critical differences in split plot and strip plot design.pptx

In this presentation you can able to understand what is split and strip plot design along with that steps to forming ANOVA table and further calculation for critical differences by which you can determine whether the treatment is on par with other

1. Critical Differences in Split plot and Strip plot Design K.ADITHYA 2022502301 TAMIL NADU AGRICULTURAL UNIVERSITY STA 502 EXPERIMENTAL DESIGNS (2+1)

2. SPLIT PLOT DESIGN A  split-plot design  is an experimental design in which researchers are interested in studying two factors in which: One of the factors is “easy” to change or vary. One of the factors is “hard” to change or vary. This type of design was developed in 1925 by mathematician Ronald Fisher for use in agricultural experiments.

3. It occurs in factors which require larger plots than for others. Eg : Experiments on Tillage and Irrigation require larger plots whereas experiments on Fertilizers and Herbicides require no larger plots. Larger plots are split into smaller plots to accommodate the other factors; different treatment where alloted at random to their respective plots – Split plot design.

4. (Sub plots) (Main plots)

5. CRITICAL DIFFERENCE Critical Difference  is used to  compare means of different treatments  that have an  equal number of replications .

6. Treatment 1 ( T1 ) is significantly different from Treatment 2 ( T2 ) as  their difference is more   than the Critical Difference   you have calculated here . (T1-T2 i.e. 10.62 – 5.21 = 5.41  >   0.978887) But Treatment 1 ( T1 ) statistically does not differ significantly from Treatment 6 ( T6 ), as  their difference is less than the Critical Difference you have calculated here  (T1-T6 i.e. 10.62 – 10.25 = 0.37  <  0.978887)

7. 1. As Treatment 1 ( T1 ) significantly  out-yielded - Treatment ( T2 )  and will likely do so again in future field trials , 2. But as Treatment 1 ( T1 ) statistically does not differ significantly from Treatment 6 ( T6 ) i.e. Treatment 1 ( T1 ) was statistically similar to Treatment 6 ( T6 ). so The   treatment effects on yield were similar The   observed differences are likely due to simply random chance  or background "noise," and The   apparent trends in treatment yields (T1>T6) would likely  not be repeated in subsequent trials comparing these same treatments.

8. CONFIDENCE LEVEL The confidence level , which we usually take either  90 or 95 percent. Confidence level can be identified by its corresponding  alpha value: A  95 percent confidence level  has an  alpha  of  5 % (p < 0.05) and a  90 percent confidence level  has an  alpha  of  10 % (p< 0.1) . A 90 percent confidence level means  there is still a 10 percent chance that, the difference was actually due to natural variation.

9. ANOVA TABLE

10. here, r=4 ; m=3 ; s=4

11. STRIP PLOT DESIGN This design is also known as split block design. When there are two factors in an experiment and both the factors require large plot sizes it is difficult to carryout the experiment in split plot design. Also the precision for measuring the interaction effect between the two factors is higher than that for measuring the main effect of either one of the two factors. Strip plot design is suitable for such experiments. In strip plot design each block or replication is divided into number of vertical and horizontal strips depending on the levels of the respective factors.

12. In this split-plot design, Irrigation was implemented first followed by a split into two parts. Two fertilizers were randomized among the split plots. In the  split-block design , the “plots” are split horizontally  and  vertically according to how many levels are present in the experiment. In other words, the first, whole-plot factor is completely crossed with a second factor.

13. Compute Correction Factor = (GT)² ------------- a X b X r TSS = Σ yijk ²- CF Trt SS = Σ Yij²/r - CF Blk SS= Σ Y..k²/ ra - CF

14. 1 ) Vertical Strip Analysis Form A x R Table and calculate RSS, ASS and Error(a) SS

15. 2) Horizontal Strip Analysis Form B x R Table and calculate RSS, BSS and Error(b) SS

16. 3) Interaction Analysis Form A xB Table and calculate BSS, Ax B SSS and Error (b) SS

17. ANOVA

18. R=3 A=3 B=4

19. THANK YOU