Version 0.24

Be aware that this package is under development and test do not 100% trust the functions!!! You’re welcome to contribute: see here for more informations.

Authors and contributors

Pierre Rivière1 maintains and coordinates the package development, writes the R functions and the book

Gaelle Van Frank2 tested the package and updated the code regarding some functions

Olivier David3 wrote the JAGS code and reviewed the R code and the book regarding some functions

Facundo Muñoz4 reformated and improved all the code regarding S3 methods, format figures with Tikz, improve section regarding contributions.

Camille Vindras5 wrote text to present method description in sensory analysis

Mathieu Thomas6,7 proposed the data format as well as the descriptive analysis regarding network section

Isabelle Goldringer2 reviewed and improved all the text of the book

1 Réseau Semences Paysannes, 3 avenue de la gare, F-47190 Aiguillon, France

2 INRA, UMR 0320, Génétique Quantitative et Evolution, Ferme du Moulon F-91190 Gif sur Yvette, France

3 INRA, UR 1404 Unité Mathématiques et Informatique Appliquées du Génome à l’Environnement, F-78352 Jouy-en-Josas, France

4 INRA, Centre Val de Loire, Unité Amélioration, Génétique et Physiologie Forestières, F-45075 Orléans, France

5 ITAB, Ferme Expérimentale, 2485 Route des Pécolets, F-26800 Etoile-sur-Rhône, France

6 CIRAD, UMR AGAP, F-34398 Montpellier, France

7 AGAP, Univ Montpellier, CIRAD, INRA, Montpellier SupAgro, Montpellier, France

Key words

R package, Participatory Plant Breeding, statistical analysis


Participatory Plant Breeding (PPB) is based on the decentralization of evaluation and selection in the fields of farmers and gardeners (Desclaux et al. 2008). All actors such as farmers, technicians, researchers, facilitators, consumers, … are involved in the decision-making process at all stages of the PPB programme. Such kind of involvement empowers all actors and responds to their needs (Sperling et al. 2001). During PPB programmes, experiments are carried out and different types of data are produced and must be analysed in order to support farmers in their selection. These data covers the history of seed management (circulation, mixture, reproduction, selection, etc), agronomic trials, organoleptic tests and molecular data. Several programmes exist to analyze these different types of data but they are not always freely available and often scattered in different softwares.

Objectives of PPBstats

PPBstats is a freely available package based on the R software (R Core Team 2018) that performs analyses on the data collected during PPB programs at four levels:

  • network of seed management,
  • agronomic trials,
  • organoleptic tests and
  • molecular analyses.

The objectives of PPBstats are

  1. to have a single package capable of performing all analyses required for PPB programmes with comprehensive documentation, and
  2. to create a community working on PPB programmes in order to improve the package, exchange on how to process data from PPB programmes and develop good practices.

Examples of analysis performed by PPBstats

After a step of data formatting a workflow of analysis is carried out. For network analysis, only descriptive methods are provided, and graphical representations are available.
For agronomic analyses, decision trees are available in order to perform the most appropriate analysis depending to the objective and the experimental constraints such as number of plots or seed available. Descriptive analyses such as barplot, radar, boxplot, interaction plot, maps can be done. Several models such as spatial analysis, mixed model, bayesian hierarchical models, AMMI and GGE are implemented. For organoleptic data, hedonic and napping analyses can be performed. For each model, a function checks if the model went well and mean comparisons as well as biplots can be visualized. For molecular data, only descriptive plots on the markers are implemented.


Contributions to PPBstats are very welcome and can be made in four different ways:

  1. testing the package and reporting bugs,
  2. improving the code,
  3. improving the documentation and
  4. translating

More information can be found here.


Desclaux, D., J. M. Nolot, Y. Chiffoleau, C. Leclerc, and E. Gozé. 2008. “Changes in the Concept of Genotype X Environment Interactions to Fit Agriculture Diversification and Decentralized Participatory Plant Breeding: Pluridisciplinary Point of View.” Euphytica 163: 533–46.

R Core Team. 2018. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.

Sperling, L., J.A. Ashby, M.E. Smith, E. Weltzien, and S. McGuire. 2001. “A Framework for Analyzing Participatory Plant Breeding Approaches and Results.” Euphytica 122 (3): 439–50.