03067naa a2200349 a 450000100080000000500110000800800410001902400360006010000190009624500880011526000090020350003870021252015430059965000240214265300270216665300240219365300240221765300450224165300260228665300230231270000260233570000180236170000180237970000140239770000160241170000150242770000200244270000200246270000160248270000180249877302010251610613042022-09-05       2020    bl uuuu     u00u1 u    #d7 a10.1007/s13253-020-00406-22DOI1 aRAEGAN HOEFLER  aDo Spatial Designs Outperform Classic Experimental Designs?.h[electronic resource]  c2020  aArticle history: Received 15 October 2019/Accepted 01 July 2020/Published 29 August 2020. This project was partially funded through a USDA_AFRI_NIFA_2018-67013-27620 award and by the Hatch Act Formula Fund WISO1984 and WIS03002. Additionally, JARN received funding from CAPES CAPES_PrInt_UFLA 88887.318846_2019-00 as Senior Visiting Professor at the University of Wisconsin-Madison.  aControlling 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 twodimensional 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. Supplementary materials accompanying this paper appear online.  aDISENO EXPERIMENTAL  aAUTOREGRESSIVE PROCESS  aEXPERIMENTAL DESIGN  aPREDICTION ACCURACY  aRANDOMIZATION-BASED EXPERIMENTAL DESIGNS  aRESPONSE TO SELECTION  aSPATIAL CORRECTION1 aGONZALEZ-BARRIOS , P.1 aMADHAV BHATTA1 aNUNES, J.A.R.1 aBERRO, I.1 aNALIN, R.S.1 aBORGES, A.1 aCOVARRUBIAS, E.1 aDIAZ-GARCIA, L.1 aQUINCKE, M.1 aGUTIERREZ, L.  tJournal of Agricultural, Biological, and Environmental Statistics, 1 December 2020, volume 25, number 4, pag.523-552, 1 December 2020. OPEN ACCESS.  Doi: https://doi.org/10.1007/s13253-020-00406-2