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STRIP PLOT DESIGN AND MULTILOCATIONS Erlina Ambarwati.

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Presentation on theme: "STRIP PLOT DESIGN AND MULTILOCATIONS Erlina Ambarwati."— Presentation transcript:

1 STRIP PLOT DESIGN AND MULTILOCATIONS Erlina Ambarwati

2 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 A & B diconfoundedkan a1a1 a2a2 a4a4 a3a3 a0a0 b3b3 b2b2 b0b0 b1b1

3 Model Linear Tabel Anova SRdfSSEMS (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) SS R SS A SS E1 SS B SS E2 SS AB SS E3 Totalrab-1SS tot 4/20/ Erlina Ambarwati

4 Penghitungan semua JK (SS) seperti pada simple split plot. Kecuali untuk errornya. 4/20/ Erlina Ambarwati

5 Konsentrasi RepGenangan I TSRTSR 10,3 9,8 9,0 9,7 10,1 9,6 11,2 11,0 10,8 10,4 10,1 10,5 10,6 9,8 9,9 9,5 11,0 184,1 II TSRTSR 11,8 10,7 10,1 10,3 11,6 10,9 12,1 11,9 12,1 12,3 11,8 11,0 11,8 11,7 10,3 10,6 10,1 9,2 200,3 II TSRTSR 10,2 9,5 9,7 10,1 10,7 9,3 11,6 10,8 11,2 9,9 9,6 10,6 10,5 10,4 10,3 9,4 10,3 185,3 91,192,3102,797,196,290,3569,7 4/20/ Erlina Ambarwati

6 4/20/ Erlina Ambarwati

7 ANOVA SRdfSSMSFhitFtab Rep. Kon. E1 Gen. E2 K*G E ,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* 5 ns 2,85 ns 6,43 9,6 4,37 Total5236,17 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; 4/20/ Erlina Ambarwati

8 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. 4/20/ Erlina Ambarwati

9 this example. 4/20/ Erlina Ambarwati

10 L AYOUT First Block I A B C D E F Block II F A E B D C Block III C B F A D E Second Block I A B C D E F Block II F A E B D C Block III C B F A D E Third Block I A B C D E F Block II F A E B D C Block III C B F A D E 4/20/ Erlina Ambarwati

11 A NOVA RCBD R EPEATED TIME Source of variation Degrees of freedom a Sums of squares (SSQ) Mean square (MS) F Blocks (B)b-1SSQ B SSQ B /(b-1)MS B /MS Em Treatment (Tr)t-1SSQ Tr1 SSQ Tr /(t-1)MS Tr /MS Em Error-main (Em)(b-1)*(t-1)SSQ Em SSQ Em /((b-1)*(t-1)) Time (Ti)(s-1)SSQ Ti SSQ Ti /(s-1)MS Ti /MS E Time X Blocks (TxB)(s-1)*(b-1)SSQ TxB SSQ TxB /((s-1)*(b-1))MS TxB /MS E Time X Treatments (TxT)(s-1)*(t-1)SSQ TxT SSQ TxT /((s-1)*(t-1))MS TxT /MS E Error (E)(s-1)*(t-1)*(b-1)SSQ E SSQ E /((s-1)*(t-1)*(b-1)) Total (Tot)f*s*b-1SSQ Tot a where t=number of treatments, s=number of times measurements are taken, and b=number of blocks or replications. 4/20/ Erlina Ambarwati

12 S AMPLE SAS GLM STATEMENTS : PROC GLM; CLASS BLOCKS TREAT; MODEL TIME1 TIME2 TIME3 = BLOCKS TREAT; REPEATED TIME /PRINTE; RUN; 4/20/ Erlina Ambarwati

13 RCB REPEATED AT MORE THAN ONE LOCATION Field 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. 4/20/ Erlina Ambarwati

14 L AY OUT MULTILOCATIONS 4/20/ Erlina Ambarwati

15 A NOVA MULTILOCATIONS Source of variation Degrees of freedom a Sums of squares (SSQ) Mean square (MS) F Locations (L)l-1SSQ L SSQ L /(l-1)MS L /MS El Error for Locations (El)l*(b-1)SSQ El SSQ El /(l*(b-1)) Treatments (Tr)t-1SSQ Tr SSQ Tr /(t-1)MS Tr /MS E Treatments X Locations (TxL)(t-1)*(l-1)SSQ TxL SSQ TxL /((t-1)*(l-1))MS TxL /MS E Error (E)l*(t-1)*(b-1)SSQ E SSQ E /(l*(t-1)*(b-1)) Total (Tot)l*t*b-1SSQ Tot a where l=number of locations, t=number of treatments and b=number of blocks or replications. 4/20/ Erlina Ambarwati

16 S AMPLE SAS GLM STATEMENTS: PROC GLM; CLASS LOCS BLOCKS TREATS; MODEL WHATEVER = LOCS LOCS(BLOCKS) TREATS TREATS*LOCS ; TEST H = LOCS E = LOCS(BLOCKS); RUN; 4/20/ Erlina Ambarwati


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