Do Spatial Designs Outperform Classic Experimental Designs?
-
Published:2020-08-29
Issue:4
Volume:25
Page:523-552
-
ISSN:1085-7117
-
Container-title:Journal of Agricultural, Biological and Environmental Statistics
-
language:en
-
Short-container-title:JABES
Author:
Hoefler Raegan, González-Barrios Pablo, Bhatta Madhav, Nunes Jose A. R., Berro Ines, Nalin Rafael S., Borges Alejandra, Covarrubias Eduardo, Diaz-Garcia Luis, Quincke Martin, Gutierrez LuciaORCID
Abstract
AbstractControlling 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 $$\times $$
×
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.Supplementary materials accompanying this paper appear online.
Funder
Agricultural Research Service Hatch Act Formula Grant CAPES
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Statistics, Probability and Uncertainty,General Agricultural and Biological Sciences,Agricultural and Biological Sciences (miscellaneous),General Environmental Science,Statistics and Probability
Reference84 articles.
1. Bartlett, M. S. (1978). “Nearest Neighbour Models in the Analysis of Field Experiments.” Journal of the Royal Statistical Society: Series B (Methodological) 40(2):147–58. 2. Beeck, C.P., Cowling, W.A., Smith, A.B. and Cullis, B.R. (2010). “Analysis of yield and oil from a series of canola breeding trials. Part I. Fitting factor analytic mixed models with pedigree information.” Genome, 53(11):992-1001. 3. Besag, J. and Kempton, R. (1986). “Statistical Analysis of Field Experiments Using Neighbouring Plots.” Biometrics 42(2):231-251. 4. Bhatta, M.,L., Gutierrez., L,. Cammarota, F., Cardozo, S. Germán, B. Gómez-Guerrero, M.F. Pardo, V. Lanaro, M. Sayas, and Castro A.J. (2020). “Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (Hordeum vulgare L.).” G3: Genes, Genomes, Genetics, 10(3): 1113-1124. 5. Borges, A., González-Reymundez, A., Ernst, O., M. Cadenazzi, O., Terra, J., and Gutiérrez, L. (2019). “Can Spatial Modeling Substitute for Experimental Design in Agricultural Experiments?” Crop Science 59(1):44–53.
Cited by
20 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|