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Procedures for Standard Evaluation
and Data Management of Advanced
Potato Clones
Module 2. Healthy Tuber Yield Trials
International Cooperators’ Guide
© International Potato Center (CIP), 2014
ISBN: 978-92-9060-448-8
DOI: 10.4160/9789290604488
Digital version
CIP publications contribute to important development information to
the public arena. Readers are encouraged to quote or reproduce
material from them in their own publications. As copyright holder, CIP
requests acknowledgement and a copy of the publication where the
citation or material appears. Please send a copy to the Communication
and Public Awareness Department at the address below.
International Potato Center
P.O. Box 1558, Lima 12, Peru
[email protected] • www.cipotato.org
Citation
De Haan, S.; Forbes, A.; Amoros, W.; Gastelo M.; Salas, E.; Hualla V.; De
Mendiburu F.; Bonierbale M. 2014. Procedures for Standard Evaluation
and Data Management of Advanced Potato Clones. Module 2: Healthy
Tuber Yield Trials. International Cooperators Guide. Lima (Peru).
International Potato Center. 44 p.
Edition and Layout
Sofia Tejada
Setiembre 2014
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INDEX
INTRODUCTION
MODULE 2: HEALTHY TUBER YIELD TRIALS
LOCATION
GENETIC MATERIALS
EXPERIMENTAL DESIGN
OTHERS DESIGNS
FIELD MANAGEMENT AND INFORMATION ON ENVIRONMENTAL FACTORS
GEOGRAPHICAL INFORMATION SYSTEM (GIS) – TAKING WAYPOINT
EVALUATION PARAMETERS
Period of vegetative Development
Number of tubers planted (NTP)
Number of emerged plants/Plot (NPE)
Plant growth Habit (PGH)
Plant uniformity (Plant_Unif)
Plant vigor (Plant_Vigor)
Flowering degree (Flowering)
Senescence (SE)
Period Of Harvest
Number of plants harvested (NPH)
Number of stolons (Num_Stolon)
Lenght of stolons (Leng_Stolon)
Tuber appearance (Tuber_Apper)
Tuber uniformity (Tub_Unif)
Tuber Size (Tub_Size)
Number marketable tubers category I/plot (NMTCI)
Number marketable tubers category ii/plot (NMTCII)
Number of non-marketable tubers/plot (NNOMTP)
Marketable tuber weight category I/plot (MTWCI)
Marketable tuber weight category II/plot (MTWCII)
Non-marketable tuber weight/plot (NOMTWP)
CALCULATED OF VARIABLES
OTHER EVALUATIONS
EXTERNAL DEFECTS
INTERNAL PROBLEMS
PERCENTAGE OF DEFECTED TUBERS
DATA RECORDING
DATA ANALYSIS
QUANTITATIVE CONTINUOUS/DISCRETE VARIABLES
QUANTITATIVE ORDINAL (PSEUDO- QUANTITATIVE) VARIABLE
QUALITATIVE ORDINAL VARIABLES
EXAMPLE AND DATA INTERPRETATION
VALIDATION OF THE EXPERIMENT
SELECTION CRITERIA
FILLING THE DATACOLLECTOR TEMPLATE OF HEALTHY TUBER YIELD
FORM: MINIMAL
FORM: INSTALLATION
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FORM: MATERIAL_LIST
FORM: CROP MANAGEMENT
FORMS: HOBO DATA, WEATHER DATA, SOIL ANALYSIS
FORM: VAR_LIST
FORM: FIELDBOOK
ANNEX 1
SPLIT-PLOT DESIGN
Characteristics
Randomization
PARTIALLY BALANCED LATTICE DESIGN
Characteristics
Randomization
EXPERIMENTAL DESIGN FOR LARGE NUMBER OF GENOTYPES
ALPHA-LATTICE
Characteristics
Randomization
AUGMENTED BLOCK DESIGN
Characteristics
Randomization
BIBLIOGRAPHY
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I.- INTRODUCTION
The International Potato Center (CIP) currently manages potato
selection trials in no less than 50 different countries in Africa, Asia
and Latin America. Each region or sub-region has scientists in
charge of monitoring breeding advances and varietal selection. The
procedures presented in this basic guide are designed to assist CIP
staff in organizing trials and data collection in such a way that data
can be shared, centrally stored and uploaded to the Global Trial
Data Management System.
Among breeders and collaborators we do at least need
agreements about: (i) the most important traits to be observed
and measured, (ii) standardized procedures and formats to record
data, (iii) a user-friendly and practical global system to upload,
store and share data.
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II.- MODULE 2: HEALTHY TUBER YIELD TRIALS
Healthy Tuber Yield Trials can be conducted with 1 up to 30 clones
and is recommended for advanced materials that have already
shown superior performance in intentional exposure trials for key
traits.
Location
Season 1: During the first
season, yield trials are
established in a location
representative
of
the
targeted production area.
However, the number and
quality of the seed used
might force this first
evaluation to be located in
an experimental station.
Season 2 onward: Yield trials
are established in one or
more locations representative
of the targeted production
areas. Yield trials can be
combined with on and offfarm
experiments,
management
trials,
participatory selection and/or
GxE interaction trials.
Genetic Materials
Clones or varieties from CIP and/or national breeding programs
can be evaluated. At least two of the most commonly-used
varieties should be used as controls. High-quality seed of the same
origin should be used as control varieties and clones. The tuber
yield trial in the first season requires at least 40 seed tubers per
entry (10 plants per row), to be planted in three replications in one
location. During the following seasons, the plot size and number of
locations should be increased depending on seed availability.
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Experimental Design
The Healthy Tuber Yield Trials uses a Randomized Complete Block
Design (RCBD), where replications of clones are planted in blocks
and within each block all genotypes are randomized.
In the RCBD design, all treatments (advanced clones/varieties)
are grouped into uniform blocks of equal size. The main purpose
of blocking is to reduce experimental error by eliminating
sources of heterogeneity such as soil fertility or field slopes.
With a predictable pattern of field variability, plot shape and
block orientation can be carefully chosen so that the
experimental conditions within each block are as uniform as
possible.
When the pattern of field variability is unidirectional, long and
narrow blocks should be used. When the pattern of variability is
not predictable, blocks should be as square or rectangular plots of
double or multiple rows. These are preferable to long, single row
plots. Single row plot should not be carried out because of the
inter-plot competition (border affects due to neighbor plot within
a block).
Ideally, Healthy Tuber Yield Trials must be carried out in at least
three locations. The clear advantage to conduct tuber yield trials in
three locations is that this saves time, because in potato trials
temporal variation of test environments can be replaced by spatial
variation of test environments (locations).
Trials carried out across locations allow for the separation of
effects due to genotypes, genotype by environment interaction
and plot error. Furthermore, with three divergent locations it is
possible to determine for each genotype stability parameters,
which must be considered as an additional character associated
with yield.
The randomization process for a RCBD design is applied to each of
the blocks. Randomizing can be done with “DataCollector”.
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Analysis of Variance (ANOVA) is used to analyze the data collected
in a RCBD. The three sources of variability used in the statistical
model are the treatment (variety/potato clone), the blocks
(repetition) and the experimental error for each environment.
Others Designs
Depending on the type of trial and its objectives, additional trial
designs (such as a split-plot design and lattice design) can be
applied. Brief descriptions and the randomization processes of
those designs are provided in the annex1. Further information can
also be obtained from technical manuals dealing with experimental
trial designs (Gomez and Gomez, 1984).
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IRONMENTAL FACTORS
III.- FIELD MANAGEMENT AND INFORMATION
ON ENVIRONMENTAL FACTORS
Field management should follow standard agronomic practices and
local procedures to protect the crop from pests and diseases.
Meteorological data and soil analyses are ideally collected as to
identify spatial patterns among experimental sides and agroecological zones. Climatic data may be easily accessible only for
on-station trials, whereas availability for other experiments may
depend on the proximity of the test site to a meteorological
station.
Geographical Information System (GIS) – taking waypoint
For plant breeders, the strength of spatial data management
systems is its capacity to provide information on test location that
can be used in supporting the analysis of genotype x environment
interactions. Ideally a so-called waypoint is taken with a GIS device
to record longitude, latitude and altitude for each trials site.
Evaluation parameters
Once the Healthy Tuber Yield Trial(s) have been established, the
following agronomic data should be collected during:
Period of vegetative development
a) Number of tubers planted (NTP): This is recorded directly at
planting.
b) Number of emerged plants/plot (NPE): This evaluation is
performed 45 days after planting, count the number of emerged
plants by plot.
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c) Plant growth habit (PGH) 1: This evaluation is performed 45
days after planting using a scale 1 to 3. (Gomez, 2004).
Scale
State
1
Erect
2
Semi-erect
3
Decumbent
Semi-erect
Erect
1
1
Description
The stems are almost vertical and the angle of
insertion between the leaves rachis and the
main stem is sharp, around 30°.
The stems have more or less a vertical growth,
but some secondary stems open up a bit and
the insertion angle between the leaves rachis
and the main stem is more open, around 45°.
The stems are more open, some secondary
stems are open to the point of reaching the
ground. From there the stems tend to recover
some vertical growth. The angle of insertion of
the leaf rachis with the main stem is very open,
from 60 to 90 °. Such plants cover the ground
very well and have most of the leaf area
exposed to sunlight.
Decumbent
2
It is not necessary to collect this variable for multiple plots or years. Once the plant
habit is established no re-recording is required.
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3
d) Plant uniformity (Plant_Unif): This evalutation is performed 45
days after planting and should be evaluated using a scale from 1 to
9. (Salas et al., 2004)
Scale
State
1
Very heterogeneous
3
Heterogeneous
5
Intermediate
7
Uniform
9
Very uniform
Description
Height, vigor, growth stage very
heterogeneous.
75% of the plants show height, vigor
and growth stage heterogeneity.
50% of the plants show height, vigor
and growth stage heterogeneity.
75% of the plants show height, vigor
and growth stage homogeneity.
100% of the plants show height, vigor,
growth stage homogeneity.
Heterogeneous
Very heterogeneous
Intermediate
3
1
5
Very uniform
Uniform
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9
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e) Plant vigor (Plant_Vigor): This evaluation is performed 45 days
after planting and should be evaluated using a scale from 1 to 9.
(Salas et al., 2004).
Scale
State
1
Very weak
3
Weak
5
Medium
7
Vigorous
9
Very
vigorous
Description
All the plants are small (< 20 cm), few leaves, weak
plants, very thin stems and/or light green color.
75% of the plants are small (< 20 cm) or all the plants
are between 20 and 30 cm, the plants have few
leaves, thin stems and/or light green color.
Intermediate or normal.
75% of the plants are over 50 cm, robust with foliage
of dark green color, thick stems and leaves very well
developed.
All the plants are over 70 cm and ground coverage is
complete. The plants are robust, with thick stems and
abundant foliage of dark green color
Very weak
Weak
1
Medium
Very vigorous
Vigorous
5
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3
7
9
f) Flowering degree (Flowering) 2: This evaluation is preformed 60
days after planting and recorded using a scale from 1 to 7
(Bioversity & CIP, 2009; Gomez, 2004)
Scale
State
0
No bud
1
Aborted bud
3
Low
5
Moderate
7
Profuse
Description
No inflorescence although inflorescence are
rudimentary and consequently of buttons.
Presence
of
small or
rudimentary
inflorescences that can show an abortion or
abscission point at the joint of the pedicel.
Flowering is scarce with the presence of 2 to
3 flowers (buds, flower buds, flowers, fruits
and flower abscissions) per inflorescence.
Flowering is moderate with 8 to 12 flowers
(buds, flower buds, flowers, fruits and flower
abscissions) per inflorescence.
Profuse flowering with 20 or more flowers
(buds, flower buds, flowers, fruits and flower
abscissions) per inflorescence.
Aborted Bud
No Bud
0
Low
3
1
Moderate
Profuse
5
7
2
It is not necessary to collect this variable for multiple plots or years. Once the
flowering degree is established no re-recording is required.
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g) Senescence (SE): This evaluation is performed 70 or 90 days
after planting (Depending on the vegetative period of the
genotypes) and should be evaluated using a scale from 1 to 9.
(Amoros & Gastelo, 2011. Personal communication)
Scale
State
Description
1
Very late
All the plants still show green foliage and flowers
3
Late
Most of the plants are still green, flowering is over and
berries might be formed.
5
Medium
7
Early
9
Very early
The plants are still being green or on the onset of
senescence, there may be a slight yellowing. The angle
of insertion of the leaves on the stems may have
become more obtuse than in the younger plants of the
same clone. The formation of berries can be advanced
and abundant in fertile clones.
The plants have senescent foliage, yellowing is more
advanced but the stems may still be upright. If berries
are present, their color will turn from green to pale
green or yellow green.
The plants are completely senescent, yellowing is
complete and uniform, and the stems are decumbent.
1
3
Early
5
Very early
7
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Medium
Late
Very late
9
Certainly observations on disease and pest damage can also be
recorded. Because the purpose of the trial is to evaluate yield
under standard or optimum crop management, integrated crop
management practices should be used to control pests and
diseases.
The harvest period
The foliage should be burned or cut 10 to 15 days prior to
harvesting. It is recommended that evaluations are conducted in
the following sequence:
a) Number of plants harvested (NPH)
b) Number of stolons (Num_Stolon) 3: Overall assessment of the
number and length of the stolons based on inspection of the
stolons using a 1 to 9 scale. (Amoros & Gastelo, 2011. Personal
communication)
Scale
State
Description
1
Very few
Plants show no stolon or very few (0 to 4).
3
Few
Plants with 5 to 10 stolons.
5
Medium
Plants with 11 to 15 stolons.
7
High
Plants with 16 to 25 stolons.
9
Very high
Plants with more than 25 stolons.
3
It is not necessary to collect this variable for multiple plots or years. Once the variable
is established no re-recording is required.
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c) Lenght of stolons (Leng_Stolon) 4
Scale
State
Description
1
Very short
X ≤ 20 cm long.
3
Short
20 cm <X ≤ 40 cm long.
5
Medium
40 cm < X ≤ 60 cm long.
7
Long
60 cm < X ≤ 80 cm long.
9
Very long
X > 80 cm long.
1
3
5
Very long
Long
7
4
Medium
Short
Very short
9
It is not necessary to collect this variable for multiple plots or years. Once the variable
is established no re-recording is required.
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d) Tuber Appearance (Tuber_Apper): (Amoros & Gastelo, 2011.
Personal communication)
Scale
State
1
Very poor
3
Poor
Low yield, some misshapes but non uniform size
5
Regular
Medium yield, good shape but non uniform size
7
Good
Good yield, good shape and uniform size
9
Very good
High yield, good shape and very uniform size
Very poor
Description
Very low yield, totally misshapes and non-uniform
size
Regular
1
Very good
5
9
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e) Tuber uniformity (Tub_Unif): Overall assessment of tuber
uniformity is based on the inspection of the harvested tubers using
a 1 to 9 scale. (Amoros & Gastelo, 2011. Personal communication)
Scale
State
1
Very heterogeneous
3
Heterogeneous
5
Intermediate
7
Uniform
9
Very uniform
Very heterogeneous
1
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Description
All tuber sizes are present (from very
small to large)
All tuber sizes are present but there is
a predominant size
There are only 2 or 3 tuber sizes with
a predominant size
Only two sizes are present with a
predominant tuber size
Only one tuber size
Intermediate
Very uniform
5
9
f) Tuber size (Tub_size): Data is collected using a 1 to 9 scale.
(Amoros & Gastelo, 2011. Personal communication)
Scale
1
State
Description
1
Very small
Most tubers are very small (<2cm).
3
Small
Tubers are small, between 2 and 4cm.
5
Medium
Tubers are between 4 and 6cm
7
Large
Tubers are large, between 6 and 9 cm.
9
Very large
Tubers are over 9 cm.
Very small
7
3
Large
Small
5
9
Medium
Very large
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g) Number marketable tubers category I/plot (NMTCI): Count the
number of marketable tubers for category I with weighing
between 200-300 g or tubers of 60 mm.
h) Number marketable tubers category II/plot (NMTCII): Count
the number of marketable tubers category II with weighing
between 80-200 g or tubers between 30-60 mm.
These categories I and II are arbitrary and can be change according
to the country or region where are being evaluated. Each evaluator
is free to use locally relevant criteria; however, each category
should be defined in order to facilitate comparison of data
between countries.
i) Number of non-marketable tubers/plot (NNoMTP): Count the
number of non marketable tubers with weighing less of 80 g or less
of 30 mm.
j) Marketable tuber weight category I/plot (MTWCI): Weigh
marketable tuber category I/plot. The unit of measure is Kilograms.
k)Marketable tuber weight category II/plot (MTWCII): Weigh the
marketable tuber category II/plot. The unit of measure is
Kilograms.
m) Non-marketable tuber weight/plot (NoMTWP): Weigh the
non-marketable tuber/plot. The unit of measure is Kilograms.
Data should be checked for any errors made during collection or
transcription and checked as soon as possible afterwards so that
corrections can be made where necessary.
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Calculated of variables
Several variables can be derived from the raw data the Healthy
Tuber Yield Trials. We consider: Total Tuber Yield, Marketable
Tuber Yield and Average Tuber Weight as a must.
Variable
Abbreviations
Unit
Formula
Percentage of
Plants Emerged
PPE
Percentage
Percentage of
Plants Harvested
PPH
Percentage
Number
Marketable
Tubers/Plot
NMTP
Count
Total Number of
Tubers/Plot
𝐍𝐌𝐓𝐏 = NMTCI + NMTCII
TNTP
Count
Total Number of
Tubers/Plant
TNTPL
Count
𝐓𝐍𝐓𝐏 = NMTP + NNoMTP
Number
Marketable
Tubers/Plant
NMTPL
Count
Total Tuber
Weight/Plot
TTWP
kg
Total Tuber
Weight/Plant
TTWPL
kg/pl
Total Tuber Yield
Adjusted
TTYA
tons/ha
Total Tuber Yield
No Adjusted
TTYNA
tons/ha
𝐏𝐏𝐄 =
𝐏𝐏𝐇 =
NPE ∗ 100
NTP
NPH ∗ 100
NTP
𝐓𝐍𝐓𝐏𝐋 =
𝐍𝐌𝐓𝐏𝐋 =
TNTP
NPH
NMTP
NPH
𝐓𝐓𝐖𝐏 = MTWCI + MTWCII
+ NoMTWP
𝐓𝐓𝐖𝐏𝐋 =
𝐓𝐓𝐘𝐀 =
TTWP
NPH
TTWPL ∗ PLD
1000
𝐓𝐓𝐘𝐍𝐀 = �
TTWP
� ∗ 10
PLS
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Variable
Marketable Tuber
Weight/Plot
Marketable Tuber
Weight/Plant
Marketable Tuber
Yield Adjusted
Marketable Tuber
Yield No Adjusted
Average Tuber
Weight
Average
marketable tuber
weight
Abbreviations
Unit
MTWP
kg
MTWPL
kg/pl
MTYA
tons/ha
MTYNA
tons/ha
ATW
g
ATMW
g
Formula
𝐌𝐓𝐖𝐏 = MTWCI + MTWCII
MTWP
NPH
MTWPL ∗ PLD
𝐌𝐓𝐘𝐀 =
1000
MTWP
𝐌𝐓𝐘𝐍𝐀 = �
� ∗ 10
PLS
TTWP
𝐀𝐓𝐖 = �
� ∗ 1000
TNTP
𝐌𝐓𝐖𝐏𝐋 =
MTWP
𝐀𝐓𝐌𝐖 = �
� ∗ 1000
NMTP
Where: PLS= Net plot size and PLD=Planting Density.
Other evaluations
A random sample of 10 tubers per clone should be cut
transversally and checked for:
a) External defects: such as cracking, secondary growth and warts,
and
24|P á g i n a
b) Internal problems: Such as hollow heart, black spots, heat
necrosis, and rot. Internal defects should be reported at harvest
time. This is critical for estimating processing quality.
c) Percentage of defected tubers: For each entry, the number of
affected tubers is recorded on the tuber yield datasheet.
Data recording
CIP together with partners are promoting the use of Data Collector
software that helps to standardize and ensure data quality (Simon
et al., 2012); it is part of the International Potato Center’s Global
Data Management System and assists researchers in data analysis
by automatically calculating the variables for Healthy Tuber Yield
Trials.
The
information
should
be
recorded
onto
form
DataCollector.
Phase
Component
Minimal - basic
data
Experimental
design
Field
Field
Installation and management
characterization and evaluation
dates
to define
List of
selection
materials
environments
Climate data
Soil analysis
Experiment
results
Observed and
calculated
variables
Method
Registration Form
List
Minimal
List
Installation
List
Crop_Management
List
Material_list
Weather
station
Soil
analysis
Weather_data
Soil_Analysis
Fieldbook
25|P á g i n a
Data analysis
Types of variables:
Quantitative continuous and discrete variables: Numeric
variables following approximately a normal distribution (e.g.
Total Tuber Yield, Dry Matter, etc...) are analyzed using
parametric statistics.
Quantitative
ordinal
(pseudoquantitative)
variable: Numeric variables
which
show
in
their
distribution strong deviation
from a normal distribution
(e.g. The percent of plant
infection (which is used in
evaluating clonal resistance
to a disease). This variable,
which
represents
the
evaluator’s estimation of the
damage, is more a rank than
a measurement.
Qualitative ordinal
variables:
Data cannot be measured,
they are ranked or attached
to a rating scale. (e.g. Scores
with a scale of 1 to 9 for
plant uniformity or scores
with a scale 1 to 3 for plant
growth
habit).
Ordinal
variables are analyzed and
compared
using
nonparametric methods of
analysis.
Data Quality Control:
Simple statistics such as mean, standard error, frequency
distribution and boxplots should be used to explore the data. Yield
data are analyzed using variance analysis (ANOVA) and means are
compared using statistical comparison tests such as LSD, Tukey,
Waller-Duncan, and Bonferroni. Orthogonal contrasts and Dunnett
tests can be used to compare the advanced clones with the
control(s). The analysis of residuals is recommended to test the
validity of the model and to analyze the behavior of the variance
(homogeneous or not). All analysis can be performed using
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DataCollector Software or other statistical packages that facilitates
analysis and reports of the results.
Example and Data interpretation
Validation of the experiment: An experimental trial for tuber yield
evaluation is considered to have been carried out under
appropriate conditions if the experiment’s coefficient of variation
does not exceed 30%.
Selection criteria: Performance of each advanced clone is
compared with the performance of the control(s). It is important
to consider the commercial yield of the entry rather than the total
yield. In most situations, the ability of a clone to develop numerous
small tubers will be viewed as a negative characteristic.
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IV.- FILLING THE DataCollector TEMPLATE OF
HEALTHY TUBER YIELD
Form: Minimal
DataCollector software will
complete this information
according with your
locality.
Be sure to complete the
“Begin date” and the “End
date”. Please write and
single quote before the
date The correct format
date is: ‘yyyy-mm-dd.
e.g. ‘2014-04-07
[Back to “Data Recording” table]
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Form: Installation
Please complete this form according your experimental design.
Note: Consider the net plot size.
[Back to “Data Recording” table]
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Form: Material_List
Here you need to complete the code clones in the column "Institutional
number", pedigree information and mark with "x" the clone or genotype control.
Form: Crop management
Summarize all procedures that were performer in the experiment (date of
planting, evaluation dates and all field management data that you consider
important. The correct format date is: yyyy-mm-dd, please write and single
quote before the date. e.g. ‘2014-04-07.
[Back to “Data Recording” table]
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Forms: Soil analysis & Weather data
Complete this information with your experiemtnal data.
[Back to “Data Recording” table]
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Form: Var_List
Type on Selection direction (+) and on Selection weight (1) depending on the
variable to analyze.
[Back to “Data Recording” table]
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Form: Fieldbook
Allows the entry of observed and calculated data of the variables that were
performed according to the experimental design.
[Back to “Data Recording” table]
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V.- ANNEX 1
A brief description of commonly used incomplete block designs
Split-Plot Design
Characteristics
The split-plot design is a special kind of incomplete block design.
The underlying principle of the split-plot design is that whole plots,
subject to one or more treatments (factor A), are divided into
subplots to which one or more additional treatments are applied
(factor B). Thus, each whole plot may be considered as a block for
subplot treatments (factor B), but only as an incomplete block as
far as the full set of treatments is concerned (factor A + B). The
design may be used when an additional factor (such as planting
density or fertilizer use) is to be incorporated into an experiment
to increase its scope.
Randomization
Randomization is a two-stage process. First, factor A treatments
are randomized over the whole plot; then factor B treatments are
randomized within the subplots.
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Partially Balanced Lattice Design
Characteristics
The partially balanced lattice design is recommended when the
number of treatments is very large or when the experimental units
are very heterogeneous. Lattice designs are incomplete block
designs. Each block does not contain all treatments, so the
precision of comparison between treatments differs depending if
the treatments belong to the same block or not. The lattice design
(also called double lattice or square lattice), is a partially balanced
design in which the number of treatments is a perfect square (9,
16, 25, 36, 49, 64, 81, 121 etc.) and the number of treatments
within each block is equal to the square-root of the total number
of treatments. This design needs two or multiples of two
replications. The experimental units within each incomplete block
should be as homogeneous as possible.
Randomizing
Treatments are arranged in the form of a square (step 1).
Treatments are grouped by row, and then by columns. The row
grouping is generally known as X grouping. The group of
treatments in one row will form a block.
All the rows (blocks) will make one repetition (step 2). The column
grouping is generally known as Y grouping. The group of
treatments in one column will constitute another block. This Y
grouping will form the other repetition (step 3). The X grouping
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and Y grouping ensure that treatments occurring together in the
same block once do not appear together in the same block again.
For each repetition, the randomization is a three-stage process:
the blocks are randomized, each treatment is randomized within
each block (step 4), and ultimately a treatment is randomly
assigned to each plot.
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Experimental design for large number of genotypes
Phenotypic data are vitally important for assessment of the withinenvironment error structure for each of the trials that will be used
later in the MET analysis. Therefore, appropriate control of local
variability through efficient experimental design is of key
importance. Spatial variability in the field is a universal
phenomenon that affects the detection of differences among
treatments in agricultural experiments by inflating the estimated
experimental error variance. Recently, efficient experimental
designs (both unreplicated and replicated) have been developed,
assuming that observations are not independent in that
contiguous plots in the field may be spatially correlated (Cullis et
al., 2006; Martin et al., 2004).
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Alpha-lattice
Characteristics
It is common in many experiments have a large number of seedlots
and a small number of replicates. In this case, one of the most
suitable design is the Alpha-lattice; an incomplete block design
that divide the replicates into incomplete blocks that contain a
fraction of the total number of treatments.
Treatments are distributed among the blocks so that all pairs occur
in the same incomplete-block in nearly equal frequency.
The breeder should beer in mind these conditions (Patterson &
Williams, 1976):
→ The number of genotypes (v) must be multiple of the size
block(k).
→ The number of block (s) is calculated dividing the number of
genotypes and the block size: s=v/k .
→ The parameters for Alpha design are divided in four cases:
Case I:
Case II:
Case III:
Case IV:
When r=2 and k ≤ s;
When r=3, the number of blocks(s) is odd and
k ≤ s.
When r=3, the number of blocks (s) IS even and
k ≤ s-1;
When r=4, the number of blocks (s) is odd but
not a multiple of 3, and k ≤ s.
→ The range of number of replication (r) can vary from two
until four (r=2,3,4).
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Randomizing
→ Example 1
An example of the randomization in Alpha design with two
replication (r = 2), 9 treatments (v = 9) and block size (k = 3). Thus,
the number of blocks is s =3.
→ Example 2
Another example with T=32 treatments {T1, T2, T3,…,T32},
two replications (r=2) and block size k=4. Thus the number
of block is s=8.
A graphical representation of the example 2,
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Augmented block design
Characteristics
The augmented designs are very useful for testing many genotypes
or clones but taking as limiting the number of seed tubers.
These designs just make one repetition for treatment and include
checks or controls which are systematically replicated in each
block to control the heterogeneity of the environment. The
repeated checks measure the spatial variation and the
experimental units (without repetition) are assessed on the basis
of adjacent checks.
The experiments are usually conducted using an augmented
randomized complete block design, even more incomplete block
designs.
Randomizing
Let c: number of different checks per block
Let r: number of blocks=number of replicates of a check
Let V = 30 genotipos {V1,V2,…..V30}
and the number of
checks(#checks) = 3 {a,b,c}
Number of blocks: ((10)/(c-1))+1 = (10/2)+1 = 6
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VI.- BIBLIOGRAPHY
Bioversity International; International Potato Center (CIP) 2009. Key access
and utilization descriptors for cultivated potato genetic resources.
http://www.bioversityinternational.org/nc/publications/publication/issue/key
_access_and_utilization_descriptors_for_cultivated_potato_genetic_resources
.html
Cullis, B. R.; Smith, A. B.; &Coombes, N. E. (2006). On the design of early
generation variety trials with correlated data. Journal of Agricultural, Biological,
and Environmental Statistics, 11 (4), 381-393.
International Potato Center (CIP). 2006. Procedures for standard evaluation
trials of advanced potato clones. An International Cooperators’ Guide.
Gomez, K.A.; Gomez, A.A. 1984. Statistical Procedures in Agricultural Research,
New York, Chichester, etc.: Wiley, 2nd edition, paperback, pp. 680
Gomez, R. (CIP). 2004. Guía para las caracterizaciones morfológicas básicas en
colecciones de papas nativas: Sexta aproximación. Conservación In Situ de
Cultivos Nativos y sus Parientes Silvestres INIEA. Seminario - Taller Nacional de
Caracterización In Situ. Lima (Peru). INIEA. pp. 44-71.
Martin, R.; Eccleston, J.; & Chan, B. (2004) Efficient factorial experiments when
the data are spatially correlated. Journal of Statistical Planning and Inference,
126(1), 377-395.
Patterson, H.D. & Williams, E. R. (1976). A new class of resolvable incomplete
block designs. Biome- trika, 63(1), 83-92.
Salas, E.; Mihovilovich, E.; Amoros, W.; Bonierbale, M. (CIP). 2004. Estimation
of variability for PLRV resistance in tropically adapted potatoes. American
Journal of Potato Research. (USA). ISSN 1099-209X. 81(1):86
Simon, R., Salas, E., Eyzaguirre, R., Hualla, V., de Haan, S., & Bonierbale, M.
(2012). Desarrollo de un software estadístico para estandarización y chequeo
de calidad de datos del campo en raíces y tubérculos (GDET4RT) para el
mejoramiento de cultivos. In 12th International Conference, ALAP, Uberlandia
Brazil.
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z
The International Potato Center (known by its Spanish acronym CIP)
is a research-for-development organization with a focus on potato,
sweetpotato, and Andean roots and tubers. CIP is dedicated to
delivering sustainable sczzience-based solutions to the pressing
world issues of hunger, poverty, gender equity, climate change and
the preservation of our Earth’s fragile biodiversity and natural
resources.
www.cipotato.org
CIP is a member of CGIAR.
CGIAR is a global agriculture research partnership for a food-secure
future. Its science is carried out by the 15 research centers who are
members of the CGIAR Consortium in collaboration with hundreds of
partner organizations.
www.cgiar.org
International Potato Center • Av. La Molina 1895, La Molina •
Apartado 1558 Lima 12, Perú
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