Optimizing the Allocation of Trials to Sub-regions in Crop Variety Testing with Multiple Years and Locations

Abstract Field experiments in crop variety testing are conducted in multiple environments. When the targeted growing area can be stratified into sub-regions (zones), a design problem arises regarding the number of trials to be performed in each sub-region. We propose an analytical solution and a computational approach for optimal allocation of trials to sub-regions in multi-environment crop variety testing for multi-year experiments. For our purpose, we use a linear mixed model and we determine allocations that are optimal for the prediction of pairwise linear contrasts of genotype effects. For computations, we use the OptimalDesign package. The analytical results are illustrated by a real data example.

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Publication Details

Journal
Journal of Agricultural Biological and Environmental Statistics
Published
2024-11-04
DOI
https://doi.org/10.1007/s13253-024-00659-1
Primary Topic
Genetics and Plant Breeding
Type
article
Field-Weighted Citation Impact
0.00

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article

Optimizing the Allocation of Trials to Sub-regions in Crop Variety Testing with Multiple Years and Locations

Maryna Prus, Hans‐Peter Piepho
Journal of Agricultural Biological and Environmental Statistics
Genetics and Plant Breeding
article

Optimizing the Allocation of Trials to Sub-regions in Crop Variety Testing with Multiple Years and Locations

Maryna Prus, Hans‐Peter Piepho
article en

Abstract

Abstract Field experiments in crop variety testing are conducted in multiple environments. When the targeted growing area can be stratified into sub-regions (zones), a design problem arises regarding the number of trials to be performed in each sub-region. We propose an analytical solution and a computational approach for optimal allocation of trials to sub-regions in multi-environment crop variety testing for multi-year experiments. For our purpose, we use a linear mixed model and we determine allocations that are optimal for the prediction of pairwise linear contrasts of genotype effects. For computations, we use the OptimalDesign package. The analytical results are illustrated by a real data example.

Journal of Agricultural Biological and Environmental Statistics
University of Hohenheim (DE)
Universität Hohenheim, Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 89%
Genetics and Plant Breeding
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