Dr. M. Shamshad

Sugarcane Breeder && Computational Biology Researcher

Recent posts

Strip Plot Design

10 minute read

Updated:

A Strip Plot Design (also called a Criss-Cross Design) is an experimental layout used when two factors are both difficult or costly to randomise at the level of individual plots. It is a natural extension of the split plot concept but treats both factors symmetrically — neither is nested within the other.

In a strip plot:

  • Factor A (the row factor) is applied to horizontal strips running across each block
  • Factor B (the column factor) is applied to vertical strips running down each block
  • The intersection of a row strip and a column strip forms the experimental unit for the interaction A×B

Split Plot Design

5 minute read

Updated:

A Split Plot Design is a type of experimental design used when one or more factors are difficult or expensive to randomize at the level of individual experimental units. It is widely used in agricultural field trials, industrial experiments, and manufacturing studies.

The design partitions experimental units into two levels:

  • Whole plots — the larger units to which the hard-to-change factor (the whole-plot factor) is applied
  • Subplots — subdivisions of whole plots, to which the easy-to-change factor (the subplot factor) is applied

Factorial Experimental Design

9 minute read

Updated:

A Factorial Experimental Design is an experimental strategy in which two or more factors are varied simultaneously, and all possible combinations of their levels are studied. This allows researchers to:

  • Estimate the main effect of each factor
  • Detect interactions between factors
  • Draw conclusions over a wide range of conditions
  • Use experimental resources more efficiently than one-factor-at-a-time (OFAT) experiments

Honeycomb Design Analysis in R

2 minute read

Updated:

The Honeycomb (HC) design, developed by Fasoulas (1988) and later extended by Kyriakou and Fasoulas, is a field layout method used in plant breeding to improve the efficiency of mass selection under field variability. In this design, plants are arranged in a triangular (hexagonal) grid, so that each plant is surrounded by exactly six nearest neighbours at equal distances. This uniform spatial arrangement ensures that every plant experiences a similar level of competition, reducing environmental bias caused by uneven spacing or directional field effects.

Spatial Analysis with AR1 × AR1 Model: Theory & Complete R Analysis

22 minute read

Updated:

Field experiments are routinely affected by spatial heterogeneity — systematic variation in soil fertility, moisture, drainage, pH, and microclimate that creates patches of high and low performance across the trial. When this variation is ignored, it inflates the residual variance, reduces heritability estimates, biases treatment comparisons, and misranks genotypes. The AR1 × AR1 model (first-order autoregressive process in both row and column directions) is the gold standard for capturing and removing this spatial structure from field trial data.

Partially Replicated (p-rep) Design: Theory & Complete R Analysis

18 minute read

Updated:

The Partially Replicated (p-rep) design — formally developed by Cullis, Smith & Coombes (2006) — is the modern standard for Stage 1 multi-environment plant breeding trials. It overcomes the key limitation of the augmented design (zero replication of test entries) by replicating a controlled fraction (typically 20–30 %) of test entries twice, while the remainder appear only once. This provides direct within-trial error estimation for all genotypes and enables powerful spatial modelling of field heterogeneity.

Augmented Design: Theory & Complete R Analysis

16 minute read

Updated:

The Augmented Design — proposed by Federer (1956) — is a field experimental design specifically developed for early-generation plant breeding trials, where a large number of new (unreplicated) test entries are evaluated alongside a small set of replicated check (standard) varieties. It allows breeders to screen hundreds of genotypes within a single trial without the cost of fully replicating every entry, while still enabling valid statistical inference through the checks.

Alpha (α) Lattice Design: Theory, Layout & Complete R Analysis

14 minute read

Updated:

The Alpha (α) Lattice Design — introduced by Patterson & Williams (1976) — is an incomplete block design built for large-scale experiments where the number of treatments exceeds the practical block size. It is the standard design for plant breeding trials evaluating hundreds of genotypes, offering superior error control over RCBD while remaining flexible in treatment and block size combinations.