During the last few years, many journal articles have shown the
usefulness of the Additive Main Effects and Multiplicative
Interaction (AMMI) model for analyzing regional yield trials. AMMI
helps agronomists and breeders in several ways: to understand or
model complex data sets, especially the interactions; to estimate
yields more accurately, even with less data; to make better
selections; and to design more efficient yield-trial experiments.
This book is the first systematic treatment of these topics,
collecting concepts from the scattered literature and also
presenting many new results. Although agricultural applications are
emphasized here, AMMI is applicable to two-way data tables
containing one kind of data, either replicated or not, so AMMI
appears in many areas of science and technology.
The volume's first seven chapters review the agricultural and
statistical principles and the final chapter indicates the
difference that AMMI can make for agricultural research and world
food supplies. This book will be of great value to agricultural
scientists throughout the world, enabling them to learn more from
their data and thereby make greater progress.
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