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Volume 53, Issue 5
Review & Interpretation

A Simple Protocol for AMMI Analysis of Yield Trials

Hugh G. Gauch Jr.

Corresponding Author

E-mail address: hgg1@cornell.edu

Crop and Soil Sciences, Cornell Univ., Ithaca, NY, 14853

Corresponding author (E-mail address: hgg1@cornell.edu).Search for more papers by this author
First published: 01 September 2013
Citations: 18

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ABSTRACT

The Additive Main effects and Multiplicative Interaction (AMMI) model has been used extensively for analysis of multi‐environment yield trials for two main purposes: understanding complex genotype × environment interactions and increasing accuracy. Nevertheless, AMMI analyses implementing best practices have been rare. Accordingly, this paper presents a simple protocol with four steps: (i) analysis of variance, (ii) model diagnosis, (iii) mega‐environment delineation, and (iv) agricultural recommendations. This protocol is illustrated with an international bread wheat trial. This paper concerns a basic and common application of AMMI: yield‐trial analysis without consideration of special structure or additional data for either genotypes or environments. Best practices involve using both treatment and experimental designs to gain accuracy and exploiting both broad and narrow adaptations to increase yields.

Number of times cited according to CrossRef: 18

  • Comparing yield trial locations based on their elicited expressions of genetic variance among soybean cultivars, Crop Science, 10.1002/csc2.20066, 60, 3, (1313-1324), (2020).
  • Genotype × Environment Interactions and Stability in Organic Wheat, Crop Science, 10.2135/cropsci2018.02.0147, 59, 1, (25-32), (2019).
  • Mean Performance and Stability in Multi‐Environment Trials II: Selection Based on Multiple Traits, Agronomy Journal, 10.2134/agronj2019.03.0221, 111, 6, (2961-2969), (2019).
  • Mean Performance and Stability in Multi‐Environment Trials I: Combining Features of AMMI and BLUP Techniques, Agronomy Journal, 10.2134/agronj2019.03.0220, 111, 6, (2949-2960), (2019).
  • Screening oat germplasm for better adaptation to cold stress in the Southern Great Plains of the United States, Journal of Agronomy and Crop Science, 10.1111/jac.12318, 205, 2, (213-219), (2018).
  • Nonparametric Resampling Methods for Testing Multiplicative Terms in AMMI and GGE Models for Multienvironment Trials, Crop Science, 10.2135/cropsci2017.10.0615, 58, 2, (752-761), (2018).
  • AMMI Bayesian Models to Study Stability and Adaptability in Maize, Agronomy Journal, 10.2134/agronj2017.11.0668, 110, 5, (1765-1776), (2018).
  • Constrained AMMI Model: Application to Polish Winter Wheat Post‐Registration Data, Crop Science, 10.2135/cropsci2017.06.0347, 58, 4, (1458-1469), (2018).
  • Can Tall Guinea‐Race Sorghum Hybrids Deliver Yield Advantage to Smallholder Farmers in West and Central Africa?, Crop Science, 10.2135/cropsci2016.09.0765, 57, 2, (833-842), (2017).
  • Variation in Yield, Starch, and Protein of Dry Pea Grown Across Montana, Agronomy Journal, 10.2134/agronj2016.07.0401, 109, 4, (1491-1501), (2017).
  • Variety × Environment × Management Interaction of Diseases and Yield in Selected Common Bean Varieties, Agronomy Journal, 10.2134/agronj2016.11.0681, 109, 6, (2450-2462), (2017).
  • Cross‐Validation in AMMI and GGE Models: A Comparison of Methods, Crop Science, 10.2135/cropsci2016.07.0613, 57, 1, (264-274), (2017).
  • Multienvironmental Evaluation of Dry Pea and Lentil Cultivars in Montana using the AMMI Model, Crop Science, 10.2135/cropsci2015.01.0032, 56, 2, (520-529), (2016).
  • Modeling Genotype × Environment Interaction for Genomic Selection with Unbalanced Data from a Wheat Breeding Program, Crop Science, 10.2135/cropsci2015.04.0207, 56, 5, (2165-2179), (2016).
  • AMMI Analysis of Four‐Way Genotype × Location × Management × Year Data from a Wheat Trial in Poland, Crop Science, 10.2135/cropsci2015.03.0152, 56, 5, (2157-2164), (2016).
  • What Should Students in Plant Breeding Know About the Statistical Aspects of Genotype × Environment Interactions?, Crop Science, 10.2135/cropsci2015.06.0375, 56, 5, (2119-2140), (2016).
  • A Weighted AMMI Algorithm to Study Genotype‐by‐Environment Interaction and QTL‐by‐Environment Interaction, Crop Science, 10.2135/cropsci2013.07.0462, 54, 4, (1555-1570), (2014).
  • Genotype × Environment Interaction of Maize Grain Yield Using AMMI Biplots, Crop Science, 10.2135/cropsci2013.07.0448, 54, 5, (1992-1999), (2014).