RESEARCH ON A WEATHER SERVICE FOR SCHEDULING THE

Transcription

RESEARCH ON A WEATHER SERVICE FOR
SCHEDULING THE IRRIGATION OF WINTER WHEAT
IN THE ORANGE FREE STATE REGION
by
DE JAGER
VAN ZYL
KELBE
SINGELS
J M
W H
B E
A
Research Report
to
THE WATER RESEARCH COMMISSION
by
DEPARTMENT OF AGROMETEOROLOGY
UNIVERSITY OF THE ORANGE FREE STATE
ISBN:
0 908 356 70 6
CONTENTS
CHAPTER
1
PAGE
INTRODUCTION
GENERAL
THE CONCEPT OF EVAPORATION FROM A NATURAL
VEGETATIVE SURFACE
OBJECTIVES
THE MODEL
2
THE VALIDATION OF PUTU 9
7
3
THE DESCRIPTION OF PUTU 9.86
18
4
THE ROOT DEVELOPMENT AND SOIL WATER EXTRACTION
SUB-MODEL FOR WHEAT
34
MODEL DEVELOPMENT AND VALIDATION
5
MICROMETEOROLOGICAL METHODS FOR ESTIMATING
EVAPOTRANSPIRATION
60
6
DESCRIPTION OF THE STRESS EVENTS DURING 1985
79
7
PUTU 9.86 VALIDATION
88
THE COOPERATIVE EXPERIMENTS
8
THE EXPERIMENTS OF 1982 AND 1983
115
9
132
li
OPERATION OF T H E IRRIGATION
SERVICE
10
THE AUTOMATIC WEATHER STATIONS
151
11
COMPUTATION AND DISSEMINATION O F
INFORMATION
179
12
THE INFLUENCE OF WEATHER UPON DECISION MAKING
FOR O P T I M A L IRRIGATION SCHEDULING
183
13
SUMMARY
19 7
ACKNOWLEDGEMENTS
199
REFERENCES
202
APPENDIX I - M A T H E M A T I C S
207
APPENDIX II - SOIL PHYSICAL PROPERTIES
212
APPENDIX III (a)
(b)
DIE SIMULERING VAN PLANTHOOGTE
VAN KORING ONDER BESPROEIING
VERFYNING VAN 'N BLAARGROEIMODEL
VIR KORING ONDER BESPROEIING
PUTU 9.86
220
224
APPENDIX IV
- LISTING:
APPENDIX V
- LISTING: REGISTER
240
APPENDIX VI
- LISTING:
241
APPENDIX VII
- LISTING: PRINT DAILY,
MONTHLY SUMMARY
EREF
233
243
APPENDIX VIII - LISTING: TRANSFORM AUTO
DATA AND FILE
246
APPENDIX IX
253
APPENDIX X
- COMPARISON OF WEATHER
VARIABLES AND TESTS ON
EQUIPMENT
- RESULTS FROM THE COOPERATIVE EXPERIMENTS OF 1982/83
iii
25 5
CHAPTER
1
INTRODUCTION
GENERAL
Atmospheric
water
conditions
(the
weather) primarily
requirements of vegetative crops.
determine
It is only when
the
water
becomes limiting that soil physical properties and crop physiological characteristics assume a significant role.
Hence, thorough
understanding of atmospheric influences is imperative for an
depth consideration of water use by crops.
Thus,
in-
any investiga-
tion into irrigation scheduling must involve the weather.
Irrigation
demands,
becomes
the
rate
necessary when,
of water supply from the soil
growth and hence production.
situation
is
via
scheduling
atmospheric
limits
the use of a computer
and
the
mathematical
It thus follows that irriga-
could most effectively be
undertaken
computerized model which takes into account the weather.
technique
provides
plant
The best method of analysing such a
simulation of the natural process.
tion
in response to
a sound basis for determining the
using
a
Such a
efficient
use of irrigation water.
Particularly
ages,
has
during the past three years of serious water short-
has a premium been placed upon irrigation efficiency.
furthermore,
become necessary to strive
maximum production from limited water supplies.
towards
It
obtaining
The major losses
in production are generally incurred as a result of poor cultivation practices.
Coupled with this is bad irrigation management.
For example, untimely or inadequate irrigation can be responsible
for depressing yield.
lation
out
Excessive irrigation producing deep perco-
of the root zone,
cause of water wastage.
on the other
hand, is the
There is little doubt that
prime
scheduling
irrigation according to atmospheric demand should be practised as
it
will
contribute to the alleviation of both these
problems.
rect
Such
production.
tionalizing
will
technique will limit water losses due to incor-
assessment of the amounts of water required for
economic
be
The work described
the interaction of weather,
making
soil.
and
on
Agrometeorology,
the experimental site
on
the
raThis
possible
the
The work was conducted by
of cooperative experiments with farmers in the
district
successful
here is aimed at
plant and
done in the computer in a manner
scheduling and planning of irrigation.
means
irrigation
of
the
Hartswater
Department
West Campus of the University
of
of
the
Orange Free State.
THE CONCEPT OF EVAPORATION FROM A NATURAL VEGETATIVE SURFACE
Penman
the
(1948)
defined the term potential evapotranspiration
evaporation
water.
The
from a short grass surface well
supplied
significance of the interaction between
implicit assumption was that evapotranspiration from all
ever,
could be referenced to short grass
with
vegetative
architecture and the atmosphere was then not fully realized.
surfaces
as
evaporation.
The
cropped
How-
crop height and shape seriously influence both the conduc-
tance of water vapour away from the surface,
of the surface.
These markedly influence water removal from the
vegetative surface.
of
It is thus possible to find, for given sets
atmospheric conditions,
transpiration
cover.
and the temperature
different rates of potential
corresponding
to
different types
of
evapo-
vegetative
To overcome this uncertainty and to account for the role
played by evaporation from a soil surface under incomplete
tative
cover,
the
vege-
following definitions will be adhered to
in
this treatise.
The
total evaporation from a natural vegetative surface,
defined as the sum of evaporation from the sub-stomatal
E,
is
cavities
of leaves, Ev, plus the evaporation from the surface of the soil,
Es.
Thus
E = Ev + Es
Transpiration is,
..(1.1)
in effect, the evaporation from the cell walls
in the sub-stomatal cavity.
The term, maximum evaporation from a given crop replaces the term
potential evapotranspiration.
of
meeting
denote
atmospheric demand,
Thus,
when soil water is capable
the symbol Em will be
used
to
maximum total evaporation from a specific crop surface in
a given growth stage.
Em = Ev + Es
..(1.2)
Total evaporation and evapotranspiration are entirely synonymous.
Although the term total evaporation (E) is here
term
preferred;
evapotranspiration (ET) will also be used.
the
In which case
the symbols in Eqn 1.2 become:
ETm = Tm + Es
The
..(1.3)
above definitions follow proposals by Monteith
went
on
to emphasize that total evaporation (E)
(1985).
has
the
He
same
number of syllables as evapotranspiration, occupies slightly less
space
on
the
printed
page
and,
unlike
ET,
has
no
extra-
terrestrial connotations.
The
concept of evaporation from a short grass surface well
plied with water is recognized.
defined
with
as
Reference evaporation,
the evaporation from a short grass surface
adequate water.
sup-
Er,
is
supplied
Similarly evaporation from a large
open
water surface will be defined by the symbol Eo.
OBJECTIVES
A
convenient way of scheduling irrigation on a number of
bouring
station.
farms
These
is
via the use of weather data
from
a
neighcentral
data should be representative of conditions oc-
curring on the various farms.
Such data may then be processed by
computer to provide simulations of
irrigation
upon
plots in the area.
estimations
leads
conditions on many individual
Since such a technique is
of actual atmospheric
evaporative
to the most efficient use of water.
follows upon an initial project (De Jager,
Van
Rooyen,
with
it
project
Bristow and
1982) which developed a suitable computer programme
which
efficiently.
demand
The present
Van Zyl,
based
irrigation
This
scheduling
could
be
managed
project takes the work one step
more
further
and
investigates the practical application of the model.
The objectives of the project were to:
(i)
prove the accuracy and viability of computer irrigation
scheduling
(ii)
demonstrate
the application of a central
computerized
service for scheduling the irrigation of wheat on individual farms using weather data,
(iii)
standardise
the operational procedure for such a
sys-
tem,
(iv)
install
a
large lysimeter and test
the
various
aspects of the simulation model used,
ticular -
(a)
the crop evaporation formulae,
and
accuracy
of
in par-
(b)
crop
hydraulic conductance relationships
sible for the onset of water stress
addition,
micrometeorological
respon-
utilizing, in
techniques and in-
fra-red therraometry,
(v)
further
verify the leaf water potential and leaf
area
index sub-models.
NOTE:
It had been the intention to construct two
but
finances
limited the project to one.
lysimeters,
Data
from
Rietrivier Experimental Station were to have been used,
but the drought made this impossible.
The
investigation
experiments
on
included cooperative
field
experiments
and
the Agrometeorological experimental site on
the
West Campus of the UOFS.
The research project commenced on 1 October, 1982.
CHAPTER 2
THE VALIDATION OF PUTU 9
PUTU
9 is the model developed in the previous project {De
et al,
ject.
9.86
1982).
Jager
It was utilized in the first stages of this pro-
PUTU 9 was modified from an hourly time step model to PUTU
which works in daily time steps.
interesting
However,
it was
deemed
and necessary to validate the initial model in order
to determine problems that could be rectified in PUTU 9.86, Also,
PUTU 9 may well be utilized in the future.
PROCEDURE
Essentially
tion
an accurate simulation model for scheduling
irriga-
must provide reliable simulation of crop water use and
onset
of water stress.
the
Relative evaporation is defined as
the
ratio of total evaporation (E) to maximum total evaporation (Em).
The
presence
of
crop water stress is indicated by a
relative evaporation less than unity.
versus
place
and
time
and permits quantitative comparisons between
evaporation
water
Accurate
simulation
of
stress
Evaporation models have to be
took
measurements
measured
by a model produces a suitable validation of a
use model.
of
Hence a scenario of E/Em
clearly illustrates when the onset of
simulation.
value
validated
total
crop
for
both bare soil (Es) and cropped surfaces (E).
The
necessary measurements were made and simulations carried out
in the 1984 and 1985 seasons on West Campus.
Measured values of
evaporation were obtained From the lysimeter.
Automatic weather
station data were utilized to simulate E and Em.
For both years,
the scenario of the simulated and measured ratio
are illustrated in Figs.
2.1 and 2.4.
Comparisons between mea-
sured and simulated daily crop water use,
2.2,
2.3 for bare soil conditions,
the wheat crop.
E,
are shown in Figs.
and in Figs
Simulation indices,
2.5 and 2.6 for
SI ( Willmott,
1982), and
correlation coefficients were calculated and are shown in each of
the
relevant figures.
A simulation index of SI = 0,7 generally
indicates acceptable accuracy in biological experiments.
Figs
2.2,
2.3,
2.5,
and
2.6
do not include
days
irrigation
took
place because it was not possible to
on
which
determine
evaporation from the lysimeter without an accurate measurement of
the rainfall or irrigation in the lysimeter.
rainfall
In 1984 and
1985,
and irrigation amounts were estimated directly from
the
lysimeter record.
RESULTS AND DISCUSSION
The figures and statistical tests indicate that reasonable agreement
between
simulation
indices were acceptable for bare soil
both years.
ably
simulated and measured values were obtained.
accurate
(SI = 0,69) .
conditions
The
in
For the wheat cover, the model proved to be acceptin 1985 (SI = 0,89) but less acceptable
in
1984
The accentuated scatter in particularly Fig.
2.2 and 2.5 is due
to an inadequate resolution setting on the datalogger used.
2.4
indicates that under incomplete vegetative cover (DOY 234 to
250) in 1984,
PUTU 9 over estimated E.
true from DOY 213 to 235 in 1985.
DOY
Fig.
275
to 330 in 1984,
The same was
partially
Furthermore, with full canopy,
PUTU 9 predicted stress which was
evident in the lysimeter readings.
not
The crop coefficient (F) for
simulating the degree of stress in PUTU 9 is defined by
E = F.Em
where,
F = [Fie . Fh + Fg.(l-Fle)].
..{2.1)
These three F-terms are dimensionless constants between zero
unity.
and
Originally they were described as follows:
Fie = -0,2 + 0,7 LAI (Ritchie, 1972) where LAI = leaf area index,
Fh
= 1/ [1 + Exp (PSIC - PSIL)] and
Fg
= EXP [0,4. days since surface was wetted]
PSIL and PSIC are leaf water potential and stress threshold
leaf water potential respectively.
Evaporation from the soil, Es, was computed from
Es = Fg.(1 - Fie).Em
Hence, for bare soil, Es = Fg . Em
..<2.2)
The
initial
over-estimation of E for an
incomplete
cover could have been due to use of the Ritchie ( 1972)
vegetative
equation.
A better estimator might be obtained by replacing Em by Er. Trial
and error methods were employed to rectify these problems.
These
suggested replacing the Ritchie (1972) equation by
Fie = 0,186.LAI for Fie < 0,9,
and
..(2.3)
Es = Fg.(1 - Fie).Er
..(2.4)
The equation describing evaporation from bare soil
which will be
developed in Chapter 7, viz.
Fg = EXP(-a . VDEF1)
..(7.3)
where a is a constant, was used while establishing Eqn 2.3. Thus,
in
both the PUTU 9 and PUTU 9.86 runs of 1984 and 1985
Eqn
7.3
replaced the original Fg = EXP(-0,4.time).
Meyer and Green (1981) reported F values for a full wheat
varying
between
however,
was
1,1 and 1,3.
estimated
daily mean weather data.
Maximum total evaporation
using the normal Penman
equation
This probably under estimated Em
canopy
(Em),
with
under
windy conditions for crop heights of approximately 0,7 m.
From Fig.
stress
2.4.
it appears as if the model predicts the onset of
too early.
prediction
This could also have resulted from the
over
of E earlier in the season which would simulate
more
rapid depletion in soil water reserves.
10
CONCLUSION
PUTU
9 with Eqn 7.3,
for determining the fraction of
reference
evaporation evaporated from a bare soil surface,
was shown to be
reliable.
throughout
Use
of
this equation for bare soil
the
period 1 March to 30 June yielded a SI > 0,77.
The model, PUTU 9, was considered to have reliably computed water
use
by
stress
the
the
wheat crop in 1985 (SI = 0,89)
occurred.
little
water
The poor performance in 1984 was ascribed
model being over sensitive to stress.
rectify
when
It was attempted
to
to
this in PUTU 9.86 (the daily version of PUTU 9) by using
an exponential root distribution and by iterating for leaf
potential and transpiration rate (see Chapter 4 ) .
11
water
•
1984
30-
«S20
z o
«
. . i
i 11
i
DOY
1985
•I.
S
FIG.
2.1
) and simulated (
) scenarios of the ratio of evaporation from a bare soil
to reference evaporation on West Campus during 1984 and 1985. Simulations were undertaken with
PUTU 9.
- Measured (
DOY
CUMULATIVE
80
1984
J.G
E»
|tnrn!
' 100
90
- 90
*
3.1
/
f
T3 2.7
UJ z.i
Q
UJ
H
13
s
1.3_
.9_
• o
8
.1
0
+
•
+
+ ++ 0
D5
S-Ind«x
•
.?SS
Con-elation
=
.666
n
=
93
.
.8^
u
rvs
<n
MEASURED
FIG.
2.2
-
Es (mm/d)
Relationships between cumulative (o) and daily (+)
•easured
and
simulated (by PUTU
9)
evaporation
from a bare soil during 1984 on West Campus.
13
CUMULATIVE
3.8.
TO
1985
E,
Imml
80
- 90
3.3.
-10
Z.7.
- 70
LU
2.2.
Q
LU
i.e.
CO
0
1 .!_
0
0+
•
0
0
,0°
S-Index
Correlation
•
=
.862
.783
93
.8.
ni
»
MEASURED
FIG.
2.3
-
Es (nwn/d)
Relationships between cumulative {o) and daily (+)
measured
and
simulated (by PUTU
9)
evaporation
from a bare soil during 1985 on West Campus.
14
mm
1984
40-
IS-H
X_*
L
M
• • -
-
\
O
O
O
—
DOY
m
mm
i
uz
0
.1.1 II .1
1 ..
L
1
,11
1
1
Hll
1985
1 <
•»•
It
M
AA
05
O *
03
0
3
0 1
oo
o
o
DOY
FIG.
2.4
- Measured (
o
CO
) and simulated (
) scenarios of the ratio of total evaporation, E, to maximum
evaporation, Em, on Uest Campus during 1984 and 1985. Simulations were conducted using PUTU 9.
CUMULATIVE
E
(
300
1
mm
)
«00
400
1984
3.4-
a.4
-300
7.3.
s.a.
'
0
•
0
*
UJ
Q 3.1.
UJ
ID B.J.
2
(/)
i.e.
S Index
•
.G87
Correlation
«
.51?
n
-
78
IT
MEASURED E (mm/d)
FIG. 2.5 -
Relationships between cumulative (o) and daily {+)
total
and
evaporation from a wheat crop
simulated
(by
Campus
16
as
PUTU 9) during 1984
measured
on
West
CUMULATIVE
9.4
1985
J
200
300
300
V*
7.3
A
A
S.3
-200
S.2
*
•
A °
1.2
111
Q
UJ
3.1_
2.1-
S-Index
.B8S
C«-r*UUi>i
.792
62
.8.
1
ID
MEASURED
FIG.
2.6
-
E (mm/d)
Relationships between cumulative <o) and daily (+)
total
and
evaporation from a wheat crop
simulated
as
(by PUTU 9) on West Campus
1985.
17
measured
during
CHAPTER 3
THE DESCRIPTION OF PUTU 9.86
The PUTU model was first created for maize in 1973.
construction
and King,
in Fig.
was
described
(1974).
3.1.
by De Jager
(1974) and De
Jager
The computing stages (modules) are described
In the model the partitioning of dry matter
ceeds as described in Fig.
3.2.
pro-
A version of PUTU specifically
for wheat, PUTU 6, followed in 1981.
for
Its initial
All the important functions
this model are described by De Jager,
Botha and Van
Vuuren
(1981) .
PUTU
6
was
PUTU
9.
modified for
irrigation
scheduling
It is described in detail by De Jager
and
et
re-named
al.,
1982.
While PUTU 9 utilizes most of the functions of PUTU 6 it computes
hourly
time
steps.
It has subsequently become
daily time steps are adequate,
scheduling
is concerned.
and necessary,
Hence,
apparent
where
that
irrigation
the daily irrigation
version
PUTU 9.86 was developed. Computation of daily values of Em and Er
from
the
hourly
data from the automatic
weather
station
was
carried out using the programme in Appendix VI.
An
attractive feature of PUTU models is their modular
tion.
construc-
During 1986 the computer programme was completely restruc-
tured and simplified to make the sequence of operations described
in Fig 3.1 and 3.2.
easy to follow.
9.86 reads:
18
The main programme for PUTU
5
10
20
25
29
30
40
50
60
62
64
72
75
80
85
86
90
100
101
130
170
172
180
190
200
210
220
230
240
250
260
270
280
290
300
310
320
330
340
350
360
370
380
390
400
410
420
430
500
510
520
530
535
540
*tt******«***************«**t**********t*t******t**tt****t«*t**t*
•PUTU9-86
NAAM:PUTU9-86
OP DISK:PROGRAM #2
LPRINT "Latest up-date on
1986.08.19 "
ttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttttt
OPTION BASE 1
DIM X(366,8),GMT(12),GMR(12),GMS(12),GME(12),MONL(12),MEND(12),
DOYMES<12)
GOSUB
GOSUB
GOSUB
GOSUB
GOSUB
GOSUB
GOSUB
'MEAN DATA
'FUNCTIONS
•INITIAL CONDITIONS
•WEATHER DATA INPUT
'PARAMETERS
'ZERO
'TITLE PAGE
2500
15000
9500
10500
3100
10000
5000
'ttttttttttttttttttttttttt
'BEGINNING OF CALCULATION
•I************************
FOR D = BEGIN TO FINISH
IF
IF
IF
IF
IF
IF
IF
'
GOSUB 5500
GOSUB 6000
STAGE > 1 THEN
GOSUB 1400
GOTO 540
STAGE > 2 THEN
GOSUB 1500
GOTO 520
STAGE > 3 THEN
GOSUB 1600
GOTO 500
STAGE > 4 THEN
GOSUB 1700
GOTO 500
STAGE >5 THEN
GOSUB 1800
GOTO 500
STAGE > 6 THEN
GOSUB 1900
GOTO 500
STAGE > 7 THEN
GOSUB 2000
GOTO 500
STAGE 8
GOSUB 2100
'ENVIRONMENTAL VARIABLES
'WATER BALANCE
230
'PREREST PERIOD
260
•REST PERIOD
290
•TILLERING
320
'STEM EXTENTION
'BOOTING
350
380
•ANTHESIS
410
'GRAIN FILLING
'RIPENING
GOSUB 12800
GOSUB 11500
GOSUB 12000
GOSUB 7000
' IF D=FINISH THEN GOSUB 14100
NEXT D
19
'ASSIMILATION
•TRANSLOCATION
'TOTALS
•WRITE RESULTS
'WRITE CARRY OVER FILE
The
new model is exceedingly user friendly and the results would
appear aa follows:
PUTU9 IRRIGATION SCHEDULING OF WHEAT
Run date
06-16-1987
Tiae : 15:26:39
LYSIMETER
PLANT POPULATION
206
Um'2)
EXTBNTION ENDED ON DOY
PLANTING DATE
1 / 7 / 1984
CULTIVAR
33T4
277
SOIL DESCRIPTION:SANDY LOAM
SOIL MOISTURE
MAXIMUM
200
IU/BI
MINIMUM
76
INITIAL
200
EFFECTIVE ROOTING DBPTH
1 .8B
1984
DOY
FH
LAI
IB BAIN PEHC
WATER POTENTIAL
S2
S3
ST
L
(XI
xlO
(%)
:*».
<MP»*100)
Total rainfall in re«t period: 0
GROWTH STAGE 3 - TILLERING PERIOD
183
0
0
0
0
0
-1
-1
0 -70
Bv
HU
EB
IDD)
(••*10)
0
0
14
Ely*
2
0
0 231
-1
-1
-2
233
96
6
YRED = 6 TTRAN3 : 15 TPTRANS= 16 TLYS: 85 C=
TSOILVAP=
54
WUSE= 81
DEEPEHC: 231
GROWTH STAGE 4 STEM EXTENTION PERIOD
0
0 231
-1
-1
-2
234
96
6
26
0 238
-1
-1
-2
235
96
8
0
0 249
-7 -17
-6
276
98
85
YRED=
1 TTRANS =201 TPTRANS=207 TLYS= 199 C =
DEEPERC: 249
T9OILVAP=
83
WUSB= 311
GROWTH STAGE 5 BOOTING
0
1 249
-8 -16
-7
277
98
85
-73
14
57
6
6
13
623 BL/C=.46 GL/C=.OO SL/C=.26
TAW: 209
281
98
85
0
0 249 -11 -14 -10
YRSD=
1 TTRANS= 18 TPTRANSs 19 TLYS=
36 C=
DEEPERCr 249
TSOILVAP: 83
WUSE= 330
GROWTH STAGE 6 ANTHESI9
282
98
85
0
0 249 -12 -14 -11
-85
43
41
37
0
79
8738 BL/C=.57 GL/C=.OO SL/C=.13
TAWr
96 HT= . 54
-73
-74
15
16
60
59
6
6
8
7
22
45
-88
40 112
99
6
45
7888 BL/C:,61 GL/C=.OO 3L/C=.1O
TAW= 114
-82
-86
41
44
41
41
37
1
45
97
37
291
98
76
0
0 249 -11 -20 -14 -96
51
80
71
0
93
YRED:
1 TTRANS: 66 TPTRANS= 67 TLYS: 78 C:10958 BL/C=.45 GL/Cr.19 SL/C=.11
DEEPERC: 249
TSOILVAP:
85
WUSE= 397
TAW = 64 HT=.64
GROWTH STAGE 7 GRAIN FILLING
292
98
76
0
0 249 -16 -23 -18 -106
52 109
96
0 112
326
27
10
0
0 249 -164 -164 -164 -279
81
88
0
0
13
YREDi 27 TTRANS=149 TPTRANS=287 TLYS= 220 C=15359 BL/C=.32 CL/C=.42 SL/C=.O8
DEEPERC- 249
TSOILVAP:
86
WUSE- 547
TAW:
0
GROWTH STAGE 8 RIPENING
327
22
10
0
0 249 -164 -164 -164 -294
82 125
0
0
10
328
19
7
0
0 249 -164 -164 -164 -304
83 162
0
0
18
335
98
0
0
5 249 -41 -103 -25 -111
89
336
97
0
0
4 249 -51 -69 -40 -121
90
Environmental potential yield for 1984
4547 kg/h»
TOTAL DEEP PERCOLATION
249 , EVAPORATION FROM THE SOIL
TAL WATER USE
561
70
47
62
41
1
0
SURFACE
86
0
0
AND
TO
S2, S3 and ST denote the second and third soil layers and total
root zone respectively. L denotes leaf.
20
Running
time on the SPERRY PC is approximately 20 minutes for
complete
a
season. However, when compiled, this is reduced to less
than five minutes.
The IBM PC requires twice this length of time
The complete coding of the programme for PUTU 9.86 is to be found
in APPENDIX IV.
DOCUMENTATION
The
wheat
stages.
growing
season is divided into
number
of
growth
These are illustrated in Fig. 3.3.
To promote the understanding of PUTU 9.86,
of the model,
a
a
to assist in the use
and to facilitate future modification of the model
brief documentation of the system is here provided.
This will
be achieved by giving:
1.
the
location (line number) and titles of
The
titles,
in fact,
the
subroutines.
describe the operations performed by
each subroutine, and
2.
a description,
together with source,
of the
parameters,
functions and triggers included in the model.
The logistics of the computer programme may be studied by following the sequence of model operations here given.
It is
followed
by a reference list whereby subroutines may be quickly found.
21
SEQUENCE OF OPERATIONS IN PUTU 9.86
3100
9500
14000
14245
ESTABLISH PARAMETERS AND BOUNDARY CONDITIONS
PARAMETERS
ESTABLISH INITIAL CONDITIONS
COEFFICIENTS SOIL WATER EXTRACTION CURVE
ALTER INITIAL CONDITIONS
10500
10800
14600
14700
DATA
WEATHER DATA INPUT
HAIL
READ RAIN, IRRIGATION AND SPECIFIC DAYS TO RECORD
CREATE RAIN, IRRIGATION AND HAIL FILES
8500
9000
5500
ENVIRONMENTAL VARIABLES
TEMPERATURE FUNCTIONS AND PROCEDURES
TOTAL EVAPORATION
1400
1500
1600
1700
1800
1900
2000
2100
TRIGGER AND EXECUTE APPROPRIATE GROWTH STAGE
STAGE 1
PRE-REST PERIOD
STAGE 2
REST PERIOD
STAGE 3
TILLERING PERIOD
STAGE 4
STEM EXTENSION
STAGE 5
BOOTING
STAGE 6
ANTHESIS
STAGE 7
GRAIN FILLING
STAGE 8
RIPENING
6000
5700
13000
8000
9800
13300
13700
2700
WATER BALANCE
CRITICAL LEAF WATER POTENTIAL
SIMULATE ROOT DEVELOPMENT
SOILWATER POTENTIAL FROM DESORPTION CURVE
SOILWATER CONTENT
SIMULATE SOIL-ROOT CONDUCTANCE AND LEAF WATER POTENTIAL
EXTRACT WATER FROM EACH LAYER
DISTRIBUTION OF INFILTRATED WATER
10850
11500
12800
PRODUCTION
CROP HEIGHT
10900
TRANSLOCATION OF ASSIMILATE
PHOTOSYNTHYSIS AND ASSIMILATION
7000
14095
RECORD RESULTS
RECORD RESULTS OF SIMULATIONS
RE-WRITE CARRY-FORWARD FILE
2400
2500
5000
5300
6500
COMPUTATIONAL PROCEDURE ASSISTANCE
DOY ON WHICH MONTHS END
RESET
13900
MEAN DATA
10000
SET VARIABLES TO ZERO
TITLE PAGE
12000
SUB TOTALS
TABLE HEADINGS
12500
MEANS
DOY
11000
GEN ARTIFICIAL WEATHER DATA
15000
THE FUNCTIONS
22
LEAF GROWTH
QUICK REFERENCE FOR FINDING SUBROUTINES
1000-
2000-
30005000-
600070008000900010000-
110001200013000-
14000-
15000-
1400
1500
1600
1700
1800
1900
2000
2100
2400
2500
2700
3100
5000
5300
5500
5700
6000
6500
7000
8000
8500
9000
9500
9800
10000
10500
10800
10850
10900
11000
11500
12000
12500
12800
13000
13300
13700
13900
14000
14095
14245
14600
14700
15000
PRE-REST PERIOD
STAGE 1
REST PERIOD
STAGE 2
TILLERING PERIOD
STAGE 3
STEM EXTENSION
STAGE 4
BOOTING
STAGE 5
ANTHESIS
STAGE 6
GRAIN FILLING
STAGE 7
RIPENING
STAGE 8
RESET
MEAN DATA
DISTRIBUTION OF INFILTRATION WATER
PARAMETERS
TITLE PAGE
TABLE HEADINGS
CRITICAL LEAF WATER POTENTIAL
WATER BALANCE
DOY
RECORD RESULTS OF SIMULATIONS
SOILWATER POTENTIAL FROM DESORPTION CURVE
TEMPERATURE FUNCTIONS AND PROCEDURES
TOTAL EVAPORATION
ESTABLISH INITIAL CONDITIONS
SOILWATER CONTENT
SET VARIABLES TO ZERO
WEATHER DATA INPUT
HAIL
CROP HEIGHT
LEAF GROWTH
GENERATE ARTIFICAL WEATHER DATA
TRANSLOCATION OF ASSIMILATE
SUB TOTALS
MEANS
PHOTOSYNTHYSIS AND ASSIMILATION
SIMULATE ROOT DEVELOPMENT
SIMULATE SOIL-ROOT CONDUCTANCE AND LEAF WATER
POTENTIAL
EXTRACT WATER FROM EACH LAYER
DOY ON WHICH MONTHS END
SOIL WATER EXTRACTION CURVE COEFFICIENTS
RE-WRITE CARRY-FORWARD FILE
ALTER INITIAL CONDITIONS
READ RAIN,IRRIGATION AND SPECIFIC DAYS TO
RECORD
CREATE RAIN,IRRIGATION AND HAIL FILES
THE FUNCTIONS
23
PARAMETERS, TRIGGERS AND FUNCTIONS
The
major parameters,
phenological triggers and functions
lized in PUTU 9.86 constitute the essence of the model.
uti-
They are
now described.
Parameters
V01, V15, V16
The volumetric water content (mm/m) at soil water
potentials of 10, 1500 and 1600 kPa respectively.
1600
kPa
represents the
lowest
(driest)
in soil water potential in the root zone,
in the
W01, W15, W16
The
limit
but not
surface layer
layer water content (mm) at soil water poten-
tials of 10, 1500 and 1600 kPa respectively.
PAW
The layer plant available water (mm)
TPAW
The profile available water content (mm)
SPL
= 500 kg/ha.
SEEDMAS = 0,03 g.
The specific leaf ratio
The mass of a wheat seed
The units of heat accumulation above or below a basal temperature
are growing degree days (DD) or cold units (CD) respectively.
BO
= 8 C.
Basal temperature for calculating growing degree
days .
HUMX
= 20 'C.
The daily maximum temperature for
growing degree days
calculating
CUC
= 7,4 CU.
Vernalisation requirements.
units
The minimum coJd
( 8 'C required to end the tillering
stage
(see
cold
unit
APPENDIX III)
CONl
- 21,8.
Additional
degree days per unit of
deficit (DD/CU) {see APPENDIX III)
COO
= 0,012.
Constant
for calculating respiration (Kaiser,
1976) (not required by this version of PUTU)
WEQ
= 30
kg/(ha mm).
Dry matter accumulation equivalent to
water use (not used in this version of PUTU)
EFFMAX
= 20%.
PAR
The photosynthetic efficiency of utilization
(Penman, 1971).
Active
of
PAR represents Photosynthetically
Radiation in W/m
(not used in this version
of
PUTU)
FE
= 68 * 10~(-9) kg/J.
The photo-chemical equivalent (Pen-
man, 1971) (not used in this version of PUTU)
RGR
= 0,0182.
Rate of vertical root penetration m/d
(Botha,
total roots found below 0,97 of the
current
1983)
AP
=2,1%
The
rooting depth (see Chapter 4)
KSP0
= -1,2 * 10"(-3) mm/Id kPa m pl). Maximum conductance of
the root zone {defined in Chapter 4 and found by
trial
and error)
HUC =
145
DD
critical heat requirement for transition
tillering to stem extension.
25
from
For
convenience in future modelling,
the parameters
COO,
WEQ,
EFFMAX, and FE are retained here.
Phenological triggers
The
various growth stages adopted for the wheat growth model are
described in Fig. 3.3. They are defined as follows:
STAGE 1
JR is the DOY of commencement of the rest period.
period
The
pre-reat
is a dummy period stretching from January 1 to the day on
which
the
rest period would hypothetically
have
occurred
had
there
been a standing crop on January 1 of the first year of the
data series.
STAGE 2 - THE REST PERIOD
JP
is the day of the year on which planting occurred.
It
ends
the rest period.
STAGE 3 - THE TILLERING PERIOD
Stage 3 ends when accumulated heat equals HUC.
critical
heat
(in DD) required to bring the
The latter is the
specific
cultivar
from planting through tillering to the stem extension stage.
is adjusted according to vernalisation.
26
It
STAGE 4 - THE STEM EXTENSION PERIOD
Stem
extension ends when DOY = ENDEXT.
This ENDEXT is provided
from field observations by the farm manager.
STAGE 5 - BOOTING
Booting ends when DOY = ENDBOOT = ENDEXT + 5.
STAGE 6 - ANTHESIS
Anthesis ends when DOY = ENDANT = ENDBOOT + 10.
STAGE 7 - GRAIN FILLING
Grain filling ends when DOY = ENDFILL = ENDANT + 35.
STAGE 8 - RIPENING
Physical maturity is reached when DOY = ENDRIPE r ENDFILL + 10.
The functions
FNDLENGTH - Maximum
day length for each day of the year.
It is
an empirical function determined from LIST (1958)
FNMJD
- Radiation
at the top of the atmosphere.
It
is
an
empirical function determined from LIST (1958)
FNDLYR
- Angstrom's
solar
formula
for
radiation from the
shine hours
27
estimating
daily
incoming
fraction of possible
sun-
FN2
- An alternative to FN3
FN3
- Stomatal conduction as a function of leaf water potential.
(Bristow, K.L., De Jager, J.M. and Van Zyl, W.H., 1981)
FN5
- Soil evaporation from the soil surface, Es
FN6
- The temperature limiting factor on photosynthesis
FN8
- The
function
S/(S+ ) in the Penman equation.
function of temperature
FM9
- Ratio
is
a
It
is
a
(Schulze, 1975)
of transpiration to maximum total evaporation.
function of leaf area
index
(originally
It
Ritchie,
1972, changed to FN9 = 0,186.LAI ... Eqn 2.3 in Chapter 7)
FN10 - Priestley-Taylor formula (Priestley and Taylor, 1972)
FN12 - Construction
tosynthesis
respiration is equal to
0,5 of daytime pho-
(Kaiser, 1976)
FN13 - Night respiration as a function of nighttime
temperature
(Kaiser, 1976)
FN14 - Leaf
area
per
unit plant development as a
growing degree days
(APPENDIX III)
28
function
of
FN15 -
Exponential
soil water extraction function
FN16 -
Calculation
of
FN17 -
Relative
(APPENDIX I)
leaf water potential using FN15
root development assumed to be a logistic func-
tion of days of the growing season
FN18 -
The
normalized natural logarithm of the relative volume-
tric
soil
water content for use in the
computation
of
soil-root conductance (KSP line 13330). KSP, the soil-toroot conductance in a given layer is proportional to
square
the
root of root mass and to the ratio of the natural
logarithm
of
the current relative (to
V16)
volumetric
water content divided by the natural logarithm of
ratio
of
point
field capacity to volumetric permanent wilting
as computed by FN18
(Botha,
1983;
Bennie and Botha,
1985).
The functions FNDLENGTH,
FN12,
AND
FNMJD,
FNDLYR,
FN2,
FN13 are not used in PUTU 9-86,
FN6,
FN8, FN10,
but are retained for
future reference.
The yield function
For irrigation scheduling the simplified yield model of Doorenbos
and Kassam (1979) was included in PUTU.
Ky are shown in Fig. 3.3.
The relevant values
of
This means that the photosynthesis and
29
translocation
tion.
The
routines were not used in this particular applicaDoorenbos
and Kassam (1979) yield
concept of relative yield deficit,
deficit, RED.
model
uses
the
RYD, and relative evaporation
Thus, by definition
RYD - 1 - Y/Yo
and
RED = 1 - E/Em
The fundamental yield equation then reads:
RYD = ky RED
Hence,
Y = Yo - ky RED
For the ith growth stage REDi - 1 - Ei/Emi
For a season of n growth stages each having its own kyi
n
Y = Yo[l - ;£, kyi REDi
i
..<3.1)
PUTU 9.86, including Eqn 3.1, may be used to compute yield.
ADDITIONAL OPERATING INSTRUCTIONS
A
simplistic
weather
generator
(subroutine
included for purposes of model development.
following the screen instructions.
30
1100)
has
been
It may be called by
P U TU
SCHEMATIC FOR THE
COMPUTER PROGRAMME
WHEAT CROP ECOSYSTEM
OPERATIONAL CELLS
PARAMETERS
1
INITIATE]
INITIATE
I
ENVIRONMENTAL
DRIVING FORCE
PRODUCTION
I
ENVIRONMENTAL
DRIVING FORCE
STATUS
PRODUCTION
1
i
ARTITION
PARTITION
\
RECORD
TEST
-No-
END
Fig.
STATUS
—NO-
END
3.1 -
Schematic
flow
structure
of
diagram which outlines
the computer programme for
seasonal dynamic model of a wheat crop.
31
the
general
PUTU,
the
•X4
X5
DA
(Alfa)
GD <— X8 *— GL <
I
(Phi)
X
(Beta)
t
CD -*— X9 * — CL *•
TR
X6<
BD«
X7
XI
BL «•
X2«
SL *-
BA
(Theta)
DMG
DB
RD*
X3 t— RL
I
BB
(Rho)
CB
Symbol definition:
DMG
GD
CD
BD
SD
RD
TR
BA
BB
B
=
E
=
=
=
=
=
=
=
=
Daily dry matter assimi lation (kg/(ha d))
Dead grain
GL = Live grain
Dead culms
CL = Live culms
Dead leaves
BL = Live leaves
Dead stubble
SL = Live stubble (Reserves)
Dead Roots
RL = Live roots
Senesced material
DA = Above ground dead
Above ground live
DB = Beneath surface dead
Beneath surface live
D = CA + DB
BA + BB
CA = Total mass standing above
surface
CB = Total beneath surface
C = CA + CB
Alfa, Beta, Phi, Theta, Rho = Partitioning factors
XI, X2,
X9 = Dry matter coefficients
Fig. 3.2 - Diagramatic presentation of the partitioning of
tosynthate to the different plant components.
32
pho-
Stage
Description
Modified
Zadoks Key
Ky
Tillering
Booting 4nthe= Grain f i l l i ng
sis
Stem
Extention
Ripening
00 - 39
40 - 49
50-69
70-79
80-89
90-97
0,2
0,2
0,2
0,65
0,55
0,0
t
in
to
m
o
Trigger
x
LU
o
a
5
F i g . 3.3 -
hX
LU
Q
DB
1/1
II
3
LU
O
O
CO
Diagram depicting the growth stages utilized in PUTU
9.86, the corresponding Zadoks delimitation, the
phenological criteria triggering progress to the
next stage and Ky the wheat yield - relative evaporation deficit coefficient (Doorenbos and Kassam,
1979) applicable in each growth stage.
33
CHAPTER 4
THE ROOT DEVELOPMENT AND SOIL WATER EXTRACTION
SUB-MODEL FOR WHEAT
INTRODUCTION
The
accurate
water
mathematical simulation of
extraction
from
the soil root zone is
confronting crop growth modellers.
ling
root
development
a
major
problem
The PUTU 9 irrigation schedu-
model used a simple double layered soil sub-model.
deemed
and
It was
necessary to improve the reliability thereof by modelling
a multi-layered root zone.
Improvements to PUTU 9 regarded as necessary, were:
(i)
the
inclusion of capillary flow upwards into the
root
zone from a wet substrate,
(ii)
more
accurate simulation of the influence
of
weather
conditions upon the onset of plant water stress, and
(iii)
the need to account for small increments in water which
can only be accomplished using a multi-layered soil.
Recent
sible.
scientific developments have made complex simulation feaImproved
mathematics have reduced the computations
quired to manageable proportions.
the
water
desorption
in
Relationships for
soils have been refined
34
re-
describing
by
De
Jong
(1983);
Campbell and Campbell (1983);
Hutson and Cass (1985) and others.
Mottram (1985);
Furthermore,
Schulze,
Botha (1983),
Botha, Bennie and Burger, (1983) and Bennie and Botha (1985) have
expressed
the hydraulic conductance of the soil-plant system
in
terms of the rooting density and the soil water content.
The initial objective of this study was to simulate mathematically the root distribution and water extraction from a soil profile
divided into a number of layers.
for
the
simulation
It is important that
of vertical water movement
by
routines
unsaturated
capillary flow be added at some time in the future.
The
major problem encountered was the simulation of the feedback
mechanism operating in the soil-plant-atmosphere system when
crop is experiencing water stress.
the
Mathematical analysis of such
a feedback system requires the solution of one equation (D'Arcy's
law) for two unknowns viz. leaf water potential, PSI1, and transpiration rate,
this
will
T.
A computer iteration technique for achieving
be described.
The theory was developed for a
wheat
crop, but has also been successfully extended to maize (De Jager,
Mottram and Singels, 1986).
All
line
numbers quoted in the ensuing discussion refer to
listing of the programme for PUTU 9-86 in Appendix IV.
35
the
THEORY
Electrical analogue of the soil-plant hydraulic system
A
simple
electrical analogue and D'Arcy's law
model
the soil-plant hydraulic process.
model
is depicted in Fig.
4.1.
was
adopted
to
For a layered soil the
Note particularly
the
diodes
which prevent moisture flow from moist to dry soil layers via the
root system.
This was rather difficult to simulate.
The resul-
tant programming is found in lines 13715-13810 (see APPENDIX IV).
The model was originally developed for hourly time steps.
subsequently
accuracy.
found
that
a daily time
step
provided
It was
adequate
The development of the hourly model is described by De
Jager and Singels (1986).
Symbol definition
The symbols used are the following:
A
-
root distribution exponent (/m),
Ap
-
percentage of roots found below the depth 0,97.ZEFFo (%),
DZRT -
thickness of the individual soil layers
DOG
-
day of the growing season,
DOY
-
day of the year,
Es
-
evaporation from the soil surface (mm/h), (SOILVAP)
K
-
hydraulic conductance (nun/(d kPa) ) ,
KSPEFF -
(m),
effective soil-root hydraulic conductance (mm/(h kPa}),
36
k
-
hydraulic conductivity ((mm/d)/(m kPa)),
PSI
-
water potential (kPa),
PSIST -
effective soil water potential (kPa),
RGR
-
vertical root growth rate (m/d)
R
-
root mass per unit of ground surface at soil depth
z(kg/m Z ),
RM
-
the total amount of root per unit surface area for
entire root zone down to depth ZEFF relative to
seasonal maximum value,
the
the
RPROPn -
the proportion of roots per unit ground surface
found in the nth soil layer,
area
T
-
transpiration rate (mm/h),
V
-
volumetric soil water content (mm/m)
V01
-
volumetric soil water content at 10 kPa
(mm/m).
Similarly V15, V16 and W01, W15 and W16 are values of V
and W at 10, 1500 and 1600 kPa..
W
-
soil water content (mm)
ZEFF -
current effective rooting depth (m)
ZEFFo -
maximum effective root depth (m)
1
-
subscript denoting leaf,
N
-
there are N soil layers,
n
-
subscript denoting the nth soil layer,
o
-
subscript denoting the maximum value,
p
-
subscript denoting the total water pathway from
into the roots and through the leaf surface,
s
-
subscript denoting soil, or, soil-root interface where
conductance is concerned,
v
-
subscript denoting vegetation water pathway
x
-
subscript denoting xylem sap
z
-
depth in the soil measured positive downwards from
soil surface {m),
37
soil
the
The general mathematical expressions
The
derivation of the equations describing the root distribution
patterns
here,
are provided in Appendix I.
the
symbol
variable
In the
model
considered
RM replaces the symbol x
used
in
Appendix I.
Water
flow through the soil-plant system is governed by D'Arcy's
law, viz.
T
=
- k(£pSI/<£z)
...{4.1)
Furthermore, the total plant transpiration rate is given by
Tn
. . . <4 . 2 >
n=l
where,
the contribution to the total transpiration from the
nth
soil layer is given by
Tn
=
-Ksn
. (PSIx - PSIsn)
...(4.3)
Eqn 4.3 is the appropriate form of D'Arcy's equation.
Water non-stress situations
Campbell
and Campbell (1983) presented solutions of Eqn 4.1,
terms of resistances,
The
exists.
solution here described will be undertaken in terms of
ductances.
the
for the case when no water stress
in
con-
The simple electrical circuit equations pertaining to
analogue illustrated in Fig.
stress by assuming:
38
4.1 were solved for water non-
(a)
that
the
conductance through the plant from root
to
leaf
sub-stomatal cavity is infinitely larger than the conductance at the soil-root interface,
(b)
that
and
all leaves in the crop are at approximately
potential viz. PSIx =
Under such conditions,
same
PSI1.
the vegetation can, and does, accommodate
atmospheric evaporative demand.
in each soil layer;
the
PSI1
Given the soil water
potential
may now be found from
Ksn.PSIsn
-
T
PSI1 =
...(4.4)
Ksn
This may be written
PSI1
=
PSIST - T/KSPEFF
or
T
=
Here, KSPEFF and PSIST
-KSPEFF(PSIL - PSIST)
...(4.5)
are defined as the effective soil hydrau-
lic conductance and water potential respectively.
Hence, the effective soil hydraulic conductance,
defined from Eqn 4.4.
It is the algebraic sum of the
soil-root interface conductances in each soil layer.
KSPEFF =
KSPEFF,
^
n=l
Ksn
39
is
hydraulic
Thus,
...(4.6)
Similarly,
from
the effective soil water potential, PSIST, is defined
Eqn 4.4.
weighting
plant
It is the mean soil water potential obtained
each
layer's water potential according to
hydraulic conductance.
its
by
soil-
This PSIST offers an estimate of
the soil water potential effectively sensed by the plant.
Thus,
for no water stress, (PSIST) is defined by
PSIST
=
^T
(Ksn . PSIsn) / KSPEFF
...(4.7)
Eqn 4.5 is a form of D'Arcy's law and thus justifies the concepts
and terminology for KSPEFF and PSIST.
In the simulation process PSIsn
for each layer is estimated from
the previous day's soil water status.
In
the original PUTU 9 model of De Jager et al. (1982) PSIST was
simply
defined as the mean soil water potential over the
entire
root profile and the overall hydraulic conductance as
Kp - 1/(12,5 + [1 + 10 EXP(-5(PSIST + 100))]
This
function
Furthermore
was.
the
soil-root
stant
the first assumption (viz.
Kp
maximum conductance at the soil-root
then equal to 0,08 mm/(h bar) as determined by De
al . (1982).
soil
reflects
Note that 0,08 = 1/12,5.
= Ks).
interface
Jager
et
It is thus apparent that
conductance was assumed to decline exponentially
with
water potential, PSIsn, from an initial approximately
con-
value
of
0,08 mm/(h bar).
denominator.
40
See the second
term
in
the
Water stressed condition
In the water stressed condition,
requires
(see
Eqn
atmospheric evaporative
4.5) that PSI1 must decrease
threshold value for normal transpiration.
is
evidenced
at
below
its
In nature water stress
by decreased stem elongation and leaf
and stomatal closure.
initiated
to
demand
development
Each of these symptoms is not
necessarily
the same given threshold leaf water potential
{or
soil water content) (see Ritchie, 1981).
Futhermore,
a low soil water content induces resistance to water
flow at the soil-root interface. This
reduces Ks and also limits
the flow of water through the system.
Both phenomena decrease Kp
with resultant decrease in PSIx (= PSI1).
stomatal
closure.
This condition is simulated by decreasing
until D'Arcy's law is satisfied.
and
T
can
iteration
This, in turn, induces
The only manner in which
be found from Eqn 4.5 for this situation
technique.
Once
the
which is below threshold value,
is
T
PSI1
by
required steady-state
an
PSIl,
has been reached; leaf and stem
(PSIx = PSIl) dehydration exist. These inhibit growth.
Root distribution and depth
The
results of Chaudhary and Bhatnagar (1980) and Kmoch,
Fox
and Kochler (1957),
root
4.1)
suggest that the distribution of
mass (and hence water extraction)
with depth.
for
Ramig,
decreases
wheat
exponentially
Hence, an exponential model was postulated (see Fig.
simulating
distribution.
the control of
water extraction
by
root
Such equation in terms of root length (L) is given
41
by
L
where
Lo
Later
root
=
Lo . EXP(-A . z)
...(4.8
= the root length per unit area at the
soil
mass will be replaced by root length,
surface.
but the
same
arguments will hold.
The
proportion of roots per unit area of land surface within the
nth
soil layer,
4.8.
RPROPn,
was derived (see Appendix I) from
It takes the form
RPROPn
=
EXP[- A . zn-1] - EXP[- A . zn]
The constant A is a function of crop growth stage.
uated
by
terns.
...(4.9)
It was eval-
assuming that 2,7% (Ap = 2,7) of the roots
beneath the rooting depth 0,97 ZEFF.
after
Eqn
examination
Its
theoretical
values
distribution
was verified by the goodness
agreed with measured values of
Table 4.1). Based upon this
found
This assumption was reached
of a number of wheat root
validity
are
with
RPROPn
patwhich
(see
assumption, A may be evaluated using
Eqn 4.10 in the manner described in Appendix I.
A
=
-(In 0,03) / 0,97 . ZEFF
...(4.10)
Originally, the rooting depth was modelled using regression equations found from the data reported by Botha,
current effective rooting depth, ZEFF, was
ZEFF
=
0,0074 * DOG
for
42
(1983).
From this
defined as
DOG < 47
...(4.11)
or
ZEFF
=
0,0151 * DOG - 0,3282
for
DOG > 47
...(4.12)
Later, in PUTU 9-86 however, a simplification, viz
ZEFF s RGR * DOG
...(4.13)
where RGR = 0,0182 m/d, was used (see Botha, 1983).
Accounting for a restricted root zone
Modeling
root
distribution
and water extraction
from
a
containing
a
difficult.
Three possible assumptions, amongst others, are:
1.
The
same
soil
layer impermeable to root or water penetration
root proportions and amounts in each layer
values
they
would
have
had,
had
assume
there
is
the
been
no
restriction.
2.
The
root
proportions in each layer remain the same as
for
the no-restriction case, but the total amount of roots which
would have developed under no restriction is divided between
the active layers.
3.
The total amount of root developes as normal,
distribution
pattern
changes
from
an
but the
exponential
root
to
a
cylindrical or semi-conal one,
PUTU
2
9.86 provides the computational steps for assumptions 1 and
(see lines 13167-13172).
In the present study the model
assumption 1 was used.
43
for
Soil hydraulic conductance
The
equation describing hydraulic conductance of
interface was taken from Botha (1983).
-6
Ksn = 5,1065 * 10
the
soil-root
It reads:
-10
+ 0,7847 * 10
ln(RDEN) * In(PSIsn/-47,7)
(mm/(h bar))
...(4.14)
Where the root density in each layer, RDEN, is given by
RDEN LT(ZEFF)
is
RPROPn . LT(ZEFF) / DZRT
the total root length per unit ground
entire root zone, currently of depth ZEFF.
of
...(4.15)
the soil layer.
area
in
an
DZRT is the thickness
LT(ZEFF) was estimated following Botha
et.
al . (1983) using, for DOG > 10.
2
LT(ZEFF) = ,806 -,2044 DOG + 0,00645 DOG
3
- 0,00003108 DOG
(km/m*}
Bennie
...(4,16)
and Botha (1985) modified Eqn 4.14 by
placing
soil-root
hydraulic conductance directly proportional to the square root of
rooting
density and the natural logorithm of relative soil water
depletion.
The latter was expressed as the natural logarithm of
the ratio between the current layer volumetric water content (Vn)
and
V16
(the
-1600 kPa.
mathematical
volumetric
i.e.
permanent
water
wilting
content
point).
at
which
This is
PSIsn
a
transformation of the initial results and does
44
-
simple
not
represent a change in the fundamental theory.
This principle, in
a form which facilitates fine-tuning of the model,
cluded
was
in PUTU 9.86.
Tuning the constant of
was then
in-
proportionality
simplified by normalising the rooting density as well as the
natural logarithm of relative soil water depletion terras.
lization
was
achieved by replacing LT(ZEFF) by
relative total "root mass",
seasonal maximum root mass.
RM,
a
Norma-
hypothetical
normalized to a value of 100 at
The logistic expression assumed
to
reflect this relationship is given by FN17, viz.
RM = 100/(1 + EXP(-,06 x (DOG - 50))
The
relative volumetric water content was normalized
..(4.17)
using
the
multiplier ln[V01/V16], hence
FN18 = ln(Vn/V16) / ln(V01/V16)
This
..(4.18)
yielded a final expression for soil hydraulic conductance in
a given layer (see Line No 13330) of
Ksn = KSPEFFo
(RPROPn * RM)
/ 10 . FN18
..(4.19)
By trial and error it was found that the appropriate value of the
maximum
soil-root hydraulic conductance per unit soil
per plant was
KSPEFFo = -1,2
x 10
45
mm/(d kPa m pi).
thickness
Extraction of water from the multi-layered soil
The
initial
zone.
wheat model PUTU 9 utilized a double
It became evident however,
layered
root
that a multi-layered root zone
was required to improve simulation of water dynamics in the
tem.
sys-
The original double layered model had evolved from a model
developed
by
Mallett and De Jager (1974).
The double
layered
model is reliable for large rainfall amounts and irrigations {see
Van Zyl,
De Jager and Van Rooyen, 1981).
The influence of water
quantities smaller than say 20 mm/d however,
becomes lost in the
large volume of soil found in the second layer during the
part
of
the season.
Hence the decision to introduce
layered root zone into PUTU 9.
latter
a
nine-
A layer thickness of 0,15 m meant
that available water in a soil layer amounted to approximately 15
ram. Hence
that
water changes greater than approximately 5 mm (30% of
available)
were now accurately accounted
for.
As
daily-
transpiration, at crop maturity generally exceeds this value, the
error
inherent in the model was expected to be less than one day
per wetting event.
ting
Improving accuracy at the expense of
compu-
time is possible by the simple introduction of layers thin-
ner than 0,15m.
A diagrammatic presentation of the new root sub-
model is given in Fig. 4.1.
The
new water extraction sub-model essentially
which penetrate linearly with respect to time.
simulates
The root mass is
exponentially distributed throughout the entire root zone at
time.
46
roots
any
Total
root
mass
per unit surface area (expressed
in
relative
units 0-100) was assumed to change as a logistic function of time
(see Eqn 4.17).
The
model first determines the effective soil
PSIST,
the
for the entire root profile.
soil layers,
water
potential,
This is the mean,
over all
of the water potential in each layer
weighted
according
to
potential
and transpiration rate are then computed by
using
The
Eqn
current root-soil conductance .
4.19.
each
layer.
expressed as a function of the square root of
water contents in each layer was computed
The
routines
water
iteration
and the natural logarithm of the ratio of current
volumetric
new
They are listed in
root
relative
using
version of PUTU for wheat which includes
is entitled PUTU 9-86.
APPENDIX III.
been
leaf
4.5 and the root-to-soil conductance in
latter,
mass
The
Eqn
these
detail
in
Since the basic structure of PUTU 9-86 ha3 already
described in Chapter 3;
the computation of only the
leaf
water potential and soil water extraction will be outlined here.
The soil water desorption curve
A
model for soil water potential in terms of soil water
was
sought.
The
model
computerised (SOILCHR).
based
Campbell
(1983)
was
De Jong (1982) produced a similar model
upon the well known Clapp and Hornberger (1978) equations.
Schulze,
terms
of Campbell and
content
Hutson
and Cass (1985) report empirical
equations
of clay content based upon unpublished results of
Mottram
reported
(1985)
by
reports similar
Burger,
Bennie,
equations.
Similar
Botha and Du Preez
47
in
Hutson.
equations
(1979)
apply
mainly to sandy soils however.
While the spline function of the original PUTU9 functioned satisfactorily,
it
was decided to streamline the computations.
The
Hutson equations were used to determine soil water contents
cor-
responding
to soil water potentials of 30 kPa (V30) and 500
(V50)
fit
and
a curve through these two
points.
A
detailed
description of this procedure is given in Appendix I.
so obtained was used as the soil water extraction
the
plant
layer
water
accuracy
procedure
The curve
curve.
Since
commences experiencing resistance to water flow
soil
values,
kPa
the
potential lies somewhere
is required in this
soil
water contents at
between
region.
maximum
these
Utilizing
(V01),
when
two
this
minimum
(V15), permanent wilting (V16) and profile available water (TPAW)
could be calculated. Accuracy was within approximately 5 mm/m (5%
on 100 mm/m).
hand.
rived
This was deemed accurate enough for the purpose at
The elementary mathematics involved in,
from,
and results
this model are given in APPENDIX I and APPENDIX
deII
respectively.
SEQUENCE
OF
OPERATIONS FOR COMPUTING EXTRACTION OF
WATER
FROM
EACH LAYER OF THE MULTI-LAYERED SOIL
In the computer programme the individual soil layers are
L.
48
denoted
INITIAL CONDITIONS
Establish for the entire run
Line #
9556
9590
Layer thickness DZRT (m)
Root-soil conductance coefficient
mm/(d kPa m pi)
-1,2 x 10
Maximum effective rooting depth ZEFFO
m)
Now calculate for each layer :
9591-9594
Initial water content V(L) (mm/ra), W{L) (mm)
3130-3137
Soil water extraction coefficients M(L), PO(L)
3139-3147
Wilting point V15(L) (mm/m), W15(L) (mm)
Field capacities V01(L) (mm/m), WO1(L) (mm)
DAILY STATUS AND ENVIRONMENTAL VARIABLES
Calculate for each day:
8000-8040
Soil water potential in each layer
9090-9120
Evaporation from soil surface
2840-2890
Runoff and infiltration RUNOF (mm)
2910-3090
Rainfall and/or irrigation water recharge in
layer (mm)
1300-13040
Current rooting depth
13050-13172
Proportion of roots in each soil layer
13160-13166
Relative total root length per unit surface area
in entire current rooting depth RM - assumed a
logistic function of days since planting, DOG
13167
Number of layers, DZRT thick, into which roots have
penetrated NOLAY
13320
Root proportion in each layer RPROP(L)
49
PSIS(L) (kPa)
SOILVAP (mm)
INFIL
(mm)
each
ZEFF (m)
RPROP(L)
13330
Root-soil conductance of each layer KSP(L)
(mm/(d kPa layer))
13340
Effective root-soil conductance
profile KSPEFF {mm/(d kPa))
of
entire
soil
13380
Effective soil water potential
profile PSIST (kPa)
for
entire
soil
ITERATION ROUTINE
13380-13680
Iterate for crop mean leaf water potential,
and transpiration rate, T
PSIL,
WATER EXTRACTION ROUTINE
13700-13810
Extract water (transpiration) from each soil layer
TS(L), (mm/layer)
13830-13880
Calculate new water content V(L) (mm/m) and W(L) (m)
in each layer
Procedure for the preliminary validation
Root distribution
The assumption expressed by Eqn 4.10 was tested against the
reported
of
80
series
by Botha (1983).
He seeded Zaragoza wheat at the rate
kg/ha in rows 300 mm apart.
The soil was
in which an orthic A-horizon overlies a
apedal B-horizon of eolian sand.
TREATMENT
I -
TREATMENT
II -
TREATMENT III -
data
an
Annandale
yellowish
Three treatments
brown
were applied:
Conventional 250 mm mouldboard ploughing,
250
mm mouldboard
500 mm deep ripping,
ploughing
and
followed
by
Same as Treatment II but the traffic zones
were loosened by deep ripping after planting.
50
2
Root
length density was determined in soil plots of area 10 ra .
Samples
were taken down to 2 m depth at two weekly intervals
in
each treatment.
Simulated values of the root proportions in each layer obtained
using
Eqn
rooting
4.9 (A found from Eqn 4.10) and the
known
effective
depth (2 m) on given days were compared with the
sponding measured values.
corre-
The results are shown in Table 4.1 and
4.2.
Water extraction
The
parameter values in Eqn 4.11 through 4.16 pertain to soil of
the Annandale series as found on the Vaalharts Irrigation Scheme.
It is dangerous to extrapolate such results to other
ces.
As a preliminary test of the theory however, it was deemed
expeditious
to investigate how accurately this model would simu-
late water extraction from a Bainsvlei series,
and
circumstan-
a modified (Singels,
because test data
1984) multi-layered version of PUTU
9
were already available.
This
model computed the daily water balance by extracting
transpirative losses from the water in each soil layer.
water contents were compared with field gravimetric
made in dryland wheat.
at
Baunton
daily
Computed
measurements
The data were collected by Singels (1984)
on a Bainsvlei series which was 1,5m
51
deep
and
at
Hopefield on a Bainsvlei series 2 m deep.
priate
as water stress had occurred during the seasons
gated.
from
These data were appro-
Weather
input
data for the calculations were
the Soils and Irrigation Research Institute.
The
investiobtained
results
are reported in Table 4.3.
The iteration approach for solving for PSI1
conditions
model.
was
and T under stressed
tested in the Singels (1984) multi-layered
soil
It incorporates Equations 4.5, 4.6, 4.7, 4.9, 4.10, 4.11,
4.12, 4.14, 4.15 and 4.16.
Occasionally
PSIsn
in,
from
other
layer.
the
say,
soil
predicted values of PSI1 were higher than
the nth layer.
the
This would cause water to flow
layers to the leaf and then to
the
dry
soil
While this is possible in an electrical network it is not
biologically feasible.
Hence, statements eliminating such flow
from the computation were included.
Such procedure is represen-
ted by the diodes shown in Fig. 4.1 and simulated by lines 1376013820 in the computer programme.
It was subsequently found that
the model performed minimally better without this routine.
Hence
it
still
was
circumvented for the present application.
available for future use when needed.
52
It is
The
root
distribution
simulated
Eqn
4.9
the proportion of roots in the different
(see Table 4.2).
Eqn
model described by
accurately
soil
It is evident that the assumption described by
4.10 is valid for the particular set of conditions
gated.
layers
investi-
It deserves however to be stressed that it is an extreme-
ly dangerous practice to extrapolate results obtained in one soil
type
and for a single depth to other conditions.
Unfortunately
measurements for only a 2m rooting depth were available. Furthermore,
while
1982),
indicated
appeared
for
all
the simulation indices,
SI
(after
Willmott,
acceptable reliability (SI > 0,8), the
model
to be more accurate for the Treatments II and III
the conventional ploughing Treatment I.
than
Results for
other
rooting depths and other soil types should be undertaken to prove
the
be
general reliability of the model.
Should this relationship
verifiable for other effective rooting depths,
soil
types
model
for
found around Hartswater,
and
it will provide
root development as the only input required
for
a
the
simple
for
Eqn
4.9, 4.10 and 4.16 are the day of the growing season (DOG) and an
estimate of the effective rooting depth.
Results
of tests on the accuracy of simulation of
water contents are presented in Table 4.3.
conclusively
hydraulic
layered
These results did not
validate the mathematical expressions utilized
conductance.
The
slopes and intercepts differ
greatly from unity and zero respectively.
53
soil
for
too
The rz and SI however,
are sufficiently high (r
technique
tion.
had scientific merit and deserved
further
investiga-
It was evident, from the rapid depletion of soil water in
each
layer,
that Eqn 4.14
tance in each layer.
entire
et.
> 0,6 and SI > 0,7) to suggest that the
over estimated the hydraulic conduc-
The soil-plant hydraulic conductance of the
wheat crop can be as high as 0,08 mm/(h bar)
al, ,
0,05 mm/(h
1982).
bar)
in an individual,
150 mm thick,
layer.
disproportionately high and could explain the
rapid
water extraction from wet,
tion,
Jager,
The conductances predicted by Eqn 4.14 reached
seems
problem
{De
densely rooted
was later overcome by using the root
This
excessively
layers.
This
development
equa-
Eqn 4.13 and the Bennie and Botha (1983) conductance rela-
tionship LINE 13330 (see APPENDIX IV).
2
The SI and r
obtained however,
were superior to those found
Singels (pers com) and De Jager,
by
et al. (1982) - the first for a
non-iterative multi-layered model and the second for a non-iterative
these
double layered soil.
equations
were
Hence,
the iteration procedure
deemed to have improved the
accuracy
simulation
of water extraction from individual soil
the
of
onset
water stress conditions.
Considering
and
of
layers
and
that
the
equations were applicable to an Annandale series,
these prelimi-
nary
applicable
tests
were stringent.
Use of
parameters
to
Bainsvlei series could only improve the degree of compatability.
54
CONCLUSIONS OF PRELIMINARY VALIDATION
For
an
Annandale series soil at Vaalharts,
in which the
wheat
roots had penetrated to depths of approximately 2 m, root distribution
was successfully simulated by assuming that
decreased exponentially with depth.
was
evaluated
by
root
length
The constant in the exponent
assuming that 2,7 % of the total
roots
were
found below 0,97 of the current rooting depth.
The theory and computational procedures for solving the
cal
electri-
analogue equations describing water extraction from a multi-
layered soil profile by iteration were developed.
The exponential root distribution model and the hydraulic conductance
iterative
daily
soil
model were reasonably successful in
water
content under dryland
wheat
simulating
throughout
two
different growing seasons on two independent farms.
The functions used appeared to have over estimated soil hydraulic
conductance.
required.
Further
The
refinement of the relevant equations
iterative approach to modelling a
multi-layered
root profile had however demonstrated sufficient simulation
wess
pro-
to warrant further development and inclusion in the irriga-
tion scheduling model.
4.19)
was
These modifications (Eqn 4.17,
4.18 and
were introduced and the model tested on data collected
1984 and 1985.
55
in
Unit area
j?oori£A/cw
(m)
LAYER
NO.
1
2
3
4
**$
FIG.
4.1 -
Diagrammatic representation of the multi-layered
root zone model and its electrical analogue.
56
TABLE 4 . 1 -
Measured (Botha, 1983) and simulated values of the
proportion
of the total roots found in each
soil
layer, RPROPn.
DATE
LAYER
TREATMENT 1
TREATMENT 2
TREATMENT 3
SIMULATED
MEASURED
MEASURED
MEASURED
150
300
450
600
750
900
1050
,36
,30
,19
,08
,00
,06
,02
,29
,18
,03
,02
,32
,20
,16
,12
,15
,02
,02
,33
,22
,15
,10
,12
,05
,03
DOY 93
150
300
450
600
750
900
1050
,24
,19
,12
,13
,24
,05
,03
,38
,28
,14
,10
,08
,02
,01
,26
,19
,17
,12
,13
,10
,03
,33
,22
,15
,10
,12
,05
,03
DOY 113
150
300
450
600
750
900
1050
,24
,16
,17
,15
,14
,26
,23
,23
,09
,11
,06
,03
,35
,22
,16
,11
,12
,04
,01
,33
,22
,15
,10
,12
,05
,03
mm
DOY 7 9
,03
,22
,14
,11
,11
57
TABLE 4.2 -
Coefficients of determination (r ), mean
differences (MAD),
and
intercepts
fractions
of
soil layers,
absolute
simulation indices (SI), slopes
for
the comparison
total roots found in
RPROPn,
of
simulated
the
different
to the measured values.
latter were obtained from Botha et al (1983).
root depth was approximately 2 m in all cases.
The
The
SI
was determined according to Willmott (1982).
TREATMENT
INTERCEPT
2
SLOPE
1
79
,73
,03
,93
,04
,96
1
93
,94
,01
,61
,04
,87
1
113
1,42
-,06
,79
,04
,89
2
79
1,00
,00
,91
,02
,98
2
93
,74
,04
,97
,03
,97
2
113
1 ,03
,00
,84
,03
,95
3
79
,97
,00
,95
,02
,99
3
93
1,40
-.06
,94
,03
,96
3
113
,89
,02
,99
,01
,99
58
MAD
SIMULATION
INDEX (SI)
DAY
TABLE 4.3 -
Test of accuracy of the simulation of soil water content in each soil layer at
Baunton and Hopefield.
Slope and intercept of the linear regression are given with
coefficients of determination (r ) and Simulation Index (SI) after Willmott (1982).
LOCATION
SOIL LATER
SLOPE
HOPEFIELD
SIMULATION
INDEX (SI)
CORRELATION
COEFFICIENT
(—/•2
(••)
BAUNTON
INTERCEPT
0-150
0,53
63
,79
,62
.71
150-400
1,00
19
,69
.47
,66
400-650
1,11
-23
,78
.61
,83
650-900
1,47
-45
.91
,82
,88
0-150
0,56
26
,69
,48
,81
150-360
0,70
25
,81
,65
,88
360-575
0,79
19
.74
,55
,86
575 790
0,95
3
.84
.70
.91
790-1000
0,90
6
.87
.76
,92
CHAPTER 5
MICROMETEOROLOGICAL METHODS FOR ESTIMATING EVAPOTRANSPIRATION
INTRODUCTION
The
reliability of the PUTU 9.86 irrigation scheduling model
totally dependant upon the Penman-Monteith equation (Thorn,
for
estimating total evaporation.
that its accuracy be verified.
It is
therefore
use
chapter
imperative
Later as reported
7 it was shown that the Penman-Monteith adapted
with data from the automatic weather station
accurate estimates of total evaporation.
pheric stability was also investigated.
budget method (Thom,
1975)
This was done during 1985 using a
full set of micrometeorological observations.
in
is
also
for
provided
The influence of atmosThe Bowen ratio energy
1975) and the iteration technique (De Jager
et al, 1982) and (Bristow et al, 1982) for estimating evapotranspiration were also verified.
60
THE MODIFIED PENMAN-MONTEITH EQUATION
List of symbols
Em
:
Maximum total evaporation (mm/h)
u
:
wind speed (m/s)
s
:
Slope of the saturated
curve (Pa/aC)
%
:
Thermodynamic value
of
vapour
the
pressure
temperature
psychrometric constant
(Pa/°C)
Qn
:
Net radiation (W/m )
G
:
Soil heat flux density (W/m2)
/Oa
:
Air density (kg/m )
Cp
:
Specific heat of air (J/(kg °C)
#a
:
Aerodynamic conductance of the atmosphere (m/s)
<£e
:
Difference between the saturated vapour pressure and
vapour pressure of air at a specific temperature (abar)
L
:
Latent heat of evaporation of water (J/kg)
(6s
:
Conductance for water vapour exchange through the surface of a whole wheat crop (=0,03 m/s see Russell
(1980))
ra
:
Aerodynamic resistance of the atmosphere (s/m)
z
:
Height above ground level at which
made (m)
d
:
Zero plane displacement level (m)
zo
:
Roughness parameter
k
:
Von Karman's constant (0,41)
u*
:
Friction velocity (m/s)
im
:
Stability function
Ri
:
Richardson number
g
:
Gravitational acceleration (m/s )
:
Average air temperature ( K)
—
T
o
61
measurements
were
The Penman-Monteith equation (Thorn,
1975) for estimating maximum
total evaporation in mm/h ia given by
Em
= 3600
[a(Qn-G)/(s+ #*)
+
( CpêflSa)/(s+ 6*)]/L
..(5.1)
where 3600 ensures the coherency of units, and
%t =
From
( 1+rfa/sSs)
Thorn, (1975),
..(5.2)
the
generalized expression for
the
wind-
profile relationship is
du/dz = [u*/k(z-d)]Sm
Here
the stability function,
depending
..(5.3)
<fim,
is greater or less than unity
upon the stability conditions existing in
the
atmos-
phere .
Aerodynamic
resistance
(ra)
derived from Eqn 5.3.
ra =
«5a -
for neutral conditions
(4m=l)
is
Thus
2
2
In (z-d)/zo)] /k u which may be substituted in
1/ra
. . (5.4)
It can be shown, following the same argument as Thom (1975) that
2
For
stable
)
. . (5.5)
conditions Webb (1970),
Munn (1966) and Lumley
Panofsky (1964) developed the relationship
62
and
-1
6m = (l-5Ri)
....Ri > 0
While, for unstable conditions,
..(5.6)
Dyer and Hicks (1970) and Busin-
ger (1966) proposed
-0,25
4m - (l-16Ri)
....Ri < 0
..(5.7)
where, the Richardson number is given by
2
Ri = g/T(dT/dz) / (du/dz)
..(5.8)
Initially, the objective of the study was to estimate Em from Eqn
5.1 utilizing first Eqn 5.4 and then Eqn 5.5 and to compare these
estimates separately with hourly lysimeter observations, so as to
evaluate the effects of stability upon Eqn 5.1.
Materials and methods
The
study was carried out on the experimental plot at
Campus
of the University of the Orange Free State.
the
West
Hourly mea-
surements of Em from irrigated spring wheat were obtained from a
50 t,
10m~
mates
of
lysimeter surrounded by 4 ha of wheat.
Em were calculated from Eqn 5.1 using
Hourly esti-
the
following
from a Middleton 970 net radiometer
installed
measurements made in the wheat field.
net radiation
1,5m above ground level,
soil
heat
flux
density from three soil
imbedded 5mm below ground level,
63
heat
flux
sensors
wind
speed using a Gill three cup anemometer
installed
1,5m
above ground level, and
wet and dry
bulb temperatures from an aspirated
psychrometer
installed 1,5m above ground level.
The stability function was calculated from Eqn 5.6,
5.7 and
5.8
using the following:
wind
speeds
at 0,7 and 2,0m above an adjacent grass
site,
and
-
air temperatures at 0,7m and 2,0m above grass level.
Sixty three values (N) under varying weather conditions were used
for the comparisons.
ter than 3.
and
The wheat crop had a leaf area index grea-
The entire area under wheat including the lysimeter
the grass site were supplied with adequate water to
moisture stress.
maximum
rate
Thus evaporation from the crop proceeded at the
at
all times
during
the
experiment.
statistical comparisons plus the simulation index,
by
prevent
Willmott (1982) were applied.
Standard
SI, described
Hourly Em estimated from
Eqn
5.1, not adjusted for atmospheric stability (i.e. using Eqn 5.4),
were
compared
estimates
Eqn
to
lysimeter values.
In
addition,
hourly
from Eqn 5.1 adjusted for atmospheric stability
5.5 and 5>m,
computed from either Eqn 5.6 or Eqn
compared against measured evaporation.
64
5.7,
Em
using
were
Results and discussion
Table 5.1
the
4m
presents a
values.
summary
of the frequency distribution
of
The stability function differed from unity
by
more
than 10% in 51% (32 observations) of the cases.
0,60
< 4m <= 0,80 i.e.(0,36 < im
63
events
(13%).
The
case
<= 0,64) occurred in 8 out
This implies that the adjustments to
of
4m
lay
between 64% and 36% in 13% of the cases.
This range and distri-
bution
stringently
was
deemed
adequately varied to
test
the
stability theory.
Fig. 5.1 and 5.2 compare Em measured with Em estimated for 4m = 1 ,
and for dim f 1 respectively. Table 5.2 is a summary of the hourly
relationship between Em measured and Em estimated,
according
to
the two methods.
It
is
evident that the Penman-Monteith equation unadjusted
atmospheric
A
stability provided accurate estimates of hourly
mean difference of 0,03 mm/h and simulation index of
highly acceptable for practical irrigation scheduling.
of
0,03
for
mm/h
is half the error that could
be
Em.
0,97
is
An error
tolerated
when
keeping an accurate daily soil water budget.
No
improved
equation
accuracy
was
obtained
when
the
was adjusted for atmospheric stability.
Penman-Monteith
The index of
agreement is marginally worse at 0,94 as compared to 0,97.
mean absolute difference of 0,10 mm/h is also minutely worse.
65
The
Conclusion
The
Penman-Monteith equation was used to compute hourly evapora-
tion.
The mean absolute difference between measured and computed
values was 0,09 mm/h in the range of atmospheric stabilities
<i&m < 1.8.
0,6
Estimates of maximum evaporation rate from for wheat
obtained from the Penman-Monteith equation,
will not be improved
by adjusting it for atmospheric stability.
Similar
mean
good
agreement was attained with daily
estimates.
absolute difference between measured and calculated
was
0,5
mm/d.
Here,
The
values
correcting for atmospheric stability
did
produce improvement,
ESTIMATION OF NET
SOLAR RADIATION
An
RADIATION MINUS SOIL HEAT
FLUX
FROM
TOTAL
important relationship utilized in the Penman-Monteith
equa-
tion, when automatic weather stations are used, is that for estimating net radiation minus soil heat flux (Qn - G) from
total
solar radiation.
incoming
This relationship is necessary as it is
not possible to measure net radiation on a continuous basis using
an automatic weather station.
The relationship found by &e Jager
et al (1982) was re-determined (Eqn 5.9) using measurements
during 1985.
The goodness of fit is reported in Table 5.4.
made
The
then existing relationship was replaced by this new function.
reads:
{Qn - G) = 0,75 St - 72 W/m2
66
..(5.9)
It
DAILY VALUES
Putu 9.86 requires daily values of evaporation.
to
It was necessary
check whether the daily values from the Penman-Monteith equa-
tion were as accurate as the hourly values have been shown to be.
The results reported in Table 5.3 verify that this is in fact so.
Hourly values were calculated using Eqn 5.1 and the micrometeorological data.
These hourly values were then integrated over the
daylight period and the necessary comparisons carried out.
It
is
apparent
from Fig 5.1 that
correcting
for
atmospheric
stability minutely improved the accuracy of estimate. Thia is not
reflected
between
in
Table 5.3 however.
measured
The low
and estimated values (0,5
absolute
mm/d)
difference
is
however,
highly acceptable for accurate irrigation scheduling.
ASSUMPTION
CONCERNING
AERODYNAMIC CONDUCTANCE
WIND
MEASUREMENT,
CROP
HEIGHT,
AND
The height of the anemometer on the automatic weather station was
3.15m.
Hence, using these data implies that wind speed at this
height above neighbouring short grass approximates the wind speed
at the same height above the wheat crop.
shown
that
In Chapter 7 it will be
automatic weather station data may
used in the Penman-Monteith equation.
And it may thus be
cluded that the above is a workable assumption.
regarding
the
effect of measurement height are
this stage however.
67
successfully
be
con-
Certain comments
appropriate
at
The
Penman-Monteith equation as utilized in PUTU 9 and PUTU 9.86
uses aerodynamic conductance to account for the influence of wind
upon crop total evaporation.
The form of this equation is
F(u) = 0a = k2u/[ln{(3.15-d)/zo)]
..(5.10)
where,
k
is
von Karman's constant,
and d and zo are
the
zero
plane
displacement and roughness heights respectively.
Aerodynamic conductance, tfa, corresponds to the wind function of
the Penman equation F(u).
mic
conductance,
displacement
present
crop
the
height
is
application
clearly evident from
upon
Eqn
zero
5.10.
Hence as the wheat crop developed,
d
The
of
increased,
effectively increasing aerodynamic conductance as
z
is
unchanging height of the anemometer on the automatic weather
station.
This situation caused a marked increase in
crop evaporation.
of
computed in this
procedure assumed d to be a constant fraction (0,63)
height.
thereby
as
The strong dependence of the aerodyna-
0,7 m,
exceed
For example, on windy days with a crop height
crop total evaporation computed using Eqn.
computed
simulated
reference
evaporation by as much as
5.1 could
5
mm/d.
Such arte fac would markedly influence the soil water budget.
The
sensitivity of aerodynamic conductance calculated using
the
assumption regarding wind regime above wheat and short grass
are
clearly
the
illustrated
in Fig.
5.3.
68
The latter
exaggerates
situation
because it shows results for a measurement height of 2
m whereas the automatic station anemometer height was 3.15m.
The
results
use
of
however do support the alternative argument for the
Er (d constant at 0.0315 m) instead of Em computed with
height increasing with time.
that
the
short
Use of Er embraces the
wind speed at the same distance above wheat
grass
surfaces
are
more nearly
equal
than
crop
assumption
crop
are
and
those
resulting from the initial assumption.
The desire to account for crop architecture is the motivation for
using Em. It was hoped that this step would eliminate some of the
anomalies generally found in the use of crop coefficients.
respective
station
of
data;
whether
it
is
Em
or Er are used
apparent the the
installed as high as possible,
with
the
anemometer
but not so high as to
Ir-
automatic
should
be
completely
outride the influence of the boundary layer.
THE BOWEN METHOD AND THE ITERATION TECHNIQUE
Two
alternatives to the Penman-Monteith equation for
crop
total evaporation are available.
estimating
They are the Bowen ratio
energy budget method (Bowen) and the iteration technique
The
former
1975).
The
is a standard micrometeorological
micrometeorological
(Iter).
technique
iteration technique
has
(Thorn,
been
fully described by De Jager et al (1982) and Bristow and De Jager
( 1982).
69
During
1985 hourly micrometeorological investigations were under-
taken
to estimate evapotranspiration utilizing the Bowen
que.
Measurements
were
techni-
carried out from DOY 261 onwards
at
a
stage when the leaf area index was greater than 5.
Inter-comparisons
between these estimates and measurements
the lysimeter are reported in Table 5.5.
relatively
values
to
elements
the
especially when
The two methods however
evapotranspiration to a large extent.
table
The results indicated a
high correlation coefficient,
are considered.
daily
under-estimate
This is possibly attribu-
small vertical gradients
observed (see Table 5.6).
from
in
the
meteorological
The slopes of 0,97 and 0,91
(together with high correlation coefficients) for temperature and
relative humidity,
gradients.
ment
respectively,
are indicative of low vertical
In order to try and explain the poor Elys/Iter agree-
in 1985;
the comparisons were repeated,
but with assuming
that the relative humidity at the momentum exchange level was 10%
higher
than
that at 2m height.
These comparisons
between
the
iteration technique (Itera - Table 5.5) and the lysimeter reflected
a decrease in the correlation coefficient,
simulation index.
humidity
than
but an
improved
It was therefore suggested that the
relative
at the effective crop surface (Rho) was actually higher
what was measured at the momentum exchange level.
A
difference (Rho-Rhr) in relative humidity would have resulted
a
better estimation of E in 1985 using the iteration
is the relative humidity at a 2m high reference level.
the
other hand,
careful tests conducted in 1986
70
in
technique.
Rhr
the
high
On
confirmed
the accuracy of the original iteration techniques (Bristow and de
Jager
1982).
Measurement error could probably explain the
poor
agreement of 1985.
Good agreement was obtained between the Bowen ratio method and
the iteration technique (Table 5.5).
The
canopy
surface temperature estimated
technique
(TO-iter)
utilizing
an
compares
using
favourably with
aspirated psychrometer (TOMES)
the
iteration
values
measured
installed
at
the
momentum exchange level within the wheat and an infra-red thermometer (IRT) (see Table 5.7).
It
is evident from these results that none of the highly complex
techniques described approach the accuracy of the Penman-Monteith
equation.
ratio
However, neither the iteration technique nor the Bowen
method should be viewed over critically on this
evidence.
The iteration technique can be greatly simplified using relationships of the type reported in Table 5.6.
Futhermore,
iterating
for relative humidity at the momentum exchange level (Rho) rather
than canopy temperature (Tempo) would greatly simplify the procedure.
be
The need to measure Rho - always a difficult matter, would
eliminated.
attractive
This could indeed make the iteration
alternative.
method
The high simulation index obtained from
comparisons between the Bowen and iteration techniques is
interesting
and
an
suggest that the iteration
further investigation.
71
technique
indeed
deserves
1,50
_
1,00
_
E
£
D
UJ
K
3
CO
iuO,75
E
t-
LLJ
0,50
_
0,25
0,25
0,50
ETm
Fig. 5.1 -
A
comparison
0,75
1,00
ESTIMATED |mm/h
between
Em measured
1,25
and
1,5 0
estimated
using the Penman-Monteith equation assuming neutral
atmospheric conditions (i.e. *m = 1 ) .
72
1,50
0,25
0,25
050
0,75
ETm
Fig. 5.2 -
A
comparison
using
the
1,00
1,25
ESTIMATED |mm/h
between Em
measured
and
estimated
Penman-Monteith equation corrected
atmospheric stability as measured over grass
4m = f(Ri)).
73
for
(i.e.
o
N
O
I
.3.
II
0
Fig. 5.3 -
Variation
in
aerodynamic
plane displacement.
74
conductance
with
zero
TABLE 5 . 1 -
Frequency distribution of the stability function,
Frequency
(n)
Class interval
1,20
1,10
1 ,00
0,90
0,80
0,60
\
DID
\
^ DID K
K. win x
\
(Dm
x
< <bm <
< im <
TABLE 5.2 -
5
2
3
28
17
8
1,80
1,20
1 ,10
1 ,00
0,90
0,80
Summary
Percentage frequency
(n/N x 100) : N = 63
8
3
5
44
27
13
of the statistical tests carried out
tween
hourly values of Em measured and
mated
with
(S>m
= f(Ri)) and without
Em
(6m
beesti=
1
adjustment for the existing atmospheric stability
Appropriate
coefficient
Comparison
with 4m = 1
Comparison
with 4m = f(Ri)
Correlation coefficient (r)
0,91
0,89
Slope (m)
0,97
0,88
Intercept (b)
0,05 mm/h
0,11 mm/h
Simulation index
0,97
0,94
Systematic error
0,03 mm/h
0,04 mm/h
Unsystematic error
0,12 mm/h
0,13 mm/h
Mean residual
0,09 mm/h
0,10 mm/h
Mean difference
0,03 mm/h
0,03 mm/h
Mean absolute difference
0,09 mm/h
0,10 mm/h
75
TABLE
5.3
-
Statistical
values
analysis of agreement
of total evaporation
the lysimeter,
teorological
tion,
1)
or
from microme-
data using the Penman-Monteith
Em, assuming
adjusting
daily
(mm/d) as measured by
Elys, and calculated
atmospheric
for atmospheric
neutrality
stability
equa(4m =
(4m
=
Elys (mm/d)
Em
(mm/d)
m=l ftm=f(Ri)
Y
X
n
SLOPE
INTERCEPT (mm/d)
r
MEAN OF MEASURED VALUES
MAD (mm/d)
SIMULATION INDEX
TABLE 5.4
between
-
9
0,82
0,99
0,90
6,07
0,6 5
0,94
9
0,88
0,72
0,88
5,89
0,52
0,93
Statistical analysis of relationship between hourly
net radiation minus soil heat flux and
incoming solar radiant flux density, St.
Y
X
Qn-G
St
(W/m*)
96
0 ,75
W/m
-72
0 ,96
n
SLOPE
INTERCEPT
r
76
1
hourly
TABLE
5.5 -
Statistical
analysis of the accuracy of different
methods of measuring total evaporation, Em
Y
Bowen
Elys
Elys
X
Iter
Iter
Bowen
Elys
*Itera **It86
mm/h
mm/d
mm/h
mm/d
mm/h
n
60
9
79
9
75
9
75
SLOPE
0,80
0,73
0,64
0,81
0,74
0,86
0,54
INTERCEPT
0,18
2,32
0,41
2,67
0,28
1,73
0,39
CORRELATION
COEFFICIENT
0,78
0,73
0,58
0,73
0,75
0,68
0,50
MAD
0,16
1,33
0,28
2,00
0,19
1,05
0,25
SIMULATION INDEX
0,85
0,66
0,66
0,56
0,83
0,74
0,80
mm/d
mm/h
SYSTEMATIC ERROR
0,16
UNSYSTEMATIC ERROR
0,17
MEAN RESIDUAL
0, 14
MEAN DIFFERENCE
0,09
mm/h
. 61
o, 49
o, 29
o, 68
o, 18
o, 80
0,00
1,33
MAX ABSOLUTE
DIFFERENCE
MEAN Y
Elys
0,52
0,43
3,60
0,72
5,60
0,71
6,00
0,71
o, 55
0,55
MEAN X
*Itera are values obtained in 1985 with the iteration technique
assuming that the relative humidity at the momentum exchange
level was 10% higher than that at the 2m height.
**It86 are values obtained in 1986 using the original
iteration technique.
77
unaltered
TABLE 5.6 -
Statistical analysis of relationship between
tempera-
ture
exchange
level
and
in
relative humidity at the momentum
the
wheat crop (o) and
the
corresponding
elements at a reference height of 2m (r).
X
Y
Tempo
Temp
Rho
Rhr
115
n
0,97
SLOPE
0,63
INTERCEPT
0,99
r
1,31
MEAN OF MEASURED VALUES 21,31
0,36
MAD
0,99
SIMULATION INDEX
TABLE 5.7 -
Statistical
115
0,91
9,25
0,95
46,82
5,99
0,95
analysis
of agreement
between
crop
surface temperature calculated using the iteration
technique (TO-iter) and measured by thermometer at
the momentum exchange level (TOMES) and the infrared thermometer (IRT).
Y
X
TO-iter
TOMES IRT
n
66
25
SLOPE
1,15
1,03
INTERCEPT ( C)
-1,90 -0,31
r
0,92 0,95
MEAN OF MEASURED VALUES(°C) 26,33 29,65
MAD ( °C)
2,43
1,90
SIMULATION INDEX
0,92
0,95
78
CHAPTER 6
DESCRIPTION OF THE STRESS EVENTS DURING 1985
In order to develop and validate the PUTU models, it was necessary to identify stress periods in the wheat growing seasons.
The
procedures
are
adopted
in obtaining the results discussed here
reported in Chapter 7.
describe
the
The objective of this chapter will be
stress events used to validate the
capability
to
of
PUTU 9.86 to determine the onset of crop water stress.
RESULTS
Leaf
water potential will be considered to be a positive entity.
The scenario describing two stress events in 1985 are depicted in
Fig. 6.1.
It is evident that during stress, leaf water potential
rises to almost 3,0 MPa.
difference
in
leaf
From Fig.
water
potential
6.2 it may be seen that the
and
foliage
temperature
between the stress and control plots provides an excellent
indi-
cation of crop water stress.
De
Jager,
water
Bristow and Van Zyl (1984) provide evidence that leaf
potential
in the wheat crop maintains
during the middle part of each day.
a
constant
This is clearly illustrated
in Fig. 6.3 under conditions with no water stress.
non
level
This phenome-
was repeatedly observed on many occasions during the experi-
ments .
79
In 1985,
DOY
Plot 3
320.
The
development
Fig. 6.4.
(as described in Chapter 7) was stressed around
response
of
of
leaf water
potential
during
water stress is depicted in the four
graphs
the
of
It is evident that in the stressed plot (Plot 3 ) , leaf
water potential gradually increased to a level exceeding 2,5 MPa.
The plateau during day-light hours was still evident.
Crop
foliage
stress.
temperatures
also provide
useful
indicators
of
Once again the progressive development of stress in Plot
3 around DOY 320 may be followed by comparing (experimental Plotwet plot) foliage temperatures, as measured by infrared thermometer,
The
on stress and control plots (Fig. 6.2 and 6.5 through 6.7).
effect of cloud in the 14th hour of DAY 320 is also
evident
from Fig. 6.6.
All these figures (6.1 through 6.7) clearly indicate that
occurred
stress
around DAY 285 in Plot 2 and around DOY 320 in Plot
3.
CONCLUSIONS
Bearing in mind the convention of positive leaf water potentials,
it may be concluded that,
Plot 2 and Plot 3.
stress
deepened
approximately
until
development
started
stress events took place
2700
DOY
286
to
where
leaf
water
potential
when
irrigation
took
gradually
place.
Leaf
from 2200 kPa on DOY
3000 kPa on DOY 322.
80
water
318
and
reached
kPa on DOY 284 and then remained above
was recorded around DOY 320.
rising
in
In Plot 2 leaf water potentials indicate that
commenced on DOY 280 (leaf water potential 1500 kPa)
gradually
kPa
in 1985,
A
2400
similar
potential
to
around
49 21
6
34
15
56
0
50 18
0
31
17
60
84
46 14
0
25
16
58
76
286 289 294
297
303
308
318
IRRIGATION (mm)
TIME (DOY)
H
O
y Y T
T
STRESS 2
^
4,
STRESS 3
3 , control
the, SflfflS
2, control
the same
•
W
3,0 o_
•
J
o: 2 0
• ^
^ i
yh/f/
*
t.o
Cloud
PL0T
2
° PLOT 3
PÂ/
300
290
280
#
*
CONTROL
310
320
330
TIME (DOY)
Fig.
6.1 - Scenario
stress
1985.
of
plots
noon Bean crop leaf water
potential
and the control plot at West
Campus
Irrigation dates and amounts are indicated.
81
on
in
10
1,0
8
LLJ
0,6
UJ
DC
UJ
oo
2 _ 2i
0,2
o
u_
0
290
300
310
320
330
TIME (DOY)
Fig. 6.2 - Scenario of difference in leaf water potential,
and
foliage
temperature,
IRT,
control plots on West Campus 1985.
82
between
stress
PSIL,
and
DOY 3 1 8
•
6
2.0
I : t
•
8 °
* •
•
Si 1 - °
-
PLOT 2
0
*
10
12
PLOT 3
CONTROL
14
16
TIME < h )
DOY 310
~
3,0
•
•
•
S z.o
*
I
•
*
*
O
•
5 •* •
0
(
5
8
*
* <
•
UJ
I—
# PLOT 2
*
fe i.o
s
10
12
0
PLOT 3
*
CONTROL
14
16
TIME ( h )
Fig.
6.3 - Hourly crop lean leaf water potential in Plot 2,
3
and
Plot
the Control Plot on DOY 310 and DOY 318 before
the onset of water stress, West Campus 1985.
83
DOY 320
DOY 322
3,0 •
J.O
•
2,0
o
o
o
*
•
*
*
*
o
o
o
•
o
*
2.0
*
-
*
*
o
0
0
o
b
o
*
*
o
*
*
*
*
*
•
-
1.0
1,0
-
PLOT 3
*
*
o run 3
CONTROL
*
-
1
COWTWL
0
10
16
u
S
10
TIME (h)
17
I*
">
TIME (h)
00
DOY 319
DOY 323
1.0
J,0 -
o
o
•
*
o
0
O
o
UJ
0
0
Q
*
•
o
o
2.0
0
*
*
s
*
*
*
•
*
•
£
t—
'.0
•
O PLOT 3
o
PLOT 3
*
*
cotrrnoi.
CONTHOL
•
'1
M
1
12
10
TIME
u
10
12
TIME <h)
FIG. 6.1 - HOURLY CROP MEAN LEAF WATER POTENTIAL ON PLOT 3 AMD THE CONTROL PLOT ON DOY 319 THROUGH DAY 323
ILLUSTRATING THE EFFECT OF WORSENING MATER STRESS UPON LEAF WATER POTENTIAL ON WEST CAMPUS IN 1985.
35
•
CONTROL
•
PLOT 2
O
PLOT 3
30
•a:
en
25
fi
CD
20
15
10
12
14
16
TIME ( h )
Fig. 6.5 - Comparison of Plot 2,
Plot 3 and Control plot
temperatures on DOY 285 at West Campus in 1985.
85
foliage
40
-
30
-
25
-
PLOT 3
•
CONTROL
IR!
35
•
:RAT
LJ
UJ
QJj
AGt
I—
p
1
20
Cloud
15
10
12
TIME
14
16
(h)
Fig. 6.6 - Comparison of hourly wheat foliage temperatures in the
Control
plot
and Plot 3 on DOY 320
1985.
86
at
West
Campus
cc
x.
UJ
CX.
I i i
TIME (h)
Fig. 6.7 - Comparison of Plot 3 and Control plot foliage temperatures on DOY 322 at West Campus in 1985.
87
CHAPTER
7
PUTU 9.86 VALIDATION
OBJECTIVE
The
objectives of this phase of the project were a) to establish
suitable
use,
facilities
and b) to
lating
for accurately measuring wheat
crop
water
test and improve the computer routines for simu-
water use and the onset of crop water stress in the
PUTU
9.86 irrigation scheduling model.
PROCEDURE
The lysimeter
The
lysimeter
was installed in the middle of
area on West Campus.
10 m x 2 m
deep.
six
experimental
Its dimensions are 3,16 x 3,1 x 2,1 m, i.e.
This
bin
system provided by Klerkseale*,
with
the
was installed above
Potchefstroom.
a
mechanical
It was provided
steel sheets arranged 100 mm above the bottom
of
tank in such a way as to permit water to seep between the
for
collection
and removal.
Upon this base 12 mm
chips were evenly spread to a depth of 200 mm.
was
covered with 4,4 mm thick (grade U64)
filament
needle
September,
approximated
1983,
50 t.
plates
granite
This porous layer
non-woven
punched Polyester Geofabric
of
the
continuous
(Bidim*}.
the bin was filled with soil.
Its total
During
mass
The soil in the lysimeter may be classified
*Bidim - manufactured by Kaymac in S.A. and supplied by Noel Hunt
Geofabrics (Pty) Ltd, P O Box 34179, Jeppestown 2043. Any reference thereto and to Klerkscale does not represent an endorsement
by the UOFS.
as Hutton form and Middelburg series.
orthic
A
It was collected from
and red apedal B horizons of a neighbouring
the
Bainsvlei
soil.
The
accuracy of the Penman-Monteith equation has been proven
Chapter 5.
form
of
stant
Reference evaporation,
Er,
was simulated using the
the Penman-Monteith equation used in PUTU 9 for a
crop
height of 50 mm.
in
Maximum total evaporation Em
conwas
estimated using crop height modelled as in APPENDIX III.
An
underground water piping system was installed to supply the 5
ha
experimental area
surrounding the lysimeter with
irrigation
water (see Fig. 7.0).
In 1984,
ter.
ted
3 ha of SST-4 wheat were established around the lysirae-
It was planted on 1984.07.02 in 300 mm wide rows and resulin a plant density of 206 pl/m .
Three experimental
plots
were utilized - the lysimeter, Plot 1 (120 x 5m) which received a
stress
which
well
treatment in the reproductive phase and Plot 2 (120 x 5m)
was stressed in the vegetative phase.
watered up to 1984.10.26.
The lysimeter
The entire field
received
was
772
kg/ha of 2:3:0 (21) fertilizer.
In 1985, 4 ha of SST-44 wheat were established around the lysimeter.
It was planted in 100 mm wide rows at a seeding rate of 220
pl/m
(90 kg/ha) on 1985,07.01.
89
In 1985 six experimental plots were used.
1,
2,
Five plots,
numbered
C, 3 and 4, were sited in a north-south line 80 m east of
the lysimeter.
installed
They were irrigated by a set of line sprinklers,
exclusively for this purpose.
The soil in the
sited 120 m south-east of the lysimeter,
sixth
(wet)
plot,
was
held
close
to maximum soil water content throughout the season.
All
plots
were approximately 10 x 10 m in
plot had three neutron probe
of
side
access
0,5 m.
tubes.
size.
Each
experimental
access tubes imbedded in a triangle
A raingauge was placed one metre south of
Each plot centre was 2,25 m west of a
th<-
sprinkler
riser.
The
soil
of
the experimental terrain is of
Sterkspruit series.
form,
Fig. 7.0 illustrates the soil profile in the
pit prepared for the lysimeter.
750 mm.
Sterkspruit
A calcerous layer is present at
No soil water extraction was measured at this depth
in
the plots.
Tensiometers, for monitoring soil water potential, were installed
in the lysimeter (1984 and 1985) and Plots A and B in 1984.
porous cups were placed at depths of 150 mm,
300 mm,
The
600 mm and
900 mm.
In
1985 neutron probe access tubes were installed in the lysime-
ter (4 tubes) and experimental Plots 1,
each).
2,
C,
3 and 4 (3 tubes
Measurements were taken at 150 mm intervals down to 1200
mm in the lysimeter and to 800 mm in the plots.
90
The irrigation treatments for 1985 applied are illustrated in Fig
6.1.
to
Essentially C, the control plot, was given adequate water
prevent water stress.
284) was permitted.
4
(+_ DOY 319).
In Plot 1 and 2 early stress (+, DOY
While stress was allowed late in Plot 3 and
Irrigation and rainfall events are recorded in
Table 7.5.
Soil water potential, crop height, leaf area and foliage temperatures were measured at regular intervals.
Total evaporation was
measured in the lysimeter.
Infra-red thermometer readings were made in all three sites using
the Teletemp AG24 unit.
0,89
It was initially calibrated on
and 0,91 following the method of Berliner,
Green
(1984).
error
of
setting
Calibration yielded an r
of 1,0;
Oosterhuis
a
0,33C and an unsystematic error of 0,1 "C on
which
was subsequently used in the field
setting
and
systematic
the
0,89
measurements.
The standard error of estimate of the regression was 0,12 C.
RESULTS
It .must
be
emphasized that all results quoted
obtained
during
strictly
reserved for verification of model reliability.
model
development.
dard statistical tests were applied.
The data
for
1984
were
for
1985
were
The symbols used to denote
the different test parameters are described in Table 7.1•
91
Stan-
Evaporation through the soil surface, Es
The PUTU 9 model used a soil surface drying function which was an
exclusive function of time, viz.
Fg
-
EXP(-0,4 t)
...(7.1)
where,
Fg is the proportion of maximum evaporation
surface soil water status, and
permitted
by
t is the number of days since the last irrigation or rainfall event.
Thus
Fg = Es / Er,
if it is assumed that evaporation
from
wet
bare soil equates to evaporation from a short grass surface, Er.
The problem with this equation is that the surface loses water at
a
rate
It
is
rather
independent of the soil water content in the top
however logical to expect such water loss to
to
soil
water content in the top layer
Instead of Eqn 7.1,
the
that
soil
layer.
be
than
related
to
time
it was assumed that water evaporated through
surface stems solely from the top 100 mm of
the drying rate in the top soil layer bears an
soil
and
exponential
relationship to water content in this layer.
Essentially, this means that Fg is a function of the total amount
of water evaporated since the last wetting event.
equal
top
The latter is
to the depletion below maximum soil water content
layer.
Consequently,
the proposed model has the
92
in
the
defining
equation
tag/ ivDEFl = -a . Fg,
which
may
be
VDEF1 = 0.
solved given the boundary condition Fg =
1
when
Thus,
Fg
where
..(7.2)
VDEF1
=
EXP(-a . VDEF1)
. . (7.3)
in mm/m is the soil water deficit (decrement
below
field capacity) in the top soil layer and <a> is a constant.
The
value of a = 0,03 m/mm was obtained by assuming that Fg decreased
to approximately 0,1 when the soil water content of the top layer
is depleted by 77 mm/m
(approximately 75 % of plant available wa-
ter) .
The model represented by Eqn 7.3 was tested against data measured
in
the
accuracy
lysimeter
following two wetting events
of the model is reflected
in
Fig.
tests yielded an SI = 0,99 and MAD = 0,03.
Eqn
in
7.1.
1984.
The
Statistical
It was concluded that
7,3 was an accurate model for describing water loss
through
the soil surface.
Model1 ing
The
data
collected were used to determine the onset
of
stress
periods in the various plots {see Chapter 6) and to test the PUTU
9 model which is also described in Chapter 2.
93
The 1984 data were
used further to fine tune the PUTU 9.86 model. In particular, the
leaf
water potential and soil water potential observations
needed
to evaluate KSPo,
were
the maximum soil-root conductance,
by
trial and error. The best value found was KSPo = -1,2 x 10
mm/(d kPa m pi).
Relative vegetative evaporation, Fh
The scenario of
simulated relative vegetative
is given in Fig.
onset
and
7.2, 7.4, 7.5 and 7.6.
duration
the
Fh,
The ease with which the
of water stress may be
scenario of Fh is evident from these.
evaporation,
identified
from
a
The good agreement between
scenario in Plot 1 and Plot 2 in 1985 (Fig.
7.5)
indicates
that the model is consistent.
The
timing
7.4.
of stress in 1984 (see Chapter 6) is shown
in
Fig.
This was compared with stress incidence recorded by IRT and
Scholander bomb.
infra-red
in Plot 1 and 2, on DOY 285 {1984),
o
temperatures were 3,7 C higher than in the lysimeter.
Furthermore,
leaf
Firstly,
water potential was 2100kPa on DOY
286.
model
therefore probably did not predict a partial stress
might
have
been present.
Thereafter,
IRT
records
The
which
indicated
stress in Plot 1 on DOY 296 and 297 as did a leaf water potential
of
3400 kPa on DOY 303 and DOY 304.
In Plot 2,
the IRT
record
showed stress from DOY 281 to 285 and also on DOY 292, then again
from
DOY
296 to 298 as well as on DOY 303 and 304.
Leaf
water
potential measurements reinforced this with values on DOY 286 and
94
303
in excess of 2700 kPa.
rately
reflects
The scenario of Fh in Fig 7.4
all these as days on which
crop
accu-
water
stress
occurred.
In 1985 more critical tests on model ability to simulate onset of
stress were undertaken.
were described.
on
In Chapter 6, two stress events in 1985
One commenced in Plot 2 on DOY 281 and the other
DOY 320 in Plot 3.
by comparing Fig.
The reliability of the model was assessed
7.5 and 7.6 with Fig.
6.1 through 6.7.
The
accuracy with which the onset of the stress periods described
Chapter
6
are identifiable in Fig.
reliability
puted
Fh
7.5 and 7.6 attest
of PUTU 9.86 for scheduling irrigation.
-
89% on DOY 277 and Fh = 62% on DOY
predicted the onset of stress two days early.
319
to
the
PUTU
com-
It
thus
278.
On DOY 318 and DOY
simulated Fh equalled 80% and 14% respectively.
These
sults indicate that the model simulated the onset of this
cular
in
re-
parti-
stress period to within an accuracy of one or two days.
Relative total evaporation
The
scenario of relative total evaporation for 1984 and 1985
given in Fig.
sured
E/Em
7.3.
is
The sharp fluctuations in the ratio of mea-
were due to the compounding influence
either lysimeter measurement or simulation.
in trends in this ratio are apparent.
of
error
in
Reasonable agreement
Particularly noteworthy is
the
agreement in the gradual decrease in relative evaporation in
the
latter part of the grain filling stage in 1984
occurred.
tion
when
The grain filling period ended on DOY 325.
stress
Examina-
of the simulated ratio at the end of the 1985 season
95
indi-
cates that the function describing the decrease in relative total
evaporation with decreasing LAI at this time requires refinement.
The model over-estimated measured values.
place
late
in
This,
however,
the season and would affect neither
took
yield
nor
scheduling.
Soil water content
Results of comparisons between measured and simulated soil
content
deeper
are
reported in Fig.
than 450 mm.
fluctuations
in
7.1).
layers
soil
In depths shallower than this,
rapid
content
The trends in soil water content in the
were simulated excellently (cf high r
This indicates an accurate water extraction
absolute
layers
the
both measured and simulated soil water
make analysis difficult.
various
7.7 through 7.9 for
water
in
Table
model.
magnitude of soil water content in the different layers
does not always correspond exactly to the measured values.
tended
The
This
to cause a low simulation index of 0,73 for both the
mm and 600 mm layers.
However,
as will be seen, the water from
the entire root profile can nonetheless be accurately
simulated,
notwithstanding
poor simulation of water content in the
dual
It is the total water use that is of
layers.
450
where irrigation scheduling is concerned.
indivi-
importance
The SI > 0,82 for the
layers
below 600 mm depths is highly acceptable.
during
dry-down
The agreement
down as stress commenced around DOY
320
seems
soil
water
good.
It
may
be concluded that the root distribution and
extraction model functioned satisfactorily.
96
Water use
Results
water
The
obtained from modelling (1984) and validation (1985)
use from the entire root zone are reported in
excellent
Table
7.2.
agreement between measurement and simulation
self evident-
are
The results of the statistical tests applied
the results in Table 7.2 are given in Table 7.3.
of
on
The high SI in
Table 7.3 indicates a most reliable model.
General
It
is
best
difficult to decide between 5 kPa or 10 kPa which is
limit for soil water potential at which maximum soil
content occurs.
There is support in the literature for
either (see Mottram, 1985).
Simulation
use.
water
adopting
The effect of using each was tested.
runs were undertaken with both (see version I and
as described in Table 7.2).
selection
of
the
It is evident from Table 7.2,
either would provide accurate simulation of
II
that
water
Maximum soil water content did not seem to affect simulated
yields
much
(see
tests in Table 7.4) when
the
Doorenbos
and
Kassam (1979) model was used.
Since
soil water contents were simulated to within the range
uncertainty
limit;
in
Neutron
probe measurements using
the
10
of
kPa
it appears that this might be the better parameter.
Comparisons
between
measured and simulated yields are given
Table 7.4.
While the number of values used here is too small to
represent an exhaustive test of the model,
97
in
it appears from these
results
that the Doorenbos and Kassam (1979) yield sub-model
used in PUTU 9.86 is in need of refinement.
as
The SI of 0,76 with
maximum water content taken at 5kPa, approaches acceptablity.
Study
tial
of the simulations showed that simulated leaf water potenmaintained
conditions
stress.
and
a
low value (less than 1
MPa)
then rapidly approached 3 MPa at
in
non-stress
the
onset
of
The rate of change and non-stress values of this varia-
ble are lower than those measured.
requires refinement.
proach
Hence,
this sub-model
Although leaf water potential does not ap-
this stressed condition in the manner measured,
indicate
the
also
time of onset of stress accurately.
it
does
This is
that is required from an irrigation scheduling model.
all
This plant
water response may be rectified by modifying the function
(after
Botha, 1983) describing soil root hydraulic conductance, KSP.
order
crease
to improve the model,
KSP must be made initially
to
rapidly with decreasing soil water content and then
In
deless
rapidly as the soil water content in the layer approaches wilting
point.
CONCLUSION
Many
doubt
more data would be required before conclusions
could
be reached.
Particularly the simulation
water potential requires attention.
here
presented
it
beyond
of
water
may be concluded that the
use by the crop is also accurately
PUTU
9.86
simulated.
such it offers a reliable model for scheduling irrigation.
98
leaf
However, from the evidence
accurately simulates the onset and duration of water stress.
total
any
model
The
As
FIG. 7.0 -
The soil profile in the pit in which the lysimeter
was installed and the furrow excavated for the
irrigation water supply pipe.
99
Statistical
Table 7.1
for
analysis of the accuracy of
PUTU9-86
simulating soil water content in five
diffe-
rent soil Layers during 1985.
DEPTH (mm)
450
n
23
600
750
23
23
1050
1200
23
22
a
0,81
0,69
0,73
0,93
b (mm/m)
0,84
23,65
29, 72
14,48
0,24
r
0,81
0,81
0,87
0,96
0,90
137,78
141 ,52
150,52
176,74
205,45
MAD (mm/m) 31,74
30,00
21,09
8,09
26,91
0,23
0,22
0,14
0,05
0, 13
MD (mm/m) -31 ,30
-27,64
-15,96
2,70
26, 55
0,73
0,73
0,86
0,98
0,82
MEAN (Y)
MAD/Y
SI
1, 15
n
denotes
the
comparisons,
a
the slope of the regression curve,
b
the intercept on the y-axis,
r
the correlation coefficient,
Y
the measured values,
Y
the mean value of Y,
MAD
the mean absolute difference between measured and
values,
MD
the
and
SI
the index of agreement (here
Willmott (1982).
number
of
sets
of
values
used
in
the
simulated
mean difference between measured and simulated values,
100
named
simulation
index) of
TABLE 7.2 -
Water use in the different growth stages and for
the season as measured (lysimeter) and calculated
by PUTU 9.86 in 1984 and 1985.
Four different
versions of the model as defined by I to l\ below
were tested.
WATER USE IN
YEAR
GROWTH
STAGE
DURATION
(days)
CALCULATED
MEASURED
II
3
4
5
6
7
1985
54
38
5
10
35
TOTAL
1984
3
4
5
6
7
51
43
5
10
35
TOTAL
108
191
36
59
254
105
187
36
62
258
92
186
36
62
260
649
648
636
85
199
*
220
87
228
18
65
155
618
553
78
(mm)
III
79
96
94
228
202
13
223
256
17
64
141
613
574
554
17
66
Description of the significant modifications yielding the
(I-IV) different versions of PUTU 9.86 tested.
VERSION
-
Em = f(crop height)
Fie = 0,186*LAI
V01 at 10 kPa
II -
Em = f(crop height)
Fie = 0,186*LAI
V01 at 5 kPa
III -
Em = f(crop height)
FlefRitchie, 1972)
V01 at 10 kPa
IV -
Em = Er
Fle(Ritchie, 1972)
V01 at 10 kPa
I
Missing data
101
49
196
four
TABLE
- Statistical analysis of the accuracy of PUTli 9-86
for simulating total water use in the different
growth stages.
Five growth stages for 1985 and
four growth stages for 1984 and the entire season
totals were considered.
7.3a
VERSION
1985
1984
OF
MODEL
Y
SI
Y
MAD
SI
(mm)
I
216
II
III
IV
216
1,00
1,00
( mm )
240
240
240
240
2,,5
7,,2
MAD
0 ,99
1 ,00
0 ,98
0 ,99
35
11
41
26
Y - measured mean daily water use over all periods considered
TABLE 7 . 3b -
YEAR
1984
1985
Comparison
IV
Statistical analysis of the accuracy of the four
versions of PUTli 9.86 for simulating mean daily
water use in the different growth stages.
VERSION
OF MODEL
I
II
III
IV
I
II
SI
MAD
(mm/d)
(mm/d
0,92
0,97
0,87
0,83
1 ,00
1 ,00
0 ,94
0 ,52
1 ,27
0 ,95
0 ,12
0 ,18
5,01
4
5,01
5,01
5,01
5,36
5,36
4
4
4
5
5
of the SI and MAD for versions I and II with III
illustrate the improvement brought about by
the relationship Fie = 0,186 * LAI.
102
introduction
and
of
TABLE 7.3c -
Analysis of accuracy of version I of PUTL 9.86 for
simulating seven-day totals of crop water use.
1984
1985
17
0,82
7 ,82
0,92
35,68
4,59
12,86
1,83
0,95
18
0,84
5,32
0,97
33,42
3,87
11 ,58
-0,02
0,98
STATISTICAL
PARAMETER
n
a
b (mm)
r
MEAN (Y)
MAD (mm)
MAD/Y (%)
MD (mm >
SI
A
seven day irrigation cycle was considered as this is the shor-
test probable.
the
The mean difference of MD <,8mm means that over
entire season the error in computed water use
less than 2,5%.
will
average
The expected average error in a given irrigation
period is <19%.
103
Percentage
TABLE 7.4 -
9.86
yield
reduction as simulated by
for the various growth stages and the
PL'TU
final
simulated yield and measured final yield.
YIELD REDUCTION
MAX.
WATER
CONTENT
AT
3
4
(kPa)
(X)
(%)
FINAL Y IELD
STAGE
5
6
(%)
CALCULATED
7
(X)
{%)
1
1
1
MEASURED
STD.DEV.
MEASURED
(kg/ha)
(kg/ha)
(kg/ha)
1
1
6 510
6 510
5 240
3
7
6 230
5 320
5 710
470
11
1985 L
5
10
5
5
0
0
0
0
C
5
10
on on
PLOT
1
1
1
0
5
10
5
5
1
1
40
40
1
1
3 500
2 520
4 170
490
17
2
5
10
5
5
1
1
1
7
25
40
4
4
4 200
3 010
4 650
370
3
5
10
5
5
1
1
1
1
0
10
11
18
5 740
4 550
5 560
860
4
5
10
5
5
1
1
1
2
2
17
13
18
5 520
3 990
4 780
750
1984 L
5
10
6
6
0
1
1
10
1
1
0
24
5 740
4 760
5
10
6
6
1
3
1
3
19
23
39
39
2 370
1 820
5
10
6
6
3
6
1
0
6
6
40
41
3 100
2 870
5 380
940
1
1
2
3
WET PLOT
Statistical tests on final yield
5 kPa
r = 0,87
SI = 0,80
MAD = 638 kg/ha
y = 5018 kg/ha
10 kPa
r = 0,78
SI = 0,64
MAD = 1125 kg/ha
y = 5018 kg/ha
L denotes the lysimeter
y denotes the mean measured yield
104
Water applications during the experiments of 1984
and 1985.
TABLE 7.5 -
1985
1984
( mm )
165
2.6
1. 1
179
0.9
180
186
24.1
71 . 1
199
200 159.9
4.86
214
1 .49
216
4.29
219
1.13
222
22.0
223
235 26.39
240
18.0
244
1. 1
4.22
254
257
258
260
262
263
264
265
269
270
271
275
276
282
284
289
291
292
293
296
299
300
303
304
305
306
313
317
320
322
DOY
PLOT
1
DOY
( mm)
( mm )
25 .0
25 .0
25 .0
25 .0
25 .0
25 .0
25 .0
25 .0
25 .0
25 .0
25 .0
25 .0
0.0
22.23
25 .0
25 .0
7.41
0.97
20.3
9.98
2.58
20.94
0.0
25 .0
0 .0
25 .0
25 .0
25 .0
12.0
1.7
25 .0
25 .0
4. 19
11.66
25 .0
0.0
7.22
2.33
0.02
PLOT
C
1
I mm
198
200
201
206
211
214
221
234
245
249
252
258
260
263
266
269
271
272
274
276
279
282
284
285
286
287
289
292
294
297
299
300
302
305
306
315
318
(mm)
(mm)
(mm)
(mm)
(mm)
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
50.0
90.0
90.0
90.0
90.0
90.0
90.0 102.0
92.0
2 .9
1 .6
58 .0
15 .4
8 .9
13 .8
17 .4
8 .5
14 .1
15 .4
17 .9
114.4 114.4
30 .5
26 .9
18.1
18.1
20.1
9.9
0.0
0.9
38.0
50.2
48.0
2 .5 14.9
3 . 1 6.1
32.0
12.4
7.6
18.0
10.9
4.6
14.0
12.4
6.1
21.0
12.9
4.1
20.0
0.0
78.8
0.0
31.4
0.0
25.0
5.7
33.6
7.0
31.9
46.8
45.6
43.3
41.9
39.4
0 .2
7 .8
6 .5 104.0
84.0
76.0
0.0
0.0
2 .0
24 .1
26 .2
1 .8
34 .5
8 .5
12 .3
3 .2
8 .4
1 .2
25 .0
5.5
15.70
2.7
3.8
25 .0
105
Model
,8
Lystmeter
,7
,6
,5
,4
o
en
,3
• 2-
,1-
—i—
90
92
94
96
98
100
102
104
106
o f the
ratio
— • —
108
110
112
DOY
FIG.
7.1 - S i m u l a t e d
the
soil
wetting
and
measured
surface,
events
values
Es, to reference
during
the
1984
of evaporation
evaporation,
season.
throunh
£r, f o l l o w i n c i
two
100
i
I
280
290
300
S10
320
330
TIME (DOY)
Scenario of relative transpiration, Fh, as simulated by PUTU 9.86 for the lysimeter during 1984.
FIG 7.2a -
100
280
TIME (DOY)
FIG 7.2b
-
Scenario of relative transpiration, Fh, as simulated by PUTU 9.86 for the lysimeter during 1985.
107
160
LYSIMETER
2
|
I
290
300
310
320
330
340
TIME (DOY)
FIG 7.3a
-
Scenario of lysimetrically Beasured and relative
total evaporation simulated using PUTU 9.86 during
1984.
150
LYSIMETER
200
FIG 7.3b
-
310
330
TIME (DOY)
Scenario of lysinetrically Measured and relative
total evaporation simulated using PUTU 9.86 during
1985.
108
100
280
300
320
(DOY)
100
|
kl
a:
10
280
300
TIME (DOY)
FIG 7 . 4
Scenario of simulated relative total evaporation,
during 1984 on Plot 1 and Plot 2.
109
Fh,
i
I
u
10
270
290
310
390
TIME (DOY)
100
8
I
310
270
330
TIME
FIG 7.5
Scenario of simulated relative total crop evaporation,
Fh, on Plot 1 and Plot 2 in 1985.
110
100
o
I
m
I
u
270
290
310
330
TIME (DOY)
I
10 -J
270
290
310
330
TIME (DOY)
FIG 7 . 6
Scenario of simulated relative crop evaporation,
Fh, on Plot C and Plot 3 during 1985.
Ill
250
240 230-
DEPTH : 1050mm
220-
i WETTING EVENT
210 \
Cb
200
D
'D
1M-
180170-
I
160160140150120110-
100
180
FIG 7.7
200
r
220
i
240
i
i
260
i
i
2S0
i
i
500
i
l
320
Measured (O ) and simulated (-) values of soil water
content at 1 0 5 0 M depth in the lysiaeter during
1985.
112
250
k.
180
200
220
240
260
280
500
280
300
TIME (DOY)
250
180
200
320
TIME (DOY)
FIG 7 . 8
Measured
( 0 ) and s i m u l a t e d (-) v a l u e s of s o i l
water
content
at
450mm and 600mm depth
in t h e
lysimeter
d u r i n g 1985.
113
230
180
200
250
220
240
260
280
500
320
ODD
240 -
DEPTH : 1200mm
230220k
210200
190180-
8
I
170160 -
a. 150-
§
140 150120110 100
180
FIG 7.9
200
320
220
Measured (0 ) and simulated (-) values of soil water
content at 750mn and 1200mm depth in the lysimeter
during 1985.
114
CHAPTER 8
The objective of the cooperative experiments was to
(a)
obtain experience with the running of a weather service
for
scheduling irrigation on farms at Hartswater,
(b)
establish
what
type
of
participating farmers,
(c)
information
was
required
from
and
collect whatever data possible for testing the models and if
possible, demonstrate the reliability of the technique.
The
data
collected are reported in APPENDIX
X.
Reference
to
figures or tables in APPENDIX X will be preceded by X.
METHOD
Scheduling
In 1982 the first four farmers listed in Table 8.1 assisted
with
the
farmers
were
Table
8.1.
demonstrations
joined
in
Cultivation
Table X.21.
gation
1983
and
tests.
These same four
by the two other farmers shown
practices
adopted during both years
in
are
given
in
In 1982 preliminary tests were carried out on irri-
scheduling using weather data collected from the
115
routine
weather
method
station installed at Vaalharts Winery Cooperative.
in
which
weather data were collected
is
The
described
in
detail in Chapter 10 and by De Jager et al (1982}.
Essentially each farmer managed his plots according to his normal
practice and another set of plots on the same farm were scheduled
from
the UOFS.
simulations,
Based upon the prevailing weather and
computer
recommendations on when to irrigate were telephoni-
cally conveyed to the farmers.
They then proceeded to
irrigate
according to their normal practice.
Due to the severe drought of 1983, the supply of irrigation water
to
all the participants,
drastically rationed.
except for Site F in Bultfontein,
was
Farmers were able to utilize only approxi-
mately one half of their irrigable land.
Field observations
Monitoring
intervals
of plant growth and soil water took place
at Site A and B in 1982 and 1983.
at
weekly
Leaf water
poten-
tials were obtained using the Scholander pressure chamber.
dawn
and
noon
Simultaneously,
long,
in-row,
observations were taken on eight
leaf
area
leaf
samples.
indices were determined from
destructive samples.
Pre-
0,5
m
The total leaf area of each
sample was determined using an optical leaf area meter.
Further-
more, gravimetric soil water measurements were made in the 0-300,
300-600
and
600-900 mm soil layers.
taken at each level.
replications
were
In 1983 the gravimetric measurements
were
116
Seven
also
carried
out at Site F.
The only observation made at
all
four sites in 1982 and 1983 was that of crop height.
In
1982 each participant provided three irrigation plots of
size shown in Table 8.1.
All monitoring of plants and soil
done in the middle plot.
At the end of the season,
of
yield
within
a 1 m x 1 m square wire frame
the
was
ten samples
were
obtained.
Samples were taken at 10 m intervals down the length of the plot;
except at Site B,
where 20 m intervals were employed.
was positioned randomly at the prescribed distance.
bad patches avoided nor good patches sought.
were
by
the farmer.
The cultivation
during 1982 are as shown in Table X.21.
samples
during
were
1982.
taken
Furthermore,
three
practices
In 1983,
on each plot instead of
Neither were
The same procedures
carried out on the centre plot of the closest
controlled
The frame
the
plots
adopted
fifteen yield
ten
utilized
samples were extracted from
random
locations in each plot.
At
Site F (Buitfontein) soil water conditions were merely
moni-
tored throughout the growing season and therefore no other comparisons were possible.
Here a line move system was in operation.
Weather data from the UOFS was used for the simulations at
fontein.
The
Bult-
farmer provided the relevant rainfall and irriga-
tion information.
117
During
This
1983,
is
an
irrigation strategy was designed for
described in Table 8.2.
Here,
Site
A.
was
to
the objective
improve
total
production for the farm with
supply.
Establishing the most effective strategy remains a mana-
ger's major problem.
the
limited
water
Hence, it was decided that this matter was
deserving of special attention.
Building on the strategy out-
lined in Table 8.2, a theoretical solution is provided in Chapter
12.
The
wheat grain yields obtained in the 1982 season are given
in
Table 8.4a. Yield losses due to the experimentation were marginal
and none of the farmers requested compensation.
These
experiments
were designed primarily
scheduling procedures.
comparing
yields
yields.
to
demonstrate
the
It was not possible to test the system by
Assume
a
10% coefficient of
on irrigation plots on a farm.
variation
What is required
is
in
an
experiment designed to prove that the model and farmer irrigation
schedules
10%
plots produced equal yields.
repititions
practical
tion.
a
decrement in yield due to the computer scheduling treatment,
with 80% reliability (i.e.
teen
To endeavour to show
experience.
randomized plots per farm).
It
to undertake such trials in the actual farming
Hence,
primarily
(36
80% of the time) would require
at
from
much
is
of irrigation by
useful
computer
information as possible
functioning of the model was also
118
collected.
to
not
situa-
1983 onwards the demonstrations were
scheduling
As
eigh-
aimed
obtain
on
the
The dates upon which irrigation took place are given in Table X.I
and
X.8.
For practical considerations such as labour and water
availability,
dates
managers
were not able to irrigate on
requested by the computer scheduling programme.
recommendations
been adhered to,
exact
Had
the
then one irrigation would have
been saved at Site A in 1982 (see Table X.I).
DOY
the
An irrigation
on
245 instead of irrigations on DOY 237 and 251 were recommen-
ded by the model.
At Site B, the model would have postponed the
irrigation of DOY 278 until later.
At Site C,
an irrigation on
DOY 240 was recommended in place of those which eventually occurred
on DOY 235 and 244.
With such good correspondence
between
the scheduling by the farmer and the computerised
technique,
is
This agreement
not surprising that the yields were similar.
did however suggest,
it
at this early stage, that the frequency and
timing
of
irrigation scheduled by
deemed
necessary by the farmers.
computer
approximated
that
In the four trials irrigation
was applied 33 times by both farmers and UOFS.
Irrigations were
scheduled simultaneously on 82% of the occassions.
The hydraulic crop factor,
tion.
Fh,
was used for scheduling
irriga-
Fh is defined as the relative vegetative evaporation rate,
i.e.
Ev/Evm.
80%,
irrigation was recommended.
suing
When
the value of Fh dropped below approximately
A forecast of Fh for the
en-
week was obtained by inputting the previous week's weather
data commencing with the current soil water content and leaf area
index as determined by the time of the immediate previous
tation.
The
values
compu-
of Fh obtained during the 1982 season
119
are
given in Fig. X.7.
It is evident from this figure that theoreti-
cally water stress had occurred.
In Fig.
X.14 through X.18 the scenario of Fh for the 1983 season
at four sites is given.
At Site F (see Fig. X.20b), a policy of
maintenance irrigation was practised.
The area under irrigation
was too large to meet atmospheric evaporative demand required
ensure maximum yield.
water
content
Hence, a steady decrease in mean root zone
was expected as the season progressed.
evident from Fig. X.20b.
valuable
experience
to
This
is
Monitoring of this experiment, provided
in the scheduling of line
move
irrigation
systems and illustrated the results of poor irrigation planning.
Results
describing
the water dynamics at Site A and Site
1982 and 1983 are given in Table X.2,
X.9 and X.10.
tests on the data are reported in Table 8.3.
B
in
Statistical
The lack of agree-
ment (low SI) between calculated and measured water use at Site A
gave
cause
movement
for concern.
It could have been
of water from a water table.
to errors in the gravimetric technique.
however,
were reassuring.
caused
by
upward
It was most probably due
The results from Site B
These results emphasized the need for
the model to be tested against the lysimeter measurements.
Leaf
area index measurements for the 1982 and 1983
illustrated in Fig. X.4 and X.12.
than
At
seasons
are
Only at leaf area indices less
three does the leaf area sub-model play a significant role.
leaf
area indices higher than three,
120
evaporation
from
the
crop, Ev, approaches maximum total evaporation Em.
These results
were not as favourable as those obtained by De Jager §_t a_i^ (1982)
and hence indicated that the leaf area sub-model needed refining.
A simulation index SI (Willmott, 1982) of 0,7 is generally accepted
as
an indication of acceptable reliability
biological
Table
the
processes.
8.4c
modelling
The Si-index for 1982 and 1983 given
suggest that (with the exception of Site A in
original leaf area sub-model was
velopment
when
acceptable.
in
1982)
Further
de-
and validation of this sub-model was undertaken.
The
results are reported in Appendix III.
The SI,
absolute
,83 and ,12 respectively,
difference) obtained of ,97,
r and MAD
(mean
indicate that the revised sub-model was sufficiently accurate.
Noon
and
pre-dawn measured and simulated leaf water
potentials
are presented in Fig. X.6 and X.13 plotted from the data in Table
X.5
and X.ll.
It is evident that simulated values under
mated actual potentials (taken positive) at pre-dawn.
measured
positive,
At
noon,
values,
taken
the need for refinement of the leaf
water
leaf water potential exceeded simulated
suggesting
esti-
potential model.
The
been
poor
due
performance of this sub-model during 1983
could
to the generally dry conditions brought about
water restrictions applied during this time.
by
have
the
These circumstan-
ces precipitated the desire to improve PUTU 9 and resulted in the
creation of PUTU 9.86.
121
During
1983
proportion
stressed conditions probably prevailed for
of
the time.
a
good
This provided a critical test of
the
leaf water potential sub-model. The model consistently underestimated noon leaf water potential by approximately 8 bar.
It is apparent that simulated pre-dawn leaf water potentials were
also lower than corresponding measured values.
been
due
This could
have
to the spline functions utilized to describe the
soil
water desorption curve.
could
have caused the error.
subsequently
I).
An improved desorption curve
included in the model (see Chapter 4
and
was
APPENDIX
The crop hydraulic conductance in PUTU 9 also appeared to be
too high.
It
Underestimation of soil water potential
This sub-model was also modified later (Chapter 4 ) .
was concluded that the leaf water potential sub-model of PUTU
9 functioned inaccurately.
eliminated
by
Much of the error could probably
decreasing the crop hydraulic
conductance.
be
The
nature of the water stress factor however, still produced reasonable simulations of the onset of crop water stress.
Tests on the soil water sub-model
The results of monitoring water use at Site A,
in
Table
X.2,
X.9 and X.10.
B and F are given
It must be borne
in
mind
gravimetric samples could only be taken to a depth of 0,9 m.
Sites
this.
B
and
Plant
F roots reached depths considerably
available
soil water content may be
122
greater
expected
that
At
than
to
increase
root
with depth in the root zone of wheat due to
density.
estimated
Hence the gravimetric sampling
soil water in the total root zone.
decreasing
probably
under-
Most of the water
extraction required for supporting crop growth takes place in the
second soil layer of the model which occurred between 0,1m
and
the simulated depth of the effective root zone.
depth
Hence most
attention was focussed upon the volumetric soil water contents in
this layer (9%0-
Little water (approximately 12 mm) is present
in the top soil layer for only short periods.
Hence,
the effect
of errors in this layer in this type of model is minimal.
The
results
listed in Table 8.4c for 1982 show that
the
model
accurately simulated 9v2 While, this result includes a spurious
correlation
due
to the common dependence of both simulated
and
measured values upon simulated effective rooting depth, such temporal increase in soil water in this layer does constitute a real
natural
phenomenon.
ignored.
in Fig.
lated
X.19 and X.20.
values
prevailed,
the
deep
results should not
A.
season
The agreement between measured and simu-
is not as good as was expected.
that after DOY 240,
the
when a full
It
is
however,
vegetative
canopy
agreement is better than early in the season
at
It is unlikely that crop water stress occurs early
in
when roots have the capability of growing
into
root zones diminishing the consequences of a poor model
this stage.
clearly
be
The 1983 scenario of soil water content are illustrated
interesting
Site
Hence the 1982 Ov
wet
in
Measured soil water content was underestimated as is
evident from these figures.
123
These discrepancies erapha-
size
the
water
urgent need for accurately
content.
In particular,
determining
maximum
soil
had the field water capacity at
Site B in 1983 been assumed lower,
exceptionally good
agreement
between measured and simulated values would have resulted.
It is apparent from Fig.
too
X.20 that management at Site F selected
large an area to irrigate.
root
zone
veloped.
progressively
The
The result was that the
dried down as the growing
applications
second
season
de-
water
per
of approximately 6mm of
irrigation event were insufficient to accommodate daily atmospheric demand.
From
the
above information it was concluded
determination
of
values
where
an
accurate
the field water capacity is essential
site at which the model is to be applied.
SI
that
at
Furthermore, relevant
in Table 8.3 suggest that apart from Site B in
something obviously went wrong,
performed reasonably.
any
the soil water
1983,
sub-model
Model input data were too few for conclu-
sions to be drawn about Site F.
These results reflect that much water stress occurred due to
limited supply of irrigation water.
trated
Site
The Fh-index scenario illus-
particularly well the gradual depletion of soil water
F {see Fig.
X.20c).
the
at
The simplicity with which crop water
stress may be identified with this index is self-evident.
124
The
number of stress days identified by the model at
are
given in Table 8.5.
of stress days and yield.
ments
each
site
There is poor agreement between number
This is to be expected as the adjust-
in yield reduction coefficient {ky in Chapter 3) with
age
have not been taken into account.
The variability in gravimetrically determined soil water
was
deemed
too large to provide measurements against
test the water use model.
have
to
distant.
the
destination
to
250
km
was therefore decided to reserve model testing for
West Campus Site and concentrate on establishing
dardizing
which
This is true particularly when samples
be collected and transported to a
It
content
and
stan-
the most effective modus operandi for running a compu-
ter based scheduling service using the field investigations.
125
TABLE
8.1 -
List
of participants in the field
demonstrations
and tests of 1982 and 1983.
SITE
LOCATION
DEPTH OF
WATER TABLE
IRRIGATION
PLOT SIZE
m)
(m)
IRRIGATION
SYSTEM
A
Hartswater
1 ,0
6 X 200
Flood
B
Magagong
2 ,3
6 X 287
Flood
C
Tadcaster
6 X 118
Flood
D
Tadcaster
6 X 90
Flood
E
Magagong
6 X 118
Flood
F
Bultfontein
Line move
126
TABLE
8.2 -
Scheduling strategy designed to improve total farm
production at Site A in 1983.
DATE
SUPPLY
PERIOD
NO
IRRIG.
AMOUNT
IRRIGATION
PLOT
GROWING STAGE
DAYS AFTER
PLANTING
(Storage
unit)
JUNE
01
03
05-10
—
1/2
1/2
Plant
0
JULY
14
16
15
15
1/2
a
b
Secondary roots
Secondary roots
29
29
AUGUST
15
19
20
20
1
1/2
a
b
Start stem extention
Start stem extention
61
62
SEPTEMBER
05
07
19
21
23
23
25
25
1
1/2
1
1/2
a
b
a
b
Late stem/start anthesis
Late stem/start anthesis
End anthesis
Anthesis
83
80
97
92
27
27
1
1/2
1
a
b
a
Soft dough
Soft dough
Hard dough
OCTOBER
05
07
20*
1
113
108
128
a - Two blocks of approximately 3,2 ha each (6,4 ha)
b - One block of 3,5 ha.
*
Should no rain occur, ignore the irrigations planned for
21/9 and 7/10 on b and apply one extra on treatment a
(20/10).
NOTE:
It may be that irrigation on 7/10/83 on treatment b will
not be permitted because of the {the so-called 10% evaporation
loss) further restrictions.
If Zaragosa is planted then postpone irrigation on 15/8 by approximately 7 days. Thereafter all irrigations will be delayed by 7
days because Zaragosa flowers 10 days later than does T4.
Fertilizer recommendation:
a - as usual (130 kg N and 35 P)/ha
b - (100 kg N and 25 kg P)/ha.
127
TABLE 8.3 -
Tests
on
accuracy of PUTU 9 for estimating water
using
gravimetric soil water measurements from
use
Harts-
water in 1982 and 1983.
SITE A
1982
WUSE
12
n (#)
a (interc) 0,39
b (slope)
9, 18
28,00
Y (mean)
r (correl) 0,61
0,60
SI
MAD
21,50
0,77
MAD/Y
n
a
b
Y
r
SI
MAD
MAD/Y
PAN F
WUSE
7
0 t 85
10,46
60,14
0,62
0,77
6,29
0,10
0,41
0,17
0,54
0,65
0,58
0,40
0,73
SITE A
1983
SITE B
PAN F
0,79
0,20
1,06
0,55
0,72
0,17
0,18
SITE B
WUSE
PAN F
WUSE
10
0,71
4,18
35,06
0,76
0,85
17,52
0,50
0,86
-0,03
0,59
0,67
0,76
0,31
0,52
7
0,29
19,95
28,26
0,40
0,64
14,20
0,50
PAN F
0, 42
0, 33
0, 56
o, 56
75
o, 25
o, 45
o,
Y
are the measured values
SI
is the simulation index of Willmott
MAD
denotes mean absolute difference
128
SITE F
WUSE
4
0,15
32,32
36,27
0,11
0,40
16,10
0,44
U982)
(measured - simulated)
PAN F
0,99
0,45
1 ,02
0,41
0,32
0,52
0,51
TABLE 8.4a
-
Wheat grain yields obtained in 1982.
F A R M E R
STANDARD
DEVIATION
YIELD
SITE
U 0 F S
(t/ha)
YIELD
STANDARD
DEVIATION
(t/ha}
(t/ha)
(t/ha)
A
5,4
0,9
5,0
0,9
B
7,6
0,8
6,8
1,0
C
6,8
0,8
6,8
0,6
D
4,2
0, 5
3,7
0,7
TABLE 8.4b
-
Wheat grain yield and standard deviations obtained
on the Experiment, Whole Farm and Control plot
sites in 1983.
U O F S
SITE
EXPERIMENT
(t/ha)
F A R M E R S
STANDARD
DEVIATION
WHOLE FARM
CONTROL PLOT
STANDARD
DEVIATION
(kg/ha)
(t/ha)
(t/ha)
(kg/ha)
615
4,20
4, 14
756
A
3, 10
B
5,35
1 341
5,30
6,97
643
C
6,60
610
2,70
5,27
823
D
5, 12
430
5,40
6,08
489
E
4,23
1 039
6,10
4,89
675
4,74
5,48
MEAN OF
ABOVE
F
4,89
4,35
201
4,13
129
TABLE 8.4C - Statistical reliability tests carried out on PUTU 9 during 1982 and 1983.
TEAR
a
SITE
VARIABLE
(VAR)
2
r
RELIABILITY
INDEX
(SI)
MEAN
DIFFERENCE
MEAN OF
ABSOLUTE
DIFFERENCE
(MAD)
MAD
X 100
MEAN
MAX ABSOLUTE DIFFERENCE
1983
A
LAI < 3
0,91
0,82
0,37
0,5
52
1,6
1983
B
LAI < 3
0,59
0,83
-0,40
0,6
32
1,2
1982
A
Noon leaf water
potential
(bar)
0,22
0,50
-2,30
3,9
24
8,8
1982
A
Pre-dawn leaf water
potential
(bar)
0,71
0,71
-2,42
2,5
55
4,6
1982
B
LAI
0,42
0,71
1,63
1.7
40
5,2
1982
A
LAI
0,23
0,62
1,38
2,1
60
6,1
1982
B
Noon leaf water
potential
(bar)
0,03
0,36
-2,67
4,9
29
10,2
Pre dawn leaf water
potential
(bar)
0,10
0,55
-2,03
2,8
68
6,0
1982
B
1982
A
6v2 ( mm layer " l)
0,71
0,84
27,40
30,0
26
69,0
1982
B
0v2 (mm layer" )
0,94
0,98
1,20
7,4
7
43,0
1982
A
Qvl (mm layer
0,02
0,41
5,20
6,8
65
21,0
1
)
TABLE 8.5 -
Showing
the
number
of theoretical
stress
experienced and yields measured during 1983.
UOFS
FARMER
SITE
YIELD
STRESS
YIELD
(kg/ha )
(d)
(kg/ha)
(d)
A
3,1
14
4,2
5
B
5,3
8
5,3
8
C
6,6
34
2,7
28
D
5,1
16
5,4
11
E
4,2
32
6,1
22
131
STRESS
days
CHAPTER
OBJECTIVE
The
objective of the work was to obtain experience in scheduling
irrigation
model
best
for
a number of farmers
and weather input data.
using
computer
simulation
From the experience gained,
procedure for managing an effective service
was
the
standard-
ized.
PROCEDURE
In 1984,
with
the
farmers at Hartswater made available 19 plots to assist
experiments.
A list of the
experimental
sites
is
provided in Table 9.1.
Participants
forecast
necessary.
however,
were
advised
that they would be provided
of the date when irrigation of their plots
The
had
with
a
become
final decision on when and how much to irrigate
remained their responsibility.
In view of the cuts in
water supply during 1984 it was deemed inadvisable in any way
interfere with participants' farming practice.
132
to
Mr Japie Smit kindly agreed to irrigate one entire plot at weekly
intervals
to ensure that no stress occurred on it and Mr Van Wyk
also agreed to irrigate
ble,
a small plot (10 m x 10 m ) .
When possi-
comparisons between infra-red temperature on these and
normal plots were made (see Table 9.2).
tered
the
by the IRT were inaccurate,
bead thermistor.
determine
under
Air temperatures regis-
because of
poor exposure
Hence the foliage-air differences
reported in Table 9.2 are unreliable.
irrigation at Hartswater.
Yield
undertaken using fifteen repetitions per plot from
area
on
entire
farm
two farms.
Mean yields were obtained
on the other sites.
The results are
to
expected
determinations
were
these
of
(DIFF)
The Smit plot served
the approximate maximum yield which might be
optimal
the
a i m
for
reported
the
in
Table 9.3.
In
July,
1984 an automatic weather station was erected
on
the
meteorological site at the Vaalharts Agricultural Cooperative and
data
was collected weekly on a magnetic tape cassette.
In 1985
the telemeterized automatic station was commissioned {see Chapter
10) .
An intensive programme on a few farms was conducted in 1985.
objective
The
was to carefully monitor operations on just six sites.
The six experimental sites chosen are shown in Table 9.8.
133
RESULTS AND DISCUSSIONS
It
became
site
evident
that certain information pertinent
in
and the cultural practices adopted were required to
an effective scheduling operation.
each
ensure
An example of the details for
farmer Japie Smit is given in Table 9.4.
The
PUTU 9 model was used to simulate the water dynamics of
situation.
Initially,
the
weekly projections were made (Table 9.5)
and the information distributed.
Subsequently, the procedure had
to be modified and computerized in order to save time.
listing of the weekly advisory from PUTU 9
A typical
upon which scheduling
projections were based is given in Table 9.6.
The few IRT observations
van
made on the farms of Japie Smit and
Wyk are reported in Table 9.2.
P.
In all cases the foliage of
the wet plot was cooler than the surrounding wheat.
PUTU
9
was
re-coded in GW Basic for use in IBM
compatibles.
This version was utilized subsequently.
Scheduling technique
At
the
1984.
model
weather
onset the procedures of 1982 and 1983
Here
for
irrigation
were
events were forecasted by
adopted
running
the forthcoming week using the immediate past
data.
personnel time.
However,
the
week's
this absorbed excessive computing
Prognoses of the earliest date on which
in
and
irriga-
tion would next be required were thus made by dividing the
esti-
mated
would
soil water extraction remaining before water stress
134
occur
by
the
estimated mean daily total
ensuing period (see Meyer and Green,
total
evaporation
tained
from
evaporation
1981).
for
Estimates of
the
mean
for the relevant crop growth stage were
the previous experiments in this
project.
ob-
It
recommended that columns "VOLGENDE BESPROEIINGSDATUM" and
is
"REDE"
should be added to Table 9.6.
Weather
data
and information collection was performed
and
the
model simulations proceeded most satisfactorily.
The large num-
ber
data
of
lated,
experimental sites and the big volume of
however, became unmanageable.
This was true of computing
time and the man-hours available to the project.
will also cause
was
soil
manipu-
This situation
concern in an extensive advisory programme.
It
therefore decided to alter the procedure and group sites
type and planting date.
Thereafter it was necessary merely
to simulate daily maximum wheat crop evaporation,
category,
rather
by
than for each plot.
Em,
for
each
This cut computing
time
considerably.
The
different categories of soil and cultivation practice
used,
is as follows:
SOIL TYPE
PLANTING DATE
CATEGORY
Fine Sand
Loamy Sand
Sandy Loam
Sand Clay Loam
G
G
G
G
CATEGORY
06/1-9
06/9-14
06/15-20
06/21-30
1
2
3
4
p
p
p
p
0
1
2
3
Thus, each week the daily Em was computed for each category using
PUTU
9.
discette.
The
Em data series for a category was then
A separate file was created for each group.
135
filed
on
New
programmes EREF and REGISTER (see APPENDIX V and
written
to
use these files.
VI),
REGISTER computed the soil
extraction and stress situation on each specific plot.
ple
of the listing produced by REGISTER is given in
These
water
An examTable
9.6.
results were then used manually for prognosis of the irri-
gation date included in Table 9.5.
ling
were
decision
The reason for each schedu-
was always explained.
These
explanations
were
coded as follows:
R
-
refill
the
root
reason (eg.
zone with water
for
secondary root development,
strategic
utilisa-
tion of dam storage, etc.)
S
An
-
refill because of the danger of water stress
indication
of the limits of the categories
into
which
the
farms in this particular study were divided is evident from Table
9.1.
Data was initially transmitted telephonically to the secretary of
Mr
Hugo Hamraan (Chief Extension Officer,
ture,
obtain
Vaalharts).
Farmers
were
Department of Agricul-
requested to call
the advice contained in Table 9.5.
weekly
to
Since each farmer
is
expected to order water (i.e to re-fill his storage dam) from the
bailiff on a Wednesday;
the projections were made available
noon each Wednesday.
136
by
Farmers' response however,
to
follow
their
was disappointing. Managers preferred
own irrigation scheduling
procedure.
It
is
evident that should such a system be implemented, it will have to
be preceded by an extensive public relations exercise.
ration
will
have
to be given to
providing
Conside-
telephonic
advice
directly to individual farmers. When the price of water increases
and economic pressures become critical; then will managers be
circumspect than is the case at present.
use
An education
more
and water
efficiency awareness campaign should be an integral part
of
an irrigation service.
The
need
for grouping stations could fall away should a
large,
fast computer be dedicated solely to the weather based scheduling
operation.
the
The method of predicting future needs by
previous
quite
weeks' weather data into the future would then
practical.
provided
projecting
with
Alternatively,
individual farmers
could
daily Em values which they could apply in
own personal computer or calculator.
be
be
their
The latter would certainly
cultivate interest in, and awareness to, saving water.
Nature of advice
Farmers
are
keen to know how effectively they
operate.
Hence
some form of monitoring of their performance is most valuable.
137
The best form of monitoring, apart from the weekly advisories, is
by
graphical presentations.
Managers appreciate regular scena-
rios of:
(a)
The irrigation need index,
Fh,
which is basically relative
vegetative evaporation (transpiration) defined by
Fh = Ev/Evm
There are numerous examples of such scenario throughout this
thesis
(Fig.
X.7,
X.14 through X.18,
and Fig.
7.2,
7,4
through 7.6).
(b)
Leaf water potential - see Fig. 9.1.
(c)
Deep percolation out of the root zone - see Fig. 9.2.
At
the
the conclusion of the 1985 season,
an analytical summary
type shown in Table 9.7 generated acute interest and
of
proved
most significant.
SUMMARY
The
summary to this chapter has particular significance
operation of a weather service for irrigation scheduling.
to
the
Hence
it has been awarded a chapter of its own (see Chapter 11).
The
automatic weather station was successfully used
estimates
to
provide
of Em suitable for the irrigation scheduling of winter
138
wheat.
Computing water requirements for individual plots was too
time
consuming and plots with similar soils and
were
grouped together.
operational
operating
veloped.
of
tion
procedures
dates
The computer software necessary and two
plots)
for
a weather based irrigation scheduling system were
de-
(single plot and groups
of
Forecasting the next irrigation date either for a group
sites or for an individual site are possible.
The distribu-
of the information however presents problems
attention
the
planting
in the future.
deserves
Direct telephonic communication
farmers is recommended.
postal service.
and
with
Consideration should be given to
a
The delays however, might prove unacceptable.
Strategy
Because
mers
The
of the drought and resultant limited water supply,
were unable to utilize the entire land area on their farms.
specific
irrigated.
question
posed was how large an
area
should
be
Careful analysis and discussion resulted in the stra-
tegy outlined in Table 8.2 for the farm of Japie
of
far-
Srait.
Because
the seriousness of this problem an econometrical analysis was
undertaken.
guidance
and
It is described in Chapter 12.
a possible basis for advice on
planning.
139
The findings provide
future
irrigation
Results
The
automatic weather station was successfully connected via the
normal Posts and Telegraph telephonic network, to the computer in
the Department of Agrometeorology at the UOFS.
ble
dialling for real-time data acquisition
All
the
torily.
from
Bioemfontein.
initial and technical problems in the system were
cessfully eliminated (see Chapter 10).
Monday,
This made possi-
Wednesday and Friday.
The
suc-
Data was collected every
The system functioned
satisfac-
original automatic weather station was moved
from
Vaalharts Cooperative to the farm of Mr Japie Smit because of the
personal
supervision he provided.
He furthermore permitted use
of his private telephone and assisted with the necessary
tions .
140
connec-
CO
-10 _l
£
o
-20 -
Q_
-30 -
-40
CO
-50 t
195
180
210
225
240
255
DAY OF YEAR
FIG 9.1
-
Scenario of simulated leaf water potential at Site
B during the 1985 growing season.
141
270
-
SITE A
SITE B
SITE E
DAY OF YEAR
FIG 9.2
-
Scenario of simulated deep percolation out of the root
zone at Sites A,
B, and E during the 1985 growing
season.
142
TABLE
9.1
- Planting
dates
characteristics
(category
P)
and
soil
phys
(category G) For the vari
•- sites
at H a r t s w a t e r d u r i n g 1 9 8 4 .
PLANTING
DATE
SO IL TYPE
CAT
S3[TE IDENTIFICATION
G P
NAME
NO
1 0
w
3E6 (1 )
3E6 (2 )
1/6/84
4/6/84
FINE SAND
FINE SAND
5C6
6C6
22/6/84
26/6/84
FINE SAND
FINE SAND
1,3
1, 5
1,3
1, 5
JAPIE SMIT
E
F
12/6/84
12/6/84
LOAMY SAND
1 ,3
1, 5 +
O MARITZ
5C3
LOAMY SAND
LOAMY SAND
1,3
6C4
14/6/84
15/6/84
1 ,3
1+
1+
4C6
15/6/84
LOAMY SAND
1,3
1, 5
A G VAN NIEKERK 3B1
3B2
11/6/84
18/6/84
LOAMY SAND
LOAMY SAND
1,3
1,3
1,5
J HUMAN
1D6
13/6/84
LOAMY SAND
0,9
0, 9
1D5
14/6/84
15/6/84
LOAMY SAND
LOAMY SAND
1,3
1,3
1,5 +
3D5
1 3
2 1
VAN ROOYEN
P VAN WYK
H J VTLJOEN
S M HUMAN
ROOTING WATER
DEPTH
TABLE
MODELLED DEPTH
1,3
1,3
1 ,5
1, 5
1, 5
1, 5 +
2 3
J L MELLET
313
25/6/84
LOAMY SAND
1,3
1, 5 +
3 1
F WOLHUTER
3M1 1
12/6/84
SANDY LOAM
1,3
I.5
4 1
JOHAN SMIT
JX
13/6/84
SAND CLAY
LOAM
1,3
1, 5 +
K STRAUSS
1E6 ( 1)
14/6/84
SAND CLAY
LOAM
1,3
1,5 +
K STRAUSS
1E3 (2 )
6/6/84
SAND CLAY
LOAM
1,3
1 ,5 +
4 0
143
Wheat
TABLE 9.2 -
foliage
l i a g e - air
by
P
van
and
(FOLIAGE)
temperature differences
infra-red
gated
temperatures
thermometer
conventionally
Wyk
and
(DIFF
o n small,
irrigated
Japie Smit
at
and
ma? as
we 1 1
plots
Hart swater
!• n
irri-
on sii
during
1984.
F1 VAN WYK
DATE
840918
841002
841009
CONVENT IONAL
WET
FOLIAGE
21 ,3
21 ,9
JAPIE SMIT
DIFF
FOLIAGE
DIFF
— O f Z.
2 2 ,3
-5,3
-5,2
2 2 ,2
21 , 1
20 ,4
-7,0
-7,6
-7,9
23, 4
24, 0
23, 4
-5 , 0
17 ,6
18 ,3
18 ,7
18 ,6
-0,8
-0,8
20, 5
21, 0
-5 ,7
-1,0
-1,1
21, 4
19, 8
-4, 0
-4 ,5
-4, 9
-4, 9
-5 ,5
144
WET
FOLIAGE
CONVENTIONAL
DIFF FOLIAGE
20,7
2 0,4
19,2
-1 ,2
22,6
22,6
22,4
18,4
18,0
17,5
18,4
23 ,0
DI FF
23 , 7
0, 1
-0 ,5
-2 i 7
-2 ,8
-2 ,8
23 ,8
24 • 1
23 ,4
-1 » 7
-1 ,0
-2 ,4
0 ,9
0 ,3
-0 ,4
0
20 - 7
-3
-3
-3
-3
-2 ,9
21 , 5
21 ,0
21 r 5
,0
,6
,4
,5
TABLE
9.3
- Mean
farm
yields
Hartswater during
PARTICIPANTS
SITE
on
the
different
sites
the 1984 s e a s o n .
COMMENTS
YIELDS
NUMBER OF
IRRIGATIONS
( t/ha
J
HUMAN
S M
HUMAN
6,6
6,6
11
J
MELLET
4,93
7
0
MARITZ
4,3
6
JOHAN SMIT
4,2
2
JAPIE SMIT
5,8
9
(CONTROL PLOT)
6,9
W
6,3
SAUNDERS
A G
P
H J
C
VAN N I E K E R K
V A N WYK
VILJOEN
WOLHUTER
at
(irrigated
5 , 38
weekly)
1 8 % hail d a m a g e
19
5
4,7
7
3 ,9
7
5,4
9
145
TABLE
Details of experimental site and cultivation prac
tices at site SMIT, HARTSWATER, 1984.
9.4-
NAME OF FILE
NAME OF DISK
VHS
N'ICO ( 1
JAPIE SMIT
NAME
BLOK E
FARM
TELEPHONE 2002 (HARTSWATER
DATE
SOIL
SOIL DEPTH
1m
FORM
LOAMY SAND
CLAY CONTENT 10 %
FIELD WATER CAPACITY 164
PLANT AVAILABLE WATER
WATER TABLE DEPTH
> 1 5,
1,3
ROOTING DEPTH
CHARACTERISTICS
SERIES
SILT+CLAY
kg/ha
kg/ha
kg/ha
kg/ha
kg/ha
325
514
840726
840924
840814
841002
SPLINE FUNCTION FOR SOIL WATER DESORPTION CURVES
P.)
9v
(In kPa)
PI
P2
P3
P4
=
=
:
=
%
m
FERTILIZER APPLICATIONS
TYPE 3.2.1.(25) AMOUNT
AMS
N
148 (BEFORE PLANTING)
N
25
(AFTER PLANTING)
P
27
K
13
CULTIVAR
SARAGOZA
SEED DENS ITY
85
kg/ha
PLANT/DENSITY
160
(pl/m )
PLANTING DATE
840612
IRRIGATION DATES
840604
840910
841025
ln(
12
mm/m
m
m
(mm/m)
5.94
5.30
4.42
3.82
V1
V2
V3
V4
LAI ON DOY 200 = 0 , 5
SIZE OF CONTROL PLOT 1/14 ha
SIZE OF PLOTS
3,5 ha
YIELD
5,6 t/ha
IRRIGATED EACH WEEK
7,1 t/ha
CONTROL
5,8 t/ha
TOTAL RAINFALL ON PLOT
TOTAL WATER USED FOR IRRIGATION
64 mm
415 mm
146
= 71.24
- 77.69
= 9 3.40
= 114.57
840828
841015
TABLE 9.5
-
The form upon which weekly irrigation
prognoses were reported.
DEPARTMENT AGROMETEORLOGY,
UOFS
scheduling
WEEKLY IRRIGATION ASSESSMENT
DATE OF ISSUE
CAT
G P
SITE IDENTIFICATION
NAME
NO
1 0
W VAN ROOYEN
3E6(1)
3E6{2)
1 3
P VAN WYK
5C6
6C6
2 1
JAPIE SMIT
E
F
0 MARITZ
5C3
6C4
H J VILJOEN
4C6
A G VAN NIEKERK 3B1
3B2
1D6
J HUMAN
S M HUMAN
1D5
3D5
2 3
J L MELLET
313
>->
{ 1* )
F WOLHUTER
3M11
4 1
JOHAN SMIT
K STRAUSS
JX
1E6{ 1)
4 0
K STRAUSS
1E3( 2)
3
PREVIOUS
IRRIGATION
DATE AMOUNT
EARLIEST NEXT
IRRIGATION
DATE (2*)
REASON(3*)
1*
CAT G P - The irrigation category into which the irrigation
plot with the relevant Identification No falls. The soil type eg.
sand is denoted by G, the planting date (see Table II.2 Appendix
II) by P.
2*
The earliest next irrigation date is the estimated earliest
date on which it will be required to irrigate the relevant plot
3*
Column three provides one of two reasons for the next
i rrigation :
viz.
R - refill soil profile with water for a strategic reason
eg secondary roots
S - danger of the onset of water stress.
14'
TABLE 9.6
Example of the water use advise note produced by
program REGISTER using data stored by PUTU 9.
"Persentasie stremming" equals (1-Fh) * 100.
WATERVERBRUIKSADVIESNOTA
W VAN ROOVEN 3E6 (1)
24/9/84 - 1/10/84
GRONDTIPE: FYNSAND
BERAAMDE WORTELDIEPTE OP DIE LAASTE DAG VAN DIE WEEK:
KALENDERDAG
WATERVERBRUIK
KUMULATIEWE
WATERVERBRUIK
BESPROEIING
1300 nun
PERSENTASIE
STREMMING
mm/d)
(mm )
269.0
9.8
44.8
0.0
0.0
270.0
9.9
54. 7
0.0
0.0
271 .0
7 .7
62. 5
0.0
0.0
272.0
7.3
69.8
0.0
0.0
273.0
5.7
75.5
0.0
0.0
274.0
6.9
82.5
0.0
0.0
275.0
6.7
39.2
50.0
0.0
( mm
WATERVERBRUIKSADVIESNOTA
W VAN ROOYEN 3E6 (2)
24/9/84 - 1/10/84
GRONDTIPE: FYNSAND
BERAAMDE WORTELDIEPTE OP DIE LAASTE DAG VAN DIE WEEK:
148
1300mm
TABLE 9.7 -
Analysis
of irrigation scheduling which could
supplied to farmer at the end of the seas m .
1 Frequency of irrigation
9
2 Application per irrigation
3 Total water irrigated
50
Irani)
4 Total water use by crop
5 Total evaporation
(mm)
4 50
(mm)
490
through soil surface
6 Total atmospheric evaporative demand
(mm)
(mm)
7 Total deep percolation out of root zone (mm)
8 Total rainfall
(mm)
70
934
120
40
9 Water supplied to farm
(mm)
500
10 Efficiencies of :
Irrigation management
[(3 + 8-7 )/(3 + 8+14) ]%
Water use
[4/(3+8+14)]%
83
Transport
[3/9]%
90
Management other than water
11 Expected final yield
12 Actual final yield
13 Production
[12/11]%
(kg/ha)
63
85
5500
(kg/ha)
4700
coefficients
Irrigation water use
[12/3] kg/(ha mm)
10
Total water use [12/(3+8-15)] kg/(ha mm)
12
14 Initial soil water content
15 Final soil water content
(mm)
(mm)
149
100
100
:••
TABLE 9.8 -
SITE
The six sites used during 1985
CLAY
SILT &
SOIL CLASS
CLAY
D. 3d
10
12
Loamy sand
B.A.c
10
12
Loamy sand
C .A.c
10
12
Loamy sand
D.3d
18
18
Sand clay
loam
E. jx
18
8
Sand clay
loam
E.3mll
13
14
Loam
150
CHAPTER 10
THE AUTOMATIC WEATHER STATIONS
INTRODUCTION
Three automatic weather stations were installed by the Department
of Agricultural Meteorology,
purposes.
Station
UOFS,
for routine and experimental
#1 {DIE BULT) is located in the
Departmental
Observatory for routine weather observations and calibrations. It
was
commissioned in February 1984.
{WEST
Station #2
CAMPUS) was erected at the experimental site on
campus
for research work.
Vaalharts
and
In October 1983,
in 1984,
that
west
Station #3 was initially deployed at
but was linked by telemetery system in
moved to a farm at Hartswater.
information
the
1985
In view of the nature of the
is required from each
of
the
stations,
the
sensors and data format varied between the stations. However, all
three stations have the same basic components.
The
data
logger for the automatic weather station consisted
the
basic
Scientific,
of
electronics
Logan,
by Ecosystems,
of the CR21 unit supplied by Campbell
*
Utah . It was assembled as the Envirologger
Pretoria
.
The latter provide a convenient dis-
tribution board, lightning protection and signal conditioning for
certain
of the weather sensors.
The environmental
housing
and
support systems were provided by Ecosystems.
* Reference to any manufacturer,trade name or agent is for information and does not represent an endorsement by the Department
of Agrometeorology, UOFS or the Water Research Commission.
151
FIG 10.1.
The automatic weather station on the WEST CAMPUS site,
showing
the components common to all the automatic
weather stations.
Lightning
Met One
Anemometer
152
Conductor
The automatic weather stations were mounted on metal tripods (Fig
10.1)
with the radiation and wind sensor arm raised to
mately
3m
shelter
for
lightning
right.
above the ground.
Each station had an
the temperature and humidity sensors.
conductor
approxi-
environmental
There
(aluminium rod) mounted on the
is
centre
A radiation shield protects the sealed fibreglass
a
up-
(Model
021) environmental enclosure in which the Envirologger (CR21) was
housed.
The CR21 is battery operated with a real time
clock,
a
serial data interface, a programmable analogue-to-digital converter,
has
and full floating point mathematics capabilities.
a
scan rate of 10s,
which samples the
The CR21
individual
sensors
according to user-specified commands programmed in an input table
(*4).
The data is processed in a 64 location intermediate memory
before
final
specified
storage
output
in a 608 word memory according
programmes
stored in
three
to
separate
usertables
(*1,2,3). Final storage is completed only at the output intervals
specified.
pied,
in each output Table.
When the memory is fully
occu-
the old data is overwritten by the new data. The memory is
dumped automatically to cassette and can be accessed manually
by
the user.
CR21-DATALOGGER
The
CR21
counting
accepts 7 analog inputs (Channels 1-7) and
inputs (Channels 8 &. 9 ) .
are through HIGH and LOW ports.
channels
two
Input to the analog
The low is common to all
and is +1V with respect to ground.
pulse
channels
analog
It is important
to
make sure that this port (LOW) is not earthed. The pulse channels
are labelled HIGH and EARTH.
153
The
pulse count channels have a maximum detectable frequency
of
150 Hz (Channel 8) and 50 Hz (Channel 9 ) . Each pulse must exceed
3.5V
from a base of <1.5V to be detectable.
While not used in this experiment,
inputs
and
the CR21 also has two
four control output ports.
binary
These digital input
and
output ports can be used with special input and output programmes
for controlling external devices (eg to turn sensors/equipment on
or off).
SENSORS AND POWER SUPPLY
All three automatic stations had the following basic sensors:1
-
LI-COR (MODEL LI200SB) SILICON PYRANOMETER
2
-
101 TEMPERATURE
PROBE
comprised of
a
Fenwal
Electronic
UUT51J1 thermistor encased in a water proof probe
3
-
HYGROMETRIX XNAM 10205 RELATIVE HUMIDITY SENSOR and
ECO MODEL XNAM/82R INTERFACE
4
-
MET-ONE (MODEL 014A) CONTACT 3-CUP ANEMOMETER
5
-
MET-ONE (MODEL 024A) WIND DIRECTION SENSOR (potentiometer)
6
-
OSK764 TIPPING BUCKET RAINGAUGE.
The
pyranometer
and wind sensors were mounted approximately
3m
above the ground (Fig 10.1). The temperature and humidity sensors
were
housed in an environmental shelter approximately 1.5m
standard height) above the ground.
The three remaining
(the
channels
on the CR21 monitored different sensors at the various stations.
There were periods at all three stations when data was lost. This
154
was
usually due to power failure;
some problems.
power
although lightning did
cause
Since the data was usually retrieved once a week,
failures
did result in the loss of as much as a
week
of
data. The problem of power failure appears to have been solved by
the use of a trickle charger and regular battery maintenance.
DATA
The CR21 displays and stores a maximum value of 6999 in
internal
memory.
cassette
The
data in memory is automatically dumped to
tapes at regular intervals. The tapes were retrieved and the data
is transfered from the cassette drive to fixed or soft disc using
the Campbell Scientific Inc C-20 cassette interface and Sperry PC
model 30 computer.
The data was stored in files according to the
following nomenclature:-
1 - Hourly mean data and daily means
File =
MTH-YR.STN
2 - Monthly summary of daily means
File =
MTH-YR.MON
where MTH = MONTH ; YR = YEAR ; & STN = STATION
station abrieviations used were
= OBS ( DIE BUILT
)
= EXP ( WEST CAMPUS )
Any alterations or corrections to the data format, usually caused
by
changing the CR21 programme tables,
are noted at the end
of
each month. The GW BASIC coded computer programme (Appendix VII)
checks
for
these
corrections while listing
155
the
data
in
the
formats shown in Table 10.1.
means
or
estimates
differed
totals
of
in a separate file (MTH-YR.MON),
reference
for
The programme also stores the daily
evaporation
(Er).
as
These
well
as
programmes
the different stations since the data
format
necessary
corrections vary between stations.
However the
structure
is similar in all cases to the programme for the
and
basic
WEST
CAMPUS station (Appendix VII).
Because of the problems arising from the necessity to correct the
raw
data
each
subsequently
developed
store
the
These
formats
of
a
BASIC
program
(Appendix
VIII)
was
to make the appropriate corrections
and
data in files with the format shown
requirements.
months
time,
in
were chosen in order to reduce the
Table
data
10.2.
storage
Using these formats it is now possible to store 10
hourly data on a two-sided double density 5.25
disc, as opposed to 3-4 months with the original format.
156
flexy
TABLE 10.1 - Examples of a daily and monthly summary printouts
z
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1
10.2 - N o m e n c l a t u r e
MON-YR-tt.UUR
MON-YR-S.DAG
MDN-YR-tt.WND
MON-YR-#.TER
==
==
==
«*=
and
HOURLY
DAILY
DAILY
TOTAL
format
files
DATA
DATA
WIND
F!EFERfiNC!
FORMAT
1
for c o r r e c t e d data
(DF
EVAPOfSAT I ON
1
FILES
1
1
MON-YR-#.DAG
1
1
FIELDS
SIZE
STRING
YEAR
DOY
TOTAL MISSING DATA
MEAN RADIATION
TEMP.MAX.
TEMP.MIN.
TEMP.AVERAGE
RADIATION AVERAGE
WIND AVERAGE
RAIN
LYSIMETER
LYST STAND.DIVIATION
CHANGE IN LYSIMETER
12
13
14
( 1 2)
.
UK
;
.
1
1
(
I
1
1
1
1
1
1
1
1
1
1
1
F 6..2
F 6.:
F 5.1
F b. J1
F b.1X.
F 7.1
(3. 3)
(6 -4)
( l .7)
(17 .6)
(23 .6)
(29 .<b)
(35 .6)
(41 .6)
(47 .5)
(52 .6)
(58 .b)
(64 .7)
FIELDS
SIZE
STRIivt
YEAR
DOY
TIME
RADIATION
TEMPERATURE
R.H.
WIND
LYSIMETER
RAIN
12
13
12
F 7.:
i—
F 6.:2
F 6.:
F
6.:
• >
1
1
I
W/m
I

7.2
6.
b.2
b.
6. 1
5. 1
^j
(3, 3i
(6, 2)
(g ,7)
( 1- ,6)
(21 ,6?
(27 ,6)
(33 ,6)
(39 ,5)
i
W/ni
!
i
•/
i
m/s
i
mV
mm
i
i
MON-YR-#.WND
i
!
J
S IZE
FIELDS
STRING
UNIT
j
1
1
1
1
1
1
1
1
1
1
1
I
1
1
1
r
YEAR
DOY
WIND SPEED
WIND MAX.
WIND MIN.
WIND FREGUENCY
WIND FREQUENCY
WIND FREQUENCY
WIND FREQUENCY
WIND FREQUENCY
WIND FREQUENCY
WIND FREQUENCY
WIND FREQUENCY
RADIATION TOTAL
I
I3
0-45
45-90
90-135
135-180
180-225
225-270
270-315
315-360
F 6. 2
F 6. 2
F 6. 2
F 6. 3
F 6. 3
F 6. 2
F 6. 3
F 6. 3
F b. 3
F b .3
F b. 3
F 6. 2
58
(1,2)
(3, 3)
(6,6)
(12 ,6)
(IB ,6)
(24 ,6)
(30 ,6)
(36 ,6)
(42 ,6)
(48 ,6)
(54
(60 ',6)
<bb ,6)
(72 ,6)
1
m/s
i
m/5
1
m/s
i
7./100 1
V. /100 1
7. /100 1
X'/IOC 1
•/./loo I
^/100 1
1
•/. /1 oo
1
'/./100
MJ/m" 2 1
STATION #1 : DIE BULT
The automatic weather station in the Department of Agrometeorology
Observatory (Fig.
standard
daily
10.2) was used primarily to supplement the
meteorological measurements.
It has
been
extensively by the Agronomy and Agricultural Engineering
ments,
their
and
the Institute for Ground Water Studies to
automatic weather stations.
used
Departcalibrate
It was also used as an alter-
nate site when the WEST CAMPUS station failed.
FIG 10.2.
Photograph of the automatic weather station located in
the UOFS Observatory.
159
The
automatic
described
weather station comprised the basic unit
plus a second temperature probe used to
temperature
at a depth of approximately 30 cm.
monitor
which
soil
The other tempe-
rature and humidity sensors were located in a standard
Screen
already
Stevenson
also housed other standard meteorological
instru-
ments. The two remaining channels on the CR21 were not used.
The calibration for the pyranometer is shown in Table
10.3.
The
rain gauge and temperature sensors were compared against standards
when
the
against
station
the
was installed.
Prufchien
calibrated
against
The
anemometer.
anemometer
The
was
humidity
a standard aspirated
Asaman
checked
sensor
was
Physchrometer
(Table 10.3).
The
user-specified
tables
for
input
(*4) and
output
(¥1,2,3)
station #1 are given in Table 10.4
intermediate
storage locations.
and
require
Output Table*l provides
2
o
mean values of total radiation (kW/m )
direction).
amount
rainfall (mm). Output Tables*2 & *3 provide
identification,
as
the
provides
the
deg,
time
and
station
battery voltage and daily total rainfall as well
other
sensors.
Table*2
also
the maximum and minimum temperatures and their time
occurred,
wind
programme also logs the
daily mean values of the
occurrence,
it
hourly
m/s,
std.dev.of
of
26
temperatures ( C ) , rela-
tive humidity {%) and the wind speed and vector (m/s,
This
programme
while Table*3 provides the maximum wind 3peed,
when
as well as the relative frequency distribution
direction for eight points of the compass
10.1).
160
(see
of
of
Table
The data from the CR21, as generated by the user-specified tables
(*1,2,3) are stored in files MTH-YR.OBS in the following format:-
01+TABLE*
where
02+####
03+####
04+####
05+#### etc.
the individual values in the second and subsequent
fields
of each record are dependent upon the different programme tables.
For this station the records were generated as follows:
02+DATE
01+0002
02+STATION ID
01+0003
02+MAXIMUM WIND SPEED
03+TIME OF MAXIMUM
04+MEAN WIND SPEED
05+RELATIVE FREQUENCY OF WIND DIRECTION IN THE
06
"
07
"
08
"
09
"
10
"
11
"
M
12
01+0249
02+RAIN
03+TIME
04+RADIATION
05+TEMPERATURE
06+RELATIVE HUMIDITY
07+SOIL TEMPERATURE
08+WIND SPEED
09+WIKD VECTOR
10+WIND DIRECTION
11+STANDARD DEVIATION OF WIND DIRECTION
01+0001
03+BATTERY VOLTAGE
05+TIME OF MAXIMUM
04+MAXIMUM TEMP
07+TIME OF MINIMUM
06+MINIMUM TEMP
08+RAINFALL
09+RADIATION
10+MEAN TEMPERATURE
11+MEAN RELATIVE HUMIDITY
03+TIME OF RAIN
161
NNE
ENE
ESE
SSE
SSW
WSW
WNW
NNW
TABLE 10.3 - The calibration details for the different sensors.
STATION
#
SENSOR
DATE
CORRELASLOPE
TION COEFFICIENT
0001
PYRANOMETER
(PY5753)
27-02-84
0001
HYGROMETRIX
# 8502
17-09-84
17-09-84
0002
PYRANOMETER
(PY5059)
24-05-83
0002
HYGROMETRIX
# 8503
20-01-84
12-10-85
18-07-86
0002
LYSIMETER
15-05-84
13-09-85
13-09-85
0002
MET-ONE
ANEMOMETER
0003
PYRANOMETER
(PY5060)
0003
HYGROMETRIX
# 11086
*
0,991
0,985
OFFSET
0,1901
0
1,146
1,141
-24,99
-19,68
0,100
0,981
0,988
0,912
0,989
0,322
0,153
0,152
CALIBRATION
INSTRUMENT
MANUFACTURER
CERTIFICATE
0
MANUFACTURER
CERTIFICATE
-10,64
-16,94
ASSMAN
WET-DRY BULB
WET-DRY BULB
0
0
0
WEIGHBRIDGE
WEIGHBRIDGE
WEIGHBRIDGE
18-07-86
16-10-84
16-10-84
calibration by Miss Weideman
*ASSMAN
*WET-DRY BULB
PRUFCHIEN
0,994
0,982
0,100
0
1,072
1,149
-17,79
-10,69
MANUFACTURER
CERTIFICATE
*ASSMAN
*VET-DRY BULB
- aspirated Assman
unaspirated wet 4 dry bulb
162
TABLE 10.4 - The input and output programme tables for DIE BULT.
CH21 Output T.t>le Coding Form
C R 2 1 Input Table Coid.13 Focrn
CR2I ' "
ciei ID .
» W * . Cttl U - — .
*1 *nd cd»»*i Ibl (of #•* h u f l M ..wnq i
IU - i
mttf
Output Tmt biUiuvl tmlnuta) 0 1 ^ _ k ^ _ .
OutpulTlblcNumtKi i l .
Prr^ram No
Input Program
Range (EUI
(EU/IUl
Output 10 No
Ourput Proqtim anil D i l i Dncripltcxi
F.njl Output IEU)
Srnsor Description And Calibtjtion
Param 2 drvrip
Pdrsm I dcvnp
OHVL-I |EU)
ProdraTn No
Pi/«nftn 1
f
Parjfn«tcr 2
1
IV. MV. PC R r t u « . (
It-
-2
12
O- M1o 1
few „-••
M" V
kW
13
O
—
13
o
72 •
21
O
11
i?
21:
1
"S+*-J*-wt««.
z
21:
3
23
O
<V. HV. DC K H u r n l
TtMCrf ATu
3
c
t?£
7
31:
32
»
»_
O
33
]
>/A
[31
Si
12
33
41:
Si
«.
12
IV.HV.DCIIrtwi
SOIL.
!
cn
Co
s
J
41:
7
42
T£»<»e<*Tu«£
I
1
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W. MU DClACBa..™,,!
j r?tLAiiJlT
u f ^
hHA.
{ "
SI:
**"
S2
S3
51:
tJ%*MA-
-
IVMV.IKHC Runwi
c
vol:s
i
01-
62
71:
72
a
71:
T2
73
81
H2
.11
am
Wind
(5.789)(councs/occj+l
KPH - (O.029S)(cauntj/Bln)+l
<«d —
dneaomecer
1-4S = /
Fulse
Count!
81
S2 O • O "*
ra/*
8
1
j a i o • ^>
ivj
1 t l p / n • 1 r.lp/0.03937 In
Tipping Bucket IU1
Variable
Pulse Count*
91: *
SCIENTIFIC.
92
O-2O
INC.
"Hurtdrethj of
nn Inch
93
9
RftwfnLL
1
I
j.
0
CAMPBELL
91
^T
SCIENTIFIC.
INC
-o
._
J
i
CI121 Output Table Coding Form
CH21 O u l p u l Table Codlii'i f o r m
CR21ID
Ouqxj* D f**nfe«n I. 2 » n l 3 *Jr-ni»y u b l .
_ Smi D*n
Si-wi Tiw .
• * • - M l Op-—'
OutpulT\tbl«Nun*«t<l.Zo<31_dL_^ Ouipm Tun* knttrvd i™ul«) (U
Ourpul Program *nd Data Description
Trio
Output ID No
P.rWd^np.
-
o 1
NOTE
O u l p u l T a b l e N U M I L M 1 1 . 2 o r Jl
Pa ram 1 dcvtip
oo
_
_
PtagnmN.
'
Output I D No
Ourpul Piogram and DtU [ V U P I I A K I
Numtw>
Param 1 desoip
P«im 2 dncrip
I'umtnl
(•VKjiauNn
Paijmcwr 2
1
-/r
1
11:
~ / *
2
A/\iT£Ay
Vot-7*>c£ ft/A
A 2
12
OOOl
n
Ti*-t •*
!
11:
^3
I?.
S
ii
Ô
22;
CD
2.1
/A
O
//!
f
32:
t
2t
22:
21
4
O)
41:
I
5"^
42
41.
V
O
ZL
5
"/*
i:
Si.
| 52
f
T
I 5-i
O
T>.
62
61:
7
PC */A
i
W-*M
21:
*C
S
o
-
St+*^
7
71:
1
81:
1
91:
^ /
72:
*>-
S2
^
73
o
71:
72
81:
| 82
r
9
SCIE
1 92:
N Tl f I C .
INC
7]
C A M P B E L L
S C I C N
TtflC.
91
o
STATION #2 : WEST CAMPUS
The
automatic weather station on the WEST CAMPUS (Fig.10.1)
installed
of
over a short grass cover for the continuous monitoring
the appropriate hourly and daily meteorological data
experimental
basic
unit
lysimeter
was
plot (Fig.10.3).
already
situated
mentioned.
at
the
The station was comprised of
the
It was also
connected
some 80m to the north (Fig.10.3).
to
The
the
CR21
monitored the amplified signal from the load cell incorporated in
the mechanical weighbridge of the lysimeter.
The resistive
vol-
tage divider and translator circuit used are shown in Fig. 10.4.
FIG. 10.3. -
The experimental plot showing the location of the
automatic weather station, lysimeter and other
instruments.
165
The
two
evaluate
to
remaining channels on the CR21 were initially
soil ooisture probes.
used
to
They have subsequently been used
compare other sensors against the automatic
weather
station
sensors.
The
calibration for the pyranometer is shown in Table 10.3.
raingauge
the
and temperatures were checked against
system
Hygrometrix
1984.
was
Xnam
initially
RH
Prior to this
relative humidity.
against
the
installed
The
standards
when
1983).
The
(November
Bensor (#8503) W B B installed
in
February
a wet bulb temperature was used to estimate
The Hygrometrix sensor was initially
unaspirated wet and dry bulb
January 20 and 25,1984 (Table 10.3).
temperatures
checked
between
Subsequent calibrations are
shown in Table 10.3.
49.9k
HSIMETER
AMP.
24.9k
CR21
24.9k
FIG. 10.4. -
The
The resistive voltage divider and translator cir
cuit coupling the lysimeter to the CR21.
lysimeter was connected to the CR21 on January
calibrated soon after (Table 10.3).
166
27,1984
and
On September 9,1985 the load
cell
on the lysimeter was replaced with a more sensitive one and
re-calibrated
for the CR21.
The CR21 monitored the voltage drop
between the positive terminal and earth,
while the voltage
drop
between the negative terminal and earth was monitored manually on
a Keithly digital multimeter at selected times.
The
BASIC
(Appendix
each
programme
for printing the hourly
and
daily
VII) also provided a table of mean hourly
means
values
for
month together with their standard deviations (Table 10.5}.
These
will eventually be incorporated in the programme so as
to
provide standard checks on the raw data.
The
input
station
The
data
(*4)
and output(* 1,2,3 ) programme
tables
(Table 10.6) require 23 intermediate storage
for
this
locations.
format generated by these tables and saved on disc
files MTH-YR.EXP are as follows:
01+0001
02+DATE
03+TIME
04+RADIATION
05+TEMPERATURE
06+LYSIMETER
10+WIND SPEED
11+LYSIMETER-CONSTANT
01+0002
02+STAT1ON ID
01+0003
02+RADIATION
03+MEAN WIND SPEED
04+RELATIVE FREQUENCY OF WIND DIRECTION IN THE NNE
05
"
ENE
06
"
ESE
07
"
SSE
08
"
SSW
09
"
WSW
10
"
WNW
11
»
01+0249
02+RAIN
03+BATTERY VOLTAGE
04+RAINFALL
05+LYSIMETER
06 + STANDARD DEVIATION" IN LYSIMETER
03+TIME OF RAIN
167
in
TABLE
10.5 - An example of mean hourly values for
dim*r-B6.»xp
MEAN HOURLY
Tin*
1.00
2.00
3.00
4.00
S.OO
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
16.00
17.00
18.00
19.00
20.00
21.00
22.00
23.00
24.00
Rad
0.23
0.22
0.26
0.24
0.24
0.43
28 .36
141.07
286.00
372.80
454.93
506.33
634.56
576.28
552.33
413.44
262.3?
87.77
14.98
0.19
0.21
0.16
0.22
0.22
VALUES
Rri Huni
Tmp
18.68
17.90
17.11
16.41
15.78
15.09
39.38
61.77
66.77
72.99
76.86
79,45
81.65
78.92
72.63
67.20
62.64
57.69
52.24
46.33
43.S3
42.61
41.58
41.67
47.89
52.47
54.71
37.97
59.37
61.14
14.94
16.48
19.04
21 .04
22.96
24.81
26.88
28.16
29.06
29.36
29.26
28.41
2<J.O7
23.31
21.66
20.91
20.17
19.37
Uind tp«td
!.64 1721 .00
t.76 1721.00
!.41 1721.00
i.66
1721.00
>.98 1720.83
!.94 1720.63
1.32 1720.83
i 1.05
1720.63
1.48
1721,17
i 1.57
1721.83
1.61
1660.40
1.36
1667,00
1.46
1661,80
(.46 1702.80
t1.21
1711.60
1,41 1709.80
tt.14
1708.00
3.77 1706.60
3.57 1712.60
3.10
1725.80
1.91
1735.40
2.87 1746.80
2.70
1752.60
2.81 1753.20
Lyi
J2.31
1 .60
196.23
196.23
195.47
195.71
195.63
195.63
195.63
195.36
194.98
195.33
141.15
133.30
126.03
134.12
143.99
142.29
140.95
139.80
144.39
1S9.46
172.78
190.80
199.67
200.69
•
STANDARD DEVIATION
1.00
2.00
3.00
0.10
o.ii
i .24
: .74
o.os
o.io
; .49
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
12.00
13.00
14.00
15.00
16.00
17.00
I B . 00
19.00
20.00
21.00
22.00
23.00
24.00
0.08
't '.10
o.ii
15.83
69.77
128.65
161.12
182.90
180.71
52.44
83.07
48.36
44.63
38.84
38.67
10.78
0.07
0.10
0.14
0.13
o.ii
; .04
;>A9
1 .92
1 .43
J>.36
: .53
< .53
< .97
i 1.28
: .63
:1.40
i'.98
1'.84
; .61
* 1.97
* 1.74
'1.34
* 1.23
* 1.07
:9.88
168
21.28
19.86
17.62
15.32
14.75
13.68
14.34
13.60
13.76
17.38
21.76
22.68
20.32
14.49
12.49
11.89
11.50
11.62
23.53
27.52
26.97
24.85
23.95
22.76
.19
1 .55
.25
1 .15
.47
.45
.68
.16
1).75
.17
.57
.05
1J.77
11.30
).3?
.08
1.17
.25
3.65
1.07
1.12
1.83
WEST
CAMPUS
TABLE
10.6 - The input and output programme tables for WEST CAMPUS
CR2I li.|>ut Table Oi.liny Form
ID
rJoJCMSCK
Sum Dalo
f\T\
CR21 Output Table Coding f o r m
C«2I ID.
Storl Timt .
Odd
_ SIMI 0 K <
. Sun Time .
•> •*»•»*• a J * • a n t O B M I
, EU .
| mjfc«h, IMVl
7*
Oul|.ulT»b)e^k»Ill>^f ( l , 2 o f 31
£Z
Sonar Ovrlpoon tnd CilrbuBon
RanoilEUl
=t
60
OutputT™ InHnnll<T<n>1cl)03
Ourpul ID No
Fifwt(Xjipui (EUJ
Multiplier
(KU/IU)
Program No
Input Program
..;•.•„. » ,
Ur
12
2
1
|
Pjnm 2 dorrirl
OHvellEUI
o ' o
13
°
23
°
33
O
* ^ ^ /
Progrwn Kx
111.
*
|l3
O
21
O
13
O
(V. MV. DC R>i«»nl
2
2i
i
21:
S/
IV. MV. r j C R n u « . l
3
3
31:
CD
'
32
IOOO
32:
3
°C
4
|
i
to
*^r"
41:
7
j 51
*+•
41:
O
£/
<v. MV, tXT * AC Rm>nnnl
TV
S
1
t
f—
6
(V. MV. CK & AC H m « w v . |
S3 - 1 1 - 2 1
u
62
63
71:
72:
73
62
I
| 7
i
„
1 Ulnrf Speed —
1 contjet jfiKKnntter
| l-i5 a / .
Pulse Count s
Tipping Bucket R«lni;<ge
v*rl«ble
KPH -
71:
A / "
73
-S'
n ,0?3B)(counts/a l n ) * l
(0
HI
82 O • o «
I tlp/« - 1 tlp/O.0J937 In
Pulse Counts
91: *
C/l*f8f[t
SCIENTIFIC,
92
INC.
O'2a
ZI
83 a
~it
R3
JBl
Hurtdretha ot
an Inch
93
0
T^
CAMPBtLL
91:
-^7
SCISNTIFIC.
INC
O
CH21 Output T»bl« Coding Form
CK21 O u t p u t Table C o d i n g f o r m
CR21 ID
SiJrt O J I .
OOO2.
CR21 ID.
Suit Tun« .
NOTE Srimouvuir
t -Ki J E
Piram 2 dficrtp.
Progwm No.
P««« m i
w/o
ItuiputlDMa
Oulpul Piognm irwt DiU DncripHon
Output ID No
Ourpul Proyam and DiU D^vripHaa
Param 1 dncrip.
Ourpul T n x Inwnul (nanul«| 03
Output T i t J , Number ( 1 . 2 « 3 ] .
Output Tifalt NumUt (1. 2 or 31
P*ram 2 d( scrip.
Parojn I devhp
Parjnvicf 2
FWitjiAnt f &
P*ffliiwttl 1
"UAn\c w Z
1
12
OOOZ.
21: S O
32:
41
f*/A
\23
O
33
O
42
3
43 O
52:
3
O
'
H/A
13 O
12
ZI.5S
/
\\
22 J?
32:
tr
tr
~1
I
6
61.
62-
71
72:
61
7
71
S
~T
81:
82:
9
9
92
CAMPBELL
SCIENTIFIC.
INC.
93
i
31
T
91
f t(
72
7)
92
'M
S C 1 E H T t f I C , INC.
O
STATION #3 : VAALHARTS (JUNE-DECEMBER,1984 and 1985)
This automatic weather station was deployed at the Vaalharts Wine
Cooperative Meteorological Site during the wheat season of
The
station
Stevenson
was
screen
comprised
of
vacant.
basic
data
However
the
All the remaining channels
to
were
The user-specific programmes for the CR21 were the
same as those of Station #1 (Table 10.4).
station
unit.
was replaced by a radiation shield mounted
the main upright of the tripod.
left
the
1984.
The automatic
was retrieved on cassette tapes once a
weather
week
from
Vaalharts.
In June, 1985 the automatic weather station was moved to the farm
of
Japie Smit at Hartswater to enable
linkage
between
the CR21 and the Department
Sperry PC at the UOFS.
unit)
to
an
supplied
16VAC
a
of agrometeorology
asynchronous dial-up ITT Data Modem
supply
(Fig.
interface
(Model
The SC232 was
1161)
powered
10.5) drawn from the 12 VDC battery of
by
the
The 12VDC lead-acid battery was continuously charged by
220VAC
trickle
telecommunication
The CR21 was interfaced (SC232
by the General Post Office.
station.
direct
trickle charger.
The 220VAC mains
required
charger and modem were supplied by Mr Smit,
for
the
as was
the
telephone line.
Before the third station was installed at Vaalharts/Hartswater it
was erected alongside Station #1 and the sensors compared
10.7.
All
pyranometer,
the
sensors
were highly
correlated
(Table
(r>0.99).
The
hygrometrix and met-one sensors also had regression
171
SENSOR
DISTRIBUTION
BOARD
• J 6 VAC
/POWER
SOURCE
ENVIROLOGGEH
FIG. 10.5 -
Photograph of the components of the telecommunication system.
UOFS
Dept. Agrometeorology
FIG. 10.6 -
Schematic diagram of the telecommunication system
linking the automatic weather station with UOFS.
172
TABLE 10.7 -
CORRELATIONS AND REGRESSION ANALYSIS OF STATION #1
AND STATION #3 AT THE OBSERVATORY (DIE BUILT)
CORRELATION
SENSOR
INTERCEPT
SLOPE
RADIATION
1 ,00
0,92
-0,004
TEMPERATURE
0,99
1,21
-3,76
REL. HUMIDITY
1,00
1,07
14, 18
WIND SPEED
0,99
1 ,01
-0,04
coefficients (slope)
that were within 8% of unity. The intercept
for the humidity sensor however showed a consistent difference of
>14%.
This
sensors
difference
could
be
and that exhibited
by
the
caused by the use of a radiation
temperature
shield
for
Station #3 instead of a Stevenson screen.
The
hardware for this station is shown in the schematic
in Fig.
10.6.
cooperation
and
of
Vaalharts
problems
which
diagram
The procedure for accessing the CR21 required the
two manually operated switch boards at the
Post Office.
were
Initially this
quickly rectified.
caused
some
Direct access
UOFS
minor
to
the
station via the telecommunication linkage was scheduled for three
specific times each week (Monday,
Wednesday and Friday). Mr Smit
arranged to switch the telephone modem to the CR21 for an hour on
each occasion.
During which time the data stored on the internal
173
memory
was dumped to the Sperry PC at the
encountered
excessive
with
noise
conditions.
and
data
over
the
on
UOFS.
Problems
system which are believed to
the telephone line during
be
were
due
extremely
windy
Since the UOFS switch board closed at regular
times
weekends it was not always possible to
before
retrieve
the memory locations were updated with
information.
the
some
latest
The cassette tape, however, operated simultaneously
and independently of the telecommunications and consequently
data
lost
to
via
telecommunication linkage could be
the
obtained
by
physically retrieving the cassette from Hartswater.
Internal
storage requirements had to be minimized.
available storage on the CR21 is 608 words.
The
maximum
In order to
ensure
at least 36 hours between data retrievals (Friday to Monday), the
user-specified output Table *1 had to be altered. Only the information essential for numerical simulation modelling (Table. 10.9)
was
stored.
Table *2 and *3 were not used.
In this way only
4
intermediate storage locations were required.
Once
would
communication with the CR21 had been established,
respond
to the commands listed in Table
10.8.
the CR21
Thus
the
individual sensors or any portion of the internal memory could be
monitored at any time. All responses from the CR21 were displayed
on the Sperry PC video and subsequently saved in specified files.
174
The
data
was
received and saved in files
with
the
foLlowing
format:
01+0001
02+DATE
03+TIME
01+0249
02+RAIN
03+TIME OF RAINFALL
TABLE 10.8.
THE
04+RADIATION
05+TEMPERATURE
07 WIND SPEED
COMMANDS AND RESPONSES
FOR
TELECOMMUNICATION
WITH THE CR21
VALID COMMANDS
RESPONSE FROM CR21
A
ADVANCE AND DUM
B
BACKUP TO PREVI
C
(DOY & TIME)
(BATTERY VOLTAGE)
(READING SENSOR #1
#2
D:#### T:####
00:####
01 :####
02:####
etc
#n
HHMMG
8
where HH=HOUR
MM=MINUTES
0n:####
RESET CLOCK
(CURRENT MEMORY LOCATION) ??:####
D "(DUMP MEMORY FROM CURRENT
MEMORY LOCATION TO THE
MOST RECENT STORAGE)
01:000n
E (END TRANSMISSION)
CLOSES TRANSMISSION
### (POSITION MEMORY LOCATION)
175
02:DOY
03:TIME
04:DATA etc
TABLE 10.9 - The input and output tables for HARTSWATKR STATION.
Cft2 1 Oulpul Table Coding I'orm
Clt2l Input Tabl-- CoJinjj Form
CH21ID.
Sun Dale ^_^
_ Stan Dai*
CR21ID.
Sjjrt Turn
Sun fu
-ltX. V u lE™ ) «.« S lJ"* U . inpulUoH.. 9 «*.|V| mjkvoh.
OulpulTmclnliTMllminulol 03
Oulpul TiW*Nuro6«iU.2 « 3 |
Scn»» DcicnptJan andCjIibrjhon
RingrtEUP
I.-.
F.njIOutpul IEU1
MultifJwr
IFU/IU]
f)r<^gmm No
Ifipul Program
55?
OuûllDNo
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9
O
SUMMARY AND CONCLUSIONS
The
WEST CAMPUS station has been operational for more than 24000
hours during which time approximately 1300 hours down-time occurred.
This
failures
but
5%
in data was due almost
entirely
which went undetected for several days in
exceeded
time) .
loss
A
similar down-time for DIE BULT
failure.
problem
22 days in January 1985 {"50% of the
However,
in
to
most
power
cases,
total
down-
was also due to
power
the case of the third station,
the power
was rectified and the low data loss was due entirely
to
excessive noise on the telecommunication lines.
Once
the automatic weather station had been
standards,
they
department
of
Institute
weather
the
of
were
readily
Agronomy,
Ground
Water
deployed
Agricultural
Studies
calibrated
in
the
field.
Engineering
compared
against
and
their
the
Automatic
stations with DIE BULT Station before deploying them
field.
The
in
In some cases this was accomplished by undergraduate
students with little difficulty. The major problem appeared to be
installing
these
CR21
the
user specified programme Tables.
The design
programme tables requires a thorough understanding of
capabilities
and
available
software.
The
CR21
different output programmes,
intermediate storage and
passing
would be difficult to
capabilities
suitable training.
which
However,
use
has
of
the
22
parameter
without
once designed they can be installed
with little difficulty by undergraduates.
177
The
ASCII
simple,
data
readable
from
the automatic weather
form
that
can
be
easily
station
is
in
interpreted
a
from
printouts (Table 10.10). It is readily accessed using string form
in
BASIC
programmes.
disseminating
form
The
BASIC
programmes developed
the automatic weather station data are modular
and can easily be modified to suit other users.
been used by the Institutes of Ground Water Studies and
mental Studies for the analysis
They
for
in
have
Environ-
and presentation of experimental
data.
TABLE 10.10 - The data format for the automatic weather station.
TABLE #1
DOY
TIME
(RADIATION) (TEMPERATURE) (HUMIDITY)
01:0001
02:0xxx
03:xx00
04:0.xxx
05:xx.xx
06:xx.xx etc
01:0001
02:0xxx
03:xx00
04:0.xxx
05:xx.xx
06:xx.xx etc
01:0001
02:0xxx
03:xx00
04:0.xxx
05:xx.xx
06:xx.xx etc
178
CHAPTER 11
COMPUTATION AND DISSEMINATION OF INFORMATION
A
wealth of experience in the operation of irrigation scheduling
service using weather data was obtained during the period 1982 to
1985.
This
modus
chapter
serves as a short summary
describing
the
operandi such service should adopt.
Objective of weather service for scheduling irrigation
The
objectives
(i)
keep
of
such a service are:
accurate
account
of water use
and
soil
water
budget on individual irrigation plots on diverse farms,
(ii)
provide
weekly
advisories to managers which
indicate
the day on which the next irrigation should be applied.
(iii)
regular
monitoring of the performance of
managers
of
enterprises.
(iv)
assist managers in planning strategies and
scheduling
at the outset of the season.
The
prediction accuracy of the onset of crop water stress should
be approximately two days.
179
Description
To
curtail
computing time a simulation model using
steps should be used.
daily
An hourly (PUTU 9) and daily (PLTU
time
9.86)
version are available.
Where
should
order
a large number of farmers are involved,
be
to
individual
grouped according to soil type and planting
limit computing time.
logistically the most sound.
Such arrangement
is
plots
date
in
probably
The necessary computer software has
been developed.
Hourly values of crop total evaporation,
from weather data.
Em,
should be computed
The daily values of Em are found by integra-
ting these over the daylight period.
Dissemination of data
Information most valuable to managers is outlined under the above
stated
onset
objectives.
It includes:
of water stress;
indication of the danger
the daily water use over the past
of
seven
days; the percolation of water out of the root zone; the expected
timing
of the next irrigation;
monitoring of the current
available water in the root zone,
gers' performance.
and an assessment of the mana-
The necessary standard forms and figures for
disseminating this information have been designed.
found in Table 9.4,
such
as
Fig.
plant
9.5,
9.6 and 9.7.
7.2 and 7.4 etc,
Fig.
They can
Graphical presentations
9.1 and 9.2 offer
effective ways of communicating information to managers.
; 80
be
simple
Deep percolation losses,
are
illustrated
1985
season.
aware,
could
had
illustrating poor irrigation management
in Fig.
Indeed,
9.2
Fig 9.2 made the farmer involved
for the first time,
prove to be.
occurred
This is an actual case from
just how serious such
the
fully
mismanagement
The knowledge that so much deep percolation
early in the wheat growing season was
of
immense
value to him.
Managers greatly appreciate personal contact.
the
weather service should regularly visit clients.
meeting
the needs of the manager,
certainly lead to an improved
be
valuable
Such feedback will
service.
summary of performance provided by Table 9.7 also proved
particularly
of
Apart from
this is a source of
information to the weather service operator.
The
The operators
useful when conveying information to
the
to
farm
managers.
Strategy in times of restricted water supply
Information
most
urgently required by farmers involves
drought
sensitivity to and water requirement of crops during given growth
stages
throughout the growing season.
timeous visits to the farm manager.
181
Hence the necessity
for
Decision making regarding the most suitable strategy
restricted
vice.
water
in times
supply form an indispensable part of the
Some form of strategy planning is always required.
indications
as to how this might be achieved,
Chapter 12 and Table 8.2.
182
are
of
ser-
Useful
provided
in
CHAPTER 12
THE
INFLUENCE
OF WEATHER UPON DECISION
MAKING
FOR
OPTIMAL IRRIGATION SCHEDULING
INTRODUCTION
When
water restrictions apply,
period
1983-86,
as was the situation during
the
the farmer is faced with the dilemma of finding
the most profitable irrigation strategy.
His objective must
be
to maximize whole farm profit with the given water supply, rather
than the maximization of mean farm yield (t/ha), or the maximization of water use efficiency (kg/(ha mm)).
It is precisely this
question with which the farm manager confronts the adviser.
The
procedures of profit maximization are well described by Doll
and Orazem (1984) and the total dependence of such analysis
suitable
crop-water production functions
upon
is illustrated by Ayer
and Hoyt ( 1981 ) .
OBJECTIVE
The
aim
of
this work was to establish a
irrigation strategies which maximize:
(i)
(ii)
yield, or
water use efficiency, or
(iii)
profits per hectare, or
(iv)
whole farm profit.
183
theory
for
planning
THEORY
An analysis of the economics of irrigation scheduling requires
a
crop-water production function.
The ensuing argument ran best be
followed with reference to Fig.
12.1.
Simple linear production
curves have been reported for wheat by Streutker (1983a), Doorenbos and Kassam (1979) and Hanks and Hill (1980).
Similar curves
are available for other crops (see Streutker, 1983b).
Production
functons are not linear however (see Ayer and Hoyt, 1981).
the
Hence
construction of theoretical or experimental crop-water
duction
Here,
functions
is a pre-requisite to this type of
pro-
analysis.
a simple function for wheat will be derived and applied to
illustrate the principles involved.
9.86
wheat
crop growth model.
It was found using the PUTU
Such production
functions
dependent upon climate and soil physical and chemical
istics.
are
character-
They must be constructed for each specific situation and
hence a reliable crop growth model greatly simplifies analysis.
The
parameters necessary for analysing the influence of
upon
the
profits
of irrigating winter wheat
were
weather
defined
as
follows:
Y is the total physical product (TPP) or output (yield) (t/ha).
X
is
the
variable input - in this case the quantity
applied (ha mm).
18-1
of
water
APP
is the average physical product
APP = Y/X for the straight line intersecting the origin
and
the point on the production curve corresponding to the water
application under consideration (t/ha mm)) - see Fig. 12.1a
MPP,
the marginal physical product,
is the change in output re-
sulting from unit change in input
(MPP -AY/AX
the ccrop-water production curve)
(t/ha
at any given X on
mm) - see Fig. 12.1a.
Py
is price per unit output
(R / t ) .
Px
is price per unit variable input
TC
is the total cost in Rands
TVC
is the total variable cost;
TVC = Px.X
(R/ha).
TVP
is the total value product;
TVP = Py.Y
(R/ha).
TFC
is the total fixed cost per unit area
(R/(ha mm)).
(R/ha).
(R/ha).
2
VMP is the value of the marginal physical product (R/(ha mm)).
The simple profit model
Doll and Orazem (1984) express the simple profit equation as:
Profit = TVP - TC
..(12.1)
- TVP - TVC - TFC
= Py . Y - Px . X
Where
Y
=
f(X)
..(12.2)
- TFC
..(12.3)
and is known
as
the
crop-water
function. Thus Profit - Py . f(X) - (Px .X) - TFC
185
production
To maximize profit with respect to water application
^Profit/jX = Py.&Y/AX
- Px = 0
= Py.MPP - Px = 0,
Hence, for
by definition.
maximum profit, the following must be true
MPP = Px/Py
. . ( 12.4)
Px/Py is known as the price ratio line.
The condition for maximum profit expressed by Eqn 12.4 is
Py.MPP = Px or VMP = Px
where,
value
..(12.5)
VMP is the value of the marginal product,
i.e.
the Rand
of the yield associated with each succeeding unit
plied water (R/(ha
of
ap-
mm)).
The three stages of production
Doll and Orazem (1984) describe three stages of production.
STAGE I - here MPP > APP
As
X
increases,
APP
increases throughout STAGE I
maximum at the end of STAGE I.
reaching
a
Hence STAGE I ends when
<S APP / b X = 0 and MPP = APP
Doll and Orazem
enterprise
do,
(1984) maintain that,
operates
in
this
range.
in practice, no successful
It
is
illogical
because each increase in applied water attains an
186
so
to
increased
mean
yield (APP).
Only when STAGE II is reached is the strategy
economically sound.
is
a
maximum.
Within STAGE I a point is reached where
Thereafter MPP decreases.
This is known as
MPP
the
region of diminishing returns.
STAGE II - here MPP <_ APP
Here,
as X increases, MPP is decreasing.
Thus, this stage falls
entirely within the region of diminishing returns.
In this stage
however, MPP is always greater than zero.
STAGE III - here MPP < 0
Production
attain
in this stage is irrational because the farmer
the same production with a smaller input (water
could
applica-
tion) .
The elasticity of production, €p, is defined:
percent change in output
tp =
( dimensionless )
percent change in input
£p
= AY/Y / A X / X = X/Y.AY / A X = AY/AX / Y/X
Generally,
interval,
for
is
the
economic
interval
systems,
the
bounded by
possible
variable
corresponding to 0 < £p < 1 (Doll and Orazem,
APP
then £p = 1.
= MPP / APP
production
input
1984).
values
When MPP =
Theoretically this is the beginning of
STAGE
MPP > APP then €p > 1 (STAGE I) a 1% change in
input
II.
When
will
produce a greater than 1% change in output.
187
When MPP = 0,
£p
=0,
STAGE III is reached.
uneconomical.
to
proceed
Production beyond this point
is
Hence irrigation scheduling must be planned always
in STAGE II - which is in the realm
of
diminishing
returns.
Water use or irrigation efficiency, v.w, is defined:
Cw
= Y/X.
(NOTE €w - APP in this case. )
when APP is a maximum (see Xm in Fig.
this
coincides
Furthermore,
with
It reaches a
12.1a).
neither maximum MPP
maximum
As will be seen,
nor
maximum
profit.
it coincidea with the end of STAGE I for an irriga-
tion enterprise.
IRRIGATION STRATEGIES
(i)
Maximum
yield
12.1a.
This
Xo mm.
In
is
obtained at the apex
corresponds
practice
to a water
(A)
of
Fig.
application
water application
should
of
never
exceed Xo as this is complete wasteage.
(ii)
Maximum
area
(iii)
irrigation water use efficiency per unit
occurs
when
APP = MPP
i.e.
when
land
6APP/&X - 0.
Maximum profits per ha are attained however when VMP
Px (see Eqn 12.5),
physical
product
=
i.e. when the value of the marginal
equals the price per unit
188
of
water
applied (see Fig. 12.1c).
Only when the price of water
is zero will the profit maximizing and yield maximizing
level of water coincide.
(iv)
Maximum whole farm,
result
This never occurs.
or region, profit does not however
by ensuring maximum profits (Case iii) on
hectare of irrigated land.
The latter is determined by
the amount of water available.
profits
each
Whole farm, or region,
are maximised by increasing the area irrigated
and applying less water than that level which maximizes
profit per unit area on condition that APP > MPP.
The
solution to this problem (Strategy iv) is site specific
and
totally dependent upon the shape of the production function.
The
latter
and
is
cultivation
principle,
of course a function of climate,
practices.
derived
The relevant
soil character
fundamental
equimarginal
by Doll and Orazem (1981) for the case where
input (water) is limited, reads:
Maximum whole farm, or region, profits are attained when the
marginal product of the input is the same in each enterprise
(irrigated unit).
The solution to the problem thus reduces to numerical computation
of the production function.
189
The pertinent rules are:
1.
The
amount of water applied to each unit of land
irrigated
must ensure operation within STAGE II, and
2.
Equal
amounts
provided
lots.
of
water
must be
applied
to
each
unit,
the same type of irrigation system is used on
a]I
If not, the criterion.
AY2/ A Y I = Pyl/Py2 ...
etc.
applies where 1,
2...etc refer
to
different types of system.
The
theoretical
procedure required to maximise whole
farm,
or
region, profits from irrigation will now be described.
Given
a water supply S (m ),
upper limits.
let 'lo' and 'up' denote lower and
Compute from the production function the limits of
STAGE 11 on a single hectare, thus:
1.
Lower limit
Xlo = X where APP = MPP or Xm
Upper limit Xup = X where MPP = Px
(maximum profit per unit
area).
2.
The total number of hectare able to be
lie between Hlo = €s
irrigated,
H,
will
. S/YuplO and Hup = Cs . S/lO.Xlo
C s = efficiency of supply of water to the lands.
(Note: 1 ha mm = 10m3 )
3.
For irrigated units of size Hu, the most effective number of
units
N
would
be all integer numbers between
Hup/Hu.
190
Hlo/Hu
and
APPLICATION OF THIS THEORY TO IRRIGATION PRACTICE
A simple example will be used to illustrate use of the theory.
Theoretical crop-water production function
A hypothetical crop water production function was created.
was
done by selecting several irrigation strategies
12.0)
from
and
running the PUTU 9.86 model with weather
the West Campus.
No rainfall was assumed.
This
(see
Table
input
data
The scheduling
ensured that no stress occured during secondary root
development
and anthesis. A soil layer depth of 0,15 m and the lysimeter soil
were
selected.
The
plotted in Fig. 12.2.
results obtained from this
procedure
are
From this curve the following results were
extracted:
1.
the maximum yield (6,6 t/ha) was obtained at a water
appli-
cation of Xo = 620 mm.
2.
the
maximum
water use efficiency of 12,3 kg/{ha
mm)
area
occurred at Xm = 460 mm.
3.
values
made
of APP and MPP from which strategy decisions may
are
quoted in Table 12.1.
STAGE II,
the region
be
of
diminishing returns resides between water applications of Xd
=
360 mm and Xo = 620 mm,
or between eight and 14
irriga-
tions .
4.
Whole
mm,
farm profits may be maximised between 460 < X
i.e.
10 and 14 irrigations.
case of flood irrigation Px = 0.
191
<
620
It is asumed that in the
5.
the
number
of plots irrigated may be calculated given
the
size of plot and the water supply available, S.
Significance to West OFS
While the production curve,
Fig.
12.2, is theoretical it should
be reasonably representative of wheat in the central and
OFS.
western
At Hartswater, evaporation will be higher, but the soil and
cultivation
practices are typical of the region.
As a starting
point therefore the results are worthy of scrutiny.
The 1983 strategy reported for Site A in Table 8.2 was an attempt
to
solve
matter
was
(eight)
It
the problem subjectively.
obtained,
Much experience
with
but the recommended irrigation
fell two short of the theoretical
minimum
the
frequency
requirement.
might therefore have been more profitable to have selected
a
strategy encompassing a frequency of ten for Hartswater.
The
suggested
practice
produce
in
frequency
Hartswater.
of 10 to 14 agrees well
with
Here 11 irrigations are
6-7 t/ha of wheat grain (see Table
9.3,
general
expected
8.4,
X.I
to
and
X.8) .
The
Fig.
MPP and APP derived from the hypothetical production
12.2
and
described
in Table 12.1 could prove
curve,
useful
preliminary irrigation strategy planning in the western OFS.
192
in
PRODUCTION FUNCTION
WATER APPLIED
MPP = AY/AX
APP = Y/X
b)
MARGINAL- PHYSICAL PRODUCT
E
STAGE III
Q_
Q_
WATER APPLIED
Xm where MPP = APP
c)
VALUE OF MARGINAL PRODUCT
WATER APPLIED
FIG.
12.1 -
Figures illustrating how the crop-water production
function, Fig. 12.1a, nay be used to analyse the
various stages of irrigation and to determine Xm,
Xo, APP and MPP. (After Ayer and Hoyt, 1981)
193
10
8
6
4
Xo
Xm
n
0
160
320
480
640
800
WATER APPLIED, X (ha mrr)
FIG.
12.2 -
The
crop-water
hypothetical
weather
production
curve
using
several
irrigation strategies together
for West Campus in the wheat crop
model PUTU 9.86.
with
growth
The water application producing
the
onset of diminishing returns,
use
efficiency and the maximum yield are
by Xd, Xm and Xo respectively.
194
maximum
water
denoted
TABLE 12.0 -
Day
of
the
irrigations,
9.86
year
and
(DOY)
on
amounts,
which
theoretical
were applied in
in order to generate the hypothetical
PUTU
crop-
water production curve for wheat on West Campus.
DOY
180
IRRIGATIONS
45
45
200
215
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
220
225
45
240
45
250
45
260
270
278
45
45
45
280
45
290
300
310
(mm)
45
45
320
195
45
45
TABLE 12.1 -
Values
of APP and MPP derived from the
hypothetical crop water production curve.
IRRIGATION
FREQUENCY
WATER
{mm)
APP
(kg/{ha mm))
MPP
(kg/(ha mm))
300
340
6,5
8,5
11,4
25,0
360
380
9,4
10,2
27,8
25,0
420
11,6
21,0
10
460
12,2
12,3
11
500
12,0
9, 5
12
540
11,7
6,6
13
580
620
11,3
10,5
2 ,8
0,0
196
CHAPTER 13
SUMMARY
The
use
of weather data from an automatic weather
station
scheduling the irrigation of winter wheat was investigated.
method devised,
numerous
for
The
accommodated individual plots of land as well as
farms.
The
study
was carried out
with
experiments
conducted on the experimental site at the Agrometeorological
Ob-
servatory on the West Campus of the University of the Orange Free
State,
Bloemfontein
and on several farms in the Hartswater dis-
trict.
An
existing crop growth simulation model was refined and
for
the purpose of scheduling irrigation.
entailed
including
Major
modifications
a multi-layered soil and a water
routine using an exponential rooting distribution.
controlling
total evaporation loss from the
extraction
The function
vegetative
leaf cover was also refined.
tested
The
surface
under
incomplete
model
shown
to provide accurate simulations of water use and the onset
of crop water stress in the 1985 season on West Campus.
sons
were conducted in a lysimeter and on
plots.
It,
certain
with
an irrigation supply system was
during the experimental period.
197
Compari-
experimental
The lysimeter was installed specially for this
together
was
purpose.
established
Micrometeorological
tal
measurements were utilized on the experimen-
site to validate the modified Penman-Monteith
equation
estimating crop total evaporation using weather data.
modified
for
A slightly
version of this model was and is now being utilized
to
estimate crop total evaporation from data provided by the automatic
weather station.
Other micrometeorological techniques were
tested, but found to be inferior to the Penman-Monteith equation.
An
automatic weather station was commissioned in
provide
could
weather
data for the service.
Hartswater
It was shown that
be telemeterized via the normal telephonic
connection
UOFS
and
Hence,
the
network.
was effected via a manually operated exchange at
the
manual exchange in the
data
district
transmission and acquisition
of
technical
the
for
The necessary computer
the
function
The
initial
software
was
the manipulation and compilation of the data
and
for the simulation procedures.
for
The
problems encountered with the initial commissioning of
system were overcome.
developed
data
Hartswater.
should
satisfactorily under all conditions in any region.
to
Furthermore,
the modus operandi
disseminating irrigation scheduling prognoses to
farm managers was standardised.
individual
A good understanding was created
with certain farmers in the Hartswater area.
They are now recep-
tive to the establishment of such a service in the region.
A
theory
for planning irrigation
scheduling
conditions of limited water supply was devised.
198
strategies
under
ACKNOWLEDGEMENTS
The
research
here reported was financed by the
Water
Research
Commission and carried out under the research project entitled:
RESEARCH
ON
A WEATHER SERVICE FOR SCHEDULING THE IRRIGATION
OF WINTER WHEAT IN THE ORANGE FREE STATE REGION
The evaluation committee responsible for the project consisted of
the following members:
NAME
ORGANISATION
Chairman:
Dr
G C
Members:
Mr
Dr
Dr
Dr
Mr
D S
Dr
Water Research Commission
van der Merwe
Reid
du Pisani
Mottram
F Pretorius
J Joubert
J J Human
R du T Burger
W Snyman
de Kock
J du Toit
P c
A L
R
P
D
Prof
Prof
Dr J
Dr J
Dr J
Committee
Secretary: Mr
Green
F F
Marais
Dept of Water Affairs
S.A. Weather Bureau
Dept of Agric. & Water Supply
Dept of Water Affairs
University of the OFS
University of the OFS
The financial assistance received from the Water Research Commission
and
invaluable contributions made by the
evaluation committee are gratefully acknowledged.
199
members
of
the
Furthermore,
the
authors wish to convey their sincere gratitude
to the following persons and institutions:
Mrs DAWN DE KLERK for typing the manuscript.
The many TECHNICAL ASSISTANTS whose arduous,
it
data
was
time consuming task
to collect the crop field data as well as undertake
manipulation.
Their contribution is greatly
acknowledged
and appreciation expressed for their unselfish acceptance of
inconvenience
ter.
all
the
imposed while making the regular trips to Hartswa-
The technical assistants recognized are:
JOHAN VAN RENSBURG,
PETRUS STEYN,
HENDRIK
MICHAEL FLESLAND,
MYNHARD,
GERHARD DE
JONGH, NICO BARNARD, MARTHIE HOULTZHAUSEN, SUSANN GRIESSEL, THABO
MARe, ANDRIES JACKSON and ANDRe JOUBERT.
Dr
L K
Oosthuizen (UOFS) for valuable assistance and
guidance
in establishing the theory for maximizing profits from wheat crop
production under irrigation.
The cooperation of Mr Hugo Hamman, Chief Extension Officer of the
Department
herewith
tributing
of Agriculture {Experimental Station at Vaalharts ) is
acknowledged
for his exceptional assistance with
information during 1984.
cooperative.
200
His office staff
were
dismost
MR JAPIE SMIT is recognized.
Without his cooperation and assis-
tance the experiments could not have been undertaken.
meterized
self
the
The tele-
weather station was installed on his farm and he
him-
completed the telephonic connection thrice weekly to permit
transmission
of weather data.
It was due
mainly
to
his
encouragement and enthusiasm that so many farmers became involved
in the project.
The large financial contribution of the University of the
Free
Orange
State towards the purchasing of the lysimeter is gratefully
acknowledged.
The Hydrological Research Unit of the Department of Water Affairs
is
thanked
for providing the large bin for
the
lysimeter.
particular the efforts of Dr Peter MacRobert Reid in this
In
regard
are acknowledged.
Malcomess are acknowledged for providing the tractor and all
the
implements for cultivating the wheat.
The
cause
Bloemfontein Municipality were extremely sympathetic to
and
made a special dispensation whereby the
project
the
was
permitted to receive water during the drought when water restrictions were enforced throughout the entire area.
201
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206
APPENDIX
I
MATHEMATICS
This
section
is
aimed
purely at
documenting
the
elementary
mathematics useful in various parts of the report.
DESCRIPTION OF ROOT DISTRIBUTION
PATTERN
Let x denote the root mass per unit land surface area.
The defining equation for root mass distribution with soil
depth
( z ) is
<ix / iz
- - ax
For the boundary conditions
x - Xo
at a = 0, and
x = 0
at a = cw
-az
The solution is
x = Xo e
. . . ( 1 )
Let ZEFF = Effective root depth i.e. currently the depth to which
roots effectively extract water.
Then the mean value is given by
CO
x = fx. ST. I ZEFF
U
o
= Xo / (ZEFF . a )
207
. . . ( 2)
Evaluation of a
Practical application of Eqn 1 requires knowledge of the value of
a.
Let XT(z)
= the total amount of roots contained in the
profile to depth z.
soil
Let XT(ZEFF) = the total root amount in the entire effective root
zone .
Utilizing
the assumption that XT(z) / XT(ZEFF) - 0,97 occurs
at
depth 0.97ZEFF it is possible to evaluate a.
Firstly
-az
XT(ZEFF) =
/x z = Xo
/e
XT(ZEFF)
From
Eqn
-az co
]0
=
Xo [- I/a e
=
-az a,
Xo/at1 - e
]0
...(3)
=
Xo/a
...(4)
3
using
the boundary
condition
assumption
-a 0.97ZEFF
XT(0,97ZEFF) - Xo/l[l - e
], and
208
prescribed
by
the
-a 0.97ZEFF
XT(0,97ZEFF>> / (XT(ZEFF)) = (1 - e
) - 0,97
-a ZEFF
i.e. e
= 0,03
or
a = -{ln[0,03]) / 0.97ZEFF
...(5)
Evaluation of root density within a given soil layer, xn
The
amount of roots per unit of land surface found at depth z in
the soil layer is given by
-az
x = Xo e
...(1)
Integration between two levels (znl,
zn2) = xn,
the root amount
in the nth layer
zn2
zn2
I -az
x
znl
<£z
= Xo
/ e
Oz
znl
Xo[-l/k e
-az zn2
]
znl
-azn2
Xo/k[e
-aznl
- e
209
]
...(6)
Experimental evaluation of a
The value of a may also be found
(i)
given Xo and XT using Eqn. 4.
a
(ii)
=
Thus
Xo/XT(ZEFF)
...(8)
Alternatively should the mean value x, ZEFF and Xo be known
then
a = Xo/x.ZEFF
.. . (9)
may be used.
SOIL WATER DESORPTION CURVE
Consider,
PSIS = soil water potential in kPa
V = volumetric water content in mm/m
Let
A
P = ln(PSIS)
plot
of
...(10)
P versus V for desorption curves appeared to
exponential curve.
Thus,
be
the expression P = Po EXP(-mV) may be
fitted to such curve.
Hence, if
an
P = Po EXP(-m.V)
...(11)
P/Po - EXP(-m.V)
...<12)
and, ln(P/Po) = -mV
...(13)
Thus,
...(14)
V = -1/m ln(P.Po)
210
Should
PSIS
the
and
Given V;
constants Po and m be known for
the
fitted
curve;
V can be computed provided one or the other is
known,
the corresponding soil water potential may be computed
using Eqn 10.
And given P;
the corresponding volumetric water
content can be found using Eqn 14.
The fundamental equation describing the simulation curve is thus,
ln(PSIS) = Po EXP(-mV)
or
..(15)
PSIS = EXP(Po EXP(-mV)
..(16)
Determination of m and Po
m
and
Po may be determined for a given curve
points on the curve with coordinates (PI,
by
selecting
VI) and (P2, V2).
two
The
constant m is then calculated from
m = ln(Pl/P2) / (V2 - VI)
...(17)
The constant Po may be calculated using
Po - P/EXP(-mV)
. . . (18)
211
APPENDIX II
SOIL PHYSICAL PROPERTIES
Soil physical characteristics as calculated
equations (See Schulze, Hutson and Cass, 1985)
PLOT
DEPTH
1
VIO
mm/m
CLAY
%
mm
15,2
18,2
94
108
196
92
104
179
257
225
525
825
14,6
16,4
36,4
199
207
290
157
164
250
118
126
209
91
100
189
90
97
173
225
525
19,6
28,4
38,6
220
178
216
260
139
175
218
114
153
199
109
143
181
825
LYSIMETER
17,6
BELOW
15,2
TOP
159
172
257
299
212
202
170
159
= 0,
EQUATIONS : VIO
V30
V100 =
V500 =
V1500 where V is given in
1387 +
0 ,0943 +
0 ,0582 +
0 ,0262 +
0 ,0344 +
mm/mm.
Soil characteristics
equations.
CLAY
%
m
15,2
18,2
as
105
131
121
computed
*
*
*
*
*
CLAY
CLAY
CLAY
CLAY
CLAY
(% j
(% )
(% )
(% }
(% )
in P U T U - 9
V5
mm/m
V10
14,84
17, 16
43,02
240
252
333
201
214
296
159
172
257
126
140
226
200
206
289
157
164
250
220
257
299
211
P0
1,225
1,525
1 ,825
38
,00927
,00941
,00988
2,225
2,525
2,825
14,6
16,4
36,4
,00913
,00941
,00988
14,25
15,92
40, 15
239
244
3,225
3,525
3,825
19,6
28,4
38,6
,00941
,00956
,00988
18, 16
26,82
44,31
258
LYSIMETER
17 ,6
BELOW
15.2
,00927
,00927
16,43
14,84
251
240
326
294
336
212
V3 0
from
V 100 V500
the
Hutson
V1500
mm/m
V1600
mm/m
94
108
196
76
91
179
75
90
178
124
132
219
91
100
189
73
83
172
72
82
172
178
216
260
146
184
229
1 14
153
199
97
136
182
96
135
170
159
137
126
105
94
87
76
86
75
mm/m mm/m m m / m mm/m
201
Hutson
01
92
94
0,0041 6
0,00428
0,0041 3
0,0044 7
0,0038 1
the
VI 5 00
mm/m
38
3
TOP
V500
mm/m
121
133
215
2
m
V100
mm/m
202
214
297
225
525
825
DEPTH
V30
mm/m
with
181
Regression
equations for the estimation of soil water
retention
and the persentage variation accounted for by the regression.
SOIL
TOP
REGRESSION EQUATION
EQUATION
NO.
3.1
95
= 21,11 + O.44C + l,29Si
+ 1.06OM - U , 91BD
81
5,4
= 6,81
71
6,4
3.2
95
3.3
91500 = 15,26 + 0.28C + 0,05Si
+2,32OM - 10.6BD
80
4,1
91500 r 3,02 + 0 ,44C
66
5,2
67
5,1
H 0,87Si
= 35,56 + 0 , 3C h
- 19.8BD
72
8,1
= 6,4 + 0, 57C
61
9,4
3.4
+ 0 ,62C
Z
BELOW
S.E. OF
ESTIMATES
VARIANCE
ACCOUNTED
FOR (%)
3.5
91500 = -0,01 + O,67C - 0.003C
3.6
95
3.7
95
3.8
91500 = -1,93 + 0.31C + 0,59Si
+ 2.9BD
81
4,2
91500 = 2,54 + 0 ,4C
76
4,7
79
4,4
3.9
2
3. 10 91500 = -3,54 + 0.78C - 0.004C
The soil physical characteristics computed using the equations of
Mottram (1985) in the PUTU-9 procedure.
DEPTH
V5
mm/m
V10
mm/m
V30
mm/m
V500
mm/m
VI 500
mra/m
V1600
mm/m
1,225
15,2
1,525
18,2
38
1,825
2,225 14,6
2,525 16,6
2,825 36,4
3,225 19,6
3,525 28,4
3,825 36,4
LYSIMETER
251
270
515
270
261
510
306
400
523
227
244
470
244
235
464
278
362
478
201
215
421
216
207
413
247
320
428
161
170
345
172
163
334
200
352
150
158
324
160
151
313
187
238
331
149
157
323
159
150
312
186
237
330
,014
,013
,007
,013
,013
,007
,012
,009
,007
69, 15
61 ,79
95, 18
66,02
58, 33
80,90
78,79
67,50
99,25
TOP
BELOW
271
240
246
216
219
189
176
148
165
137
164
136
,014
,014
77 ,05
54,69
m
CLAY
%
17,6
15,2
255
213
m
P0
Approximate range
lysimeter in 1985.
in values measured by Neutron
DEPTH
PLOT2
V10 V1500
mm/m
V10
PLOT1
V10 V1500
mm
mm/m
PLOT 3
V1500
mm/m
probe
in
the
PLOT4
V10 VI500
mm/m
PLOTS
V10 V1500
mm/m
150
250
60
250
50
240
80
240
50
260
60
300
290
80
270
90
270
110
210
90
260
90
450
290
130
300
190
300
150
300
150
310
150
600
315
250
330
-
320
-
320
230
330
-
750
360
320
322
-
380
-
380
290
380
-
900
390
360
320
-
390
-
390
-
390
-
LYSIMETER
150
190
90
300
200
100
450
200
90
600
200
105
750
200
105
900
200
105
1050
220
109
1200
250
115
214
Plots
Lysimeter
Location of the pits at which sand,silt and clay percentages were measured at the experimental
site on West Campus during 1985.
Sand, silt and clay percentages measured on the different
irrigation plots on West Campus 1985 after the method of Day
( 1965) .
PLOT
POSITION
DEPTH
SAND
SILT
CLAY
100
300
500
81 .46
77 . 16
73 .98
16 .8
7 .0
4. 5
1 .3
13.
22. 2
100
300
500
60 .69
49 . 12
47 .72
7 .8
7 .4
6. 2
31 . 1
36 .6
44 .2
100
300
500
56 .06
46.61
44.06
9.6
10.01
10.0
34 . 9
39.7
38. 7
100
300
500
84 27
76 73
71 22
2.8
12.6
4.2
8.4
18.4
25.2
100
300
500
61 .2
60 . 1
55 .43
7 .4
6 .8
6 .3
30 .6
33 .0
34 .7
100
300
500
48.45
46.39
51.89
6.0
12.0
10.3
35.2
37.4
32.5
100
300
500
80.85
74.91
70.20
7. 1
4.6
5.6
9.3
20.7
24.6
100
300
500
54 .6
48 .51
46 .26
7 .0
6 .0
34 .2
43 . 2
47 , 7
100
300
500
63.66
46.22
45.57
11.2
8.2
8.8
26.8
38.6
38.4
100
300
500
73.02
54.58
49.2
4.0
4.2
3.6
22.8
40.8
46 .6
100
300
500
44.60
41. 18
40.32
7 .0
8.0
5.8
48.0
52.4
53.0
( mm)
216
4. 5
PLOT
POSITION
DEPTH
SAND
SILT
CLAY
(nun)
LYSIMETER
1
100
300
500
82 .84
77 .86
41 .3
3 .0
2 .6
3 .6
15 .2
18 . 2
54 .6
100
300
500
81 .12
81 .58
60. 18
4 .0
4 .0
2 .4
14 .6
16 . A
36 , 4
100
300
500
76.64
73.34
55.32
7.4
6.4
5.2
19.6
28.4
38.6
100
300
500
81 .50
59.68
54.78
4.0
3.2
4.2
8.2
37.6
40.6
100
300
500
81.9
75.72
62.48
8.4
4.6
4.2
14.0
18.0
32.8
100
300
500
82.20
82.10
81.22
10.2
1.0
3.2
18.4
19.6
14.8
116
100
300
500
81 .38
81 .92
80.32
217
2.6
3.2
4.0
16.4
15.8
15.6
Soil physical characteristics for the five farms at Hartswater.
CLAY
SILT
COARSE
(X)
SAND
MED
(X)
FINE
(X)
2
2, 98
20,22
63,84
1
2
3
4
14
14,4 4
16,64
16
2
2, 84
2, 22
3, 80
18,58
18,16
63,94
62,58
61,2
B
Subsoi 1
1
2
3
4
5
24
27
16
14
10
2
3
2
2
1
1,8
1, 54
2, 0
2, 08
2, 48
14,92
14
17,22
17,54
17,42
B
Topsoi1
1
2
3
4
13
10
22
22,4
1
2
3, 6
3, 6
2, 82
5, 0
2, 28
1,66
12
2
FARM
NO
(X)
A
Subsoi1
A
Subsoi1
12
B
Subsoi1
ORGANIC
MATERIAL
(X)
m
Po
,00913
12,89
1,7
1,6
2,0
,00927
,00927
,00927
14,17
14,43
15,4
59,36
56,66
65,72
65,66
68,48
2,45
2,90
1,6
1,4
1,4
,00941
,00956
,00927
,00927
,00913
21,71
25,32
15,4
14, 17
11 ,87
19,96
22
16
14,76
63,96
62
58
60
1,8
1,6
1,6
0,98
,00913
,00913
,00956
,00941
13,37
11 ,87
20,52
20,33
2, 16
22,72
61,72
0,8
,00913
12,89
5, 44
2, 56
22,4
16,16
61,02
68,44
1,0
2,33
,00913
,00913
11 ,87
12,09
2,1
B
1
2
10
2
10,4 2,6
B
1
2
12
14
2
4
4, 96
3, 28
23,48
16,84
59,9
62,76
2,09
2,6
,00913
,00926
12,89
14,17
E
Subsoil
1
2
3
14
15
16
5,6 10, 56
1
11, 18
2
11, 44
19,64
20,62
21 ,64
52,88
55,74
52,28
2,2
2,4
2,2
,00927
,00913
,00927
14, 17
14,51
15,4
E
Topsoi1
1
2
3
10
9
11
2
13, 64
1
7, 54
2,6 12, 64
28,44
26,64
23,58
48,26
56,94
52,60
1,7
1,5
2,0
,00913
,00899
,00913
11,87
11 ,24
12,31
D
Subsoi1
1
2
3
8
8
8
1,6
2
2
1, 82
1, 62
3, 46
26, 14
20,0
23,16
65,78
69,98
65,68
1,1
1,1
1,0
,00899
,00899
,00899
10,84
10,84
10,84
D
Topsoil
1
2
3
4
8
8
8
2
0
1
1 ,46
2, 68
1, 94
28,36
27,46
20,6
62,64
63,0
68,84
1,2
1,1
1,3
,00899
,00899
,00899
10,84
10,84
10,84
TopBoil
218
FARM
NO
CLAY
<*)
COARSE
SAND
MED
FINE
SILT
ORGANIC
MATERIAL
m
Po
t«
C
Subsoil
1
2
3
9,6
7
10
0,4
1
3
4,0
4 ,52
1,88
22,14
23,78
18,28
66,88
65,4
70,0
1,1
1 ,05
1,11
,00913
,00899
,00913
11 ,66
10,00
11 ,87
C
Topsoil
1
2
3
5,6
7
6
2,4
3
2
4 , 36
4,88
5,10
28,48
23,28
26,60
61,78
65,46
62,90
1,0
1,4
1,1
,00899
,00899
,00899
9,82
10,37
10,00
219
APPENDIX
a.
III
DIE SIMULERING VAN PLANTHOOGTE VAN KORING ONDER BESPROEIING
Doel
Die
ontwikkeling van 'n model wat die planthoogte van
besproei-
ingskoring (cv:SST44) akkuraat simuleer.
Metodes en resultate
Waarnemings
jare
(Tabel
van
planthoogte op verskeie plekke en oor
III.l)
is gebruik om 'n model
te
verskeie
ontwikkel
wat
planthoogte simuleer.
Akkumulatiewe hitte-eenhede na plant(Akkhe) en dae na plant (DOG)
is
oorveeg
vir die primere inset.
Deur middel van
statistiese
passing op gemete planthoogtes (tot en met blomdatum) is lineere,
eksponensiele, logistiese en polinomiese verwantskappe tussen elk
van bogenoemde veranderlikes en planthoogte ondersoek. Die kleinste
standaardfout
van beraming (Sb = 74 ram) is
verkry
met
'n
derde graad polinoom met DOG as insetveranderlike:
Hoog = 12.74 - 0.882*DOG + 0.0891*DOG~2 - 2.09 * 10"(-4)*DOG~3
..(III.l)
waar Hoog - planthoogte (mm)
DOG - dae na plant
220
Vgl.
III.l
en
gemete planthoogtes word grafies in
Fig.
III.l
gei1lustreer.
In
die
PUTU 9-model word hierdie verwantskap aangewend
aerodinamiese
skatting
konduktansie te beraam.
'n Fout van ,1m
om
die
in
die
van planthoogte sal lei tot 'n fout so klein as 10%
in
die beraming van aerodinamiese konduktansie.
Gedurende
die vegetatiewe groeifase word die planthoogte volgens
Vgl.III.l bereken.
perk
na
aJ. ,1981)
Gedurende die reproduktiewe stadium (die tyd-
groeistadium
75
volgens die
sisteem
van
Human
et.
bly die hoogte konstant op die hoogte wat bereik is op
groeistadium 75. Wanneer Akkhe as insetveranderlike gebruik word,
lewer 'n liniere verwantskap die beste passing met Sb = 112 mm:
Hoog = -10.34 + 1.482*Akkhe
..(III.2)
Gevolgtrekkings
Vir
die doeleindes van besproeiingskedulering in
die
Vrystaat-
streek m.b.v.
PUTU 9-reeks modelle, is Vgl.III.l 'n bevredigende
model vir die
simulering van planthoogte oor die groeiseisoen.
221
BOO
^
i
—
GEMETE WAARDE
MODEL
•
/
B
,
/
mm
600
,
UJ
o
o
400
•
•
i
B
3
i
200
>
i
•
i
1
\
i
i
40
20
60
80
100
DAE NA PLANT
FIG.
III.l. -
Gemete
die
planthoogtes van verskeie aanplantings oor
groeiseisoen en die kromme (Vgl.
III.l)
wat
planthoogte oor die vegetatiewe groeistadium simuleer.
222
TABEL
PLEK
III.l - Plantdatums,
blomdatums en plantdigthede van die
verskillende aanplantings wat gebruik is om die
model
vir die berekening van planthoogte te
ontwikkel.
LIGGING
JAAR
PLANT
DATUM
BLOMDATUM
kalenderdag)
O
PLANTDIGTH1ED
(plante/m
UOVS
29 7'
26 11'
1985
184
287
220
UOVS
29 7'
26 11'
1984
183
285
220
UOVS
29 7'
24 50'
1980
165
268
140
HAW
27 57* 24 50'
1983
164
263
258
HAW
27 57' 24 50'
1982
167
271
383
HAW
27 57' 24 50'
1982
176
271
233
HAW
27 57' 24 50'
1981
161
270
220
JKEMP 27 55' 24 50'
1981
159
272
220
UOVS - UOVS WES-KAMPUS
HAW
- HARTSWATER
JKEMP - JAN KEMPDORP
22 3
b.
VERFYNING VAN
'N BLAARGROEIMODEL VIR KORIN'G OSDER BESPROEI ING
Inleiding
Blaaroppervlakte
het 'n groot invloed op die tempo van waterver-
bruik deur 'n koringgewas.
Die akkurate skatting daarvan is
noodsaaklik vir susksesvolle besproeiingskedulering.
gepoog
dus
Daar is dus
om 'n bestaande blaargroeimodel(de Jager et.al.,1982)
te
verbeter.
Teorie
Die
teorie van die bestaande sub-model in PUTU-9
verduidelik
in De Jager et.
al •
(1982).
word
volledig
Kortliks simuleer die
model blaargroei met behulp van die volgende vergelykings:
Dlap = He * Q10 * Fw
..(III.3)
Lap = Lap + Dlap
..(III.4)
Boi = Lap * Npl
. . (III.5 )
He = (Trax + Tmin)/2 - Bo
224
en Tmx < To
..(II1.6)
waar,
Dlap
- die
daaglikse
toename
in
blaararea
per
plant ( mz/plant )
Lap
- die blaaroppervakte per plant ( m /plant)
Boi
- die
blaaroppervakte
per
oppervlakte
grond
{mz /m3-)
He
- die daaglikse hitte-eenhede bo 8 °C ( *C >
Npl
- die plantdigtheid (plante/m2)
Q10
Fw
- groeitempo koeffisient (m2/{°C plant))
- die berekende waterstremmingsfaktor (De Jager
et. al., 1982)
- daaglikse maksimum temperatuur tot 'n maksimum
van 20 °C ( °C)
Tmx
o
Tmin
- daaglikse minimum temepratuur ( C)
Bo
- basistemperatuur vir blaargroei (8 C)
To
o
- hoogste lugtemperatuur vir normale blaargroei (20 C
Die waarde van Q10 wissel na gelang van die groeistadium van
die
gewas, soos volg:
Q10 = 2 * 10~(-5)
vir 0 < DOG <= 60 (stoelatadium)
..(III.7)
Q10 s 2,5 * 10"(-4)
vir 60<DOG<=Bld-Pld (pypstadium)
..(III.8)
Q10 = -1.5 * 10~{-4) vir DOG>Bld-Pld (reproduktiewe stadium)
..(111.ft)
waar
DOG - die aantal dae na plant
Bid - blomdatum ( kalenderdag)
Pld - plantdatum (kalenderdag)
225
Die
gebruik van bogenoemde
aekere
leemtes
model vir besproeiingskedulering het
aan die lig gebring.
In
teenstelling
met
die
werklikheid, word positiewe blaargroei gesimuleer nadat die vlagblaar reeds volwasse is.
Die aanvang van die pypstadium (waarty-
dens die groeitempo die hoogste is), is konstant geneem as 60 dae
na plant ongeag weerstoestande tydens die stoelstadium. Die model
het
ook nie onderlinge kompetisie vir lig by hoe blaaroppervlak;-
tes in aanmerking geneem nie.
Verfyning
Die akkurate berekening van die tydsduurtes van die
verskillende
groeistadia is noodsaaklik omdat die verskillende QlO-waardes vir
elke
groeistadium
planthoogte
grootliks verskil.
Uit gemete
waardea
van
en blaaroppervlaktes kon die werklike duurte van die
stoelstadium beraam word. Hieruit het dit geblyk dat die gewas *n
kritieke
die
hoeveelheid hitte-eenhede (Akkhec)
einde
van
die stoelstadium bereik
volgens die sisteem van Human et.al.,
word
akkumuleer
voordat
(groeistadium
39
1981). Hierdie hoeveelheid
hang onder andere af van die vernalisasietekort (Verndef) wat die
gewas
ondervind
het.
'n Lineere verwantskap tussen
Akkhec
en
Verndef is ontwikkel:
Akkhec = 145 + Vcon * Verndef
Verndef = Verne - Akkke
..(III.10)
..(III. 11)
226
waar Akkhec - kritieke hoeveelheid akkumulatiewe
hitte-eenhede ( C.d)
Verndef - vernalisasietekort ( C.d)
o
o
Vcon
- konstante
( C hitte/ C koue)
Verne
- kouebehoefte van SST44 {°C.d)
Akkke
- Akkumulatiewe koue-eenhede in stoelstadium
(°C.d) (sien Vgl. III. 12)
Die waarde van Vcon is 21,8
o
o
C/ C wat verkry is d.m.v. die beste
passing van die een aanplanting waar Verndef > 0 was.
Die daaglikse koue verantwoordelik vir vernalisasie is soos
volg
bereken:
Ke = Bv - (Tmaks+Tmin)/2
en
0 < Ke < 2
..{III.12)
o
waar Bv - basistemperatuur vir vernalisasie (8 C)
Troaks - daaglikse maksimum temperatuur ( C)
o
Tmin - daaglikse minimum temperatuur ( C)
Ke - koue-eenhede ( C)
o
Die waarde van Bv is geneem as 8 C,
met ander woorde byvoorbeeld
is die skeidingspunt tussen hitte vir groei (sien Vgl.
koue vir vernalisasie (sien Vgl.
111,12).
Hierdie
III.6) en
verwantskap
word geillustreer in Fig. III.2.
Volgens
Maas
temperatuur
en
Arkin (1980) het enige daaglikse gemiddelde
o
onder 6 C dieselfde vernalisasie-effek - vandaar die
boonste limiet vir Ke.
227
Vanuit
die
bert,1984)
SST101
relatiewe koue-behoeftes van
en die kwantitatiewe kouebehoeftes
duurte
et•al.,
dige
(Jou-
Scheepers69,
r
n
kouebe-
van 7,4 { C.d) het.
van die pypstadium (groeistadia 40-59 volgens
1981) kon nie susksesvol gesimuleer word nie.
Human
'n Volle-
ondersoek na die fenologie van verskeie cultivars
die vooruitsig gestel.
van
van
en Belinda (Joubert,1982) is beraam dat SST44
hoefte (Verndef)
Die
koringcultivars
word
in
Tot tyd en wyl, word die waargenome datum
aarverskyning as inset in die model gebruik om die einde van
die pypstadium aan te dui (sien Vgls. III.14 en III.15).
Q10 = 2 * lCT(-5) vir Akkhe<Akkhec {stoelstadium)
..(III. 13)
Q10 = 2,5 * 10"(-4) vir DOG< Epd-Pld (pypstadium)
..(III.14)
Q10 = -1 , 5*10"(-4) vir DOG > Epd-Pld (reproduktiewe stadium)
. . ( 111 . 1 5 )
Waar
Om
Epd
- datum van die
(kalenderdag).
einde
van
die
kompetisie by hoe blaaroppervlaktes in ag te
III.3 verander na Vgl.
III.16 en Vgls.
sioneel in die model
ingesluit.
oppervlaktes
as Boio.
groter
pypstadium
neem,
is
Vgl.
III.17 en III.18 is adi-
Kompetisie vind plaas by blaarDie daaglikse toename
in
blaar-
oppervlakte per plant onder hierdie toestande word dan gereduseer
eweredig
tot
die
verhouding tussen die
(Nplo) en die werklike plantdigtheid (Npl).
is geneem as 140 plante/m
optimum
plantdigtheid
Die waarde van
Nplo
aangesien die Q10 waardes verkry is by
228
hierdie
plantdigtheid.
'n Waarde van 2,3 vir Boio het die beste
passing met getnete waardes gelewer.
Dlap
Vgls.
=
He * Q10 * Fw * Komp
..{til. 16)
Komp - Nplo/Npl vir Npl>Nplo en Boi>Boio
..{III. 17)
Komp = 1 vir Npl < Nplo en/of Boi<=Boio
..(III. 18)
Fw
..(III. 19}
= 1 vir DOG > = Epd - P l d
III. 4, III.5 en III.6 word net so behou vir die verbeterde
mode 1.
Veriflering
Die
model is getoets teen gemete waardes
irideks
van
III.3).
nie.
vier
van
blaaroppervlakte-
seisoene op verskeie lokaliteite
(sien
Hierdie aanplantings het geen waterstremming
ondervind
'n Gladde kromme is deur die gemete waardes getrek om
ster ingsvarias ie te elimineer.
Die gewasfaktor
mende
die
Beide
en Fie waardes van elke tien dae is vergelyk met ooreenstemberekende
analises.
was,
mon-
(Fie) van Ritchie
(1972) is bereken vanaf Boi waardes van bogenoemde kromme.
Boi
Tabel
Wanneer
waardes deur middel
van
verskeie
statistiese
beide gemete en berekende waardes van Fie = 1
is dit gelgnoreer. Die simulering van blaargroei tydens die
reproduktiewe
waarnemings.
fase is nie
Resultate
getoets nie weens 'n
van die statistiese
toetse
gebrek
wat
aan
die
akkuraatheid van die model weerspieel word in Tabel III.2 gegee.
229
VERNALISASIE
BLAAR EN FENOLOGIESE ONTWIKKELING
12
a
Q
s
8
s
LJJ
LU
I
12
20
EFFEKTIEWE TEMPERATUUR C O
FIG.
III.2 -
Die verwantskap tussen temperatuur en koue-eenhede
vir vernalisasie en hitte-eenhede vir blaargroei
Boos gebruik in die verbeterde model.
230
28
TABEL III.2 -
Die
waardes
wat
verkry
van verskeie statistiese
is
met die toets
parameters
tussen
gemete
berekende waardes van blaaroppervLak-indeks
en
(BOI)
en die gewas faktor (Fie).
Statistiese parameter
BOI
b
1.02
0.78
c
0.04
0.09
r
0.93
0.83
d
0.96
0.91
diff
0.69
0.12
11
29
se
b
(%)
Fie
- helling van die regressie tussen berekende en gemete
waardes
c
- afsnit van regressie tussen gemete en berekende waardes
r
- korrelasie ko£ffisie'nt
d
- akkuraatheidsindeks (Willmott, 1982)
diff
- gemiddelde
absolute verskil tussen gemete
en
berekende
waardes
se
- sistematiese fout as'n persentasie van die totale fout
(Willmott, 1982)
231
TABEL III.3 -
Plantdatums,
en
datums
van die einde van pypstadium
plantdigthede van aanplantings wat gebruik
is
vir die verifiering van die model.
PLEK
JAAR
PLANTDATUM
DATUM VAN
EINDE PYP
kalenderdag
PLANTDIGTHEID
plante/m^
uovs
1985
184
276
220
uovs
1984
183
277
220
uovs
1980
165
261
140
HAW
1983
164
249
258
HAW
1982
167
257
383
HAW
1982
176
257
233
HAW
1981
161
263
220
JKEMP
1981
159
253
220
1 -
Temperature vanaf kalenderdag 164-208 was nie beskikbaar nie
en langtermyn geraiddeldes is vir modellopies gebruik.
2 -
Datum van einde van pypstadium is beraam.
232
APPENDIX IV - LISTING: PUTU 9.86
5
'PUTU9-86
NAAM;PUTU9-ti6
OP DISK:PROGRAM #2 (a)
10
LPRINT "Latest up-date on
]y86.0B.19 "
20
25 ' u i n i i i i i i i i i t t l t t l l i t t t l l l l i t l i i i i i i i M t t i t i i i i i i t i i t i i i i i i i l i i
26
FIKLD WATER CAPACITY AT -10
27 LPKINT
28
29
OPTION BASE 1
30
40
DIM XI366,8 1 , GMT I 12) , LiMkl 12) , CMS < 12) ,UME< U ) , MONL I 12) , MLNDl 12) , IHA MKh i 1L
SO
'MEAN DATA
GOSUB 2500
60
'FUNCTIONS
GOSUB 15000
62
1
INITIAL, CONDITIONS
GOSUB 9500
64
'WEATHER DATA INPUT
GOSUB 10500
72
1
PARAMBTERS
GOSUB 3100
75
'ZERO
GOSUB 10000
80
GOSUB 5000
'TITLE PAGE
85
86
I S I IJ 1 1 I I t I t t * I <1 4 I • I t I 1 •
yo
'BEGINNING OF CALCULATION
100
itliuiilillllinlullili
101
130
FOR D - BEGIN TO FINISH
170
172
'ENVIRONMENTAL VARIABLES
GOSUB 5500
1B0
'WATER BALANCE
GOSUB 6000
190
IF STAGE > 1 THEN 230
200
'PREREST PERIOD
GOSUB 1400
210
GOTO 540
220
230
IF STAGE > 2 THEN 26 0
'REST PERIOD
240
GOSUB 1500
250
GOTO 520
260
'TILLERING
270
GOSUB ] BOO
280
GOTO 500
290
IK STAGE > 4 THEN 320
'STEM EXTENTION
300
GOSUB 1700
310
GOTO 500
'BOOTING
320
IF STAGE >5 THEN 350
330
GOSUB 1800
340
GOTO 500
350
'ANTHESIS
360
GOSUB 1900
370
GOTO 500
380
390
•GRAIN FILLING
GOSUB 2000
400
GOTO 500
410
' STAOE 8
420
'RIPENING
GOSUB 2100
430
500
510
5 20
530
GOSUB 12800
GO3UB 11500
GOSUB 12000
GOSUB 7000
'ASSIMILATION
'TKANSLOCATION
'TOTALS
'WHITE RESULTS
5.15
540
542
5*3
550
557
••lid
600
6 10
614
bIS
61b
6*0
630
640
650
'
IK D - K I N 1 S M
THEN GOSIIB
'WRITE CARRY OVER FILE
14 1 0 0
NEXT D
I)
r U-
tfUStiH
I
;
YEAR
= YEAR
+
1
:
GOSUB
13900
'RECORD RESULTS ON DOY 365/6
75JJ1I
GOTO h I II
I.USl'H 2 41)0
GOSUU 1 0 0 0 0
linSlitl SliUO
Ih « y < > 1 I M t M HL'(l
'RESET
'ZERO
'MID-SEASON
INITIAL CONDITIONS
UKlil 1st - 1
i ; u l u 1130
'WEATHER DATA
'TABLE HEADING
i . u s i l t lil:,UU
(Ji)SUU S400
GuTO 150
' iiiltiiiui
140U
I 405
1410
1415
14 20
1430
1450
Hi.il
1470
1 4M0
1490
1500
I50S
1S10
1515
1520
1525
1530
1535
1540
151 L
1542
1543
1545
1550
I55S
1560
156S
1570
1575
1580
I 5H5
I5M0
1595
1600
160S
1606
F: IF
1610
"St»ae 1 pre-rest period"
lltllllltMllllllltKIMtMIKiltllllllllllltlitllltlltllMIMM
II- STAGE = I AND i)= BtLi IN THEN
PRINT "GROWTH STAGE 1 PRE-REST PERIOD"
II- 11 < JH THEN linn
STAUE=2
THEh=0
GOSUB 10000
l.l'HINT " UKOWTH STAGE 2 - REST PERIOD
RETURN
'
"Stage
'liMtMIIIIIUtMlllltll
IK D<JP THEN 1585
STACE=3
l.l'HINT " Total rainfall
DOG : 0
TRLH=O
TTHANK = 0
TPTRAHS - 0
GOSUB SHOO
L
» io~4
C r L-H
CL = CU
Q
z
' ZERO
DOY
;D
2 rest period"
tlllltltlUtttlltMltltllllllllllUMIMl
in real per iod:"; TREE
' TO ESTABLISH SOIL WATER CONTENT
IF SO WISHED
.B
gio=2»io-(-5)
K Y = . 2 : HY=I
HUsO
HUT
= HDCO
liOHUB 1 2 5 0 0
'MEANS
i.'jiUlt 1 0 0 0 0
'ZERO
I.PKINT " T.ROWTH STAIiK .) - TILLERING PERIOD
RETURN
IF HU>=HUCO AND VTEL-0 THEN VERNDEF=CUC-CU :VTEL=1:
HUC<HUCO THEN HUCrHUCO
IF HUtHUC THEN 1665 : 'IF D < ENOTIL THEN 1665
HUC;HUC0+C0N1«VERNDB
1619
MTAGE=4
1620 g = .8
1625
1)10 = 2.5*10"(-4)
1627
RT = TTRANS/TPTRANS
1630
YRED(STAGE-11=
KYt(l-RT)
1635
K Y : . 2 : 'KYrl
1637
LPRINT USING " Y R E D M I * TTRAMSzlll TPTRANS=I## TLYS- f*l C=tt*ff BL/CGL/C=.## Sl,/C=.ll" ;YRED(STAGE-I ) » 100; TTRANS ; TPTRANS ; TLYS ;C; BL/C; GL/C ; Sl./C
163B LPRINT USING " DEEPERC:|«##
TSO1 l.V AC- • 11 t
WUSb-'flM
EPEHC;TSEVAP;WUSE;TAW
1640
TTRANS=0
:TPTRAN3:0 ! TLYS-0
LPRINT
1645
GROWTH STAGE 4 STEM EXTENT1ON PERIOD"
ENDTIL=D
1650
1660
RETURN
1665
REM
1670
GO-SUB 10850
'CROP HEIGHT
1672 GOSUB 5700
•PSIC
1
1673
GOSUB 10900
LEAKGROWTH
1674
• Jiisi li 108O0
'HAIL
1675
BKTA:DELLAP»NPL«SPL
16H0
RHOs . I I I W
'TRANSLOCATE TO ROOTS
1681
TRANSLOCATE TO 3TEMS
P H U . UDMG
TRANSLOCATE TO GRAIN
16B2
ALPHAiO
RESERVES IN STUBBLE
THETA - UMIi- ( A L P H A « I'll I • H K T A . H I K . l
16R3
RETURN
1684
1685
1690
1700
Stage 4 stea extention
1705
IMtltlllllMIt
1710
IF 0 <. ENDEXT THEN 1756
1715
STAGE=5
1720
ENDBOOT-ENDEXT+5
1725
«10:-l.5I10"(-41
1727
RT - TTRAN3/TPTHANS
1730
YREDISTAGE-1)=
KY*(I-RTI
1732
LPRINT USING " YRED=#*t TTRANS=#tl TPTRANS=#»# T L Y S : ill
Illll B L / C :
G L / C - . I I S L / C : . # t " ;YRED(STAGE-I ) * 100 ; TTRANS ;TPTRANS ;TL.YS ;C ; BL/C ;GL/C , S L/C
1733 LPR1NT USING " DEKPERC=I«I*
TSO1 LVAP= t « * «
WUSE^MM
TAWÎIIf
EPERC;TSEVAP;WUSE;TAW
1735
TTRANSÔ
:TPTRANS;0 : TLYSÔ
1740
K Y : . 6 5 : "KY=1
1745
GROWTH STAGE 5 BOOTING
LPHINT
1750
RETURN
1756
1759
GOSUB 10850
'SEE LINES 1670- 11.
GO3UB 5700: OOSUB 10900: QOSUB 10800
1760
BETA=DELLAP»NPL>3PL
1765
PHI:.2*DMG
1770
RHO:.
1775
ALPHAsO
1780
THETA = DM -(ALPHA + BKTA+ PHI+RHO)
1785
RETURN
1790
'llltMttttlltltlllllltllltlllitlMIUtllllllltlMMIMIIMlllll
1795
'
"Stsce 5
booting"
1SO0
1805
'ItlUIIIIIUItllUltlllUIIMIIttltltttllMlitlMIIUMIMttlll
1810
IF D < ENDBOOT THSN 1860
1816
.STAGE - 6
1820
ENDANT=HNDBOOTtlO
1821
CO
HT : TTBAN3/TPTRAN3
IHii.t LHKINT uaiNt. " VRKD-#il TTRANS=##* T P T H A N S - M I TLYS = I** C=ttt*4 BL/C= . M 2110 IF D<ENDRIPE THEN 2153
Gl./C':. tf SL/C= .*#"; Y«8D(3TAUE- I »» 100;TTRANS;TPTRAN8;TLYS;C;BL/C;GL/C;SL/C
2115 Y = YO*( 1 - ( VKEDI 3)+YRED< 4 ) +YREDI5 1+YRED16 I tYKfclM 7 ) ) |
IHU-l LHKINT UHlNti ' l>febPEHC = M # #
T3OILVAF=IIt*
WUSE-tilf
TAW-ilM
HT= . 2120 LPRINT "
Environaentsl potential yield for "J VEAK;'1 ";:LPRINT USIS'
If •' ;UI-M'hHC: THF.VAP; WUSK ; TAW ;HT
" If 11# " ; Y : : LPRINT " kg/ha 11
IH26 TTKANS=U
:T»"THANSsO : TLYSSO
2125 LPRINT U3ING "TOTAL DEEP PERCOLATION
m i , EVAPORATION FROM THE S O U
|M:IU KV-.to : ' K V M
RFACK • •»*
AND TOTAL WATER USE I «• t" ; DKKl'KRO; T3EVAP; WUSE
IH-10 I.l-N I N1 likoWI'll STAtiK ti ANTHES1S"
Z130 INPUT "Do you wteh to repeat the calculation Tor a further season I Y/N
IHMI kk'lUKN
ANSWERS
IHliO '
2 140 IP ANSWER* <>"Y" THEN 16000
1864 i.uSI'H :WUi). OuSUH 10900: UOSUB 10800
: GOSUB 10850
'SEE LINES 1670-1683 2150 GOTO 560
IKS5 HKIA=0
2153
IH70 1'H I = . £ • U M U
2 155 G O S U U 5700 : G O S U B 10900
1875 K H O - . 1 M.'l-K;
2 1 5 6 G O S U B 10800
1H8K ALI'ltA = O
2 160 AI,PHA-0:BETA = 0: PHI =0 : RUO = 0 : T H E T A ^ O
IHMf) IMETA = UM<;- (AI,fHA*BKTAtPHI * R H O )
2170 KETUKN
1U90
RETURN
2180 '
IM V .
'iiiiiiiiiiiiiiiilitiliiMttitiniiiiiuiiiiiiiiiiiiiiiitlltlltlUtll
2190
1SU0
•
2400
'RESET ALL STATUS VAR. TO ZERO AND COEFFICIENTS .RE-ESTABLISH INITIAL
i'i;:i
i',1"
'4llil4llH(<IMlttl<llltlllltttlllltlllllllllllilllMltllti<ttl>tl>l
IK I K b N D A N T T H E N 197U
2401
2402
'
C O N D I T I O N S AND REST PHASE
' lltlUttlillltllltlillllltllllltllltillllltullltlllllttlllttltlllti
Slttge 6
anthems
iiiiiiitiiiiiiiiiiiitiiiiiiiiiiiiniiiiitiiilliiitiiiitlllliilllliti
IH25 STAliE = 7
24 10 TP
- 0
1930 fcNDh I1.L-EN[>ANT*35
2420 IIUPREV - 0
IM42 RT - TTRANS/Tl'THANH
2430 HU
=0
1943 YRKD(STACE~1 )= K H I 1 - R T )
2440 31NKF = 1
iai4 Ll'HINT USING " YHtb = ### 1'1 HANS - # # I TPTKANS=#*# TLYS= tit C-ttttt BL/C=.I# 2450 DREP : 0
GL./C=.f* SL/C= . #t" ; YHtUISTAGE- 1 1«100; TTRANS ; TPTRAN3 ; TLYS ;C; BL/C; GL/C ; SL/C
2460 DOC
= 0
19*5 LPKINT USING " DEEPfcRCitttt
TSOlLVAP=####
WUSK=l##i
TAW-I#I#
HT=. 2470 TREE = 0
If "iUEKPEHCiTSEVAH;WUSK;TAW;HT
2480 STAGE ^ 2
1947 TTkANS-0
: Tl'TRANH : 0 : TLYS = 0
2485 BL;O : GL = 0 : C L = 0 : S L - 0 : RL = O : CuO : CB= 0 : CA = O
I960 K Y = . S 5 ! ' KY- 1
2490 X 1 : 0 : X 2 = 0 : X 3 = 0:X4 = 0 : X S - 0 : X 6 - 0 : X I - 0 : X 8 = O : X 9 = 0
1957 L P H I N T "GROWTH S T A G S 7 GRAIN F I L L I N G "
2499
IllllMltitnillllllltilMltltlinttMtllllllllKinilKllllllllKli
I960 RETURN
2500
'
MEAN
DATA
1976
1980
ia85
IU90
1995
2000
GOSUli 5700: COSUB 10900: GOSUB 10800 : GOSUB 10850
'SEE LINES 1670-1683
BETA:0:PHI:0:RHO-0
:ALPHA-DMG
THETA = DM(i-(ALPHA + Bl-:TAtPHItRHO)
RETURN
tllllliltnittilliiimitn
(titttiiiitttttiiiiitiititiiiiltiiui
'
STAGE 7
Grain filling
2530
2540
2550
2560
2570
2580
DATA 31,28,31,30,31,30,31,31,30,31,30,31
FOR 1=1 TO 12
READMONL(I|
! • PR I NT HONL ( I )
NEXT I
DATA 22.9,21 .9, 19.8, 15.6, 11.4,8. 1,8.5, 10.fi, 14.6, 18,20. 1 ,22. 1
FOR 1^1 TO 12
2020
2025
2030
2032
IF D<ENDFILL THEN 2055
STAGE s 8
ENDRIPEÊNDFILL*10
RT = TTkANS/TPTRANS
2600
2610
2620
2630
NEXT I
DATA 9.3,8.4,7.9,7.9,8. 1,7.8,8.3,9. I ,9,9.2,9 . 8 , y.9
FOR I>1 TO 12
READ CMS( I )
: 'PRINT GM3rs
2640
NEXT I
KY*(I-RT)
2037 LPRINT USING " YRED=»t» TTRANS=#tf TPTRANS=### TLYS= fl* Csff##f| BL/C=.## 2650 DATA 84.6,81.9,76.7,53.7,23.5,7.0,90.0,90.0,90.0,90.0,90.0,68.1
GL/C=.I# SL/C=.##";YHED(STAGE-1)«100;TTRANS.TPTRANS;TLYS;C;BL/C;CL/C;3L/C
2660 FOR 1=1 TO 12
2038 LPRINT USING " DEEPBKC-ff##
THol l.VAH" 11 * I
WUSE = ***f
TAW-*f|l
" ; DE 2670 READ GMH ( I )
: 'PRINT GMR ( I )
EPE«C;T3FVAP;WUSE;TAW
2671 NEXT 1
2039 TTRANS = O
;TPTRANS = 0 : TI.YS = O
2675 DATA 12,11,10,9,8,5,5,8,9,10,11,12
2045 LPRINT "GROWTH STAGE 8 RIPENING "
2676 FOR t=l TO 12
2050 RETURN
2677 READ GME( I I
: 'PRINT GMBlI)
2055
'
2678 NEXT I
2058 GOSUB 5700: GOSUB 10900: GOSUB 10800
'SEE LINES 1670-1683 2686
'INPUT "NAME OF FILE ON WHICH TO STORE I-WATER CONTENTS AND 2-CROP
2059 IF L A K 0 THEN LAI=0
VARIABLES " ; SOWATI, GROt
2060 BETA=0:PHI=0:RHO=0:ALPHA^DMG
2687 SOWATI = "THBTAV"
: GROI - "PHYS"
2070 THKTAMlMG-l Al I'HA* BhTA t PH 1 tHhui
2688
KILL SOWATI
! KILL GROI
2080 RETURN
2689 OPEN 0RO|
FOR APPEND AS 12
: OPEN SOWATI
FOR APPEND AS #3
rsj
<J1
2090
' * • » • « « « I l l l l l l t l l l l t « M ( « I I I M t l l » «» » • > • ! » I K « * * « I l l l l l i « * « t « * I I
2100
'
STAGE 8
RIPBNINO
26MU
2691
2700
2710
2H3O
2H4O
2850
2HK0
ZHi'll
2880
2890
•-•HID
2920
2930
2940
2950
2960
2970
29H0
2985
2990
3000
3010
3020
30:10
3050
3060
3070
3000
3082
3084
3086
3087
HIHH
3090
3099
3100
3101
31 10
31Z0
3 130
3M0
3141
3142
3143
3144
314S
3146
3147
314H
3149
3150
3200
3210
3220
cri
HFTIJkN
IIIIIIIIIIIMIIIIlllll
UISTHI bUll ON OF INFILTRATION
WATEH THROUGH SOIL
PROFILE
Illlllllltllltllllll
II- XlU,:i> - 0 ANH X(H,6l - 0 THEN 3090
RUNOK - .2
1NF1 I. - X(1).:U + IU
.1
IF INK 1L i 511 THEN HUNOP
.05
IF INI-I I, <• 2ft THEN RUNUF
0
II- INF II. < IS THEN HHNOF
I N H L = (XI I), 3 I tXlU.til ) • l l - R U N O F )
W( I |:W| I | + INF1L-SO1LVAP
1
LPHINT W|1)
IF W[])<:W0lI 1 I THEN 29ti0
PEKCI I »=W| I >-W0l I I )
W< 1 )-WOI I I I
VIII : WflI/HZRT
PSlS(l) = FNTION15 1P0I1),M(II,V(I))
'PR I NT W( II,V[ 1},PKRC( 1 >,PS 1S( 1)
IF INKlLrO THEN 30MU
FOR L = 2 T O 9
W ( L ) ;W( LI +l'FRC< L- 1 1
IF W| I,) <W0l (l,| THEN 3050
PEKl'll.) - W(L) - WO M l . I
W|L)
= W0K1.)
V ( D = WILI/U/.HT
PS1SILI = FNT1ON1 5( l'0(l,) ,M(L) , V I L M
'PRINT USING • 1 I M « H '-,W(L) ;V( L) ;PERC(L) ; HS13(L)
NEXT L
DEEPERC ^ UEEPEHC + PEBC(9)
1
L P R I N T "DEEP PERCOLATION " ;DEEPERC
FOR L=I T O 9
PtHC:(L)=0
NEXT L
RETURN
llltMIIIIIIII
PARAMETERS
'llllllllltlllllllllllllllUlMltllllllltlttlMlllllllltltlllllilllttll
GOSUB 14000
TPAWiO
FOR L=l T O a
PERC(L)=0
VOIIL)=FNTION16|M(LI,2.3
,PO(L))
'1.609 FOR VO AT 5 kPa
VI5IL)=FNTION16(M(L),7.3,PO(L))
V16(L)=FNTIONI6(M<L1,7.4,POIL)I
WO I ( I, | : V O 1 [ L M D Z H T
WI5(L|3V151L|«DZHT
H16(LI=VI6ILI
PAWf D ^ W O l ( L I - W J 6 I L)
'PHINT W15(L),W0l(L),W(L),W16(L),TPAW
NEXT L
SPL
= 500
'SPECIFIC LEAF RATIO k(/ha
KAISER....SpL=50f/B~2
SEEDMAS-.03«10"<-3)
'kg/aeed
i*2
SOOka/ha
LEAFAREA
3270
TO - 25
3275 VTEL=0
3280
HO - 8
'DAILY MAX TEMP FOR CALC O F HEAT UNITS
HUMX=20
3290
'MINIMUM COLD UN1TSCH REQUIRED TO END TILLERING
CUC = 7 . 4
3294
'ADDITIONAL DD PER UNIT OJDEFIC1T DD/CU
COHl - 21
3296
.8
3320 COO=.012
•PHOTO EFFICIENCY 1*1
EFFMAX=20
3330
'PHOTO CHEMICAL EKWIVALENT
k«\J
FE=6fi«10"(-9)
3340
WEQ=40
'fcg/(ha • • )
3360
' DM
3370
' KC/Ha
33BO VO=7000
'RATE OF VERTICAL ROOT GROWTH HATE m/d
341 1 RGR s . 0 18
•X O F TOTAL ROOTS FOUND BELOW 0.97
AP = 2 . 1
3412
AP = A P / 1 0 0
3413
RMO = 1 0 0
•kg/(h« p i )
3414
KSPO - -I.2*10"(-3 I
'MAX CONDUCTANCE OF ROOT ZONE ••/(d kPa • pi )
3416
KSPO = KSPO»DZRT*NPL
3417
3420
RETURN
3430
5000
TITLE PACE
5010
5020
LPRINT TAB ( 7 ) ; " PUTU9 IRRIGATION SCHEDULING O F WHEAT1"
5030
Run date : ";DATE«;" T
5035 'LPRINT "
: 1 ";T1ME|
5110 'LPRINT
5130
LPRINT TAB(7);Nt
5140 'LPRINT
5150
LPRINT TAB<10>,"PLANT POPULATION " ;TAB<38),"CULT!VAR";TAB(55 I,"PLANT1N
TE"
5160
LPRINT TAB!16);NPL;" (/«"2)";TAB<451;C«;TABI57);PDAY; I";PMONTH;"/";VF
5165
LPRINT TAB( 6),"BXTENTION ENDED ON DOY ";ENDBXT
5170
LP81NT
5180
LPRINT T A B ( 1 0 ) SOIL DESCRIPTION:";W«
5210
LPRINT TAB(10) SOIL MOISTURE (••/•!"
5230
LPRINT TAB!15) MAXIMUM":TAB(30) ;"MlN1MUM";TAB<40>;"INITIAL";TAB I 50) ; I
CTIVE ROOTING DEPTH"
5240
LPRINT USING '"
»l#
lit
tit
;V01<2);V15(2);VINIT(2);2EFFO;
5242
LPRINT "•"
5250 '
5300
iillllllltllltl
iiiiillliiiiiiltlltiiitillltlKMiiiilllltl
5350 '
TABLE HEADING
5400 'ItlltlllUlllllttltllUltlMllltllMtlltlMllllttDltlMIMttDI
5410 'LPRINT
5420 LPRINT TAB<7);YEAR
5430 LPRINT
DOY
FH LAI IK RAIN PERC
WATER POTENTIAL HU
5440 LPRINT
Ely«"
EB
32
S3 ST
L"
5443 LPRINT"
x 10
(MPaMOO)
(XI
(••)
(XI
5445 LPRINT
0)
5450 RETURN
5460
t i t i t
n i l
t i i i i i u i i i i i i i i i t i i i i i i i i
i i i i t i i i u
5470
ENVIRONMENTAL VARIABLES AND LIMITING FACTORS
5500
5510 ' l l l l l l l l t t l l l U I M t t l l l t l l l M M I I t l t l l l l l t l l t l M t l i l U I U i l l l t t l
5520 NMJU FNDLENCTH(D)
' MAX DAYLBNGTH
5530 S O S K N M J I J I O )
' DLY PO3S RAD
5555 K T - X l h , 4 )
I i.
5 5 til)
55HO
5!><M>
fitiOl)
56 l()
5fci!0
5690
5700
S70i>
57 10
5 7 20
58OS
600U
- M
!(•
6U4O
6050
6052
Mill 1
6055
6056
6057
6058
6060
6490
649 I
6500
6510
6520
6530
6540
6550
6560
6570
6580
6590
659L
6592
69»9
7000
71 12
7115
7130
do,
7)32
7135
7137
AKL<7
7142
7500
7510
7520
7530
7540.
7590
-
'MEANS
7592 OOSUB 12500
1
IF X(D,7> <) 22 THEN 7690
7595
7598 IF P3IL>-800 THEN FUDGB--700 ELSE FUDGE-0
7600 LPH1NT USING " f * « M " ; D; F«« 100 ; LA I * 10 ; X I D, 6 ) ; XI D, 3 I ; DEEPERC; PSISI 2 ) / 10 ; \->.
liUSUB 9000
3>/10;P3IST/lO;(PS IL+FUDGE I/I0.HU/10;PVi10;THAN3»10;3OILVAP«10;X(D,81 * I"
I'AK : . 5 * HFDM
7 60 5
RETURN
7610 PRINT 1 2 , USING till ";D,DOG,KW»100,LAI•10,BL,RL,CL,GL,C,SL,DMG,TREE,
1 mi MIII I I I I I I I
1
THANSi10.PTRAN3*10,(TRANS+301LVAPI/PVIIOO,X(D,8I/PV«100,YRED*100,Y,YO>
CRITICAL LEAK WATER P O T E N T I A L
7611
I l l t l l l l l t l l l l l l l l l l l l
1
7612
•ZfcHO
GOSUB 1110(11)
I'SK : -1600 - 11.7 • (DOQ-50)
7690 RETURN
I)- I'SK > -1600 THEN H.S1C = - 1 6 0 0
7700
IK I'SIl" < -2300 THEN I'SIC = -2300
SOILWATER POTENTIAL
8000
KKTUKN
IttlltlttlDII
IllllllllllMIMIMIMMIIItMltlltllllttlllllllll
'lllllllllllllMllllllllllllllllltllMllllllllllllltlllllllllltll
8001
B010 FOR L-l TO 9
WATKH BALANCE
8020 PS1S(L) = FNT10N15(PO(L).MIL),V|L))
i
iitiiiiiiiiiiiiMtitiiniitiiiiiiiiniitittiiiiiiiti
8025
'PRINT USING "ItttM" ,PSIS(L);
GOHUB 13000
'BOOT DEVELOPMENT
8030 NEXT L
QOSUU B0O0
'SOIL WATER POTENTIAL
8040 RETUHN
GOSUH 13300
'SOIL-HOOT CONDUCTANCE,
8498
'WATER POTENTIALS EACH SOIL LAYER, PSlleaf, PH
8499
'EXTRACT WATER FROM EACH LAYER
8500
TKMPRATURE ROUTINES
FW s I-II
8501
i t • i
II- FW >1 THEN KW=!
8510 IF X(D,1}= -99.9 THEN X(D,1)=OMT
IK KW <0 THEN HWiO
8S20
IF X(D,2|= -99.9 THEN X(D,2|:GMT
•PHI NT USING '•*(*#«.tt";FH;FH
8530 •r=i x(ii, i j +x (ii, z i )/2
'TEMPERATURE, HEAT
GOSDB 2700
•ttt-WKT SOIL
8540
UPRT=X(D,1 1
RETURN
'IMIIIIIIIIIIIIIItlltllMIMIIMIIItltlttmMMtlMIIIMIIMIIIIItlll
8550 IF X ( D , U > H U M X THEN UPRT = HUMX
1
8S60 EFFGT-(UPRT*X(D,2))/2
DOY
'IIIIMItllllMlttllllMlltKIIMItllMDt
1111
I II11 • 11 II11 H I M It I
8570 IF EFFCT<BO THEN EFFGT^BO
8572 HUPHEVrHU
IF DAYSO66 THEN MONLIZ)=29
8575 DELHU : EKFGT - BO
B580 HU=HUt(BFFGT-BO)
'HEAT UNITS
KOH M-l TO MONTH
8582 DELCU = BO-(X(D,I)+XID,2)1/2
If H : 1 THEN 6 5 70
8584
IF DELCU <- 0 THEN 0KLCU=0
JI=JI+MONLIM-1)
8586
IF DELCU >2 THEN DELCU s 2
NEXT M
8588 CU = CU + DELCU
DOY -JI+DAY
8600 FTV=FNTlON6(T,T0l
MONL(2)=28
86 10 TN1TE=X(D,1l/3+X|D,2)/2
RETURN
8630 RETURN
8631
ItMllltllllttltllMIMIIIIMtMUIIIIMtllllltllllllMlllttMlllllin
8640
TO RECORD DAILY VALUES AND STORE ON TO DISK
9000 •
TOTAL EVAPORATION
IIHIttM
9001
PRINT #3,USING "Iffl ";D,DOG,
llllllllllllllllllMlllllltlllllttltllttllllltllMIIIIIIUIMIIIllttl
PRINT t3,USING "tt.ltf ";- PS I 3 I 1)/1000,-PSIS(2>/1000,-PS IS(3)/I 000,-PS IS(49010 ' Calculates potential and crop evaporation
1/1000,-PSISI5 1/1000,-PSI3(6)/1000,-P3I3(7}/1000,-PSIS(8)/1000,-P3IS(9)/10 9020 H.B.&* RFDM
9030 Z : FNTIONB(T)
9040 FLE=FNTION9(LAI)
PRINT #3,USING "tt.ttl ";-PSIST/1000,-P3IL/1000,
VEG COVER FACTOR
9042 I F STAGB>7 OR 3TAOK = 7 THEN FLE- . ;i
PRINT t3, USING " I * * 11 ";V(1),V(2),V(3),V(4) ,V(5),V(6),V(7>,V(8),V(9),
9044
PRINT #3, USING "Illfl ';PAWL)1>,PAHLI2),PAWL<3),PAWLt4),PAWL(5),PAWL(6),P
I F DOG < ENDANT+3 1 OR STAGE<7 THEN 9050
9046 FLE - . 9 - .O62«(IKXJ-(SNDANT.3I i )
),PAWL!8),PAWL(UJ,
9050
PRINT #3,USING "**tt ";PPAW
I F FLE < . 1 THEN F L E - . I
9060 I F FLE > . 9 THEN FLB=.9
9070 P V - F N T I O N 1 0 ( Z , H t i 10"6
9075 PV = X ( D , 5 )
•IF J X O 1 0
THEN 7690
9080 PTRAN3 : FLE » PV
IF STAGED I THEN 7690
9090 VI DBF i V01( 1 ) - V ( 1 )
IF S T A G E = 2 THEN 7 6 9 0
HI-DM : l-Nl)I.YHISO,KT,NMX I
CiOSUB H500
KS - KNRADlLA I I
1
Dl,V MEAN RFD
TEMP
' R A D I A T I O N FACTOR
'HKK E V A P O T R A N S P I R A T I O N
1
9 1 00
91 10
9U0
94HO
9490
9500
I I - v m l - K < (I T H E N
VIUKK ; 0
t'ti - K N T I O N 2 I I V I U K I - I
-Si JI I . V A I ' ; H i * 1 l - K L h ) » X n ) , 7 ) :
KKTUHN
'PRINT
USING
" M i l l .##"
;FG;V1DEF;SOILVAP
•tlMltltlUMMIIIMIMIIUI)«UIIMIIll*ltMltt)ltlllttt«t(t«ltltttl
INITIAL
CONDITIONS
'tllllMltlMIIIII)MMMIMIIIItltlllttMMMItlttllUlttltt*ltlMMI
9S1U
9515
9520
9540
9545
9SS0
9552
95bO
Mf.65
9567
95fcH
9670
9571!
9576
9580
9ftMJ
95H5
9587
9591
9595
9600
9tiOS
96 10
9615
9620
9625
9630
96 4 0
9650
9655
9670
9680
9690
9695
9700
9710
9760
9 7 80
9790
9 7 99
9800
9H01
9B10
9X20
9H3O
9H31
9832
9840
00
INPUT "NAMh OK CAHKY OVKR FILE
" ; CO!
O P E N '' 1" , » I ,CO»
ESTABLISH THE DOV ON WHICH THE MONTHS END
U O 3 U H 1.1900
'STATION
INPUT > I , MS
'SOIL DESCRIPTION
I N P U T • I ,WJ
'WHEAT CULT1VAR
IN HUT • i .fi
t 1 , HOY , IMKi, I'M< (NTH , H>A* , J I>, STAGE , BEG I N , F l N I SH
INPUT
'PLANT POPULATlON/»~2
INI'tPl l l . N P I .
INPUT « I , KNUKXTi ENUT11.
INPUT t1,HUCO
•I.IIUCl.l
9850 V(L) = VINIT(L)
9H60 NEXT L
9870 RETURN
iiitlllltittlttllliltitluiMIIIMintlllltiltlltiiniiiiililltltllll
9999
10000 '
SET ALL VARIABLES TO ZERO
•HTKM EXTKNTION AND TILLERING ENDED ON UOY
'CR1T MIN HEAT REyU I RF.U-K.ND T I l.l.KH 1 Nii
HUC-HUCO
I N PUT f 1 , Z K K K ) , DZHT , HT , L A I , HLJ, CO , TTRANS , TPTRANS , WUSE , TSEVAP . DKEPEBC , RT
INPUT * I , V U . Y , V K A K
Vii
= VO»IU0U
: V s YMU00
:
YEAR : Y B A R U 0 0 O
K ) H L = 1 TO 9
INPUT f l . V I N I T I M
' I N I T I A L S O I L MOISTURE ( V o l u « e t r i C • a / m )
W ( L ) = V I N 1 T I 1 . 1 «U/RT
V l l , ) = V1N1T (I-1
NfcXT IKOK L ; 1 TO 9
INPUT f l , M ( L )
NKXT 1.
KOR L = I TO 9
INPUT # 1 , P O ( L J
KEXT I.
CLOSE I I
CLS
LOCATE 6 , 2 0
PRINT USING " YOU AHE IN VEAR
####";YEAR
MONTH = PMONTH
! DAY - PDAY
UAY8 = 365
IF YEAH MOD 4=0 THEN 1MYS=366
UUSUB 6500
JP=DOY
PRINT " PLANTING DQV
";JP
PRINT " UKOWTH STAGE
";STAGE
INPUT " UO YOU WISH TO ALTER ANY INITIAL CONDITIONS
(Y/N)";AN3W»
IF ANSW» : "V" TlltN UOSUB I4'^5O
RETURN
3O1LWATER
CONTKNT
WyyzO: INPUT "ENTKR 9 IF YOU WISH TO ESTABLISH
IK wwn-i' THKN 9H70
FOR L=1 TO 9
PRINT "1.AYEK No. " ; I.
1INPUT "INITIAL SOIL WATER
;VIN1T(L)
3OILWATER
AT
PLANTING
10020
10030
10040
10050
10060
10070
I00B0
10085
10090
10100
10 110
10120
10 130
10140
10150
1UI60
10170
101B0
10190
10200
10205
10210
10220
10230
10Z40
10250
10260
10270
10280
I0Z90
10300
10310
10320
10330
10340
10350
10351
10360
10500
10501
10502
1U5O3
10504
10510
10540
10542
10550
10560
10570
IX 0
TREfcNrO
TTOT=0
TDREIN=O
:.kH.u
SP:0
PS12=0
TPS1L = 0
A1, = O
VMIN=WOI(9J/D2RT
TFG=0
'TTRANS-O
'TPTRANa=0
TVEK=0
TPV=0
TBWPÔ
TGWP=0
TK=0
TF1=O
TFH=0
TFTPÔ
TDHC=0
TFCiO
TFLK=O
TFTR=0
TFTL=0
TEFFÔ
TFFMAX=0
IKESP-O
TMAIN=0
ASSIM-0
TP-0
DMG=0
RETURN
'
'IIIIIIKIIIttllllllllllllllllllKltllllllllllllllilllllllllllltllllM
'
TO READ DATA
'lllltlltltltttllllllltllllllllllltMllltlllitllltllllllllllllMllllli
INPUT " IN ORDER TO CiENERATE SIMPLE DATA SERIES ENTER I
IF 40 = 1 THEN 11000
' DATA GENERATOR
INPUT" IN ORDER TO READ IRRIGATION FILE ENTER 1 "' ; Www
INPUT " N » e of WEATHER data file eg. B: 2008-83 " . D»
OPEN *I",tl,DI
IF D$="B:I985" THEN START=60
FOR I=START TO BEGIN - 1 + 4
INPUT f1,K»
NKXT 1
";Q4
ELSE START =6 1
' DATALLER HET 4 OPSKRIFTE
I 4 I I I • I i > i I • •> • > I I I 1 > • t 1 •1 • • I •• I • • • • • • 1 r• I < • • I • • > • . • • i , i , i i . • •i .
10899
10900 '
LEAF GROWTH
10901
'•MIMMMttttMltttttltttttltttlttlttlttttllltltlltMllttttltll
10907 FLGRO=FW
10908 IK Q10<0 AND F L Q R O U THEN FLGRO:I
10910 l)E L1.AP= KNT 1 ON Z0 ( Q I 0 , DELKU , FLGRO )
10920 IF LAI> = 2.3 AND NPL>=140 THKN UELLAP- DKI.LAPI KNLHEIX NP1,)
10930 LAP=LAP • DELLAP » NPL
10940 LAI=LAP
10950 IF LAI < 0 THEN LAI =: 0
X(D,8)=VALtMlD*(X»,43,6) )
10960 IF LAI > 8.5 THEN LAI = 8.5
X(D,4)=VAL(MIDS(X»,^5 ,6))
10970 RETURN
\ I n , fi ) = V A 1 . ( M 1 U $ ( X I , 37 ,(> ) )
10999 't
IIMIIIUIIttttlUIIIIIMItltllllttlllltltllllllllttlllltlllll
I t IJ<=MENU(MI THKN 1 0 6 6 0
1000 •
SIMPLE WEATHtR GENERATOR
1001
H>H K = l T O 7 : I K K-4
OK E = ti T H E N 1 0 7 1 0
1070
IK XID.K. I O - 9 9 . 9 THKN 10710
1080 FOR D:1 TO DAYS
XMl.S3;X(D,EI
1090 IK IK=MEND< I ) THEN
1 1 1 10
PRINT USING " MiHHing value of ele'unt * • on day Iff* "jK;D
100 1 = 1 + 1
X < D,E)=X < D-1,11
1 10 X(D,I)=GMT(I)
IK E=3 THEN X(D,K)=O
120 X(D,2)=GMT(I)
NEXT K
130 X(0,31-0
IK X ( D , 4 ) = - 9 9 . 9 THKN \ | D , U = G M S ( M ) : PRINT USING " Hissing value of ele
140 X(D,4)=GM3<I)
4 on day till ";D
150 X(D,5)=GME<I)
IF X ( D , 8 ) = -99.9 AND 1><233 THEN X(D.8) = 2
: PRINT USING " M i a u m a vs
160 IF D MOD 10 = 0 THEN X(D,3)=GMRI I )/3
eleaent 8 on day M I * ";D
170 NEXT D
IF X(D,H1 --ay .9 AND \}>-23'.i THEN X|D,8) = 4
: PRINT USING ' M m a i n g v1 180 RETURN
M98
I t- I .-•flit
H on day ( M l * IB
PHI NT USING
• • M I . M 1 ; M ; D , X ( D . 1 ) , X ( D > 2 ) , X 1 D , 3 ] . X { U , 4 ) - X I D , 5 ) , X I D , 7 ) , X ( D , 8 1499
'lltllttlltttniltltMlttlllttttltllttlltltlllMtllllttMlltltllltlll
1500 •
TRANSUM:ATION
1501
1530 Q L B O L M L P H A
1540 BLsBL+BETA
1550 CL^CLtPHI
1560 RL=RL+SHO
1 S80 SL=SLtTHETA
1590 TLAI=BL/3PL
IF ANTW»="K' THEN CHAHA-2&
1600 GD=QD+X6*OL
IF A N T ¥ » = T THEN CHASAsiS
1610
IF ANTW|:"2' THEN IHANA=I9
1620 BDnBDtXltBL
IF ANTW|="3' THEN GHARA=31
1630
IK ANTW»:'4 THEN CHAKA=3 7
3D-aD+)(2«SL
X ( DOYIRR.6 ) = VAL < Ml [)» < XI .CHAHA.6I ) :PHINT DOY IKR , X I DOY IRR , 6 > : GOTO 10743 1640
1650
SL+GDtCD*8D+SD
CLOSE II
1660 CB=RL+RD
KETUHN
1670 C=CA+CB
IIIUIIIII4IIKI
1680
HAIL
1690
1700
HAILPC=0
17 10 'Nil C INCL STAND DEAD
FOB 1=1 TO DOYSHA1L
1770 RETURN
IF D O DOYHAI I.I I ) THEN
1998
HAILPC = HA1LP( 1 )
1999 ' M t l t t U t M l l t l l l l l l M t i l t M l l l l l t t t t l l t l t l t t t U l l l l l l l l l l l i l l l l t l l l
NEXT I
12000
SUB TOTALS
RETURN
12001
INPUT
ENTKH NUMIIK.K 0 > MONTH I N WHICH YOU S T A R T
Ki)R D - H H . I N T o (• I N1SH
INPUT | 1 , X «
PM.K.NIX* I
• | ' < J H THKN X 1 = M " m
: GOTO 1 0 6 0 0
X I I ) . 1 J V A U M I l i t i X$ , 7 , 6 ) )
J i ( D ( 2 ) = V A L ( M I D $ ( X $ , 1 ;) , b ) )
X I t ) , :i I : V , \ I . ( H I i n I X I , 19 , 6 ) )
X I 11. 7 ) - V A l . t M l l H ( X I , 3 I , 6 I )
10746
10747
10748
10749
107 50
10751
10759
10760
10.799
10800
MIHill
10805
10810
10820
10830
1O840
10845
10849
10850
10851
10860
10862
10870
10880
ro
" ,M
i
1-2010
DELHT=FNTIONI9(UOG,FW) - FNTION19(DOC-1,FHI
HT = HT t DELHT
IF KT>1 THEN HT=1
RETURN
12020 IK STAIJE >2 TIIKN DOG - DOG+1
12030 "I Krl> = Tkhl>tKKI>
12040 1 T H = TPWPV
1 2050 TShVAP-TSKVAI'»KlH I.VAP
12060 'SMH1:SKHHKKD
12070 'RAINKAI I
1 20B0 IW-KN-TKKKN + Xl I), 3 )
111 UO
I .• I I i '
12120
12)30
12140
12150
12 160
IZ 17 0
12172
12175
12176
1218U
12190
12210
12220
12230
12240
12250
12260
12270
I 22MO
12290
1 2 3 00
12310
12 3 2 0
12330
12340
IZ3S0
I 2JbU
I 24(10
12401
12499
12500
12501
12510
12520
12530
12535
12540
I255D
I25&S
12560
12570
12580
12590
12600
ro
O
THH: = TRKMJitH, 3]
1
THKKN: I i>l a 1 r a i n l ' n l 1 t o r a p p r o p r i a t e p e r i o d
'Tkhk.-ioirtj r M i n t a l l up t o p r e s e n t d a t e
1
TTOT: I nt H I i n f t o r a p p r o p r i a t e a e a n i n g i n t e r v a l
TUMG^TDMIitDMt;
TTOT-TTOT*T
'TKi=Tn;tK;
I IHASS;T1HANS«TKANS
' tt(lllll*l«<1111111•11M*Mt
'['I'TKANS^'I'l'THANStHTKANS
' •ItlllllttllltttllllUMMMI
TLVS=TL¥S+X( I),H)
WUSF. = WU.SK • SO I I.VAP +TKAN3
Dkl-'PERC - DKKPPkHC + PERC19I
'THWI) = TBtal'»Ht>lJ
'SUM SOILWATbK
•TK-TKtK
'TKTHsThTH+HTH
•TFLE=TFLE+FLE
'fFTHsTFTB+FTB
•TFCaTFC*FC
'TKT=TKT+FT
"TKH=TFH*FH
'TKKMAX-TKhMAXtKKKMAX
TP=TP4P
•THESP-TKKS1'*KKHI'
'TMAIN-TMAIN+MAINR
'TOT PHO
126 10
12620
12630
12640
12650
12660
1Z670
12690
12700
12761
12762
12763
12764
12765
12766
12770
12777
12780
12790
12791
12800
12810
12820
12830
12840
128 50
12860
12B70
12880
128U5
12890
12900
12920
12930
12935
12940
129 86
13000
TFFHAX=TFFMAX/JX
TEFFiTEFF/JX
T8KD=TRFD/JX
13010
13015
13020
13030
13O40
13050
13060
13070
13080
13090
13 100
13110
13 120
13130
13140
13150
IF DOC - O THEN 13175
IF STAUE > 7 THEN 13175
RI)EVF = R(]K*DOU
ZBPF=RDBVF
TUM(i^TDMQ/JX
MASSIM=AS8IM/JX
TV-TP/JX
TPViTPV/JX
TkEKN-TKEEN/JX
SRFU=SHFD/10"6
IF STAGE < 3 THEN 12780
FRAC0=GWC
FRACL=BL/C
FRACS^3L/C
PRACS = S I . / C
KRAORrRL/C
TBHP=TBWP/JX
TBWP-TBWP/JX
TGWP=TUWV/JX
RETURN
'
'tltlllMlltlltttlllltMIIMIIIItlMltllMUIIItOMIttlllltllllllltl
'
PHOTO3VNTHYS13 AND ASSIMILATION
'IIIIIIIMtllllltltlMttlllltlMtllMIIIMIIIIIillllKlllllllllllllli
K=FH • FTP » FS
'EFFICIENCY FACTOR
EFF - EFFMAX t F
EFF - EFF /100
J/<nT2 A) KFFECT1VE RADIATION FOR PHOTOSVNTH
EFS - EFF « PAH « 10*6
P - EFS * FE • H
hf/(ha d)
P = P * 10*4
P = Wky ITHANS
'SVNT RESP
S¥NR=RNT1ON12(P)
MA1NR=FNTION13(COO,TNITE,C)
RESP=SYNR+MA1NR
DM(J = P-HKHP
'LPRINT USING "Milt.It ' ;FH;FTP;KS;SYNR;MA 1NH;DMU;P;RESP
RETURN
iiiiixiiiiiiniiixiiiiii
ROOT DEVELOPMENT
HtTUHN
MI-ANS
i • • < • t I I < t I 1 ( I I I I I i • • • > I ZEFFO THKN 2EFF s ZHFPO
A = -l.OO(AP)/( .97IRDEVF>
KMASS=0
FOR Ls] TO 9
RPROP<L}=0
ZTOP=ZBOT
?.BOTrL»DZRT
RPROP(L)=(BXP(-A»ZTOP)-BXP(-A«ZBOT)J/.97 9
RMASS=RMA33*RPROP{L)
'PRINT
NEXT L
L, R P R O P ( l . ) ,F N T I 0 N 1 81 V (1 . 1 * 2 . V 1 6 I L ) ,V l l l l l . l )
RMASS
13 16b
i 11 I . I .
13167
K M : I- NT 11 >N I 7 I MHO, DOU I
13730
FOR L^l TO 9
I K
13740
13750
13760
13770
13780
13790
13800
13810
13820
13823
13824
13825
13826
13HZ7
13H28
13829
13830
13840
13845
1 3847
13850
13860
111862
13864
13865
13866
13B7O
13880
13882
13885
13890
13899
13900
13910
13912
1 3914
13922
13924
13926
1 3926
13940
13950
13960
13970
13990
1 3999
14000
14001
14010
T 3 {I.) - - { P S IS ( L 1 - P S I L 1 * K 3 P ( L I
KM <
15 T H E N
HM :
1 5
• |>K I NT KM
NIH.AVz !NT(ZfcKK/DZHT+ I . 00 I I
H>K l.s I T i l H
RPKOPll.)=KPHOP| I.t/HMASS
Ik I.< = NOI.AY THEN 13171!
1 3 Ififl
1316 9
1 .1170
RPKUP|LI=0
13171
i II I 7 2 NEXT t.
I J I 7 S HETUKN
1
Itllllll Mlllllllll
i:>*yu
SOU. HtiOT CONDUCTANCE AND LEAF WATER POTENTIAL
1 3300
MIIIIIIIIIIIIIIIIMIItKtUMllllllllllllilllilMlllllllillltlltltK
I 3301
IK l i m ; - (1 THEN I.SH'JO
I 3302
13309 h . S l ' h H - : ! ) : I'S1ST = O : WMPS1S-0
13310 H>k l . - l TO «
13320
HPKOPI I.) :RFBOP( 1.1/HMASS
I :i j :i o K S P U . l = K S P O M K P R O J M I . H R M I " . 5 « FNTION I 8 ( V ( L) + 1 . V 1 6 ( L I , V0 1 ( L) I / 1 0
13340 K8PKFF - KSPEKF t k S I M L I
13350 WMPSIS ; WMPHIS t KSI'I I.) i l ' S I S I 1.1
"PHI NT L . K S I ' E h h , h S P ( L> , PS 1 S T , RPHOP( L )
131170 NEXT I.
psisr = WMPSIS / KapEFK
' WEIGHTED MEAN PSIS
I:S3HIJ
1339(1
I 3395
13400
134 10
13420
I 34 40
13450
13470
13480
13500
13510
13520
13530
13540
13550
1 3560
13570
13580
1 3600
13610
13620
I 3630
I 3640
13650
13660
13680
13690
13700
I 37 10
13715
'PR INT PS 1ST
GOSUB 5700
LFLAG=0
PSlL=PKISTtPTHANS/KSPEKF
'PRINT KS1NO
ftl«ft«#
;PS1L;PSIST
l:> I II I = 0
PSILO=-3 500
PSlT=tPSIHlfPSlLO)11
FHsFNT10N3(PSIT,PSICI
IF HI.. I TMKN
FH= 1
IF I-H < 0 THKN FH = O
IF PS IT>-3500 THEN 13 540
PK1NT "ULTRA-STHESS, PS IL = -3000, ON DOY ";0
STOP
TRANS^FHtPTRANH
PS1 l.-PS 1 ST + THANS/KSPfcKF
'PRINT PMIT,KH,THANa,PSII,
TEST-PS1L-PSIT
IF TESTJ5 THEN 13610
IF TEST>-5 THEN 13700
GOTO 13640
REM PS IT TOO NEGATlVt
P31LO=P3IT
GOTO 134 70
REM PS IT T(» LITTLE NEGATIVE
PSIHI=PSIT
GOTO 134 70
EXTRACT WATER FROM EACH SOIL LAYER
ItMMIIlMIIIIMMtlllllll
'PRINT USING "ftfff";PSIST; PSIL; :PRINT USING
ttlt.t
, TRANS; PTRANS
14020
14030
14040
14050
14060
'IF T3(L)>=0 THEN 13810
•LFLAG-1
'K3PEFF - KSPEFF - KSP(L)
'WMPSI3 = WMPSI3 - KSP(L) * PSIS(I.)
'KSP(L»=0
NEXT L
IF LFLAG=1 THEN 13400
TBANS : 0
TAW = 0
WTEST = W(II-TSI11-SO1LVAP
GOTO 13H.
IF WTEST < W16 ( 1 ) THEN T S U I -W(l) - W I 6 U ) :W(1)-W16(1I
W( 1 ) = WTEST
V(l) : W( 1 | / DZRT
: TRANS - TS1 I I
AW(1) - W(l) - WI6(1| : TAW = T A W *• AW ( 1 ) PAWL{ 1 ):AW1 1 I/PAWI 1 )• I Ou
FOR L=2 TO 9
1
IF WTEST < W161L1 THEN TS ( L ) ; W(L) - W16 1L) : W ( L ) -W I 6 (1,) GOTO 13 H;
W(L> - WTKST
TRANS = TRANS+TSIL)
V(U=W(U/DZRT
AW(U=WIL)-WI6(L)
TAW;TAW+AW(L)
H A W U 1.1 -AW( L)/VAW( L) • 100
'PRINT L
' P R I N T USING
"tMM";
NEXT L
PPAW;TAW/TPAW»100
•FH^TRANS/PTRANS
RETURN
L ; W( L ) ; V( L ) ; TRANS* 10 ; T 3 ( LI * 1 0 ; TAW
'IIDIttlllltllMIIItlMlIttMltlttllttltlttltlltltllttltllltltltllll
1
DOY ON WHICH THE MONTHS END
'ItllKtllltllltltlUlttMtlttMltlMltltlllllllltlttlHIIHIltlllt
DAYS = 365
IF YEAR MOD 4 ^ 0 THEN DAYS - 366
MEND(I) : 3 1
MEND(2> =
59
IE DAYS = 366 THEN MEND(2t-6O
MENDG = MEND(2)
FOR 1=3 TO 12
MENI)( I )=MENDG • M O N H I )
MENDG = MEND(I I
NEXT I
RETURN
COEFFICIENTS SOIL WATER EXTRACTION CURVE
DATA .00899,.009 72, .00913, .00927, .00913, .0U913, .00913, .00913, .00913
FOR L=I TO 9
READ MIL)
: 'PRINT M(L)
NEXT L
DATA 9.56,13.65,14.SI,14.84,10.45,12.20,12.20, 12.2,12. 2
FOR L=l TO 9
14O7U
1 4UHIJ
14085
1 4090
1 4091
1 In-.* I
I 4095
HEAD PO(L>
NKXT I.
V1N1T(L>:1SO
RETURN
: 'PHINT PO(L)
'IIIIIIIIIIMIIIIIlllltllllllllllllMltltllllllllllltMUItltlllll
SUM TO HE-WRITE CARRY OVEK PILE
CLS: LOCATE 7,20:INPUT "ENTER NAME OF CARRY OVER FILE ",CO|
OPEN " ,11,ro»
,Nt:PHINT *I,W|:PHINT #1,C|
II.- 1 N T
*###"; 1>,1)OU,PMONTH,PDAY, JP, STAGE, BEG JN, FINISH
14 130 I'M I N T
14132 PR I NT • 1 , US INU «l»t";NPL, KNDEXT,ENDTIL,HUCO
I l l l l l ;ZEFFO,DZRT,HT,LAI ,HU,CIJ
.USING
14 140
PttlNT
,\>a IN<; III II";TTHANS,TPTRANS,WUSE,TSEVAP,DEEPEHC,RT
14 142
PRINT
14 144
,USING tt.ttl" ;YO/I 000,Y/1000,YEAR/1000
PRINT
'. II :.n FOR L = TO 9
14160
PR I NT I,USING •f##f;VINlT(l. 1
14 170 NEXT 1.
14 180 EOH L-l TO 9
.nil!
I 4 190 CHI NT I I .USING 1 m i
14200
NtXT L
I 4210 KOR I.-1 TO 9
PHI NT 11,USING 1 1 • 1 . f 1 " ; P O (I,)
I 4230 NEXT 1.
CLOSE fl
14235
RETURN
l (.• in
1 4 1 00
14 1 HI
1 4 1 20
1424 1
1 1 t t 1 1 1 1 1 1 1 i t 1 1 1 . , 1 > 1 . 1 1 1 1 1 • 1 1 t 1 1 « 1 1 n 1 1 t « 1 . 1 1 l 11 t 1 1 1 > t 1 1 1 » 1 i i 1
14244
14245
SUB TO ALTER INITIAL CONDITIONS
14247 ' t l l l l l l t t d l l l l t l l t l M t l l l l l l l l l l t l l l l H I I I t i l l l l l l t t l l M l l l t t M l
14250 CLS:COLOR 11,0,0:LOCATE 2,20,1,4,6:PRINT "STATION";TAB(35);N»
14255 COLOR 11,12:LOCATE 3,20
:PRINT "N»=
" :COLOR 11,0,0
14260 LOCATE 5,20
:PHI NT "SOIL DESCRIPTION";TAB(40);W$
14 265 COLOR 1 1 , 12 : LOCATE b,2U
:COLOR 11,0,0
:PH1NT *!*• =
14270 LOCATE 8,20
:PHI NT "WHEAT CULT IVAR";TAB(3 5 I ;C$
14275 COLOR 11 , 12:LOCATE 9,20
:COLOR 11,0,0
:PR I NT "C* =
I 4280 LOCATE 11,20
:I'K1NT " HI.ANTPOPULATION/hii"1 ; TAB ( 40 ) ; NPL
142S5 COLOR 11.)2:LOCATE 12,20
:PRINT "NPL =
:COLOR 11,0,0
14290 LOCATE 14,20 :PRINT "STEM EXTENTION";TAB(35);ENDEXT
14295 COLOR II, 12:LOCATE 15,20
PHI NT "ENDEXT =
":COLOR 11,0,0
I 4:iou LOCATE 17,20 I PRINT "T 1 LLEHI NU" ; TAB( 35 1 ; ENDT11.
14305 COLOR 1 1 , 12;LOCATE IB,20 :PS 1 NT "ENDTIL =
11,0,0
":COLOH
1 1
= • ; HU
14 360 LOCATE 5,2:PR I NT"UK
14 365 LOCATE 7,2:PR 1NT'CU
= ";cu
14 370 LOCATE 9,2:PR INT"TTRANS = ";TTRANh
143BO LOCATE 1 I,2:PR INT"TPTHANS-";TPTRANS
14385 LOCATE I 3,2:PR INT'WUSE - ";WUSE
143U0 LOCATE 15,2:PR1NT"TSEVAP= ";TSEVAP
14 400 LOCATE 17,2:PRINT"RT";RT
14405 LOCATE 19,2:PR1NT"DKEPERC=";DEEPbRC:COLOR 11,0
14430 PK1NT "MAKE CORRECTIONS IF INCORRECT ELSE PRESS CONT. ( K 5 ) " ; :STOP:CLS
14440 COLOR I 1,12:FOR L=2 TO 18 STEP 2:LOCATE L,1:PRINT "V1NIT(";L/2 ; " I =
V1NITIL/2):NEXT L :COLOR 11,0:PRINT
14445 PRINT "CORRECT INITIAL SOIL MOISTURE IF INCORRECT ELSE PRESS CONT.IFSI
RI NT:PR1 NT:STOP:CLS
14450 FOR L^l TO 9 :W<L)-V1NIT(L}»DZRT:V IL)=V IN1T(L):NEXT L
14460 COLOR H.lZiPOR L=2 TO 18 STEP 2:LOCATE L,l:PRINT "M(";L/2;")2|:NEXT L :COLOR I 1,0:PRINT
14465 PRINT "CORRECT IF INCORRECT ELSE PKESS CONT.(F5|"; :PR1NT:PR1NT:STOP:CI
14 470 COLOR U,I2:FOK L=2 TO 18 STEP 2:LOCATE L,1:PRINT P0(";L/2;")=
L/2):NEXT L ;COLOR I 1 ,0:PRINT
!4475 PRINT "CORRECT IF INCORRECT ELSE PRESS CONT. (F5) " ;: P R I N T : P R I N T : S T O P : i 1
';PMONTH; '
14 4 90 COLOR 11,12:LOCATE I , :PR1NT"PMONTM
1
;PDAY;"
14 495 COLOR II 12:LOCATE 3, :PR I NT"PDAY
1
:STAGE;"
14 500 COLOR
12:LOCATE 5, :PR1NT"STAOE
•;JP;"
1 4 S10 COLOR
I 2:LOCATE V . :PR[NT"JP
•;DOY;"
14 520 COLOR
12:LOCATE 9, :PRINT"DOY
'; DOG; "
14 530 COLOR
,12:LOCATE 1 I,I;PRINT'DOU
1
;BEG IN;"
14540 COLOR
,12:LOCATK 13,1:PRINT"BEIMN
'-.FINISH;'
14 545 COLOR
,12:LOCATE 15,1:PRINT'FlN1SH
;YBAR;"
I 4547 COLOR
,12:LOCATE 17,1:PRINT"YEAH
:COLOR 11,0
14550 PRINT MAKE CORRECTIONS IF INCORRECT ELSE PRESS CONT.(F5>";:STOP:CLS
14555 GOSUB 14 100
14 560 RETURN
14599
14600 ' READ RAIN, IRRIGATION, HAIL AND SPECIFIC DAYS TO RECORD
14601
'llllltllltlltllllilllllllllllltMMIIIIMIIIMIIIIMIttllMllttll
14602 OPEN " I " , #4, "WCWIN"
14605 INPUT I4.NRAIN
'NUMBER Ot DAYS ON WHICH IT RAINED
146 10 FOR I - I To NRAIN
11615 INPUT 14, DOYRAINl!),RA1N(1)
14620 NEXT I
14625 INPUT 14, NIR
•NUMDEK OF DAYS ON WHICH IRRIGATED
14630 FOR 1=1 TO NIR
14635 INPUT 14, DOYIRtI),IHRIGII)
1 4 3 1 0 LOCATE 20,20 :PRINT "SOIL LAYER DEPTH";TAB!40 I ;DZRT
14640 NEXT I
.14315 COLOR 11,12:LOCATE 2 1,20 :PRINT "DZRT =
":COLOR
14645 INPUT »4, NHA1L
0,0
•NUMBtR OF DAYS ON WHICH IT HAILED
14317 LOCATE 22 10:PR]NT
MAKE CORRECTIONS IF INCORRECT ELSE PRESS CONT. <F5I";:S 14650 FOR 1=1 TO NKAIL
TOP:CLS
14655 INPUT II. 1XIYHAI I.I I I , M A 1 I.P( 1 i PERCENTAGE HAIL
14320 LOCATE 2,20 :COLOR 11,12:PRINT "MAX EFFECTIVE ROOTING DEPTH";TAB)55);ZEFF 14660 NEXT I
'NUMBER OF DAYS ON WHICH TO TEST MODfcl
O
14665 INPUT 14, NMEAS
14670 FOR I = 1 TO NMEAS
14325 CLOSE I 1
14675 INPUT #4, D O Y H E S U )
14330 LOCATE 5,20 :PRINT"CR1T M1N HEAT REQUIRED To END OF TI LLER1 N(i" ;TAB ( 65 ) ; HU
14680 NEXT 1
CO
;YO:COLOR 11,0
146S5 'NEXT NM
14333 COLOR II,12:LOCATE B,20:PH1NT"YO=
";Y:COLOR 11,0
14695 RETURN
14334 COLOR 11,I2:LOCATE I 0,20:PR[NT"Y
11
: COLOR 11,12
14699
: PR I NT
14335 COLOR I 1,0: LOCATE 6.20
CORRECTIONS IF INCORRECT ELSE PRESS CO 14700 '
TO CREATE RAIN, HAIL AND IRRIGATION FILES
14337 LOCATE 1I,10;COLOR 11,0:PRINT 'MAKE
14701
llllllltlllttlMlMtlltllltllllllllllllllllltiiiiililiiittlllltlll
NT.(F5>";:PRINT :PR 1 NT :STOP:CLS
14710 OPBN " I " , #4, "WCWIN"
14340 COLOR 1 I , I2:1,OCATE 1 , 2 : PRI NT'HT = •; H T
14720 INPUT »4,COUNT
14350 LOCATE 3,2:PHI NT"LA 1
= ";LAI
147 30
1474 0
H J 50
I 4760
14762
14764
14 ;<>(.
I476H
L477O
M771
11772
14773
14774
14775
14778
I 47HO
I47H2
I 47H1
1 4784
1479U
14999
15000
15010
15020
15030
15040
IS050
1 5060
15070
15080
15090
15100
151 10
15120
15130
15140
1SI&0
15160
15170
15180
15190
15200
15210
15220
H)K ] : 1 T(l COUNT
INMJT (4, 1)0* HA IN. AMOUNT
X(DOYNAIN,3] i AMOUNT
NhXT' I
INPUT #4.COUNT
KOH I : I TO COUNT
INPUT #4, DOY1HH,AMOUNT
X(UOYIKK,&) : AMULINT
NEXT I
INPUT ti.COUNT
KOH I = I TO COUNT
I NPUT 1-4 , llOYHAI I., AMOUNT
XI DOYHAIL, 7 ) = AMOUNT
Nl-XT 1
INPUT 14 . C O I J N T
FOR I : I I d COUNT
INPUT #4, DOVMKAS
X(DOYMKAS,HI - 22
NKXT 1
RETURN
THK FUNCTIONS
1
t t
DEF KNSINKIKW)=(I-KW)».06
/NPL
DEF K N L H K U I N P L ) : 140
DEF FNDtBNGTH(D)i|Z.15+1.93«COS((D+9I/3B5«2«3.142B5)
'DAVLENGTH
DtK * NMJ [> ( I)) = (30. 8 4 * 1 2 . 65»COS( | D + 9 ) / 365» 2 • 3 . 142H5) )
' MJ «"-2 d"-l
DtF KNI)L.VH(SO,KT,NM)()=SO*( .25+.7S • KT / NMX )
' MJ «*-2 d"-l
[>EF FNRAD(l-AIt= I - EXP(-.7»LAI)
DfcK KNTU>N2(('Sl . PSIfr} = I-EXPl-8 . 00000 1 E-03» ( HSI - I PSIC-400) ) ) ' FH
DEI- FNT1ON3IPSL , PS I C ) = 1 / ( I tEXP( 2 . 00000 1 E-03* ( PS1C-PSI)))
' KW
DEF KNTION4 1PS1,PSlC>=l/<1+IXPI.004*(PSI-(PSIC-2O0|I)
DEF FNTION5I KG.KLK, (>V)^FG» ( 1-FLE) »PV
' Es
DKF KNTION6(T,T()|;KXP(-.OO48» IT-T0I *2)
'Ft
DfcF KNTK)N8(T»=.402t.017»T-.00015»T"Z
'B/(«+0)
DEK KNT1ON9ILAI I = . 186*LAI
'Fie
DEF FNTlONlOIZ.HIcZi1.35»H/245O/lO0O
'Em
DBF FNT1ONIUHU)=(-4]7Otl7.1*HU-.02S93»HU"2+17.09*10~|-6)*HU"3
-4 .O349» 10' ( - 9 M H U * 4 )» 10
'dG/dC
DtF FNTION!2(P1=:.5«P
' SYNRESP
DEF FNTIONI 3(l-00,TNITE,C)=COOt( . 04 4 + . 00 1 9 • TN ITE+ . 00 1 *TN 1TE* 2 ) «C 'MA1NRESP
DKK KNTION14(H1I,NP1.| = . 85/ (1 • 482* EXPI - - 0 1 2 » HU II « NPL» 1 0 * ( - 4 )
'LAP
DHF FNTlONI5(P0t.,ML,VI,)=-liXP(P01-*EXP(-ML*VLM
'PSI3
Dth FNTION)6(HL,Pl,,P0l.l: (- 1 /ML* LOG ( PL/POL) )
DEK KNT1ON17 (RMO, l)O(JtzkM0t 1 / (1 *EXP( - . 06* ( DOU-50 M )
' REL ROOTMASS
DEF
KNT1OMH(V,VIB,V0UI
( LOG ( V/ V 1 6 > ) / LOG ( VO 1 / V I 6 »
• 5240 UEF (-NTIONiy(OOG,l-'W) = ( (-2.09«10 - (-4)*DOG*3t8.9l4999E-02»DOC-2-.88 19«DOU+l
2.749)/ 1000)*KW
'HT
LS250 DBF KNTION20(«IO,!)ELHU,KLGHO) =Q10 • DKLHU t FLGRO
' DELLAP
'FJ
15260 l>th KNTION2 1 I V1LIEKI s EXP| - . 03 « V 1 DKF )
' vio
1526 2 DEF KNT]ON22(t:t,AVI = . 1 3B7 • . 004 I 6*CLA¥
15270 RETURN
16000 PRINT "END"
16020 CLOSE
16030 tNII
APPENDIX V - LISTING:
REGISTER
in R.EM FRdBRAM 7U WRITE EACH FARMER'S WATER USE RECORD ON PAPER (NOME RFGISTFR)
?O npT 1HN RASF 0
i n m r i A M WATFRcnnni ,wvOoo> ,nci6ft> ,AMnuNT<rK>o> ,Fwrio<3on» ,wrprFP<3i">o>
^.O INPUT'FILE NAME", A*
60 OPEN " I " ,ttl ,A*
70 INPUT ttl .TITLE*,PAYS
SO IF M1IU <TITLE* ,4,It="I" THEN GRnNn*="F YNSAND"
90 IF MIM<TITLE*,4,1) = "2" THEN GRDND*="L EEMSftND"
100 IF MICK (TITLE* ,4 ,1 >="4" THEN GROND*= "SANDILE ILEEM"
I 10 IF MlD*tTITLE*,4,l>="3" THEN GRONP* = "SANrjLE£M"
120 PRINT TITLE*
130 FDR N=l TD DAYS
140 INPUT *l,P(N> ,UV(N),UDIEP(N)
150 PRINT
D<N> ,UIVIN> ,WD1EP(N>
160 NEXT N
170 INPUT "START ING ANCi FINAL PATE OF WEEK < COMMAS BETWEEN TWO DATES)".DAT*,DATU
Ml
1 R" INPLIT"HOW MANY FARMERS'1 , FARMS
190 FOR FARMERSM Tt) FARMS
200 INPUT "FARMER NAME AND BLOCt",N*
210 INPUT"MAXI MUM ROOTING DEPTH (ffl)" ;WORTEL
220 INFLiT"WATER EXTRACTION ON THE LAST DAY OF PREVIDLJ5 WEEh I mm) " sAK- WATER < DAYS71
230 INPUT"ANY CHANCE PF STREMMING (1 OR 0)"jANSWER
74CI IF ANSWFR= I THFN ?*fl
?F.II R H T O nno
260 • INPUT "DAY OF WEEI. an which stremming should o c c u r " , CiDC
270 N=DAYS-i+DDD
reo REM
290
300
310
320
FOR N=DArs-6 + M"'D TO DAYS+1
FWDO(NI=FWDO(N-1>+50
IF FWD0(N)>100 THEN FWD0(N)=100
NEXT N
J30
^40
INPUT"IMUME>ER OF RAIN AND IRRIGATION DAYS" ,ANTWnORD
I F ANTWDOR[i-:| THEN 3 6 1
3T.0
360
361
370
380
390
GOSUB 490
GOTO 4 70
FOft-0000=1 TO ANTUODRD
INPUT"DAY OF WEEK",DAB
N=DAYB-7+DftG
INPUT"AMOUNT(MMI " .AMOUNT(N)
391 NEXT QQOQ
400 GOSUB 490
4tO FDR N=DAYS-6 TO DAYS
420 AMOUNT(N1=0
430 AKKWATER<N)>=0
440 FWD0(N>=0
450 FW=O
460 NEXT N
4 70 NEXT FARMERS
4S0 END
490 REM SUBROUTINE CALCULATES AND PRINTS WATER USE
500 REM
•510 REM
52fi REM
530 FOR N=DArS-6 TO DAYS
540 AhKWATER(N)=AKKWATERIN-l)*WV(Ni
•550 IF AMOUNT(N) ;0 THEN AKK WATER (N) ÂKKWATER <N-i )+WV (N>-AMOUNT (N)
560 IF AMDUNT<NI K) THEN FW=J
IF FW=1 THEN FWDO<N)=O
IF Akt-.'WATEft (N) <0 THEN AKKWATER (N) =0
580
590 IF WDIEP(N)>WORTEL THEN WDIEP(N)sWORTEL
600 NEXT N
£10 PRINT
620 PRINT
630 PRINT
640
LPR I NT " * * # » * » - * # » * * * » * I I * « # * » * » » » « « » » « » * * » * » * » * * » * * * » « * # * » » » » » » » » « ••••*•*•••**
**•**•••#**"
6P0 t PRINT-'WATFRVERBRUIKSADYIFSNOTA"
66O L PR I NT"
"
670 LPRINT
680 LPRINT N*
690 LPRINT
700 LPRINT DAT*)" - ";DATUM*
710 LPRINT
720 LPftlNT "GRONDTIPE:"jGROND*
730 LPRINT
740 LPRINT-'BERAAMDE WORTELDIEPTE OP DIE LAASTE DAG VAN DIE WEEK : " ,WD 1EP ( DAYS I • 1'
00 s"mm"
750 LPRINT
760 LPRINT'KALENDERDAG
AKUMULAT1EWE
BESPROEIING
PEI
WATERVERBRUIK
SfcNIrtblb
HU LPRINT"
EMM ING"
7B0 LPRINT"
mm/d
X"
790 LPRINT"
800
810
820
83O
840
REM
FOR N=DAYS-6 TO DAYS
LPRINT USING "•#».»
NEXT N
LPRINT"
850 LPRINT"»<
•*•#**#•#••"
860 RETURN
"jDINI ,WV(N) ,At;KWATER<N) ,AMOUNT<N) ,FWDD(N)
.>.•....•..•...••«••••>«••••>•.•••>••••••
APPENDIX VI - LISTING:
EREF
5
III
45
46
50
60
70
75
76
80
92
84
86
B9
U W R I l t VttLUtS UF I t ILHLCULfllEU HHLH1 HUUKLY VALUfcS I U
Liltit .
: l - i l t b l rtUlilKlCM'l IINM OK PROGftAUHE ON B6-O7-O?:
F'ROGRAM Nfi..f1:TEREFB4 . &AK
OP I I UN hHiit 1
DIM W 132" .TT- > ,51 I 32 , 2 " 1 . T (32 .25 I , RH (32 , 2^'* ,S <3r ,25> TL < 32) , TMAX ( 366)
I"1M > O H . D I 1 6 6 I . Ttf1O6*>) , DOf ( 3 D > ONK (32)
PIM RA[ltr(3t,6) .TEMPCl(3ib6) .RHDI366I .WINCiStK 3661 ,RAIND(366) , T M I N ( 3 6 6 )
Ci|M TEMF(31 ,?4I .WINtiS<31 , r 4 ) .RAIN (31 ,ZA) ,X I 3L>> .RAP (31 , 2 4 ) ,TER(366)
nosue
GOSUB
UOSUB
GO5UB
PUN
i•:«:••
2",1i.i
"Siii'ni.
T^-i-'O
PARAMETERS
-LEES PATALEER
'liEREKENINGS
'SI-RTF RESULTATE
E)9
90
95
SLJBR0U1INE FAROMETERS
I III.
EiO=S
1 10
1 BO
200
3020
2030
2040
RV ^ 1 2 . 8 5
RETURN
:
1NLEES VAU UEERELEMENTE
INPUI
206P
2070
2080
20B5
2090
2J00
21 10
21 11
2112
212(i
2130
2140
215O
1' 1 6i.i
2170
2175
218U
G0= - 1 : MEMX = 2O : FW=1
CLS :
THE MINIMUM HT[<RAULIC RESISTANCE OF THE VEGETAUQN 5,/m
T T =i
••A^KOF1N[J VAN MAANCi
leg.
AUCi
I":(-1LE»
INPUT "NUMBEFl OF CATS IN DATA F I L E " | E I N D
OPEN '• I " ,#1 , F I L E *
FOR I - I TO EIND
TMXÔlTMN-40
FDR M=l TD 24
INPUT 111 ,A»
D = V A L ( M I P * l f i * , 3 , 3 > ) : I F 1-1 THEN D l - D
IF I=CIN[l THEN DF = D
DOT(I)=D
RACK I , K )
-VAL ( M I D * ( A * , B , 7 ) )
TErtP<l,m =VAL (MID* (A» , 1 5 , 6 ) I
RHll,H)
=Vfil i n H H I A I ,:•! , < • ) )
wlNH'Ji I ,H) -vfiL i n l f i * ifl* ,27 . 6 ) )
R A I N ( 1 , H I -VAL trtlU*<Al , 3 V , S ) )
(jOSUb TWO
IF K D n K I I I - I THEN 222U
RAtHnUI -KHULMDJ *RALK I ,M)
II- tE«P( I ,H) <TMN THEN TMN- TEMPI 1 ,H)
IF TEMP*I,H>>TMX THEN TMX-TEMP(I,H)
RHD < D)"RHDIP) »RH( 1 .HI
RAlNLilin-HAINOILU tfmlhll 1 ,H>
2191
220O
22O1
22 IO
222V
NtXF
2222
TnAX<D)-If1X : l n l N ( L U - I M N
MINOSDllu •HINCJSDICD / 2 4
w[Nn<;rj<[>> - W I N C I M i m i . w I N I i s i i . i n
PRINT
H :
USINb
If
KONK ( I ) i
I
THtN
224<->
iKADLH ID -RHUU < U) »3t.0O» t O" 1-6 (
" • « • * . » • » » - ;Li , H . I M A X I D l , T M I N l D >
. R A I N D (CO .
NEK T 1
RETURN
2240
24OO
2410
2450
5000
5001
5010
5015
i050
toss
UERBANO TUSS£N SVI CPTR.) ,SVCl(NIPf>)
6038
6060
ti R n - G
FOR 1=1 TO EIND
IF h ONh ( I ) = 1 THEN 5 3 9 0
FOR H=? TO 18
I F R A D ( I , H ) < 0 THtN R A D ( I , H ) = O
5030
5040
RADt I ,H) = ( ( R A D t l , H I « . 5 1 3 - 6 0 ) )
505O
I F R A D U . H K O THEN R A D t l , H » = O
5055
DPl_r>DOV I I ) -PLDAT-H : I F DPLD s O THEN DPLD-0
5080
HOOG - - : . 0 9 " 1 0 - ( - 4 ) « ( D P L D 3 ) + . 0 8 9 1 4 8 * ( D P L D - 2 ) - . 8 B 1 9 9 » » (DPLOI * 1 2 . 7 4 :
5081
HDOG = HOOG/I 0 0 0
5100
I F HOOG >.B THEN HOOG=.8
5110
IF HOOG < .0*THEN HOOG=.05
5130
01=.63»HOOG : P1R = . 6 3 * . 0 5
5140
Z 0 = . 1 3 " H 0 0 G ; 2Dr< = . 1 3 « . O 5
E 1 = 6 . 1 I « E X P < 5 3 4 7 . ( 1 / 2 7 3 - 1 / ( 2 7 3 * T E M P ( I ,H> ) > >
•5240
E2=RH(],H)»E1/100
DELTA = E I / < 2 7 3 » T E M P I I ,H> > " 2 » ( 6 7 9 0 . 5 - 5 . 0 2 8 0 8 * ( 2 7 3 * T E M P )
5260
5270
5200
S2C35
E3=E1-E2
I F W I N D S U ,H)«-0 THEN UIINDSCI , H ) - . l
RA-<LDG( ( 2 - D I ) / 2 0 > "2> / < . 4 1 m2 <• W I N D S d . H ) )
RAR=tLOG( (2- D 1 R ) / Z O R ) T ) / C . 4 I - 2 • M I N D S M . H M
KDNflî .'RA
IfDNAR^l /RAR
GAMA= . 6 6 * I 1 +KOm/ . 0 3 )
GAMAFi- .hi,' C 1 • • DNAft/ , 0 3 1
KONDUKTANSIE WAN GEWASOPPERVLAKTE UIDRD BERAAM DP 0 . 0 3 • / »
l/RC-'. .'2^-').'.>2'.2l
LEP=(RA[:l [ ,11) *EiELTA+l .2-H.H 0«E3»KONA) / (DELTA+GAMA)
LEPR=(RA£t(I ,H) wtiELTA* 1 . 2 * 1010»E3»K0NflHl / (DELTA+GAMAR)
LEP»LEP«3600/(2454*10"3)
5295
5300
5305
5310
5220
5330
5335
5340
5345
535O
5360
5330
TD85
5370
f.4O0
.5410
T-500
D=DO*(It
TER(D) •
TERCCI)
NCXT H
PRINT WINDSD(D) .RADtMtK ,TEF;(D) rHODG
RETURN
'
QM r.ftAGLU.SE
DATA OF DISKET TE
srio •
5900
6C-10
6015
6020
iO40
f<E TURN
1
UI T * - ' i 9S4F-RF"
DPEN " A " , » 4 , L t l Tt
VIR EERSTE LOFIE VERVANG " A " MET "Q"
FOR D=DI TO PF
PRINT H4 ,USING-«#II« . # • " ;DjTMAX(D> ;TMIN(O) jftAIND(D) ;RADD(D) j
NEXT D
TER(D) )TEf1 <D>
7OI.I0
7002
7OO9
7010
7020
7030
7040
7050
7060
7070
7U8II
7090
BOt'O
BOP.
SUBROUTINE Oil VERMISTE I'ATA TE VERVANG
IF
IF
IF
IF
hUNKIl! = 1 THEN 8010
H > 7 AND H C 18 THEN 7080
RAO( t ,H ><-<?? AND H= I THEN f(AI'<l,H)'O
RAD(I,H)\-99 THEN RAIlU ,H) =RAl' i 1 , H- 1 )
IF
IF
IF
IF
[F
IF
IF
IF
IF
TEMP( I ,H> . . - 9 9
AND H= 1 THEN TEMP ( I ,H> = - 9 9
TEMPI I . H ) < - 9 9 THEN TEMP v - 9 9 THEN W l N D S t 1 , H I ^ W l N D S < I , H - 1 )
R A I N t I , H ) • -<?•?
AND H = l THEN RA IN ( I ,H) - 0
h r t I N t 1 , H ) - . - 9 9 THEN RAIN ( I , H ) ' R A I N t I , H - 1 1 I GOTO
RAD ( I , H I
- . - 9 9 THEN RALiCHLn
=-99.9 ! KDNk(I) R A I N I I . H I - . - 9 9 THEN RAIND(D) = - 9 9 . 9 I KONK ( 1 ) W1NPS • - 9 9 THEN W I N D S D < D ) = - 9 9 . 9 : KONK(I I •
RETURN
BO 1 O
1
1
1
A P P E N D I X VII - L I S T I N G :
PRINT DAILY, MONTHLY
SUMMARY
10 REM
20
REM
30
RfM
40
REM
50
REM
60
SEM
70
REM
80
REM
90
REM
100 REM
110 REM
120
130
140
5,24)
150
160
1 70
180
190
200
210
220
230
240
250
260
270
2B0
290
300
310
BRUCE-86
• PROGRAM ; "BRUCE" ;AGROMETEOROLOGY MET-SITE 2 - E x p r r i m r n U I
( P r i n t s d a i l y «• m o n t h l y t u i M u r i t l of met
b r u c r kvlbe
: O4/3/I9B5
plot
( 10/01/1986
•..•...•...•.•.•••••....•.•••••*•••••«••«•••.•••.......•.•..•••••••••-•
OPTION BASE 1
CLEAfi
tilM A*(B0> , X U A > ,MON<33,16> , J U L I A N ( 1 3 ) .MONTH* < 12 > , 1 ( 2 . 1 6 ) , S X ( j , 2 4 ) ,SN<
0.05,
2.5E06
0,
I,
2,
LAMBDA
READ FIN,RECORD,HEIGHT, CROP ,
10 - . 13»CROP
, " ENE
ESE
SBE
DATA "
" , " NNE
s s u •'
DATA - W5W " , " UNW " , " NNW
FOR l - l TO 9
READ DIR*
NEXT 1
DATA O,3I ,59 ,90,120,151 ,1B1 ,212,243,273,304,334,365
INPUT -TEAR"jYEAR
INPUT "LAST LYS1METER WEIGHT mV'tWEIGHT
TR-TEAB
FOR
I-l TO 13
READ JULIAN!1)
IF INI (YR/41-TR/4 A N D I >2 THEN JUI. I AN I I I « Jl IL 1 AN I I i * ]
NEXT I
DATA - JANUARY »,« FEBRUARY",- MARCH »," APRIL
","
MAY
"," J
UNE
JULY "," AUGUST "."SEPTEMBER"," OCTOBER ",« NOVEMBER"," PECEMBER"
320
FOft 1-1 TO 12
330
READ MONTH*(I>
340
NEXT 1
330
CLOSE
360
INPUT -FILE REQUIRED <»<J. " B . M A R C H . O A T f j F l L E *
370 OPEN "I" ,»l.FILE*
3B0
WHT-UEIGHT
390
I PRINT CHR« (271 *CHRf < 7fc) +CHR* (48) +CHR* (49)
4O0
RECORD-I i TIME-100 : IRBIG-0
410
IF RECORD-I AND EOF(1)-O THEN INPUT »1.A* ELSE IF EOF(1> GOTO 2140
420
RECORD-I
430
IF MID»(A*,1 ,2)-"O9" THEN STOP
440
IF M1D«(A»,4 ,4)-"0249" THEN LPRINT USING '•••• .*» " I VOL <n I D* t A* ,23 ,6) ) | 16
OTO 410
450
P-LEN(A»)
460
FOR 1 - 1 TO P S T E P 1 0
470
K . V A L <MID» < A » , 1 , 2 ) )
4 BO
I IK )-WAl ( M i n i I A* , 1 + 2 , 6 ) )
490
NEXT I
SOO
IF P<78
GOTO 330
310
INPUT HI,A«
520
IF MlD*(Af,4,4) 0249" THEN (.PRINT USING "••••••" |VAL (MID* (AS ,23,6) ) I I
GOTO 510
530
540
IF niDtlAt ,t ,2>--0<»- GOTO 450
RECDfiO-2
550
NUM-K
360 REM BRANCH TO CORRECT TABLE
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
570
IF XCl>-2 GOTO 960 ELSE IF X(1)-3 GOTO t33O
580 REM
TABLE »1
590
IF ( O ) - I O O Oh TIME-10O IHFN 60T0 1570
6O0
LPRINT
610
IF I I 3 I O TIME THEN GOSUB 2620
620
LPRINT USING "•••"(TIME/1 Cm :
630
TIME-TIMEM0O
640
IF NUM-ll THEN III6)-0 ELSE IF N U M X 1 THEM X ( 1 * > - X U 2 >
630
FOB 1-4 TO 12
660
IF 1-4 THEN X U >-X ( I > • 10u'</360
670
IF 1=6 THEN GOTO 760
680
IF 1-7 THEN X(l>-X( 11-10
690
'
IF I-10 THEN GOSUB 2O00
700
IF 1-12 THEN GOSUB 21OO
720
IF I -1 I THEN LPRINT USING "••«*«.*"|X(I>|ELS£ IF 1-12 AND X(t)-O THEN LP
RINT ••
";ELSE LPRINT USING "•««>.«»"|Xt I > |
730
IF X M X - 9 9 THEN X(l)--99.99 I GOTO 760
740
I(1 , 1)"Z(I , I»+X(lI
750
2<2,It-Z(2,l»*l
760
NEXT 1
770
WEIGHT-X(ll)
780
2(1,I2)-(WHT-WCIBHT>
790
I(1,12>-Z<1,12)*.152
BOO
GDSUB 1840
S10
LPRINT USING "•••#.••"tEP!
820
IF EP«..O1 IHEN EP=--01
IF IRRIGK) THEN IH*-"v»t" ELSE IR«="no"
1140
LPRINT
1150
LPRINT CHW*(27H-CHR*134>
llt.ii
LPHINT "A-P»n
t»«i> " |TAB<20> |" I r r m t i o n ( " ; IR» ; " > " s
1170
JF 2(2,5X12 THEN MON < DATE ,2» — 99 .99 ELSE MON (DATE ,2) -I (1 ,5) /I »2,3>
1180
IF
2(2,7X12
THEN
MON<DATE,3>—99.99
ELSE HON (BATE ,3 > -1 I 1 , 7 > 11 < 2 ,TI
1190
IF {(2,8X12 THEN MON (DATE ,4 ) --99 .99 ELSE MON (DATE , 4 I - 2 ( 1 ,8 t II (2,B)
1200
IF 2(2,9X12 THEN MON < DATE ,51—99 .99 ELSE MON (DATE ,3> -I ( 1 ,9 t /I(2,»l
1210
IF 1(2,10X12 THEN MON(DATE,6)—99.99 ELSE MON < DATE ,6 t -2 ( 1 , 101 fl 12, 1 0
1220
)
IF X(4)<0 THEN HONCDATE ,flt--99,99 ELSE MON(DATE,8>-K(4 I«2O/25
123O
MON(DATE,9>-1(1,12*
1240
MON(DATE,10)-EVAP
1250
FOR 1-1 TO 2
1240
FOR J-1 TO 16
127O
Z(l,J)-0
1260
NEXT J
1290
NEXT I
13OO
IRfilG-0 • X U 2 J - 0
1310
GOTO 4 1 0
1320
REM
TABLE
«
133O
IF M 2 X - 1 0 THEN MON (DATE , 11 --99.99 ELSE MON ( PATE , I ) - * <2 I »96 .4
1340
LPRINT
1350
830
IF AE<S(K ( 12)/EP) >999 THEN LPRINT "
" | ELSE LPRINT USING " * • # • . « •
LPRINT
1360
"|X(12t/EP;
LPRINT
137O
B40
IF X(161-0 THEM BOTO B70
LPRINT "WIND-DIRECTION - RELATIVE FREQUENCY fi I [N 45 DEGREE SECTORS"
138O
B50
LPRINT USING "••#§.•"|K(16);
LPRINT
1390
86O
LPRINT "•••)
O
-> 43 ->
90 -> 133 -> 180 -> 223 -> 210
LPRINT "d»gr».«
1400
B7O
EP = -99.99
-> 360"
-> 315
880
IRRIG-IRR1G*X(16) i X(16)'O
LPRINT *r»qu»ficy
1410
890
N-INT(X(3)/100I
S-0
1420
900
IF X(4)>-I THEM SX (1 ,N>-SXU ,N>»X<4> i SN( 1 ,N) -SN ( 1 ,N> +1 : S5<I,N)-SS( 1430
FOR 1-3 TO 10
1 ,N)»-X (4) "2
IF I I I K O THEN X (I (—.9999
1440
910
IF X(5t>-99 THEN SX(2,Nt-SX(2,N)*X(3) I SN(2,N>-SN(2,N1•I
SS(2,N>-SS
IF ( ( I K S THEN GOTO I4B0
1450
(2,N)*X(5)"2
N-I
1460
920
IF X<6>>0 THEN SX (3 ,N> -SX (3 ,N1 -»X <6>
SS(5,N>-SS(3
SN(5,N)-SNC3,N>*1
S-X(I)
147O
,N)»X(6> "2
LPRINT USING "••#••.•#• | M 1 ) » I O O |
1480
930
I F X ( 7 > > 0 THEN S X ( 3 , N ) - S X ( 3 , N ) * X < 7 >
SS(3,N)-SS(3
SN(3,N)-SN(3,N)+1
NEXT 1
1490
,NI*»(71"2
TIME-100
I50O
940
IF <<10)>0 THEN SX(4.Nt-SX(4,N)*X(10)
: SS(4,N)=SS
SN(4,N)»SN(4,N)
LPRINT
1510
(4,N) *% ( 10) "2
LPRINT
1520
950
GOTO 410
MON(0ATE,7t-(N-l>
1530
94O Htr\
TABLE 12
-99.99 " ELSE LI-K
IF MON(DATE,7)<0 THEN LPRINT " H i g h M t frtqucncy ->
1540
970
LPRINT
Highest frequency -> "|DIR«lN-l>
INT
9B0
IF P 0 K 2 4 1 THEN ( ( 4 > -X (4 ) » 2 0 / 2 3
IF F1N-1 THEN GOSUB 2140
15T.O
990
LPf*INT CHR*(27»«CHR« (331
GOTO 410
156<J
1000
LPRINT "Ave " i
REM
SUBROUTINE TO PfclNT HEADINGS
1570
ELSE LPRINT USING "••«.••" 11 ( 1 ,4 1580
1010
I F Z ( 2 , 4 ) < 2 2 THEN LPRINT "
LPRINT CHR*(12)1
)II(2,4> j
LPRINT CHR*(27) »CHf**(69t
159O
FOR 1=3 TQ 10
1020
LPRINT TAB(65)("D " t X<2>
1600
IF 1=6 THEN GOTO 105O
IO30
LPRINT CHR»(14)lCHR*<27)*CHR«(78)|CMR«(27)*CHR»(33>
16IO
IF 2(2,1X22 THEN LPRINT "
'I ELSE LPRINT USING "««««.«*"; 2 ( I 1620
1040
LPRINT "UNIVERSITY"|CHR*(27)*CHR»(B1»!"
of t h e " : C H R « ( 2 7 I * C H R * ( 7 8 ) J" ORAN
,I>/2 (2,11 t
GE FREE STATE"
1050
NEXT 1
LPRINT "
P«p*r-t»*nt
Agro»t>orol09y "
1630
1060 LPRINT
LPRINT CHRK27I *CHR*(BO) | "
Eipfr.mfntjl
Plot"
1640
1070 LPRINT "Total 11 ;
LPRINT CHR*(13) )CHR*(27) +CH>R«(69> |CHR« (27> *CHR* ( 34 t
1650
1060
LPRINT TAB146) USING "•«»••.UN"|(HEIGHT-WHT)|
LPRINT
1660
1090
LFR1NF USING "••• . Utt" ; 7 (1 , 1 ?) ;
YR-r£AR-1900
I 6 70
1IO0
IF EVAP>.1 THEN LPRINT USING "•••#.••"|EVAP|ELSE LPRINT "
FOR 1-1 TO 13
1680
1110
IF EVAP>.I THEN LPRINT USING "••§».••" 11(I ,12)/EVAP|ELSE LPRINT "
I F X ( 2 X - JULIAN(!> GOTO 1710
14,90
NEXT I
"i
1700
IF * ( 2 1 - J U L I A N I I ) THEN F I N - 1
1120
L P R I N T US1NS - • « . • - ( X < 4 >
1710
1-1-1
1720
1130
MHT-UEIGHT
MONTH-I
1730
00
I 74O
I73O
176O
1770
1 7BO
E*/
DATE-K <2> -JUL IANC I )
IF DATE-1 THEN F 1 N-O
LPRINT "
Date I"iDATE|"/-(MONTH*(1> | " / - I TEAR , TAB(60> 1"DOY « - | « ( 2 )
LPRINT CMR*<27>*CHR*(33>
LPRINT " T n » Rcdtn
Temp
«.M. CJI ibritiont
Hind
Ly»«tr
El
RAIN"
Ep
1790
LPRINT "
W/m" ;CHR« ( 2 7 ) [ " I , " ;CHR»<B1 > |CHR* ( 2 7 ) l " » " |SPC<4> i C H R * < 2 ? > l
tCHR«<79> | C M R * ( 7 7 ) t " « " l"C
X
T ( C)
RH<Z>
m/»
mV
M/hr
m/hr
LPRINT ! HP»(77> .<_HHf '.ii >
1B0O
LPRINT TAB146I USING "•••••-i"[WEIGHT(
1610
EVAP-0
1B20
GOTO 600
1830
POTENTIAL EVAPOTRANSPORATION
1B40 REM
IF K <4> <-99 OR X (5) <-9<? OR X<7><-99 OR X U O K - 9 9 THEN RETURN
IB5O
I860
TEMP-X(5)+?73.15
1B7O
C 0 N S I - . 4 0 I 9 9 1 4 • .017251 • X (5) - .0001483 • X <SI • X<3)
1BB0
IF I H X H B THEN ER-0 ELSE ER'CGNST • ( ( X (4) - I 4B) / . 8 8 ) /LAMBDA »3*>OO
1B90
RH-<LOG<(HEIGHT-,64 • CROP> / ZOt ) " 2 / ( .4 1 » . 4 1 • X ( i O t )
190O
EVS-EXP(32.5763-6790.499/TEMP-5.02BO8»L0G<TEMP)>/( .000«A2»TEMP )
1910
VAP- I 1 - X < 7 ) / 1 0 0 ) ' E V S / I O O O
1920
EV-(I-CONSTI«VAP/RH »3400
1930
EP'ERtEV
1940
EVAP-EVAP+EP
195O
RETURN
I960 REM
RADIATION
197O
X< D-X ( I)»10OO/360
1990
RETURN
200O REM
UIND CORRECTIONS
2010
2020
2080
RETURN
209O REM
LTSIMETER CORRECTIONS
2100
IF WEIGHT < O THEN HEIGHT * X<I-1>
211O
X<12)><UEIGHT-X<11))
2120
X<I2)-X<12)».152
2I3O RETURM
2L4O REM
MONTHLY SUMMARY
2150
GOSUB 2670 : 'PRINT HOURLY MEANS
2160
INPUT "FILE FOR STORING MONTHLY SUMMARY(eg.8:JAN.MQN)";f1LE«
7170
DPEN "O" ,»2,F1LEI
2180
LPRINT CHR«(l?t:
2190
LPRINT TAB<45>i"O " jMONTH
220O
LPRtNT CHR*I14);CHft»(27)*CHR«(7B):CHRI(27)»CHR«(33)
221O
LPRIN1 "UNIVERSirr" ;CHR»(27)*CHR«(611 :" of the" :CHRt(27)*CHR*<7B1 ; " ORAN
GE FREE STATE"
7220
LPRINT "
Dfpjrtnpnl
fl?ro«itteorolo<)y"
223O
LPRINT CHR« (2 7) 'CHR* (BO) ; "
Etperinwtal Plot"
224O
LPRINT CHflt11?) ;CHR«127>*CHR«<t9) SCHB«(27>+CHRI 134 I
2250
LPRINT
226O
LPRINT TAP <2O) jMONTHt (MONTH) ; •'-•• : TEAR i
2270
LPRINT
2280
LPRINT CHR«(77)*CHR«(33)
229O
LPRINT " Dty R i d t n
Temp
R . H . Ca! i b r * t i o n *
Wind
R«in
Tot»l
Total "
2300
LPRINT
total
mea
speed angel
mean
•
Ep"
231O
LPRINT "
MJ/«-,CHR*(27)*CHR«(ll5)*CHRf<49)
l»2"tSPC
<6> ("O-|CHR«<27)'K>IR*(H5J»CHRt(4a) |CHR«(27) tCHRf (49) |"C
C
1RH
m/% deg
ma
•ni/day"
2320 LPRINT CHR»(77)*CHR*<34>
233O
FOR I - I TO 10
2340
2350
2360
2370
23B0
;3<fO
240O
flINT
MONI32.D-0
M0N(33.I)-0
NEXT 1
FOR 1-1 TO DATE
LPRINT USING "•••»" I
FOR J-l TO 10
IF J-7 THEN LPRINT DIR*(MONII,Jl)| ELSE If J-8 AND M0N(l,J)-0 THEN LP
; ELSE LPRINT USING "•
"( ELSE IF M a N ( I , J X - 9 O THEN LPRINT "
" -.MONCt ,J) ,
IF MON(I,Jt>-99 THEN MON(32,J)-M0N(32,J)+MON(I,J)
2410
IF M 0 N U , J ) > - 9 9 THEN MON ( 33 , J) -MON 133 , J > • I
2420
PRINT
«2,M0N(I,J)
2430
MONtI,J>-O
2440
NEKT J
245O
LPRINT
2460
NEXT I
2470
MON(33,B)-1
24SO
F1N=O
2490
2500 LPRINT CHH»(27)*CHR*(33)
2510 LPRINT "MEfiN"J
FDR J-l TO 10
752O
2^30
IF J-7 THEN LPRINT
", ELSE LPRINT USING "••••.••"|MON(32.J>/MOH
133.Jl I
254O
PRINT •2,MON(32,J)/MON(33,Jt
2530
NEXT J
2!M>O
FOR 1-1 TO 17
2570
LPRINT
258O
NEXT I
2390
LPRINT CHft*(27>»CHR*<34)
26O0
CLOSE
26 IO
END
262O REM
2630
2640
2630
266O
2670 REM
26B0
2690
2700
2710
2 720
M, King
tut.
Hour-i y
H i m
FOR I-TIME*1OO TO XC31-100 STEP IOO
LPRINT USING " • # • " ! I / t O O
NEI1T I
RETURN
LPRINT CMR«(12)
LPRINT
LPRINT TAB( lt>> ; F I L E *
LPRINT "
Time
FOR 1=1 TO 2*
: LPRINT
Rad
TAB ( 10 > J "MEAN HOURLY VALUES"
Temp
Rel Hufli Wind cpeed
Ly* 11
2730
LPRINT USING -••#•««.me1 : 1 ;S*U ,I I/SN(I ,I) :5<I2,I I/SN(2,It jSX(3,1 I
/SN(3.H :SX(4,I>/SN(4,I) |SX(3,I) /SN(?,n
NEXT I
2740
2760
T77O
2 780
281O
2B30
284O
LPRINT i LPRINT : LPRINT TAB(101;"STANDARD DEVIATION" ! LPRINT
"STANDARD C'EW I AT ION
FOR 1=1 TO 24
FOR J=l 10 5
SS(J, I(=SQR( (SS(J,l>-<S« <J, I > "2/5N< J,I) ) > /<5N<J,It-I ) )
IF SN<J,I><2 THEN 5S I J,I)--999.99
NEXT J
LPRINT USING "(t»#K»IIH .•»•• ,1 jSSIl , I ) ; SS ( 2 , I ) :SS(3,I t ;SS<4 ,1) jSS<5.t
NEXT I
RETURN
A P P E N D I X VIII - L I S T I N G :
TRANSFORM AUTO DATA AND FILE
10
20
30
40
3O
60
70
BO
70
1OO
I 10
120
13O
140
130
160
170
180
185
190
3O0
210
220
230
2*0
230
260
270
2B0
29O
30O
31O
320
UNE
33O
34O
330
360
370
3SO
410
420
430
440
441
442
430
*h:<
470
4 BO
49O
3OO
510
32O
330
540
350
3*0
570
seo
590
600
610
620
EKPL0TS4
• PROGRAM | TO TRANSFORM AUTOMATIC WEATHER DATA WITH CORRECTION TO
DATA FILES
HOURLt IFtla- 2) AND DAILY <Fl 1•-3)
•
bruci k«lb» i 24/6/196 6
Conv«rt»d 4rom "BRUCE-B4-
•
•
•
OPT ION BASE 1
CLEAR
DIM A i l B O I . I I I t l ,f1ON<33,16) .JULIAN!13) .MONTH*(12) , 1 ( 2 , 1 6 ) .SX(3,241
DIM SN<5,74> , S S t 3 , 2 4 )
DATA
O, 1 , 2 , 0 . O 3 , 2.3EO6, O.2O , O
READ F I N , RECORD, HEIGHT, CROP, LAMBDA , CRP
, EPI
10 • .I3«CROP : It - . I3»CRP
MIN-99.9"? •- WMIN-99.99 : MAX = -9<?,99 : WHAX--99.99 : M1S-0
DATA "
",NNE • ' , " ENE • ' , " EBE " , * SS€ " , '
SEW "
DATA WSM " , "
WNW " , "
NNW "
FOR 1-1 TO 9
READ D 1 R * ! I )
NEXT I
DATA 0 , 3 1 , 3 9 , 9 0 , 1 2 0 , 1 5 1 , 1 8 1 , 2 1 2 , 2 4 3 , 2 7 3 , 3 0 4 , 3 3 4 , 3 6 3
INPUT "TEAR"|TEAR
INPUT "LAST LTSIhETER HEIGHT >|V|MEIOHT
TR-YEAR
FOR 1-1 TO 13
READ JUL IAN<I)
IF I N T ( Y R / 4 ) - T R / 4 AND I>2 THEN J U L I A N ( I ) " J U L l A N ( I » * t
NEXT I
DATA " JANUARY " , - FEBRUART*," MARCH " , " APRIL
•,*
HAY
•,"
J
JULY - , * AUGUST - .-SEPTEMBER- , * OCTOBER " , - HOVCHKR-,- DCCEriBER*
FOR I - l TO 12
READ MONTH*(1)
NEXT I
CLOSE
INPUT -SOURCE FILE REQUIRED
< • » . C iMON-VR.EXP > ' | F 1 L E *
OPEN " I - , » I , F 1 L E *
INPUT •OUTPUT HOURLY FILE REQUIRED <•». AihON-YR-« .UUH)' |F tLE*
OPEN - 0 " , i 2 , F I L E *
INPUT -OUTPUT OAILT FILE REQUIRED
( a g . BiMON-YR-V.DAO)- ( FILC«
OPEN -O-,«3,F1LE»
INPUT -ENTER NAME OUTPUT WIND FILE l a g . BiMON-YR-* .HND)" |F1LE«
OPEN -O",114 . F I L E *
HHT-WEIGHT
RECORD-I i IRRIQ-O
IF RECORO-t AND E O F I U - 0 THEN INPUT * 1 ,A* ELSE IF EOF t I t 6OT0 176O
RECORD- 1
IF M I D « ! A * , I , 2 ) - - O 9 - THEN STOP
I F H I D * I A * , 4 , 4 > - " 0 2 4 9 - THEN GOTO 13B0
P-LEN<A*»
FOR I « l TO P STEP 10
K-VALIM]D*<A*,I , 2 ) >
X<K)-VAL!MID*(A*,I*2,6))
NEJIT I
IF P<7» BOTO * I O
INPUT t l , A «
IF M I D * ( A * , 4 , 4 > - * 0 2 4 9 - THEN GOTO 1610
IF H 1 0 « < A * , l . 2 I - - O 9 - 80T0 310
RECDRD-2
NUH-K
BRANCH TO CORRECT TABLE
>>>>>>>>>>>>>>>»»>»»>>>>>>>>>>>>>»>>>>
630
640
650
6 70
671
660
690
700
710
720
770
780
790
eoo
eio
820
B30
840
B50
852
B54
856
B5B
860
870
HHO
B90
900
910
93O
940
N-O
970
<?80
IF Kill"! GOTO IO4O ELSE IF X d > - 3 GOTO 1300
'
TABLE • ,
IF K(3)-IOO THEN GOTO 1460
DO*1 - X<2>
PRINT #2, "B4":
PRINT «2, USING -••••| X<2]| iPRINT X<2>,
PRINT »2, USING "••"; X O> / 10Ot iPR I NT X(3>
IF NUM-ll THEN X d 6 > « 0 ELSE IF N U H M 1 THEN X d 6 ) - X d 2 )
FOR 1-4 TO 12
IF 1-4 THEN GOSUB 2080
IF 1-5 AND ( ( I X - V 0 THEN GOTO 720
IF 1=6 THEN GOTO BBO
IF [-7 THEN GOSUB 1800
IF 1-10 THEN GO5UB 1930
IF I'll THEN 0O5UB 198O
IF 1-12 AND WEIGHT<0 THEN WEIGHT - XU-ll
IF 1-12 THEN X d >-(WEIGHT-ldl-l> >•.32222
IF X d X - 9 9 THEN Xd»--99.99
IF 1-6 OR 1-6 OR 1-9 OR I-J2 THEN GOTO B50
IF X d X - 9 9 THEN nIS - MJS • 1
IF X d X - 9 9 OR XIM--99.99 THEN GOTO 880
IF 1=10 AND XdO>>WMAX THEN WHAX-X(IO)
IF 1-10 AND X d O X W M l N THEN UHIN-IIIOI
IF 1-5 AND X<3> >MAX
THEN MAX-X (9)
IF 1-5 AND X<5> <MIN THEN HIN-X(3)
Z<1,I>-Z(1,I>*XIII
I04O
1050
106O
1O7O
1090
too
105
110
120
140
1130
160
170
173
180
190
1205
1210
1220
123O
1240
1230
1260
127O
1280
1290
I<?,I>-Z<2,I>»1
NEXT I
P R I N T »2, USING " « • * • . # # " |X<4> | aPttINT § 2 , U S ING - • • » . » • " | X 15) ; X (7) i X (10) |
2(1 , 1 2 > " ( W H T - W E I G H T > n . 3 2 2
WEIGHT=X(11)
130O
P H I N T N2,USING " # # # # . » " | M E I G H T |
1310
IF R A I N - 0 THEN P R I N T # 2 , "
.O" ELSE PRINT « 2 , U S I N G "•••. *" |RAIN i RAI 1320
1330
N-INTIX O t / l O O )
1340
IF X ( 4 ) > - |
THEN S X d t N > - S X d , N > * X M ) • S N I I ,NI-fiN(l ,NI »1 I S S ( 1 ,N)-SS
1345
(I,N)*X<4>"2
1350
990
IF
1360
THEN SX (2 ,N> -SX ( 2 ,N> •» (5) I SN ( 2 ,N )-SN ( 2 ,Nt • I S S ( 2 , N ) - S S
1370
(2 ,N) <-X (5) "2
1380
10O0
IF X(6)>0
THEN S X ( 5 , N > = S X ( 5 , N > + X ( 6 ) l SN<5,N)-SN<3,N>+1 t
1390
S ( 5 , N ) + X(61"2
1400
I0IO
IF XI7l>0
THEN S X ( 3 , N I = S X < 3 , N ) + X 1 7 )
: SN(3,N)-5N(3,N)• I
SS(3,N)-S
1410
S(3,N>*Xl?t"2
1420
1020
IF XIIODO
THEN SX ( 4 ,N> »SX ( 4 ,N) • X «2O/23
IF Z(2,7)
- O THEN Z(2,7) - -99.99
IF Z<2,10) - 0 THEN Z(2,10) - -99.9V
IF I<2,4) - O THEN Z(2,4I
- -99.99
IF Z(2,5) - O THEN Z(2,5) - -99.99
IF X<4)<0 THEN X < 4 > — 99.9
IF X<3>
'..-99 THEN X <3> --999OO' i X <6) --999O0 •
PRINT «3, USING -MHM1DOY|
PRtMT N3, USING "H«««|lilS)
PHINT «3, USING " tt»M» , ••• I Z< 1 , 4 >/Z< 2 , 4> I
=3
PRINT «3, USING ••«(•».••" lMAX,nlN|Z(i ,31/1 (2,3) |Z (I ,71/1(2,7>»
PRINT #3, USING "•••.••" |Z I I ,lOt/I(2,10) ,iWINDt-Zd
, 101 /It2,*OI
PRINT 113, USING •••«.•"
[X(4)l
PHINT «3, USING -••••.«" |X(3)/lOO0,X(6)/10O0|
PRINT « 3 , USING "««••)«.•-| (HEIGHT-MHT)
MIN-99.99 : MAX--99.99 : M1S-0
EP1 - O
UMT •WEIGHT
FOR I-l TO 2
FOR J-l TO 16
Z(I ,J)-0
NEXT J
NEXT I
Xd2)-0
GOTO 4 70
TABUE
S-0 i PRINT * 4 , " B 4 H |
PRINT « 4 , USING " • * • " |DOT|
PRINT M , USING " • « . • • " jWINDY|
PRINT 114, USING " • # • - • • " (HflAX |WMIN(
Ht1IN-99.99 1 WMAX--99.99 I MIS-0
FOR 1-3 TO 10
IF I I I K O THEN X d ) - - . 9 " » 9 9
IF X ( I ) < S THEN GOTO 1400
N»I
s-x |
NEXT I
IF X I 2 X 0 THEN PRINT K4 , •• -99 . 99"ELSE PRINT K4,USING "•»•.»•" |X (2»-86 .1
IF FIN-1 THEN OOSUB 1760
GOTO 4 70
146O
1470
14B0
1490
I5OO
15U>
132u
1330
1540
155O
15*0
1370
1580
1390
1600
TR-TEAR-1900
FOR [-1 10 13
IF X ( 2 ) < - JULIANU) GOTO 1310
ME XT I
IF I<2>-JULIAMll) THEN F I N - 1
1-1-1
HDNTH-I
DATE-* <2>-JULIANU >
IF DATE-I THEN F I N - 0
EVAP-0
GOTO 665
.RAINFALL
1600
1B10
182O
IB30
18<O
1830
I860
1970
I860
R i l a t i v > humidity it-cm 3 ,1m
IF X < 7 K - 9 9 THEN RETURN ELSE IF X12>>]9 GOTO 1890
TD-X<5)*273.15
TU»X<7)*273.15
es-EKf > (52.3763 - 6790.499/TD - 3.02808 • LOG <TD> >/ TO/ .000*62
Efl-E IP (32.3763 - 6790.4'?V/TW - 3.02SOB • LOG < TW> )/TW/.OOO462
£A-Efl-(1 .2»1004»lOOOI /LAMBOfl*<TD-TH»
X(7>-EA/ES«100
RETURN
ie<?0
190O
1910
1V20
IF K(2>-20 AND X I 3 K I O O 0 GOTO 1B2O
IF K(2)<26 THEN X ( 7 ) « X ( 7 1 - 2 9 , 2 9 » 2
X ( 7 I - X ( 7 ) - 10
RETURN
RAIN - V A L < M t D » I A « , 2 3 I i > ) « 2 0 / 2 3
GOTO 470
1610
1620
1630
R*UN GOTO 370
.RAINFALL
1760
1780
1790
CLOSE
END
>»20/25
.END
1930
1940
193O
I960
.Hind c e r r t t t ions
1970
IF XM0K.0 OR I(2>>240 THEN RETURN
IF X(2)><70
THEN X (10) - ( X (10) - .447) *b+ .447
IF X<2)<90 THEN XUO>-X < 10» * 6 * . 4 4 7
RETURN
1980
1990
2000
2010
2020
2030
2040
I F K<2>>256 AND « ( 2 X 2 8 3 THEN GOTO 2070
I F X<2>-303 AND K < 3 K 1 0 THEN X C 11 >-X< 11 >+S24
I F X<2)<27 Of* K ( 2 ) > 4 0 THEN RETURN
X ( 1 1 ) -X ( 6 X 1 O 0 0 - 1 3 0 0
I F X f l M M O O O THEN t ( I 1 ) -X ( I 1) / 1 000
RETURN
2050
2060
2070
IF X(2)-282 flND X(3)>1400 THEN RETURN
X d l t - X (6) -1000
RETURN
2080
2090
2100
X(4)-X(4t»1000/360
RETURN
.LTSIMETER CORftECTH
.RADIATION
APPENDIX
IX
COMPARISONS OF WEATHER VARIABLES AND TESTS ON EQUIPMENT
Relationship between measurements on West Campus and "Die Bult"
As
explained,
it
was necessary to determine
the
relationship
between values of certain weather elements measured at "Die Bult"
and
corresponding measurements made at West Campus in
replace missing data.
temperature,
order
to
Results of regressions involving radiation
relative humidity and wind are given in Table IX.1.
The high correlation coefficients and low mean absolute differences
mean that the regression equations should provide reasonably
reliable estimates.
It
was deemed necessary to check whether the data logger in
the
automatic weather station was providing reliable records of lysimeter changes in mass.
This was done by comparing values regis-
tered on the CR21 with those measured on the Keithly digital volt
meter.
again
The results of the tests are given in Table IX.2.
the
high SI and low MAD indicate that the CR21 was
tioning accurately for the measurements made over both daily
hourly intervals.
253
Once
funcand
TABLE
IX.1 -
Statistical
analysis of relationship between West
Campus and Die Bult measured values of hourly mean
( W / m M and daily ( M J / ( m 2 d M radiant flux
density,
hourly
temperature, hourly relative humiditv
and
hourly mean wind speed.
RAD
Y
- West
Campus
X - Die Bult
W/m 2
n
SLOPE
INTERCEPT
r
MAD
SIMULATION INDEX
TABLE
IX.2 -
57
1,06
37,34
0,99
437,77
61 ,44
0,98
OCT85 SEPT85
RH
WIND
%
m/s
23
25
28
28
1 ,07 1,03
0,72
1 ,08
6,68
2, 12 - 0 , 10 -15,24
0,96
0,99
0,98
0,98
19,60
15,75 18,7
59,46
2,30
3,38
0, 55 10,68
0,59
0,90
0,98
0,88
Y
X
E Kei thly
E CR2 1
mm/d mm/h
n
9
68
0,86
0,70
0,98
5,31
0,20
0,99
0,91
0,07
0,94
0,71
0,07
0,97
(mm/-)
r
MAD (mm/-)
SIMULATION INDEX
slope in the daily values is seriously affected by a
outlier.
cluded
On
29
1 , 23
1 ,36
0, 88
1 , 83
1,77
0,59
Statistical analysis of agreement between
lysimeter total evaporation,
E, registered on Keithly
and CR21 recorders on a daily or an hourly basis.
SLOPE
INTERCEPT
The
AUG85 OCT8 5 OCT8 5
RAD
RAD
TEMP
MJ/d
MJ/d
°C
grounds of the evidence
that the lysimeter connected
satisfactorily.
254
in this table it was
to the CR21 was
simple
con-
functioning
APPENDIX X
RESULTS FROM THE COOPERATIVE EXPERIMENTS OF 1982 AND 1983
This
appendix
reflects
the data collected in
the
cooperative
experiments during 1982 and 1983.
INVENTORY
TABLES
FIGURES
1982
1982
X.I
Irrigation record
X.4
LAI
X.2
Water use
X.6
Leaf water potentials
X.3
LAI
X.7
Scenario of Fh
X.5
1983
1983
X.8
Irrigation and rain
X.12
LAI
X.9
Water use
X.13
X.10 Water use
X.14
Scenario of Fh - Site A
X.ll Crop morphological
and physiological data
X.15
Scenario of Fh - Site B
X.16
Scenario of Fh - Site C
X.17
Scenario of Fh - Site D
X.18
Scenario of Fh - Site E
X.19
Soil water content
X.20
Soil water content
X.21 Cultivation practices
255
TABLE X.I -
Day
of
four
the year on which irrigation took place
sites
in
the
Vaalhaarts/Hartswater
on
trials
during 1982.
A
FREQUENCY
C
B
D
FARM
UOFS
FARM
UOFS
FARM
UOFS
FARM
UOFS
176
176
167
167
155
155
174
174
223
223
203
203
196
196
217
217
237
237
222
222
217
225
225
251
215
235
235
242
242
266
266
249
249
251
251
279
279
260
260
293
293
271
271
306
306
278
278
287
287
266
266
303
303
279
279
10
10
7
8
8
8
256
225
230
244
235
258
244
265
265
258
279
279
8
7
TABLE X.2a - A-pan evaporation, rainfall, irrigation and measured
and calculated water use and pan factors for Site A
during 1982.
PERIOD
WATER USE
PAN FACTORS
A-PAN
EVAP. RAINFALL IRRIGATION MEAS CALC MEAS CALC CALC/MEAS
( mm )
222-228
229-235
236-242
243-249
250-256
257-263
264-270
271-277
178-284
285-291
292-298
299-305
TOTAL
(mm }
43
49
49
49
60
59
56
72
61
31
11
5 11
4
3
58
49
5
6 16
1
636
62
(mm )
( mm) { m m )
62 (223) 13
18
0, 3
0,4
20
9
62 ( 237 ) 20
0,4
34
43
0,3
12
62 (251 ) 21
0,4
55
0,8
47
52
0,5
59
62 (266) 26
0,6
44
68
62 (278) 30
0,5
64
31
45
1,0
62 (293) 47
0,8
63
25
62
0,5
372
336
0.4
0,2
0,7
0,9
0,9
0,9
1 ,0
1 ,0
1,0
1,5
1,1
1,3
1,3
0,5
1,8
3,0
2, 3
1,1
2,0
1,7
2,0
1.5
1,4
2,6
572
TABLE X.2b - A-pan evaporation, rainfall, irrigation and measured
and calculated water use and pan factors for Site B,
during 1982.
WATER USE
PAN FACTORS
A _D A M
PERIOD
EVAP. RAINFALL IRRIGATION MEAS CALC MEAS CALC CALC/MEAS
(mm)
(mm)
222-228
229-235
43
49
236-242
243-249
250-256
257-263
264-270
271-277
278-284
285-291
292-298
299-305
49
49
60
59
56
72
61
31
58
49
TOTAL
636
75 (222)
75 (235)
(mm) {mm)
30
35
75 (249)
*
43
51**
48
( mm )
75 (303)
65
53
33**
72
68
48
46
69
56
53
66
68
64
45
63
62
675
421
411
75 (260)
11
5
5
113
9 5
6 16 1
72
t* Ignore
75 (271)
75 (278)
75 (287)
* Est imate
257
0,7
0,7
1,0
1,1
0,9
0,6
1,0
1,1
1,6
0,8
1,4
0,9
1,0
0,9
0,9
1,2
1,0
1,0
1,5
1,1
1,3
0 ,9
0 ,8
1 ,0
2 ,0
1 ,0
0 ,9
0 ,9
1 ,4
0 ,9
Table
DATE
X.3
- Leaf area indices measured and
1982.
SITE A
DOY
CAL
calculated
LEAF AREA INDEX
SITE B
MEAS
CAL
MEAS
08/10
222
0,8
0,8
2,0
2,0
08/17
229
0,9
1,7
2,8
3,1
08/24
236
1,6
2,6
3,8
3,8
08/31
243
3,3
4,1
5,1
4,9
09/07
250
4,4
4,4 •
6,3
6,1
09/14
257
6,1
8,1
7,8
5,8
09/21
264
7,9
5,3
9,8
7,2
09/28
271
9,7
4,7
8,7
5,6
10/05
278
8,1
2
7,3
2,1
10/12
285
6,9
3,5
6,3
2,7
10/19
292
5,6
2,2
5,1
2,2
10/26
299
4,3
3,6
3,8
3,8
2 58
during
10
ncomputed
+ measured
8-
260
240
220
280
300
DAY OF THE YEAR
FIG. X.4a -
Measured and calculated leaf area indices at site A
during 1982.
10
98-
• computed
+ measured
7654321 -
220
240
260
280
300
DAY OF THE YEAR
FIG. X.4b -
Measured and calculated leaf area indices at s i t e B
during 1982
259
Table X.5a -
Pre-dawn and
site A, 1982.
noon leaf water potential
(bar)
at
LEAF WATER POTENTIAL
DATE
08/10
08/17
08/24
08/31
09/07
09/14
09/21
09/28
10/05
10/12
10/19
10/26
TABLE X.5b -
DOY
PRE-DAWN
NOON
CALC
MEAS
CALC
MEAS
222
229
236
243
250
257
264
271
278
0,1
0,1
1,1
2,6
0,4
6,8
0,5
4,0
2,6
4,7
4,0
3,8
9,8
10,9
10,9
16,2
12,2
285
292
299
1,6
1,3
11,4
1,6
2,3
6,4
4,0
4,0
1,6
4,1
18,4
15 ,8
14,9
13,9
16 ,9
15 ,7
17 ,0
19,5
19, 1
16,0
12,1
18,0
Pre-dawn
1982.
1,1
6,7
6 ,9
16,3
19,2
25,3
10,8
13, 1
18, 1
and noon leaf water potential at site
DATE
DOY
PRE-DAWN
08/10
08/17
08/24
08/31
09/07
09/14
09/21
09/28
10/05
10/12
10/19
10/26
CALC
MEAS
222
229
236
243
0,1
0,4
1,2
2,0
3,0
4,4
6,2
7,5
250
0,4
7,0
0,5
5,3
0,6
257
264
271
278
285
292
299
2,0
3,1
3,9
3,0
1> 1
7,7
1,6
1,3
1,5
7,3
3,9
260
MOON
CALC
10,0
11,5
11,1
17,4
12,3
7,1
16,5
19,2
25,2
10,7
13, 1
18,1
MEAS
14,9
14,2
13,5
15,0
18, 1
17 , 3
19, 1
16,8
18,9
16,5
—
B
2.8
• computed
• measured
I
220
240
I
i
f
260
DAY OF THE YEAR
280
500
260
280
300
1.2
1.1 -
D computed
1-
+ measured
0.9 -
0.80.7-
03-
0.1 240
220
DAY OF THE YEAR
FIG.
X.6 -
Noon
1982
and pre-dawn leaf water potential at Site
261
A,
'.Or
0.6-
OJS
0(4-
0.2
220
240
260
DAY
FIG.
X.7a
-
280
300
OF THE YEAR
Scenario of the Hydraulic scheduling factor, Fh, on
Site A during 1982.
1.0
0.8
0,6
0.4
0,2-
0
220
240
FIG.
X.7b
-
:80
260
DAY
Too
OF THE YEAR
Scenario of the hydraulic scheduling factor, Fh, on
Site B, 1982.
262
TABLE X.8a -
Day of the year (DOY) on which rainfall
occurred and the amounts at the six Sites
investigated.
SITE
DAY OF THE YEAR
RAIN
A
168
205
263
282
291
( mm )
11
25
9
32
13
B
168
205
263
280
291
11
25
9
32
13
C
168
205
263
282
291
11
25
7
38
12
D
168
205
263
282
291
11
25
7
38
12
E
168
205
281
20
24
20
F
148
206
282
283
288
311
20
40
9
6
10
13
263
TABLE X.8b -
Day of the year on which irrigation took place on
six
sites
in
the
Vaalharts/Hartswater
and
Bultfontein trails during 1983.
A
B
C
D
E
F
FARM UOFS
FARM UOFS
FARM UOFS
FARM UOFS
FARM UOFS
FARM UOFS
167
167
164
164
165
165
157
157
165
165
140
140
214
214
192
192
185
-
187
187
201
201
147
147
235
-
221
221
222
222
221
221
224
224
196
196
250
250
-
250
264
264
238
-
242
-
224
224
264
265
257
-
-
300
248
248
259
259
238
238
278
278
263
-
279
279
276
276
250
250
299
299
-
265
300
300
264
264
278
278
278
278
300
300
292
292
306
306
76
77
44
76
264
65
10
10
A-pan
evaporation,
rainfall,
irrigation
and
measured and simulated water use and pan factors
for Site A during 1983.
TABLE X.9 -
WATER USE
PERIOD
A-PAN
RAINFALL
IRRI
MEAS
SIM
(mm)
(mm)
( mm )
( mm)
(mm)
207-216
217-221
222-229
230-235
236-242
243-249
250-256
257-263
264-270
271-277
278-291
292-298
TOTAL
25,0
68*
214
52,5
38,5
50,0
63,0
46,0
57,9
44, 1
50,5
84,0
54,0
608, 5
*
-
24,2
10,4
10,4
14,6
250
9 (263)
265
32 (282)
13 (291)
54
278
33,2
32,2
39,8
80,7
80,0
350,5
14,7
12,6
17,3
33,9
45,7
50, 1
46,2
48,8
55,9
107,2
5,2
PAN FACTORS
MEAS
SIM
0,37
0,46
0,27
0,21
0,23
0,72
0,56
0,90
1 ,60
0,27
0,73
0,24
0,45
0,68
0,73
1,09
0,80
1, 12
1,11
1,28
0,10
0,52
1 ,67
3,24
1,60
1,51
1,43
437,6
Evaporation on two of the days was estimated.
265
SJIM/MEAS
1,24
0,69
TABLE X.lOa - A-pan
evaporation,
rainfall,
irrigation
and
measured and simulated water use and pan factors
for Site B during 1983.
PERIOD
RAINFALL
A-PAN
(mm )
( mm)
207-216
217-221
222-229
230-235
236-242
243-249
250-256
257-263
264-270
271-277
278-291
292-298
TOTAL
IRRI
( mm )
68,0*
52,5
38,5
50,0
63,0
46,0
57,9
44, 1
50,5
84,0
54,0
608,5
WATER USE
SIM
MEAS
( mm)
27,9
(mm)
10,8
44, 5
16,7
15,8
14,9
49, 1
28,9
15,3
14,0
34,4
16 ,9
56,2
51,6
42,9
56,8
123,0
47,1
197,8
468,5
MEAS
PAN FACTORS
S IM SI M/MEAS
0,41
0,16
0,39
0,85
0,43
0,37
0,27
1 ,07
0,50
0,29
0,36
0,69
0,27
1,22
0,89
0,97
1 , 12
1,46
0,87
0,34
0,84
1,86
1 ,00
1,14
1 , 78
221
250
9 (263)
265
32 (280) 278
13 (291)
54
TABLE X.lOb - A-pan
evaporation,
rainfall,
irrigation
measured and simulated water use and pan
for Site F during 1983.
PERIOD
A-PAN
RAINFALL
IRRI
( mm)
(mm )
{mm)
31 ,9
196
61 , 4 * 40 (206)
23,7
224
34,5
195-201
202-216
217-222
223-229
230-235
236-244
245-250
251-264
265-270
271-280
281-292
293-299
300-311
31,6
55,0*
238
59,0
250
96,3
264
50,7
278
108,3
88,3 16 (282) 292
60,3 10 (288)
95,4 13 (311 ) 306
TOTAL
851 ,7
* Evaporation
W A T E R USE
MEAS
SIM
(
. mm)
49,,8
33 ; ,0
13.,9
48,,4
MEAS
( mm )
17,2
41 ,3
16,4
27,4
25,8
24,3
0,8
2,7
4,0
16 ,6
16,3
0, 15
0, 18
30,5
44,5
0,51
0,47
on two of the d a y s w a s e s t i m a t e d
266
PAN FACTORS
SIM
SIM/MEAS
0,54
0,67
0,27
0,79
0,82
0,44
0,01
0,03
0,08
252,8
79
and
factors
1 , 56
0 ,54
0 ,59
1 ,40
0 ,35
1 ,25
0 ,46
0 , 56
TABLE X. lla - Crop Morphological and physiological data and soil water contents measured at Site A,
1983.
LAI
DOT
CROP
HEIGHT
GROWTH
STAGE
BOOT
DEPTH
MEAS
CALC
(bar) (bar)
(•)
r-o
186
193
200
207
216
221
229
235
242
249
256
263
270
277
291
298
0
0
0,1
0,1
0,1
0,1
0,2
0,2
0,3
0,3
0,4
0,4
0,7
0,7
0,7
Tillering 0,2
0,2
0,3
0,4
0,5
0,6
0,8
0,8
0,9
Heading
1,0
1,0
Flowering 1,0
1,0
1,0
1,0
MEASURED
OBHOO 12HOO
0,2
0,3
0,7
1
1,3
1,5
1,9
2,2
1,8
2,4
2,8
1,1
0,5
0
0,2
0,2
0,3
0,4
0,8
1,8
2,5
3,5
5,1
6,7
7,9
7
5,6
2,3
3,4
1,9
2,2
1,4
1,2
3,1
1,3
2,3
9,6
1
5,5
4,4
7,6
10,8
2,2
19,6
16,6
8,4
17,3
17
18,1
15
21,3
22,9
24,2
24,5
22,6
25,3
38.2
9,6
CALCULATED
0 6 H 0 0 121100
(bar) (bar)
3
0,1
0,3
0,2
0,4
0,1
0,1
0,2
1,7
4,1
1,1
1,5
3,9
2,5
50
6,6
6,2
5,6
2,7
8,1
6.9
8,1
9,3
12,8
16,7
10,9
11,5
13,1
14,4
50
MEASURED LAYER 8g
,0,3
,3,6
,6-,9
(*)
(*)
<*)
7.6
7,5
6,3
10,5
10,3
10
11,5
9,9
8
10
10,6
11.4
7,1
6,1
5,1
4,8
9,2
6,1
8,5
3
10,5
10,5
11,8
11,8
11,3
11,2
11,6
12,7
11,8
11,6
11,1
11,1
11,8
10,9
11.5
5,9
6,9
9,6
8,9
8,5
10,8
8,6
,3
9,2
10,9
TABLE X.llb
-
Crop Morphological
1983.
and p h y s i o l o g i c a l
oo
186
193
200
207
216
221
229
235
242
249
256
263
270
277
291
298
CROP
HEIGHT
GROWTH
STAGE
ROOT
DEPTH
(•)
(•)
0,1 Tillering
0,1
0,2
0,2
0,2
0,2
0,3
0,3
0,4
Heading
0,4
0,7
Flowering
0,8
0,7
0,7
0,8
0,2
0,2
0,3
0,3
0,4
0,5
0,6
0,8
0,9
0,9
1
1.3
1,3
1,5
1,7
MEAS
CALC
MEASURED
06H00 12HOO
(bar) (bar)
0,8
0,7
1,3
1,8
2,6
3,2
2,6
2
4.1
3
1,3
water contents
LAI
DOY
data and s o i l
0,5
0,7
0,7
0,8
0,9
1
1.3
2,2
3,2
4
5,6
6,8
7
5,6
3.1
3,3
5
1,5
13,6
3,5
0,9
14,8
17,8
16,9
1,5
7,8
15
13,2
17,3
17,3
22,4
25,3
24
21,6
5,3
24
CALCULATED
06H00 12H00
(bar) (bar)
0,3
0,1
0,4
0,2
0,4
0,1
0,2
0,2
1,9
5,3
1,5
1,8
4
2,6
5,1
6,6
6,5
5,6
2,8
8,2
6,9
8,2
9,5
13,2
15
11,5
11,5
13,2
14,6
•eaaured at S i t e B,
MEASURED LAYER eg
,0-,3
,3,6
6-,9
(*)
<%>
<*>
4,3
6
6
5
9,5
4,5
3,6
4,5
3,1
2,7
6,5
8,2
6,3
7
6,2
6,2
5,3
6.1
5,1
3,8
3.4
6.1
3,9
5,6
5,3
4,3
6,5
5,2
3,7
6,9
6
15, 1
2,5
4, 1
5,5
2,3
6,2
4,4
2,4
5,1
3, 1
2,5
4.8
+ LAI calculated (unstressed)
c LAI measured
{ Irrigation
1 Rain
180
FIG. X.12a
-
280
Measured and simulated leaf area indices,
1983
Site
B,
+ LAI calculated (unstressed)
• LAI measured
\ Irrigation
Rain
HUE (DOY)
FIG. X.12.2 -
Measured and simulated leaf area indices,
1983.
269
Site A,
50
• measured
+ calculate^
f
180
200
220
240
260
280
TIME (DOY)
FIG X. 13a (1) -
Pre-dawn leaf water potential at Site A, 1983*
D measured
+ calculated
I
220
240
280
HUE (DOY)
FIG. X.13a (2) -
Noon leaf water potential at Site A, 1983
270
50
28 - D measured
26- + calculated
242220 181614121086 -
420
180
200
220
TIME (DOY)
FIG. X.13b(l) -
Pre-dawn leaf water potential at Site B, 1983
50
28 26-
• measured
+ calculated
2422 201816-
f
141210 8
642180
200
220
240
280
280
(DOY)
FIG. X.13b (2)
-
Noon l e a f water p o t e n t i a l a t S i t e B, 1983
271
| 1
1
H*in
1
Irrigation
*
Fh catculition
so
*• •
1
Fh forecast
\
'" r'i
240
zeo
X.14a
^ 1
1
260
DAY
FIG.
OF
380
I/
300
1Z0
THE YEAR
Scenario of the hydraulic scheduling
on Site A, 1983 for the UOFS plot.
-
•
1
'•
factor,
Fh,
100
f
-
fh
cil:
•• fh forecast
if
SO
220
240
260
DAY
FIG.
X.14b
-
eao
300
OF THE YEAR
Scenario of the hydraulic scheduling factor,
on Site A, 1983 for the farmer plot.
272
Fh,
I 00 I
L.
J-i.
—
Th C i l c u l a L u U
• .
Fh f o r e c a s t
I
2L 0
240
260
DAY
FIG.
X.15a
-
280
300
OF THE YEAR
on Site B, 1983 for the UOFS plot.
factor,
Fh,
on Site B, 1983 for the farmer plot.
Fh,
100
f Ifrigalion
— I li
calculated
•• Fh (
240
FIG.
X.15b -
280
260
PAY
Oc
300
THE YEAR
273
100
| III m
t
[rrirjaLion
- Fh
calculated
- • Fli f
240
120
Z60
DAY
FIG. X.16a
-
260
300
OF THE YEAR
on Site C, 1983 for the UOFS plot.
factor, Fh,
ioo , — L
I Ham
75
f
1r? y
-
Ft: ukuiated
lition
-• Hi forecast
SO
220
?*u
DAY
FIG.
X.16b -
zeo
OF THE YEAR
?60
300
on Site €, 1983 for the farmer plot.
274
Fh,
25
220
240
260
DAY
FIG.
X.17a
-
OF
THE
260
300
J20
YEAR
Scenario of hydraulic scheduling
Site D, 1983 for the UOFS plot.
factor,
Fh,
on
Fh,
on
lOOr
I Ha in
T Irrigation
- fh calculated
•• Fh forecast
50
2B0
DAY
FIG.
X.17b
-
303
OF THE YEAR
Scenario of hydraulic scheduling factor,
Site D, 1983 for the farmer plot.
275
I Run
V Irrigation
-n,
LdUttliU'd
••Hi
DAY
FIG. X.18a -
OF THE YEAR
on Site E, 1983 for the UOFS plot
factor, Fh,
100
I Rain
f
Irrigation
— Fh cal
••Mi
forccasl
t*>
260
220
DAY
FIG. X.18b -
ZOO
300
OF THE YEAR
Scenario of the hydraulic scheduling factor, Fh,
on Site E, 1983 for the farmer plot.
276
200
-ISO
100
50
ro
o
u
u
\ Std D«v seaiurad 0v2
I 1
Aneaaured
200
220
240
260
DAY C1F THE YEAR - DOY
FIG. X.19 -
Gravimetrically measured and simulated values of
the volumetric water content in the second soil
layer, Ov^ at Site A, during 1983.
ISO
-
100 -
i/i
SO
o
o
o
UJ
to
1
StJ Dav of
nciiurgd
•
Measured 9^-,
Svj
SlmuUtod 0,,,
I
jl
200
210
DAY
FIG.
~
X.20a -
OF
I
•
THE
I
2QQ
260
YEAR
layer 0v2 , at Site B, during 1983.
ISO
100
o
1/)
o
a
l
B
DAY
FIG. X.20b
-
HO
i
OF THE YEAR
HO
t a i
280
3DD
(DOYJ
layer, Qv% , at Site F, during 1983.
278
1
75
I
1
I
1 In-iMtion
— Th calculated
•• Hi forewfcl
1
II
1
^,
SO -
2$ -
i
i
zoo
I
A. !
?tO
\ ,/
3PO
L.
or mr
FIG.
X.20c -
on Bite F, 1983.
279
Fh,
TABLE X.20
-
Cultivation
practices
TIME OF PLANTING
SITE
PLANT DENSITY
CALENDER
the
CULTIROW WIDTH VAR
different faners during 1982.
SOIL DESCRIPTION
IRRIGATION
PLOT SIZE
FERTILIZER
DOT
(.
no
oo
o
adopted by
)
(kg/ha) (-•)
(•
)
(•)
A
82.06.25
176
223
85
167
6
T4
Mangano loaiy sand:
10* clay, 2% silt,
1,8* course sand,
69* fine sand
6,5 x 200
B
82.06.16
167
383
150
167
6
T4
Sand:
94* sand,
6X clay
6,5 x 287
C
82.06.23
174
383
T4
Sand:
94* sand
6* clay
6,5 x 118
D
82.06.04
155
383
T4
6,5 x 90
300 kg/ha
3.2.1 (25)
500 kg/ha
Aioniui
Sulphate
TABLE X.21 -
Cultivation practices during 1983.
Row widths on all farms were 167 mm except
width of 200 mra was used.
A
-
:
SST 44
Fertilizer
:
350 kg/ha
250 kg/ha
100 kg/ha
-
row
HARTSWATER
Cultivar
B
("or Site C where a
255 plants/ha
3.2.4.(25)
ASN (27)
ASN (27) top dressing
MAGAGONG
Cultivar
:
T4
Fertilizer
:
200 kg/ha
2.3.4.(30)
150 kg/ha
Ammonium Sulphate applied before planting
(June 11, 1983.
300 kg/ha Ammonium Sulphate top dressing applied on
July 11, ]y83.
C
-
2 58 pi ants/ha
TADrASTER
Cultivar
:
T4
Fertilizer
:
250 kg/ha
3.2.1 (25) applied before planting.
250 kg/ha
Ammonium Sulphate top dressing
applied
with the first irrigation after planting, viz. August 10, 1983.
D
-
2 78 piants/ha
TADCASTER
I ultivar
:
T4
Fertilizer
:
350 kg/ha
3.2.1 (25)
460 kg/ha ASN (27)
All the fertilizer was applied before planting.
V
-
4 36 plants/ha
MAGAGONG
I ult Lvar
:
Zaragoza
'Z6i"> plants/ha
Fertilizer
:
500 kg/ha
3.2.1 (25)
3 00 kg/ha Ammonium Sulphate
270 kg/ha Ammonium Sulphate top
on August 30, 1983.
dressing
applied

RESEARCH ON A WEATHER SERVICE FOR SCHEDULING THE

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