Irrigation and nitrogen effects on Wampum spring wheat by John Craig Wallace
A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE in Soils
Montana State University
© Copyright by John Craig Wallace (1982)
Abstract:
Current recommendations for scheduling irrigations of wheat in Montana call for allowing depletion of
50% of the soil's available water holding capacity before starting irrigation. The objectives of this investigation were to measure the response of 'Wampum' spring wheat to nitrogen and irrigation variables, and to determine if the 50% depletion recommendation should be modified for varying nitrogen fertility levels.
A field plot experiment was established near Fairfield, Montana in 1981, to investigate the response of
'Wampum' spring wheat to varying nitrogen and irrigation levels. The study employed a line source sprinkler system in a continuous variable design. Irrigation treatments consisted of successively decreasing amounts of water with all treatments irrigated with equal frequency.
Wampum grain yields increased with increasing applications of both nitrogen and water, and an interaction between these factors was indicated.
A greenhouse pot study was initiated in the fall of 1981, to further study Wampum’s response to irrigation and nitrogen, by varying both the timing and amount of irrigation. A randomized complete block design was used, in which irrigation treatments consisted of allowing 25, 50, or 75% of the plant available water to be depleted before irrigation.
The interaction between nitrogen fertility and maximum allowed depletion was not significant over the treatment range used. Therefore, the maximum allowable depletion concept, and the currently recommended values, should be applicable to fields having a broad range in nitrogen fertility status without requiring modification.

STATEMENT OF PERMISSION TO COPY
In presenting this thesis in partial fulfillment of the requirements for an advanced degree at Montana State
University^ I agree that the library shall make it freely available for inspection. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by my major professor, or, in his absence, by the Director of Libraries. It is understood that any copying or publication of this thesis for financial gain shall not be allowed without my written permission.
Signature.
Date_____

Approved:
IRRIGATION AND NITROGEN EFFECTS
ON WAMPUM SPRING WHEAT by
JOHN CRAIG WALLACE
A thesis submitted in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE in
Soils
Head, Major Department
Dean
MONTANA STATE UNIVERSITY
Bozeman, Montana
December, 1982

U i
This research was partially supported by the Montana
Fertilizer Tonnage Tax? the Test and Demonstration Branch,
Tennesee Valley Authority? and contributions by several fertilizer manufacturers and dealers.
Appreciation is expressed to Dre Paul 0. Kresge for the inspiration to pursue a graduate degree, and support arid constructive criticism during the completion of this degree.
Dr. James W. Bauder and Dr. A. Hayden Ferguson contributed much in their service as members of my graduate committee.
Special thanks go to Dr. Gerald Westesen for his help in designing the irrigation system, and Evan Vervick of
Fairfield, Montana who provided the field space for the experimental plot.
Extra special thanks for their tireless efforts in the field, greenhouse, and laboratory go to Raymond F. Guthrie and Margaret J. Babits for their expert help in all phases of the experimental work.
Great gratitude is expressed to my wife Michele for her patience, encouragement, and words of support, without which this project could not have been completed.

TABLE OF CONTENTS
Page ii
ACKNOWLEDGEMENTSoo*®*®®®®®®®®®®®®®®®®**®*®®®***®®®*®®*® ill
TABLE OF CONTENTS*@®®®»»®*@®®®*®®®®®®®®®®®® ®®**e®»*»*®®
L 1ST OF TABLES@®«®®®®**®e®®*®»®®»®®®®®®®®*@®®*®®®****** iv vi
L 1ST OF FIGURES®®®*@®®®®®®®@*®®®*®®®®®®®®o*®®*®®®e@®®®®
ABSTRACTe®®®®®®®*®®**®®®®®®®*®®***®®®*®®®® * ® ®*®*®®®®®®® ix xi
Chapter
I. INTRODUCTION e o o e e e o o o o e e e e o o o o e o o o o e o e o o e . o e o o o e
REVIEW OF LITERATURE* e e e * e e e e e e e » e o e e o e e e e e o e e e
Effects of Crop Growth Stage.*••*».»••*.....
Continuous Variable Designs..... .
3.
MATERIAIiS AND METHODS
Field Study...............o.
Site Selection Criteria.
Greenhouse Study.................o...........
4. RESULTS AND DISCUSSION O. ...... 9.9.9.00. .....009
FIELD STUDY..........................o.......
Line Source Irrigation System Performance.®
Above Ground Dry Matter Yield®...®.®®..®..®
Grain Yield® ®* ®g®®®®**®®®®**®*®®®®®®®®®®®®#
Harvest Index®®@®®®®*®»®e®®®*®e®a®®®**®»®**
■ Grain Protein.e®®®®®®*®®®®®**®®*®®®®®®*®#®®
D l SCUSSlOn® o e e e ® e e e © e e e e e e e e o e o e e e ® e e e e o e e e
GREENHOUSE STUDYg®*®®®®®®®*®®*®®®**®®®®®®®®®*
W ater Use Efficiencyo®®®*®.®*®®®®®®*®®®®®®®
Above Ground Dry Matter Y i e l d .
.
Grain YieId*®®®®®®®®®®®®®®®®®®®.®®®®®®®®®®®
Harvest Indexg*®®®®®®®®*®®®®®®#®*®®®®®®*®®®
'4
5
7
14
19
19
19
27
34
34
34
37
38
40
40
42
43
43
43
45
46
48

V
3^ -Pl- t
I
H 50 rt t H
JL n©
52
3fi©lc3 ComponentSe
@ @ 0 0 0 0 0 0 0
e @
@ * 0 0 0 0 0 0 0 0 0 0
* * @ 5
55
27 S© "50
Msin Hesds vs. Tiller H e s d s e @ * *»»»c»®»»@ e @» 57
D i SCUSSlOneoeeeoeoeeoeeeeeeeeoeeeeoeeoooeeo 53
5 o GENERAL CONCLUSIONS*o************************** 00
AR 3
A* INDIVIDUAL PLOT DATA, ANALYSES OF VARIANCES,
AND REGRESSION EQUATIONS*@e o @
@@@@@*@@*c*o*@ @ @ : 04
Be FIELD PLOT RAINFALL RECORD**** * 97
Ce LITERATURE CITED
* &
* * * * *©** o * e *o**************** 99

vi
LIST OF TABLES
Table Page
I.
Field Pre-treatment Soil Test Results......... 21
2.
Field Pre-treatment Soil Nitrate-Nitrogen
22
3.
Field Fertilizer Treatment Set................. 23
4.
Field Irrigation Record........................ 26
5.
Greenhouse Irrigation Treatment S
6.
Greenhouse Fertilizer N Applied e t .
29
30
7.
Irrigation Depths, as a Function of Distance from the Line Source. Measured on 2 June, during 1.35 Hour Irrigation.................. 36
8.
Irrigation Depths, as a Function of Distance from the Line Source, and Position along the Line. Measured on 22 June, during
5.2 Hour Irrigation.......................... 36
9.
Field Above Ground Dry Matter Yield............ 65
Analysis of Variance of Above Ground Dry
Matter Yield Data»© © © ©,*
© © © © © © © © © © © o © © © © * © © © © ©
6 n .
Field Grain Yield©
© © © © © © © o © * © © © © © © © © © © © © © © © © © © ©
67
12.
Analysis of Variance of Grain Yield Data. ..... 68
13.
Field Harvest Index©©©©*©©©©©*©*©***©©©»*©©©©©© 69
14.
Analysis of Variance of Harvest Index Data..... 70
15.
Field Grain Protein Percentage................. 71

'
I'
■ ■
I; vii
16.
Analysis of Variance of Grain Protein
Percentage Data...................
. . . . . . . . . . .
72
17.
Greenhouse Cumulative Water Use................ 73
18.
Analysis of Variance of Cumulative
Water Use Oata
.
............................... 74
19.
Analysis of Variance for Regression of
Cumulative Water U s e 74.
20.
Greenhouse Water Use Efficiency................ 75
21.
Analysis of Variance of Water Use •
Efficiency Data e . . . . . . . . . . . . . . . . . .
........... 76
■
■
22, Analysis of Variance for Regression of
Water U se Efficie n c y
Greenhouse Above Ground Dry Matter Yield...... 77
24.
Analysis of Variance of Above Ground
Dry Matter Yield Data.
. . . . . . . . . 6 .
............ 78
Analysis of Variance for Regression of
Above Ground Dry Matter Yield.
. . . . . . . . . . . . . . .
78
26.
Greenhouse Gr
. . . . . . . . . o .
...
. . . . . . . . . . .
79
27.
Analysis of Variance of Grain Yield Data.
. . . . . .
80
28
.
Analysis of Variance for Regression of Grain Yield...................
. . . . . . . . . . . .
8.0
29.
Greenhouse Harvest Index.
........................ .
81
30.
Analysis of Variance of Harvest Index Data..... 82
31.
Greenhouse Grain Protein Percentage......
. . . . . .
83
32.
Analysis of Variance of Grain Protein a t a 84
33.
Analysis of Variance for Regression of
Grain Protein Percentage.
........... .......................
84

34.
35.
36.
37.
V i i i
Greenhouse Heading Date..................
Analysis of Variance of Heading Date Data.
Greenhouse Duration of Heading Period...
e e o e • e e
Analysis of Variance of Duration of
85
86
87
88
89 38.
39.
40.
Greenhouse Plant Height........................
Analysis of Variance of Plant Height Data......
Analysis of Variance for Regression
41.
42.
43.
Greenhouse Heads per Plant...................,.
Analysis of Variance of Heads per Plant Data...
Greenhouse Number of Seeds per Head............
44.
45.
46.
47.
Analysis of Variance of Seeds per Head Data....
Greenhouse Mass per Seed......... .
Analysis of Variance of Mass per Seed Data.....
90
91
92
93
94
95
> 6
98

ix
LIST OF FIGURES
Figure
I. Field Plot Layout,.....
00 000000 00000900 0 09000 0 .
2
.
Average Irrigation Application Rate as a
Function of Distance from the Line Source....
Page
24
35
3.
4.
Field Above Ground Dry Matter Yield. <i..........
Field Grain Y i e l d .
@ .......o.
38
39
5.
6
.
Field Harvest Index.....e......*.......*.......
Field Grain Protein Percentage..
7.
8
.
Greenhouse Cumulative Water Use.........b......
Greenhouse Water Use Efficiency..............
9.
Greenhouse Above Ground Dry Matter Yield.
O O O O O O
10 .
Greenhouse Grain Yield? Average of Three
Irrigation Strategies. . @..... *..... *.. *.......
11
.
12
.
Greenhouse Harvest Index 000000090 '0 9000000000900
Greenhouse Grain Protein Percentage, Average of. Three Irrigation Strategies...............
13.
Greenhouse Plant Height.......
00090000000000000
14.
Greenhouse Number of Heads per Plant, Average of Three Irrigation Strategies...............
41
42
44
45
47
48
50
52
53
15.
Greenhouse Number of Seeds per Head............
55
56
16.
57
17.
18.
Regression Analysis of Cumulative Water Use....
Regression Analysis of Water Use Efficiency..v.
74
76

19. Regression Analysis of Above Ground Dry
Matter yield..........................o.......'
Regression Analysis of Grain Yield e e e e • e e • e o o o o
21. Regression Analysis of Grain Protein
Percentage..........o........................
22. Regression Analysis of Plant Height..
9 9 9 0 0 9 9 9 9 9
78
80
84
90

xi
ABSTRACT
Current recommendations for scheduling irrigations o f ; wheat in Montana call for allowing depletion of 50% of the soil's available water holding capacity before starting irrigation. The objectives of this investigation were to measure the response of 'Wampum' spring wheat to nitrogen and irrigation variables, and to determine if the 50% depletion recommendation should be modified for varying nitrogen fertility levels.
A field plot e x p e r i m e n t was estab l i s h e d near
Fairfield, Montana in 1981, to investigate the response of
’Wampum' spring wheat to varying nitrogen and irrigation levels. The study employed a line source sprinkler system in a continuous variable design. irrigation treatments consisted of successively decreasing amounts of water with all treatments irrigated with equal frequency.
W a m p u m grain yields i n c r e a s e d w i t h increasing applications of both nitrogen and water, and an interaction, between these factors was indicated.
A greenhouse pot study was initiated in the fall of
1981, to further study Wampum’s response to irrigation and nitrogen, by varying both the timing and a mount of irrigation. A randomized complete block design was used, in which irrigation treatments consisted of allowing 25, 50, or
75% of the plant available water to be depleted before irrigation.
The interaction between nitrogen fertility and maximum allowed depletion was not significant over the treatment range used. Therefore, the maximum allowable depletion concept, and the currently recommended values, should be applicable to fields having a broad range in nitrogen fertility status without requiring modification.

INTRODUCTION
Irrigation allows the farmer to control the soil water status which, under dryland conditions, is the most limiting ana least controllable crop production factor. Water should be provided in the correct amount for the prevailing soil, crop, and climatic conditions to maximize the benefit of irrigation. In addition, the crop demand for water is modified by the amount of nitrogen (N), and other essential plant nutrients available to the crop.
Many researchers have reported the yield of wheat in relation to the amount of water used by the crop. It is impractical for a farmer to apply a particular amount of water to reach a specific yield goal. Rather, he can only observe the degree of plant water stress and the amount of depletion of the soil water to estimate the optimum timing and application amount of irrigation water. The net amount of w a t e r applied during the g r o w i n g season, will be determined by the Weather conditions for that season, and the p h y s i o l o g i c a l n a t u r e of the c r o p . C u r r e n t recommendations for the amount of. maximum allowable soil water depletion (MAD) for spring wheat, are typically 50 to
70% (Bauder et al., 1982; Eisenhauer, et al,, 1980; Thompson

2 and Fisqkbachy 1 9 8 0 , Trimmery 1980.). These recommendations the actual N status of the field to arrive at the MAD value.
Interaction between N status and cumulative water use has been reported by some researchers under both dryland (Brdwny
1971), and irrigated conditions (Ehlig and L e M e r t y 1976).
The objective of tnis research was to evaluate the potential interaction between M status and an optimum value of MAD.
In 1 9 8 1 a field e x p e r i m e n t was e s t a b l i s h e d w i t h
8W a m p u m 8 spring wheat (Triticum aestivum L J , using a line source irrigation system, developed by Hanks (1976) , in a continuous variable design (CVD). The yield of wheat was strongly affected by both N status and irrigation amount=.
The response surface indicated an interaction between these two factors.
A greenhouse experiment was initiated to evaluate the desirability of modifying the MAD value for differing levels of N fertility. Three irrigaton strategies were used corres­ ponding to MAD values of 25%, 50%, and 75%. These treatments were applied in factorial combination with five N rates. The experiment was conducted with six replications in a randomized, complete block design. This experiment indicated that the interaction between N status and MAD was

3 not significant over the treatment range used. The MAD concept, and values derived from experimental plots, should be applicable to production systems having a wide range in N status, without requiring modification. An analysis of yield components revealed no significant effects of the water treatments oh heads per plant, seeds per head, or mass per seed.

CHAPTER 2
REVIEW OF LITERATURE
Observations of the relation of crop yields to the amount of water used by the crop have been reported since the early 1700's, when Langley Batty reported that fruit drop of peaches and other tree fruits GOUlti be reduced by shading and watering during late May and June (Salter and
Goode, 1962). Statistical studies were begun in the late
1800's to relate annual rainfall records to crop yield records* Lawes and Gilbert (1880) reported a correlation between wheat yields and rainfall over the period 1832-1878.
These early correlative techniques were hampered by using seasonal time frames. At the turn of the Century, Russian researchers began to relate periodic rainfall to the crop phenologic stage, with dramatically improved results.
Broundv (1899) reported a close correlation between the yield of oats and amount of rain falling in the ten day period proceeding heading. Brounov coined the term 'critical period', to refer t o .a period Of Crop Sensitivity to any meteorological factor. The factor of interest in his work was water availability. This has been the most common use of the phrase since that time.

■ : .5
Cumulative Water Use
Yield of forage and vegetative crops generally begins with the first increment of water use by the crop, and increases in a linear fashion with increasing cumulative water use (Downey* 1972? Salter and Goode, 1967). Total dry matter production of cereal crops follows this type of relationship? seed yield does not because of the critical period phenomenon (Downey, 1972? Slavik, 1965)»
In the field, crops are hot exposed to conditions of constant s o n moisture,availability. There is a period of
IOw stress following irrigation or significant rainfall, followed by a progressive increase in plant water stress because of the extraction of soil water (Downey, 1972?
Salter and Goode, 1967? Slavik,1965),
One method of investigating the effect of near constant soil moisture stress is to lower the osmotic potential of the soil solution and maintain a high soil mdisture content
(Downey, 1972), Downey (1972) summarized, the results of three researchers who used this approach, A linear, or nearly linear, response in yield was obtained in all cases.
This suggests that when soil water potential limits plant water use, grain yield is a simple, linear function of water use

In the field, a crop is progressively stressed by soil water removal until the soil profile is recharged. In most irrigation scheduling programs, the parameter of interest is the allowed soil moisture depletion before irrigation is started. Dubetz (1961) grew spring wheat in pots in the greenhouse on a loamy and a sandy soil. He allowed depletion of 25%, 50%, and 75% of the plant available water before irrigating, across five N fertility levels. Significant grain y ield diffe r e n c e s w e r e observed b e t w e e n water t r e a t m e n t s on the l o a m but not the sandy soil. This indicates that water availability remained high in the sandy soil, probably due to the high hydraulic conductivity of this type of soil in the range of moisture contents studied^
In contrast the response to water on the loamy soil shows that water was limiting at the commonly recommended MAD of
50%. Ehlig and LeMert (1976) studied the response of winter wheat to differing amounts of seasonal evapotranspiration
(Et). Irrigation treatments were imposed by applying varying fractions of the water used by wheat in a well watered w e i g h i n g lysimeter. Grain y ield was reduced in all treatments drier than 90% of maximum evapotranspiration
(Etm a X^e There was no increase in water use efficiency due to a restricted water supply. Heading and flowering dates

'
7 were earlier on the plots with restricted water supplies and the yield response obtained may have been due to the longer period of assimilation in the well watered plots.
■
Effects of Crop Growth Stage
■ . . . .
Downey (1972) summarized the results of fourteen published irrigation experiments oh nine different grain crops. For each of the fourteen experiments/ EtZEtmax was plotted against yield (Y) / maximum yield (Ymax). These data show no yield for any treatment with seasonal water use less than half of that required to achieve maximum yield. This observation supports the hypothesis that one half of the seasonal Et is required to bring the Crop to the reproductive phase. Yields averaged 50% of Ymax and ranged from 30% to 80% of Ymax at Et/Etmax=70%. Yields ranged from
40% to 95% of X max for Et/Etm a x =80%. The broad range of yield values associated with each Et deficit (Etg) level arises from two primary causes. First, the plant stress indicators measured were not the same in all cases. Leaf water potential, measured by thermocouple psychrometry or pressure bomb, were used in most of the experiments, while some other /data used in the s u m m a r y w e r e based on controlling Et by lowering soil water potential. Any

8 relationship between soil.water potential and seasonal Et is subject to large errors because the plant response to soil water potential is strongly affected by the prevailing climatic conditions. Stegman et al«,(1976) carried out a stepwise regression procedure to determine the relative importance of soil moisture, ambient air temperature, wet. bulb temperature, solar radiation intensity, and wind velocity on leaf water potential. They concluded that ambient air temperature is a critical factor in determining the effect of a particular level of soil water potential oh leaf water potentials The second cause of the wide range of results in Downey's summary is the critical period pheno­ menon. If a particular a m o u n t of Etg occurs during a critical period in the plants" I ife , there w ill be a relatively large yield reduction. An Etg of equal magnitude occuring in a non-critical period will Cause little yield loss, or may even cause a slight increase in final yield over that of a nonrsfressed plant (Singh, 1981).
Many studies have been conducted to determine the time and duration of the critical periods in wheat. Robins and
Domingo (1962) imposed irrigation treatments by eliminating irrigation at each of four phenological stages of spring wheat. Four levels of N fertility were applied in factorial

;
.
.
' combination with the irrigation treatments^ Their results show an increase in yield with increasing amount of water, used, modified by the timing of irrigation treatments^ in relation to the critical periods of crop sensitivity to moisture stress. They identified the critical periods for wheat as the boot to maturity stages, while moisture stress during the early vegetative (pre-boot) and late grain fill stages did not cause a significant yield reduction. There were no measurable interactions between N and irrigation levels except for total dry matter in one of two years.
Robins and Domingo recommended deferring irrigation until evident during the period of vegetative growth.
Schreiber and Stanberry (1965) obtained similar results with, barley for four stages? I) plant establishment, 2) tillering to boot, 3) pollination, and 4) grain fill to maturity. They allowed soil moisture depletion to 40.4 kPa tension in 'wet' treatments, and 1.0 to 1.4 MPa in 'dry' treatments. Moisture stress timing was described as? continuously wet, wilted before irrigation during early growth, and periodic wilting up to pollination. No yield effects were found for the irrigation treatments. Therefore, irrigation prior to pollination was not recommended.

intensity and duration to investigate the drought sensitiv­ ity of barley at differing development stages. Treatments with stress in the pre-boot stages developed late tillers> which were considered insignificant to yield and were not. harvested. Regression analyses showed yield reductions of
14% for stress in the jointing stage, 8% in booting stage, and 4% if stress occurred during the heading period.
Singh (1981) also investigated intensity and duration of drought stress to arrive at an optimal sequencing of
Etj's for winter wheat. Singh related wheat yields to
Et^d1s for four seasons and divided the Y/Ef ratios into classes of optimal (<17% yield reduction), and suboptimal
(51 to 78% yield reduction) irrigation levels. He concluded that the timing of a particular amount of Etj3 was of less importance than the intensity, in determining final yield.
His data show a linear relation between percent yield reduction and percent Etd for all timing sequences. Singh also looked at the effect of hardening on the drought tolerance of the winter wheat. He found that without prior conditioning in the vegetative period, any Et^ occurring in the boot to heading period caused a reduction of the Y/Et ratio* When the crop was conditioned by. Et^9s of 10 to 18%

11 in the vegetative period,, the crop could tolerate Et^'s of
30 to 35% in the boot to heading stages, and as much as 39% for flowering to grain development stages, without reducing the Y/Et ratio. Singh concluded that the three growth stages could be rated in order of sensitivity to drought as? booting to heading > flowering to grain development > vegetative stage. However, when water supplies are expected to be inadequate for full irrigation, the anticipated Etj should be evenly distributed over the growing season to benefit from the hardening effect. By allowing some Etj during the v e g e t a t i v e period, the sensitivity of. the critical period is lessened, and the net yield reduction with some Etd in all periods will be less than if all of the
Etj occurs in the least sensitive period. This differs from the common conception of critical period scheduling, where it is recommended that no Etj be allowed during the critical period, thereby concentrating the seasonal Etj completely in the less critical growth stages.
Stegman et al. (I976) arrived at a deficit irrigation scheduling technique by looking at the level, of Etj which caused stomata! closure and reduced photosynthesis. They related the stornate closing threshold to percent soil mois­ ture depletion and ambient air temperature. The stomate

12 closing thresholds accounted for the differing drought sensitivies of the growth stages. When this scheduling method (MAD modified by ambient air temperature) was used^
15 to 21% savings in applied irrigation water were realized, compared to conventional scheduling at 40 to 50% MAD, These water savings were made in simulation runs using a ten year weather record.
Brown (1971), working with dryland winter wheat, found that fertilizing with N at 67 dr, 268 kg/ha increased grain yield, consumptive water use (CU), and water use efficiency.
The water use was keyed to crop growth stage and showed a progressively greater amount of CU for the fertilized treat- ments from about the boot stage to full ripe. The increase , in CU of the dryland crops was met by greater depletion of the deeper soil layers. Brown also noted that the lower leaves remained green longer at the higher N rates.
described by Slavik (1965) in a review of worldwide litera­ ture prior to 1965. Many researchers have shown a regular decrease in yields with a decrease in osmotic potential of expressed cell sap. The response to decreases in total potential is more obscure. According to Slavik, ^Although drought reduces the yield of spring cereals at all stages of

13 their ontogeny, it is still possible to delimit to some extent periods in which the damage, is greatest? this is so when the growing point experiences a water deficit.™ Drought during the period of rapid leaf growth will reduce the number of fertile tillers? during spikelet formation, the number of Spikelets per spike? in anthesis, the total number
Of grains? and during grain formation, the weight per seed.
Slavik also states that "During ontogenesis, high hydration levels are associated with the maintenance of the physiolog­ ical characteristics of "youth6, chiefly the prolongation of cell division and of extension growth and, associated with these, morphological characteristics such as fewer larger stomata per unit area and a thinner cuticle.™ Young leaves have a higher p h o t o s y n t h e tic activity, w h i c h in turn increases the growth rate. Slavik also notes the hardening effect in pointing out that sudden drought periods in a previously unstressed crop are much more serious than when the crop is grown at a more constant but lower level of stress. However, if drought stress can be entirely avoided by irrigation, the prolongation of youthfullhess can be exploited to achieve maximum yields.

Continuous Variable Designs
In conventional experimental designs treatments ate applied in discrete units to plots which are spatially randomized. This arrangement requires borders between treat­ ments, and at the perimeter of the experimental area. These borders involve additional costs of materials and labor. A continuously varying treatment set can be more efficient for d e m o n s t r a t i n g crop responses and generating response surfaces which are visually apparent in the field , and do not require statistical analysis (Hundtoft and Wu, 1974).
One of the first reported uses of the continuous variable design (CVD) was by Fox (1973). He applied 5.5kg/ha increments of N to consecutive individual plants in a row of
N deficient corn, giving treatments from O to 200 kg/ha N to demonstrate the response curve concept to students. Fox remarked on the small amount of labor, and land required for the design and the large number of data points generated to elucidate the shape of the response curve. He also mentioned the possibility of placing two treatment sets at right angles to one another to create a systematically arranged factorial combination which would yield a Visible response surface for observable responses such as plant height. Addi­ tional development of the CVD was done by Fox and I-Pai Wu,

15 with an interdisciplinary team at the University of Hawaii on the two variable^ response surface, methodology. In 1974,
Hundtoft and Wu published a paper advancing a regression analysis technique for two factor CVD's capable of removing treatment by location interactions and providing valid regression coefficients and confidence intervals. Basically, the method establishes a response vector for each variable and a lack of fit term including the location effects, by comparison of individual means with pooled subgroup means.
They verified the methodology by creating a hypothetical field with an intrinsic yield gradient, random variability
.
in individual sample responses, and the two treatment response vectors. The analysis of the. simulated experiment d e m o n s t r a t e d that the technique gave "appropriately, conservative" inferences as to the effects of the treatment set used. To date there have been no published results of
CVD experiments using this analysis technique.
Bauder (1975) used trickle irrigation and small i n c r e m e n t s of N, to establish a continuous variable irrigation by N response surface on silage corn. The trickle system produced the desired irrigation pattern but was
Costly and labor intensive to install and operate.
Hanks et al. (1974) developed a line source sprinkler
/.W
y

16 irrigation system, suitable for applying a continuously varying amount of irrigation water across an experimental plot. The system consisted of a single stationary irrigation pipe with impact sprinkler heads closely spaced so as to give a large overlap between heads. This layout produced a c o n s t a n t l y va r y i n g but un i f o r m gradient in water application, with the heaviest application at the center line of the plot grading to zero applied water at the outside edges. When a fertility gradient is applied at right angles to the irrigation gradient the response surface emerges.
The following design criteria were found necessary to achieve the line source effects
1. Sprinklers must be closely spaced along the line, so that spacing is less than 25% of wetted diameter
2. Individual sprinklers must have a radially decreasing pattern of water application.
When these conditions were met, the pattern of water application was uniform along the line's length (about 30 m.) and smoothly decreased from the center outward. The system performed adequately at line pressures from 0.3 to
0.4 MPa when the wind velocity .was less than about 0.95 m/s

17 if wind was perpendicular to the line or less than 2.39 m/s if parallel to the line. Hanks et al. (1974) point out several limitations of this system? it may only be operated in very calm wind conditions? all irrigation treatments must be applied at the same frequency? the high application rate at the.center of the line must be kept low enough to prevent runoff between treatment levels? application rates should be monitored during each irrigation because of the extreme sensitivity to wind conditions. In addition, the useability of the design is h a m p e r e d by its i n a pplicability to s t a t i s t i c a l a n a l y s i s . H a n k s (1980) a d d r e s s e d the randomization weakness of the CVD, especially when using the line source sprinkler system. A nonrandomized placement of both treatments makes it impossible to arrive at a valid error term with which to make comparisons or. tests of significance. While randomization of the irrigation treat­ ments is impossible due to the nature of the irrigation system, the second treatment could be randomized within replications. When this is done the visible response in the field is no longer s y t e m a t i c a l Iy arranged. There is, however, a valid error term for making inferences about the second treatment effects and the second treatment by irrigation interactions.

Bauder et ale (1975) compared results of paired produc­ tion function experiments using a CVD and a randomized block, split plot design (RBSD)0 The results from the CVD were equivalent to those from the RBSD, although the analyses of the CVD were done using standard methods and ignoring the randomization restriction. They point but that the CVD design required about 1/4 as much land as the RBSD, even though the CVD had apprximately 10 times as many treat­ ment combinations. Bauder notes that the greater number of treatment combinations comes with the price of a smaller sampling unit with attendant higher sample variability, and a larger number of samples to be analyzed.

CHAPTER 3
MATERIALS AND METHODS
Field Study
On I J u n e , 19 81, a line source irrigation and N fertilizer experiment was set up on established eW a m p U m 8 spring wheat (Tritieum aestivum L.) on the Evan Vervick farm near Fairfield, Montana. The objectives of this experiment were tor 1
1. Determine the yield response of W a m p u m spring wheat to N and water.
2. Evaluate the line source CVD as a method of gathering production function data.
N W 1 / 4
NWl/4,T22N,R2W.
Site Selection Criteria. An experimental site was sought which could provides
1. A source of irrigation water within 50 feet of the experimental area. 1
2. Established spring wheat at the t i m e s location was being sought.
3. The best drainage possible (The spring of 1981 was extremely wet with record flooding in several

20 areas. The site originally chosen, at the MSU
Horticulture Farm was too wet to be planted by
I June,82o)
4. The capability to not irrigate the experimental plot when'the rest of the field was irrigated,
At the selected site the w h e a t was in the early tillering stage on I June, 1981 when the treatments were applied. The field was flood irrigated from a ditch at the northwest corner and the experimental plot was placed in the northeast corner to prevent flooding by the irrigation water .
while allowing the farmer access to the ditch at the high corner of the field. The w h e a t had been planted on
28 April, 1981 in 20.3 cm rows at the rate of 103 kg/ha, with an International Harvester model 150 drill. Emergence was on 9 May, 1981. The farmer had broadcast (pre-plant)
135 kg N/ha, 0 phosphorus (P), and 28 kg potassium (K)Zha of bulk blended, dry mix materials. Additional fertilizer materials were banded with the seed in the amount of 45 kg
P/ha and 22 kg K/ha. Weeds were controlled by a tank mix of lR o n o x e (153 cm3/ha) and Bahvel (146 c m 3 / ha), Soil samples were taken to 120 cm depth, or the maximum depth possible, with a hand driven probe. Samples were analyzed for N, P, K, sulfur (S), organic matter percentage COM), and

pH. Soil moisture was estimated by feel as 50% of available water holding capacity (AWHC) in the zero to 60 cm depth, and 100% of AWHC in the 60 to 120 cm depth. The initial soil test results other than N are shown in Table I. The pre-treatment, soil nitrate-nitrogen levels are shown in
Table 2.
Table I. Field Pre-treatment Soil Test Results
(0 to 30 cm depth).
Block SO4-S
(ppm)
I
2
3
4
10
49
53
44
P
(ppm)
K
(ppm)
14 319
6 ' 236
9 194
6 248
OM ,
(%)
2.1
1 = 8
1.5
1.8
8.1
8.2
8.3
8.3
E»Ce
(mmhos/cm)
■ o ■
0.6
0.7
0.6
0.6
A line source irrigation system similar to that of
Hanks et al. (1974) was assembled from ten 6.1 m sections of
7.6 cm irrigation pipe with latch type, quick-connect couplers. Royal Coach model 10120 impact sprinkler heads with 5.2 mm range nozzles were mounted on 2.54 cm diameter risers 30.5 cm high, at each pipe coupler. The sprinklers are rated at 32 m wetted diameter and 0.58 1/s flow rate, when operated at 414 kPa. A pressure gauge was inserted on the first riser to monitor the sprinkler operating pressure.

22
Table 2. Field Pre-treatment Soil Nitrate-Nitrogen Results*
Block
2
I
3
4
Depth
(cm)
0-30
0-30
30-60
0-30
30—60
60-90
90-120
0-30
30-60
60-90
90-120
5.0
2.4
3.4
2.4
2.1
2 . 6
4.0
3,3
3.2
3.4
6 . 2
Water supply was from an adjacent canal of the Green­ field Irrigation District. A centrifugal pump (Berkely Pump
Co., Berkely CA, model 46OH-75 with 30.8 cm impeller) with an outlet pressure gauge, powered by a six cylinder Ford industrial motor mounted on a trailer chassis was used. The duration of each irrigation event was measured by an hour meter wired through an oil pressure switch. The procedure for each irrigation was to start the engine, prime the pump via an exhaust stack venturi, open the outlet valve to fill the irrigation pipe, and adjust the engine throttle to maintain 414 KPa at the first sprinkler. This procedure took less than five minutes and the hour meter stopped

23 within 10 seconds of fuel outage. The duration of an irrigation, in
1 / 1 0
hour increments, was used in conjuntion with measured application rates to determine the amount of water applied in each irrigation.
Fertilizer treatments were applied perpendicular to the sprinkler line in 2x20 m strips. The irrigation application amount decreased to zero at approximately 16 m from the pipe. The overall plot dimension was 48 meters in length by
32 meters in width. Fertilizer treatments ranged from 0 to
125 kg N/ha as ammonium nitrate (NH
4
N O
3
), in 25 kg/ha increments, with or wi t h o u t 50 kg P/ha as treble superphosphate as shown in.Table 3.
Table 3. Field Fertilizer Treatment Set.
Treatment
1 0
1 1
1 2
8
6
7
I .
2
3
4
5
0
25
50
75
1 0 0
125
125
1 0 0
75
50
25
0
P (kg/ha)
0
0
0
0
50
50
50
50
50
50
0
0

24
The twelve fertilizer treatments were replicated four times by repeating the sequence twice along the pipe and rotating treatments 1 80 degrees on each side of the line.
The plot layout is shown in Figure I. Fertilizer materials were broadcast by hand after which a 1.7 hour irrigation was applied.
r ^ — I — I
L
O
W
M
E
D
L
0
W
H
I
G
H
H
I
G
H
I R R I G A T I O N ^ M
T R E A T M E N T q
T T T T T T T T T T i
1 1
!
I I I I
I
I u l 1 1 h o j 9 i 2 3
± ! i
7 8 9 10 ! 11112
5 1 6 T j a j s i i o j i i j l 2 12 11 10 9 8 7 6 5 4 j 3 I 2 j I
I
I U l I
1 1 i
I I I I I I
F E R T I L I Z E R
T R E A T M E N T i r r i g a t i o n p i p e l i n e ^
I
s p r i n k l e r ^
Rep 3 Rep4
Figure I. Field Plot Layout.
A Tru-check rain gauge with 6.35 x 5.84 cm opening and an evaporation pan
( # 2
washtub) were installed at the site.
The rain gauge was seeded with approximately I cm of mineral oil to prevent evaporation.

25
Three irrigations were applied during the season, as shown in Table 4. Rain gauges and catch-cans were used, during tne irrigations on 2 June and 22 June, to measure the application rate and distribution pattern of the line source system. On 2 June, six rain gauges were installed in a transect perpendicular to the pipeline, at the center sprinkler. Water appliation rates were measured during a
1.35 hour irrigation. On 22 June the original transect was supplemented by two rows of catch-cans, perpendicular to the second sprinkler within the plot area at each end. The application rate was measured during 5.2 hours of operation.
The application depths from the catch cans were not used for absolute measurements, but they served to indicate the longitudinal uniformity of the water distribution profile.
Each transect during both measurments had cans or rain gauges p l a c e d at 5, 10, and 15 m, north and south of the line. The average a p p l i c a t i o n rate per hour for the irrigation treatment levels were determined by plotting the average hourly application rates measured by the rain gauges, and averaging the rates from the high and low ends of tne irrigation treatment locations.
The plot was accidentally flooded by ponded irrigation water from the rest of the field on 29 June and again on 20

26
JuIy
0
No additional irrigation water was applied through the line source after 22 June*
Table 4 o Field Irrigation Record.
Date Time (Hours)
Start End Elapsed Avg.
Water Applied (mm)
Low Med.
High
6 - 2
6-15
6 - 2 2
6 . 1
7.8
10.3
7.8
1 0 . 1
15.3
1.7
2.3
5:0
15.6
2 1 . 1
46.0
4.3
5.9
12.7
17.6
23.9
51,9
24.9
33.7
73.3
Total 9.0
82.7
22.9
93.4
1 3 1 . 9
The grain was at the hard dough stage on 25 Aug . a n d was harvested on
8
Sept., using 80 cm wide Jari mowers.
Visual observation, of the plot indicated that the response to water was less than anticipated. The water treatments were defined by harvesting in 5 m long swaths perpendicular to the sprinkler line with 80 cm alleys between levels.
This yielded samples of about 4 m^ at three qualitative irrigation levels. The bundles, including straw, grain, and fie l d - d r y moisture, were w e i g h e d on a spring s c a l e &
Bundles were threshed in the field using, a trailer mounted plot thresher (Almaco Experimental Plot Thresher ,Allan
Machine Co., Ames IA). The samples were dried at 40 C in a forced air cabinet for 72 hours and stored. The field

27 threshed grain samples were later recleaned with a blower type seed cleaner (Built by Bill’s Welding, Pullman WA) and weighed. Grain samples were later milled on a Cyclone laboratory mill (DB Corp., Boulder CO) and analyzed for protein content by infrared reflectance spectrometry
(Technicon InfraAnalyzer 400).
Data from this experiment were statistically analyzed using the M S U S T A T c o m p u t e r i z e d statistical package, developed by Lund (1979). Two factor analysis of variance and least significant difference were calculated for all measured parameters.
Greenhouse Study
A greenhouse study was initiated with the following objectives:
1. Evaluate any interaction between N level and maximum allowable depletion (MAD) for Wampum spring wheat.
2. Verify the response surface parameters of the field study, by varying frequency and amount of irrigation rather than amount only.
3. Determine the degree to which the production function determinations in the greenhouse were

28 comparable to those from the field study.
Soil was obtained from long term nutrient depletion plots at the Arthur Post Field Research Laboratory. This soil had been continuously cropped with small grains for four years and was very low in N and organic matter. This soil was chosen to allow N treatment effects to be clearly expressed. Approximately the top 10 cm of the Ap horizon were used. The soil was dumped on a flatbed trailer, mixed, and allowed to air dry. A composite soil sample was analyzed for major nutrients, pH, and organic matter by the
MSU Soil Testing Lab with the results as follows:
NO3-N
(ppm)
P
(ppm)
K
(ppm) pH OM
(%)
EC
(iranhos/cm)
5.0
17.7
351 7i9
1 . 8
One third MPa' and 15 MPa mo i s t u r e contents were determined with a pressure plate apparatus, showing 27.6% Y
15 MPa. The net A W H C , based on these measurements, wais
14.1% (mass basis).
Four thousand grams of air dry soil were placed in each of 102 pots to be used in the experiment (5 N rgtes, 3 water rates,
6
replications, plus
1 2
pots used for borders).
Gravimetric moisture determination showed 5% moisture for a

29 dry soil mass per pot of 3823 g. At 14.1%. AWHC (mass basis) each pot had 539 g of AWHC. Moisture treatments which allowed depletion of 25, 50, and 75% of AWHC were achieved by filling the pots to field capacity (total mass upper limit = 5100 g) and allowing depletion of soil water to the specified lower mass limit before refilling the soil to field capacity. The Upper and lower mass limits for water treatments are shown in Table 5. The method of maintaining water treatments is similar to that of Sosulski et al, (196 3), and Dubetz (1961)„
Table 5. Greehouse Irrigation Treatment Set.
Treatment MAD Total mass limits
(g/pot) lower
•dry
8 upper
8
wet
8
Wl
W2
25%
50%
75%
4990
4860
47 3 0
5100
5100
5100
Two replications of each treatment were selected as reference pots for maintenance of the moisture treatments.
Reference pots were weighed daily and when the average

w e i g h t of the retference pots w a s near or b e l o w the l o w e r mass limit, all replications of that treatment were watered w i t h an a m o u n t of w a t e r equal to the a v e r a g e d e p l e t i o n of the reference pots. All pots were brought to field capacity once per week, to c o r rect for slig h t d i f f e r e n c e s in w a t e r use between the six replications of each treatment :
Nitrogen fertilizer was applied at five rates, in factorial combination with the water treatments for a total of 15 treatments. The N rates are shown in Table
6
.
Table
6
. Greenhouse Fertilizer N Applied per Treatment.
Treatment g N g NH
4
NO
3 ml Stock sol.
NO
NI
0 . 2
0
25
N2.
0
.4
0.57
1.14
50
N3
0 . 6
1 .71
75
2 . 2 9
1 0 0
An ammonium nitrate (NH
4
NO3) solution with a concentration of 0.2g N/25 ml HgO was used as the N source. The solution was applied at. the volumes shown in Table
6
, by trickling the solution from a 25 ml autopipette into the dry soil while stirring the soil with a large knife. Every pot

31 also recieved a background fertilization of B, K, and S as ortho phosphoric acid (H
3
PO
4
) and potassium sulfate (KgSO^) solution, using the same technique. Each pot recieved 50 ml of the solution which provided 0,2 g P, 0.2 g K, and 0.08 g
S per pot. All pots were filled to field capacity and allowed to stand for approximately two weeks to allow the moisture to distribute evenly throughout the pot.
On 4 October, 1981, 20 seeds of W ampum spring wheat were planted in each pot. Four rows of pots fit on each greenhouse bench so the inner two rows were made one block and tne outer two rows another block. One row of four pots at the end of the bench next to the windows was designated as a border to buffer the extreme temperature fluctuation next to the windows. Pots were arranged in a hexagonal all treatments. Emergence began on
8
October, 1981 (day I), and was essentially 100% on day 3. All pots were maintained near field capacity until day
6
to promote establishment.
Moisture treatments were imposed beginning on day
8
. Between day 26 and day 40 all pots were thinned to 10 plants, weeded, and the plants tied to bamboo stakes to prevent stem breakage or lodging. Qn day
6 6
all pots were sprayed with
Malatnion to. control aphids.

32
The NO and NI treatments recieved the last irrigation on day 99, and N2, N3, and N4 treatments recieved the last irrigation on day .107. All pots were weighed on day 116 for final moisture depletion values. All plants were harvested on day 117. The plants were harvested by breaking off the heads and bagging main and tiller heads separately. The straw was cut at the soil surface and bagged. All heads and straw were dryed in a forced air drying cabinet at 27 C for four days. Dry straw and head weights were recorded, the heads were threshed and winnowed by hand. The difference between head weights and clean grain weights was added to
' ' ' the straw weight to account for the chaff from the heads.
Grains from each sample were counted with an electronic seed counter (The Old Mill Co., Model 850-2, Savage, MD).
These data were used to determine seeds per head. Grain was milled in a Cyclone laboratory mill (UD Corp., Boulder, CO) and a five gram sample analyzed for protein on an infrared reflectance spectrometer (Technicon InfraAhalyzer 400),
Moisture in tne milled grain was determined by oven drying
36 two gram subsamples at 140 C for 20 minutes. Protein contents were corrected to 14% moisture basis.
Two factor analysis of variance and least significant difference were calculated for all measured parameters.

■ .
■
Least s q u a r e s r e g r e s s i o n a n a l y s e s w e r e c a r r i e d out for s e l e c t e d p a r a m e t e r s , w h i c h w e r e s i g n i f i c a n t l y a f f ected by the treatments applied, appeared to be repeatable, and could be f i t t e d by an e q u a t i o n w i t h a r e a s o n a b l e b i o l o g i g i c a l basis. The MSUSTAT computerized statistical package (Lund,
1979) was used for the analyses.

CHAPTER 4
RESULTS AND DISCUSSION
Two- and t h r e e - d i m e n s i o n a l graphs, and tables w e r e p r e p a r e d to i l l u s t r a t e r e s p o n s e of w h e a t to N and w a t e r treatments. Highlights of analysis of variance, results will also be presented. C o m p l e t e r e s p o n s e data, r e s ults of s t a t i s t i c a l a n a l y s e s and s i g n i f i c a n t r e g r e s s i o n e q u a t i o n s are included in Appendix A.
FIELD STUDY
Line Source Irrigation System Performance
The line source irrigation system produced the desired pattern of water application, with the highest application rate at the center of the plot, decreasing smoothly to zero at the p e r i m e t e r of the w e t t e d area. S o m e s k e w i n g of the d i s t r i b u t i o n p a t t e r n by w i n d w a s n o t e d , e v e n t h o u g h irrigating on windy days was avoided.
The line source system proved to be an efficient method of a p p l y i n g c o n t i n u o u s l y v a r y i n g i r r i g a t i o n a m o u n t s and therefore suitable for obtaining production function data.
However, to achieve quantitative irrigation levels, a larger n u m b e r of rain gaug e s w o u l d be n e e d e d than w e r e used in this e x p e r i m e n t , and g a u g e s should be used to m e a s u r e every

35 irrigation event because of changes in the distribution pattern caused by even light winds.
Figure 2 shows the average distribution pattern as a function of distance from the line source.
5 2 0 -
< 1 0 -
— CALCULATED
— EXTRAPOLATED
■ MEASURED 2 JUNE
• MEASURED 22 JUNE
North-*- LINE SOURCE -►South
DI STANCE FROM LINE SOURCE (m)
Figure 2. Average Irrigation Application Rate as a Function of Distance from the Line Source.
Table 7 shows the water application measured on 2 June during 1.35 hours of irrigation. Table
8
shows the water application on 22 June during 5.2 hours of operation.

36
Table 7. Irrigation Depths, the Line Source. as a Function of Distance from
Measured on 2 June, during 1.35
Hour Irrigation.
Distance from line
(N=north, S=South)
(meters)
Irrigation depth
(mm)
N-15
N-IO
N- 5
S- 5
S-IO
S-15
: 5.0
14. ■
23.
19.
14.
' -
Table
8
. Irrigation Depths, as a Function of Distance from the Line Source, and Position along the Line.
Measured on
2 2
I Irrigation.
Distance from line
(N=north, S=SOUth)
(meters) west-
1
/
Irrigation depth
(mm) center^/
I/
/
2 /
N-15
N-IO
N- 5
S- 5
S-IO
28.
47.
53.
59.
48.
6.5
28.
56.
6 4
8 2
5 6
.
.
.
5.1
25.
4 4
.
,
51.
5 9
4 5
.
.
5.2
Measured perpendicular to second sprinkler from west edge of plot area.
Measured perpendicular to sprinkler in center of plot area.
Measured perpendicular to second sprinkler from east edge of plot area.

37
Above Ground Dry Mafcfcer Yield
A b o v e grou n d dry m a t t e r p r o d u c t i o n (AGDM) in c r e a s e d w i t h N a p p l i c a t i o n s up to a p p r o x i m a t e l y 75 kg N/ha in the w e t t e s t i r r i g a t i o n t r e a t m e n t , and up. to 125 kg N / h a at the l o w arid m e d i u m i r r i g a t i o n l e v e l s . I n c r e a s i n g w a t e r applications resulted in increased, above ground dry matter production at all N rates.
Overall, the yields obtained were lower than expected for a net N application of over 250 kg/ha. The soil profile at the experimental plot had been truncated by previous land grading, leaving the low organic matter, fine, calcareous B ho r i z o n at the surface. Consequently, soil physical conditions may have limited the yields.
. ’ " ' ' •
Results of the line source CVD experiment cannot be used for statistical inferences because of the non-random allocation of treatments within the experimental area
(Hanks, 1980). There is however, an obvious increase in AGDM yield with increasing amounts of irrigation, as shown by the response surface in Figure 3. Irrigation levels were a source of significant variation in yield (P=.
0 0 1
), .when statistically analyzed as a randomized, complete block (RCB) design. Nitrogen levels and N x water interaction were not significant (for.N, P=.103? for N x water, P=.969).

38
6 0 0 -
5 0 0 -
4 0 0 -
APPLIED
NITROGEN
( k g / h a )
IRRIGATION
LEVEL
Figure 3. Field Above Ground Dry Matter Yield.
Grain Yield
Figure 4 shows grain yield in relation to N and water.
A significant (P=.041) response to water is apparent, especially at the low and intermediate rates of N, as shown in Figure 3. Nitrogen fertilization also increased grain

39 yield, having the greatest effect per unit applied at the low to medium rates, and decreasing in effectiveness at the higher rates. However, statistical analyses for a RCB design imply an insignificant response to N (P-.079) and N x water (P-.998), because of the high sample variability in this experiment.
320 —
3 0 0 -
2 8 0 -
2 6 0 -
220
-
APPLIED
NITROGEN
(kg/ha)
Figure 4
.
Field Grain Yield
Lo w
IRRIGATION
LEVEL

40
Harvest Index
Harvest index (HI), the ratio of grain yield to AGDMV was erratic in relation to K applications as shown In
Figure 5, There was a substantial decrease in HI with increasing applications of irrigation water. Analyzed as a but HI response to irrigation level was highly significant
(PC.001). No interaction between N and water was indicated
(P=.676).
Grain Protein '
Grain protein increased with N applications up to the highest N level. Irrigation had very little effect, although the wettest irrigation treatment tended to have the highest protein percentage at each N rate. Analyses for a
RCB design showed a significant (PC.001) response to N level. irrigation effects were nonsignificant (P=.139), as was the N x water interaction CP=.887). The protein response to N and water rates is shown in Figure
6
.

41 h- .46
cc .45
. 4 4 -
I R R I f i A T I O N I F V F l
■ L o w
# Med.
A High
APPLIED NITROGEN (kg/ha)
Figure 5. Field Harvest Index

42
IRRIGATION LEVEL
■ Low
• Med.
A High
APPLIED NITROGEN (kg/ha)
Figure
6
. Field Grain Protein Percentage
Discussion
The wheat responses to N and water that have been p r esented are not suitable for the d e v e l o p m e n t of a quantitative response surface because they are in terms of growth factors applied, rather than total budgets for N and water. However, the data presented do serve to indicate the nature of the response of W a m p u m wheat to these growth factors. The experimental method used in this experiment would be suitable for generating a transferrable response

43 surface, or development Of a yield m o d e l , if all of the input parameters were accurately measured or estimated.
GREENHOUSE STUDY
Cumulative Water Use
C u m u l a t i v e water use was less for the 75% MAD irrigation strategy than for the 25 and 50% treatments, as shown in Figure 7. The two wetter treatments probably held water at relatively high potentials so that their water use was Climatically determined. At 75% allowed depletion, soil factors began to limit water use and the seasonal water use was substantially reduced. Nitrogen fertilization affected the w ater use, a s ;seen in the increase in CU with N fertilization within each irrigation treatment. Two factor analysis of variance showed highly significant (PC.001) t r e a t m e n t effects on water use by N, water, and the
N K water interaction.
Water Use Efficiency
Water use efficiency (WUE) is strongly affected by the
I ■ - ■ „ level of N fertilization, under both dryland and irrigted
Downey, 1972). Deficit irrigation scheduling strategies

44 assume that low soil water availability can increase WUE
(Singh, 1981 ; Stegman et al., 1976). Figure
8
shows the pronounced effect of N on WUE which was highly significant
(PC.001). Irrigation strategy also had a significant effect on WUE (P*.003), But there was no significant interaction between the two factors (P=.561).
The highest WUE was achieved with a high level of N and a high value of HAD. However, increased MAD values also reduced yields.
0 14
-
M A D
A 2 5 %
S 50%
■ 75%
2 0.4
NITROGEN (g/pot)
Figure 7. Greenhouse Cumulative Water Use

45
▲ 25%
• 50%
■ 75%
I 0.4
NITROGEN ( g / p o t )
Figure
8
. Greenhouse Water Use Efficiency.
Above Ground Dry Matter Yield
Above ground dry matter yield (AGDM) was strongly affected by N and irrigation strategy. The response to N, shown in Figure 9, is typical for a growth factor subject to the 'law of diminishing returns' (Mitchell, 1970).
Above ground dry matter yield was only moderately reduced at the highest value of MAD. Greater yield reduction would occur at 75% MAD in a field situation, because of the greater climatically induced water demand and the spatial variability of AWHC. Solar radiation levels in

46 the greenhouse are considerably lower than in the field because of the glass' incomplete transmission of sunlight, the lower angle of the sun, and shorter day length in the winter. In addition, the average relative humidity during the experiment was 40 to 50%, much higher than could be expected in most Montana wheat growing areas. The response of AGDM to irrigation strategy and M was highly significant
(P=.008, and PC.OQl, r e s p e c t i v e l y ) . There w as also a significant (P=.014) interaction between N application and irrigation strategies.
The highest AGDM yield ocurred at the highest N rate and lowest allowed depletion level. However, the difference between 25% and 50% MAD was not statistically significant.
Fifty percent allowed depletion scheduling would reduce the frequency of irrigation relative to 25% depletion. .
Grain Yield
Grain yield responded significantly to N (P<.001), as shown in Figure 10. The highest N rate, 0.8 g N/pot, was not significantly different from the
0 = 6
g/pot rate, indicating that a yield plateau was reached. The response of grain yield to irrigation strategies was not significant (P=,726)# nor w a s the N x M A D i n t e r a c t i o n (P=.5 0
6
). The

47 nonsignificance of irrigation strategies is in contrast to the results of Dubetz (1961 ), who obtained significant increases in grain yield with decreasing values of MAD in a very similar experiment. The difference is probably due to
Dubetz
1
use of pots which were about twice as large as those used in this study. Irrigation would have been half as frequent with the larger pots so the low water potentials in the high MAD treatments would have been of longer duration.
This would cause the irrigation treatments to have a greater effect on the final yield.
NITROGEN
(g/pot) 50%
MAXIMUM
ALLOWED
DEPLETION 0 75%
Figure 9. Greenhouse Above Ground Dry Matter Yield.

48
NITROGEN ( g / p o t )
Figure 10. G r eenhouse Grain Yield, Av e r a g e of Three
Irrigation Strategies. (Vertical bars indicate range of mean responses to irrigation.)
The d i f f e r e n c e in r e s p o n s e of A G D M and g r a i n yield to water treatments indicates that much of the increased growth due to a r e d u c e d M A D w a s v e g e t a t i v e growth, w h i c h did not enhance grain yield. This effe c t w o u l d be r e d uced by allowing greater depletions in the early vegetative period a n d m i n i m i z i n g s t r e s s d u r i n g f l o w e r i n g a n d g r a i n development.
Harvest Index
Harvest index (HI) was reduced at the NI rate compared to NO. The NI pla n t s be g a n to show v i s ible N d e f i c i e n c y

\
49 symptoms by day 35 (four leaf stage) of the experiment, while the NO plants were visibly chlorotic by day 26 (three leaf stage)f and.never developed normal leaf area. Because the NO set w a s N deficient relatively early in the vegetative period, both vegetative and grain yields were reduced, resulting in a normal HI. The NI set developed visible deficiency symptoms after a large proportion of the vegetative yield was produced, but before grain development, resulting in a reduced value of HI. The N2 and N3 levels increased HI substantially but N4 had a reduced HI. The N2. and NS rates appeared to have supplied adequate but not excessive nitrate, At N4, the leaf growth was luxuriant and many late tillers were formed which did not bear grain at the time of main head maturity. The response of HI to N rates is shown in Figure 11.
Harvest index is an indicator of the efficiency of N partitioning in the plant. When an appropriate amount of N was supplied, HI and grain yield were both high. When excessive N was applied, grain yield was barely increased while AGDM continued to rise, giving a lowered value of. HI.

50
Figure 11.
Grain Protein
Grain protein levels show a highly significant
(PC.001) response to N level. There was no significant interaction between N and irrigation strategy (P=.825). All treatment combinations had protein contents above 12% (14% moisture basis). There was a trend towards increased protein with increasing MAD, as shown in Figure 12, although this effect was not statistically significant (P=.17 0). This contrasts with the results of Dubetz (1961) who obtained significant decreases in grain protein percentage with increasing values of MAD. As in the case of grain yield,

51 this difference is probably attributable to the longer intervals of low soil w ater p o tentials at high M A D 8 s associated with the larger pots which Dubetz used. Only 4 kg of soil per pot were used in this experiment and the resulting high frequency of irrigation (approximately every
48 hours during peak water use in the 75% MAD treatment) reduced the effective difference b e t w e e n irrigation strategies relative, to what it would have been if larger
' pots had been used. In Dubetz0 study a loamy and a sandy soil were compared. The sandy soil required more frequent irrigation than the loam, and the sandy soil showed no significant irrigation effects on grain yield or protein percentage. The wheat on sandy soil in Dubetz0 large pots behaved very similarly to the wheat in loamy soil and smaller pots used for this study.

52 z 1 4 -
O 1 3 -
NITROGEN (g/pot)
Figure 12. Greehouse Grain Protein Percentage, Average of
Three Irrigation Strategies. (Vertical bars indicate the range of mean responses to the irrigation stratgies.)
Plant Height
Increasing levels of both N and W were highly signifi­ cant in increasing the height of wheat (for N, P<.001; for water, P = .
O O 6; for N x W, P = .
012), as illustrated by
Figure 13. All plants in the experiment were tied to bamboo stakes so lodging was not a problem. In the field however, excessive height, coupled with weak, high moisture straw, can cause lodging. This condition is often associated with available N levels which are in excess of the established N requirements.
Plants at the NO level were visibly dwarfed with very

53 little leaf growth. At NI the plants were nearly normal in height but still had relatively little leaf growth. The N2 and N3 levels were of normal (average 82-86 cm) height but the leaf mass continued to increase in this range. At the N4 rate, height remained the same as for N2 and N3 but the leaf mass was obviously very high and these plants would have been very susceptable to lodging, if they had not been mechanically supported.
▲ 2 5 %
• 5 0 %
■ 7 5 %
0.4
NITROGEN (fl/ pot )
(
Figure 13. Greenhouse Plant Height.

54
Earliness
Heading and pollination dates were observed in the reference pots during the daily weighings* The data were statistically analysed as a completely randomized design with two replications.
Heading dates averaged one day earlier for each 25% increase in MAD, while pollination dates averaged less than one day different across all W treatments. Because of this, the duration of the heading period (heading to pollination) was one day greater for the 75% MAD treatment than for the
25 and 50% MAD treatments.
Nitrogen applications delayed heading and pollination, each about one day per 0.4 g N/pot. The duration of flowering was not affected by N rate. These trends in earliness were not statistically significant.
Yield Components
, Heads per plant. The amount of tillering was low in the greenhouse, as shown in Figure 14, probably due to the steady, warm temperature. Partially as a consequence of this, the NO thru N2 treatments averaged essentially one head per seed planted. The N3 and N significantly increased heads, per plant, but even at the N4

55 rate there was an average of only
1 . 6
heads per plant.
Irrigation strategies had no significant effect on heads per piant.
0.4
NITROGEN (fl/pot)
(
Figure 14. Greenhouse Number of Heads per Plant, Average of
Three Irrigation Strategies. (Vertical bars indicate range of mean responses to the irrigation strategies.)
An additional factor suppressing heads per plant was that only heads bearing grain at the time of main head maturity were counted. The N3 and, especially, N4 pots had many more tiller heads formed than were counted, but these did not bear grain. Late tillers were still being formed in the N4 pots when irrigation was stopped to ripen the grain.
Seeds per head. The number of seeds per head, shown in
Figure 15, increased nearly linearly from NO to N2, then remained fairly constant. Irrigation strategy had no significant effect on this parameter.

56
< 2 0 .
▲ 2 5 %
• 5 0 %
■ 7 5 %
0.4
NITROGEN ( g/ pot )
C
Figure 15. Greenhouse Number of Seeds per Head.
Hass per seed. Mass per seed increased from the NO rate to the N2 rate, stabilized at approximately 33 mg/seed at N2 and N3, then dropped significantly at the N4 rate, as shown by Figure 16. The drop in mean mass per seed at the highest N rate was compensated for, and probably caused by, an increase in the number of heads per plant, and thereby, the number of seeds produced. Water treatments did not significantly affect seed mass, but there was a trend towards increasing seed mass with higher values of MAD.

57 w 30_
5 26
M A D
A 2 5 %
• 5 0 %
■ 7 5 %
0.4
NITROGEN (g/ pot)
(
Figure 16. Greenhouse Mass per Seed.
Main Heads vs. Tiller Heads
The grain yield of the main heads reached a maximum at
N3 and declined about 10% at N4. This was compensated by an increase in tne grain production of tiller heads, which did not significantly contribute to yield at the lower N rates.
Irrigation strategy had a much greater effect on tiller grain production than on main head grain production. The
75% M A D treatment caused a 30% reduction in tiller grain production in the N3 and N4 treatments compared to the 25 and 50% MAD treatments. The main head grain production was much less affected by MAD levels, presumably due to the less favorable position of tiller stems relative to the trans­ piration stream.

58
The average mass per seed for tiller heads was about
30% less than for main heads. Many of the tiller grains were visibly shrivelled at harvest, because they were formed late in the season and never filled properly. The number of seeds per tiller head was approximately one half that of the main heads in N3 and N4 plants, the only ones to have significant tiller development.
Discussion
This e x p e r i m e n t was d e signed to evaluate the suitability of 50% MAD irrigation scheduling to irrigated spring wheat production. The grain yield response to MAD and to the N x MAD interaction were not significant. The
50% MAD scheduling method appears, to be appropriate for spring wheat regardless of the field's N fertility level.
While 75% MAD scheduling did not significantly reduce grain yield in the greenhouse, it would very likely reduce yield in tne field because of the variation in AWHC in soils.
Allowing 75% MAD at the field soil moisture monitoring station would not leave enough reserve water to account for the variation in AWHO, unless the soil in a particular field was extraordinarily uniform. .
Anotner objective of this experiment was to verify the
I

59 response of wheat to N and water which was obtained from the field study. While the environmental differences between the field and greenhouse are large* and the responses obtained are not numerically comparable, the greenhouse and field responses do have the same patterns. If all of the environmental parameters could be measured and scaled to relative values, the responses obtained in any particular environment should be transferrable to other environments and seasons

Chapter 5
GENERAL CONCLUSIONS
Current recommendations for scheduling irrigations of wheat in Montana are based on 50% MAD before irrigating.
This investigation was undertaken to evaluate the response of 'Wampum' spring wheat to irrigation and N fertilizer, and to evaluate any interaction between N fertility status and an optimum value of MAD.
The results f r o m the field e x p e r i m e n t yielded a qualitative picture of the response of wampum wheat to N and
I irrigation. The data from this experiment were not adequate for developing a transferrable grain yield response surface because of uncertainties in the total N and water budgets which the crop experienced. The line source design is suited to obtaining production function data, it is an efficient technique for developing wheat response surfaces and yield models, if the N and water available to the crop are completely quantified.
In the greenhouse, irrigation levels varied in.both amount and frequency according to the three levels of MAD.
There was no significant effect of either MAD level or the N x MAD interaction on wheat grain yield. This indicates that the 50% MAD scheduling method can be used with confidence on

61 wheat crops having a wide range in N status and yield potentials. There is a close c o rrelation b e t w e e n N fertility, seasonal water use, and y i e l d in wheat.
Scheduling irrigation according to a threshold level of soil moisture depletion controls the timing and amount of individual irrigation events, rather than the amount of seasonal water use. If two wheat fields of differing N status are scheduled using the 50% MAD technique, the crop with the higher N fertility and growth rate will deplete the soil water more rapidly, be irrigated more frequently, have a greater seasonal water use, and have a higher final yield.
Each of these crops would still exhibit the correlation between seasonal water use and final yield, even though irrigations were scheduled in the same way.
if the gree n h o u s e and field p r oduction results w e r e comparable and mutually reinforcing. The results indicate that w h i l e the data f rom the two e x p e r i m e n t s are qualitatively similar,: they are not numerically comparable.
There were large differences between the two environments, ana it was difficult to quantify those differences. If.all of the g r o w t h factors in t h e s e , or any, d i s s i m i l a r environments could be accurately measured, and if the growth

6 2 factors and response parameters were scaled to relative values, the production function data should be comparable.
When this is done, the response surface developed from data obt a i n e d in any environment, or season, should be transferrable to other environments and seasons for which the greenhouse environment is too different from that in. the field, and the number and detail of required measurements makes the greenhouse impractical for obtaining production function data which can be appplied to a field situation, without substantial modification.

APPENDICES

64
APPENDIX A
INDIVIDUAL PLOT DATA ,
ANALYSES OF VARIANCES,
AND REGRESSION EQUATIONS

65
Table 9. Field Above Ground Dry Matter Yield*
(Yield in g/m2 „)
Individual Plot Data
Applied M Rate (kg/ha)
Water Rep .
1
/
Rate
LOW I
2
3
4
IP
2
P
3
P
4
P
0
25 50 75 100
125
4 4 8 . 9
4 5 5 . 9
4 55.9
4 1 8 . 3
514.4
682.7
6 0 1.7
4 4 4 . 7
3 2 5.6
381.7
478.0
4 1 2 . 3
5 1 4 . 4
4 4 9 . 1 4 4 9 . 1
385.5
3 9 6 . 9
4 7 9 . 3
478.0
515.5
6 2 3 . 7
3 3 3 . 5 4 5 1.3
675.6
6 0 3 . 2
500.3
4 9 4 . 0
4 5 8 . 2 4 4 2 . 2
3 7 8 . 0
522.5
4 5 5.8
4 3 0 . 9
6 0 7 . 1
3 8 9 . 1
4 8 2 . 8
439.7
678.2
5 5 5 . 9
510.3
561.3
725.7
2 9 4 . 8 4 4 2 . 2 5 6 7 . 0 572.6
5 9 5 . 0 533.0
4 4 0 . 8 4 5 8 . 1 4 8 6 . 9 557.5
MED I
2
3
4
IP
2P
3P
4P
Mean
4 0 8.2
6 8 0 . 4
5 8 4 . 5
4 3 8 . 3
7 9 3 . 8
2 9 4.8
2 2 6 . 8
2 8 9 . 0
4 6 4 . 5
5 9 6 . 2 5 5 5 . 6
5 4 4 . 3 5 8 4 . 5
555.2
6 0 7 . 9
505.2
5 0 0 . 3
7 7 4 . 7
4 5 1 . 3
801.8
4 0 9 . 2
464.9
3 6 7 . 2
355.8
5 8 4 . 2
5 3 0.9
5 5 1.3
5 0 4 . 5 5 0 3 . 6
7 5 2 . 1
5 2 0.7
7 3 2 . 4
4 5 5 . 9
7 4 4 . 5
7 59.8
7 6 7.4
7 2 1 . 6
5 1 1 . 4
7 0 8 . 7
4 7 4.4
2 8 3 . 5
651.2
5 2 7 . 7
4 2 6 . 6 7 2 5 . 7
637.1
5 8 3 . 4
7 52.1
5 1 4 . 4
589.7
729.8
6 8 9.7
4 19.6
7 1 3 . 1
526.1
616.8
HIGH I :
IP
2
P
5 8 4 * 5 6 5 4 . 7
2
■ 6 8 0 . 4 5 1 0.3
3 .
1 0 0 3 . 1
6 1 2 . 3
8 5 8.7
1 0 1 1 . 7
4 5 2 6.1
9 3 5 . 2
255.7
5 4 4 . 3
9 2 3 . 6
4 8 9 . 2
5 8 4 . 5
4 5 3 . 6
9 9 3 . 7
567.0
3P
2 19.8
2 4 4 . 6
283.5
3 6 2 . 4
7 48.4
5 3 2 . 3
520.7
5 33.6
8 1 9.6
538.4
759.9
589.7
4 2 5 . 2 556.1
504.0
462.9
9 4 8 . 6
4 1 9 . 6
9 1 4 . 1
4 7 6 . 3
5 2 3 . 4
5 67.0
Mean 561.0
573.5
6 4 4 . 0 624.5
6 1 4 . 3
6 7 1.1
4 2 3.8
7 7 8.2
494.6
708.7
5 8 9.2
788.4
324.0
597.2
LSD 0.05= 1 6 4
.
6
,LSD q 9 oi “
2 1 7 «5
I/ 'P' after rep. means treatment with 50 kg/ha phosphorus.

66
Table 10« Analysis of Variance of Above Ground Dry Matter
Yield Data.I/
SOURCE DF S.S.
MoSo F-VALUE
Blocks
Nitrogen
Wafer
NxWater
Residual
2
1 0
5
771200.
2 5 7 1 0 0
1 9 4 3 0 0
.
.
3 8 8 5 0
.
1.875
3 0 2 1 0 0
6 9 4 8 0
123 2 5 4 8 0 0 0
.
1 5 1 0 0 0
.
7 . 291
.
6 9 4 8
.
0 . 3 3 5 4
.
2 0 7 2 0
.
P-VALUE
.103
.001
.969
I/ ANOVA invalid due to non-random placement of treatments«

67
Table Il0 Field Grain Yield* (Yield in g/m2 *)
Individual Plot Data
Rate
LOW I
2
3
4
IP
3P
4P
Mean
MED I
2
3
4
IP
2P
3P
4P
0
25 75
1 0 0
125
2 3 7 * 4
2 7 3.1
2 33.8
1 9 1.4
365.7
2 1 4 . 6
1 3 8 . 2
1 14.3
2 8 5 . 8
2 4 7 . 0
198.8
1 85.0
3 48.4
2 2 0 . 6
1 92.9
191.1
2 2 7 . 9
2 3 2 . 0
2 2 2 . 8
1 4 4 . 7
3 8 6 . 5
2 4 2 . 8
2 0 1 . 4
2 36.3
272.3
2 3 2 . 8
2 55.1
2 6 9 . 6
200.4
275.6
3 25.9
1 8 4 . 9
353.7
3 4 5 . 3
256.2
2 1 7 . 6
1 95.1
2 3 2.8
246.3
2 6 2 . 5
250.6
255 * 5
2 8 2.6
239.3
2 8 4 . 2
264.8
350.2
2 4 2.7
2 2 1 . 1 233.7
2 3 6 . 8 256.1
2 5 9 . 7 271.2
2 2 2 . 4
I 303.7
279.4
3 7 6.1
4 0 0 . 8 4 1 9.1
3 3 3 . 4 286.8
2 8 5 . 5 2 4 8 . 6 2 2 2 . 1
2 1 2 . 6
273.5
1 9 3 . 6
4 1 1 . 4
1 4 6*7
1 1 7 . 4
109.8
269.8
2 4 4 . 2
3 9 2*5
3 1 9 . 6
2 3 2 . 3
4 6 5 * 1
367.5
3 3 8 . 2
4 2 4.4
2 1 1 . 2 1 7 3 . 2 2 1 5.1
197.6
1 6 5 . 1 275.9
156.5
2 5 0 . 1 1 7 6.2
3 5 0 . 6
2 4 2 . 2
3 9 1 . 6
95.7
2 2 9 . 3
3 1 4 . 5
3 0 1 . 8
3 16.3
3 6 4 . 9
213.1
3 3 7 . 6
233.9
Mean
HIGH I
2
3
4
IP
2P
3P
4P
2 2 6 . 0
2 8 5 . 7
3 0 8 . 5
4 5 6 . 4
2 3 1 . 7
3 5 0.9
1 3 2.2
91.4
1 1 6.3
2 5 7.8
2 7 1 . 3
336.9
2 4 7 . 9
4 3 5 . 2
3 0 5 . 3
2 9 8 . 3
4 7 0 . 9
2 5 1 . 3 178.7
463.6
5 3 1 . 0
2 4 2 ; 6 1 9 7.5
1 0 6 * 1
146.8
1 8 9 . 6
2 0 6 . 9
302.7
388.9
260.8
401.9
222.3
419.4
260.1
235.4
174.8
2 8 0 . 8
2 8 6 . 4
2 6 6.4
3 9 9 . 2
1 9 0 . 8
4 8 8 . 2
2 1 0 . 2
2 5 6 . 2
1 8 9 . 2
299.9
331.3
2 18.9
3 8 2 . 4
2 1 3 . 3
366.0
301.1
3 8 4.1
144.4
Mean 2 4 6 . 6 278.8
2 9 7 . 3 295.5
285.8
2 9 2.7
LSD 0*05= 82.7, LSD
1 0 9 . 4
I/ 1P1 after rep* means treatment with 50 kg/ha phosphorus*

/
68
Table 12«> Analysis of Variance of Grain Yield Data.
SOURCE DF s.s.
M 0Se F-VALUE P-VALUE
Blocks 3
Nitrogen 5
Water 2
NxWater
1 0
Residual 123
403000.
134300.
5 3 0 4 0
.
10610.
34030.
8 5 1 4
.
644300.
17010.
8 5 1 . 4
5 2 3 9
.
2.025
3 . 2 4 8
0 . 1 6 2 5
.079
.041
.998
J,/ ANOVA invalid due to non-random placement of treatments.

69
Table 13o Field Harvest Index,
Individual Plot Data
Applied N Rate (kg/ha)
Rate
LOW I
2
MED
2
3
4
IP
2P
3P
4P
Mean
I
3
4
IP
2P
3P
4P
Mean
HIGH I
2
3
4
IP
2P .
3P
Mean
0
.545
.490
.468
.442
.518
.498
.518
.380
.482
.444
25
.529 , .556
.599
.513
.458
.536
.483
.424
i388
.550
.443
.516 ■ .56 1
.4 85
.579
6462
.468
.432
6 517
.434
.572
.485
;440
.417
.491
.506
.484
.509
.527
.486
.483
.507
.468
.425
.440
.481
.489
.453
.515
.486
.455 .
.507
.440
.462
.375
.517
.416
.410
.502
.496
.434
.405
.476
.503
.488
.526
»464
.580
.423
.450
.428
.483
.522
.487
.466
.394
.534
.348
.446
.411
.451
75
.500
.477
.494
.445
.553
.453
.424
.413
.470
.568
.534
.523
.444
.586
.519
.441
.430
.506
.520
. 501
.480
.413
.552
.441
.423
.378
.465
.538
.500
.421
.455
.534
.441
.490
.334
.547
.487
.486
.474
.553
.338
.435
.433
.469
100
.518
125
.616
.!570
.527
.
.523
.429
.417
.430
.569
.557
6559 ■ .472
.482
.441
.483
.455
.493
.557
.413
.512
.434
.529
.508 ■
.474
.445
.484
.464
.494
. 517
.491
.432
.516
.511 .
. 487
.446
.487
LSD 0.05=
.0 4 6 , LSD
0
.
0 1
= '061
I/
5
P
8
after rep, means treatment with 50 kg/ha phosphorus.

70
Table 14. Analysis of Variance of Harvest Index Data. I/
SOURCE DF S .
S .
■
0
Blocks 3
Nitrogen 5
Water 2
NxWater 10
Residual 123
.
0 * 1 9 0 8 0 . 0 6 3 6 1
0.005798
0 . 0 0 1 1 6
0.03018
0.01509
0.01207
0.001207
0.1976
0 . 0 0 1 6 0 6
F-VALUE P-VALUE
0 . 7 2 1 9
9.393
0. 7 5 1 3
.611
< . 0 0 1
'
.676
.
2/ ANOVA invalid due to non-random placement of treatments.

71
Table 15, Field Grain Protein Percentage*
Individual Plot Data
Applied
N
Rate (kg/ha)
Rate
0
25 50 75
LOW I
2
3
10.4
10.4
9.5
9.9
IP .
2P
10.3
'
■
1 0 . 8
3P 9 . 1
4P 9.3
Mean
10.7
10.5
8.9
10.4
1 1 . 1
1 0 . 8
9.5
10.0
1 0 . 2
1 1 . 0
11 . 4
1 1 . 2
9.5
1 1 . 0
9.9
■
1 1 . 0
1 1 . 6
10.7
9.8
10.5
11.6
11.2
1 0 . 6
11.4
9
.
9 6
'
10.24
1 0 . 5 9
100 125
1 1
:
2
'
11.1
10.7
1 1 . 2
11.6
.
11.4
11 . 4
11.6
■
1 1 , 1
12.4
11.8
11.6
9.8
11.3
10.9
12.0
11.04
11.60
MED I
2
3 '
4
IP
2P
3
P
4P
10.8
10.4
9.1
9.8
1 1 . 0
1 1 . 2
9.2
8.9
10.5
11 . 1
9.1
10.3
11.3
1 0 . 8
8.8
8.9
10 . 9
10.9
9.1
10.4
11.7
1 0 . 9
9.9
9*9
11.3
11.9
9.9
10.4
12.0
11.3
11.8
11 . 1
10.9
1 2 . 0
12,2
11.9
10.9
11.5
12.4
1 1 . 2 1 2 . 1 11.8
10.3
10.6
1 0 . 0 11.1
■
1 0 . 7 11.1
Mean
HIGH I
2
3
4 ■
IP
2
P
3P
4P
10.05
10.10
1 0 . 2
10.4
9.7
10.5
10.9
9.7
8.8
8.7
10.8
11.0
10.1
1 1 . 1
1 1 . 0
11.5
9.2
9.2
1 0 . 4 6
1 1 . 1
11.0
10 . 3
10.95
11.4
11.7
10.8
1 1 . 2 4
1 1 . 2
11.7
11.3
11.61
1 1 . 1
11.9
11.2
■
11.3
10.9
12 . 2 12.1
1 1 . 1 11.3
11 . 4 10.9
1 1 . 4 11.6
11 . 7 12.0
1 0 . 5
10.4
10.9
1 1 . 2
10.9
12.0
11.2
11.3
Mean 10.49
1 0 . 8 9
1 1 , 2 2
1 1.55
1 1 .46
lSD 0.05= 0
.
4 8
, LSD.
.64
I/ 'P' after rep, means treatment with 50 kg/ha phosphorus.

72
Table 16. Analysis of Variance of Grain Protein Percentage
Data. A/
SOURCE DF S.S.
H .
S .
F-VALUE P-VALUE
Blocks 3
Nitrogen 5
Water
NxWater
2
1 0
Residual 123
2 9 . 4 2
44.95
1.003
2.052
2 8 . 9 9
'
9 . 8 0 7
8 . 990
0.5017
0 . 2 0 5 2
38.14
2 . 1 2 9
0.8708
< . 001
.121
.563
■
I/ ANOVA invalid due to non-random placement of treatments.

73
Table 17. Greenhouse Cumulative Water Use. (Water use in literA/jpot)
Individual Plot Data
N Rate (g/pot)
Rep.
MAD
(%)
25
6
I
2
3
4
5
0 . 0
0.2
0.4
0,6 0.8
9 . 3 1 6
8 . 3 9 0
'
1 4 . 2 7 6 1 8 . 4 1 0 1 8 . 6 4 4 1 8 . 6 0 0
13 . 1 2 6 17. 1 8 9 1 7 . 5 5 9 19.125
.
9 . 1 3 0 1 4 . 9 2 0 18 . 0 8 1 1 8 . 5 2 9 1 6 . 9 2 5
8 . 9 3 0
1 0 . 1 4 5
9 . 1 9 9
1 3 . 6 9 9
1 3 . 9 3 1
1 3 . 2 3 8
16.893
1 7 . 9 5 9
17.327
19 . 1 3 7
1 9 . 4 9 9
1 9 . 4 7 2
18.645
20.944
50
Mean
6
I
2
3
4
5
75
Mean
6
I
2
3
4
5
9 . 1 8 5
8 . 7 3 1
8 .185
8 . 7 7 2
6.967
9 . 7 8 2
8.268
8.451
7.702
7 . 6 6 5
8.369
7.371
8.782
7 . 9 0 6
1 3 . 8 6 5
13.263
13.277
1 3 . 5 3 6
1 2 . 4 7 9
1 3 . 3 6 6
1 3 . 6 4 5
13.261
14 . 1 0 3
13.115
13.182
12 . 0 0 4
1 3 . 4 4 5
12.827
1 7 . 6 4 3 18 . 8 0 7 19 . 1 0 3
16.972
18.0.34
17.073
16i761
1 9 . 3 2 9
17 . 5 4 1
19 . 2 3 3
1 8 . 9 4 4
1 8 . 4 1 6
1 7 . 2 6 9
1 9 . 3 0 7
1 9 . 3 6 3
20.035
18.636
19.139
17.582
21.620
19.311
1 7 . 6 1 8
; 18.755
1 9 . 3 8 7
15.075
14.888
15.411
1 5 . 1 8 1
1 5 . 5 2 9
15.212
16.282
15.425
16. 5 1 1
14.527
1 5 . 9 2 9 15 . 4 3 6
'
1 6 . 7 6 0 1 6 . 6 7 8 1 7 . 9 5 6
1 5 . 3 7 9 1 5 . 8 6 9 15.434
Mean
7 . 9 6 6 13.113
1 5 . 3 4 0 1 5 . 7 3 3 16.174
LSD O eQ5= 0.74, LSD
0
.01= 0o98

74
Table 18» Analysis of Variance of Cumulative
Water Use Datae
M .
S .
F-VALUE P-VALUE SOURCE DF S eSe
Blocks
Water
5 2 5 . 0 9
2 7 6 .23
nitrogen 4 1 1 8 5
.
NxWater 8 2 6 . 3 3
Residual 70 2 8 .92
5 . 0 1 8
3 8 . 1 2
2 9 6 » 2
3 . 2 9 1
.4131
92.27
7 17.1
7.967
<.001
<.001
< » 0 0 1
Figure 17 « Regression Analysis of Cumulative Water Use,
Combination Regression Equations
CWU = 1 2
,
0 7 0
-
1 3 , 7 3 0 [log (MAD +
I)] + 3 8 , 7 3 0 log CN + I)
Where s CWU = cumulative water use in l/pot
MAD in percent
N in g/pot
/ ■
Fits var „ r-partial B
MAD
- . 5 3 7 3
N
Intercept =
1 2 , 0 7 0
.9373
R-squared =
.8843
-13730,
3 8 3 7 0
.
SE (B) P-value
6 2 2 1
4 1 1 8
.
.047
.
< » 0 0 1
Table 19» Analysis of '
Water Use,
•
Source Def e .
Regress, 2
Residual 1 2
Total 14
1 . 8 9 8
2 . 4 8 4
2 . 1 4 6
E+7 45.85
<.001
E
+7 2 . 0 7 0 E
+ 6
E
+ 8
M .
S .
E
+ 8 9
.
4 9
CI
F-value P-value

75
Table 20o Greenhouse Water Use Efficiency=,
(Water use efficiency in g grain/1 water,)
Individual Plot Data
W Rate (g/pot)
MAD Rep,
(%)
25 I
2
3
'4
5
6
0 . 0
.2565
.3313
.2815
.2990
.2612
.2544
0 , 2
.4231
.3786
.3840
.3847
.3690
.3800
0.4
.5606■
.5544
.5708
.5316
.4844
.5310
0.6
0.8
.6538
.5752
.5500
■
.5872
.6557
.6349
.6375
.6825
.4762
.6227
.6403
.6487
50
Mean
I
2
3
4'
.
6
5
.2807
.2818
,3005
.2645
.2899
.2556
.2842
75
Mean
I
2
3
4
5
6
.2794
:
.2623
.3301
.2569
.3242
.2642
.2593
Mean .2828
.3866
.4049
.3721
.3783
.3606
.3486
.3642
.3715
.3531
.4674
.4225
.3982
.3845
.4218
.4079
.5388
.4755
.5406
.4990
.6002
.4936
.5581
.5278
.5718
*6643
.7092
.6133
.5913
.6548
.6341
.6399
.6858
.6134
.6342
.6063
.5506
.6523
.6238
.6963
.5875
.6089
.5366
'.6761
.6683
.6573
»6385
'
.6309
.7232
.7387
.6405
.7014
.7854
'
.7952
.
.7197
.6789
.4783
.6872
. 8 476
.6071
.7148
LSD 0,05s .0605, LSD ■o.ois «08°3

Table 21o Analysi? of Variance of Water
Use Efficiency Data0
SOURCE DF s*s* MVS.
F-VALUE . P-VALUE
Blocks
Water
Nitrogen
NxWater
4
8
Residual 70
5
2
*01087
.06890
1 . 9 4 6
.03948
.1934
.00217
.03445
12.47
.4865
176.1
*00493
.00276
1 . 7 8 6
< . 0 0 1
<*001
.094
Figure 18V Regression Analysis of Water Use Efficiency*
Combination Regression Equations
WUE = *2498 + *1174 MAD + *4943 N
Where: WUE = water use efficiency in g grain/1 water
MAD in percent
N in g/pot
Fits var* r-partial
MAD .
N
Intercept= *2498
R-squared= *8814
*4183 '
*9372
B SE (B) P-value
.1174
*07358 : .137
*4943 *05310 <*001
Table 22* Analysis of Variance for Regression of
Water use.Efficiency*
Source
Regress*
D
0
f e
2
Residual
1 2
Total 14
.
. 3 018
.04050
.3424
M * S .
. 1 5 0 9
.00338
F-value .
P-value
4 4 . 6 0
< * 0 0 1

77
Table 23. Greenhouse Above Ground Dry Matter Yield.
(Yield in g/pot.)
Individual Plot Data
N Rate (g/pot)
MAD Rep.
(%)
25
6
I
2
3
4
5
50
Mean
I
2
3
4
■
6
5
0.2
6.7
.
6.8
6.0
5.6
6.5
6.5
6.3
7.2
6.9
6.5
6.6
6.5
17.1
14.3
16.1
14 .8
15.3
13.8
6.6
■ 15.2
15.6
15.7
14.5
14.6
12.6
13.9
25.0
24.3
25.1
24.1
2 4 . 6
23.9
23.5
4 . 4
26 . 7
24.1
25.5
24.6
25.4
23.8
30.5
28.8
30.4
31.5
2 9 . 0
32.4
30.4
30.6
30.7
28.7
29.3
29 . 8
3 0 . 6
0.8
31.2
.
32.2
27.3
34.5
35.0
34.3
32.4
33.4
31.5
34.7
32.6
35.5
35.8
75
Mean
6
I
2
3
4
5
Mean
6.3
5.7
6.2
6.4
5.7
5.9
6 . 0
14.5
15.6
16.4
16.0
14.4
14 . 4
14.6
15.2
24 . 3
22.4
2 4 . 1
25 . 9
23.1
2 3 . 6
23.9
23.8
2 9 . 9
2 6 . 1
27.5
2 6 . 1
30.6
2 9 . 2
25.3
27.5
33.9
32 . 2
28.1
31.7
30.8
31 . 5
28.2
30.4
LSD O eO S ss LSD o . 0 1 = 2 e ^

SOURCE DF S.S.
78
Table 24o Analysis of Variance of Above Ground Dry Matter
Yield Data.
M.S.
F-VALUE P-VALUE
Blocks
Water
2
5 2.251
33.36
Nitrogen 4 8221.
NxWater
8
41.69
Residual 70 139.3
0.450
16.68
2055.'
5.212
1.990
< . 0 0 1
< . 0 0 1
.014
■■
8.382
1033.
2.619
Figure 19. Regression Analyses of Above Ground Dry Matter
Yield.
Combination Regression Equations
AGDM = 8.630 - 9.144 (log MAD + I) + 105 (log N + I)
Where: AGDM = above ground dry matter yield in g/pot
MAD in percent
N in g/pot
Fits vat.
MAD r-partial
-.3530
N
Intercept= 8.630
.9885
R-squared= .9773
B
-9.144
105.0
SE (B)
6.997
4.632
P-value
.2158
< . 0 0 1
Table 25. Analysis of Variance for Regression of Above
Ground Dry Matter Yield.
Source
Regress.
Residual
1 2
Total
D.f.
2
14
S.S.
1351.
31.43
1383.
MoSe
675.7
2.619
F-value
258.0
P-value
< . 0 0 1

Table 26. Greenhouse Grain Yield. (Yield in g/pot.)
MAD
(%)
25
Rep.
o.o
6
2
I
2.39
2.78
3
4
2.57
2 . 6 7
5 ■ 2.65
2. 3 4
Mean 2.57
50
6
I
2
3
4
5
2 * 4 6
2.46
2.32 .
2. 0 2
2 . 5 0
2.35
Individual Plot Data
Q
.2
6. 0 4
4.97
5 . 7 3
5.27
5.14
5.03
N Rate (g/pot)
,
0.4
■
0.6
0.8
1 0 . 3 2
9.53
1 2 . 1 9
■
1 0 . 1 0
■
10*23
11.23
10.32
8 . 9 8
' 12.15
8.06
11.61
8. 7 0 12.43
13.41
9. 2 0 1 3 . 2 9 1 3.22
■ •
•
5 . 3 6
• 9.51
1 2.05
11.29
5.37
4.94
5.12
4.50
4. 6 6
4.97
8.07
9. 7 5
8.52
10.06
9. 5 4
9.79
.
1 3 . 1 9
■
1 2 . 2 0
11.62
1 0 . 0 0
1 1 . 6 8
10.47
1 0 . 6 3
1 2 . 6 3
12.94
11.75
14.21
12.33
Mean 2.35
4.93
1 1 . 7 0 12.24
75 I
2
3
4
5
6
Mean
2 . 0 2
2 . 5 3
2 * 1 5
2 . 3 9
2 . 3 2
2 . 0 5
2.24
4. 9 8
6.13
5.57
4. 7 8
5.17
5. 4 1
5.34
9.29
8.62
9. 8 9
10.93
8.91
9.91
10.07
9.72
10.57
11.23
10.67
12.51
1 2 . 3 2
7.59
10.81
13.80
9* 8 8
13.13
11.11
12.19
9.37
11.58

80
Table 27. Analysis of Variance of Grain Yield Data.
SOURCE DF S.S.
F-VALUE P-VALUE
Blocks
Water
Nitrogen 4 1218.
NxWater
5
2
8
Residual 70
3.102
0.761
8.548
81.31
Ms S.
0.621
0.381
304.5
1,069
1.162
0.328
262.1
0.920
.727
<.001
.506
Figure 20. Regression Analysis of Grain Yield.
Combination Regression Equations
GY = 2.871 - 1.468 (log MAD I) 39.77 (log N +1)
Where: GY = grain yield in g/pot
MAD in percent
N in g/pot
Fits var. r-partial
MAD
N
Intercept= 2.871
R-squared= .9448
-.1005
,9720
B
-1 .4 6 8
39.77
SE (B) P-value
4.195
.7325
2.777
<.001
Table 28. Analysis of Variance for Regression of Grain
Yield.
Source
Regress.
Residual
2 '1 9 3 .2
12 11.29
Total
D.f.
14
S .S .
204.5
96.60
.9412
F-value
102.6
P-value
<.001

81
Table
2 9
. Greenhouse Harvest Index.
'
MAD
(%)
25
50
75
Rep.
I
2
3
4 ■
5
6
Mean
I
2
3
4
5
6
0 . 0
.379
.388
.372
.411
.402 .
■ ■ ;
.385
.387
.382
.387
.381
.385
.382
Mean .371
I
2
3
4
5
6
.354
.408
.336
.419
.393
.342
Mean , .375
Individual Plot Data
N Rate (g/pot)
.353
.348
.356
' .356
.336
.364
.352
.344
.315
.353
.308
.370
.358
.341
.319
.374
.348
.332
.359
.371
.351 •
.387
.395
.405
.365
.343
.387
.383
.385
.410
.422
.386
.420
.421
.407
.380
.332
.388
.354
.409
.399
,417
0.6
0,8
.400
.351
.400
.386
.328
.349
.295
.337
'
.429
.383
.410 ' ' .385
,396 .346
.431
.379
.407
.357
.357
.4113
.391
.405
.408
.409
.409
.422
.300
.392
.365
.317
.374
.360
„400
.344
.379
.360
.429
.352
.414
.361
.387
.332
LSD e01= '0437

82
Table 30. Analysis of Variance of Harvest Index Data.
SOURCE DF s .
s .
M.S. F--VALUE P-VALUE
Blocks
Water
5
2
Nitrogen 4
NxWater 8
Residual 70
.002767
.002257
.02625
.004821
.05714
.000553
. 0 0 1 1 2 9 1.383
.006562' 8 . 038
. 0 0 0603 0.738
. 0 0 0 8 1 6
.2567
<.001
.6591
■

83
Table 31, Greenhouse Grain Protein Percentage,
(14% moisture basis.)
Individual Plot Data
MAD
(%)
25
Rep.
0.0
0.2
I
2
3
4
5
6
Mean 13.4
50 .
I .
2 13.3
3 .
12.2
4
5
14.0
13.4
6 13.7
13.1
12.2
12.7
14.0
12.2
12.2
13.6
12.1
13 .6 ■ 13 .6
13.5
13.8
12,7
12.5
13.0
12.2
13.1
13.4
14.2
75
Mean
I
2
3
4
5
6
Mean
13.5
13.8
13.8
12.8
13.3
12.9
13.5
13.4
13.1
12.6
12.2
11.2
13.7
14.3
14.1
13.0
M Rate (g/pot)
0.4
12.3
12.7
1 2 .2
12*0
13.8
0 ,6
12.6 '
13.5
1 3 ,0
12.8
14 ,4
13.9
12.7
12.7
1 2 .7
1263
13.1
13.8
13,8
1 3 .1 ;
11 .9
1 2 .2
14.5
13.7
13.7
13 .2
13.4
13.0
12i8
12.3
14 .4
15.4
13.7
13 .6
1 3 .5
13.5
12.8
14.8
1 4 .3
15.7
14.1
LSD 0.05= 0.72, LSD 0e01= 0e96
0.8
16.0
14.1
14.5
15,4
14.8
15i9
15.1
166 0 '
14,4:
14.6
15.6
15.7
14.7
15.2
1 4 .0
13,8
14.8
1 5 .9 • ..
16.4

Table 32. Analysis of Variance of Grain Protein Percentage
Data.
SOURCE DF M.S.
F-VALUE P-VALUE
Blocks
Water
5
2
Nitrogen .
NxWater 8
Residual 70
S.S.
24.15
1.419
58.28
1.692
27.52
4.830
. ’ 8.7093
14.57
0.2116
0.3931
1 . 80 4
37.06
0.538
.170
<.001
.825
Figure 21. Regression Analyses of Grain Protein Percentage.
Combination Regression Equations
Protein = 12.51 + 0.6 MAD + 2.083 N
Where: Protein in percent at 148 moisture
MAD in percent
N in g/pot
Fits var.
r-partial
MAD
N
.2154 .
.7279
Intercept= 12.51 .
R-squared= .5403
B
.6000
2 .0 83
SE (B)
.7851
,5666
'
P-value
.459
.003
Table 33. Analysis of Variance for Regression of Grain
Protein Percentage.
Source
Regress.
Residual 12
Total
D.f.
2
14
S e Se
5.433
10.06
M.S. '
2.717
0.3852
•
F-value
' 7.052
■■ ■- ■
P-value
.009

85
Table 34. Greenhouse Heading Datec (Days post-emergence®)
MAD
(S)
25
Rep.
50
Mean
I
2
3
4
5
6
Mean
75 I
2
3
4
5
6
Mean
I
2
.3
4
5
6
62
-
63
—
-
0.0
62.5
61
-
62
—
61.5
-
— *
61
61
61.0
Individual Plot Data
N Rate (g/pot)
.
—
—
0.2
-
62
-.
61.5
-
—
61
-
63
62.0
61
61 '
- ’
61.0
LSD OoOSss 2.6, LSD O.Ol” 3o5
0.4
61
—
-
67
64.0
—
64
T
61
62.5
—
-
62
61
61.5
0.6
—
-
67
67
-
67.0
61
63
—
—
62.0
-
61
—
64
62.5
64.0
62
-
62
—
62.0
0.8
-
67
—
—
-
61
64.0
61
—
67

8 6
Table 35. Analysis of Variance of Heading Date Data.
F-VALUE P-VALUE SOURCE DF
Water
2
Nitrogen 4
NxWater
8
Residual 15
24.80
24.87
20.53
69.00
MeSe
12.40
6.217
2.567
4.600
2.696
1.351
0.558
.099
.297
.796 •

87
Table 36. Greenhouse Duration of Heading Period. (Days.)
MAD
(%)
25
Rep.
0 . 0
6
I 5
.
-
2
3
6
4 ' -
5
-
50
Mean
6
I
2
3
4
5
Mean
75
,
I
2
3
*&
5 '
1 0
6
-
-
6
5.5
5.5
-
6
-
■
5
— t
€W
Individual Plot Data
N Rate (g/pot)
0 . 2
0.4
0 . 6
—
-
6
5
-
6
6
-
™
W .
6
6
— ■
"
6
6 . 0
6
6
—
5.5
■
6
—
-
■
-
6 . 0
6
7
6.5
-
■ —
6
7
-
6
-
8
—
-
«*•
6
—
■
8
7.0
'
Mean
8 . 0 6 . 0
.
LSD q .05=
1
.
4
,
LSD
0
.
01
= l o 9
6.5
7.0
0 . 8
5.0
7
7
6
-
—
8
7.0
6
.
-
—
—
4

88
Table 37. Analysis of Variance of Duration of Heading
Period Data. ■
SOURCE DF ■ S .
S .
M.So
F-VALUE P-VALUE
Water
2
Nitrogen 4
NxWater
8
Residual 15
5.400
2 .133
1 0.27
1 8 . 5 0
2 . 7 0 0
0.5 3 3 3
1 . 2 8 3
1 . 2 3 3
2.189
0. 4 3 2 4
1 . 041
.145
.785
.450

89
Table 38o Greenhouse Plant Height= (Height in cm.)
Individual Plot Data
MAD
(%>
25
Rep.
3
4
5
2
I '
6
59
55
58
56
58
Mean 56.8
50
6
I
2
3
4
5
53 '
61
52
53
54
58
Mean 55.2
75
6
I
2
3
4
5
57
56
55
58
55
59
81
79
, 74
78
80
76
74
76
78
79
80
78
76
80
76
83
75
68
76.3
Mean 56.7
77.5
LSD 0.05=
3
.
1 4
,
LSD
0
.
01
*
4.17
86
86
88
87
84
82
8 5 . 5
78
85
85
83
80
84
82.5
0,4
85
85
84
87
89
85
85.8
80
83
82
85
79
86
82.5
83
86
84
90
87
86
86.0
0.6
81
88
83
84
86
79
83,5
84
86
84
87
84
85
79
80
75
90
75
77
79.3
80
89
83
89
86
84
85.2

\
90
Table 3 9
.
Analysis of variance of Plant Height Data.
SOURCE DF .
S .
S .
M .
S . F-VALUE P-VALUE
Blocks
Water
Nitrogen
NxWater
Residual
5 1 8 7 . 4
2 8 3 . 2 9
4 1 0 3 8 0
.
8
1 4 7 . 4
70 521.4
3 7 . 4 8
4 1 . 6 4 5.591
2 5 9 5
1 8.42
2.473
7
.
3 4 8 i4
.
4 4 9
•
.006
<.001
. 0 2 0
Figure 22 . Regression Analyses of Plant Height.
Combination Regression Equations
Height.= 65.69 - 14.14 log MAD + 100.7 log N
Where: Height in cm
MAD in percent
N in g/pot
Fits var . r-partial
MAD - . 1 4 2 2
N
Intercept= 65.69
.8396
R-squared= .7068
B SE (B) P-value
-
1 4 . 1 4 2 8.41
.628
1 0 0 . 7 18.81
<.001
Table 40.
Analysis of
Height.
Variance for Regression of Plant
Source
Regress.
Residual
Total
D.f. S .
S .
2 1 2 4 9
.
12 5 18.2
14 1 7 6 7
.
.
M.S.
6 2 4 . 4
4 3 . 1 8
F-value P-value
14.46 .001
V
'
; ■;
I

91
Table 41o Greenhouse Heads per Plante
MAD
($)
25
Repe
6
I
2
3
4
5
50
Mean
6
I
2
3
5
Mean
1 , 0 0
75
6
I 1.0
2
.
1.0
3
4
1.0
1.0
5 IoO
1.0
Mean
1 , 0 0
1.02
1.0
1.0
1.0
1.0
1.0
1.0
0.0
1
,
0
.
1.0
1 , 1
1.0
1.0
1.0
Individual Plot Data
N Rate (g/pot)
1.00
1 , 0 2
1 , 0
1.0
1 , 1
1 . 0
1 , 0
1.0
1.0
1.0
1 , 1
1.0
1.0
1.0
0.2
1.0
1.0
1 . 0
1.0
1 , 0
1.0
1 , 0 2
1.0
1 , 0
1 , 0
1 . 1
1.0
1 . 0
0.4
1.0
1.0
1 , 1
1 , 0
1 . 0
1.0
1 , 0 2
1.02
1.1
1.1
1 . 1
1.0
1.0
1. 0 5
0.6
1.2
1.4
1.2
1 . 6
1.5
1 . 0
1 . 2 1.8
1 . 0 1 . 6
'
1 . 1
-
1.9
1.18
1.57
1.1
1.4
1.0
1.3
1.3
1 . 1
1.20
1 . 1
1.3
1.0
1 . 2
1.3
1.8
1 , 2 8
0.8
1.8
1 . 6
1.6
.
1.4
1.5
1.5
1.57
1.0
1.0
1.6
1.5
1.7
1.7
1.42
LSD ,18 f
LSD £
I o O i si 0 e 2 4

Table 42. Analysis of Variance of Heads per Plant Data.
SOURCE DP s .
s .
M.S.
F-VALUE P-VALUE
Blocks 5
Water
2
Nitrogen 4
NxWater
8
Residual 70
0 . 0 7 0 3 3
0 . 0 0 0 6 7
3 . 5 0 2
0. 1 3 0 4
1.718
0 . 0 1 4 0 7
0 . 0 0 0 3 3
0 . 8 7 5 4
0 . 0 1 6 3 1
0 . 0 2 4 5 4
0. 0 1 3 6
35.67
0.6644
'
.987
< .001
.722

93
Table 43o Greenhouse Number of Seeds per Head„
Individual Plot Data
MAD
(%)
25
1
Rep.
0 . 0
I 8.9
2 9.3
3 9.7
4 .
9.5
5 9.6
6 7.3
Mean 9.05
50
6
I
2
3
4
5
9.6
8 . 6
8.5
6.6
9.5
8 . 2
75
Mean
6
I
2
3
4
5
Mean
8.50
7.6
9,1
8.2
8.5
8.0
6.9
8.05
0.2
22 . 2
14.9
20.9
18.6
17.6
17.1
18.55
19.8
16.8
16.7
13.7
15.6
16.9
16.58
19.1
23.5
17.5
16.2
1 8 i
0
16,6
18.48
0.4
34.9
31.2
3 0 . 5
26.8
2 5 . 5
35.7
2 9 . 2 7
2 5 . 9
3 0 . 2
27.0
2 6 . 1
32.6
31.8
2 8.93
2 5 . 2
27.7
30.1
24.4
30 . 5
31.3
2 8.20
2 9 . 7 8
29 . 3
27.8
3 1 . 6
28.3
2 6 . 5
13.8
26,22
0.6
32.7
2 2 . 9
32.7
30.5
3 7 , 6
3 5 . 4
30.97
36.6
23 . 5
3 5
«
8
27 . 6
22.3
32.9
26.62
4 1 . 9
32.8
25.7
23.9
19.9
19.6
27.30
0.8
23.3
26.6'
34.4
24.7
29.8
26,0
27.47
20.4
23.0
29.3
30.2
28.1
28.7

94
Table 44. Analysis of Variance of Seeds per Head Data.
SOURCE DF M. S. .
F-VALUE P-VALUE
Blocks 5 116.7
Water
2
4 1 . 6 4
Nitrogen 4 ' 5832.
NxWater
8 8 3 . 4 4
Residual 70 1189.
2 3 .34
2 0 .82
1 4 5 8
.
1 0 .43
1 6 .99
1 . 2 2 5
85.81
,
0.6138
.300
<.001
.765

.95
Table 45, Greenhouse Mass per Seed, (Mass in mg,)
MAD
(%)
25.
1
Rep.
I
2
3
4
5
6
.
0 , 0
2 6 . 8 5
2 9 . 8 9
2 4 . 0 2
28.11
2 7 . 6 0
32.05
28 .09
Individual Plot Data
N Rate (g/pot)
0 . 2
27.21
3 3.36
27.42
28.33
2 9 . 2 0
2 9.42
2 9 .16
0.4
2 9 . 5 7
30.54
3 0 .81
3 3 . 5 1
3 4 . 1 2
34.46
32.17
0.6
3 1 . 1 0
3 1 . 4 6
3 0 . 9 9
3 3 .20
3 3 . 0 6
3 4 .16
3 2 .33
50
Mean
6
I
2
3
4
5
25.63
27.12
28*60 ■
27.29
3 0 . 6 1
27.83
32.85
26.32
2 8 . 6 6
'
29.87
29.41
27.85 .
29.41
75
Mean
6
I
2
3
4
5
2 6 . 5 8
27.80
2 6 * 2 2
28.12
2 9 . 0 0
2 9 . 7 1
27.90
26.07
26.09
2 9.01
2 9 .51
28.72
32.59
Mean 28.67
LSD q oos= 1,45, LSD Q eQl” 3,19
3 1 . 1 6
32.28
31.56
3 5 . 0 5
2 9 . 2 6
3 0 . 7 9
3 1 . 6 8
31.12
35.70
3 3.02
3 3 . 2 5
3 2 . 4 9
32.17
3 2 . 9 6
3 2 . 7 3
35.32
3 2 . 6 3
2 9 . 1 6
3 6 . 6 6
3 4 . 8 9
3 3 .57
3 2 . 8 3
3 1 . 1 1
3 3 . 7 7
3 6.79
3 5 .81
3 0 . 6 0
3 3 . 4 9
0.8
27.43
28.15
23.43
26.15
28.17
26.76
26.68
33.15
27.17
27.65
27.78
33.67
28.67
2 9 . 6 8
32
.
94
30.12
31.95
31.03
36.07
28.14
31.71

Table 46.
Analysis of Variance of Mass per Seed Data.
SOURCE DF ■ s .
s .
M. So F-VALUE P-VALUE '
Blocks 5
Water
2
Nitrogen 4
NxWater
8
Residual 70
5 6 . 1 0
2 4 . 1 2
3 5 5 . 8
6 5 .26
3 0 3 . 9
1 1 . 2 2
1 2 . 0 6
8 8 . 9 5
8 . 1 5 7
4 . 3 4 2
2 . 7 7 8
2 0.49
1 . 8 7 9
.067
< . 001
.077

APPENDIX B
FIELD PLOT RAINFALL RECORD

98
Table 47 .
Field Plot Rainfall Record
Period day-month
Cumulative Rainfall
Amount for Period
(mm) from to
— —
1 1 - 6
16-6
18-6
23-6
29-6
8-7
16-7
28-7
5-8
1 2 - 8
2 1 - 8
Total
1 1 - 6
16—6
18-6 .
23-6
29-6
8-7
16-7
2 8 - 7
5-8
1 2 - 8
2 1 - 8
25-8
0 . 0
31.0
0 . 0
0.9
1.3
7.7
1 1 . 9
0.8
3.6
7.1
17.8
1 . 0
83.1

LITERATURE CITED

1 0 0
Bauder
7
J
0
W. 1981. Irrigating with limited water supplies.
Montana Coop. Ext. Serv
0
Circular No.1262,
_________.., R.J. H a n k s
7
and D.W. 1975. Crop production function determinations as influenced by irrigation and nitrogen fertilization using a continuous variable design. Soil Sci. Son. Am. Proc. 39:13.87-92.
L
0
D
0
King, and G
0
L
0
Westesen. 1982. Scheduling irrigations with evaporation panSi Montana Coop. Ext.
Serv. Bull. No. 1262.
B r o w n
7
P
0
L
0
1971. Water use and soil water depletion by d r y l a n d w i n t e r w h e a t as a f f e c t e d by n i t r o g e n fertilization, Agron
0
J
0
63:43^6.
D o w n e y
7
L
0
A. 1 9 7 2
.
W a t e r - y i e l d relatio n s h i p s for non-forage crops, J . Xrr. and Drain. Div., Proc
0
Am.
Soc
0
Civ. Engin, 98 (IRl):1 07-15.
Dubetz
7
S, 1961. Effect of soil type, soil moisture, and nitrogen fertilizer on the growth of spring wheat. Can.
J. Soil Sci. 4 1
:
4 4
-
5 1
.
E h l i g
7
C.F. and R.D, LeMert. 1976. W a t e r use and productivity of wheat under five irrigation treatments.
Soil Sci. Soc
0
Am. J
0
40:750-755.
Eisenhauer
7
D
0
E
0 7
P
0
E
0
Fischbach
7
W.L. T r i m m e r
7
and ReA
0
Krohn
0
1980. Irrigation scheduling using soil moisture blocks on deep soil. Ch
0
C
0
p.1-19. JLa Irrigation scheduling notebook. Third Rev. Nebraska Coop. Ext,
Serv., Lincoln, Nebraska. .
Fox
7
R
0
L
0
1973. Agronomic investigations using continuous function experimental designs - nitrogen fertilization of sweet corn, Agron, J. 65:454-6.
H a n k s
7
R
0
J
0 7
J. Keller, and: J.W, Bauder. 197 4. Line source sprinkler plot irrigator for continuous variable.
water and fertilizer studies on small areas. Dept
0
of
Agric. and Irr
0
Engin., Utah State Univ., Logan
7
Utah.
Pub. No. 211 (d)-7.

101
________ J. Keller , VePe Rasmussen, and GoDgWilson 6 1976.
Line source sprinkler for continuous variable irrigation-crop production studies. Soil Sci
0
Soc. Am.
J. 40s426—9 o
____ ■
1980. Statistical analysis of results from continuous variable designs. Soil Sci. Soc. Am. J. 44:886-887.
Hundtoft, EoBo, and I-Pai Wu. 1974. Continuous function designs for d e m o n s t r a t i o n and research. Hawaii
Coop. Ext. Serv. Misc. Pub. 115.
Lawes J.B. and J.H. Gilbert. 1880. Our climate and our wheat-crops, j. Royal Agric. Soc. (s.2) 16s173-210.
Lund, R.E. 1979. MSDSTAT — an interactive statistical analysis package. S t a t i s tical C e n t e r , Dept. Math
Sci., Montana State Univ., Bozeman, Montana. Tech. Rep.
Printed Jan. 1982.
Mitchell, R.L. 1970. Crop growth and culture. Iowa State
Univ. Press, Ames, Iowa.
Mogensen, V.O. 1980. Drought sensitivity at various growth stages of barley in relation to relative evapotranspira- tion and water stress. Agron. J. 72$1033-1038.
Robins, J.S. and C.E. Domingo. 1962. Moisture and nitrogen effects on irrigated spring wheat. Agron. J. 54:135-
138.
Salter, P.J., and J.E. Goode. 1967. Crop responses to water at different stages of growth. Research Review
No. 2. C o m m o n w e a l t h B u reau of H o r ticulture and
Plantation Crops. Commonwealth Agricultural Bureaux,
Parnham Royal, Bucks, England.
S c h r e i b e r , H.A., and C.O. S t a n b e r ry. 1965. Barley production as influenced by timing of soil moisture and timing of nitrogen applications. Agron. J. 57:442-5.
Singh, S.D. 1981. Moisture sensitive growth stages in wheat. Agron. J. 73:387-391.

102
Slavik, B e 1965o Response of cereals and grasses to water. p.22 /-40 I r
FoLo Milthorpe and J.D, Ivins (eds.) The growth of cereals and grasses. Proceedings of the 12th
Easter School in A g r i c u l t u r a l science. Univ. of
Nottingham. Butterworth, London.
Sosulski, F.W., E0A0 Paul, and W.L. Hutcheon. 1963. The influence of soil moisture, nitrogen fertilization, and temperature on quality and amino acid composition of thatcher wheat. Can. J. Soil Sci. 43s219*^28.
Stegman, E.C., L.H. Schiele, and A. Bauer. 1976. Plant water stress criteria for irrigation scheduling. Trans.
Am. Soc. Agrici Engin. v.19 no.5:850-5.
Thompson, T.L., and P.E. Fischbach. 19 80. IRRIGATE - a computerized irrigation scheduling program. Ch. C, p. 34-55. Jn Irrigation scheduling notebook. Nebraska
Coop. Ext. Serv. Lincoln, Nebraska.
Trimmer, Walter. 1980. Irrigating wheat. Ch. B, p.26-30.
In irrigation scheduling notebook. University of
Nebraska Coop. Ext. Serv. Lincoln, Nebraska.

M O N T A N A S T A T E U N I V E R S I T Y L I B R A R I E S stks N378.W1545@Theses
Irrigation and nitrogen effects on Wampu
RL
3 1762 00112305 6