The blog contains basics of strip plot design, randomization, ANOVA model, all the formulas and solved example along with demonstration in Agri Analyze. (Reading time 15 min.)<\/i><\/span><\/p>\n The Strip Plot Design (SPD) is The vertical and horizontal strip plots are Randomization and Layout Planning for Strip Plot Step Step Layout Example:<\/b><\/span><\/p>\n A sample layout of strip-plot design with six varieties (V1, V2, V3, V4, V5 and V6) as a horizontal factor and three nitrogen rates (N1, N2 and N3) as a vertical factor in three replications.<\/span><\/p>\n <\/p>\n Example In the previous chapter, this dataset A strip plot Factors:<\/span><\/b><\/p>\n Horizontal Factor (A – Irrigation A1: Low Irrigation<\/span><\/p>\n A2: Medium Irrigation<\/span><\/p>\n A3: High Irrigation<\/span><\/p>\n Vertical Factor (B – Fertilizer B1: Organic Fertilizer<\/span><\/p>\n B2: Inorganic Fertilizer<\/span><\/p>\n B3: Mixed Fertilizer<\/span><\/p>\n Treatments<\/span><\/b><\/p>\n<\/td>\n R1<\/span><\/b><\/p>\n<\/td>\n R2<\/span><\/b><\/p>\n<\/td>\n R3<\/span><\/b><\/p>\n<\/td>\n R4<\/span><\/b><\/p>\n<\/td>\n<\/tr>\n A1B1<\/span><\/b><\/p>\n<\/td>\n 386<\/span><\/p>\n<\/td>\n 396<\/span><\/p>\n<\/td>\n 298<\/span><\/p>\n<\/td>\n 387<\/span><\/p>\n<\/td>\n<\/tr>\n A1B2<\/span><\/b><\/p>\n<\/td>\n 496<\/span><\/p>\n<\/td>\n 549<\/span><\/p>\n<\/td>\n 469<\/span><\/p>\n<\/td>\n 513<\/span><\/p>\n<\/td>\n<\/tr>\n A1B3<\/span><\/b><\/p>\n<\/td>\n 476<\/span><\/p>\n<\/td>\n 492<\/span><\/p>\n<\/td>\n 436<\/span><\/p>\n<\/td>\n 476<\/span><\/p>\n<\/td>\n<\/tr>\n A2B1<\/span><\/b><\/p>\n<\/td>\n 376<\/span><\/p>\n<\/td>\n 406<\/span><\/p>\n<\/td>\n 280<\/span><\/p>\n<\/td>\n 347<\/span><\/p>\n<\/td>\n<\/tr>\n A2B2<\/span><\/b><\/p>\n<\/td>\n 480<\/span><\/p>\n<\/td>\n 540<\/span><\/p>\n<\/td>\n 436<\/span><\/p>\n<\/td>\n 500<\/span><\/p>\n<\/td>\n<\/tr>\n A2B3<\/span><\/b><\/p>\n<\/td>\n 455<\/span><\/p>\n<\/td>\n 512<\/span><\/p>\n<\/td>\n 398<\/span><\/p>\n<\/td>\n 468<\/span><\/p>\n<\/td>\n<\/tr>\n A3B1<\/span><\/b><\/p>\n<\/td>\n 355<\/span><\/p>\n<\/td>\n 388<\/span><\/p>\n<\/td>\n 201<\/span><\/p>\n<\/td>\n 337<\/span><\/p>\n<\/td>\n<\/tr>\n A3B2<\/span><\/b><\/p>\n<\/td>\n 446<\/span><\/p>\n<\/td>\n 533<\/span><\/p>\n<\/td>\n 413<\/span><\/p>\n<\/td>\n 482<\/span><\/p>\n<\/td>\n<\/tr>\n A3B3<\/span><\/b><\/p>\n<\/td>\n 433<\/span><\/p>\n<\/td>\n 482<\/span><\/p>\n<\/td>\n 334<\/span><\/p>\n<\/td>\n 435<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n <\/span><\/p>\n
\nparticularly suitable for two-factor experiments where higher precision is
\nneeded for measuring the interaction effect between the factors compared to
\nmeasuring the main effects of either factor individually. This design is also
\nideal when both sets of treatments require large plots. For instance, in
\nexperiments involving spacing and ploughing treatments, cultural convenience
\nnecessitates larger plots. Ploughing strips can be arranged in one direction,
\nand spacing strips can be laid out perpendicular to the ploughing strips. This
\narrangement is achieved using:<\/span><\/span><\/p>\n\n
\nfactor)<\/span><\/span><\/li>\n
\nhorizontal factor)<\/span><\/span><\/li>\n
\nfactors.<\/span><\/span><\/li>\n<\/ul>\n
\nalways perpendicular to each other. However, their sizes are unrelated, unlike
\nthe main plot and subplot in the split plot design. The interaction plot is the
\nsmallest. In a strip plot design, the precision of the main effects of both
\nfactors is sacrificed to improve the precision of the interaction effect.<\/span><\/span><\/p>\n
\nDesign<\/span><\/b><\/p>\n
\n1: <\/span><\/b>Assign horizontal plots by dividing the
\nexperimental area into r blocks, then dividing each block into horizontal
\nstrips. Follow the randomization procedure used in RBD, and randomly assign the
\nlevels of the first factor to the horizontal strips within each of the r
\nblocks, separately and independently.<\/span><\/p>\n
\n2:<\/span><\/b> Assign vertical plots by dividing each
\nblock into b vertical strips. Follow the randomization procedure used in RBD
\nwith b treatments and r replications, and randomly assign the b levels to the
\nvertical strips within each block, separately and independently.<\/span><\/p>\n![](\"https:\/\/blogger.googleusercontent.com\/img\/b\/R29vZ2xl\/AVvXsEimlFDhV6OTyNJnuIyUS6JgUmJnhyphenhyphenTdxlefr5Nc4ZdgztL6T6l6cY6kg9YPOqLa3Ub9RHN6fX6-Sa-XtYsqRh1Xfv-8Ln4bVf-yFmmLDFg24d9uIS-BBrwEN61GCcBPfpCSJS-oA0NT01_evn1McjDEVrAOqoBEZpa9rs-UfiYCKt9MC8EdCj-DJ2PloQM\/s762\/strip_plot1.png\")
<\/a><\/div>\n<\/a><\/div>\n<\/a><\/p>\n<\/a><\/div>\n<\/a><\/div>\n
\nof Strip Plot Design<\/span><\/b><\/p>\n
\nwas used for a split-plot design and now the same dataset will be used to
\nillustrate a strip plot design.<\/span><\/p>\n
\ndesign was used to investigate the effects of irrigation levels (Horizontal
\nfactor) and fertilizer types (Vertical factor) on the yield of a particular
\ncrop. The experiment was conducted over four replicates (R1, R2, R3, R4).<\/span><\/p>\n
\nLevels):<\/span><\/b><\/p>\n
\nTypes):<\/span><\/b><\/p>\n\n\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n <\/a><\/div>\n<\/a><\/div>\n<\/a><\/div>\n