Show simple item record

Do spatial designs outperform classic experimental designs?

Creator: Hoefler, R.
Creator: González Barrios, P.
Creator: Bhatta, M.R.
Creator: Nunes, J.A.R.
Creator: Berro, I.
Creator: Nalin, R.S.
Creator: Borges, A.
Creator: Covarrubias, E.
Creator: Diaz-Garcia, L.
Creator: Quincke, M.
Creator: Gutiérrez, L.
Year: 2020
Language: English
Publisher: Springer Verlag
Publisher: American Statistical Association
Publisher: International Biometrics Society
Copyright: CIMMYT manages Intellectual Assets as International Public Goods. The user is free to download, print, store and share this work. In case you want to translate or create any other derivative work and share or distribute such translation/derivative work, please contact indicating the work you want to use and the kind of use you intend; CIMMYT will contact you with the suitable license for that purpose
Type: Article
Place of Publication: New York (USA)
Pages: 523-552
Issue: 4
Volume: 25
DOI: 10.1007/s13253-020-00406-2
Keywords: Autoregressive Process
Keywords: Prediction Accuracy
Keywords: Response to Selection
Keywords: Spatial Correction
Keywords: Randomization-Based Experimental Designs
Description: Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency of methods used to control spatial variation in a wide range of scenarios using a simulation approach based on real wheat data. Specifically, classic and spatial experimental designs with and without a two-dimensional autoregressive spatial correction were evaluated in scenarios that include differing experimental unit sizes, experiment sizes, relationships among genotypes, genotype by environment interaction levels, and trait heritabilities. Fully replicated designs outperformed partially and unreplicated designs in terms of accuracy; the alpha-lattice incomplete block design was best in all scenarios of the medium-sized experiments. However, in terms of response to selection, partially replicated experiments that evaluate large population sizes were superior in most scenarios. The AR1 × AR1 spatial correction had little benefit in most scenarios except for the medium-sized experiments with the largest experimental unit size and low GE. Overall, the results from this study provide a guide to researchers designing and analyzing large field experiments
Related Datasets:
ISSN: 1085-7117
Journal: Journal of Agricultural, Biological, and Environmental Statistics

Files in this item


This item appears in the following Collection(s)

Show simple item record