This document summarizes a book titled "Crop Variety Trials: Data Management and Analysis". The book covers various topics related to managing and analyzing data from crop variety trials, including theoretical frameworks, biplot analyses, experimental designs, and software tools. It is intended to be a practical manual for researchers conducting multi-environment trials. The reviewer concludes that the book provides useful information and techniques for data analysis, especially using GGE biplot software, and would be valuable for researchers and teachers.

1. 2910 crop science, vol. 54, november–december 2014
book review
Crop Variety Trials: Data Management
and Analysis
Weikai Yan. John Wiley & Sons. 2014. 360 pp. US
$149.95. ISBN-13: 978-1-118-68864-9.
Crop improvement has three main components: (i)
germplasm enhancement (pre-breeding), (ii) devel-
opment of new crop cultivars (breeding), and (iii) culti-
var performance trials (post-breeding). Crop breeders and
agronomists invest 3 to 5 yr in pre-breeding, 7 to 10 yr
in the second component and 2 to 4 yr in the third com-
ponent. With the advent of molecular techniques, i.e., use
of molecular markers and marker-aided selection (MAS),
the time required to develop/release a cultivar may be
shortened. Nevertheless, the process of cultivar develop-
ment and release is expensive and time-consuming. Huge
amounts of monetary and personnel resources are needed
for crop cultivar development and testing.
Whether cultivars are developed via conventional
breeding methods or via MAS, all cultivars must still be
field-tested in multienvironment trials (METs). Selection
of appropriate experimental designs and statistical analyses
is extremely important. How cultivars perform in farmers’
fields is the ultimate test of their success. There are con-
sequences, for growers, of researchers’ commission of sta-
tistical errors (Type 1 and Type 2). Thus, cultivar testing
assumes considerable importance. The METs are essential
from the standpoint of identifying adapted cultivars for a
given area.
The key features of Crop Variety Trials: Data Manage-
ment and Analysis are: 18 chapters ranging from theoretical
framework for crop variety trials (Ch. 1 and Ch. 2) to bip-
lot analysis (Ch. 3 through Ch.6) to GGE biplot analyses
(Ch. 7 through Ch. 14, Ch.17) to relational databases for
crop variety trials (Ch. 15) to experimental designs used
in breeding nurseries and variety trials (Ch. 16). Chapter
18 summarizes important key points.
In Chapter 1, one finds discussion of various aspects
of heritability and how to improve variety trial efficiency.
Chapter 2 covers variety trial data and data analyses. Here,
decision making based on multiple traits is also covered.
In Chapter 3, biplot analysis is introduced and terms such
as ‘inner product property’ of a biplot and ‘singular value
decomposition’ are explained. In Chapter 4, the author
explains types of data centering, evaluation of genotypes
and environments via biplots, unique properties of GGE
(G = genotype + GE = genotype ´ environment interac-
tion) biplot, and quantitative trait loci (QTL) ´ environ-
ment interaction.
In Chapter 5, Yan discusses data scaling methods in
GGE biplot analysis and factor-analytic-based GGE biplot.
Chapter 6 is devoted to ‘frequently asked questions about
GGE biplot analysis’; many ‘what if’ scenarios are pre-
sented. The GGE biplot method is compared with addi-
tive main effects and multiplicative interaction (AMMI)
analysis. The author mentions common mistakes that
users make in interpreting biplots. Chapter 7 covers case
studies regarding spatial analysis to correct field trends and
variation. It also informs the reader that the purpose of
‘single-trial analysis’ is to assess data quality. Chapter 8
details genotype ´ location analysis, mega-environment
analysis, evaluation of test locations and genotypes, and
identification of specifically and generally adapted geno-
types. The number of test locations required for cultivar
testing is also discussed.
Various aspects of genotype ´ trait data analysis are
covered in Chapter 9, which includes genotype evaluation
on the basis of multiple traits. Independent culling, fol-
lowed by index selection, is promoted as a useful strategy.
Chapter 10 highlights association ´ environment analysis
Published in Crop Sci 54:2910–2911 (2014).
doi: 10.2135/cropsci2014.08.0002br
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2. crop science, vol. 54, november–december 2014 www.crops.org 2911
to study associations among traits. Quantitative trait loci
(QTL) and QTL ´ environment interaction (QQE) anal-
yses are also illustrated in this chapter. Location by trait
two-way data analysis is discussed in Chapter 11, wherein
how to select production regions that are suitable for
producing crop products with certain end-use quality is
shown. Environment ´ trait concept is explained here as
well. Chapter 12 is completely devoted to mega-environ-
ment analysis on the basis of multiyear data. Mega-envi-
ronment is defined and target-environment classification
is discussed.
Test-location evaluation and genotype evaluation, on
the basis of multiyear data, are, respectively, covered in
Chapters 13 and 14. Ideal location, ideal genotype, and
stability analysis concepts as well as fixed versus mixed
model choice for genotype evaluation are explained.
Chapter 15 relates to building and using a relational data-
base for crop variety trial data. Database called ‘COOL’
(Context Oriented Observation Library) and software
called ‘DUDE’ (Data Unification and Data Distillation
Engine) are described in this chapter; together the system
is referred to as ‘COOL-DUDE’. As breeders must often
choose from a variety of experimental designs for vari-
ety trials, several types of designs, including randomized
complete-block design, incomplete blocks design, row-
column design, and augmented design (for nurseries), are
described in Chapter 16. Chapter 17 contains a compre-
hensive listing and functions of various modules available
in GGEbiplot software. Data file types needed for GGE
biplot analysis are explained. Chapter 18 is a summary of
overall conclusions about crop variety trials. There, the
major bullet points are: (i) how to determine effectiveness
of crop variety trials, (ii) key points about multienviron-
ment trial data analyses, (iii) key points about single-trial
data analysis, (iv) key points about multitrait data analysis,
and (v) data management and analysis tools.
There is consistency across the main chapters (Ch. 1
through Ch. 17) in that at the beginning of each chapter,
there is a bulleted list of key points covered in respective
chapters. The number of bullets varies from one to 10.
Only Chapter 1 contains a point-wise summary at the end.
The book is well illustrated and uses actual trial data from
the author’s breeding program. References have been used
sparingly as there is only a short list of less than 100 refer-
ences. The subject index is adequate, but a more extensive
index could have enhanced the usefulness of the book.
Overall, in my opinion, Crop Variety Trials: Data Man-
agement and Analysis is a highly useful practical manual of
MET data management and data analysis techniques. The
use of GGE biplot software developed by the author has
been amply demonstrated with examples. I trust research-
ers and teachers will find the book useful in their research
and teaching. The price of the book at $149.95 appears to
be reasonable.
Manjit S. Kang*
Book Review Editor, Crop Science
Dep. of Plant Pathology
Kansas State Univ.
Manhattan, KS 66506-5502
*Corresponding author (manjit5264@yahoo.com).