Crop Science Journal of Natural Resources and Life Sciences Education
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Published online 16 January 2008
Published in Crop Sci 48:317-330 (2008)
© 2008 Crop Science Society of America
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Testing Wheat in Variable Environments: Genotype, Environment, Interaction Effects, and Grouping Test Locations

Kraig L. Roozebooma,*, William T. Schapaugha, Mitchell R. Tuinstraa, Richard L. Vanderlipa and George A. Millikenb

a Dep. of Agronomy, 2004 Throckmorton Hall, Kansas State Univ., Manhattan, KS 66506-5504
b Dep. of Statistics, 101 Dickens Hall, Kansas State Univ., Manhattan, KS 66506-5504. Contribution No. 07-226-J Kansas Agricultural Experiment Station, Kansas State Univ

* Corresponding author (kraig{at}ksu.edu).

Selection of winter wheat (Triticum aestivum L.) genotypes requires testing programs with complementary locations that sample environments of interest with minimal duplication. The goal of the current study was to improve prediction of genotype performance in the highly variable environments of the central Great Plains in the United States by estimating the contributions of genotype, location, and year to wheat yield variability and identifying subgroups of test locations that minimize crossover genotype-by-environment interaction. Variance components were estimated from Kansas wheat performance data from 17 locations from 1982 to 2002. Annual data sets balanced for genotypes and environments were used to generate genotype, genotype-by-environment biplots that could objectively separate locations into groups with the same top-yielding genotype. Location, year, and their interaction introduced the greatest proportion of the variability in wheat performance test yields. Frequency of common grouping during the 21-year period was used to construct six groups of test locations representing unique target environments. Evaluation of the six groups using results from two subsequent years revealed that they generally agreed with location groups observed in the previous 21 years. Smaller regional genotype-by-environment variance component estimates compared with statewide estimates further confirmed the effectiveness of the pro posed six regions for reducing genotype-by-environment interaction.

Abbreviations: AMMI, additive main effects and multiplicative interaction • G, genotype • GE, genotype-by-environment • GGE, genotype, genotype-by-environment • GL, genotype-by-location • GLY, genotype-by-location-by-year • GY, genotype-by-year • L, location • LY, location-by-year • MSE, mean square error • SREG, sites regression model • SS, sum of squares • TSS, total sum of squares • Y, year


The authors would like to acknowledge the consistent quality and quantity of work supplied by the cooperating agronomists who conducted the field studies that supplied the data for this study: Dr. Mark Claassen, Pat Evans, Dr. Allan Fritz, Dr. Barney Gordon, Dr. Bill Heer, Dr. Keith Janssen, Dr. James Long, Dr. T. Joe Martin, Dr. Vic Martin, Dr. Alan Schlegel, Ted Walter, and Dr. Merle Witt.

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Received for publication April 13, 2007.





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