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GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists

2002 Edition, August 28, 2002

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ISBN: 978-0-8493-1338-7
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Product Details:

  • Revision: 2002 Edition, August 28, 2002
  • Published Date: August 28, 2002
  • Status: Active, Most Current
  • Document Language: English
  • Published By: CRC Press (CRC)
  • Page Count: 288
  • ANSI Approved: No
  • DoD Adopted: No

Description / Abstract:

Preface

The human population of the world is currently growing at the rate of 1 billion people per 10 to 12 years. Projections are that, by 2050, world population will increase from the current 6 billion to about 10 billion. During the past 50 years, agricultural research and technology transfer have helped increase the output of world crops two and a half-fold. Ruttan (1998), while summarizing the world's future food situation, referred to the "2–4–6–8" scenario, which means a doubling of population, a quadrupling of agricultural production, a sextupling of energy production, and an octupling of the size of the global economy by 2050. Currently, more than a billion people can be categorized as the world's absolute poor, subsisting on less than $1 of income per day, and 800 million of these do not have secure access to food (McCalla, 2001). The challenge for agricultural researchers to meet the food demand is astounding.

From the perspective of food security, the stability of agricultural production is as important as, if not more important than, the magnitude of output (Wittwer, 1998). Food production is very much a function of climate, which in itself is unpredictable; the principal characteristic of climate is variability (Wittwer, 1998). The consultative group on international agricultural research (CGIAR) warns, "Agricultural growth has to be achieved with methods that preserve the productivity of natural resources, without further damage to the Earth's previous life support systems — land, water, flora, and fauna — that are already under stress" (Harsch, 2001).

Agricultural production may be increased through increased efficiency in utilization of resources such as increased productivity per unit of land and of money, and through a better understanding and utilization of genotype-by-environment interaction (GEI). Stuber et al. (1999) considered GEI as one of the factors that gave impetus to research in the application of molecular-marker technology and genomics to plant breeding. The GEI and stability of crop performance across environments are expected to become more relevant issues in the 21st century as greater emphasis is placed on sustainable agricultural systems.

GEI is a major concern among breeders, geneticists, production agronomists, and farmers because of its universal presence and consequences. The occurrence of GEI necessitates multienvironment trials (MET) and has resulted in the development and use of the numerous measures of stability. Understanding and management of GEI has gone through several phases. The first phase was the realization of GEI as a confounding factor in cultivar selection and plant breeding early in the 20th century. The second phase was the concentrated study of GEI, which led to the development of numerous measures of stability (reviewed in Lin and Binns, 1994; Kang and Gauch, 1996; Kang, 1998). The third phase was the integration of the genotype main effect (G) and GEI. An integration of the two was needed because in practical breeding, selection only for stability is inconsequential if production level is ignored. Concepts such as crossover interaction (Baker, 1988a), stability-modified yield (Kang, 1993), rank-based statistics (Hühn, 1996), statistics to differentiate crossover from noncrossover interactions (Crossa and Cornelius, 1997), and methods of identifying the "which-won-where" pattern of MET data (Gauch and Zobel, 1997) emerged; all reflect the contemporary understanding and use of G and GEI in selection. The most recent development along this line is the development of the genotype and the genotype-by-environment (GGE) biplot methodology (Yan et al., 2000).

Since 2000, Yan has received tremendous support for his GGE biplot methodology from colleagues worldwide. The GGE biplot methodology drew the attention of many plant breeders and other researchers for two reasons. First, it explicitly and necessarily requires that genotype (G) and (GE) interaction, i.e., GGE, be regarded as integral parts in cultivar evaluation and plant breeding. Second, it presents GGE using the biplot technique (Gabriel, 1971) in a way that many important questions, such as the "which-won-where" pattern, mean performance and stability of genotypes, discriminating ability and representativeness of environments, etc., can be addressed graphically.

Although GGE biplot analysis was initially used only for dissecting GGE and visualizing MET data, its application has been extended to any data set that has a two-way structure. In the area of plant breeding in particular, the GGE biplot has been used to address important questions a breeder or researcher is likely to ask. Thus far, it has been applied to genotype-by-trait data, genotype-by-marker data, quantitive trait loci (QTL)-mapping data, diallel-cross data, and host genotype-by-pathogen strain data. Undoubtedly, with the fertile imagination of researchers engaged in crop breeding and production, additional applications to other types of two-way data will be found in time.

To facilitate the use of the GGE biplot methodology by researchers with only limited familiarity with computer applications and statistics, a Windows application called GGEbiplot has been developed (Yan, 2001). The GGEbiplot software has evolved into a comprehensive and convenient tool in quantitative genetics and plant breeding.

This book was envisioned during a meeting between Manjit Kang and Weikai Yan at the annual American Society of Agronomy meeting in Minneapolis, MN in November 2000, to make this useful technology available on a wider scale to plant breeders, geneticists, college teachers, and graduate students. The book is organized into three sections: Section I. GEI and stability analysis (Chapters 1 and 2); Section II. GGE biplot and MET data analysis (Chapters 3 to 5); and Section III. GGEbiplot software and applications in analyzing other types of two-way data (Chapters 6 through 11).

In preparing the book, we have been cognizant of the needs of researchers, teachers, and students of plant breeding, quantitative genetics, and genomics. We trust that readers will find the book stimulating and useful, as we do. The book is extensively illustrated and a person with a few courses in genetics and statistics should be able to comprehend easily the concepts and applications. It should also be useful to all researchers in other areas who must deal with large two-way data sets with complex patterns. We trust that this book will provide a powerful tool to breeders and production agronomists, and make a significant contribution toward helping meet the challenges of food production and food security that the world faces today.

Weikai Yan acknowledges that he benefited from his association and interactions with Professor L. A. Hunt at the University of Guelph and Professor D. H. Wallace at Cornell University, and from stimulating communications with Drs. Hugh Gauch and Rich Zobel (both at Cornell University at the time) — two of the major advocates of the additive main effects and multiplicative interaction effects (AMMI) model for analyzing MET data. Professor Paul L. Cornelius at the University of Kentucky and Dr. Jose Crossa at CIMMYT provided valuable comments and suggestions during the pre-publication phase of Yan et al. (2000). We thank John Sulzycki of CRC Press for his role in making this project a reality.