PPBstats website
Preface
Contact
Authors and contributors of
PPBstats
and the book
Copyright, Licence, citation
1
Philosophy of PPBstats
1.1
What is Participatory Plant Bredding ?
1.1.1
Decentralize the selection
1.1.2
Involve all actors in the breeding decision process
1.2
Objectives of
PPBstats
1.3
Contribute
1.4
Design and statistical methods according to the objectives
1.5
Let’s go: install
PPBstats
and the data used in the book!
2
Analysis of network seed circulation (
M8
)
2.1
Introduction
2.1.1
Workflow and function relations in
PPBstats
regarding network analysis
2.2
Unipart network for seed lots analysis
2.2.1
Steps with
PPBstats
2.2.2
Format the data
2.2.3
Describe the data
2.3
Unipart network for location analysis
2.3.1
Steps with
PPBstats
2.3.2
Format the data
2.3.3
Format and describe the data for each germplasm
2.3.4
Format and describe the data for each year
2.4
Bipart network analysis
2.4.1
Steps with
PPBstats
2.4.2
Format the data
2.4.3
Describe the data
3
Analysis of agronomic traits
3.1
Introduction
3.1.1
Analysis according to the objectives
3.1.2
Family of analyses
3.1.3
Experimental design
3.1.4
Decision tree
3.1.5
Workflow and function relations in
PPBstats
regarding agronomic analysis
3.1.6
Data format
3.2
Design the experiment
3.2.1
Fully-replicated (
D1
)
3.2.2
Incomplete Block Design (
D2
)
3.2.3
Row-column (
D3
)
3.2.4
Regional and satellite farms (
D4
)
3.3
Sow, note, harvest, measure …
3.4
Describe the data
3.4.1
Format the data
3.4.2
presence abscence matrix
3.4.3
histogramm
3.4.4
barplot
3.4.5
boxplot
3.4.6
interaction
3.4.7
biplot
3.4.8
radar
3.4.9
raster
3.4.10
map
3.5
Family 1 of analysis : Compare different varieties evaluated for selection in different locations.
3.5.1
Decision tree
3.5.2
Workflow and function relations in
PPBstats
regarding family 1 of analysis
3.5.3
Classic ANOVA (
M4a
)
3.5.4
Spatial analysis (
M4b
)
3.5.5
Mixted model for incomplete block design (
M5
)
3.5.6
Hierarchical Bayesian intra-location model to perform mean comparisons with each farm (
M7a
)
3.6
Family 2 of analysis : Study the response of varieties under selection over several environments
3.6.1
Decision tree
3.6.2
Workflow and function relations in
PPBstats
regarding family 2 of analysis
3.6.3
AMMI (
M6a
)
3.6.4
GGE (
M6b
)
3.6.5
Hierarchical Bayesian
\(G \times E\)
model to analyse
\(G \times E\)
interaction in the network of farms (
M7b
)
3.7
Family 3 of analysis : effects from family 1 and 2 in a network of farms
3.7.1
model 3
3.8
Family 4 of analysis : specific research questions on one farm or more
3.8.1
Study reponse to selection
3.8.2
Study local adaptation
3.8.3
Study variance within germplasm
3.9
Family 5 of analysis : multivariate analysis
3.9.1
Decision tree
3.9.2
Workflow and function relations in
PPBstats
regarding family 2 of analysis
3.9.3
Steps with
PPBstats
3.9.4
Format the data
3.9.5
Look at correlation
3.9.6
Run the analysis
3.9.7
Check outputs and results
4
Sensory analysis
4.1
Introduction
4.1.1
Analysis according to the objectives
4.1.2
Decision tree
4.1.3
Workflow and function relations in
PPBstats
regarding organoleptic analysis
4.1.4
Data format
4.2
Taste and measure …
4.3
Hedonic analysis (
M9b
)
4.3.1
Method description
4.3.2
Steps with
PPBstats
4.3.3
Format the data
4.3.4
Describe the data
4.3.5
Run the model
4.3.6
Check and visualize model outputs
4.3.7
Get and visualize mean comparisons on note
4.3.8
Get and visualize biplot regarding samples (CA) and juges (HCPC)
4.4
Napping analysis (
M9a
)
4.4.1
Method description
4.4.2
Steps with
PPBstats
4.4.3
Format the data
4.4.4
Describe the data
4.4.5
Run the model
4.4.6
Check and visualize model outputs
4.4.7
Get and visualize biplot
4.5
Rank analysis (
M9c
)
4.5.1
Method description
4.5.2
Steps with
PPBstats
4.5.3
Format the data
4.5.4
Run the model
5
Molecular analysis (
M3
)
6
Communicate results to farmers
6.1
Technical aspects
6.1.1
Run the analysis
6.1.2
Create a
.Rmd
template
6.1.3
Generate a report for each location
6.2
Organisational aspects on collective work
Acknowledgement
References
PPBstats website
PPBstats
: An
R
package for Participatory Plant Breeding statisticial analyses
4.2
Taste and measure …
!!! TO DO !!!