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Analyzing Variety by Environment Data Using Multiplicative Mixed Models and Adjustments for Spatial Field Trend

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  • Alison Smith
  • Brian Cullis
  • Robin Thompson

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  • Alison Smith & Brian Cullis & Robin Thompson, 2001. "Analyzing Variety by Environment Data Using Multiplicative Mixed Models and Adjustments for Spatial Field Trend," Biometrics, The International Biometric Society, vol. 57(4), pages 1138-1147, December.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:4:p:1138-1147
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2001.01138.x
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    Citations

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    Cited by:

    1. Gambin, Brenda L. & Coyos, Tomás & Di Mauro, Guido & Borrás, Lucas & Garibaldi, Lucas A., 2016. "Exploring genotype, management, and environmental variables influencing grain yield of late-sown maize in central Argentina," Agricultural Systems, Elsevier, vol. 146(C), pages 11-19.
    2. Joanne De Faveri & Arūnas P. Verbyla & Brian R. Cullis & Wayne S. Pitchford & Robin Thompson, 2017. "Residual Variance–Covariance Modelling in Analysis of Multivariate Data from Variety Selection Trials," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 1-22, March.
    3. T. Caliński & S. Czajka & Z. Kaczmarek & P. Krajewski & W. Pilarczyk, 2005. "Analyzing Multi-environment Variety Trials Using Randomization-Derived Mixed Models," Biometrics, The International Biometric Society, vol. 61(2), pages 448-455, June.
    4. Emi Tanaka, 2020. "Simple outlier detection for a multi‐environmental field trial," Biometrics, The International Biometric Society, vol. 76(4), pages 1374-1382, December.
    5. Sudipto Banerjee & Gregg A. Johnson, 2006. "Coregionalized Single- and Multiresolution Spatially Varying Growth Curve Modeling with Application to Weed Growth," Biometrics, The International Biometric Society, vol. 62(3), pages 864-876, September.
    6. Johannes Forkman & Hans-Peter Piepho, 2014. "Parametric bootstrap methods for testing multiplicative terms in GGE and AMMI models," Biometrics, The International Biometric Society, vol. 70(3), pages 639-647, September.
    7. Joel Jorge Nuvunga & Carlos Pereira da Silva & Luciano Antonio de Oliveira & Renato Ribeiro de Lima & Marcio Balestre, 2019. "Bayesian factor analytic model: An approach in multiple environment trials," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-26, August.
    8. S. Hadasch & J. Forkman & W. A. Malik & H. P. Piepho, 2018. "Weighted Estimation of AMMI and GGE Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(2), pages 255-275, June.
    9. Alison B. Smith & Lauren M. Borg & Beverley J. Gogel & Brian R. Cullis, 2019. "Estimation of Factor Analytic Mixed Models for the Analysis of Multi-treatment Multi-environment Trial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 573-588, December.
    10. Boby Mathew & Jens Léon & Mikko J Sillanpää, 2018. "Impact of residual covariance structures on genomic prediction ability in multi-environment trials," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-11, July.
    11. Brian R. Cullis & Alison B. Smith & Nicole A. Cocks & David G. Butler, 2020. "The Design of Early-Stage Plant Breeding Trials Using Genetic Relatedness," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(4), pages 553-578, December.
    12. Johannes Forkman, 2013. "The use of a reference variety for comparisons in incomplete series of crop variety trials," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2681-2698, December.

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