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Kết Quả Phi Lao Trên Dạng Lập Địa B


> TukeyHSD(d)

Tukey multiple comparisons of means 95% family-wise confidence level


Fit: aov(formula = DelD ~ Vung, data = Keo2)


$Vung


diff


lwr


upr p adj

III-II

-1.7345043

-1.94225056

-1.5267581 0.0000000

IV-II

-0.9196416

-1.10281777

-0.7364655 0.0000000

V-II

-1.4826716

-1.68238787

-1.2829554 0.0000000

IV-III

0.8148627

0.67352345

0.9562020 0.0000000

V-III

0.2518327

0.08962954

0.4140358 0.0003977

V-IV

-0.5630300

-0.69227729

-0.4337827 0.0000000

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> d1=LSD.test(d,"Vung")

> d1

$statistics

MSerror Df Mean CV 0.5746206 1450 1.438968 52.67922


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none Vung 4 0.05


$means

DelD std r LCL UCL Min Max Q25 Q50 Q75

II 2.5456716 0.8212694 134 2.4172173 2.6741260 0.32 6.05

2.0475 2.390

2.93

III 0.8111673 0.2652125 257 0.7184129 0.9039218 0.33 1.55

0.6300 0.800

1.00

IV 1.6260300 0.9704864 733 1.5711076 1.6809524 0.25 5.43

0.8800 1.210

2.36

V 1.0630000 0.3310540 330 0.9811452 1.1448548 0.40 2.35


$groups

0.8200 1.035

1.26

DelD groups II 2.5456716 a

IV 1.6260300 b

V 1.0630000 c

III 0.8111673 d


attr(,"class")

[1] "group"

> h=aov(DelH~Vung,data=Keo2)

> summary(h)

Df Sum Sq Mean Sq F value Pr(>F)

Vung 3 32.51 10.838 116.8 <2e-16 ***

Residuals 1450 134.56 0.093

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> TukeyHSD(h)

Tukey multiple comparisons of means 95% family-wise confidence level


Fit: aov(formula = DelH ~ Vung, data = Keo2)


$Vung


diff lwr


upr p adj

III-II

-0.1902506 -0.2737366

-0.10676462 0.0000000

IV-II

-0.3083483 -0.3819604

-0.23473622 0.0000000

V-II

0.0406730 -0.0395860

0.12093200 0.5608599

IV-III

-0.1180977 -0.1748971

-0.06129841 0.0000006

V-III

0.2309236 0.1657398

0.29610739 0.0000000

V-IV

0.3490213 0.2970813

0.40096132 0.0000000


> h1=LSD.test(h,"Vung")

> h1

$statistics

MSerror Df Mean CV 0.09279871 1450 0.821575 37.07864


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none Vung 4 0.05


$means

DelH std r LCL UCL Min Max Q25 Q50 Q75 II 1.0014179 0.3366222 134 0.9497966 1.0530393 0.40 2.00 0.7000 0.985 1.245

III 0.8111673 0.2652125 257 0.7738925 0.8484421 0.33 1.55 0.6300 0.800 1.000

IV 0.6930696 0.3217180 733 0.6709982 0.7151410 0.15 1.92 0.4300 0.670 0.920

V 1.0420909 0.2795842 330 1.0091963 1.0749855 0.40 1.75 0.8225 1.050 1.250


$groups

DelH groups V 1.0420909 a

II 1.0014179 a

III 0.8111673 b

IV 0.6930696 c


attr(,"class")

[1] "group"

> dt=aov(DelDt~Vung,data=Keo2)

> summary(dt)

Df Sum Sq Mean Sq F value Pr(>F)

Vung 3 19.44 6.479 31.33 <2e-16 ***

Residuals 1450 299.85 0.207

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> TukeyHSD(dt)

Tukey multiple comparisons of means 95% family-wise confidence level


Fit: aov(formula = DelDt ~ Vung, data = Keo2)


$Vung


diff


lwr


upr


p adj

III-II

-0.05463471

-0.17926110

0.06999169

0.6725478

IV-II

-0.24171642

-0.35160328

-0.13182956

0.0000001

V-II

0.01104116

-0.10876806

0.13085038

0.9953143

IV-III

-0.18708171

-0.27187074

-0.10229268

0.0000001

V-III

0.06567586

-0.03162935

0.16298108

0.3053474

V-IV

0.25275758

0.17522249

0.33029266

0.0000000


> dt1=LSD.test(dt,"Vung")

> dt1

$statistics

MSerror Df Mean CV 0.2067926 1450 0.9427098 48.23802


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none Vung 4 0.05


$means


DelDt

std

r LCL UCL Min Max

Q25 Q50 Q75

II 1.071716

0.3287171

134 0.9946570 1.1487759 0.50 2.20

0.85 1.000 1.25

III 1.017082

0.2186367

257 0.9614386 1.0727249 0.45 1.83

0.86 1.010 1.15

IV 0.830000

0.5381744

733 0.7970522 0.8629478 0.10 3.21

0.41 0.680 1.15

V 1.082758

0.4314047

330 1.0336531 1.1318621 0.10 1.90

0.88 1.175 1.40


$groups

DelDt groups

V 1.082758 a

II 1.071716 a

III 1.017082 a

IV 0.830000 b


attr(,"class")

[1] "group"


II. KẾT QUẢ PHI LAO TRÊN DẠNG LẬP ĐỊA B

2.1. Giai đoạn 14 tháng tuổi

> d=aov(Dgoc~CTTN,data=TP14)

> summary(d)

Df Sum Sq Mean Sq F value Pr(>F) CTTN 3 4.470 1.4901 16.38 3.43e-08 ***

Residuals 71 6.457 0.0909

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

22 observations deleted due to missingness

> d1=LSD.test(d,"CTTN")

> d1

$statistics

MSerror Df Mean CV 0.09094891 71 1.7512 17.22118


$parameters

test Fisher-LSD

p.ajusted

none

name.t ntr alpha CTTN 4 0.05


$means

Dgoc


std


r LCL UCL Min Max Q25 Q50


Q75

CT1 1.548889

0.3257470

9 1.3484462 1.749332 1.08 2.01 1.31 1.40

1.780

CT2 1.820000

0.2796855

26 1.7020698 1.937930 1.37 2.61 1.69 1.75

1.955

CT3 1.921935

0.3344689

31 1.8139338 2.029937 1.27 2.51 1.72 1.91

2.195

DC 1.166667

0.1926136

9 0.9662239 1.367109 1.02 1.53 1.02 1.11

1.150

$groups

Dgoc


groups



CT3 1.921935

CT2 1.820000

CT1 1.548889

DC 1.166667

a a b

c




attr(,"class")

[1] "group"

> h=aov(Hvn_m~CTTN,data=TP14)

> summary(h)

Df Sum Sq Mean Sq F value Pr(>F) CTTN 3 2.217 0.7391 8.395 7.52e-05 ***

Residuals 71 6.251 0.0880

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

22 observations deleted due to missingness

> h1=LSD.test(h,"CTTN")

> h1

$statistics

MSerror Df Mean CV 0.08803982 71 1.257333 23.59876


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05


$means

Hvn_m


std r LCL UCL Min Max Q25


Q50


Q75

CT1 1.0644444

0.2867103 9 0.8672334 1.261655 0.60 1.50 0.9000

1.10

1.16

CT2 1.2842308

0.2936007 26 1.1682020 1.400260 0.43 1.76 1.1325

1.27

1.48

CT3 1.3987097

0.3229008 31 1.2924493 1.504970 0.57 1.87 1.1750

1.44

1.65

DC 0.8855556

0.1969207 9 0.6883446 1.082767 0.70 1.30 0.7700

0.80

0.94

$groups

Hvn_m


groups



CT3 1.3987097

CT2 1.2842308

CT1 1.0644444

DC 0.8855556

a ab

bc c




attr(,"class")

[1] "group"

> dt=aov(Dtan_tb~CTTN,data=TP14)

> summary(dt)

Df Sum Sq Mean Sq F value Pr(>F) CTTN 3 0.942 0.31398 11.49 3.15e-06 ***

Residuals 71 1.940 0.02732

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

22 observations deleted due to missingness

> dt1=LSD.test(dt)

> dt1

Linear mixed model fit by REML ['lmerMod'] Formula: DelDt ~ (1 | Dong)

Data: DelDt1

REML criterion at convergence: 458.2522 Random effects:

Groups Name Std.Dev. Dong (Intercept) 0.0000

Residual 0.3259

Number of obs: 759, groups: Dong, 20 Fixed Effects:

(Intercept)

0.4456

convergence code 0; 0 optimizer warnings; 1 lme4 warnings

> dt1=LSD.test(dt,"CTTN")

> dt1

$statistics

MSerror Df Mean CV 0.02732181 71 0.658 25.12053


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05


$means

Dtan_tb std r LCL UCL Min Max Q25 Q50 Q75 CT1 0.5788889 0.2281143 9 0.4690272 0.6887506 0.23 0.90 0.4000 0.63 0.70

CT2 0.6792308 0.1214882 26 0.6145938 0.7438677 0.45 0.88 0.5625 0.69 0.75

CT3 0.7416129 0.1831411 31 0.6824177 0.8008081 0.35 1.00 0.6000 0.80 0.88

DC 0.3877778 0.1361780 9 0.2779161 0.4976395 0.25 0.63 0.3000 0.35 0.48


$groups

Dtan_tb groups CT3 0.7416129 a

CT2 0.6792308 ab

CT1 0.5788889 b

DC 0.3877778 c


attr(,"class")

[1] "group"

> c=aov(Bough_50cm~CTTN,data=TP14)

> s=aov(Song_chet~CTTN,data=TP14)


> summary(s)

Df Sum Sq Mean Sq F value Pr(>F)

CTTN 3 3.217 1.0724 7.23 0.000205 ***

Residuals 93 13.793 0.1483

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> s1=LSD.test(s,"CTTN")

> s1

$statistics

MSerror Df Mean CV 0.1483143 93 0.7731959 49.80832


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05


$means

Song_chet std r LCL UCL Min Max Q25 Q50 Q75 CT1 1.0000000 0.0000000 9 0.7450787 1.2549213 1 1 1 1 1

CT2 1.0000000

0.0000000 26 0.8500175 1.1499825 1

1

1

1

1

CT3 0.6078431

0.4930895 51 0.5007548 0.7149315 0

1

0

1

1

DC 0.8181818

0.4045199 11 0.5875968 1.0487668 0

1

1

1

1


$groups

Song_chet


groups


CT1 1.0000000

CT2 1.0000000

DC 0.8181818

CT3 0.6078431

a a ab b

attr(,"class")



2.2. Giai đoạn 24 tháng tuổi

> summary(d)

Df Sum Sq Mean Sq F value Pr(>F)

CTTN 3 18.79 6.263 36.88 <2e-16 ***

Residuals 113 19.19 0.170

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> d1=LSD.test(d,"CTTN")

> d1

$statistics

MSerror Df Mean CV 0.1698261 113 1.802222 22.86619


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05



$means

Dgoc std r LCL UCL Min Max Q25 Q50 Q75

CT1 2.031034

0.4830953

29 1.8794247 2.182644 1.02 3.09 1.7500 2.01

2.26

CT2 1.846190

0.4290044

21 1.6680279 2.024353 1.08 2.67 1.5000 1.85

2.01

CT3 2.011915

0.4343543

47 1.8928244 2.131005 1.27 3.50 1.7350 1.97

2.29

DC 0.931500

0.1248694

20 0.7489377 1.114062 0.64 1.15 0.8825 0.95

1.02

$groups

Dgoc


groups



CT1 2.031034

CT3 2.011915

CT2 1.846190

a a

a




DC 0.931500 b


attr(,"class")

[1] "group"

> h=aov(Hvn~CTTN,data=PLTP)

> h=aov(Hvn_m~CTTN,data=PLTP)

> summary(h)

Df Sum Sq Mean Sq F value Pr(>F) CTTN 3 7.295 2.4316 18.03 1.24e-09 ***

Residuals 113 15.237 0.1348

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> h1=LSD.test(h,"CTTN")

> h1

$statistics

MSerror Df Mean CV 0.1348376 113 1.25547 29.2482


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05


$means

Hvn_m std r LCL UCL Min Max Q25 Q50 Q75

CT1 1.397241 0.4743108 29 1.2621491 1.5323336 0.40

2.40 1.1000 1.35 1.6500

CT2 1.345238 0.3272861 21 1.1864859 1.5039903 0.85

1.95 1.1000 1.32 1.5000

CT3 1.361277 0.3605408 47 1.2551607 1.4673925 0.52

2.10 1.0750 1.30 1.5800

DC 0.707000 0.2071892 20 0.5443274 0.8696726 0.40


$groups

1.25 0.5575 0.70 0.8125

Hvn_m groups CT1 1.397241 a

CT3 1.361277 a

CT2 1.345238 a

DC 0.707000 b


attr(,"class")

[1] "group"

> dt=aov(Dtan_tb~CTTN,data=PLTP)

> summary(dt)

Df Sum Sq Mean Sq F value Pr(>F)

CTTN 3 7.166 2.3885 35.96 <2e-16 ***

Residuals 113 7.506 0.0664

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> dt1=LSD.test(dt,"CTTN")

> dt1

$statistics

MSerror Df Mean CV 0.06642651 113 1.012051 25.46644


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05


$means

Dtan_tb

std

r LCL UCL Min Max Q25 Q50 Q75

CT1 1.153793

0.3228712

29 1.0589740 1.2486122 0.65 1.98 0.950 1.13 1.300

CT2 1.016667

0.2238377

21 0.9052410 1.1280923 0.50 1.45 0.940 1.03 1.180

CT3 1.149574

0.2697016

47 1.0750934 1.2240555 0.65 1.73 0.975 1.11 1.365

DC 0.478500

0.1122157

20 0.3643227 0.5926773 0.30 0.65 0.380 0.50 0.580

$groups

Dtan_tb


groups


CT1 1.153793

CT3 1.149574

CT2 1.016667

a

a a



DC 0.478500 b


attr(,"class")

[1] "group"

> c=aov(Bough_50cm~CTTN,data=PLTP)

> summary(c)

Df Sum Sq Mean Sq F value Pr(>F) CTTN 3 513.4 171.14 14.04 7.63e-08 ***

Residuals 113 1377.1 12.19

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> c1=LSD.test(c,"CTTN")

> c1

$statistics

MSerror Df Mean CV 12.18672 113 10.70085 32.62309


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05


$means

Bough_50cm

std

r

LCL UCL Min Max Q25 Q50 Q75

CT1 11.20690

4.34560

29

9.922591 12.491203 5 20 8 10 14

CT2 10.47619

2.78602

21

8.966952 11.985429 6 16 9 10 12

CT3 12.34043

3.55868

47

11.331594 13.349257 7 23 9 12 14

DC 6.35000

2.41214

20

4.803491 7.896509 2 12 5 6 8


$groups

Bough_50cm groups CT3 12.34043 a

CT1 11.20690 ab

CT2 10.47619 b

DC 6.35000 c


attr(,"class")

[1] "group"


> s=aov(Song_chet~CTTN,data=PLTP)

> summary(s)

Df Sum Sq Mean Sq F value Pr(>F)

CTTN 3 2.17 0.7229 4.282 0.00573 **

Residuals 244 41.19 0.1688

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> s1=LSD.test(s,"CTTN")

> s1

$statistics

MSerror Df Mean CV 0.168796 244 0.7741935 53.06786


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none CTTN 4 0.05


$means

Song_chet

std

r LCL UCL Min Max Q25 Q50 Q75

CT1 0.7037037

0.4609109

54 0.5935772 0.8138302 0 1 0 1 1

CT2 0.9591837

0.1999149

49 0.8435749 1.0747924 0 1 1 1 1

CT3 0.7289720

0.4465823

107 0.6507377 0.8072062 0 1 0 1 1

DC 0.7631579

0.4308515

38 0.6318784 0.8944374 0 1 1 1 1

$groups

Song_chet


groups


CT2 0.9591837

DC 0.7631579

a

b



CT3 0.7289720 b

CT1 0.7037037 b


attr(,"class")

[1] "group"


2.3. KEO LÁ LIỀM LẬP ĐỊA B VÀ C1


> library(agricolae)

> d=aov(dg~Lapdia,data=KL)

> summary(d)

Df Sum Sq Mean Sq F value Pr(>F) Lapdia 1 29.49 29.486 24.47 1.86e-06 ***

Residuals 164 197.63 1.205

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

8 observations deleted due to missingness

> d=LSD.test(d,"Lapdia")

> d

$statistics

MSerror Df Mean CV 1.205073 164 4.213855 26.05116


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none Lapdia 2 0.05


$means

dg std r LCL UCL Min Max Q25 Q50 Q75 B 3.845000 1.033559 94 3.621433 4.068567 1.66 6.62 3.12 3.675 4.5725

C1 4.695417 1.176562 72 4.439967 4.950866 2.16 7.83 3.82 4.620 5.4100


$groups

dg groups

C1 4.695417 a

B 3.845000 b


attr(,"class")

[1] "group"

> h=aov(hvn~Lapdia,data=KL)

> summary(h)

Df Sum Sq Mean Sq F value Pr(>F) Lapdia 1 8.98 8.979 32.34 5.81e-08 ***

Residuals 164 45.54 0.278

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

8 observations deleted due to missingness

> h1=LSD.test(h,"Lapdia")

> h1

$statistics

MSerror Df Mean CV 0.2776672 164 1.651205 31.91254


$parameters

test p.ajusted name.t ntr alpha Fisher-LSD none Lapdia 2 0.05


$means

hvn std r LCL UCL Min Max Q25 Q50 Q75 B 1.447660 0.4841341 94 1.340344 1.554975 0.75 3.0 1.0500 1.35 1.800

C1 1.916944 0.5782383 72 1.794325 2.039564 0.65 3.1 1.4375 1.85 2.325


$groups

hvn groups

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