data collected at the research location
and that reported by the weather station at the Vernal airport located
approximately one mile north of the research, farm. It was found that the
daily maximum and minimum temperatures were higher at the airport than at
the farm, so the scheduling program was rerun using the airport temperatures.
Figure 29 compares the results of that computation with lysxmeter ET. A
71
-------
5O.O
120
4O.O
3O.O
h-
UJ
2O.O
IO.O
ALFALFA
CUT
ALFALFA
CUT
ALFALFA
CUT
4/26 5/16 6/O5 6/25
7/15
DATE
8/O4 8/24 9/13
IOO
80^-.
e
6OLU
4O
20
Figure 25. Cumulative lysimeter ET for alfalfa and pan evaporation.
-------
2-5
2.O
tjj
O
li.
IL.
LJ
O
U
z
Q.
.5
I I
4 DAY VALUES
APPROXIMATE
AVERAGE LINE
AVE=94f
4/26
5/16
6/O5
8/O4
8/24
6/25 7/15
DATE
Figure 26. Approximate 4-day averaee pan coefficients (ET alfalfa/E pan) for alfalfa.
9/13
-------
5O.O
4O.O
3O.O
UJ
2O.O
IO.O
PAN E
POTENTIAL ET
4/26 5/16 6/O5 6/25
7/15
DATE
8/O4 8/24 9/13
I2O
IOO
U
60
4O
2O
Figure 27. Cumulative pan evaporation and potential ET.
-------
5O.O
ISO
—i
t_n
4O.O
3O.O
h-
UJ
2O.O
10.0
O
4/26
ALFALFA
CUT
ALFALFA
CUT
ALFALFA
CUT
5/16
6/O5
6/25
7/15
DATE
8/O4
8/24
9/13
IOO
80^
O
60
4O
2O
I-
UJ
Figure 28. Cumulative lysimeter ET and computed ET for alfalfa using Jensen
irrigation scheduling program.
-------
5O.O
4O.O
3O.O
UJ
2O.O
IO.O
4/26 5/16 6/O5 6/25
7/15
DATE
8/O4 8/24 9/13
I2O
IOO
SO
SO
UJ
4O
2O
Figure 29. Cumulative lysimeter ET and computed ET using airport temperatures for
alfalfa (Jensen irrigation scheduling program).
-------
definite improvement was made in the values of predicted ET. The predicted
ET was only 8.6 percent lower than the lysimeter ET. It is expected that
if dew point temperatures from the Vernal airport were available, they
would also be lower than those for the farm, which would further improve
the prediction.
With enough manipulation of data it would be possible to predict the
lysimeter ET for this one season exactly. However, by looking at Figure 30,
which compares the cumulative measured ET of the two lysimeters, it is
obvious that it is not reasonable to expect to be able to predict ET
empirically with any more accuracy than about 10 to 15 percent, since the
direct measurement of ET varied 11.5 percent between the two lysimeters.
From the data shown, it is also apparent that the location at which the
weather information is collected can play a very important role in the
accuracy of the predicted ET.
Sprinkler System Evaluation
Results of the can-catches for single irrigations for the solid-set sprinkler
irrigation system used on the farm outside the plots are summarized in
Tables 25 and 26. The coefficient of uniformity, Cu, is defined by
Equation [4]. The low quarter potential irrigation efficiency, PE is defined
by equation [5]. The low quarter distribution uniformity, Du1 is defined
by Equation [6]. The low catch distribution uniformity, Du.. , is defined
by Equation [7].
2
The average operating pressure for all tests was 60 p.s.i. (4.22 kg/cm ).
The average wind velocity for each catch ranged from 1.27 miles per hour
(0.57 m/s) to 5.45 miles per hour (2.44 m/s) with an overall average of
2.53 miles per hour (1.13 m/s). The wind direction for each irrigation
was west-southwest. Wind speed in the range shown did not seem to have a
significant effect on uniformity of water distribution.
Table 25 shows the individual irrigation uniformities, but what is of
practical importance is the progressive composite performance shown in
Tables 27 and 28 for the system over an irrigation season. An improvement
in both Cu and Du with time occurred. The greatest improvements were in
the values of Du.
Table 29 gives the computed composite uniformities and efficiencies
synthesized for the system assuming every other irrigation was an alternate
set. An improvement was seen both with seasonal compositing and alternate
setting.
In irrigation management to control leaching, the important system parameter
is the distribution uniformity. In order to avoid a salt buildup somewhere
in the field, the minimum desired leaching fraction must be attained at
every point in the field. Therefore, if the Dulc is 80 percent and the
desired minimum leaching percentage is 0.0, the average leaching percentage
for the field would have to be 20 percent to have zero leaching in the low
77
-------
5O.O
4O.O
3O.O
00
c.
>_<
I-
2O.O
IO.O
ALFALFA
CUT
ALFALFA
CUT
ALFALFA
CUT
WEST LYSIMETER
EAST LYSIMETER
4/26 5/16 6/O5 6/25
7/15
DATE
8/O4 8/24
9/13
I2O
IOO
80
6O
o
s—'
H
UJ
4O
2O
O
Figure 30. Cumulative ET for alfalfa from east and west lysimeters.
-------
TABLE 25. SPRINKLER SYSTEM PERFORMANCE PARAMETERS FOR SINGLE IRRIGATIONS USING #30 RAINBIRD
SPRINKLERS WITH 9/64 IN. (3.6 MM) NOZZLES ON 30 FT x 50 FT!/SPACING
Date
06/05/74
06/23/74
07/04/74
07/17/74
08/02/74
08/14/74
08/24/74
Depth
Applied
5.17
3.19
3.18
3.86
4.16
3.19
4.93
Av Depth
Caught
DC
4.70
3.07
2.84
3.64
3.93
2.99
4.75
Av Depth
Low 1/4
C,
iq
3.80
2.53
2.49
2.92
3.53
2.63
4.01
Low
Catch
C,
1
3.57
2.39
2.33
2.58
3.38
2.43
3.65
Cu
86.
88.
92.
87.
94.
91.
89.
PE
Low 1/4
74.
79.
78.
76.
85.
82.
81.
Du-
Low 174
81.
82.
88.
80.
90.
88.
84.
DU
Low Catch
76.
78.
82.
71.
86.
81.
77.
- 1 foot = 0.305 m
TABLE 26. SPRINKLER SYSTEM PERFORMANCE FOR SINGLE APPLICATIONS USING #20 HILO RAINBIRD
SPRINKLERS WITH 5/32 IN. NOZZLES IN 25 FT X 30 FT SPACING!/
Date
08-07-75
08-13-75
Depth
Applied
2.18
3.05
Av Depth
Caught
DC
1.98
2.496
Av Depth
Low 1/4
c.
iq
.925
1.725
Low
Catch
c.
1
.69
1.26
Cu
73.
79.
PE
Low 1/4
43.
56.
Du
iq
Low 1/4
47.
69.
Du
Ic
Low Catch
35.
50.
— 1 foot = 0.305 m
-------
TABLE 27. SPRINKLER SYSTEM PERFORMANCE COMPOSITED TO.DATE FOR #30 RAINBIRD SPRINKLER WITH
9/64 IN. (3.6 MM) NOZZLE ON 30 FT X 50 FT- SPACING
00
o
Date
06/05/74
06/23/74
07/04/74
07/17/74
08/02/74
08/14/74
08/24/74
-/I foot =
TABLE 28.
Date
08-07-75
08-13-75
Depth Av Depth
Applied Caught
DC
5.17
8.36
11.54
15.39
19.55
22.74
27.67
0.305 m
4.70
7.77
10.61
14.25
18.18
21.17
25.92
Av Depth
Low 1/4
°lq
3.80
6.48
9.21
12.50
16.51
19.22
23.51
Low
Catch
Cl
3.57
6.19
8.89
11.93
15.79
18.39
22.60
SPRINKLER SYSTEM PERFORMANCE COMPOSITE TO DATE
5/32 IN. NOZZLE ON 25 FT X 30 Fli/ SPACING
Depth
Applied
2.18
5.23
Ave Depth
Caught
DC
1.98
4.482
Av Depth
Low 1/4
cn
.925
2.88
Low
Catch
Cl
.69
1.99
CU
86.
87.
90.
91.
94.
93.
94.
FOR #20 HILO
CU
73.
78.
PE Dun Du.,
T i // lcl 1°
Low 1/4 T i // T „ _ u
Low 1/4 Low Catch
74.
78.
80.
81.
84.
85.
85.
RAINBIRD
PE
Low 1/4
43.
55.
81.
83.
87.
88.
91.
91.
91.
SPRINKLER
76.
80.
84.
84.
87.
87.
87.
WITH
Du- Du..
lq Ic
Low 1/4 Low Catch
47.
64.
35.
44.
1 foot = 0.305 m
-------
TABLE 29. SPRINKLER SYSTEM PERFORMANCE WITH SIMULATED COMPOSITED ALTERNATE SETS FOR #30
RAINBIRD SPRINKLER WITH 9/64 IN. (3.6 MM) NOZZLE ON 30 FT X 50 FTi/SPACING
00
Date
06/05/74
06/23/74
07/04/74
07/17/74
08/02/74
08/14/74
08/24/74
-I foot
Depth
Applied
5.17
8.36
11.54
15.39
19.55
22.74
27.67
= 0.305 m
Av Depth
Caught
DC
4.70
7.77
10.61
14.25
18.18
21.17
25.92
Av Depth
Low 1/4
°n
3.80
7.11
10.11
13.24
17.19
20.07
24.95
Low
Catch
Cl
3.57
6.90
9.87
12.83
16.90
19.73
23.95
Cu
86.
95.
97.
95.
97.
97.
98.
PE
Low 1/4
74.
85.
88.
86.
88.
88.
90.
Dun
iq
Low 1/4
81.
91.
95.
93.
95.
95.
96.
Dun
Ic
Low 1/4
76.
89.
93.
90.
93.
93.
92.
-------
quarter application area. If the Du1 is 92 percent and the desired
leaching percentage is Q.O, the ayerage leaching percentage for the field
would be 8 percent. In other words, the average attainable leaching
fraction varies inversely with Du.
In evaluating a sprinkler system for leaching control, it is necessary to
know the minimum depth applied in relation to the average depth applied to
the entire field. In operation, the pressure and therefore the sprinkler
nozzle discharge varies over the field. Under strict management or
automation it is possible to operate a sprinkler system so that the supply
end of all the laterals would operate at the same pressure. If the standard
20 percent friction loss in the lateral is used, then the pressure at the
first outlet is approximately 1.2 times the pressure at the last outlet.
From Keller and Karmeli (1974) for multiple outlet lines
H = HA + R. AH + |^ [15]
m A n m — 2
where
H = line inlet pressure head
m
H, = average line pressure
A
IL = headloss adjustment = .77 for single size line
AH = headloss in line = 0.2 H
m m
EL = change in elevation =0.0 for this situation
IL = end pressure head
rearranging and substituting
HA = 1.2 Hp - .77 (.24 HD) [16]
H— i no H
— ^^
and since Q varies with the square root of H
Q. = 1.01 Qn [17]
«. LJ
where
Q = average outlet discharge
Q = discharge of last outlet
In other words, the average discharge is 1.0 percent higher than the end
discharge. If, as before, the low catch distribution efficiency is 91 percent
and the desired minimum leaching percentage is 0.0, then the average
leaching percentage for the field would be 10 percent. That means that
for the solid-set sprinkler system on the farm, operated on an alternate
set basis, the minimum leaching percentage attainable without actually
under-irrigating some area is 10 percent. If some leaching is maintained,
say 3 percent, then the minimum average leaching percentage required for the
farm is 13 percent.
82
-------
Alfalfa Yield Response
Detailed measurements of yield were taken for two alfalfa cuttings in 1974
and for three cuttings in 1975. The complete data are shown in Table 30.
The deep water table plots were not included in the 1975 experiments. A
statistical analysis of the data from the factorial experimental design
showed significance for water table depth and water quality treatments, but
not for the leaching fraction treatment. The deepest water table and the
highest quality water gave the highest yields. Table 31 shows data averaged
by treatments for 1975. The lowest leaching fraction showed the highest
yield among those treatment averages. For the two year duration of this
experiment, there was no significant treatment effect of leaching fraction
on yield even at the 10 percent level by the F test.
CROP GROWTH AS INFLUENCED BY SOIL SALINITY
1974 Corn Results
Irrigation - The funnel sampling system demonstrated the line source
sprinkler system to be effective in establishing the continuous but
uniformly variable water treatments (Figure 31). Total amounts of water
applied ranged from 17.7 inches (45.0 cm) at water level one (Wl) to 1.7
inches (4.2 cm) at W20. Wind proved to be a major problem associated with
the irrigation system. As a result, irrigation was conducted only in the
early mornings and some late afternoons when wind speed was low. This
schedule resulted in less water being applied than had been desired, but
it maintained the continuous variable water treatment.
Soil Water Content - The soil water content measured with a neutron probe
show that soil water decreased very little on any treatment. Neither
irrigation treatment nor salt levels had any appreciable effect on the
water content (Figures 32 and 33). Readings taken as late as September 18,
1974, showed high water contents in all the treatments. Upward flow from
a water table at about 7 feet (2.13 m) was the most likely source of the
water causing the water contents to stay high, since total natural
precipitation was only 0.5 inches (1.27 cm) for the entire growing season.
This plot was originally chosen because the water table was deeper than at
any other place on the Vernal farm, but apparently the amount of water
movement upward from the water table was still significant.
Salinity
In the application of salt to control the osmotic potential, intentions were
to evenly distribute the salt in the top two feet (60 cm) of soil. Achieving
this would have resulted in the approximate desired osmotic potential of
each treatment (Table 2). However, soil samples taken before the first
irrigation showed the salt to be somewhat unevenly distributed in the top
83
-------
TABLE 30. ALFALFA DRY MATTER YIELDS FROM 1974 AND 1975 WATER .MANAGEMENT
EXPERIMENTS IN VERNAL, UTAH
Treatment
Dry matter yields
I/
7/23/74
metric tpns/hectare—
8/11/74 6/26/75 8/1/75
9/15/75
5A1
5A2
5A3
5N1
5N2
5N3
5S1
5S2
5S3
7A1
7A2
7A3
7N1
7N2
7N3
7S1
7S2
7S3
9A1
9A2
9A3
9N1
9N2
9N3
9S1
9S2
9S3
Average
±1 0.45
5.77
4.99
5.48
5.05
4.69
4.45
3.85
4.56
4.56
4.94
5.18
5.10
4.45
4.86
5.34
3.61
4.69
4.94
3.55
2.88
2.58
3.58
3.47
3.17
3.07
3.72
3.80
4.31
x metric tons/ha
4.70
4.39
4.39
4.50
4.39
4.34
4.45
4.58
4.45
4.94
5.26
4.94
4.58
4.45
4.88
4.20
4.50
4.77
3.42
3.15
2.77
3.20
3.47
3.42
2.60
2.93
3.15
4.10
= tons/ac.
8.02
5.27
5.75
4.33
2.78
2.91
5.13
5.27
3.56
6.58
5.72
5.67
5.21
5.27
5.18
3.37
4.29
4.86
—
—
—
4.95
6.03
5.14
5.83
4.16
4.76
4.88
3.33
3.24
3.20
5.41
6.86
6.02
5.41
5.52
4.33
6.44
3.61
3.57
—
—
—
4.87
5.35
4.35
3.11
3.47
4.06
4.46
3.74
3.30
2.99
4.54
4.57
4.00
5.08
5.33
4.23
5.61
4.58
4.01
—
—
—
4.26
84
-------
TABLE 31. AVERAGE DRY MATTER ALFALFA YIELDS FOR THREE CUTTINGS IN 1975
Yield .
Treatment __^ metric tons/hectare^
5 foot— water table (5) 4.38
7 foot— water table (7) 5.01
High Quality Water (A) 5.45
Middle Quality Water (N) 4.52
Low Quality Water (S) 4.12
Low Leaching Fraction (1) 5.07
Middle Leaching Fraction (2) 4.66
High Leaching Fraction (3) 4.26
-1 foot = 0.305 meters
2/
— 0.45 x metric tons/ha = tons/acre.
18 inches (46 cm) of soil. This concentration of salt was one of the factors
that may have made a second planting necessary, although seedling emergence
in the whole agricultural area was generally poor.
The tractor and fertilizer spreader proved to be a relatively easy method of
applying the salt but may have caused a serious soil compaction problem.
Since higher salt application required more trips across the plots with the
tractor and 'drill, the compaction problem in turn caused an infiltration
problem in the high salinity plots that may have affected germination. There
was noticeable difficulty getting the irrigation water into the soil without
runoff. It was necessary to irrigate for a shorter duration more frequently.
The salt became more evenly distributed in the soil profile with time.
Electrical conductivities of the soil samples taken just before harvest
showed the salt to be relatively uniformly distributed to a depth of 3 feet
(0.9 m). The higher water application levels leached the salt slightly
deeper and distributed it somewhat more evenly.
85
-------
I2O
iL)
oc
IOO
O 80
o
13 eo
Q.
CL
LJ
4O
2O
LjJ
o:
1 1 1 1 1 1 1 1 1 1 1 1
•
— •
• •
•
1 • I
. o:
* UJ
"~ • ^
z
a:
§5
I 1 i 1 1 1 1 1 1 1 1 1
1 1 1 1
•
«
•
1 1 1 1
2O 18 15 13 II 863 I I 368 II 13 15 18 2O
IRRIGATION LEVEL
Figure 31. Relative sprinkler application rate as a function of
distance from the sprinkler line.
86
-------
.0
H
Z
z
o
o
WATER
A
n 1 1
1 1 1 n
WATER TREATMENTS
mm
^"V ""fli i^^_ *^-i.
—
W
— w
w
W 1
w
1
-
^TTT-- -.^^
->^, ~~-^_
"^- •
—
Q .„_.,_
y —
ill)
6-12 7-5 7-\5 7-3O 8-1
DATE
8-26 9-18
Figure 32. Average volumetric water content in the 1-3 feet (0.3 - 0.9 m)
zone during the growing season, 1974.
H
Ui
O
O
o:
LJ
o
I \ I
SALT TREATMENTS
1
6-12 7-5 7-15 7-30 8-11 8-26 9-18
DATE
Figure 33. Average volumetric water content in the 1-3 feet (0.3-0.9 m)
zone during the growing season, 1974.
87
-------
Yield
The detailed yield data and their resulting graphs are presented in Tables
B-l, B-2, and B-3, and Figures B-l, B-2, and B-3 of Appendix B. Oven dry
matter has been expressed both as Kg/ha and grams/plant to isolate the
compaction effect.
Figure 34 shows the average grain and dry matter yield plotted as a function
of salt and irrigation levels. The data show a nearly linear relationship
between yield and salt levels. There was not nearly as much influence on
yield due to irrigation treatment as due to salt treatment. This was
probably due to water coming up from the water table.
Dry matter yield on an area basis showed an 83 percent reduction over the
range of salt applied and showed 52 percent reduction over the range of
irrigation levels. Dry matter yield was expressed as grams per plant to
correct for the low plant population in the compacted high saline areas.
Expressed as grams/plant, dry matter declined 67 percent in response to
salt and 56 percent in response to water application level. Grain
production declined 96 percent over the range of salt applied. A 64 percent
grain yield reduction was found over the range of water treatments.
The effect of compaction on yield is seen in Figure 35. The yield
expressed as grams/plant has been equated to that of Kg/ha by a correction
factor. The difference in slope of the lines is due to decreased dry
matter production of plants growing in the compacted area and a fewer number
of plants in the heaviest salt treatments (Table 32).
TABLE 32. AVERAGE NUMBER OF PLANTS PER PLOT ROW AS A FUNCTION OF SALT AND
IRRIGATION TREATMENTS.
Salt level
Average number
of plants
SI
10
S2
9
S3
10
S4
10
S5
9
S6
9
S7
9
S8
5
S9
6
S10
4
Water level Wl W3 W5 W7 W9 Wll W13 W15 W17 W19
Average number 99899 8 8 7 7 7
of plants
Although the average number of plants did not significantly decrease in the
first seven salt levels, the dry matter produced per plant within the
compacted area showed a steady decrease starting about salt treatment S3.
There were no plants growing in the compacted area of S10.
Monitoring Devices
Limited success was achieved with the salinity monitoring instruments. The
EC of the soil solution extract was the only data to give a good measure of
88
-------
SALT LEVEL
246
c
o
IT
til
o:
Q
-• GRAIN
• DRY MATTER
16 12 8
WATER LEVEL
Figure 34. Dry matter and grain yields as influenced by salt and
water levels.
89
-------
vO
O
IO
o
JC
\
co 8
c
o
o
"u
I 6
E 6
N_X
Q
J
UJ
a:
LU
H
1
o:
Q
o
--• PROJECTED
5 6
SALT LEVEL
8
10
Figure 35. Projected and actual dry matter yields as a function of salt and irrigation levels.
-------
°f
Pr°f±le f°r the course * the growing
Salinity sensors - The salinity sensor results were useful in differentiating
between the salt and water treatments (Table 33). The salt levels are §
i^thf r ^ I magnitude in the readings and the irrigation treatments
by the range in the readings over time. The sensors in the high water
treatments picked up the movement of the applied salt very early The
lowest water application levels required the entire summer to leach the
salt down to one foot (30 cm). Because the salinity sensors were all
located at 12 inches, they were insensitive in describing the salinity
status of the total soil profile. Their cost prohibited using them in any
TABLE 33.
ELECTRICAL CONDUCTIVITY OF THE SOIL SOLUTION AS MEASURED BY
SALINITY SENSORS AS A FUNCTION OF TIME AND SITE AT THE 30 CM
DEPTH
Block
Salt
level
Water
Level
Sampling date
7-15 7-30 8-11 8-24
mmhos/cm at 25°C
9-18
1
2
3
4
1
2
4
0
3
3
6
8
8
8
1
2
5
9
2
3
4
5
6
7
17
15
6
10
3
20
14
7
11
19
8
12
18
3
16
9
12
10
7
10
2.4
1.7
7.7
3.7
5.5
1.2
5.9
3.0
11.7
18.0
1.1
1.9
13.0
4.5
7.7
3.0
10.6
7.7
2.3
12.1
1.5
7.9
1.4
15.8
1.0
1.2
4.3
1.1
7.2
9.4
0.5
0.3
13.6
3.5
7.0
1.7
7.7
9.8
4.5
9.7
1.4
6.0
1.1
13.9
0.3
1.9
1.7
0.7
7.4
9.5
1.0
0.3
13.2
2.2
3.0
10.1
11.1
6.4
12.5
1.2
6.3
0.3
17.0
0.3
1.6
1.4
0.6
13.5
0.7
0.3
14.5
3.5
7.7
1.2
10.9
9.9
7.7
9.6
1.4
5.8
1.6
19.0
0.3
1.9
2.2
0.3
9.2
15.8
0.3
0.3
12.1
9.8
6.9
1.7
10.9
11.1
9.6
91
-------
Thermocouple psychrometers - The thermocouple psychrometer data proved to be
of little value. The main problem was obtaining a reliable low value
calibration reading at the wet end of the calibration curves. A consistent
low reading from the dew point microvolt meter could not be obtained. This
made it very difficult to obtain a reliable water potential reading. This
also eliminated the usefulness of the psychormeters since most of the
readings taken in field were in the lower range. Inasmuch as the water
content did not decrease very much during the season, the psychrometer
readings would have been changed mostly by salt. Lack of operating
experience may also have been a contributing factor.
Resistivity meter - The 4-probe resistivity method (described in Gupta and
Hanks, 1972) was used to monitor salt movement and distribution in the
profile. The data are presented in Table B-4 of the Appendix B. Problems
related to variable soil water content were somewhat minimized by taking
the readings just before each irrigation. The water content of the soil
just prior to irrigation was relatively constant over the course of the
growing season (Figures 36 and 37).
The 4-probe data show the general trend in the distribution of the salt. They
do not, however, show the uniformity in distribution that the soil sample
data suggest.
The results tend to indicate that the 4-probe was not well suited for routine
field use for precision measurements of profile salt distribution. The
problem may be due to lack of a reliable water content correction.
Ceramic solution samplers - The ceramic samplers worked well as a means of
obtaining soil solutions as long as the units were intact and the soil
water content was high. The data collected in the corn plots are shown in
Table B-5 in Appendix B. Salt was leached more rapidly and to a greater
depth in the high water treatments. The samples taken from the lowest water
application levels indicate that it required the entire season to leach the
salt to 4 feet (1.2 m).
1975 Corn Results in Vernal
The irrigations applied on the various dates and for different distances
from the sprinkler line are shown in Table 34. Also shown are the replication
measurements which indicate some of the practical problems of using the line
sprinkler source with wind and temporarily clogged sprinklers. There is
some variation in application for different irrigations.
The yield data are shown in Table 35. The data show yield decreases that
are consistent only for salt levels greater than S8 and for water levels
above 17. Thus it appears that there was very little carry-over effect of
the salt applied except where water application, and therefore, leaching,
was limited. The amount of water applied on level 17 was about 2.8 inches
(7 cm) above the rainfall that occurred.
92
-------
I-
Z
LU
O
o
o:
I
T 1 1 1
WATER TREATMENTS
W
1
W II
W 15"
W 19
I I
I
I
I
6-12 7-5 7-15 7-3O 8-1
DATE
8-26 9-18
Figure 36. Average volumetric water content in the 1-3 feet
(0.3 - 0.9 m) zone during the growing season, 1974.
liJ
O
O
P
.2
I I I I
SALT TREATMENTS
6-12 7-5 7-15
7-30 8-11
DATE
8-26 9-18
Figure 37. Average volumetric water content in the 1-3 feet
(0.3 - 0.9 m) zone during the growing season, 1974,
93
-------
TABLE 34. IRRIGATION APPLIED AT VERNAL, UTAH, ON THE CORN PLOTS IN 1975
Date
7-3
7-18
7-31
8-13
8-27
Totals
Rep
A
B
C
D
Ave
A
B
C
D
Ave
A
B
C
D
Ave
A
B
C
D
Ave
A
B
C
D
Ave
A
B
C
D
Ave
Distance
0.38
6.6
6.6
6.6
6.5
6.4
12.4
11.6
9.2
10.5
10.9
5.4
6.8
5.7
4.9
5.7
11.6
10.7
12.9
9.2
11.1
10.6
10.2
6.8
9.5
9.3
46.1
45.8
41.2
40.6
43.4
2.21
6.6
4.8
5.7
5.2
5.6
8.3
12.5
6.4
10.0
9.3
5.3
7.1
5.4
4.6
5.6
15.0
11.6
13.7
8.4
12.2
7.5
7.3
10.0
7.6
8.1
42.6
43.2
41.3
35.9
40.8
4.04
6.6
4.4
4.8
4.9
5.2
10.0
9.2
7.1
8.4
8.7
4.8
7.3
5.2
3.4
5.2
12.4
10.9
12.4
6.5
10.6
7.5
7.3
4.1
5.8
6.2
41.2
39.2
33.6
29.0
35.8
from sprinkler line
5.87
6.6
4.0
4.6
4.5
4.9
6.0
4.4
6.9
6.7
6.0
3.5
5.7
4.6
2.7
4.1
7.5
6.0
11.0
5.6
7.5
5.7
7.0
4.4
3.9
5.2
29.2
27.1
31.5
23.4
27.8
7.70
4.0
3.3
4.4
3.5
3.8
5.6
5.2
6.0
5.4
5.6
3.0
4.8
3.1
2.2
3.3
7.6
5.6
8.2
5.4
6.7
5.0
6.8
4.1
2.7
4.6
25.2
25.1
25.8
19.3
23.8
9.52
2.3
2.6
4.3
2.6
3.0
5.4
3.4
4.1
1.7
3.6
2.8
4.2
2.3
1.9
2.8
7.6
5.3
9.9
4.1
6.7
6.8
5.3
5.7
1.9
4.9
25.6
20.8
26.4
12.2
21.2
- meters
11.35
2.4
1.2
3.1
2.2
2.2
3.3
2.0
3.3
1.7
2.6
1.6
2.7
1.5
1.1
1.7
5.0
3.0
3.9
2.4
3.6
3.9
3.4
2.8
1.5
2.9
17.4
12.3
14.6
8.9
13.3
13.18
0.2
0.8
1.5
0.9
0.8
1.4
0.5
2.0
1.0
1.2
1.0
1.1
0.3
0.3
0.7
2.4
1.8
2.2
1.6
2.0
2.3
1.9
1.0
0.8
1.5
7.8
6.1
7.1
4.6
6.4
15.01
0
0
0.8
0.3
0.3
0.8
1.1
2.6
0.7
1.3
0
0.2
0
0.2
0.1
1.1
0
0.8
0.8
0.7
1.2
0.3
0.4
0.4
0.6
4.0
1.5
4.6
2.4
3.1
94
-------
TABLE 35. INFLUENCE OF SALINITY LEVEL IMPOSED IN 1974 ON 1975 CORN DRY
MATTER YIELDS AS INFLUENCED BY WATER LEVEL
Water
Level
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Ave.
SI
22.48
15.28
18.26
19.54
20.22
15.44
21.75
20.15
17.63
17.75
23.62
22.43
21.29
18.65
24.10
21.15
18.75
21.81
14.86
17.32
19.63
S2
26.41
23.17
20.56
24.13
25.72
23.32
25.28
26.37
26.60
23.77
26.64
22.70
23.11
23.63
21.87
26.84
23.52
19.24
21.37
19.28
23.68
S3
23.04
16.98
20.55
10.34
18.20
18.07
22.35
20.15
17.80
21.96
22.10
24.12
20.38
20.52
22.22
19.57
19.39
16.04
19.42
15.03
19.41
S4
27.35
23.77
24.83
12.72
25.44
23.32
21.75
20.68
19.84
22.57
23.61
19.01
21.87
20.57
20.64
21.15
19.08
17.96
18.77
15.35
21.01
S5
- mt/ha
27.53
19.82
27.42
21.82
24.57
22.32
21.16
24.61
19.43
23.17
22.10
16.56
20.63
19.27
18.46
18.32
16.21
15-72
17.81
16.01
20.65
S6
-
20.97
20.40
21.32
18.74
26.00
26.24
22.27
23.11
23.91
22.27
27.56
23.32
20.64
17.41
20.02
18.62
18.12
14.11
16.19
16.34
20.88
S7
18.35
20.00
18.27
17.61
22.24
23.32
20.87
23.71
22.99
18.97
22.71
21.16
21.87
16.57
17.51
17.68
14.29
12.50
15.21
18.95
19.24
S8
21.36
21.90
19-79
16.47
18.77
21.57
21.71
20.90
19-72
17.75
18.77
17.78
21.48
12.42
12.83
14.36
12.07
11.21
8.41
11.75
17.05
S9
22.09
21.52
17.11
18.76
14.26
17.49
18.51
15.70
20.32
15.65
15.74
17.18
16.94
12.12
14.08
10.72
6.34
6.40
4.51
4.24
14.48
S10
20.59
24.17
20.55
21.37
18.49
23.91
21.16
20.15
26.01
17.15
21.80
11.64
18.48
17.09
15.33
13.88
6.48
9.30
8.44
6.20
17.11
Ave
23.02
20.70
20.87
18.15
21.39
21.50
21.68
21.55
20.43
20.00
22.47
19.59
20.67
17.83
18.71
18.23
15.63
14.43
14.50
14.05
Note: Water level 1 is adjacent to the sprinkler system. The distance between
water levels was 2.5 ft (0.76 m). Averages of four replications.
Alfalfa Results in Vernal 1975
The dates and amounts of irrigation water applied for the salinized alfalfa
plot study are shown in Table 36. Two replications were measured for
irrigation applied.
The alfalfa dry matter yields are shown in Table 37. The data show no yield
decrease due to either the variable water or salinity treatments. Lack of
a yield decrease due to water level treatments could be a result of the
high water table. Unlike the corn data, there was probably no yield decrease
due to salinity treatment because the alfalfa roots were already established
95
-------
TABLE 36. IRRIGATION APPLIED AT VERNAL, UTAH, ON THE ALFALFA PLOTS IN 1975
Distance
Date
7-3
7-18
8-11
8-28
Totals
Rep
A
B
A
B
A
B
A
B
A
B
0.38
5.6
6.2
9.8
9.8
13.0
12.2
6.9
6.2
35.2
34.5
2.21
7.0
6.9
10.3
13.6
12.0
10.9
7.5
6.1
37.0
37.4
4.04
6.4
7.5
10.7
10.7
12.4
10.2
9.5
5.0
39.0
33.5
from sprinkler line
5.87
— cm
5.7
7.9
9.6
8.8
10.0
9.4
8.8
4.6
34.2
30.7
7.70
—
5.6
7.3
11.1
6.9
7.6
7.5
3.5
4.6
27.9
26.3
9.52
4.2
3.5
6.8
5.4
5.3
5.8
2.8
3.0
19.2
17.7
- meters
11.35
2.2
2.0
4.9
5.7
3.5
2.4
4.4
1.8
14.9
12.0
13.18
0.5
1.1
4.1
3.1
1.6
1.5
1.9
1.1
8.1
6.7
15.01
0.6
0.6
0
4.4
0.7
0
1.5
0
2.7
5.0
Ave 34.9 37.2 36.3 32.5 27.1 18.5 13.5
7.4
3.9
when salt was applied. Some of the roots were undoubtedly functioning below
the level where the salty water penetrated. A summary of dry matter yields
by treatment are shown in Table 38.
Corn Results in Logan in 1975
The dates and amounts of water applied by irrigation in the 1975 corn studies
at Logan are given in Table 39.
Table 40 shows the grain corn yields at Logan in 1975 as influenced by both
water and salt level. The data show a general decrease in yield as the salt
level increased. There was little indication of an interaction of salinity
level and water level because the relative yield decrease from SI to S6 was
about the same for all water levels. There was a general decrease in yield
as irrigation water applied decreased below about 12 inches (30 cm) (water
treatment level 9). The corn dry matter yields in Table 41 show the same
general trends as the dry matter yields.
96
-------
TABLE 37. ALFALFA DRY MATTER YIELDS AS INFLUENCED BY SALINITY AND WATER
LEVELS AT VERNAL, UTAH, 1975. SECOND CROP HARVESTED JULY 31,
1975.
Water
level
Wl
Wl
Wl
Wl
Wl
W2
W2
W2
W2
W2
W3
W3
W3
W3
W3
W4
W4
W4
W4
W4
W5
W5
W5
W5
W5
All
Salinity level
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Ave
Sl
6.8
5.0
6.4
8.0
6.6
7.0
8.0
7.4
8.2
7.7
6.2
5.4
7.0
8.0
6.7
6.9
7.2
6.4
8.4
7.2
6.6
4.0
4.8
8.0
5.9
6.8
S2
4.6
6.2
5.2
5.6
5.4
6.4
6.2
6.5
4.1
5.8
6.4
5.6
5.4
5.6
5.8
7.0
4.6
5-2
4.8
5'.4
7.0
5.0
6.2
5.4
5.9
5.7
S3
—
4.6
8.2
7.0
5.2
6.3
5.6
6.0
6.2
6.4
6.1
4.6
5.6
6.6
6.8
5.9
5.4
5.4
6.2
6.0
5.8
8.4
4.4
5.8
7.8
6.6
6.1
S4
mt/ha —
6.0
5.0
4.6
4.0
4.9
5.4
5.4
4.8
4.6
5.1
6.6
5.2
4.6
5.6
5.5
6.4
5.2
6.4
5.0
5.8
8.6
6.4
7.4
5.2
6.4
5.5
S5
5.6
6.0
6.8
6.0
6.1
6.0
5.8
5.4
7.2
6.1
4.8
4.6
4.2
7.2
5.2
5.8
4.8
8.8
5.6
6.3
8.2
4.0
5.6
6.0
6.0
5.9
S6
7.0
6.0
3.2
4.0
5.1
5.6
6.8
3.8
4.9
5.4
7.4
7.4
5.0
5.0
6.2
7.2
5.0
5.0
4.5
5.4
6.0
6.2
5.2
4.6
5.5
5.5
Ave
5.8
6.1
5.5
5.5
5.7
6.0
6.4
5.7
5.9
6.0
6.0
5.6
5.5
6.4
5.9
6.5
5.4
6.3
5.7
6.0
7.5
5.0
5.8
6.2
6.1
5.9
97
-------
TABLE 37. CONTINUED. THIRD CROP OF ALFALFA HARVESTED SEPTEMBER 12, 1972
Water
level
Wl
Wl
Wl
Wl
Wl
W2
W2
W2
W2
W2
W3
W3
W3
W3
W3
W4
W4
W4
W4
W4
W5
W5
W5
W5
W5
All
Salinity Level
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Rep 1
Rep 2
Rep 3
Rep 4
Ave
Ave
Sl
3.0
2.7
2.5
2.7
2.7
3.4
2.5
2.9
2.5
2.8
3.1
3.3
1.8
2.9
2.8
2.8
3.2
2.6
3.0
2.9
2.0
2.0
1.8
2.0
2.2
2.7
S2
2.7
2.5
3.6
3.4
3.1
2.5
2.9
3.2
3.6
3.1
2.8
2.8
4.3
3.3
3.3
2.8
3.0
3.1
3.4
3.1
2.2
2.6
2.2
3.4
2.6
3.0
S3
2.7
2.5
2.3
2.2
2.4
3.4
3.4
2.6
2.4
3.0
2.6
3.7
3.0
2.4
2.9
2.6
2.5
2.8
1.7
2.4
2.6
3.2
2.2
1.4
2.4
2.6
S4
— nit /ha —
2.7
3.0
3.2
3.1
3.0
2.9
3.2
3.6
3.2
3.2
3.4
3.0
3.1
3.7
3.3
2.8
3.0
2.8
4.0
3.2
2.6
2.4
2.4
3.4
2.7
3.1
S5
3.8
2.9
3.2
2.1
3.0
3.3
2.7
3.1
2.3
2.9
3.1
2.8
3.4
2.0
2.8
2.8
2.8
3.0
2.5
2.8
3.0
2.8
2.6
2.0
2.6
2.8
S6
3.0
2.9
2.3
2.3
2.6
3.8
3.4
2.0
2.9
3.0
3.0
3.5
1.8
2.2
2.6
3.0
3.4
1 9
2.1
2.6
2.6
2.8
1.8
1.8
2.2
2.6
Ave.
3.0
2.8
2.9
2.6
2.8
3.2
3.0
2.9
2.8
3.0
3.0
3.2
2.9
2.8
3.0
2.8
3.0
2.7
2.8
2.8
2.5
2.8
2.2
2.3
2.5
2.8
98
-------
TABLE 38. SUMMARY OF ALFALFA DRY MATTER YIELDS FOR TWO HARVESTS AS
AFFECTED BY SALT AND WATER TREATMENTS (VERNAL, 1975)
Water
Level
Sl
S2
Salinity Level
S3 S4 S5
S6
. Distance from
Ave _ . , _ . .
Sprinkler line
— mt/ha —
Wl
W2
W3
W4
W5
All
2nd
3rd
Sum
2nd
3rd
Sum
2nd
3rd
Sum
2nd
3rd
Sum
2nd
3rd
Sum
2nd
3rd
Sum
6
2
9
7
2
10
6
2
9
7
2
10
5
2
8
6
2
9
.6
.7
.3
.7
.8
.5
.7
.8
.5
.2
.9
.1
.9
.2
.1
.8
.7
.5
5.4
3.1
8.5
5.8
3.1
8.9
5.8
3.3
9.1
5.4
3.1
8.5
5.9
2.6
8.5
5.7
3.0
8.7
6.3
2.4
8.7
6.1
3.0
9.1
5.9
2.9
8.8
5.8
2.4
8.2
6.6
2.4
9.0
6.1
2.6
8.7
4
3
7
5
3
8
5
3
8
5
3
9
6
2
9
5
3
8
.9
.0
.9
.1
.2
.3
.5
.3
.8
.8
.2
.0
.4
.7
.1
.5
.1
.6
6.1
3.0
9.1
6.1
2.9
9.0
5.2
2.8
8.0
6.3
2.8
9.1
6.0
2.6
8.6
5.9
2.8
8.7
5.1
2.6
7.7
5.4
3.0
8.4
6.2
2.6
8.8
5.4
2.6
8.0
5.5
2.2
7.7
5.5
2.6
8.1
5
2
8
6
3
9
5
3
8
6
2
8
6
2
8
5
2
8
.7
.8
.5 0 to 3.0 m
.0
.0
.0 3.0 to 6.1 m
.9
.0
.9 6.1 to 9.1 m
.0
.8
.8 9.1 to 12.2 m
.1
.5
.6 12.2 to 15.2 m
.9
.8
./
99
-------
TABLE 39. IRRIGATION APPLIED AT VARIOUS DATES AND DISTANCES FROM THE
SPRINKLER LINE IN LOGAN IN 1975.
Date
7-2
7-9
7-16
7-22
7-29
8-6
8-12
8-18
8-25
9-4
Total
Rep
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
A
B
Distance from the sprinkler line-meters
1.5
4.4
4.5
4.2
3.6
4.6
4.5
2.5
2.6
3.6
3.9
2.2
2.7
3.0
3.4
2.7
3.6
2.5
3.3
2.3
2.8
33.4
4.6
3.9
3.9
3.8
4.3
3.9
3.9
2.3
2.5
3.6
3.7
2.1
2.4
3.0
3.2
2.9
3.2
2.6
3.1
2.3
2.8
31.6
7.6
- cm -
2.8
2.9
2.4
3.4
3.2
2.5
1.9
2.0
2.4
3.7
1.9
2.2
2.6
2.9
2.3
3.0
2.1
2.8
1.8
2.3
25.5
10.6
1.8
2.0
1.6
2.6
2.2
1.4
1.4
1.4
1.9
0.8
1.0
1.7
1.9
2.1
1.2
1.9
1.4
1.8
1.4
1.2
16.4
13.7
0.4
0.9
0.4
1.0
1.0
0.8
0.4
0.6
0.1
0.1
0.4
0.6
1.2
1.2
0.4
'0.9
0.6
0.7
0.7
0.3
6.3
100
-------
TABLE 40. CORN GRAIN YIELDS AT LOGAN IN 1975 AS INFLUENCED BY SALINITY
AND WATER LEVEL, AVERAGE OF 2 REPLICATES
Water
Level
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Ave
*
D
m
.38
1.14
1.9
2.17
3.4
4.2
4.9
5.7
6.5
7.2
8.0
8.7
9.5
10.3
11.0
11.8
12.5
12.3
14.1
SI
7.4
7.1
7.3
7.6
7.9
8.6
8.6
7.1
8.3
7.1
6.1
8.0
8.1
7.4
7.8
5-7
6.2
6.1
5.3
7.1
S2
7.3
6.8
7.1
7.7
6.2
8.4
7.5
6.4
8.3
5.4
6.2
7.3
6.8
6.0
7.5
4.5
6.5
5.8
4.6
6.5
S3
6.2
6.5
7.6
7.1
7.0
7.2
8.3
7.3
7.2
5.3
6.0
7.2
5.4
5.7
6.8
5.2
5.6
5.5
3.8
6.2
Salinity
S4
- mt/ha
7.5
6.4
6.5
6.6
6.5
7.5
7.5
6.2
7.3
5.9
5.6
6.0
4.1
7.0
6.4
4.0
5.0
3.5
3.0
5.7
Level
S5
_
6.5
5.9
6.2
4.9
4.7
5.9
6.2
5.1
5.1
3.3
3.1
8.0
2.4
5.0
4.7
2.7
3.5
3.2
2.1
4.3
S6
4.2
3.8
4.2
3.3
3.3
4.2
4.6
3.2
5.2
3.4
2.9
4.4
3.0
3.3
2.7
2.3
2.7
2.6
2.0
3.4
Ave
6.4
5.8
6.5
6.2
5.9
6.9
7.1
5.9
6.9
5.0
4.9
5.9
4.9
5.7
6.0
4.0
4.9
4.4
3.4
A
D = distance in meters from the sprinkler line
m *
1 meter =3.28 feet
101
-------
TABLE 41. CORN DRY MATTER YIELDS AT LOGAN IN 1975 AS INFLUENCED BY
SALINITY AND WATER LEVEL, AVERAGE OF TWO REPLICATES
Water
Level
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Ave
A
D
m
.38
1.14
1.9
2.17
3.4
4.2
4.9
5.7
6.5
7.2
8.0
8.7
9.5
10.3
11.0
11.8
12.5
12.3
14.1
Salinity Level
SI
15.5
16.4
15.8
16.0
15.7
17.1
17.0
13.2
16.6
13.9
12.3
14.6
15.0
12.8
14.7
10.9
11.4
11.9
10.9
14.1
S2
16.3
15.8
15.8
17.3
12.9
18.3
17.0
13.2
17.7
11.6
11.8
15.0
13.7
12.1
14.2
9.5
13.2
11.8
9.1
13.8
S3
_
14.8
15.7
14.7
15.2
14.7
15.0
16.8
15.8
15.7
11.5
12.1
14.7
10.9
11.0
11.7
10.0
11.1
10.5
7.9
13.0
S4
mt/ha -
14.8
14.3
13.9
13.7
13.6
15.8
16.1
12.9
14.6
12.0
12.1
11.9
8.8
12.2
10.6
8.4
9.8
7.8
6.7
11.8
S5
13.8
13.2
12.9
11.1
11.6
13.4
13.4
11.5
11.7
8.3
7.8
8.6
6.8
10.0
9.9
6.7
8.1
7.2
5.3
10.1
S6
10.5
9.3
9.8
8.5
8.4
9.9
10.9
8.3
11.6
7.8
7.1
9.5
7.3
8.0
7.1
6.0
6.7
6.2
5.2
8.3
Ave
14.1
14.1
13.8
13.6
12.8
14.9
15.2
12.5
14.6
10.8
10.5
12.3
10.4
11.0
11.3
8.5
10.0
9.2
7.5
11.8
D = distance in meters from the sprinkler line
1 meter =3.28 feet
102
-------
The corn yield data in Logan allowed more reliable estimates of response to
evapotranspiration to be made than the data at Vernal because there was no
flow up from a water table. Evapotranspiration estimates were made (assuming
there was no drainage or upward flow) by summing irrigation, rain, and soil
water depletion. Drainage was estimated to be near zero from lysimeter
measurements. These data are shown in Figures 38 and 39. The data indicate
that yields are strongly related to evapotranspiration although there is
considerable scatter in the data. The data in the figures indicate that
yields from plots treated with different concentrations of saline water
fit in with yields from plots treated with different concentrations of saline
water_fit in with yields from plots irrigated with the best quality water.
The yields of the two Si check treatments, one irrigated initially with canal
water in the spring (the same as the other salinity treatments), and one not
irrigated in the spring almost bracket the data. Since there is no reason to
expect yield differences in the two check treatments (SI), the differences
must be due to unmeasured variation. Thus it is concluded that the effect
of different levels of salinity on yield can be explained as an effect of
salinity on evapotranspiration. The apparent effect of salinity on evapo-
transpiration is reflected in soil water depletion, since irrigation and
rain are the same regardless of the soil salinity condition. The effect
of a high soil salinity is to decrease the water availability in the soil
and therefore increase the soil water content at any given water potential.
The influence of salinity level on water depletions is shown in Table 42,
and was to generally decrease the soil water depletion as the salinity
level increased. Depletion increased as the water applied decreased
(increasing water level treatment numbers). These results tend to show
that the effect of salinity is to reduce water availability for evapo-
transpiration and therefore crop yields are reduced.
TABLE 42. SOIL WATER DEPLETION AS INFLUENCED BY WATER AND SALINITY LEVEL
AT LOGAN IN 1975
Water
Level
SI
S2
Salinity Level
S3 S4
S5
S6
Ave
cm
3
10
19
13.2
14.7
16.8
12.7
10.4
10.4
10.8
15.1
12.1
6.2
12.0
16.3
0.6
2.8
11.0
__
4.8
—
8.7
11.0
13.3
Ave 14.9 11.2 12.7 11.5 4.8 4.8
103
-------
o:
8
(0
c
o
I 6
0)
E
Q
_J
UJ
T
T
10
o S| (irrigated in Spring)
A S, (not irrigated in Spring)
A S2
0 S3
O S
YIELD = -O.49 + O.I47ET
r2 = O.65
I
2O
3O 4O
ET (cm)
5O
6O
Figure 38. 1975 corn grain yield at Logan related to evapotranspiration.
104
-------
18
16
O 14
JC.
X
CO
c
O
O
C
•+-
-------
TESTS OF THE SOURCE-SINK MODEL UNDER LABORATORY CONDITIONS
Experiments in Saturated Flow
Laboratory trial 1 - A leaching trial was conducted using a saturated soil
in small columns. The soil was taken from a depth of 30-60 cm on the USU
experimental farm in Vernal. The objective was to test the computer model
that included a "source-sink" term for saturated flow conditions.
Two segmented soil columns were filled and saturated under the same conditions,
A leaching experiment was performed on one column and the other was segmented
after only the initial saturation. In each section of the segmented columns,
the EC of the saturation solution extracted was measured. After first drying
the soil, the EC of a 1:5 extraction was determined. The first 1:5 extraction
more nearly represented the total salinity present in the soil. Salt balance
data from several trials indicated that the EC (1:5) was a good measure of
the total salinity.
Leaching of the columns was performed by flooding the sample with a constant
head of water. During the leaching process, the volume of the effluent and
its electrical conductivity, the EC(4P), and the EC by the salinity sensors
located near the walls of the column, were measured. The setup of the
experiment is illustrated in Figure 40. The measurements taken during
leaching treatment are shown in Table 43. Measurements were taken until the
rate of change of the EC of the effluent solution was very small. Then the
soil was leached for a few more days until the EC of the influent and the
EC of the effluent was the same. This equilibrium is an indication of a
complete dissolution of salts in the soil and a complete displacement of the
initial soil solution. In the next step the EC. was changed and measure-
ments of the volume and the EC of the effluent were taken in order to compute
the apparent diffusion coefficient, D . D was computed and found to be
16.98 cm2/hr. (For the detailed data usedpin the computation of D see Table
43). The interstitial velocity, U, was 5.07 cm/hr. Assuming thatPD = x|u| is
valid, the A used for the computations was 3.35 cm. Determinations were made
for each of the five segments of the column.
For the numerical solution using the model, the column was divided into 10.5
sections and the column was represented by 12 grid points. The 12 point
was imaginary, out of the physical boundary, and was assumed always equal to
grid point no. 11. (See Figure 41).
If a, b, c, d and e represent data from the five segments, the values of the
grid points will be as follows:
1 = A 5 = (2.27B + .11C)72.38 9 = D
2 = A 6 = C 10 = E
3 = (2.23A + .15B)/2.38 7 = C 11 = E
4=B 8=D 12 =11
Values used for the initial conditions of the EC of the soil solution in the
model were the values of the extraction of the soil water from the second
unleached column that was segmented. The results of the segments were
related to the grid points as explained before (see Table 44). Values for
106
-------
OQ
c
H
ro
8*
en
(D
rt
C
XI
o
Hi
O*
O
O
H
O
H-
V
OQ
(t)
H
H-
I
3
rt
CO
*SEGMENT NUMBER
CONDUCTIVITY
BRIDGE
CONDUCTIVITY
CELL
SALINITY
SENSOR
-------
TABLE 43. RESULTS OF LEACHING TRIAL 1.
o
00
General Data
: Bulk
density
= 1.32
g/cm3
Water fraction = .506
Length of soil column = 25
Area of cross section = 80.
Et
(min)
0
25
66
108
150
191
232
273
314
354
394
434
473
513
553
591
629
667
767
861
959
1141
1515
1705
1891
Et*
-------
TABLE 43. RESULTS OF LEACHING, TRIAL 1 (CONTINUED)
t—
o
It
(min)
2060
2422
2945
3400
3900
4450
£t* Def**
(h)
31.04
36.82
45.17
54.17
64.00
74.13
The reference
(cm)
67.04
79.53
97.56
117.00
138.25
160.11
column
Pore
volume
5.30
6.29
7.71
9.25
10.93
12.65
ECef
(ymho)
4000
3640
3420
3210
3050
2980
EC(4P) mmho
l(top)
7.9
8.0
8.0
8.0
8.0
8.0
Segment :
2
8.4
8.4
8.4
8.4
8.4
8.4
1
3
8.8
8.8
8.8
8.8
8.8
8.8
2
4
9.0
8.9
8.9
8.9
8.9
8.9
3
5
8.9
8.7
8.6
8.6
8.6
8.6
4
EC
1
2240
2240
2240
2270
2210
2220
5
Salinity sensors (ymho)
2
3010
3038
3070
3130
3090
3100
3
2450
2450
2500
2470
2470
2480
4
2480
2300
1850
1990
1910
1960
5
. 2360
2340
2340
2370
2340
2370
EC of 1:5 extraction (ymho)
EC of immediate extraction (ymho)
980 1310 1420 1570 1500
6500 6000 7500 8600 9600
*Time corrected for constant velocity
** Depth of effluent
-------
Grid Ring Scale (cm)
1
2
3
4
5
6
7
8
9
IO
II
(12)
" T 1
. 1
A
_
— -
B
C
D
-
-
E
1
«*-
2.38
4.76
5.8O
7.14
9.52
IO.6O
II.9O
14.28
I5.4O
16.66
I9.O4
2O.2O
21.42
23.8O
25.O -
-
•Real boundary
Real boundary
If A, B, C, D and E represent data from the five
segments, the values of the grid points will be as follows--
1 = A 5 = (2.27B + .I1O/2.38 9 = D
2= A 6 = C 1O= E
3 = (2.23A -I- .15B)/2.38 7 = C 11 « E
4= B 8= D 12 = 11
Figure 41. The location of the grid points relative to the experimental segments.
110
-------
TABLE 44. THE INITIAL SALINITY CONDITIONS USED IN TRIAL 1
Grid point No.
1
2
3
4
5
6
7
8
9
10
11
EC solution
(mmho/cm)
6.50
6.50
6.47
6.00
6.07
7.50
7.50
8.60
8.60
9.60
9.60
Solid salts
(mmho/cm)
3.18
3.18
3.33
5.61
5.61
5.58
5.58
6.01
6.01
5.04
5.04
the initial solid salts in the sample were obtained by subtracting the salts
in the saturated solution from the salts found in the 1:5 extraction. The
solid salts were expressed as a ratio between the salts in a unit volume of
soil to the water fraction in the soil, in order to simplify additional
computations. For example: EC solution =6.50 mmho/cm. EC (1:5) = 0.98
mmho/cm. p, = 1.32 g/cm3. 0 =0.506.
b v
Solid salts = 0-98 X 5 X 1.32 - 6.5 X 0.506 =6.28 mmho/cm.
0.506
The computation of d was done by a numerical approximation. For the
saturated flow equation [10] was reduced to:
K = K for S > 0
K = 0 f or S = 0
The boundary conditions were as follows: n(0, t) = C±r top boundary. C±r is
the concentration of the influent water ( J| )z=z = 0 bottom boundary.
For equal space increments equation [18] was approximated by:
±-1 X a + CfX X b + 42 X c - d
where a = (^ - ^), b - ( - | - ^r), c = (^ - ^ '
111
-------
d = - - X d - -£-? (C^ . - - 2C\
t i 2Az^ i+l i
.
ir
The physical boundary is at z ^ at the bottom of the column. The second order
approximation for (), was used N+IZ - - . The first equation in the
linear set is: C X b + ri Xc=d-C.Xa. The N-l equation is:
/. .3 -i-X.
C^ X a + CNj+1 X (b + c) = d.
The parameter K was found by a "trial and error" procedure in which the
criterion for fitness was n
E | ECC. - ECM. | (T, - T. ,) which is a sum of
j=l 3 233
the weighted absolute deviations.
ECC is the computed EC of the effluent and ECM is the measured EC of the
effluent, both at time " j . "
The detailed program used to search for the optimal K and plot the measured
and the computed results (using the chosen K) vs. time can be found in
Appendix C.
The computed and the measured electrical conductivity of the effluent as a
function of time is shown in Figure 42. By using the D that was computed for
the column, it is possible to find a suitable K value for the model and
simulate the measured data with reasonable accuracy. The biggest deviation
of the computed data and the measured data was in the first stage of the
leaching process. Comparing the measured results with those computed without
a source term (K = 0 in Figure 42) demonstrates the improvement achieved
by including a source term in the model. When no source term was used,
the model results showed that the initial solution was completely displaced
after about two pore volumes of effluent (12 hr) had passed through the
column. For a zero source term, this would be the result expected.
Figure 43 compares the EC computed by the model and the EC(4P) measured by the
calibrated four-probe method as a function of time for the five experimental
column segments. Except for the measurements in the first ring, the EC
measured and the EC computed by the model showed reasonable agreement.
Laboratory trial 2 - The objective of trial 2 was to determine whether the
same order of magnitude of K would be obtained when the experiment was
repeated with the same soil under different initial conditions.
The procedure was similar to that used in trial 1. Initial salinity conditions,
which differed from those which existed in trial 1, were obtained by using
for the prewetting treatment, solutions of different concentrations, and
by wetting the columns in the upwards direction. The initial conditions
were measured as in trial 1 and are shown in Table 45. The data from the
112
-------
COMPUTED (K=O.O43 hr )
COMPUTED (K=OO hr" )
MEASURED
3.0
2.5 5.O 75
NUMBER OF PORE VOLUMES
IO.O
12.5
Figure 42. Computed and measured EC of the effluent vs time - Laboratory trial 1.
-------
E 6
<
o
JC ^
E 5
E
UJ 4
111
TT1 I'.
RING I
I6
O
E5
E
o .
TJi| I
RING 2
O I 2 3 4 2O 4O 6O 8O
TIME(hr)
O 2 4 6 IO 3O5O TO 9O
TIME(hr)
9
8
!?
E 6
E
O 5
UJ
1 1 1
'1'
RING 3
8
r
I 6
E
O 5
UJ
1 '
I I I I I I l
RING 4
2 4 6 2O 3O 4O 5O 6O
TIME(hr)
EC (4P)
EC Model
O 24 6 IO 3O 5O TO 9O
TIME(hr)
o
I6
a5
, ,
RING 5
O 24 6 8 2O4O6O8O
TIME (hr)
Figure 43. EC computed by the model and by the (4P) vs. time laboratory
trial 1.
114
-------
TABLE 45. THE INITIAL SALINITY CONDITIONS USED IN TRIAL 2
Grid point No.
1
2
3
4
5
6
7
8
9
10
11
EC solution
(mmho/cm)
6.50
6.50
6.11
5.50
5.56
5.70
6.07
6.40
6.40
6.40
6.40
Solid salts
(mmho/cm)
14.45
14.45
14.28
14.28
13.79
13.79
11.81
11.60
11.13
6.15
6.15
leaching treatment are shown in Table 46. D for the second trial was computed
and found to be 15.47 cm /hr., X was 3.89 cm (the detailed computation
can be found in Appendix D).
Figure 44 shows the computed and measured EC of the effluent as a function
of time and indicates that the model simulates the measured EC of the effluent
with reasonable accuracy. However, the computed curve did not simulate
the measured data very well in the initial stage of the leaching. The
optimal K found was almost the same as the one found in trial 1.
A comparison between the EC of the soil solution computed by the model and
that measured by the calibrated (4P) for the five experimental segments is
illustrated in Figure 45. A good agreement between the two methods existed
only in ring 4 and ring 5. Differences found in the upper three rings
may have been caused by the method used for wetting the sample initially.
In all of the rings, however, the EC(4P) shows the correct direction of
the process.
Experiments in Unsaturated Soil
Laboratory trial 3 - The objective of this trial was to study the possibility of
finding a "source-sink" term, as in the saturated flow experiments, to
describe salt movement in unsaturated flow conditions.
Procedure - Air dry soil was packed in a column consisting of five rings
(segments) as in trials 1 and 2. The supply of the influent was provided
by a small adjustable pump which discharged the influent into a rotating
115
-------
TABLE 46. RESULTS OF LEACHING EXPERIMENT NO. 2
General data:
Bulk density = 1.255 g/cm Average Darcian
Water fraction =0.53 velocity =2.68 cm/hr
Length of soil column = 24 cm ~ Average interstitial
Area of cross-section = 80.1 cm,, velocity =5.06 cm/hr
One pore volume = 1019 cm EC of the influent = 1920 ymho/cm
At
min
10
10
18
12
15
9
11
15
15
22
11
17
15
13
16
11
15
15
15
25
35
62
28
30
43
42
90
100
110
110
235
235
105
127
Et
min
10
20
38
50
65
74
85
100
115
137
148
165
180
193
209
220
235
250
265
290
325
387
415
445
488
530
620
720
830
940
1175
1410
1515
1642
The reference
EC of
EC of
the 1:5
It*
hr
.13
.28
.56
.76
1.02
1.16
1.33
1.57
1.81
2.17
2.33
2.62
2.87
3.14
3.38
3.54
3-78
4.00
4.26
4.68
5.19
6.19
6.94
7.44
8.09
8.74
10.19
11.79
13.59
15.38
19.43
23.63
25.33
27.37
Def
cm
.33
.75
1.59
2.05
2.75
3.12
3.57
4.22
4.84
5.82
6.24
7.03
7.69
8.43
9.05
9.49
10.14
10.74
11.42
12.56
14.43
16.60
18.60
19.95
21.68
23.43
27.33
31.61
36.44
41.23
52.10
63.36
67.91
73.38
P
.03
.06
.12
.16
.22
.25
.28
.33
.38
.46
.49
.55
.60
.66
.71
.75
.80
.84
.90
.99
1.09
1.30
1.46
1.57
1.70
1.84
2.15
2.48
2.86
3.24
4.09
4.98
5.34
5.76
column
ECef
mho /cm
6600
6600
6150
5850
5650
5600
5650
5800
6100
6300
6600
6500
6300
6400
5850
5750
5650
5500
5100
4800
4500
4300
4200
4000
3800
3800
3700
3700
3500
3500
3300
3200
3200
3100
Segment :
extraction (ymho/cm)
the solution
(ymho/cm)
1 top
18.3
18.3
16.9
12.6
11.5
10.8
10.6
10.1
9.7
9.3
9.0
8.8
8.6
8.5
8.4
8.4
8.3
8.2
8.1
8.1
8.0
7,9
7.7
7.7
7.7
7.7
7.7
7.7
7.6
7.6
7.6
7.6
7.7
7.8
7.8
1
1770
6500
EC(4P)
2
14.2
14.3
15.0
15.7
14.9
13.0
12.8
12.0
11.7
11.3
11.0
10.8
10.5
10.2
10.0
9.8
9.5
9.4
9.1
9.0
8.6
8.3
7.7
7.5
7.3
7.2
7.1
6.9
6.8
6.7
6.7
6.7
6.7
6.7
6.7
2
1650
5500
(mmho/cm)
3
14.0
14.0
14.3
14.6
14.9
15.2
15.3
14.9
13.5
12.7
11.8
11.6
11.5
11.4
11.3
11.2
11.1
11.0
10.9
10.8
10.6
10.3
9.6
9.4
9.1
8.8
8.5
7.9
7.5
7.2
7.0
6.8
6.8
6.8
6.8
3
1600
5700
4
17.5
17.5
16.5
16.0
16.0
16.4
16.9
17.3
17.7
17.1
14.6
14.3
13.5
13.2
13.0
12.8
12.7
12.5
12.4
12.4
12.1
11.3
11.5
11.3
11.0
10.6
10.3
9.3
8.7
8.4
8.1
7.8
7.6
7.6
7.6
4
1520
6400
5
15.1
15.1
14,5
14.0
13.5
13.5
13.5
13.8
14.3
14.7
14.2
13.7
12.5
12.3
12.0
11.6
11.5
11.3
11.2
11.1
10.8
10.6
10.2
10.0
9.8
9.4
9.2
8.4
7.1
7.5
7.2
6.9
6.8
6.8
6.8
5
1060
6400
t* = time corrected for a constant velocity.
116
-------
COMPUTED
MEASURED
3.O
12 16
TIME (hr.)
Figure 44. Computed and measured EC of the effluent vs. time.
-------
6.O
4.O
2.O
6.O
I
TRIAL 2
MEASURED-
COMPUTED-
I I T I
RING I
I l i I
O
2 5 IO 15 2O 25
i i i i
RING 3
2.5
7O
5.O
3.0
i i i r
RING 5
0
I
2 3 4 5 IO 15 2O 25
TIME(hr)
Figure 45. EC computed by the model and by the (4P) vs. time.
118
-------
applicator that distributed the water on the soil surface (Figure 46).
The applicator was a shallow container divided into cells by radial partitions.
The cells were divided into the correct radial size to give equal flow
onto each part of the soil. This apparatus made possible an even water
distribution even at very low water application rates,
The water content in the soil and the bulk density were determined by the
gamma ray attenuation method. The gamma ray source and the probe used to
make the measurements, were placed in a fixed position and the soil column was
placed on an elevator. This made possible the checking of the water content
at any desired soil depth by changing the elevation of the column.
Measurements of the electrical conductivity and the volume of __
After completing the experiment, determination of D was made using the~data
in Table D-l and the computer program in Appendix D. Once this was completed,
another column was prepared in the same way and this column was also leached.
This time the leaching was terminated as soon as a few cubic centimeters of
effluent had passed the bottom of the column. The column was then separated
into its five segments and the EC of the immediate solution extraction was
measured. After drying the soil, the EC(1:5) was also measured.
Data from the first column was used to find the D and K parameters and the
second column was used to test the validity of the model and parameters that
were obtained from effluent data (Cft.Zj) alone.
Trial 3 was conducted on two soil samples , taken from the USU experimental farm
in Vernal from two sites. The two replicates, related to the two soil samples
are titled as 3A and 3B.
The results of leaching the first column (the main column) of soil from both
sites are shown in Table 47 and Table 48. The measurements taken during the
irrigation of the second (the reference) columns are in Tables 49 and 50,
2
D values were computed as before and found to be 4.17 cm /hr for sample 3A
and 2.2 cm /hr in sample 3B. A values (D = X|u ) were 4.3 cm for 3A and
2.7 cm for sample 3B.
The water content was measured periodically by the gamma ray attenuation
method and the 9 values for the intermediate times were interpolated.
v
The bulk density was calculated as follows: (a) An average value of bulk
density of the entire column was determined directly by the weight of the dry
soil and the volume of the column. (b) The deviation of the bulk density of
each ring from the average was estimated by the gamma ray transmissivity of
the dry soil (before irrigation) .
The initial water content conditions were uniform at air dry water content.
The total salinity was calculated as follows: Tg = EC(1:5)X5X ty
The initial concentration of the solution was estimated at 10 mmho/cm which
was approximately the highest effluent salinity obtained in the leaching
process. The initial quantities of the solid salts were calculated as the
difference between total salinity and the salinity at the initial solution
-------
SOURCE
OF GAMMA
RAYS
PROBE
PUMP
MOTOR
Figure 46. The setup of experiment 3.
120
^^iJ \ LTN.
SCALER
COUNTER
4 ELECTRODE
PROBE
APPARATUS
CONDUCTIVITY
BRIDGE
CROSS SECTION
A
WATER
DISTRIBUTION
RING, A
-------
TABLE 47. RESULTS OF LEACHING EXPERIMENT NO. 3A
General
Data:
Ring
123
Bulk density 1.
L = 24.0 cm g/cm
EC influent 1.440 mmho/cm
t*
(h)
0
2.02
4.76
7.78
10.53
14.44
15.12
15.68
17.35
17.68
19.58
20.92
22.73
26.40
31.19
37.25
41.66
44.71
48.65
55.70
Def
(cm)
.43
1.12
1.68
2.12
2.93
3.81
5.24
7.61
10.67
12.70
14.23
16.12
19.06
ECef
mmho/cm
9.560
8.110
7.800
7.550
7.260
6.930
6.230
5.560
4.040
3.350
2.930
2.350
2.100
1
.021
.316
.365
.405
.417
.407
.414
.406
.417
.405
.401
.412
.429
.435
.448
.408
.400
.419
.385
.455
2.
.021
.021
.327
.369
.400
.417
.421
.418
.428
.421
.438
.415
.416
.427
.429
.418
.417
.414
.405
.422
6
V
3
.021
.021
.021
.329
.354
.420
.430
.417
.424
.407
.423
.426
.414
.431
.417
.415
.449
.417
.422
.433
318 1.
,304 1,
4
.333 1.377
5
1.439
f
EC(4P) mmho
4
.022
.022
.022
.022
.306
.369
.386
.391
.384
.369
.403
.388
.380
.402
.385
.395
.381
.401
.395
.402
5
.023
.023
.023
.023
.023
.374
.390
.387
.389
.358
.387
.373
.378
.385
.384
.381
.383
.365
.383
.385
1
4.3
4.2
4.2
4.2
4.8
4.8
4.8
4.8
4.7
4.7
4.6
4.6
4.7
4.6
4.7
4.9
4.9
5.1
5.2
2
7.0
6.5
6.0
6.3
5.5
5.5
5.5
5.4
5.4
5.4
5.4
5.3
5.3
5.2
5.2
5.3
5.3
5.2
3
6.7
6.9
7.1
6.8
6.6
5.8
5.5
5.3
5.2
5.1
5.0
4.8
4.8
4.7
4.7
4.7
4.7
4
7.9
9.0
9.0
8.8
8.7
8.3
8.3
7.9
6.9
5.8
5.3
5.2
5.2
5.0
5.0
5.0
5
12.2
11.0
10.2
8.7
8.2
8.0
7.9
7.8
7.7
5.9
4.9
4.7
4.7
4.6
4.6
Vda(O)
cm/h
.612
.474
-------
TABLE 48. RESULTS OF LEACHING EXPERIMENT NO. 3B
General data: -Ring: 1
2
Pb(g/cmJ) 1.481 1.354
L = 24 . cm
EC influent 1.440 mmhos/cm
Ud = .418 cm/h
t Def ECef
(h) (cm) mmho/cm 1
0.00 — — .016
2.85 — — .300
5.65 — — .317
9.21 — — .351
12.89 — — .371
24.35 .94 4.400 .397
26.59 1.68 3.950 .407
28.99 2.62 3.840 .418
34.27 4.74 3.690 .430
37.88 6.30 3.530 .430
45.37 8.86 3.220 .450
50.61 11.48 2.900 .454
63.45 16.67 2.230 .455
76.00 12.97 1.650 .457
88.17 26.78 1.590 .465
2
.015
.015
.244
.284
.303
.401
.414
.415
.417
.425
.452
.429
.439
.429
.467
3
1.346
e
V
3
.015
.015
.015
.255
.312
.401
.401
.408
.415
.395
.422
.404
.406
.417
.412
4
1.363 1
4
.015
.015
.015
.015
.242
.393
.392
.390
.389
.396
.419
.397
.403
.395
.400
5
.342
5
.015
.015
.015
.015
.015
.408
.424
.423
.422
.417
.439
.412
.429
.423
.435
TABLE 49, LEACHING OF THE REFERENCE COLUMN 3A
General data : Ringi
Bulk density (g/cmJ)
L = 24 cm
EC influent = 1.440 mmho/cm
Ud = .687 cm/h
6v
(h) 123
0 .022 .201 .022
2.13 .364 .021 .022
5.30 .389 .321 .022
7.72 .417 .384 .345
10.22 .428 .396 .393
12.97 .421 .426 .413
14.02 .471 .437 .439
14.97* .439 .427 .454
EC (1:5 extraction) of original soil
Ring
EC of immediate extraction in the end
EC of 1:5 extraction in the end pmho/
Solid salt (ymho/cm)
1
1.379
4
.022
.022
.022
.022
.311
.412
.436
.429
420 umho
iumho/cm
cm
2
1.320
5
.022
.022
.022
.022
.022
.342
.413
.421
1
3040
250
327
3 4
5
1.344 1.353 1.375
Def
(cm)
__
_ _
—
—
—
__
.01
.43
2 3
4020 5900
310 500
365 846
ECef
mmho/cm
__
__
—
—
—
__
9-700
8.270
4
7400
620
1197
5
7600
730
2016
*Gravimetric test
122
-------
TABLE 50. LEACHING OF THE REFERENCE COLUMN 3B
General data: Rinai
Bulk density (g/cm )
1
1.439
2
1.368
3
1.377
4
1.406
5
1.453
L = 24 cm
EC influent = 1.440 mmho/cm
Ud = .425 cm/h
t
(h)
0.00
3.58
6.33
9.08
12.16
22.58
1
.016
.276
.295
.328
.330
.385
2
.015
.015
.235
.260
.279
.371
e
V
3
.015
.015
.015
.230
.290
.378
4
.015
.015
.015
.015
.235
.370
5
.016
.016
.016
.016
.016
.381
Def ECef
(cm) mmho/cm
.92 4.400
22.58* .393 .363 .377 .371 .385
Ring
EC of
EC of
Solid
immediate extraction in the end (pmho/cm) 200
1:5
Salt
extraction
T J
per 1 cm
(ymho)
of soil
1700
764
225
1820
879
250
2450
979
260
2850
771
320
2950
1189
*Gravimetric test
and were expressed as solid salts per unit volume of soil. The boundary
conditions at the soil surface were determined so that the salt flow at the
soil surface would be equal to the rate that salt in the influent entered
the soil or:
_D (3C} c c_ [20]
o 3 z o o o o ir
The numerical approximation of [20] is:
DT (0.5 — TVT VD'
•) + W
}.c
CI = C2 (3D1 + 2qi DLXC •> + C3 X + 2q± DLXC
where DLXA - z0 - z, DLXB = z, - z,, DLXC -
2Di i DLXC qlx 4. rJ n + DLXC^
and W = ^—. t, MTro- [Ci (-1-5 5 ' + °2 ^
123
-------
C3 (0'5 - > + 2 DLXC
3C
The boundary condition at the bottom was (— ) _ = 0, C^ = C^_^
Applying the above boundary condition to equation [11] the first equation
in the set is :
DLXA-
-4- _ DLXB i 4. r rr
J ° L
5 -
.D -
3D + 2q DLXC J 3 L 3D + 2q DLXC
d 2D rcJ M 5 DLXC ql, + CJ a + DLXA)
3D + 2q DLXC l 1 ^ D ; 2 k DLXB^
(0.5 - ) + 2 DLXC q Cir] [21]
and the last equation in the set:
- (b+c) = d- [22]
In spite of partial success in calibrating the four-probe electrode, this
method of measuring salinity was not considered reliable enough to use for
the entire soil column. Therefore, the computations are based on the effluent
data only. The detailed program that solves this system of equations can be
found in Appendix D.
Because of the length of the computer time required for the program, the
optimal K is not found by the program itself. Each trial of K needed a
special run of the whole program.
Figures 47 and 48 show the measured and the computed EC of the effluent. In
both cases the model simulated the data with reasonable accuracy but the
shape of the measured durve was not simulated too well. In both cases the
biggest deviation from the measured data occurred at the beginning of the
process (as in trial 1). The measured data at the beginning were considerably
higher than the computed. It is probable that the poor simulation in the
first stage of leaching is a limitation of the model in that it treats the
different salt species as if they were one salt with average properties.
124
-------
IO.O
tvj
Ui
K=O.O5hr
COMPUTED
MEASURED
A = 4.3 cm
2.O
8 12
DEPTH (cm)
16
2O
Figure 47. The computed and the measured EC of the effluent vs. the depth of the effluent -
laboratory trial 3,
-------
4.5
4.O
£ 3.5
O
o
O
UJ
3.O
2.5
2.O
1.5
O
Figure 48.
8
K=O.OI8 hr
COMPUTED -
MEASURED -
X= 2.7cm
\
\
\
12 16
DEPTH (cm)
2O
24
28
The computed and the measured EC of the effluent vs. the depth of the effluent -
laboratory trial 3.
-------
The K and D parameters, obtained in the main column, were applied to the
reference columns and the salinity profile was computed. Figures 49 and 50
show the computed and the measured salinity of the soil profile. The
simulation is reasonably good. In both cases the model simulated the total
salinity better than it simulated each of the components (the solution and
the solid salt). In both cases the solid salt was underestimated in the
upper half of the profile and overestimated in the bottom half of the profile.
TESTING THE SOURCE-SINK MODEL UNDER FIELD CONDITIONS
The objective of this part of the research was to test whether data obtained
in the field could be simulated by the model.
The same source-sink term that was developed for the laboratory columns was
added to a water movement model (Hanks, et al., 1969) to simulate salt movement
under field conditions. The experimental data were obtained from a field
trial performed at the Vernal experimental farm. The field trial is fully
described by King and Hanks (1975) (see field trial 1 in that report).
The computations were done in the following order: (1) data were simulated
without using a sink-source term; (2) data were simulated using a source-sink
term with uniform parameters for the whole profile; and (3) data were simulated
using a different R value for the reference equilibrium concentration for
each soil layer.
In order to determine values of R in the field, it must be assumed that the
solid salts and the salts in the soil solution are close to an equilibrium
state. In this situation, the R value is equal to the concentration of the
soil solution at a particular depth. Thus the R values assigned to the source-
sink term are equal to the initial soil solution salinity conditions. The
test to determine whether the source-sink term is significant is made by
comparing the quantities of salt existing in the natural soil solution to
the quantity obtained by 1:5 extraction, which is used as a measure of total
salinity. If the salt quantities are equal, a value of zero is assigned to
K. The same value of K, 0.05, found by trial and error to fit the laboratory
data, was used for all the field computations.
The detailed results comparing the measured and simulated data computed in
the three different ways can be found in Table 51.
Figures 51 and 52 show the salinity profiles that were computed in the three
different ways compared to measured salinity profiles. The model that
neglected the source-sink phenomenon overestimated the salinity when the
concentration of the influent was higher than the concentration of the soil
solution (Figure 51). When the soil solution was displaced by an influent
with a lower concentration, the salinity was underestimated (Figure 52) and
the upper part of the profile was simulated better than the lower part. Thxs
was attributed to there being less solid residual salts in the upper layers
than are present in the lower layers. This result supports the argument that
127
-------
EC OF THE SOLUTION SOLID SALTS
(mmho/cm) (mmho/cm)
3.O 5.5 SO O I.O 2.O
O
TOTAL SALTS
(mmho/cm)
1.6 3.O 4.4
KJ
OO
MEASURED
COMPUTED
Figure 49. Measured and computed salinity vs. depth in the reference column - laboratory trial 3,
-------
EC OF THE SOLUTION SOLID SALTS
(mmho/cm) (mmho/cm)
1.7 2.3 2.9 O.65 O.85 I.O5 I.3O
TOTAL SALTS
(mmho/cm)
.4 1.8 2.2
MEASURED
COMPUTED
Figure 50. Measured and computed salinity vs. depth in the reference column - laboratory trial 3.
-------
TABLE 51. MEASURED AND COMPUTED EC OF THE SOIL SOLUTION IN SEVERAL TIME
POINTS.
10 cm water (EC =8.0 mnho/cm) added
Time: 0 - 0.5 hr
Time: 2.5 hr
Depth (cm) Measured
First Second Third
Computation Computation Computation
mmhos/cm
0 -
30 -
60 -
90 -
120 -
150 -
Time:
0 -
30 -
60 -
90 -
120 -
150 -
Time:
0 -
30 -
60 -
90 -
120 -
150 -
Time:
Time:
0 -
30 -
60 -
90 -
120 -
150 -
30
60
90
120
150
180
10.0 hr
30
60
90
120
150
180
24.5 hr
30
60
90
120
150
180
24.75 -
26.25
30
60
90
120
150
180
6
6
5
4
3
2
7
6
5
4
3
.2
.3
.5
.2
.3
.0
.0
.5
.3
.3
.8
217
6
7
6
4
3
2
25.75
7
6
5
4
3
2
.3
.1
.2
.7
.8
.7
8
7
5
5
6
5
8
7
6
5
6
5
8
8
6
5
6
5
.0
.7
.9
.7
.7
.1
.0
.9
.1
.6
.6
.6
.0
.0
.3
.6
.4
.4
25 cm water (EC =
.2
.8
.5
.0
.2
.9
4
4
4
4
5
6
.1
.1
.1
.2
.8
.4
5
5
5
5
5
4
5
5
5
5
5
4
5
5
5
5
5
4
.9
.2
.1
.0
.1
.4
.8
.0
.0
.0
.0
.4
.8
.0
.0
.0
.0
.3
7
6
5
4
3
3
7
6
5
4
3
3
7
6
5
4
3
3
.0
.7
.6
.7
,9
.2
.0
.6
.5
.6
.7
.1
.0
.6
.5
.6
.7
.1
4.1 mmho/cm) added
4
4
4
4
4
4
.4
.6
.7
.8
.9
.6
5
5
5
4
4
4
.0
.6
.3
.9
.6
.1
130
-------
ELECTRICAL CONDUCTIVITY (mm hos/cm)
4O
80
120
I
Q.I6O
UJ
Q
d
O
cn
4O
8
8
I I I I
Measured
K=OO5, R=Var.
K=O, No sourcel
8O
I2O
160
xX26.25 Hours
-rx i i i
Figure 51. Measured and simulated comparison using no source-sink term
(K = 0.0) and a variable source-sink term for the Vernal field
trial.
131
-------
ELECTRICAL CONDUCTIVITY (mmhos/cm)
8
4O
8O
120
I
Q.
yj
Q
I6O
O
I I I
Measured
K=O.O5 /
R=5O
Equil. (
2.5 Hours
I
4O
80
I2O
I6O
rV"
. I 26.25 Hours
i! ' i j
8
/ 24.5 Hours
I
I 49.5 Hours
Figure 52. Measured and simulated comparison using a constant source-sink
term and chemical equilibrium model for the Vernal field trial.
132
-------
the soil manifests "buffering" characteristics and that a sink-source term
should be included in the model in order to improve the salt distribution
predictions. This soil is probably representative of a non-leached arid zone
soil where large quantities of soluble salts are present in the solid state.
Computations using a source-sink term with constant K and R parameters for the
whole soil profile for the field data (as was done in the lab trials 1, 2, 3)
improved the prediction of the total salts in the soil solution of the entire
profile but still predicted the salt distribution with depth (Figure 51) very
poorly. This is because the salt content of the soil profile in the field
was not initially uniform. By using a different coefficient for each layer
in most of the cases (except for Figure 51) the model predicted the measured
data fairly well.
MODEL PREDICTIONS OF SALINITY EFFECTS ASSUMING NO SOURCE-SINK
The report of King and Hanks (1975) shows predictions made by their model
where variables of initial soil salinity, amount of irrigation, and crop root
depth were studied. Some limited computations were also made to determine the
influence of salt buildup on yield over several years. Using the same model,
additional predictions have been made in this study where the quality of
the irrigation water, irrigation uniformity and time are important variables.
All other details of the computations are the same as given by King and Hanks
(1975).
Irrigation Water Quality
Predictions of salinity effects on yield were made where irrigation amount,
initial soil salinity, and root depth were variables, as given in King and
Hanks (1975), and where another variable, irrigation water quality was added.
Figure 53 shows an example of the results of these computations. The data
show, that for low amounts of irrigation and rain, the best quality irrigation
water gives higher relative yield than the poor quality irrigation water.
However, as the amount of irrigation water increased, there was less difference
in yields due to irrigation water quality differences. As shown by King and
Hanks (1975), high initial soil salinity decreased yields. The model predicts
that even with irrigation water of very low quality (C. = 100 meg/1 = EC =
10 mmhos/cm or about -3.6 bars matric potential) good yields are obtained if
irrigation amounts are high. This result may be questionable and needs to be
field tested.
Figure 54 shows the effect of amount of irrigation water applied where upward
flow or drainage may occur and where irrigation water quality and initial
soil salinity was varied. Upward flow and drainage was the same for both
irrigation water qualities for high and low water applications. At intermediate
water applications (16 in) (40 cm), the more saline irrigation water resulted
in more upward flow than the less saline irrigation water. For a given
amount of water applied the higher the initial soil salinity the lower the
amount of upward flow.
133
-------
1.2
I.O
.8
Q
UJ
UJ .6
llJ
o: .4
.2
Cis Ciw
2Omeq/l 6.4meq/l
2O
2OO
20O
IOO
6.4
IOO
I
I
2O 4O
IRRIGATION and RAIN (cm)
6O
Figure 53.
Relative yield as related to the amount of irrigation and rain,
concentration of the irrigation water (C. ), and initial soil
solution concentration (C± ) for a mediumWrooted crop.
134
-------
24
2O
£ l6
o
O 12
U_
§ -
0
Q_
D 4
III
0
O
1-4
Q
-8
T
Cis Ciw
2Omeq/l 6.35meq/l
2O IOO
2OO 6.35
2OO !OO
DRAINAGE
I
2O 4O 6O 8O
IRRIGATION and RAIN (cm)
IOO
Figure 54. Upward flow and drainage versus irrigation and rain for deep
rooted crops at two water qualities and two initial soil
salinities.
135
-------
An additional effect of irrigation water quality as shown by the model is its
influence on the general shape of the soil salinity profile. Relationships
between the soil salinity concentration profile, irrigation water quality,
amount of irrigation and initial soil salinity are shown in Figure 55 for a
medium root depth crop. Figure 55 shows the effects of water quality on
the salinity profile for different irrigation amounts. Under low irrigation
amounts large near-surface concentrations were established while the
remainder of the salinity profile was increased slightly. With high irrigation
amounts, the tendency was to form uniform concentrations with depth. There
was, in general, no accumulation of salt at the base of the root zone. Figure
55 shows the effect of water quality at two initial profile salinities with
high irrigation amounts. The results for the low initial salinity are similar.
The results for higher level of initial soil salinity were different since
the irrigation waters were all less saline than the soil solution. Therefore,
the upper portion of the profile had a somewhat lower concentration than the
rest of the root zone. The best quality irrigation water was most efficient
in the leaching process.
Multi-Year Calculations
The calculations for a single year show that, although yield is not affected
immediately, substantial increases in soil salinity occur under some conditions
of water and salt management. If these management conditions are viewed as
farm operation schemes, it is clear that yield for a single season cannot
be the only criterion in choosing particular water and salt treatments. In
order to examine the long-term effects, predictions have been made in
cumulative fashion for periods up to 10 years. This provides data for
assessing the importance of various soil water management variables in long-
term planning.
Assessment of long-term effects of various management practices was
accomplished by using the final conditions of a single-year run as initial
conditions for the following year. Boundary conditions assumed for irrigation,
rain, and potential evapotranspiration remained the same; soil water content
was assumed to be roughly at field capacity at the start of each new year.
Figure 56 shows typical results for several 10-year sequences for a deep-
rooted crop. Relative yield and final soil salinity are plotted together
in order to show the close relationship which exists between the two. None
of the irrigation amounts applied were sufficient to cause leaching so there
was a salt accumulation on all treatments. In spite of this salt buildup,
there were no yield decreases predicted at the end of the first year for
any of the three water application treatments. Where only 6 inches (15 cm)
of water were applied each year, yield began to decrease at the end of the
second year and continued to decrease throughout the period. Where 9.5 inches
(24 cm) of water were applied each year, salinity buildup did not cause yield
reduction until after the sixth year. Where 17 inches (43 cm) of water were
applied each year, there was not sufficient salt buildup to cause a yield
decrease even at the end of 10 years.
The high yields shown by the model and obtained in the field with relatively
small amounts of irrigation water applied may be unique to the Vernal field
situation because of the large amount of upward flow that occurs (also shown
136
-------
U)
—I
FINAL SOIL SOLUTION CONCENTRATION (meq/l)
O IOO 2OO 3OO 4OO 5OO O IOO 2OO 3OO 4OO 5OO
t
tlJ
Q
I2O
I4O
ISO
Cis =
Ciw AMOUNT
64meq/l 43 cm
6.4 IO
IOO IO
i i 1 i I 1 i
' i\ ' i
\
*
\
\ \
// _
I' AMOUNT=43cm
If
1 Cis Ciw
2Omeq/l 6.4meq/l
2OO
.. 2OO
. I
I
6.4
IOO
I
Figure 55. Final soil solution concentration predictions as influenced by concentration of the
irrigation water (C. ), the amount of irrigation applied and initial soil solution
concentration (C. ).
is
-------
280
u>
00
FINAL SALINITY
-28
EO/*\
-2O
O
O -16
U_
O _io
a: '*
1
85*
-4
O
1 1 1 1 1 I 1
IRRIGATION AND RAIN ~
15 cm
24 cm
— "TOV/lll ~—
_
— • — « — . .
___„ Nr -*
\ \
\ \
\ \
\ \
\ X
[ 1 1 1 1 1 1
O 2 4 6
YEAR
8 IO
246
YEAR
8 IO
Figure 56. Relative yield, final salinity, and upward flow versus time for the deep rooted crop.
Irrigation water quality: 6.4 meq/1.
-------
In Figure 56). As salt buildup occurred with increasing time in the model
for the two lowest levels of applied water, the amount of upward flow was
relatively constant until yield reductions began, at which time the amount
of upward flow decreased.
Figure 57 shows predicted final soil salinity profiles versus time for a
medium root depth crop. The same quantity of water, 17 inches (43 cm), was
applied in each case. This amount of water would not cause leaching. All of
these data show expected trends. Soil salinity increased almost uniformly
throughout the root zone. The effect of increasing irrigation water salinity
was to hasten salinization of the soil profile and raise the maximum concen-
tration.
Calculations regarding uniformity of water applications - A final arrangement
of the basic data was made in order to show the effects of uniformity of
water application on yield. While the predictions of this model can show
that a particular irrigation rate is ideal for a given situation of soil
salinity, irrigation water quality, and crop type, application of that precise
amount of water everywhere in a field is virtually impossible. In order to
simulate this variability, a measure of uniformity of water application was
required. The approach used was to segregate an area into zones receiving
differing amounts of irrigation according to the uniformity afforded by the
water application system. Separate calculations were carried out for each
of these zones and were then combined to give general results for the entire
area. The measure of water application uniformity commonly used was:
Cu = 1 - | [23]
Where:
Cu = Coefficient of Uniformity
M = Average of Ideal Irrigation
D = Average Deviation from M (disregarding sign)
when the coefficient of uniformity equals 1, the average deviation is zero
and the ideal irrigation amount is applied everywhere. A rule of thumb to
clarify this concept is: Approximately 79 percent of the irrigated area
receives an irrigation equal to or greater than the amount (Cu X M) .
(Equation [23] is a variation of Equation [4]).
In order to completely specify an irrigation amount for any small area, the
pattern of distribution of the water application within the 79 percent of
the area must be known. For the purposes of this project, two patterns of
distribution were considered: rectangular and parabolic (Figure 58). For
a rectangular distribution, the same amount of area receives minimum irrigation
as that which receives the ideal amount. The parabolic distribution has
most of its applications clustered near the ideal. In other words, given
the same value for Cu, a parabolic distribution has more of the total_land
receiving nearly ideal irrigation than does the rectangular distribution.
139
-------
SOLUTION CONCENTRATION (meq/l)
O 2OO 4OO O
ZOO
400
6OO
I year
.
~~
63.5 meq/l
Figure 57. Predicted final soil salinity versus depth for the medium rooted crop at various times
and at three irrigation water qualities.
-------
111
o:
RECTANGULAR DISTRIBUTION
I
21 | 79%
i i
UJ
01
Cu*M M
PARABOLIC
DISTRIBUTION
Cu*M M
IRRIGATION LEVEL
Figure 58. Patterns of distribution for uniformity calculations.
141
-------
Using the concepts of coefficient of uniformity and pattern of distribution,
a variety of probable irrigation patterns were investigated. The sprinkler
system for the experimental farm at Vernal, Utah, has a measured Cu value
of 0.88 that can be approximated by a parabolic distribution pattern. This
was the most controlled irrigation application considered. The other extreme
of the range of uniformities was a rectangular distribution with Cu = 0.42.
This option was assumed to simulate the flood irrigation system previously
in use at Vernal, Utah.
Table 52 shows the data from calculations which consider irrigation uniformity.
The total area is divided into 5 blocks with different irrigation levels
that depend on uniformity. A comparison of the irrigation amounts in the
two portions of the table shows that low uniformity causes a wide range in
the irrigation application amounts. Comparison of the figures for area show
that, in addition to the wide range of the irrigation amounts for Cu = 0.42,
the extremes include more of the area. The areas are used to weight the
results for each irrigation level in order to obtain an average figure. The
remainder of the table shows the individual results for the 5 blocks for
two times. Initially, a projected change from flood irrigation to sprinkler
irrigation (a rectangular parabolic pattern) boosted yields from 83 percent
to 95 percent of maximum yield. After 5 years, average yield was still at
95 percent under sprinkler irrigation while the yield under flooding has
dropped to 80 percent. Another predicted advantage of the sprinkler system
can be seen in the figures for salt outflow. The predicted better irrigation
management gave a smaller salt outflow.
Figure 59 shows the multiyear effects of different irrigation system
uniformities on relative yield and salt outflow with equal water applications
of 21 inches (53 cm). Although the average water applied was sufficient
to cause leaching, there was a net salt buildup in the areas not leached
because of non-uniform application. This did not cause appreciable yield
decreases in the 10 years simulated. However, a sharp rise in salt outflow
occurred for Cu = 0.42 after about 7 years.
Uniformity options as presented above should be considered in relation to all
appropriate soil-plant phenomena and should be used as another management
option in planning. Uniformity can be generally considered as a measure of
control of the soil-plant system. For precise management of an area, the
above results indicate that the coefficient of uniformity should be high.
Practical limitations may preclude high uniformity. The model allows
calculations of intermediate schemes of uniformity, water application, and
salt management in addition to those presented. These schemes need not be
concerned with only achieving ideals but may also be used to predict the
consequences of various existing or proposed management schemes. This kind
of data would also be useful in planning a minimum cost irrigation system
which will meet given production, environmental quality, and water
specifications.
142
-------
TABLE 52. EXAMPLE OF UNIFORMITY CALCULATIONS - 5-YEAR SEQUENCE FOR THE
SHALLOW ROOTED CROP
Irrig
and
rain
cm
•
"/
/a
Area
Rel.
yield
Salt
outflow
T/ac
Final
soil Salt
salinity Rel . outflow
meq/1 yield T/ac
YEAR 1
Cu =
40.4
46.7
53.1
59.5
65.8
Final
soil
salinity
meq/1
YEAR 5
0.88 Parabolic distribution Irrigation and rain:
10.4
24.8
29.6
24.8
10.4
AVERAGE
Cu =
10.6
31.9
53.1
74.3
95.6
.85
.92
.96
.99
1.00
.95
0.42 Rectangular
20.0
20.0
20.0
20.0
20.0
.50
.75
.96
.96
1.00
„
-
—
.23
.55
.11
27.6
25.7
23.9
22.4
21.2
distribution
_
„
_
1.14
3.63
32.3
29.9
23.9
20.4
20.0
.84
.91
.96
.99 .03
.99 .64
.95 .14
Irrigation and rain:
.39
.68
.96
.96 1.19
1.00 3.86
53.1 cm
70.8
52.5
38.7
29.6
24.0
53.1 cm
161.6
50.5
38.7
21.1
20.0
AVERAGE
.83
.97
.80
1.02
143
-------
1.2
I.O
J -8
111
>6
I
^
_l
UJ 4
o:
.2
o
1 111 1 1 1
- 53.2 cm irrigation and rain
r"B *
MM*
— .— .
' "~~"-~ ••-_._ , ^ x
~™""/™""' _
/
_ / —
— __ — — — -"—""""
YIELD —
CALT
—J.— -- FLOOD Cu = O.42
— = . SPRINKLER Cu = O.88 —
i^ 1 T" ill j 1
2.O
*u
a
1.6 p
^
^J
3
1.2 [^
o 1
_J
^
en
.4
O
468
YEAR
IO 12
Figure 59- Relative yield and salt outflow versus time for two
coefficients of uniformity.
144
-------
SECTION VI
PUBLICATIONS
Bliesner, R. D. 1975. Sprinkler irrigation system evaluation and irrigation
management technique to maintain salt storage in the root zone. Unpublished
M.S. Thesis. Utah State University.
Childs, S. W. 1975. Model to predict the effect of salinity on crop growth.
Unpublished M.S. Thesis, Utah State University.
Childs, S. W. and R. J. Hanks. 1975. Model of soil salinity effects on crop
growth. SSSA Proc. Vol. 39, No. 4. July-August, 1975. pp. 617-622.
Childs, S. W., R. J. Hanks and L. S. Willardson. 1975. Crop yield response
to irrigation and salinity. Proc. ASCE Irrigation and Drainage Specialty
Conference, August 13-15, 1975. Logan, Utah.
Melamed, J. D. 1975. Salt movement simulation for irrigation management
considering dissolution and precipitation. Unpublished Ph.D. Dissertation,
Utah State University.
Sullivan, T. E. 1975. Determining a crop production function for corn as
influenced by irrigation and salinity levels. Unpublished M.S. Thesis,
Utah State University.
Manuscript
Melamed, J. D. 1975. Model of salt flow in the soil with a source-sink term.
Manuscript prepared for Soil Sci. Soc. Amer.
145
-------
SECTION VII
REFERENCES
Bauder, J. W., R. J. Hanks, and D. W. James. 1975. Crop production function
determinations as influenced by irrigation and nitrogen fertilization
using a continuous variable design. SSSA Proc. 39:1187-1192. No. 6.
Nov.-Dec. 1975.
Bingham, F. T. and M. J. Garber. 1970. Zonal salinization of the root system
with NaCl and boron in relation to growth and water uptake of corn plants.
SSSA Proc. 34(1):122-126.
Bresler, E. and R. J. Hanks. 1969. Numerical method for estimating simultaneous
flow of water and salt in unsaturated soils. SSSA Proc. 33:827-832.
Carnahan, B. , H. A. Luther, and J. 0. Wilkes. 1967. Applied Numerical Methods.
John Wiley, New York.
Childs, S. W. 1974. A model to predict the effect of salinity on crop growth.
Unpublished M.S. Thesis, Utah State University.
Gupta, S. C. 1972. Model for predicting distribution of salt and water in
soils. Unpublished Ph.D. Dissertation, Utah State University.
Gupta, S. C., and R. J. Hanks. 1972. Influence of water content on electrical
conductivity of soil. SSSA Proc. 36:855-857.
Hagan, R. M., H. R. Raise, and T. W. Edminister, ed. 1967. Irrigation of
Agricultural Lands. Agron. Mono. No. 11. Amer. Soc. of Agron. Madison,
Wisconsin.
Hanks, R. J., J. C. Anderson, L. G. King, S. W. Childs and J. R. Cannon. 1974.
An evaluation of farm irrigation practices as a means to control the
quality of return flow. Agr. Exp. Sta., Utah State University, Research
Report 19.
Hanks, R. J., J. Keller, and J. W. Bauder. 1974. Line source sprinkler plot
irrigator for continuous water and fertilizer studies on small plots. Utah
State University, Logan, Utah.
Hanks, R. J., A. Klute, and E. Bresler. 1969. A numeric method for estimating
infiltration, redistribution, drainage, and evaporation of water from soil.
Wat. Res. Res. 5:1064.
Hanks, R. J. and R. W. Shawcroft. 1965. An economical lysimeter for evapo-
transpiration studies. Agr. Jour. 57:634-637.
Jensen, M. E. 1969. Scheduling irrigation with computers. Jour. Soil and
Water Conservation 12:193-195.
146
-------
Jensen,^M. E., D. C. N. Robb, and C. E. Franzoy. 1970. Scheduling irrigation
using climate-crop-soil data. Jour. Irrig. and Drain. Div. ASCE 96(1):25-38.
Keller, J. and D. Karmeli. 1974. Trickle irrigation design. Rainbird
Sprinkler Corporation, Glendora, California.
Kirkham, D. and W. L. Powers. 1972. Advanced soil physics. Wiley-Interscience,
New York.
King, L. G. and R. J. Hanks. 1973. Irrigation management for the control of
quality of irrigation return flow. Office of Research and Monitoring, U.S.
Environmental Protection Agency, Washington, D. C. EPA-R2-73-265. 307 p.
King, L. G. and R. J. Hanks. 1975. Management Practices Affecting Quality and
Quantity of Irrigation Return Flow. EPA-660/2-75-005. National Environ-
mental Research Center, Corvallis, Oregon.
Lunin, J. and M. H. Gallatin. 1965. Zonal salinization of the root in relation
to plant growth. SSSA Proc. 37:522-527.
Shalhevet, J. and L. Bernstein. 1968. Effects of vertically heterogeneous soil
salinity on plant growth and water uptake. Soil Sci. 106:85-93.
U.S. Environmental Protection Agency. 1971. The Mineral quality problem in the
Colorado River Basin. Summary Report, U.S. Environmental Protection
Agency Regions VIII and IX, GPO 790845.
U.S. Salinity Laboratory Staff. 1954. Diagnosis and improvement of saline and
alkali soils. USDA Handbook 60.
Utah State University, Water Research Laboratory, 1975. Colorado River - Regional
Assessment Study. National Commission on Water Quality. Contract No.
WQ5AC054, October, 1975.
van Schilfgaarde, J., L. Berstein, J. D. Rhoades, and S. L. Rawlins. 1974.
Irrigation management for salt control. Jour. Irrig. and Drain. Div. ASCE
Vol. 100 No. IR3. September 1974.
Warrick, A. W., J. W. Bigger, and D. R. Nielsen. 1971. Simultaneous solute and
water transfer for an unsaturated soil. Water Res. Res. 7:5:1216-1225.
147
-------
SECTION VIII
APPENDICES
Page
Appendix A. Irrigation Scheduling Computer Program . . • 149
Appendix B. Corn Yields as Affected by Salt and
Water Levels ........ 165
Appendix C. A Program to Compute K ...... 183
Appendix D. The Computation of D . . . . ... 188
148
-------
APPENDIX A
IRRIGATION SCHEDULING COMPUTER PROGRAM1
This program is written in FORTRAN IV for a Univac 1108 time-sharing computer.
It will handle 4 regions with a maximum of 100 fields.
Flow Chart
A summary flow chart for the program is shown in Figure A-l.
Area Constants
For operation, several area constants must be determined. For forecasting
potential ET, the maximum potential ET, E1 , for the area must be known.
Also the Julian calendar day, t', when thisPmaximum occurs (about July 15
to July 25 in the northern hemisphere), and At, the days before and after t'
when potential ET = 0.37 E' must be determined. Wind coefficient, CW, must
be adjusted for anemometer height (0.0170 for 12 feet, 0.0185 for 2 meter,
etc.). Experimental coefficients used for the net radiation equation, line
28 subroutine VAPOR, are given in Table A-l. In addition, clear day solar
radiation for the regions of interest must be known. Table A-2 gives the
area constants evaluated for Vernal, Utah. Figure A-2 gives the clear day
solar radiation for Vernal, Utah, as determined from incoming daily solar
radiation.
TABLE A-l. EXPERIMENTAL COEFFICIENTS FOR NET RADIATION EQUATION
Region (a b)
Southern Idaho (1.22 -0.18)1
General (1.20 -0.20)*
General (1.00 0.00)J
Wright, J. L. and M. E. Jensen (in press)
2
Suggested for arid areas
3
Suggested for humid areas
"""Contribution from the Northwest Branch, Soil and Water Conservation
Research Division Agricultural Research Service, USDA, Kimberly, Idaho, 83341.
Revised by Ronald D. Bliesner, 1974.
149
-------
READ REGIONAL CONSTANTS AND
CURRENT CLIMATIC DATA FOR EACH REGION
READ LAST THREE DAYS OF
CLIMATIC DATA, SOIL MOISTURE
DEPLETION, CUMULATIVE RAIN
AND RAIN OR IRRIGATION FOR
LAST THREE DAYS
PRINT DAILY CLIMATIC SUMMARY AND
POTENTIAL ET FOR EACH REGION
I
FOR EACH REGION, FARM AND FIELD,
READ: CONTROL AND PREVIOUS STUDY
DATA: CALCULATE: DAILY ET, SOIL
MOISTURE DEPLETION, EXPECTED ET,
EXPECTED RAINFALL, DATE OF NEXT IRRIGATION
ADJUSTED LEACHING FRACTION, ALLOWABLE DEPLETION
PRINT COMPUTED DATA
STORE LAST THREE DAYS OF CLIMATIC
DATA, SOIL MOISTURE DEPLETION,
CUMULATIVE RAIN AND LAST THREE
DAYS OF RAIN OR IRRIGATIONS
i
PRINT REGIONAL DATA AND DATA
FOR EACH FARM AND FIELD
Figure A-l. Sequence of computer operations.
150
-------
8OO
3OO
4/5
5/1
9/28
Figure A-2. Clear day solar radiation for Vernal, Utah, irrigation season.
-------
TABLE A-2. AREA CONSTANTS FOR VERNAL, UTAH
E1
tp
t'
t (before)
5 (after)
= .33 in.
= 211
= 120
= 63
CW =
a =
b =
.0185
1.22
-0.18
Crop Coefficients
The program contains coefficients for third order polynomials for the
following crops.
No.
1
2
3
4
5
6
Crop
Small grains
Beans
Peas
Potatoes
Sugarbeets
Corn or sorghum
Alfalfa
Pasture
100% effective cover
at heading
bloom or about 50 days after planting
full bloom or 70 days after planting
about 80 days after planting
about 110 days after planting
about 10 days after tasseling on corn
and heading for sorghum
all season except 30 days after growth
begins in the spring and 20 days after
cuttings
all season except 30 days after growth
begins in the spring
The coefficients represent two portions of the crop curve: from planting to
effective full cover expressed as percent, and from effective full cover to
harvest expressed as days.
For alfalfa and pasture, the growing season begins when mean air temperature
reaches and remains above 41°F (5°C). Also for alfalfa, by setting the 100 per-
cent effective cover data equal to the cutting date, the adjustment for
cutting effects are made automatically. Curves for other crops can be
approximated assuming that K =0.15 from planting until emergence, it then
increases to about 1.0 at effective full cover (leaf area index = 3.5 - 4.0,
or about 90 percent ground cover, or at full bloom), and remains at this
value until harvest for some crops like sugarbeets. For other crops it
remains at this value until they begin to mature after which it decreases
to about 0.15 shortly before harvest.
Allowable Depletions
Approximate percentage available water that can be depleted from soils by the
time the soil moisture tension reaches specific values are given in Table A-3.
152
-------
Guides to allowable soil moisture depletions for a medium textured soil are
given in Table A-4. These should be used only as an initial guide and in
lieu of local experimental data. For deep rooted crops a reference soil sample
in the spring is highly desirable. For many shallow rooted crops in winter
precipitation areas the soil can be assumed to be at field capacity at planting.
TABLE A-3. APPROXIMATE AMOUNTS OF AVAILABLE WATER THAT CAN BE DEPLETED
FROM THE SOILS BY THE TIME THE SOIL MOISTURE TENSION REACHES
THE VALUES INDICATED. Adapted from Haise and Hagan (1967)
Soil Moisture
Tension
Loamy
Sand
Percentage of available water depleted in;
Fine
Sandy
Loam
Sandy
Loam
Loam
Clay
Atmospheres
Percent
Percent
Percent
Percent
Percent
0.5
1.0
2.0
5.0
10.0
70
82
89
95
99
65
75
83
92
98
57
68
78
89
97
30
57
72
87
95
13
27
46
73
91
TABLE A-4. GUIDES TO ALLOWABLE SOIL DEPLETION ON A DEEP, MEDIUM
TEXTURED SOIL.
Approximate allowable depletion, inches
No. Crop 1st 2nd other
irrigations
1
2
3
4
5
6
7
8
Small grains
Beans
Peas r,
2
Potatoes
Sugarbeets
Corn or Sorghum
Alfalfa
Pasture
3.0
1.7
2.5
1.5
2.5
2.5
6-8
3.0
3.5
2.0
3.0
2.0
3.0
3.0
6-8
3.5
4.5
2.0
3.0
2.0
3.5
3.0
6-8
3.5
In general, the allowable depletion prior to harvest can approach the
maximum available water for crops such as small grains, corns and sorghum.
Desirable soil moisture levels at harvest must be considered for potatoes
and sugarbeets.
2These values may need to be reduced significantly on coarse-textured
soils to avoid poorly shaped potatoes.
153
-------
Variables used in irrigation scheduling program
CODE VARIABLE
= Name of region
= Amount of irrigation water needed
= Date last irrigation
= Julian day representing the next 5 days
= Crop coefficient adjusted for available soil moisture
= Crop coefficient (average based on observed values)
= Expected crop coefficient next 5 days (soil moisture not limiting)
= Percentage available soil moisture remaining
= Maximum available soil moisture for the soil and crop
= Available water (DPA - DPL)
= 1.0 + AV
= Rainfall probability coefficients
= Coefficients of crop curve polynomials
= Name of crop and field
= Rough crop coefficient (Jensen-Raise)
= Windspeed coefficient (combination equation)
= Crop coefficient (lower limits)
= Crop coefficient (upper limits)
= Date
= Name of farm
= Depletion allowed
= Soil moisture depletion
= Days after NDE
= Constant in mean potential ET equation, days, before t1
= Constant in mean potential ET equation, days after t1
= Irrigation efficiency
= Daily evapotranspiration
= Future expected daily ET
= Mean maximum daily ET for the region
= Expected ET next 5 days (soil moisture not limiting)
= Potential ET (Combination method)
= Potential ET (Jensen-Haise)
= Potential ET next 5 days
= Increase in ET due to wet soil caused by rain or irrigation
= Forecast potential ET for next 5 days relative to mean (1.0 =
normal)
= Forecast
= Soil heat flux (estimated)
= Region number
= Julian day
= Day number of a month
= Day number
AIR
AIRR
AJJ5
AKC
AKC1
AKC 5
AV
AVM
AVW
AV3
B
C
CR0P
CTR
cw
D
Dl
DATE
DESC
DPA
DPL
DT
DTI
DT2
E
ET
ETA
ETAP
ETAS
E0
ETP
ETP5
ETR
FCT
F0RC
G
I
II,JJ
IID,NID
J
154
-------
L
LF
LFZ
LL
M
MBD
MN
M0N
N
N5
NCR
NCR0PS
ND
NDB
NDE
NDH
NDP
NF
NFN
NREG
NXD
NXDP
PAMT
PCT
PP
R
RL0
RN
RS
RS0
RX
SUMR
TAVG
TAX
TD
TEN
TMN
TMX
TP
TXR
T
Tl
12
UA
= Farm number (used as a loop index in subroutine SCHED)
= Leaching fractions
= Adjusted leaching fraction
= Number of farms in a region
= N + 3
= First Julian day of forecast period (NDB + N)
= Month number
= Name of month, M0N(1) = JAN, etc, M0N(13) = None
= Days of input climatic data
= Day of irrigation within the computation period
= Crop number
= Number of individual crops
= Julian day
= Beginning day
= Date of effective cover
= Date of harvest
= Date of planting
= Field number in farm L
= Number of fields in farm L
= Number of regions
= Julian date of next irrigation (no rainfall)
= Julian date of next irrigation (with expected rainfall)
= Probable amount of precipitation within specified time period
= Stage of growth (planting to effective cover)
= Probable precipitation in next 2 weeks
= Rainfall, daily
= Net longwave radiation on clear days
= Net radiation
= Solar radiation
= Cloudless day solar radiation
= Temporary rain amount
= Sum of rainfall
= (TMN + TMX)/2
= Maximum temperature, "K/100
= Dew point, °F
= Minimum temperature, °K/100
= Minimum temperature, °F
= Maximum temperature, °F
=,Day of mean maximum, ET, t'
= Constant for region (Jensen-Haise)
= Time in days for expected precipitation
= Weighting term in combination equation
= Weighting term in combination equation
= Average windspeed, mph
155
-------
VP = Average saturation vapor pressure, mb
VPD = Saturation vapor pressure at dew point, mb
VPSI - Saturation vapor pressure at TMX, mb
VPS2 = Saturation vapor pressure at TMN, mb
W = Daily wind run, miles
WK = Week number (1 = March 1)
,J) = TMN
X(2,I,J) = TMX
X(3,I,J) = RS
X(4,I,J) = TD
X(5,I,J) = W
X(6,I,J) = TAVG
X(7,I,J) = ETP
X(8,I,J) = UA
X(9,I,J) = VP
I,J) = VPD
t,J) = G
E,J) = TEN
E,J) = TAX
I,J) = RL0
I.J) = RN
E,J) = E0
156
-------
Modified Jensen computer program listing
** COMMON AC 4. 5) »C TR C4 ), TXR t 4 ) .NDC4. 15 ) . XC16 ,4 , 15 ),DESCC 5). DATE!4),
2* +CROP13) »AIRRI2I ,FDRCI15)»NI 4) .NOB 14) ,PSC( 4) .W1I7.100) .MOM 113) .IT,
3« *NCR.NDE.NDP.C CS ,8 ), BC 4 , 6) ,F TA PC 4) ,T FC t) ,DT1 C 4) ,D T7 C 4 ) . FCT C"4 ) , ET" 5.
** *SUMRTI15).DPLTC 15 ». PC 15 ) , SUHR CIS) .0 PL CIS)
5* DIMENSION CMC 4)
6* REAPC5.1) NREG
7* 1 FORHATC 5X.I5)
10* Rl3)rO.O
11* HFr1
12* SUMRINFJrO.O
13* DPLCNFI^O-O
14* SUM RT INF)-O.U
15* DPLT(NF)=0.0
16* 00 2 in,NREG
17* REAPC5.3MA CI ,J ), Jrl.5) ,C TR CU , TXRC II » CWC I)
1 8* REAM 5,103) ET AP CI ). TP CI I. DT U T) ,OT2 CI )
19* 2 READC5.104) EC 1,11 ,B CI ,2 ),BC T. Z) ,P CI ,4 »,R( 1.51 .BC I. 6 )
20* 103 FORHATC 5X.F5.2. 3F5.0)
21* 104 FORHATC 5X.EE10. 2)
22* 3 FORHATC 5X.5 AM .2F7.3.F7.4)
23* Xll,l,l)=32.
24* XJ1.1.2)r36.
25* Xll,l,3)-34.
26* Xl2tl.l)=66.
27* XI2,1,2)^73.
28* XI2.1.3 ) = 69 .
29* M ON 11) = * J AN '
30* HONC2)r«FES'
31* MONI3)=*HAR«
32* HONI4)r'APP'
33* MOKI5)=*HAY'
34* MONI6)-'JUNE*
35* MONC7)=*JULY*
35* MONC8)r "AUG •
37* HON19)='SEPT'
38* KONC10)-*OCT*
39* MONC11)='NOV'
40* MONI12) -'DE C1
41* MONI13)-'NONE *
42* 19 DO 7 1=1?NREG
,RSCC I)
44* IFC NCI) .EQ.O) PO TO 999
US* 11 FORHATC 5X.2I5.F5.1.F5 .0)
46* K=NII)*3
it 7* 00 4 J=4,K
j, g, RE A DC 5, 51 NO CI,J )i. XC1. It J> .X C2 .1, J ) . X( 3 .1 • J) .XC4. I.J ).X C~ tl.J)
49* 4 C ON TI NU E
50* 7 CONTINUE
51* WRITE 16.9)
52* 9 FORHATC 1H1)
53* 5 FORHATC 5X.15,5F 5.0)
511* DO 6 1=1. NREG
55* CALL EVAPCI »K )
56* CALL VAPORC T.K, CW)
57» 6 CALL PRINTRCI.K )
58* CALL FARHSINREG ,M1,H2.M3. M4I
59* 60 TO 19
60* 999 STOP
El* E ND
END OF COMPILATION: NO DIAGNOSTICS.
157
-------
1* SUBROUTINE ETAVGC II.ETA ,M BD.T.D.D1. AVM.OPL1
2* COMMON A(1»,5I .C TR (1 )» TXRI <4) tN 01 "«tlS) t X( 16.M .15 )» CFSCtS )t DATF« <»>f
3« +CROP1 3) .AIRRt 2) .FORCdS 1. M4) *NDB 14) « RSO( 41 » Wl (7 .100 1 i M ON ( 13 I t TO t
4* *NCR»NOE.NDP»C(3»81fB{4.GI.ETAP(41.TF<4).DTll4I.O'nM4I.FCT(4I.ETr>5.
5« + SUMRT (15) .DPLTI 15 ).R( 15 ). SOW (151
6» DIMENSION D (8 I. Cl (81
7* A Tr II
8» AV=( 1.0-1 DPL+ET A) /A VH )*1DO.
3* IFt AV.GT.0.0) GO TO 300
10* AVrO.O
11« 300 AV3-1 + AV
12* 5 IFt T.I.GT.NOE1GO TO 2
13* APrNDP
m* AE^NDE
15* PCT -100.* (A T-AP 1/fAE-AP)
16* AKClrCt NCR.l) *C (NCR.2 )*PCT+C( NCR. 3) *PCT**2*C(NCRt4 ) *PCT«*3
17* GC TO 1
18* 2 DTr II-NOE
19* AKC1 = C( NCR.51 *C (NCR.6 1*DT*C (N CR»71*DT*»2+C( NCR.9)*DT* * ?
20* 1 IF( AKC1.LT. Dt NCP) )AKC1=D( NCR)
21* IFt BKC1.GT. Cl (N CR )) AKC1=D1( NCR)
22* IFC II.GT.TP tl n GO TO 7
23* DLTrDTKIl
71* GO TO 8
25* 7 DLT-DT2 CI»
26* 8 AKC-AKC1* ALOGCA V3 )/ALOG tlOl.OI
27* ETA-AKC*« ET AP (I )/ (EXP (t tAI-TP (11 1 /T> LT )* *? )) )
28* IF» II-MBD.LT.5) ET ArET A* FCTt TJ
23* RETURN
30* E NO
1* SUBROUTINE PRINTRtl.K)
2* COMMON A«tf5>fCTRf»XllEt1flS)fCrsC( 5)t OATr( H ).
3* +CPOP131 *AIRR( 21 *FORC( 15 )t Nt 4) tNOB It) t RSC(1) »ETAP<«lltTP<4)«DTlC4».D'Pt4)»FCT(«l)iETP5.
5* +SUMRT (15) .DPLT« 15».RJ 15 J.SUHR f!5) .DFt (151
6* JJrND3(IJ
7* CALL DATEE( JJ.HN.NTO. 356)
8* WRITE (6,10) (A (I rJ ). J=1.5» .M ON (MN) .N ID
9* 10 FORPATdH ?5X.'REGION * .5 AH.5 X. "B EG IN NI NC D ATE -' »A <4 ,1 3 )
10* WRTTE(6»15I
11* 15 FORMATdH .' !) «Y T AV G R5 UA VPS VPD RN C
12* * ETP EO'l
13* WRITE (6.271
14* 27 FORMATdH I
15* 00 20 J-4.K
16» WRITE (6.25) NO (I .J 1« X( 6» I. Jl .X (3. I . J J »X( ?. It J) . X ( °. I ,J ) »X «10» It J) .
17* +X(15. It J) .X (11!. It J) .X (7.I.J ItXdEtlt J 1
18* 20 CONTINUE
19* 35 WRITE (6.401 ETP5
20* 40 FORMATdH .'FORECAST POTENTIAL ET NEXT 5 PA YS-' .F5.?,///)
21* 25 FORMATdH . 15 .F 7. 1 .F6 .0 ,F6. 1. F7 .1 .F 8. 1.F7.0.FB .1 ,2F 7. 2 )
22* RETURN
23* END
1* SUBROUTINE DA TE E( II ,M N. II 0. NT) HI
2* DIMENSION NNDU2J
3* DATA NNO/0.31.59.90.120.151.181.212.243.273.304. 34/
4« DO 10 J=2.1i
5* IFt II.LE.NNO( Jl 160 TO 12
6* 10 CONTINUE
7« J-13
8« 12 MNrJ-l
9* IID=II-NND( J-1I
10* IFt II.tT. NDH1GO TO 14
11* HN=13
12* IID=0
13* 14 RETURN
14« END
158
-------
1* SUBROUTINE EV AP (2l .rORC(15)tN(4).NDB(4),PSC(4) ,W1(7 tl 00) tM ON ( 13» t ID t
1* *NCRtNOEtNDP.C(8,3ltB(4t SJ,FTAP{4>,Tr«().DTl(it),OT;>t4>.FCT(tDPLTf 15)tR(15)tSUKP (15) iDFL<151
5* DO 10 J-4tK
7» XIGtI.JJrJXd tl tJ) + Xf 2t It J))/?.o
8* 15 X(7»ItJ» = CTRJ I.J *tXCB,I, JJ-TXR lin*Xl3t7tJJ*0.000 C73
9» 10 CONTINUE
1 D» R ET UR N
11* END
END or COMPILATION: NO DIAGNOSTICS.
1* SUBROUTINE VAPO RC I. K. CW »
2* COMMON AJ4 t5) iC TR (4 )tTXR<4) tNOCtt IS) t Xf 16,4t 15) tDESC(5 )tDATr( 4),
3* + CROPI3) tAIR»( 2) tFDRC( 15 )t N(4) tN03 (4) t RSC(<4) tUl (7 ilOO) tMON (13) » TO t
4* + NCR tNDE tNDP tC(8 18 ) tB(4t E) rCTAPC 4) tT F(4) tDTIt4)tDT2f4) tFCT ( 4 1 t ETPHt
5* + SUMRT C15) tDPLT( 15 Jt R( 15 It SU MR (151 .DPL (1^)
6« DIMENSION CH( 4)
7« DO 30 J=4,K
•DIAGNOSTIC* THE TEST FOR EQUALITY BETWEEN NDN-INTEGERS MAY NOT BE MEANINGFUL.
8* TFt X«4r ItJ) .EQ. 0.0) GO TO 35
S* XI Stlt J) = X( 5t It J?/?4.U
10« VPS1=-0.6959+0. 2946 »X 12 tl t J ) -D. 0051 95 »X(2 .T i J) * • >0 .OOC009*
11* +XI2tItJ)**3
12* VPS?--0.6959+C.2946«X tl.11JI-C.005115*X11il,J)»«2+0.DODO89«
13* *XtltItJ!**3
14* X«9tItJI = «VPSl+V"S2)/2. D
15* X(10. It J»r-0.6959+0 .2 9t 6* X( 4. It J)-0 .CU5195* X <4 11 tJ) «*2 +
IS* +O.OOOOS9*XC 4t I. J) **^
17* XI 11 tit J>-( XIGit Ii J)-( Xt If It J-1J+X (2tl iJ-1 )+X(l tit J-7) +X(2tItJ-2) +
18* +Xtl«I.J-3)+ X( 2't T. J-3) )/S.O»«r
13* Tl=0.041+0.0125 *X f6 tl tJ )-4. 534»X« E» Ti JI ** 2/lC**5
20* T2=0. 959-0. 0125 *X (6.1 tJ )+4. 534* XI Gt It J) **2/10**5
21* X(12tlt JI-C (X tl tl .J )-32 )/l. B+273. )/100.C
22* X(13tItJ)-{ (X (2 »I .J 1-32 •»/!. 3+273. J/1CO.O
23* Y=X(10tItJJ
24* Jjr NDBf II+J-4
25* EHT =0.325*0.045 *SIN (30* tJ J/3U .0-0 .5 )* 3.141G/130. >
26* X(14tlt J>-( EM T- 0.044* SORT (Y )>*!!. 71 *( X( 13 tl tJJ**4+X (12 tT.J)
27* +**4)*0.5
73* XtlSt ItJ) -0.77* XC3tIt JJ-(1.22*X(3tItJ»/«>SOJH-O.I8) *X(1««,I,J)
2g» 30 X(16tlt J>=« Tl*f XC15 tl tJ )-X( lltltJ)) +T2*15.36«(0.75 + CW (I I*
30* *Xl5.ItJ)l*( X(3t It J)-X(lCt It J)n*O.OOCB73
31* AJJ5=NDBtI)+N(I 1+3
32* IF( »JJ5.6T.TPIU»GO TO 34
33* DLTrDTlfU
34* GO TO 341
35* 34 DLTrDT2II) _ t _
36* 341 ETP5rCETAPCII/( EXP( (( AJ J5-TPI IJ J/OLT) **2 J 1J *FCT ( T)
37* 35 RETURN
38* END
END OF COMPILATION: 1 DIAGNOSTICS.
159
-------
1» SUBROUTINE FA [WSINRFG »M 1'. M2.M 3. HI I
2» COM PON AI1.5)tCTRC1).TXRC1)«'ijni1»15).XC16»1.15)»DESCC5)»DATTt1)»
3* +CROP!3) .AIPRC 2» .F ORCC 15 »t NC 1) ,NDB tl ) • RSOC1) «W1 C7 .100) .MOM Cl') • TO »
1* »NCR.NOC.NDP»C(8.8).B('t.6),FTAPf('n.rcT(=0.213
31« Cf6.2)3-«t.276E-3
32* C<6.3)r2.75&E-1
33* C«6.4)3-1.583E-S
?<>* CJ7.1I3.25
35* CI7.2)3.01087
36* C«7.3)30.0
37* CI7.1)30.0
33* C(8.1)30.25
39* Ct8 .2)30.01503
«tO* CC8,3)30.0
11* C(8.f)30.0
1 2* C(l.5)31.Q22
13* Ctl.6)38.532F.-3
II* Cll. 7)3-7. 261E-1
* 5* C(1.3)3iu!»qE-6
16* CC2.5)31.050
17* C(2.6)3-1.12E-1
18* C(2.7)3-2.611E-1
19* C(2,8)3i.s5E-6
50* C(3t5)=1.005
51* C(3.6»3-i.DIE-2
52* C(3.7|39.865E-1
53* C<3.8)3-2.21E-5
51* Cll.5)30.889
55* CI1.6)rl.667E-3
56* CC1.7)3-1.87E-1
57* CC1.8)3-i.oiE-6
58* CCS.5)30.899
160
-------
CO*
61*
62*
63*
64*
65*
65*
67*
E ft*
69*
70*
71*
72*
73*
74*
75*
76*
77*
78*
79*
80*
81»
82*
83*
84*
85*
86*
87*
8 8*
89*
90*
91*
92*
93«
94*
95*
96*
97*
93»
99*
10 0»
101*
102*
103*
104*
105*
106*
107*
108*
109*
110*
111*
Cl6.E)-1.195E-2
10
C(5.8I-2.7SE-G
C<7.5»-.5
C«7.6»r .025
Ct7.7irO.O
C(7t8 JrO.O
C«8.5lr0.87
C(8.6lrO.O
C(8»7)rO.O
C( 8 »8)ro.D
READ (5. 14 M DATE (K I. K=1.4J
FORMATC 5X.I5)
T-0.9
DO 100 Ir
WRITE(6fl3l CA (I tJ It J=li5>
13 FORMATf • REGION *,5A<»t//)
14 rORKATC 5Xtl5Ai»)
READtS.lOILL
DO 100 Lrl.LL
READJ StlOINFN
REAP»5? 14 M DESC tK ) t Krl,5»
WRI TE (6 t!5) (OES C« KJ tK =;lt5l t tD ATC( K) tKrl.it)
15 FORHATf* FARM • t5 A4 .3 Xt *D ATE OF CCMPUTATION
WRITE(6 .161
16 FORHATt 15X. 'SOTL MOISTURE DEPLETION IPPTGATTONS L*5T. MEXT + AHCU
+MT' .2X)
WRITEC6 t5171
517 FORFATt' CRCP-FLD CO EF TO DATE OPTIMUM PATE LAST RAIN-0 W
+ RAIN INS'.3X )
24 DO 110 NF=1 .NFN
REAC«5tl7JNCR.C ROPC 1 1 .CRO Pf 21 .CRO PC 71 .NDP .N DE f ND H. E t A VV, LF
17 FORMAT! 5X.I2.2A 3t A2.3I5 t3F5 .2 I
M=NCI 1+3
NN^NII)
REAOC 5. 1311 AIR" IJ ). J-lt 2J .AIR .N5. (R IJ 1 « J=4t PI
16 FORMATC 5X.ZA3.F H.lt It .10F4.2I
AKC^O.O
PCTzO.O
OTrQ. 0
DO 99 J-4.M
ET( JI-0 .0
75
RX=R« J»
SUMR!NFI=SUHR«NFI +" { J )
SUMRT INFJ-SUHRT (NF) +R (J )
IFIJ-N5-3I76.75 .75
LFl-«SUHRTf NFJ-CPLT(NF) I/SUMRT(NF)
IFCLF-LFD5.6 t4
GO TO 7
113*
114*
115*
= CDPLT(NFl/H-LF)-SUMRTf KFII/(E*AIRI
161
-------
11 6»
11 7*
11 8*
11 9*
120*
121*
122»
12 3»
121*
12 5»
126*
127*
128*
129*
13 0»
13 1»
132*
133*
131*
135*
136*
137*
138*
133*
110*
111*
11 2»
It 3*
i«m*
115*
1<46»
117*
It 8*
It 9*
150*
151*
152*
153*
IS t«
15 5*
15 6*
157*
158*
159*
160*
161*
IE 2*
163*
16 t»
165*
166*
167*
168*
169*
170*
1T1*
17 2«
76
176
29
30
232
231
130
131
31
235
2tl
88
2t2
233
23t
32
38
US
t6
i»0
53
t2
17
It
18
121
50
51
91
115
DPLINF)=O.C
SUMPt NF >rQ.O
IFf NOB! Il-NCP 1108 ,176.176
IFf NDOt D-NDH )? 9,^9.108
IFt KDBf I1+J-1-N TE 130. 30.31
PCT^IOD .[)»( NDB* It *J-1-N DP )/ (NDE-NOP t
AKC1 = C( NCR. 1) +C (NCR.2 >*PCT»C( NCR, 31 *p CT «* 2* r. ( NCR A ) «PCT»*3
IFt AKC1-DK NCRJ >231.232.232
AKC1-D1 (NCR >
AV-(1.0-DPL (NF) /AVK I* 100. 0
IFt SVU30.131 .1 31
AV=0.0
AV3-1.0+AV
AKC=AKC1*ALOG IA V3 t/ALOG IIOl.D)
GO TO 32
DTrNDBI I) «-J-t-M CE
AV-11.0-0PL (NFJ /AVM )»100. 0
NCR. 5 I »C tNCR.6 >»OT+C IN CR . 7 I * OT**2+Ct NCR. 3 )*DT* »T
98,235 .235
'31
AKCl-DtNCR »)
IFt AKC1-DK NCRI
AKC1-D1 INC=?)
GO TO 212
AKC1-0( NCRI
IFI 8VJ233.231
AVrO.O
AV3-1.0*AV
AKrrAKCl*ALOG (A V3 )/AL OG tlOl .01
ETtJ)=AKC*Xtie. IrJI
IFt «KC-F)38,121 .121
IFt RIJ-1) 112.12 .13
»X 116. It JJ
-E TR
12 It1?1
Rt J-2J-RIJ-21 +P IJ-1 I
Rt J—11 = 0.0
IFtRtJ-2J116.121.121
Rt J-3I = RI J-3J +R U-?)
RtJ-2J=0.0
IFt Pt J-31 I53.1Z1.121
ETR=ETR+R(J-3)
R tJ-31=0.0
60 TO 121
IFt Rt J-2J 111.11 .17
ETR=0.5*t F-AKCJ *(Xtl6.I.JI>
Rt J-2J = Rt J-2I-ETR
60 TO 15
IFf Rf J-3) 1121.121.18
ETR =0.3*1 F-AKC) «X tlS, I. JJ
Rt J-31 = Rt J-3I-ETR
GO TO 10
IFt ETRI 50.51.51
ETR=0.0
ETtJ)=ET»J) +ETR
DPLtNFl zDPL tNFJ -»ETt Jl-RX
OPLTINF )=DPLT tNF) +ETf JJ
IFt OPLt NF)>115.99 ,99
DPLlNFJrO.O
162
-------
173*
171*
17 5»
17 6»
17 7»
17 8»
179*
180*
181*
182*
183*
181*
185*
186*
187*
18 8*
189*
190*
191*
192*
193*
191*
195*
196*
197*
198*
199*
200*
201*
202*
203*
201*
205*
206*
207*
208*
209*
210*
211*
212*
213*
211*
215*
216*
217*
218*
219*
220*
221*
222*
223*
221*
225*
22 S*
227*
228*
229*
230*
23 1«
232*
233*
99
250
255
260
CONTINUE
MBD-NDB III+NI I)
DPArAIR *£*( 1-L.F 2)
AVWrDPA-DPL INFI
DPL«=OPLINF )
CALL SCHEDl HBO. AVW.NDH. NXDr NX t>P .1 .DPL» .« VM.O .Pll
CALL DATEEf NXDi IX ,TY.t NDH)
CALL DATEEI NXDP »JX. JY.NDHI
IF! KDBC II *N (I )•» J-ND^I 25 Ot 250.255
PCTrloO .U*l NDBK T) +N II 1+3-NOP) /( NO E-NO PI
AKC5=CI NCR, 11 *C IN CR .2 1»PC T*Cf NCR. 3) *P CT** 2+ C ( NCR * )*PCT*«3
60 TO 260
DT=NDB( II+N II )+ 3-NDE
AKC5=C< NCRr 51 *C (NCR .6 I*OT+C (NCR .71*OT**2+Cf NCR. 3 1»DT* *T
63
IY ,
.3X1
IFIAKC5.LT.niNC R) lAKCS^Di NCR)
IF( AKC5.6T. Dl (N CR ) } AKC5-DK NCPI
ETA 5= AKC5*FTP5
IFC ABS( Bl I.I) ).LT .10. G0001MG P TO 65
WRITE (6 .61) CROP .AKC5. DP LI NF I r DP A. ETAS, ATRR.KON(IX)
+ MON ( JXI .JY. AIR
61 FORMAT! 2A1r A2 .F 1. 2. 3F3. 2r 2X .2 A3.2 X. AM. 13. IX. At .I7.F6.1
BO TO 108
65 WRITE (6. 56 1 CROP .AKC^i DP LI NF 1 . DP A. ET A5 , AIR R. PONC I X) , IY, AIRR.T.L
66 FORKATC 2A1 , A2 ,F 1.2 . 3F 8. 2. 2X .2 A3r2 X. Alt 13 . 8X ,F6 .1 .3X .2 12)
108 CONTINUE
WRITEI6.2I
2 FORMAT! lX./,5Xr 'DAY IN PERIOD DAILY fT«,/l
DO 9 Jrl.M
•3 WRITE <6 .DJ1.ET IJ )
1 FORfATI 10X. I2.13X.F1.2)
WRITE(6r8)SUMRT (N F ) .DPL T( NF )
8 FOPfATI EX.' TOTAL WATER AP PL TE Dr « , F5.2. /.5X. 'CUM KULATI VT
+ LAST DAY r *.F5.2r /I
WRITE (6 .901LF2
90 FORMAT! 5Xr* ADJUSTED LEACHING FR AC TI ONr • »F5. 2 ./I
K=0
Klrf-2
ET AS OF
00 I J-K1.K2
K=K*1
RIK1-RIJJ
NPl I.KlrNDI I. Jl
XII.I.K > = XI 1. I. Jl
XI2.I.K) = XI 2.1. J)
X13.I.KI-XI 3.1. JJ
XII tl .K )rXI 1. I. Jl
XI5.I.K I = XI 5. I. J>
3 CONTINUE
110 CONTINUE
IFC ABSI Bl Itin.LT.ro.00001) ) GO TO 100
WK=INDB III+NI IJ -531/7
PP-1H.* IB II.11* PI Ir2l *WK+BI I.
* + BI It6) *WK**5I
IR PP.LT.O.O)PP=0 .0
WPITEI6.163IPP
163 FORCATI/.' PROBABLE RAIN NEXT
TWO WEEKS-'.F5.2.2X,'INCHES'.30X1
100 CONTINUE
81 RETURN
END
163
-------
1« SUBROUTINE SC HE C( »° 0. AV Vt NO Ht NX R. KX OP 11 iDPL t AVK t Dt Dl)
2« COMMON A( <4 . ^) tC T9 (1 >t TXR< M) fN DC 1i IT I • Xf 16tt 111 ) tDr5;CC 5 >» DATff (»),
3» +CROP(3) tAIRRI 21 tF DPC< IE ) t M 4 ) tNDP (4) f HSCI 4) t Wl (7 tlOOl »"OM ( 13) t ID t
4* + NCR tNDE tNOP fC tfi t8 ).B( 4 t 5) tTTAPt Ml «TP( 4) .DTI ( 4) .DT," (<» I iFCT {«!•
5» *SUMRT 115) fPPLTt 15 ) t^( 15 It SU PR (15)
6» PIHTNSION D<8). C1C8)
7» PAMT( T» WIO-T* C9 (I ,1 1+BC I. 2J«WK+"?( I, 3J *WK» WK +B(Tt 1) *
8* *WK**3*B (It 5 )* WK •«1 + B( If El »W K* *5)
9« IF«AVW.Lt.D .0 ) GO TO 10
10* BD=HBD
11* CTArO.O
12* DO 1 IlrMBDtNDH
13* CALL ET AVGI IltF TAtMBOfTfOtOlt AVH. CPLI
1H* AVW-AVW-tTA
15* IFC AVW. LE.0.0 J GO TO 2
16* 1 CONTINUE
17* CO TO 12
18* 2 NXD=II
19* NXDP=NXO
20* IF( ABSt B( It in.LT.O.ODDODGO TO 11
21* WK-(M9D-53)H
22* 16 Air II
23* T^AI-BD
21* BDrBD+T
25* IF( T.LE .11. »GO TO 15
26* BD=BD-T+li».
27* Trin.
28* 15 AVW=AVW+f>AMT< Ti WK I
?9* L=IT+1
30* ETArO.O
31* DO 3 II-L.NCH
32* CALL ETAVG( II tF TAtHBD.TfDfDlt AVMt CPL)
33* AVU^AVW-ETA
31* IFC AVW. LE.0.0 »GO TO <» '
35* 3 CONTINUE
36* 60 TO 13
37* 1 IF! TI-l.EQ.NXDP )GO TO 11
38* UKrWK»T/7
39* NXDP^TI
10* CO TO 16
11* 10 NXD-MBD
12* NXOPrNXO
13* GO TO 11
14* 12 NXD^NOH
45* 13 NXOP=NDH
46* 11 RETURN
17* END
END OF COMPILATION: NO DIAGNOSTICS.
164
-------
APPENDIX B
CORN YIELDS AS AFFECTED BY SALT AND WATER LEVELS
TABLE B-l. OVEN DRY MATTER YIELD, METRIC TON DRY MATTER/HA
Salt
Level
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
, Irrigation level
2
8.0
11.8
7.1
6.8
8.2
6.0
7.4
4.0
2.6
3.3
10.3
7.7
7.9
7.8
6.2
6.7
6.6
4.6
4.9
2.7
10.4
9.9
10.6
9.5
5.9
7.0
5.9
4.8
2.5
2.6
4
11.5
11.2
9.5
10.8
6.8
7.3
7.4
3.6
2.7
2.5
7.6
11.9
6.3
8.4
6.2
5.5
6.8
3.8
4.4
2.1
8.5
5.9
10.3
6.5
6.6
6.2
8.2
4.8
4.3
3.0
6
10.8
7.9
7.2
8.3
8.4
4.2
5.7
3.7
1.1
1.0
9.1
8.9
8.3
5.5
5.5
6.7
6.8
3.4
2.8
1.6
10.5
10.9
7.7
7.2
4.9
5.1
5.4
3.6
3.1
1.7
8
6.8
6.6
9.1
4.3
4.3
6.6
5.8
3.0
2.0
0.9
8.8
6.5
7.6
7.3
5.3
2.9
5.1
3.8
2.7
1.5
7.8
9.4
11.4
8.3
5.8
4.8
4.6
4.3
3.0
0.6
10
Block
8.7
13.9
6.2
9.6
5.0
6.1
4.5
1.3
1.7
0.8
Block
7.9
7.8
7.8
8.1
6.2
4.2
7.1
3.6
4.0
2.1
Block
8.0
6.0
3.7
6.0
4.9
5.4
4.0
3.0
1.7
2.8
12
1
12.2
11.3
6.4
6.8
5.1
4.4
4.0
2.6
0.5
2.3
2
6.8
7.2
7.5
6.8
6.0
3.0
5.2
3.1
3.6
1.7
3
9.2
8.7
8.2
5.5
2.3
5.5
4.1
4.5
2.4
2.5
14
9.4
8.4
6.7
5.5
4.4
3.3
2.0
1.7
0.4
1.4
8.1
5.6
7.9
5-7
5.8
2.6
3.4
1.7
1.6
0.1
9.1
5.1
8.9
8.4
3.9
3.5
3.3
4.2
2.6
1.6
16
7.3
11.3
5.3
4.2
2.3
2.7
2.1
0.9
1.1
t
8.7
8.5
7.3
4.6
3.9
2.6
3.4
1.3
1.0
0.4
8.9
7.6
6.7
7.5
5.5
3.2
3,3
3.1
1.1
1.1
18
8.3
8.4
6.3
3.6
1.5
0.6
0.3
t
0.2
0.0
5.5
4.1
5.5
1.9
2.1
1.7
3.4
1.1
0.1
0.0
4.8
8.0
4.3
3.9
2.0
3.1
0.8
0.7
4.2
0.4
20
9.8
8.7
8.0
4.4
3.2
1.2
1.9
0.1
0.0
0.0
6.7
3.5
4.7
5.0
4.2
2.5
2.5
1.2
0.0
0.0
5.6
5.6
7.6
3.0
3,8
1.8
3.4
0.6
0.6
0.0
165
-------
TABLE 59. CONTINUED.
Salt
level
1
2
3
4
5
6
7
8
9
10
Irrigation level
2
9.0
10.4
7.8
9.6
7.4
3.5
4.6
4.8
3.4
1.8
4
10.3
10.6
9.7
10.5
7.8
8.3
7.4
6.1
3.3
4.0
6
7.6
8.4
8.9
8.5
6.6
5.7
6.2
4.5
1.6
2.5
8
9.4
6.7
7.4
7.3
7.2
6.0
3.0
4.2
2.2
3.0
10
Block
10.4
12.2
9.2
7.1
6.6
6.2
5.0
3.8
2.5
2.3
12
4
8.5
10.3
9.0
7.8
10.2
6.8
6.2
4.2
2.0
2.7
14
9.2
6.4
8.2
7.5
4.6
4.7
4.6
2.8
0.3
2.4
16
9.3
11.9
9.5
8.4
6.4
5.1
4.9
2.0
1.2
0.6
18
9.7
9.1
5.7
6.9
4.5
1.8
0.9
0.2
6.5
0.3
20
10.3
8.5
5.4
4.7
4.3
1.6
2.0
0.3
0.0
0.0
166
-------
TABLE B-2. OVEN DRY MATTER YIELD, GRAMS DRY MATTER/PLANT
Salt
level
Irrigation level
2
4
6
8
10
12
14
16
18
20
Block 1
1
2
3
4
5
6
7
8
9
10
714
1049
631
611
816
596
734
596
385
598
1028
999
846
966
679
725
736
534
409
447
962
706
641
740
835
413
569
550
162
183
609
586
815
388
431
651
573
440
296
162
776
1242
552
854
495
605
450
193
252
150
1142
1007
571
607
504
440
396
381
69
225
842
751
598
489
440
330
202
248
56
75
654
1010
475
371
229
266
211
132
14
2
743
751
565
320
147
55
28
3
33
0
875
778
715
389
321
119
188
14
0
0
Block 2
1
2
3
4
5
6
7
8
9
10
916
685
702
693
619
665
651
688
722
479
677
1065
561
751
615
546
679
571
660
383
809
792
743
495
545
666
671
512
413
281
784
578
677
652
525
286
504
416
406
264
702
693
693
726
614
415
703
537
592
371
611
644
669
611
596
293
516
468
537
297
726
503
702
512
570
259
341
252
234
16
780
759
652
413
383
258
481
195
144
74
495
363
487
173
204
172
340
165
21
0
603
314
421
446
420
246
246
179
1
0
Block 3
1
2
3
4
5
6
7
8
9
10
924
866
949
850
587
697
587
715
371
462
759
528
916
578
651
614
816
715
647
528
941
974
685
644
486
504
532
537
468
301
693
842
1015
743
578
477
459
647
440
107
718
537
330
537
486
532
394
440
254
502
817
776
735
491
229
550
404
674
358
446
809
454
792
751
385
349
330
619
385
294
792
677
594
669
541
321
330
468
165
191
429
718
380
347
202
303
83
110
619
78
503
503
677
264
376
183
339
83
96
0
167
-------
TABLE B-2. CONTINUED
Salt
level
Irrigation level
10 12 14 16 18 20
Block 4
1
2
3
4
5
6
7
8
9
801
925
693
858
734
349
459
715
509
916
949
867
941
770
825
734
908
498
677
751
792
759
651
569
619
674
234
842
603
660
652
715
596
293
619
326
933
1090
817
636
651
614
495
571
371
759
916
801
693
1009
679
614
619
296
825
570
735
669
459
468
459
413
41
834
1065
850
751
633
504
486
299
180
867
809
512
619
449
183
92
30
963
916
759
479
421
377
156
202
37
5
10 330 710 446 537 416 479 426 108 54
168
-------
TABLE B-3. GRAIN YIELD, KG/HA
Salt
level
1
2
3
4
5
6
7
8
9
10
Irrigation level
1
2303
1735
2836
1368
319
1016
407
132
11
51
3
1748
1727
967
2030
1606
739
297
20
21
141
5
4223
1817
2101
1020
517
527
943
559
26
210
7
3187
1791
1935
2330
430
575
1255
542
14
118
9
Block
1503
5868
2704
2569
760
501
518
25
36
101
11
1
2066
1960
1881
981
844
1504
1649
199
0
0
13
2122
2278
1330
1254
312
785
94
121
0
0
15
3584
1922
1626
2075
264
46
885
55
0
0
17
1712
1914
1561
1857
490
81
495
0
0
0
19
3954
3546
1125
890
146
25
238
0
0
0
Block 2
1
2
3
4
5
6
7
8
9
10
2884
1372
763
1292
1007
1241
300
574
108
106
2451
2666
1191
913
1360
453
629
76
242
435
2127
1094
1934
1534
1454
440
626
741
406
548
2564
2650
1934
1515
740
767
1065
310
287
232
3358
1734
1084
774
754
496
1482
706
534
213
1837
2941
1191
1514
387
305
561
659
358
116
2359
763
485
1580
765
336
1766
102
29
29
1525
1239
1293
1107
164
547
238
0
0
0
3241
2307
659
210
157
246
270
80
0
0
3168
2682
1198
122
158
35
174
150
0
0
Block 3
1
2
3
4
5
6
7
8
9
10
3165
2691
2301
1410
1584
1474
477
334
550
86
3733
1747
2385
1799
1370
846
1292
544
407
527
3147
2332
2301
1633
1015
823
1272
592
312
5
3358
1916
2567
1007
1149
426
2279
252
527
229
2295
1988
878
805
871
947
893
895
703
81
2534
2599
2452
1447
1600
1427
904
856
11
160
2984
2326
2177
1859
708
695
286
320
31
107
1761
1544
656
1541
646
30
11
193
9
0
2874
1626
1241
799
142
311
587
0
52
0
3679
2535
1182
269
89
202
269
9
749
0
169
-------
TABLE B-3. CONTINUED
Salt
level
1
Irrigation level
3579
11 13 15 17
19
Block 4
1
2
3
4
5
6
7
8
9
10
1275
3867
3712
2817
2144
1537
1592
411
42
132
2744
3431
1832
2073
1323
908
1170
510
125
333
2352
2019
1371
1491
754
1715
538
407
0
220
2678
2497
798
2845
1855
951
1611
1273
91
57
1156
1510
1549
1218
1359
440
765
858
0
104
1842
2616
1221
1770
1793
2149
1082
704
144
10
2307
5118
2357
2088
1767
881
80
159
0
0
1505
676
498
1386
1176
90
102
326
149
0
1779
4052
1563
1316
634
255
465
435
0
0
3838
1532
2233
803
1326
148
183
22
0
0
170
-------
23456789
SALT LEVEL
Figure B-l. Dry matter yields as influenced by salt and irrigation levels, metric tons/ha.
IO
-------
IOOO
c 8OO
5
Q.
\
W
E
§» 600
Q
LJ
tr
ill
H
tr
Q
4OO
200
o
567
SALT LEVEL
8
10
Figure B-2. Dry matter yields in grams/plant as influenced by salt and irrigation levels.
-------
o
JC.
\
W
o
•*-
.0 2
Q
_1
UJ
1
o:
o
5 6
SALT LEVEL
8
IO
Figure B-3. Grain yields metric tons/ha as influenced by salinity and water levels.
-------
TABLE B-4. ELECTRICAL CONDUCTIVITY OF THE SOIL SOLUTION AS A FUNCTION OF TIME
AND SITE, 4-PROBE RESULTS IN MMHOS/CM <§ 25°C
Salt Water Depth
Block level level inches
2 1 19 0-6
6-12
12-18
18-24
24-36
36-48
2 1 15 0-6
6-12
12-18
18-24
24-36
36-48
2 2 11 0-6
6-12
12-18
18-24
24-36
36-48
2 2 70-6
6-12
12-18
18-24
24-36
36-48
2 3 30-6
6-12
12-18
18-24
24-36
36-48
3 3 30-6
6-12
12-18
18-24
24-36
36-48
Sampling dates
6-12
0.84
0.37
0.38
0.23
0.06
0.09
0.84
0.47
0.10
0.18
0.13
0.09
1.98
0.16
0.38
0.29
0.23
0.12
1.46
0.47
0.31
0.29
0.15
0.07
1.78
0.42
0.31
0.18
0.29
0.02
1.25
0.52
0.35
0.29
0.21
0.03
7-15
0.42
0.21
0.14
0.18
0.06
0.10
0.52
0.37
0.28
0.21
0.08
0.12
1.15
0.63
0.38
0.21
0.06
0.17
0.84
0.37
0.77
0.08
0.21
0.05
0.63
0.99
0.21
0.37
0.38
0.23
0.73
0.42
0.52
0.47
0.02
0.21
8-10
0.31
0.10
0.10
0.10
0.06
0.03
0.42
0.21
0.21
0.16
0.15
0.05
0.42
0.42
0.31
0.23
0.23
0.09
0.52
0.31
0.38
0.31
0.04
0.31
0.42
0.52
0.14
0.31
0.44
0.28
0.63
0.26
0.35
0.21
0.37
0.16
9-18
0.21
0.05
0.10
0.03
0.04
0.02
0.10
0.26
0.03
0.08
0.06
0.05
0.31
0.31
0.21
0.21
0.08
0.10
0.42
0.26
0.35
0.31
0.31
0.17
0.21
0.26
0.42
0.26
0.23
0.07
0.21
0.31
0.29
0.26
0.27
0.14
174
-------
TABLE B-4. CONTINUED
Sampling dates
Salt Water Depth
Block level level inches
3 4 70-6
6-12
12-18
18-24
24-36
36-48
3 4 11 0-6
6-12
12-18
18-24
24-36
36-48
3 4 15 0-6
6-12
12-18
18-24
24-36
36-48
3 5 19 0-6
-J -*
6-12
12-18
18-24
24-36
36-48
2 10 19 0-6
6-12
12-18
18-24
24-36
36-48
2 10 15 0-6
6-12
12-18
18-24
24-36
36-48
6-12
1.57
0.94
0.24
0.26
0.33
0.02
1.36
1.20
0.56
0.03
0.73
1.15
0.84
0.66
0.23
0.19
0.17
1.04
1.36
0.17
0.21
0.21
4.70
1.10
0.91
0.05
0.04
3.97
1.20
0.31
0.21
0.29
0.05
7-15
0.84
0.84
0.31
0.37
0.13
0.23
0.63
0.57
0.42
0.26
0.23
0.24
0.94
0.99
0.21
0.52
0.06
0.05
1.46
0.52
0.45
0.31
0.04
0.17
3.13
1.98
0.59
0.26
0.21
0.00
2.51
2.66
1.22
0.91
0.00
0.00
8-10
0.21
0.42
0.59
0.23
0.17
0.31
0,42
0.37
0.38
0.47
0.23
0.21
0.68
0.63
0.18
0.27
0.17
0.94
0.47
0.52
0.18
0.04
0.12
3.03
1.36
0,66
0.26
0.21
0.00
2.72
0.99
1.36
0.05
0.02
0.00
9-18
0.21
0.57
0.47
0.16
0,15
0.19
0.21
0.37
0.49
0.47
0.10
0.19
0.42
0.52
0.24
0.21
0.13
0.12
0.63
0.73
0.31
0.39
0.39
0.00
2.30
1.78
0.77
0.08
0.84
1.78
1.88
0.38
0.29
0.27
175
-------
TABLE B-4. CONTINUED
Sampling dates
Salt Water Depth
Block level level inches
2 9 11 0-6
6-12
12-18
18-24
24-36
36-48
2 9 70-6
6-12
12-18
18-24
24-36
36-48
2 8-9 3 0-6
6-12
12-18
18-24
24-36
36-48
3 8 30-6
6-12
12-18
18-24
24-36
36-48
3 8 70-6
6-12
12-18
18-24
24-36
36-48
3 7 11 0-6
6-12
12-18
18-24
24-36
36-48
6-12
5.22
0.84
0.80
0.68
0.14
5.22
0.63
0.77
0.18
0.50
5.64
0.21
0.94
0.44
0.06
0.03
1.15
1.46
1.08
0.13
0.13
0.31
1.46
1.51
0.97
0.08
0.21
0.17
1.25
1.20
0.97
0.23
0.23
0.10
7-15
2.82
2.14
1.08
0.99
0.00
0.00
1.36
2.72
1.14
1.25
0.00
0.00
0.52
0.42
2.16
1.17
0.00
0.00
0.84
1.51
1.01
0.75
1.19
0.00
0.84
1.88
0.35
0.63
0.92
0.00
0.63
1.10
0.77
0.44
1.02
0.00
8-10
2.61
0.26
1.57
0.13
0.94
0.00
2.09
1.04
1.39
0.13
0.94
0.00
0.21
0.47
1.36
2.09
0.00
0.00
0.21
0.42
0.70
0.37
0.65
0.00
0.21
0.47
1.46
0.03
0.88
0.00
0.31
0.63
0.52
0.57
0.50
0.16
9-18
2.19
0.52
1.25
0.08
0.44
0.21
0.78
0.66
0.16
0.19
0.19
0.94
0.21
0.97
0.08
0.86
0.00
0.21
0.47
0.59
0.16
0.17
0.17
0.10
0.47
1.01
0.26
0.06
0.40
0.21
0.37
1.32
0.03
0.08
0.45
176
-------
TABLE B-4. CONTINUED
Sampling dates
Salt Water Depth
Block level level inches
3 7 15 0-6
6-12
12-18
18-24
24-36
36-48
3 7 19 0-6
6-12
12-18
18-24
24-36
36-48
4 1 19 0-6
6-12
12-18
18-24
24-36
36-48
4 1 15 0-6
6-12
12-18
18-24
24-36
36-48
4 2 11 0-6
6-12
12-18
18-24
24-38
36-48
4 2 70-6
6-12
12-18
18-24
24-36
36-48
6-12
1.46
1.20
0.45
0.47
0.19
0.16
2.82
0.78
0.49
0.23
0.13
0.09
0.63
0.21
0.35
0.26
0.13
0.12
0.73
0.26
0.38
0.21
0.15
0.10
0.52
0.37
0.14
0.21
0.17
0.09
0.94
0.37
0.28
0.29
0.92
0.16
7-15
0.73
1.15
0.70
0.55
0.00
0.00
1.36
1.20
1.36
1.01
0.00
0.00
0.42
0.31
0.10
0.31
0.21
0.09
0.42
0.26
0.28
0.21
0.31
0.00
0.42
0.26
0.28
0.13
0.27
0.00
0.31
0.31
0.24
0.29
0.33
0.07
8-10
0.84
1.41
0.24
0.55
0.33
0.14
1.36
0.68
0.59
0.21
0.56
0.02
0.21
0.16
0.17
0.16
0.13
0.09
0.21
0.16
0.17
0.08
0.21
0.12
0.21
0.26
0.17
0.10
0.21
0.09
0.21
0.21
0.24
0.23
0.36
0.00
9-18
0.10
1.62
0.24
0.13
0.29
0.07
1.15
1.20
0.17
0.34
0.17
0.16
0.10
0.10
0.17
0.21
0.19
0.21
0.21
0.16
0.28
0.10
0.17
0.16
0.21
0.21
0.21
0.16
0.08
0.19
0.10
0.26
0.14
0.26
0.08
0.16
177
-------
TABLE B-4. CONTINUED
Sampling dates
Salt Water Depth
Block level level inches
4230-6
6-12
12-18
18-24
24-36
36-48
1 3 30-6
6-12
12-18
18-24
24-36
36-48
1 3 70-6
6-12
12-18
18-24
24-36
36-48
1 3 11 0-6
6-12
12-18
18-24
24-36
36-48
1 4 15 0-6
6-12
12-18
18-24
24-36
36-48
1 4 19 0-6
6-12
12-18
18-24
24-36
36-48
6-12
0.73
0.42
0.17
0.29
0.10
0.10
0.63
0.52
0.24
0.23
0.29
0.03
0.63
0.42
0.45
0.42
0.15
0.12
1.46
0.37
0.45
0.29
0.19
0.16
2.30
0.57
0.59
0.23
0.13
0.03
1.98
0.57
0.66
0.21
0.17
0.00
7-15
0.42
0.42
0.28
0.39
0.04
0.07
0.31
0.52
0.35
0.29
0.21
0.12
0.31
0.36
0.63
0.23
0.02
0.00
0.73
0.89
0.42
0.63
0.02
0.09
0.94
1.31
0.38
0.37
0.17
0.00
1.25
0.73
0.45
0.29
0.21
0.00
8-10
0.21
0.26
0.17
0.03
0.21
0.12
0.21
0,16
0.28
0.18
0.17
0.31
0.21
0.26
0.38
0.31
0.23
0.23
0.42
0.37
0.49
0.16
0.23
0.28
1.04
0.52
0.80
0.00
0.00
0.00
0.94
0.42
0.49
0.13
0.19
0.03
9-18
0.21
0.21
0.28
0.16
0.06
0.33
0.10
0.10
0.17
0.21
0.27
0.12
0.10
0.21
0.31
0.39
0.29
0.16
0.21
0.38
0.56
0.29
0.23
0.00
0.73
0.16
0.87
0.05
0.21
0.00
0.63
0.26
0.38
0.16
0.17
0.07
178
-------
TABLE B-4. CONTINUED
Sampling dates
Salt Water Depth
Block level level inches
4 6 19 0-6
6-12
12-18
18-24
24-36
36-48
4 6 15 0-6
6-12
12-18
18-24
24-36
36-48
4 7 11 0-6
6-12
12-18
18-24
24-36
36-48
L 1 1 0-6
H- / '
6-12
12-18
18-24
24-36
36-48
4 8 30-6
6-12
12-18
18-24
24-36
36-48
1 8 30-6
6-12
12-18
18-24
24-36
36-48
6-12
1.36
0.57
0.52
0.63
0.12
2.40
0.31
0.91
0.34
0.00
0.21
1.36
0.63
0.84
0.21
0.56
1.57
0.26
1.04
0.31
0.31
1.67
1.41
1.11
0.13
0.40
0.00
3.34
1.10
0.56
0.23
0.13
0.04
7-15
1.25
0.63
0.49
0.08
0.21
0.00
0.84
1.04
0.24
1.28
0.42
0.94
0.31
0.70
0.57
0.31
0.68
0.45
0.76
0.00
0.10
0.63
0.99
0.49
0.31
0.94
1.41
0.52
0.68
0.13
0.12
8-10
0.94
0.57
0.80
0,00
0.00
0.00
1.25
0.42
0.87
0.78
0.00
0.00
1.67
0.37
1.08
0.10
0.00
0.00
1.36
0.16
0.73
0.26
0.06
0.00
0.94
0.78
0.10
0.26
0.90
0.52
2.09
0.52
0.00
0.52
0.71
0.00
9-18
0.10
1.25
0.49
0.10
0.23
0.12
0.73
0.31
0.73
0.00
0.44
0.42
0.21
0.59
0.29
0.59
0.10
0.26
0.31
0.21
0.33
0.03
0.31
0.42
0.49
0.18
0.71
0.00
0.21
0.21
0.38
0.26
0.36
0.05
179
-------
TABLE B-4. CONTINUED
Sampling dates
Salt Water Depth
Block level level Inches
1 9 70-6
6-12
12-18
18-24
24-36
36-48
1 9 11 0-6
6-12
12-18
18-24
24-36
36-48
1 10 15 0-6
6-12
12-18
18-24
24-36
36-48
1 10 10 0-6
6-12
12-18
18-24
24-36
36-48
6-12
5.74
0.63
0.73
0.39
0.10
0.21
8.56
0.26
0.37
0.19
0.52
5.64
0.84
0.59
0.83
4.28
0.89
0.80
0.21
0.46
7-15
3.34
1.25
0.49
0.89
0.29
0.12
2.61
2.19
0.59
0.26
0.23
0.70
4.07
1.98
0.00
0.13
0.04
0.17
2.82
1,72
0.17
0.39
0.08
0.19
8-10
1.15
1.46
1.81
0.00
0.00
0.00
2.51
2.25
0.00
0.00
0.00
0.00
3.55
0.99
1.43
0.00
0.00
0.00
2.82
2.77
0.00
0.00
0.00
0.00
9-18
0.94
0.68
0.97
0.29
0.61
0.26
0.21
2.25
1,50
0.47
0.40
0.00
0.21
2.51
0.35
0.34
0.44
0.10
2.51
1.20
0.56
0.18
0.42
0.00
180
-------
TABLE B-5. ELECTRICAL CONDUCTIVITY OF WATER SAMPLES EXTRACTED FROM CERAMIC
CUPS, MMHOS/CM @ 25°C
Sampling dates
Salt
Block level
2 1
1
2
2
3
3 3
4
4
4
6
6
6
6
6
2 6
6
6
6
10
10
9
9
8-9
3 8
8
7
7
7
4 1
1
2
2
2
Water
level
19
15
11
7
3
3
7
11
15
17
13
9
5
1
5
9
13
17
19
15
11
7
3
3
7
11
15
19
19
15
11
7
3
6-13
5.5
4,7
4.4
7.3
5.2
3.4
6.8
4.4
4.9
4.3
4.3
3.7
16.7
10.9
14.6
3.7
3.6
4.6
5.9
5.2
5.5
4.4
4.0
3.4
7-9
5.3
4.8
4.8
3.8
3.4
3.8
3.7
4.2
4.8
2.2
3.2
4.3
3.4
8.4
12.3
24.6
2.6
4.6
3.6
4.1
5.2
5.3
5.0
4,3
3.8
3.7
2.3
7-17
5.8
5.2
2.7
5.5
2.6
6.8
3.6
4.2
3.3
2.8
5.5
3.4
4.6
4.4
4.2
7.7
3.5
5.2
5.0
4.3
3.8
3.7
8-1
5.6
5.2
5.7
3.6
5.19
3.6
3.4
13.2
5.0
6.0
3.7
6.6
5.0
4.3
3.8
4.2
8-12
5.8
5.3
5.9
4.2
4.8
3.3
4.2
4.9
3.8
15.2
4.6
6.7
6.1
7.8
7.7
10.3
4.1
4.2
4.7
6.4
5.0
4.7
4.1
3.7
3.2
8-27 9-18
7.0 7.6
7.5 7.3
4.8 3.7
4.3
5.8
4.3 5.1
8.2 15.6
5.3 6.3
7.8
19.2
7.1
8.2
12.3
4.8 6.6
4.3 4.2
4.5
6.4
4.9 8.0
8.8
5.0
3.8 4.3
4.0 4.9
181
-------
TABLE B-5. CONTINUED
Sampling dates
Block
1
4
1
Salt
level
3
3
3
4
4
5
5
5
5
5
5
5
5
5
6
6
7
7
8
8
9
9
10
10
Water
level
3
7
11
15
19
17
13
9
5
1
5
9
13
17
19
15
11
7
3
3
7
11
15
19
6-13
6.3
5.2
6.1
5.2
4.9
5.1
5.4
5.7
5.8
3.2
5.4
6.4
6.3
5.9
6.3
6.7
7-9
5.3
5.1
6.0
5.3
5.2
4.4
4.8
4.2
2.6
6.6
3.7
5.7
3.5
4.7
21.0
7-17
3,2
4.7
4.4
5.6
5.1
1.8
3.9
5.0
2.8
6.2
4.2
2.6
8-1
4.8
5.6
5.2
6.4
5.6
5.4
5.6
6.8
6.1
6.4
2.3
8.7
3.3
5.1
4.3
3.8
5.2
4.2
10.4
8-12
5.6
5.7
5.6
5.1
5.7
6.6
5.7
5.9
10.3
8.1
7.5
2.9
7.7
3.5
5.0
4.3
7.4
4.7
9-4
8-27
5.8
4.2
7.1
7.0
5.5
7.1
8.7
9.9
10.7
8.6
3.8
8.3
3.4
4.9
4.4
11.0
6.4
5.3
7.8
9-18
7.5
5.1
7.7
5.7
5.8
5.8
9.7
12.7
9.8
'11.3
6.4
3.2
4.8
15.5
7.7
5.6
6.3
182
-------
APPENDIX C
A program to compute K for the numerical approximation ofi
(dc/dt) - D(d2c/dz2) - U(dc/dz) + K(R - c)
NOTATION
Clfl) CONCENTRATION OF SOIL SOLUTION IN SPACE INCREMENT I
S2(I) SOLID SALT IN SOIL IN INCREMENT I
C2CI) CONCENTRATION AFTER ONE TIME INCREMENT
SZ(I) SOLID SALT AFTER ONE TIME INCREMENT
AtBtC VECTORS IN MATRIX OF COEFFICIENTS
E VECTOR OF SOLUTIONS
V REAL VELOCITY (CM/H5
D COEFFICIENT OF HYDRODYNAMIC DISPERSION AND DIFFUSION
f CM**2/H)
RtRK COEFFICIENTS IN THE BASIC EQUATION.
NF*NL SUBSCRIPTS OF THE FIRST AND THE LAST SPACE GRID POINTS
TH WATER CONTENT '
CIR CONCENTRATION OF IRRIGATION WATER (MICROMHOS)
DTI INITIAL TIME INCREMENT
DT2 VARIABLE TIME INCREMENT
DZ SPACE INCREMENT (CM)
STMAX TOTAL TIME REQUIRED (H)
DIMENSION C1C1I I tSl (11 > »C2 (11) tS2(ll )» A(11) » B( 11 >» CU1 )» E( 21)
1 tTClDO) f ECM< 100) tECC (100) »£CC1 (100 )t 6(51 tlQO)» XT (5 )tSIN( 11 )
1»CTN(1D
READ GENERAL DATA
REAO(5»101)VtD»R»TH,CIR
101 FORMAT(5UXtEl0.5) J
READ(5»103 )DTIt DZ.NF tNL»NDATA*BLtCAt CEtAS
103 FORMAT(2UXfE10.5S t3f3X»X3)t4All
READ INITIAL CONDITIONS
REAO(5»102) (CIN (I) »SIN(I)t I-NFtNL)
102 FORMAT(2E1D.5)
REAO(5»102 )(T(U tECH( I) f I-lf N DATA)
STMAX=TCNDATA)
WRITECGt 210)
210 FOR MAT (1 HI »6<»Xf *K * » 1IX t 'SUM (ECC-ECM) **2f//)
183
-------
C COMPUTE A AND C
C
NFPlrNF+1
NLMl^NL-l
C
DO 1 IrNFPl»NL
A(I )-D/(2.*DZ**2)+V/t<».*DZ)
CCI)=D/(2.*DZ**2)-V/(H.»DZ)
IFCI.EQ.NFP11 A'( I)=0.
IFCT .EQ. ND C (I)=0.
1 CONTINUE
C
RK^O.
DK-.01
SUMDEV:lQ.**20.
15 Mrl
DO 10 I=ltNL
Cl< D^CINIIJ
10 Sl(I)-SINlI)
DT2=DTI
NRKrNRK+1
C
C COMPUTE B
C
DO 2 I^
2 B(I)=(-1.)/DT2-D/DZ**2
C
C THE SOLUTION IS BY SUB TRIDAG
C
BC NL)=B(3)+C(3)
C
C COMPUTE S2
C
DO 3 I-NF.NL
S2(I)=SH I)-RK» (R-CKI) )*DT2
IFfS2(Il.LT.O.)S2 (T)=0.
3 CONTINUE
C
C COMPUTE VECTOR OF SOLUTIONS
C
DO 5 I=NFP1»NLM1
RKS-RK* (R-CK I) )
IF(S1(I).LT.1.1 RKS=0.
E(I) = «-1.)/DT2 J*C1(I)-(D/C2.*DZ**2) )* (C 1 ( 1+ 1) -2 .* Cl (I H- CUI-1 )
1 (V/CH.*DZ) )*( Cl (I+l)-Cl(I-m-RKS
IFd.EQ.NFPllEt IJ = E(I)-ClR*Ai3)
5 CONTINUE
184
-------
RKS-RK*tR-CHNL))
E(NL)=C t-l.»/DT2)*CKNL)-CD/C2.*OZ**2) )*(C1(NLM1 >-CKNU
1 (V/f 4.*nZ) )*( Cl DK=f-DK)/2.
IF( ABS< DK) .LT.O .000000001)60 TO 11
IFf NRK.GT.99)GO TO 11
SUHOEV^SUMDVl
RK1-RK
DKrQ.
RKrRK-t-DK
C
DO 13 I=l»NnATA
13 ECCIII)-ECC(I)
GO TO 15
C WRITE GENERAL DATA
C
11 WRTTE(6»201)VfDtRf TH »CIR » DTI»DZ» STM A Xt NFt NL
201 FORMATClHlf 'GENERAL DATA'//
11H t'V r*,E10.5»* (CM/H)'/
11H ,'D r',E10.5»' (CM**2/H)V
11H r *R -» »E10.5/
11H t'TH r',E10.5/
11H »'CIR r«,E10.5t* (M ICRO MHOS) V
11H »*DTI r»,E10.5ff (H)V
11H .'DZ r'tElO.St* (CM)'/
11H ,'STMAXr' »E10.5»f (H)*/
185
-------
11H ,'NF
11H t'NL -'»I5////J
C
C WRITE INITIAL CONDITIONS
C
WRITE(6t203H CIN ( I ) » I-NF» NL) t ( STN < J) »J-NFtNL)
203 FORMATdH .t»5X* 'INITIAL CONDITIONS*//
11H »'C 'tllElO.5/
11H t *S * »11E10.5)
WRlTE(6t 208)RK1
208 FORMAT*/// /1H • »K= *i E13 .8////J
WRITE(6»20«»I
20«J FORMATdHl »6Xt f TIME
-------
DO 23 I-l.NDATA
TXrim/SCALEX
IYC-51.-IECC1(I)-YMIN)/SCALEY
IYM-51.-(ECM( IJ-YMIN)/SCALEY
G(IYCtIX)rCA
G(I YM»IX)rCE
IF(IYC.EQ.IYM1GIIYM.IXJ=AS
23 CONTINUE
WRITE(6,5Q1)
501 FORMATJ1H1 .3DX. 'MEASURED AND COMPUTED VALUES OF EC VS. T IMP/)
502 FORMATC1H .E10.5.' +'.100A1)
503 FORMATC1H tllX t 'I ' r 100AU
504 FORMATC1H . HXt *~* »5 ( 19 f »-« ) t « +• J /
11H t!6Xf 5(10X»E10.5))
DO ?«t IrltlltlO
Air I
ZlrYMAX-( YMAX-YMIN )*( AI-1. ) /50.
WRITECGt 502)71, (GCItK )VK=ltlOO)
L9-I+9
2<* WRTTECGt 503) ( (G (Nl tN2 ) » N2-lt 100) t NirUfL9)
WRITE(6t502)YMIN» CGCSlt N3) tN3- 1*100)
DO- 25 1-1.5
AT=I
25 XTCI)-T(NDATA)* (AI/5.)
WRTTE(6»50'4)(XT (I) .1=1, 5)
STOP
END
SUBROUTINE TRIO AG (NF.NL .'A, B.C.D. V)
DIMENSION A(NL) »B (NL1 tC CNLlt D (ML1 tV (NLI i BETA 0. 01 J.GAMA 0. 01)
BETA(NF)-B(NF)
GAMA(NF)rO(NF)/BETA(NF)
DO 1 I=NFP1*NL
BETAtIl = BCI)-A( II*C(I-1
6AMA(II = (D{I)-A(I)*6AMAII-1))/BETA(II
VtNL)^GAMA(NL)
LAST=NL-NF
DO 2 K=ltLAST
Vc"l)=6AMA(I)-CC I)*V!I+1)/BETAIII
RETURN
END
187
-------
TABLE
LEACHING EXPERIMENTS TO COMPUTE D.
oo
00
Experiment
Column's length (cm)
U (cm/hr)
Cir (ymho/cm)
Gin (ymho/cm)
Measured and
computed values of
ECef (mho/cm) Vs.
time.
T - Time (hr)
M - Measured
C - Computed
T
2.19
2.58
3,23
3.68
3,89
4.62
5.11
6,55
7.57
8.08
9.39
11.41
6
25
5.07
2010
10190
M
9608
9005
8066
7150
6815
5899
5340
4290
3821
3597
3195
2648
C
9548
9021
7954
7184
6834
5718
5080
3711
3110
2892
2508
2215
T
3,12
3,79
4,10
5.21
5.86
6.39
7.03
7,52
8.02
8.85
9.32
10... 20
7
24
3,98
1500
10200
M
8750
7750
7350
5850
5150
4550
4050
3700
3300
Z950
2750
2450
C
8751
7759
7327
5855
5167
4571
4061
3697
3324
2966
2754
2445
T
11,03
13,51
16,46
19,26
22.05
25.01
28,89
34.17
36.34
38.21
42.87
50.17
8a
24
0.98
10990
1650
M
2790
3690
4800
5810
6700
7490
8330
9210
9410
9610
10050
10390
C
2810
3693
4801
5795
6680
7470
8330
9178
9445
9642
10032
10427
T
8.97
23,50
25.28
27.60
29.14
31.27
33.40
35.91
38.23
8b
24
0.81
10930
1680
M
1680
5320
5790
6590
7190
8130
8940
9940
10280
C
1742
5321
5893
6590
7019
7557
8035
8527
8914
M
M
M
a
i
a
0
o
e!
1-3
M
90
o
o
£
o
ffi
0
o
QTATION OF D
-------
A program to search for the best coefficient of dispersion based on
minimizing the sum of the absolute deviation.
IMPLICIT DOUBLE PRECISION
-------
WRITE (6.20 7) (Tt I),Irl,N )
WRITE(6»203MEt U.IrltN)
WRITE (6.202 )( Ct T)*!-1.N )
202 FOR MAT (1 HO .'EC CALCUL AT ED' 1 12F8.0J
203 FORMATdHO .'EC MEASURED «tl2F8.Q)
207 FOPMATdHQ r'TIME f»12F8.2J
AN^N
SM-0.
SCrQ.
SCI-Q.
DO 7 Irl ,N
7 SCI-SCI+Em
DO 8 Ir
8 SMrSM+CE
-------
TECHNICAL REPORT DATA
(I lease read Instructions on the reverse before completing)
EPA-600/2-76-226
TITLE AND SUBTITLE
IRRIGATION MANAGEMENT AFFECTING QUALITY AND QUANTITY
OF RETURN FLOW
5. REPORT DATE
September 1976 (Issuing datel
6. PERFORMING ORGANIZATION CODE
3. RECIPIENT'S ACCESSION'NO.
Lyman S. Willardson and R. John Hanks
8. PERFORMING ORGANIZATION REPORT NO.
PERFOR
ORGANIZATION NAME AND ADDRESS
Department of Agricultural and Irrigation Engineering
Department of Soil Science and Biometeorology
Utah State University
Logan, Utah 84322
10. PROGRAM ELEMENT NO.
1HB617
11. CONTRACT/GRANT NO.
R-802864
12. SPONSORING AGENCY NAME AND ADDRESS
Robert S. Kerr Environmental Research Lab,
Office of Research and Development
U.S. Environmental Protection Agency
k'da, Oklahoma 74820
- Ada, OK
13. TYPE OF REPORT AND PERIOD COVERED
Final
14. SPONSORING AGENCY CODE
EPA/600/15
15. SUPPLEMENTARY NOTES
16. ABSTRACT Management practices for control of quality and quantity of return subsurfac
flow were studied theoretically, in the laboratory, and full scale in the field.
Field water management studies using waters of different qualities and different
leaching fractions showed that the soil in the project area has a high salt buffering
capacity. The soil acted either as a source or a sink for salt depending on the
leaching fraction and the quality of water used for irrigation. Minimum average
leaching fractions attainable on a field scale were found to be controlled by the
uniformity of irrigation water application.
Digital computer models were developed that consider properties of the soil, plant,
water and environment. One model allows prediction of salt buildup and the yield
response over several years. Salt buildup in the soil eventually caused a yield
decrease. It was necessary to include a source-sink term in a salt flow model to
accurately simulate field data. Source-sink phenomena observed in the field were
confirmed by leaching tests conducted in the laboratory. Both models are potentially
useful for salt management in the field.
Production functions were developed for dry matter and grain yields of corn for vari-
able water and salt application. A relation between evapotranspiration and yield
indicates that the osmotic effect of salt in the profile reduces evapotranspiration
ap.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
Irrigation, Salinity, Water Quality,
Soil Chemical Properties
3. DISTRIBUTION STATEMENT
RELEASE TO PUBLIC
b.lDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Saline Soils, Water
Management, Water Pollu-
tion Control, Soil-Water-
Plant Relationships, Crop
Response, Irrigation
Practices
19. SECURITY CLASS (ThisReport)'
Unclassified
17C
02C
08C
07D
21. NO. OF PAGES
207
EPA Form 2220-1 (9-73)
191
U.S.GOVERNMENT PRINTING OFFICE: 1977-757-056/5493
------- |