Agriculture Reference
In-Depth Information
decisions to be made and new trails organized before
planting. This can best be achieved if all the genotype
identification codes, experimental design details and
randomization information have previously been stored
in a database. In order to carry out an analysis of variance
for a single variate assessed at one location and produce
an easily understandable but comprehensive output it
should only be necessary to enter parameters to identify
which trial is to be analyzed and the variate name.
As data are collected throughout the growing season
analysis of individual traits can be carried out soon after
data collection. Inspection of de-randomized data and
genotype averages can often serve as a good check that
there are no major errors in the data. It is important
that each variate be analyzed to determine the vari-
ability within the genotypes for particular characters.
Most database systems will automatically store means
and statistics as analyses are performed.
The mode of data entry will, to a large extent, be
determined by the method used to collect data (i.e.
automatic logging, data logging or pencil and paper).
Irrespective of how the data are collected, eventually the
data to be analyzed will be available for entry into an
analysis and storage scheme. The order that numbers
are entered can differ from one of no pattern (not a
good idea), field plot order (either going across the trial
or up the trial) or in standard order (e.g. genotype 1
replicate 1; genotype 1 replicate 2; genotype 1 repli-
cate 3; genotype 2 replicate 1; etc.). It is important that
data be entered in the order expected by the software
package.
Other features which will facilitate a rapid and effi-
cient turnover of analyzing individual traits and storing
information will include:
If multiple environments are used (say at the
advanced trial stage) then over-sites analysis (simple
analysis of variance or joint regression analysis) can be
performed using stored means from individual site anal-
ysis. If an assessment trial is grown at two (or more)
locations, and yield per plot is recorded from multiple
replicates at each site, the following procedure can be
used to obtain an analysis of variance of yield over sites:
Analyze data from each location separately and store
the genotype means on a database, along with the
error variance from the analysis
•
When each location has been analysed separately then
an analysis of variance with source terms: genotypes,
locations, genotypes
•
locations and an error term
can be produced quickly and easily. The error term
is obtained by simply pooling the error terms from
each of the individual analyses
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To interpret data from assessment trials and provide
indications of possible selection strategies then joint
regression analysis, over-site analysis, simple and mul-
tiple regressions and correlation analysis can all offer an
insight into the variability of characters and also the rela-
tionship between traits. In addition, visual inspection
of histograms and scatter diagrams can help in deci-
sion making. Multi-variate transformations (canonical
analysis, principal components analysis etc.) have been
suggested as possible aids to plant breeders by reducing
the dimensions of selection problems. If these transfor-
mations are readily and easily applied to breeding data
sets perhaps plant breeders will more readily use them.
Alongside complex analysis it should be possible to
carry out simple calculations. Simple calculations would
include addition, subtraction, multiplication and divi-
sion. Other calculations, which may be helpful, would
include expressing data as a percentage of either the
trial mean or the average performance of one or more
control line.
The ability to estimate missing values
•
To analyze only a subset of the total number of repli-
cates (e.g. in a four replicate trial data for some traits
are collected only from replicates three and four)
•
Selection
If many hundreds of lines are to be considered for selec-
tion, then computer simulation (by selecting a subset
and comparing that subset to those lines rejected) can
be a big help in either setting culling levels for different
characters, or in setting weights in an index scheme.
The speed that different selection strategies can
be compared using computers offers the potential of
•
To be able to transform (e.g. ARCSIN transformation
for percentage data) or convert (e.g. convert dates to
days after sowing or to convert plot yield into t/ha)
data before analysis
•
To derive variates from single, or multiple, data sets
before analysis (e.g. if grain yield is recorded along
with straw weight, total above ground biomass can
be derived by adding the two recorded characters)
