• TABLE OF CONTENTS
HIDE
 Main
 Appendix 1
 Appendix 2
 Appendix 3
 Appendix 4






Title: Differential establishment characteristics of Florigraze Rhizoma peanut
CITATION THUMBNAILS PAGE IMAGE ZOOMABLE
Full Citation
STANDARD VIEW MARC VIEW
Permanent Link: http://ufdc.ufl.edu/UF00094261/00001
 Material Information
Title: Differential establishment characteristics of Florigraze Rhizoma peanut
Physical Description: v. : ; 28 cm.
Language: English
Creator: Proenca, M.
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 1987
Copyright Date: 1987
 Subjects
Subject: Peanuts -- Florida   ( lcsh )
Genre: non-fiction   ( marcgt )
Spatial Coverage: United States of America -- Florida
 Notes
Bibliography: Instructor: Dr. P. Hildebrand.
General Note: "September, 1987."
Statement of Responsibility: M. Proenca.
 Record Information
Bibliographic ID: UF00094261
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: oclc - 226376931

Table of Contents
    Main
        Page i
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
    Appendix 1
        Page i
        Page ii
    Appendix 2
        Page iii
        Page iv
        Page v
    Appendix 3
        Page vi
        Page vii
        Page viii
        Page ix
        Page x
        Page xi
        Page xii
        Page xiii
        Page xiv
        Page xv
        Page xvi
    Appendix 4
        Page xvii
        Page SAS 10
        Page SAS 11
        Page SAS 12
        Page SAS 13
        Page SAS 14
        Page SAS 15
        Page SAS 16
        Page SAS 17
        Page SAS 18
        Page SAS 19
        Page SAS 20
        Page SAS 21
Full Text

Instructor: Dr. P. Hildebrand

Student: M. Proenca

















Differential Establishment


Characteristics

of

Floriqraze Rhizoma Peanut


Gainesville, September,1987.


3g-A Xo02








1


Introduction


"Florigraze rhizhoma peanut (Arachis glabrata Benth.) is a
warm season perennial forage legume having value as both a hay
and grazing crop" (Prine et al.,1981). Other uses of the plant
are, ornamental, and, as a cover crop. Its centre of origin,
according to Herman, is the central region of Brazil. As a crop,
the major attributes of Florigraze are; 1) high quality forage,
2)high nitrogen fixation, 3)drought resistance, 4) until now has
no pests in Florida, and 5) needs little or no fertilizer. Its
less desirable characteristics, at present, are; 1) time
necessary for, and, difficulty in, obtaining a dense homogenous
stand, 2) non-tolerance of waterlogged field conditions (i.e.
Florida Flatwoods), and, 3) slow canopy cover gives weeds a light
competition advantage. Florigraze prefers well drained soils and
a Ph range between 5.8 and 6.5 ( Prine et al.,1981).
The aim of this study is to determine several differential
establishment characteristics of A. qlabrata in a Gilchrist
County, Florida, field. This plot is approximately located
between Newberry and Trenton, two miles north of Highway 26, near
a Flatwoods area. The field has an area of about ten acres, and
was previously planted to watermelon; in one corner of the field,
an area of two acres, Florigraze has established extremely well,
whilst in the remaining acreage the peanut sparsely, if at all,
populates it.
This study will compare the physical (including texture) and
chemical properties of the soils in the two areas of different
stands; rhizome production will also be analysed. From analysis
of the formentioned characteristics, conclusions regarding
Florigraze establishment will be attempted.


Materials and Methods


A total of 11 locations were studied; of these, 6 were in
the area of better establishment, and the remaining 5 in the
lesser populated area. At each of the 11 chosen locations a 1m2
grid comprised by 25 squares was placed on the ground; 4 soil
samples (20cm deep), 4 penetrometer readings and 5 rhizome
samples (35 cm deep) were taken at each location; the same
relative pattern of sampling was maintained, in relation to
magnetic North.
Soil samples within a location will be mixed, so as to
obtain a sample that is more representative of the soil makeup;
chemical and physical analysis will follow. Rhizome samples will
be washed, weighed, dried, and re-weighed, so as to determine
differences in rhizome production between the two areas.







2

Non-randomized sampling was chosen, in the way of making
differences between both areas more discernible. From the
resulting data it may be possible to arrive at general
conclusions that will help better understand factors that
influence Florigraze establishment.
At the outset it was expected that the penetrometer readings
would show the existence of hardpans, more so in areas of poor
establishment. Should a hardpan be present, with, the amount and
distribution of rainfall in Florida, waterlogged conditions would
result, rendering Florigraze establishment less successful.
Allelopathy was eliminated as a possible causal agent of
differential establishment due to the fact that the whole field
had been previously planted to watermelon. If the field had
previously been planted to a wide variety of crops, then
allelopathic interactions would have been an inherent part of our
working hypothesis.
Rainfall was excluded from analysis, as, all the sites are
contained within a rectangular area of 80x170m2 (see map,
appendix 1). Within such a small distance rain fluctuations
between sights is insignificant.
By subjectively labelling sites M or L, depending on more
(M) or less (L) above ground growth, a correspondence between
rhizome and above ground growth would be possible.
Lab analysis results included; rhizome weight, and, soil,
texture, PH, % Organic Matter, and, concentration of Phosphorus
(P205), Potassium (K20), Magnesium (Mg), Calcium (Ca), Soluble
Salts, Zinc (Zn), Copper (Cu) and Manganese (Mn), in parts per
million (PPM).
Each of these factors was then individually related to
rhyzome weight through simple linear regression; the result is of
the type, Y(rhizome weight)= mX(one factor) + C(constant).
Since it is probable that no one isolated factor is respons-
ible for the differential establishment, it was necessary to
conduct an analysis where, all or some, factors intervened. This
was done using SAS, where a stepwise forward/backward routine was
used. From this analysis, equations of the type, Y= Fl(factor 1)
+........+Fn(factor N)+C(constant), resulted. Each of these
equations represent models, and the stepwise procedure results
in the best model for any number of factors, or variables.

Results


Rhizome weight was determined after drying samples at 700C
for 24 hours. The weights varied between 0.35 and 1.99g.
Significant relation between observed above ground (M or L) was
established.










Weight (q)


1.99
1.91
1.48
1.29
1.18
0.90
0.81
0.52
0.48
0.47
0.35


Yield Yield
(mT/ha) (lbs/acre)


1.96
1.88
1.46
1.27
1.16
0.89
0.80
0.51
0.47
0.46
0.34


1750
1680
1300
1140
1040
0790
0710
0460
0420
0410
0310


Penetrometer Readings are graphed at the end of the report,
under appendix 2; these results show little or no relationship
between the existence of a hardpan and reduced rhizome growth.



Texture classified all samples as sands.



Note; all of the following results are tabled further along
under this tittle. The results of the simple and selected
multiple regressions follow the table.



Soil Ph varied from 6.0 to 6.8, providing a very narrow
range.



% Organic Matter (OM) varied between 0.54 and 0.93, also
providing a narrow range.



Phosphorus (P205) varied between 16 and 72 ppm.



Potassium (K20) ranged from 8 to 36 ppm.


Magnesium (Mg) ranged from 20 to 104 ppm.


Sample












Calcium (Ca) oscillated between 144 and 440 ppm.

Soluble Salts varied between 28 and 42 ppm. In 8 of the
cases the value was 42, in the remaining 3 it was 28 ppm.

Zinc (Zn) ranged between 0.32 and 2.56 ppm.

Copper (Cu) varied from 0.16 and 1.44 ppm.

Manganese (Mn) oscillated from 1.16 to 2.32 ppm

Table of obtained values;


Weight (g)
1.99
1.91
1.48
1.29
1.18
0.90
0.81
0.52
0.48
0.47
0.35


P205(PPm)
48
40
32
40
28
72
32
44
20
36
16


OM (%)
0.73
0.61
0.61
0.61
0.93
0.54
0.67
0.61
0.73
0.73
0.61


Ph
6.3
6.5
6.1
6.8
6.1
6.0
6.5
6.8
6.7
6.5
6.5


_K20 (Pm)
16
36
12
16
12
24
16
16
20
08
08


Mg(DDm)3
32
32
20
104
32
28
44
36
40
32
28


Sol.Sal.(ppm)
42
42
28
42
28
42
42
42
42
28
42


Ca(Dpm)
272
240
144
440
228
248
336
324
320
296
248


Sample
M6
M4
M5
M2
M3
Ll
Ml
L4
L3
L5
L2


Sample
M6
M4
M5
M2
M3
Ll
Ml
L4
L3
L5
L2








5


Sample Zn(Dpm) Cu(pPm) Mn(pPm)
M6 0.32 0.32 1.80
M4 2.56 0.36 1.88
M5 0.92 0.20 1.44
M2 0.72 0.60 2.32
M3 1.28 0.24 1.68
L1 2.20 1.44 1.96
Ml 0.76 0.68 1.88
L4 0.76 0.48 1.96
L3 0.40 0.16 1.92
L5 0.44 0.68 1.96
L2 0.80 0.20 1.16


Simple linear regression revealed the following Equations
and Coefficients of Correlation (R2), when each of the variables
was plotted against rhizome weight (RW). Each of the resulting
graphs is in App. 3.


OM vs RW; Y11= 0.60 +0.620M; R2=0.02


Ph vs RW; Y11= 2.87 -0.197Ph; R2=8.41 x10-4


Sol.Sal vs RW; Y11= 1.07 -8.6xl0-5SS; R2= 9.4x 10-5


P205 vs RW; Y11= 0.58 +0.012P; R2=0.10

K20 vs RW;Y11 0.46 +0.034K; R2=0.22


Mg vs RW; Y11= 1.52 -0.016Mg; R2=0.090


Ca vs RW; Y11= 1.57 -0.002Ca; R2=0.062


Zn vs RW; Y11= 0.385 +0.49Zn; R2=0.47


Cu vs RW; Y11= 1.14 -0.22Cu; R2=0.02


Mn vs RW; Y11= 0.72 +0.174Mn; R2=0.0085








6


Multiple Linear Regression, using SAS, gave 2 important
models, one for 3, and the other for 2, variables. These models
were:

2 variable; Y11= 0.113 +0.049P -1.853Cu; R2=0.566


3 variable; Y11= 0.061 +0.045P +0.230Zn -1.887Cu; R2=0.631
The analysis using SAS is included in App.4 1.


Discussion


Factors that had little or no influence on the establishment
of florigraze, in this study, were; soil texture, existence of
hardpans, Ph, OM, soluble salts and manganese.
Rhizome weight varied considerably, from 0.34 to 1.96mT/ha
(9310-17501bs/acre, respectively).
As to what regards simple linear regression (see app. 3),
the equation with highest R2 was Zn (0.47), followed by K20
(0.22) and P205 (0.1). All the other variable models explained
less than 10%.

Considering the multiple linear regression, using SAS (app.
4), in the first part, Forward Selection, only P, K and Cu met
the 0.500 significance level; Backward Elimination, the second
part, eliminated all the variables exept P and Cu, considered
relevant at the 0.100 significance level. In the last part, Max-
R2, usually the most important, two models are outstanding; these
are, the best, 2, and, 3, variable models found, with, P and Cu,
and P, Cu and Zn, respectively. These 2 models can be seen on
pages 17 and 18 of appendix 4.

Conclusion


There appears to be a positive relation between the above
ground growth and rhizome weight; in this case the presence of
hardpans did not significantly affect rhizome production; this
may be due to the fact that the texture of the top 20cm of soil
was, in all samples, sand. Soil Ph in the 6.0-6.8 range does not


1 Three variables were excluded from the multiple
linear regression ; 1) Ph for having such a narrow range, 2)
Solution Salts for having such a small R2 value under simple
linear regression analysis, and 3) OM for having such a low
% and range.








7

show significant correlation to rhizome production (the
recommended Ph range is 5.8 to 6.5). OM, Ph, soluble salts and
Mn, have, in their respective ranges present in this field,
little or no effect on Florigraze establishment.
This field appears to have been limed with Dolomitic
limestone, as there is a strong positive correlation between Ca
and Mg.
During the course of the statistical analysis, the nutrients
that appeared to be most critical, in the sampled concentration
ranges, are, Zn, K, P, Mg, Ca and Cu. Further immediate
research, should, in my opinion, study the effects of, soil
texture, hardpans, weed competition, P, K, Cu, OM and Ph, on
Florigraze establishment.


































Appendix 1
Field Map
















*M4


*M3


"M5


-M2


*M1


.tree


-M6


.L5





- L4


.L2


Scale 1:1 000


































Appendix 2
Penetrometer Results; Graphs











70
60
50
40
30
20
10
0



70
60
50
40
30
20
.-10
O
0


70
60
50
40
30
20
10
0


70
60
50
40
30
20
10
M2 0


Small Pt. kgs/cm2









10 20 30 40 50 6

Small Pt. kgs/cm2
I I _


/ \ '\ '












I
10 20 30 40 50 6







Small Pt.kgs/cm2


70
60
50
40
30
20
10
LI 0


Small Pt. kgs/cm2
-- -- I
_____I
___ _I
____________________________________________________ ________________________


10 20


40 50 60


Small Pt. kgs/cm2


10 20


50 60


Small Pt.kgs/cm2




=^^S^=


/___ I _II_


10 20 30 40 50


10 20 30 40 50 60


70
60
50
40
30
20
10
M3 o


- -- ~


_ ii


________________________________________________ ________________________________________________ ________________________________________________ I


/










Small Pt.kgs/cm2


I p


70
60
50
40
30
20
10
0


10 20 30 40 50 60


Small Pt. kgs/cm2




H' i 3





10 20 50 40 50 6_


Small Pt. kgs/cm2


70
60
50
40
30
20
10
0


Small Pt. kqs/cm2


70
60
50
40
30
20
IO
10
L5 0


70
60
50
40
30
20
10
0

































Appendix 3
Simple Linear Regression






Organic Matter vs Rhizome Weight


Yll=o.60+0.62o.m. : R2=0.02


2.00,


1.85.


1.70_


1.55-


1.40


1,25


1.10-


0.95


0.80

(g)
0.65


0.50


0.35-


1M6
M4









,M5



M2



y
M13


YL1


. L4


'L5
L2


ORGANIC


0.70
MATTER (%)


0
0.


50


0.60


0.b0


0. )


--









Ph vs rhizome weight


Y =2.87 0.197Ph
11


R2=8.41x104


M6 '


* M4


Y1
SM5


M2


2.00-


1.85-


1.70-


1.55-


1.40-


1.25


1.10-


0.95 -


0.80-


0.65-


0.50-


0.35-


' LI


. L5


* L3


*L2
___________, -- - - -- - - -- -- -- - -


6.0


6.1
6.1


6.2


6.3


6.4


6.5


6.6


15
5.8


6.7


P h


* M3







Phosphorus vs Rhizome Weight

2
Y = -0.128+0.035P :R2=0.34
10
2
Y =0.58+0.012P :R =0.10

M6
M4


Y10


R
II
I
Z
0
M .
E


W
E
I
G
H
T
(g)


2.00 -


1.85 -


1.70


1.55


1.40


1.25


1.10


0.95


0.80 -


0.65


0,50


0,35


.L4
.L3 L5L4

,L2

16 3 5 7
P205 (ppm)


M3 .


. M5















- ", "--i "~


2.000


1.85


1.70


1.55


1.40


1.25-


1.10-


0.95-


0.80-


0.65-


0.50-


0.35-


0 28


SOLUBLE SALTS


. L5


. M6













, M2





SY11

. LI








* L3


SL2


' '2' - = _" C -,-e -


,L "
: -~.n;rO-'


(ppm)








Potassium vs Rhizome Weight


YL=0.46+0.034K : R2=0.22


2.00-


1.85-


1.70-


1.55-


1.40-


1.25-
(;-)
1.10-


0.95-


0.80-


0.65-


0.50-


0.35
0


M6.


114


M5 .



M2

M3 .


L1.


Ml "


Y11


I4 .


L5


L3.


L2.
,_- -- -i


K20 (ppm)








Magnesium vs Rhizome Weight

Y =1.52-0.016Mg :R2=0.090



2.00 *M6
.M4

1.85 -
R

S 1.70 -
I

1.55
'0 M5
1.40
E
M2
1.25 -
W I .M3

S 1.10
I

G 0.95
H L

T 0.80 m
(g)

0.65 -


0.50- L5 L4 3


0.35- L2' 11
0 I I F---
20 40 60 80 100
Mg (ppm)









Calcium vs Rhizome Weipht



Y11=1.57-0.002Ca : R2=0.062
M6
rIM4.


M5


M3




Y
L1
id'


ul-.
L5. 1J3.


L2 .


20
240


Ca(ppm)


340


2.00-


1.85-

1.70-


S1.55-

1,c-
S1.40-


1.25-


. 1.10-
(g)


0.95-


0.80-


0.65-


0.50-


0.35-
O


140


S/ ()O






Zinc vs Rhizome Weight

2
Y9= 0.32+0.65Zn : R =0.61
2
S10=0.45+0.45Zn :R =0.42
2
Yll=0.385+0.49Zn :R =0.47
2.00_ -M6 /9

M4
1.85
R
il 1.70
I
1.55
S. M5
1.40


1.25 *M2
M3

J; 1.10
J
G, 0.95
i* L1

0.80 M11
(Cg)
0.65


0.50 L3 .L4
'L5
0.35 .L2
0 1
0.30 1.10 1.90 2.70


Zn (ppm)















2.00


1.85


1,70


1.55-


1.40 -


1.25 -


1.10 -


0.95 -


0.80 -


0.65 -


0.50-


0.35 -


0.15


1.55


0.85


Cu (ppm)


Copper vs Rhizome Weight

2
Y =1.14-0.22Cu :R =0.02
11


M6
M4









SM5


M2


M3





SL1


Y11





.L3 .L4 L5


L2
L2
e















2.00-


1.85-


1.70-


1.55-


1.40-


1.25-


1.10-


0.95-


0.80-


0.65-


0.50-


0.35-
0


1.35


1.55


1.75


1.95


2.15


2.35


Mn (ppm)


Manganese ;vs Rhizome Weight

2
Y =0.72+0.174Mn :R =0.0085
11


M6 .

.M4









M5'



M2 .


M3





LY

M1.





L4
L3. L5
L5


2


'L


1.15

































Appendix 4
Multiple Linear Regression






SAS

Forward Selection Procedure for Dependent Variable WEIGHT


Stec 1 Variable K Entered


R-square = 0.22179172


Sum of Squares


Regression
Error
Total


Variable

INTERCEP


Parameter
Estimate

0.45839450
0.03444381


0.75237800
2.63989472
3.39227273

Standard
Error

0.39506886
0.02150627


Mean Square

0.75237800
0.29332164


Type II
Sum of Squares

0.39489105
0.75237800


C(p) = 3.91616268

F Prob>F


2.57 0.1437


F Prob>F

1.35 0.2758
2.57 0.1437


Bounds on condition number:


Step 2 Variable CU Entered


1.0000,


1.0000


R-square = 0.28639614


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
K
CU


Parameter
Estimate

0.58620673
0.03875314
-0.41023361


0.97153383
2.42073890
3.39227273

Standard
Error

0.42844814
0.02242273
0.48204070


Mean Square

0.48576692
0.30259236


Type II
Sum of Squares


0.56645189
0.90384717
0.21915583


C(p) = 5.00993689

F Prob>F


1.61 0.2593


F Prob>F


1.87
2.99
0.72


0.2084
0.1222
0.4195


Bounds on condition number:


Step 3


Variable P Entered


R-square = 0.61873943


Sum of Squares


Regression
Error
Total


Parameter
Estimate


2.09893291
1.29333982
3.39227273

Standard
Error


Mean Square

0.69964430
0.18476283


Type II
Sum of Squares


C(p) = 2.34805715

F Prob>F


3.79 0.0667


1.0537,


4.2150


F Prob>F


Variable







SAS


INTERCEP
p


-0.01277585
0.04220428
0.01895545
-1.71392095


0.41338220
0.01708539
0.01926735
0.64839690


0.00017648
1.12739908
0.17882937
1.29096456


Bounds on condition number: 3.5942, 23.9724


No other variables met the 0.5000 significance level for entry
into the model.


0.00
6.10
0.97
6.99


0.9762
0.0428
0.3580
0.0333







SAS

Summary of Forward Selection Procedure for Dependent Variable WEIGHT


Variable
Entered


Number Partial
In R**2


Model
R**2


c(p)


F Prob>F


1 0.2218
2 0.0646
3 0.3323


Step


0.2218
0.2864
0.6187


3.9162
5.0099
2.3481


2.5650
0.7243
6.1019


0.1437
0.4195
0.0428






SAS

Backward Elimination Procedure for Dependent Variable WEIGHT


Step 0


All Variables Entered


R-square = 0.78613136


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
K
ZN
CU
CA
MG
MN


2.66677198
0.72550075
3.39227273


Parameter
Estimate

1.21778179
0.03302447
0.03381020
-0.21387934
-1.15640713
-0.00739374
0.02261940
0.00424156


Standard
Error


1.57707191
0.02424273
0.04835383
0.58274200
1.08180619
0.00659108
0.01595667
1.19288740


Mean Square

0.38096743
0.24183358


Type II
Sum of Squares


0.14419579
0.44877154
0.11823611
0.03257629
0.27633711
0.30432058
0.48595269
0.00000306


C(p) = 8.00000000

F Prob>F


1.58 0.3841


F Prob>F


0.60
1.86
0.49
0.13
1.14
1.26
2.01
0.00


0.4963
0.2664
0.5347
0.7380
0.3635
0.3436
0.2513
0.9974


Bounds on condition number:


Step 1 Variable MN Removed


10.3142,


329.4


R-square = 0.78613046


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
K
ZN
CU
CA
MG


Parameter
Estimate

1.22083657
0.03305489
0.03387594
-0.21433819
-1.15611831
-0.00738561
0.02263055


2.66676892
0.72550380
3.39227273

Standard
Error

1.14534183
0.01964406
0.03869290
0.49214032
0.93422896
0.00535330
0.01354943


Mean Square

0.44446149
0.18137595


Type II
Sum of Squares


0.20607463
0.51355700
0.13902723
0.03440337
0.27776506
0.34523058
0.50597356


C(p) = 6.00001264

F Prob>F


2.45 0.2025


F Prob>F


1.14
2.83
0.77
0.19
1.53
1.90
2.79


0.3465
0.1677
0.4307
0.6857
0.2836
0.2398
0.1702


Bounds on condition number:


9.0720, 227.9







SAS


Step 2


Variable ZN Removed


R-square = 0.77598877


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
K
CU
CA
MG


Parameter
Estimate

0.87116579
0.03685361
0.01922476
-1.43130483
-0.00564311
0.01954768


2.63236556
0.75990717
3.39227273

Standard
Error

0.74770891
0.01611220
0.01749949
0.62992070
0.00325579
0.01057585


Mean Square

0.52647311
0.15198143


Type II
Sum of Squares


0.20631322
0.79513564
0.18342646
0.78466278
0.45657844
0.51921860


C(p) = 4.14227314

F Prob>F


3.46 0.0995


F Prob>F


1.36
5.23
1.21
5.16
3.00
3.42


0.2965
0.0709
0.3220
0.0722
0.1436
0.1238


Bounds on condition number:


Step 3 Variable K Removed


4.0046,


82.3697


R-square = 0.72191692


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
CU
CA
MG


Parameter
Estimate

0.96867437
0.04414494
-1.58383071
-0.00554457
0.01955320


2.44893909
0.94333363
3.39227273

Standard
Error

0.75511429
0.01493272
0.62493397
0.00331019
0.01075665


Mean Square

0.61223477
0.15722227


Type II
Sum of Squares


0.25872852
1.37403496
1.00986500
0.44110704
0.51951195


C(p) = 2.90075533

F Prob>F


3.89 0.0681


F Prob>F


1.65
8.74
6.42
2.81
3.30


0.2469
0.0254
0.0444
0.1450
0.1190


Bounds on condition number:


Step 4


Variable CA Removed


R-square = 0.59188403


Sum of Squares


Regression
Error
Total


2.00783205
1.38444067
3.39227273


Mean Square

0.66927735
0.19777724


C(p) = 2.72476602

F Prob>F


3.38 0.0834


4.0016,


57.4387






SAS


Variable

INTERCEPT
P
CU
MG


Parameter
Estimate

-0.06574469
0.05051373
-1.92383303
0.00421807


Bounds on condition number:


Step 5 Variable MG Removed
Step 5 Variable MG Removed


Standard
Error

0.48735687
0.01619625
0.66291013
0.00633332


3.0490,


Type II
Sum of Squares


0.00359918
1.92382852
1.66571907
0.08772852


F Prob>F


0.02
9.73
8.42
0.44


0.8965
0.0169
0.0229
0.5267


21.2768


R-square = 0.56602275


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
CU


Parameter
Estimate

0.11311872
0.04919623
-1.85380026


1.92010354
1.47216919
3.39227273

Standard
Error

0.39228725
0.01550588
0.63134508


Mean Square

0.96005177
0.18402115


Type II
Sum of Squares


0.01530130
1.85241687
1.58657282


C(p) = 1.08753001

F Prob>F


5.22 0.0355


F Prob>F


0.08
10.07
8.62


0.7804
0.0131
0.0188


Bounds on condition number:


2.9723,


11.8891


All variables in the model are significant at the 0.1000 level.






SAS


Summary of Backward Elimination Procedure for Dependent Variable WEIGHT


Number Partial
In R**2


6 0.0000
5 0.0101
4 0.0541
3 0.1300
2 0.0259


Model
R**2

0.7861
0.7760
0.7219
0.5919
0.5660


C(p)


6.0000
4.1423
2.9008
2.7248
1.0875


0.0000
0.1897
1.2069
2.8056
0.4436


F Prcb>F


0.9974
0.6857
0.3220
0.1450
0.5267


Stec


Variable
Removed






SAS


Maximum R-square Improvement for Dependent Variable WEIGHT


Step 1 Variable K Entered


R-square = 0.22179172


Sun of Squares


Regression
Error
Total


Variable

INTERCEP
K


Parameter
Estimate

0.45839450
0.03444381


0.75237800
2.63989472
3.39227273

Standard
Error

0.39506886
0.02150627


Mean Square

0.75237800
0.29332164


Type II
Sum of Squares

0.39489105
0.75237800


C(p) = 3.9161626S

F Prob>F


2.57 0.1437


F Prob>F

1.35 0.2758
2.57 0.1437


Bounds on condition number:


1.0000,


1.0000


The above model is the best 1 variables model found.


Step 2 Variable CU Entered


R-square = 0.28639614


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
K
CU


Parameter
Estimate

0.58620673
0.03875314
-0.41023361


0.97153383
2.42073890
3.39227273

Standard
Error

0.428448i4
0.02242273
0.48204070


Mean Square

0.48576692
0.30259236


Type II
Sum of Squares


-0.56645189
0.90384717
0.21915583


C(p) = 5.00993689

F Prob>F


1.61 0.2593


F Prob>F


1.87
2.99
0.72


0.2084
0.1222
0.4195


Bounds on condition number:


Step 3 Variable K Removed
Variable P Entered


1.0537,


4.2150


R-square = 0.56602275


Sum of Squares


Regression 2
Error 8
Total 10


1.92010354
1.47216919
3.39227273


Mean Square

0.96005177
0.18402115


C(p) = 1.08753001


F Prob>F


5.22 0.0355






SAS


Variable

INTERCEP


Parameter
Estimate

0.11311872
0.04919623
-1.85380026


Standard
Error

0.39228725
0.01550588
0.63134508


Type II
Sum of Squares


0.01530130
1.85241687
1.58657282


F Prob>F


0.08
10.07
8.62


0.7804
0.0131
0.0188


Bounds on condition number:


2.9723,


11.8891


The above model is the best 2 variables model found.


Step 4 Variable ZN Entered


R-square = 0.63128687


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
ZN
CU


Parameter
Estimate

0.06134074
0.04474381
0.22998727
-1.88747747


2.14149724
1.25077549
3.39227273

Standard
Error

0.38934342
0.01579419
0.20661499
0.62285442


Mean Square

0.71383241
0.17868221


Type II
Sum of Squares


0.00443520
1.43401170
0.22139370
1.64086090


C(p) = 2.17205044

F Prob>F


3.99 0.0598


F Prob>F


0.02
8.03
1.24
9.18


0.8793
0.0253
0.3024
0.0191


Bounds on condition number:


3.1760,


22.2830


The above model is the best 3 variables model found.


Step 5 Variable MG Entered


R-square = 0.68425513


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
ZN
CU


Parameter
Estimate

-0.21436925
0.04567834
0.28214939
-1.99846993


2.32118000
1.07109273
3.39227273

Standard
Error

0.47641262
0.01581427
0.21296273
0.63231706


Mean Square

0.58029500
0.17851545


Type II
Sum of Squares


0.03614384
1.48935438
0.31334795
1.78320492


C(p) = 3.42904876

F Prob>F


3.25 0.0961


F Prob>F


0.20
8.34
1.76
9.99


0.6685
0.0278
0.2334
0.0196






SAS


0.00622503


0.00620477


0.17968276


1.01 0.3545


Bounds on condition number:


Step 6 Variable ZN Removed
Variable CA Entered


3.187C,


34.8179


R-square = 0.72191692


Sum of Squares


Regression
Error
Total


Variable

INTERCEPT
P
CU
CA
MG


Parameter
Estimate

0.96867437
0.04414494
-1.58383071
-0.00554457
0.01955320


2.44893909
0.94333363
3.39227273

Standard
Error

0.75511429
0.01493272
0.62493397
0.00331019
0.01075665


Mean Square

0.61223477
0.15722227


Type II
Sum of Squares


0.25872852
1.37403496
1.00986500
0.44110704
0.51951195


C(p) = 2.90075533


F Prob>F


3.89 0.0681


F Prob>F


1.65
8.74
6.42
2.81
3.30


0.2469
0.0254
0.0444
0.1450
0.1190


Bounds on condition number:


4.0016,


57.4387


The above model is the best 4 variables model found.


Step 7 Variable K Entered


Regression
Error
Total


Variable

INTERCEP
P
K
CU
CA
MG


5 -
5
10


R-square = 0.77598877


Sum of Squares

-2.63236556
0.75990717
3.39227273


Parameter
Estimate

0.87116579
0.03685361
0.01922476
-1.43130483
-0.00564311
0.01954768


Standard
Error

0.74770891
0.01611220
0.01749949
0.62992070
0.00325579
0.01057585


Mean Square

0.52647311
0.15198143


Type II
Sum of Squares


0.20631322
0.79513564
0.18342646
0.78466278
0.45657844
0.51921860


C(p) = 4.14227314

F Prob>F


3.46 0.0995


F Prob>F


1.36
5.23
1.21
5.16
3.00
3.42


0.2965
0.0709
0.3220
0.0722
0.1436
0.1238


Bounds on condition number:


4.0046, 82.3697







SAS

The above model is the best 5 variables model found.


Step 8 Variable ZN Entered


R-square = 0.78613046


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
K
ZN
CU
CA
MG


Parameter
Estimate

1.22083657
0.03305489
0.03387594
-0.21433819
-1.15611831
-0.00738561
0.02263055


2.66676892
0.72550380
3.39227273

Standard
Error

1.14534183
0.01964406
0.03869290
0.49214032
0.93422896
0.00535330
0.01354943


Mean Square

0.44446149
0.18137595


Type II
Sum of Squares


0.20607463
0.51355700
0.13902723
0.03440337
0.27776506
0.34523058
0.50597356


C(p) = 6.00001264

F Prob>F


2.45 0.2025


F Prob>F


1.14
2.83
0.77
0.19
1.53
1.90
2.79


0.3465
0.1677
0.4307
0.6857
0.2836
0.2398
0.1702


Bounds on condition number:


9.0720,


227.9


The above model is the best 6 variables model found.


Step 9


Variable MN Entered


R-square = 0.78613136


Sum of Squares


Regression
Error
Total


Variable

INTERCEP
P
K
ZN
CU
CA
MG
MN


Parameter
Estimate

1.21778179
0.03302447
0.03381020
-0.21387934
-1.15640713
-0.00739374
0.02261940
0.00424156


2.66677198
0.72550075
3.39227273

Standard
Error

1.57707191
0.02424273
0.04835383
0.58274200
1.08180619
0.00659108
0.01595667
1.19288740


Mean Square

0.38096743
0.24183358


Type II
Sum of Squares


0.14419579
0.44877154
0.11823611
0.03257629
0.27633711
0.30432058
0.48595269
0.00000306


C(p) = 8.00000000

F Prob>F


1.58 0.3841


F Prob>F


0.60
1.86
0.49
0.13
1.14
1.26
2.01
0.00


0.4963
0.2664
0.5347
0.7380
0.3635
0.3436
0.2513
0.9974


Bounds on condition number:


10.3142, 329.4




SAS

'Ie .above model is the best 7 variables model found.

No further improvement in R-square is possible.




University of Florida Home Page
© 2004 - 2010 University of Florida George A. Smathers Libraries.
All rights reserved.

Acceptable Use, Copyright, and Disclaimer Statement
Last updated October 10, 2010 - - mvs