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STRIP PLOT DESIGN AND MULTILOCATIONS. Erlina Ambarwati. SPLIT BLOCK DESIGN (STRIP PLOT). Utamanya digunakan dalam bidang pertanian. 2 faktor, A dan B, diacak pada main plot. A diacak dengan mendatar B diacak dengan vertikal MisalnyaA: penggenangan B: penyemprotan herbisida - PowerPoint PPT Presentation
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STRIP PLOT DESIGN AND MULTILOCATIONS
Erlina Ambarwati
SPLIT BLOCK DESIGN (STRIP PLOT)
Utamanya digunakan dalam bidang pertanian.2 faktor, A dan B, diacak pada main plot.A diacak dengan mendatarB diacak dengan vertikalMisalnya A: penggenangan
B: penyemprotan herbisidaA & B diconfoundedkan
a1 a2 a4 a3 a0
b3
b2
b0
b1
Model Linear
Tabel Anova
ijkjkikkijjiijkY
SR df SS EMS (fixed treat)
Blok
A
Error 1
B
Error 2
A*B
Error 3
r-1
a-1
(r-1)(a-1)
b-1
(r-1)(b-1)
(a-1)(b-1)
(r-1)(a-1)(b-1)
SSR
SSA
SSE1
SSB
SSE2
SSAB
SSE3
Total rab-1 SStot
2
22
22
222
22
222
e
ABe
e
Be
e
Ae
ra
raab
rabb
04/22/23 3Erlina Ambarwati
Penghitungan semua JK (SS) seperti pada simple split plot. Kecuali untuk errornya.
ABEBEARtoterror
BRperror
kip
ARperror
ijp
SSSSSSSSSSSSSSSS
SSSSSSSS
CFYa
SS
SSSSSSSS
CFYb
SS
213
22
2
11
1
2
.
2
.
1
1
04/22/23 4Erlina Ambarwati
Konsentrasi
Rep
Genangan
1 2 3 4 5 6
ITSR
10,39,89,0
9,710,19,6
11,211,010,8
10,810,410,1
10,510,69,8
9,99,511,0
184,1
IITSR
11,810,710,1
10,311,610,9
12,111,912,1
12,311,811,0
11,811,710,3
10,610,19,2
200,3
IITSR
10,29,59,7
10,110,79,3
11,610,811,2
11,29,99,6
10,610,510,4
10,39,410,3
185,3
91,1 92,3 102,7 97,1 96,2 90,3 569,7
04/22/23 5Erlina Ambarwati
44,345,493,03,381,219,1205,917,3617,3651,6046
45,43,319,1294,19
94,193
5,30...3,32
90,03,305,96
5,60...4,62
30,318
4,1841903,195
81,219,1205,93
30...1,29
19,129
3,90...3,921,91
05,918
3,1853,2001,184
34,601054
7,569
3
2
1
22
22
222
22
222
222
2
E
Tot
RG
RS
E
GEN
E
KON
REP
SSCFSS
SS
CFSS
CFSS
CFSS
CFSS
CFSS
CFSS
CF
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mbarw
ati
ANOVA
SR df SS MS Fhit Ftab
Rep.
Kon.
E1
Gen.
E2
K*G
E3
2
5
10
2
4
10
20
9,05
12,19
2,81
3,30
0,90
4,45
3,44
4,52
2,44
0,28
1,15
0,23
0,44
0,17
8,71*
5ns
2,85ns
6,43
9,6
4,37
Total 52 36,17
PROC GLM; CLASS BLOCKS TREATS CROSS; MODEL WHATEVER = BLOCKS TREATS BLOCKS*TREATS CROSS CROSS*BLOCKS CROSS*TREATS; TEST H=BLOCKS TREATS E=BLOCKS*TREATS; TEST H=CROSS E=CROSS*BLOCKS;RUN;
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RCB REPEATED IN TIME
Field marks: Multiple measurements of the
same experimental subjects are made in time.
Treatments are assigned at random within blocks of adjacent subjects, each treatment once per block.
The number of blocks is the number of replications.
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this example.
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LAYOUT
First Block I A B C D E F Block II F A E B D C Block III C B F A D ESecond Block I A B C D E F Block II F A E B D C Block III C B F A D EThird Block I A B C D E F Block II F A E B D C Block III C B F A D E
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ANOVA RCBD REPEATED TIMESource of
variationDegrees of
freedoma
Sums ofsquares (SSQ)
Meansquare (MS)
F
Blocks (B) b-1 SSQB SSQB/(b-1) MSB/MSEm
Treatment (Tr) t-1 SSQTr1 SSQTr/(t-1) MSTr/MSEm
Error-main (Em) (b-1)*(t-1) SSQEm SSQEm/((b-1)*(t-1))
Time (Ti) (s-1) SSQTi SSQTi/(s-1) MSTi/MSE
Time X Blocks (TxB) (s-1)*(b-1) SSQTxB SSQTxB/((s-1)*(b-1)) MSTxB/MSE
Time X Treatments (TxT) (s-1)*(t-1) SSQTxT SSQTxT/((s-1)*(t-1)) MSTxT/MSE
Error (E) (s-1)*(t-1)*(b-1) SSQE SSQE/((s-1)*(t-1)*(b-1))
Total (Tot) f*s*b-1 SSQTot
awhere t=number of treatments, s=number of times measurements are taken, and b=number of blocks or replications.
04/22/23 11Erlina Ambarwati
SAMPLE SAS GLM STATEMENTS:
PROC GLM; CLASS BLOCKS TREAT; MODEL TIME1 TIME2 TIME3 =
BLOCKS TREAT; REPEATED TIME /PRINTE;RUN;
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RCB REPEATED AT MORE THAN ONE LOCATIONField marks: Blocks are laid out at more than one location.
Treatments are assigned at random to those blocks as below.
Treatments are assigned at random within blocks of adjacent subjects, each treatment once per block.
The number of blocks is the number of replications.
Any treatment can be adjacent to any other treatment, but not to the same treatment within the block.
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LAY OUT MULTILOCATIONS
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ANOVA MULTILOCATIONSSource of
variationDegrees of
freedoma
Sums ofsquares (SSQ)
Meansquare (MS)
F
Locations (L) l-1 SSQL SSQL/(l-1) MSL/MSEl
Error for Locations (El) l*(b-1) SSQEl SSQEl/(l*(b-1))
Treatments (Tr) t-1 SSQTr SSQTr/(t-1) MSTr/MSE
Treatments X Locations (TxL) (t-1)*(l-1) SSQTxL SSQTxL/((t-1)*(l-1)) MSTxL/MSE
Error (E) l*(t-1)*(b-1) SSQE SSQE/(l*(t-1)*(b-1))
Total (Tot) l*t*b-1 SSQTot
awhere l=number of locations, t=number of treatments and b=number of blocks or replications.
04/22/23 15Erlina Ambarwati
SAMPLE SAS GLM STATEMENTS:
PROC GLM; CLASS LOCS BLOCKS TREATS; MODEL WHATEVER = LOCS
LOCS(BLOCKS) TREATS TREATS*LOCS ;
TEST H = LOCS E = LOCS(BLOCKS);RUN;
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