Trial Designs
Table of Contents
The EBS supports several experimental designs for Breeding Trials that can be generated using different models as described in the table below.
The design parameters of the selected design model should be set, and there is also an option to define the shape of the field trial by moving the toggle switch to “Yes” position and then, setting up the field layout parameters.
Trial Designs and Models
Design | Model name | Description |
|---|---|---|
Alpha-Lattice | A kind of resolvable Incomplete Block design, wherein, not all treatments are tested in the same block. It is a resolvable design because blocks can be arranged and grouped together to form complete replications. Design parameters: Number of Occurrences (Trials), Number of Replicates, Number of Blocks. Layout parameters: Number of Rows per Block, Number of Rows per Replicate, Total Number of Field Rows, Plot Ordering (serpentine or not) Usage: When the number of treatments being tested (entries, genotypes) is very large, it is difficult to have a uniform field that can support this large number of plots, so we build small blocks (incomplete blocks) within the replication. The block size need not be large even when the number of treatments is large. This design is more flexible than the Lattice designs (which require that number of treatments should be a perfect square and block size or number of plots in each block is equal to the square root of the number of treatments). | |
Same as Alpha-Lattice. Differences:
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This model is usually used to generate the Breeding trials that are also considered International Nurseries, it generates an Alpha-Lattice design, however, there are two more options to restrict the randomization process. It is the same as Alpha-Lattice | CIMMYT, with the follow difference:
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Randomized Complete Block Design | Is the standard design for agricultural experiments, the replication is arranged in group, creating a local control factor that is called “Block”. Each block contains one plot of each entry. There are no theoretical limit for the number of blocks. The entries are randomized within the blocks. In this design, the block and the replication are the same entity. Design parameters: Number of Occurrences (Trials) and Number of Replicates (Blocks) Layout parameters: Rows per Replication (Block), Number of Field Rows and Plot Ordering (serpentine or not). Usage: When the experimental units are not homogeneous and it is possible to group the experimental units into blocks such that experimental units within each block are more homogeneous than those between blocks. It is not recommended when the number of entries are too large, being impossible to have field with a little variation even within the blocks. | |
Same as RCBD Differences:
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It is a regular RCBD but each block will be assigned to a occurrence, with that, each block can be planted in a different location. Design parameters: Number of Farms (In the case of OFT, it is the same as number of blocks or even number of locations) Layout parameters: Layout parameters are not used for this design. Usage: To be used exclusively for “On-farm trials”, that is an experiment usually for late stages of a breeding program evaluating a small number of genotypes (<15). | ||
Incomplete Block Design | Is an incomplete block design where each block is assigned to an occurrence, with that, each incomplete block can be planted in a different location. Design parameters: Number of Farms (In the case of OFT, it is the same as number of blocks or even number of locations) and Block size (plots per block). Usage: To be used exclusively for “On-farm trials”, that is an experiment usually for late stages of a breeding program evaluating a small number of genotypes (<15). Is there some restrictions in terms of number of farms, as it is the number of blocks and it is dependent of the number of entries. This design tries to create a balanced design considering the test and the check entries. The entry list must have at least 2 check entries. | |
Row and Column Design | Another kind of resolvable Incomplete Block design, it is like Alpha-Lattice but with better field variation since this design allows blocking in two directions that are perpendicular to each other. Design parameters: Number of Occurrences (Trials), Number of Replicates, Number of Row Blocks. Layout parameters: Total Number of Field Rows, Plot Ordering (serpentine or not) Usage: When the number of treatments being tested (entries, genotypes) is very large, it is difficult to have a uniform field that can support this large number of plots, so we build small blocks (incomplete blocks) within the replication. This design is appropriate if there are two sources of variation and the dimension of the field per replicate is squarish as possible. | |
Augmented RCBD | Another type of Incomplete Block design wherein some of the treatments (entries, genotypes) are replicated while the the other treatments are unreplicated. The Replicated treatments are referred to as the check while the unreplicated ones are referred to as the test. The replicated treatments follow the randomization of the RCBD (all treatments are present in each block) while the unreplicated treatments are distributed and embedded within each block. Design parameters: Number of Occurrences (Trials), Number of Blocks. Layout parameters: Total Number of Field Rows, Plot Ordering (serpentine or not) Usage: This design is often used in the early generation, when the seeds or resources are limited, when the user wants to evaluate as many genotypes as possible, and when it is difficult to maintain homogeneous blocks when comparing many genotypes. | |
Augmented designs with diagonal checks | An Incomplete Block design wherein some treatments (entries, genotypes) are replicated while the other treatments are unreplicated. The replicated treatments are referred to as the check which can then be further classified as spatial or repeated. The repeated checks and the unreplicated treatments are completely randomized across the field, while the spatial checks are positioned in a diagonal pattern. Design parameters: Number of Occurrences (Trials), Entry Role, Number of Replicates. Layout parameters: Total Number of Field Columns, Percentage of Check Plots Usage: This design is often used in the early generation, when the seeds or resources are limited and when the user wants to evaluate as many genotypes as possible. | |
Partially replicated design | A special case of Augmented design where 20% of the test entries are replicated, check entries are not classified as either spatial or repeated and no diagonal plots are included. Design parameters: Number of Occurrences (Trials), Number of Replicates. Layout parameters: Total Number of Field Rows, Plot Ordering (column order, column serpentine, row order, row serpentine) Usage: This design is often used in the early generation, when the seeds or resources are limited, when the user wants to evaluate as many genotypes as possible, and when it is difficult to maintain homogeneous blocks when comparing many genotypes. |