Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs. This item is unavailable.Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs

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.

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.