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Experimental Designs for Precision in Phenotyping

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Phenomics in Crop Plants: Trends, Options and Limitations

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Abstract

Precision phenotyping is the evaluation of a genotype’s expression in a given environment with minimum influence of experimental error. This chapter presents the basic principles of experimental designs and lists commonly used experimental designs for phenotyping crop genotypes. Experimental designs include unreplicated designs, incomplete block designs, and variable replication block designs which can also be generated using some selected software. This chapter illustrates some of such experimental designs and key directives of the software which can be used to generate and analyze these designs have also been included.

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Notes

  1. 1.

    http://cran.rstudio.com/web/packages/R.methodsS3/index.html

  2. 2.

    http://cran.rstudio.com/web/packages/R.oo/index.html

  3. 3.

    http://www.austatgen.org/files/software/downloads

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Author information

Authors and Affiliations

  1. International Center for Agricultural Research in the Dry Areas (ICARDA), Amman, Jordan

    Murari Singh & Khaled El-Shama’a

Authors
  1. Murari Singh
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  2. Khaled El-Shama’a
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Corresponding author

Correspondence to Murari Singh .

Editor information

Editors and Affiliations

  1. Division of Crop Improvement, ICAR - Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

    Jitendra Kumar

  2. Division of Crop Improvement, ICAR - Indian Institute of Pulses Research, Kalyanpur, Kanpur, Uttar Pradesh, India

    Aditya Pratap

  3. Rabat - Instituts, International Center for Agricultural Research in the Dry Areas, Rabat, Morocco

    Shiv Kumar

Appendix

Appendix

Some key codes used in generating the experimental designs under various tables.

16.1.1 A.16.1 GenStat Code for Table 16.2 (Geno Stands for Genotypes)

AGLATIN [PRINT=design; ANALYSE=Yes] NROWS=6; NSQUARES=1;\

TREATMENTFACTORS=!p(Geno); ROWS=Rows; COLUMNS=Columns; SEED=27257

16.1.2 A.16.2 GenStat Code for Table 16.3 (Rep, Plots, and Geno Stand for Replications or Complete Blocks, Plots Within Block and Genotypes Respectively)

AGHIERARCHICAL [PRINT=design; ANALYSE=Yes;SEED=2534]\

BLOCKFACTORS=Rep,Plots; TREATMENTFACTORS=*,!p(Geno); LEVELS=4,12

16.1.3 A.16.3 R Language Code for Table 16.6

library(DiGGer)

trep <- rep(c(1, 2, 8), c(20, 10, 3))

design <- DiGGer(33, 8, 8, TreatmentRep = trep)

design <- run(design)

getDesign(design)

layout <- getDesign(design)

des.plot(layout, seq(1, 20), col = 5, new = TRUE)

des.plot(layout, seq(21, 30), col = 6, new = FALSE)

des.plot(layout, seq(31, 33), col = 7, new = FALSE)

16.1.4 A.16.4 Further Details on GenStat Menu and R-Program

16.1.4.1 A.16.4.1 Generate an α-Design Using GenStat

To generate randomizations using GenStat statistical package (Payne 2011), go to its “Stats” menu, “Design” sub menu, and then “Select Design …” item (see the screenshot below):

figure a

This will pop-up the dialog box listing several special analyses (see the screenshot below):

figure b

Select “alpha designs” option, then click “OK” button, and answer the series of questions on number of treatments (within the range 20–100), number of blocks per replication, number of replications, and the labels that should be assigned to the factors. Using the “Spread” menu and further “Data in GenStat” and item from “New” sub menu, one can obtain the randomized plan in the GenStat spreadsheet as shown in the following screenshot. For more than 100 genotypes, one may use CycDesigN software (Whitaker et al. 2002).

figure c

The plan in Table 16.4, in 40 genotypes in blocks of size 5 and 3 replications, can be obtained by running the following code:

AGALPHA [PRINT=design] LEVELS=40; NREPLICATES=3; NBLOCKS=8;\

 TREATMENTS=Geno;\

 REPLICATES=Rep;\

 BLOCKS=Blk;\

 UNITS=Plot;\

 SEED=1592654

16.1.4.2 A.16.4. 2R-Package DiGGer Codes for Table 16.6.

Generate Design for Partial Replications Using DiGGer and R Language:

To use DiGGer tool, one needs to carry out required installation for the R package and download the following zip files “R.methodsS3_*.zip”,Footnote 1 “R.oo_*.zip”,Footnote 2 and “DiGGer_*.zip”Footnote 3 where “*” in the filenames denotes the current version available. Then one may start the R program, go to the “Packages” menu, and select “Install package(s) from local zip files…”. Find the downloaded files and let R install them.

figure d

Once DiGGer packages are installed, the following codes are used to generate the experimental design in the Table 16.6.

figure e
figure f
figure g

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Singh, M., El-Shama’a, K. (2015). Experimental Designs for Precision in Phenotyping. In: Kumar, J., Pratap, A., Kumar, S. (eds) Phenomics in Crop Plants: Trends, Options and Limitations. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2226-2_16

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