Do spatial designs outperform classic experimental designs?

dc.creatorHoefler, R.
dc.creatorGonzález Barrios, P.
dc.creatorBhatta, M.R.
dc.creatorNunes, J.A.R.
dc.creatorBerro, I.
dc.creatorNalin, R.S.
dc.creatorBorges, A.
dc.creatorCovarrubias-Pazaran, G.
dc.creatorDiaz-Garcia, L.
dc.creatorQuincke, M.
dc.creatorGutiérrez, L.
dc.date2020-11-28T01:00:17Z
dc.date2020-11-28T01:00:17Z
dc.date2020
dc.date.accessioned2023-07-17T20:06:23Z
dc.date.available2023-07-17T20:06:23Z
dc.identifierhttps://hdl.handle.net/10883/21013
dc.identifier10.1007/s13253-020-00406-2
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7512799
dc.descriptionControlling 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
dc.description523-552
dc.languageEnglish
dc.publisherSpringer Verlag
dc.publisherAmerican Statistical Association
dc.publisherInternational Biometrics Society
dc.relationhttps://link.springer.com/article/10.1007%2Fs13253-020-00406-2#Sec32
dc.rightsCIMMYT 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 CIMMYT-Knowledge-Center@cgiar.org 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
dc.rightsOpen Access
dc.source4
dc.source25
dc.source1085-7117
dc.sourceJournal of Agricultural, Biological, and Environmental Statistics
dc.subjectAGRICULTURAL SCIENCES AND BIOTECHNOLOGY
dc.subjectAutoregressive Process
dc.subjectPrediction Accuracy
dc.subjectResponse to Selection
dc.subjectSpatial Correction
dc.subjectRandomization-Based Experimental Designs
dc.subjectEXPERIMENTAL DESIGN
dc.subjectSELECTION RESPONSES
dc.subjectSPATIAL ANALYSIS
dc.titleDo spatial designs outperform classic experimental designs?
dc.typeArticle
dc.typePublished Version
dc.coverageNew York (USA)


Este ítem pertenece a la siguiente institución