Notre Mission : étudier les interactions entre la plante et son environnement qui ont un impact sur l'état sanitaire des plantes, leur croissance et leur productivité. Il s'agit d'interactions avec des organismes microbiens ou des insectes, qu’ils soient pathogènes ou bénéfiques.

Nos objectifs

  • Comprendre comment une plante interagit avec des micro-organismes et des bioagresseurs.
  • Analyser l'impact de ces interactions sur la santé de la plante
  • Proposer des solutions pour assurer une bonne productivité des cultures

Principales cultures concernées

Riz, blé, banane, café, cacao, canne à sucre, manioc, orge, agrume, Aeschynomene

Organismes étudiés

Virus, Bactéries, Champignons, Nématodes, Insectes

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21 to 28 of 28 Results
Figure_7_Figure_S3.moving_averages.r
16 déc. 2021 - Data for: "The Dark side of shade: How microclimates drive the epidemiological mechanisms of coffee berry disease"
R Syntax - 29,5 KB - MD5: b01bc2a3811026bde6bbfddc1ecae139
codes for making figures
Figure_S1.model_goodness_of_fit.r
16 déc. 2021 - Data for: "The Dark side of shade: How microclimates drive the epidemiological mechanisms of coffee berry disease"
R Syntax - 4,9 KB - MD5: 0f17add8ded3749e4f2054dce74b22fa
codes for making figures
make.r
16 déc. 2021 - Data for: "The Dark side of shade: How microclimates drive the epidemiological mechanisms of coffee berry disease"
R Syntax - 840 B - MD5: 789d3a3af703fd1b1ef2bdc20c0203fc
Description of the code steps
R_analyses_seir_cbd.Rproj
16 déc. 2021 - Data for: "The Dark side of shade: How microclimates drive the epidemiological mechanisms of coffee berry disease"
Unknown - 218 B - MD5: 584b2434d675b192dc1614bd62b5b93e
R project
RESfinal21_09_2021.rda
16 déc. 2021 - Data for: "The Dark side of shade: How microclimates drive the epidemiological mechanisms of coffee berry disease"
R Data - 202,1 MB - MD5: fc70b42d42bfd4a839910992e9291737
MCMC chains - output of MecaStat
Table_S1.Rhat.r
16 déc. 2021 - Data for: "The Dark side of shade: How microclimates drive the epidemiological mechanisms of coffee berry disease"
R Syntax - 944 B - MD5: 961005af4090bf555ef2e720fd94509e
codes for computation of the R.hat
Dexi model for: "Improved forecasting of coffee leaf rust by qualitative modeling: design and expert validation of the ExpeRoya model"
16 déc. 2021
Motisi, Natacha; Bommel, Pierre; Leclerc, Grégoire; Robin, Marie-Hélène; Aubertot, Jean-Noël; Arias Butron, Andrea; Merle, Isabelle; Treminio, Edwin; Avelino, Jacques, 2021, "Dexi model for: "Improved forecasting of coffee leaf rust by qualitative modeling: design and expert validation of the ExpeRoya model"", https://doi.org/10.18167/DVN1/LSYFJR, CIRAD Dataverse, V1
Dexi version of ExpeRoya model. The model structure is a tree that aggregates variables in a hierarchical way according to available knowledge on coffee leaf rust (CLR) and coffea arabica (the host) dynamics. ExpeRoya integrates 229 multiple interactions that exist within the CLR...
ExpeRoya_dataverse.dxi
16 déc. 2021 - Dexi model for: "Improved forecasting of coffee leaf rust by qualitative modeling: design and expert validation of the ExpeRoya model"
Unknown - 56,8 KB - MD5: 80ead10fbf1147e3eabbf96787bbeae2
Dexi structure of the ExpeRoya model
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