Archives

Hubert, Marteau

Email: marteau@egce.cnrs-gif.fr

In charge of the following activities:


Beekeeper:
In charge of the follow-up of about ten colonies on the site of Gif-sur-Yvette. In collaboration with Jean-Christophe Sandoz’s team, production of queens, males and workers.
In collaboration with Lionel Garnery, monitoring of the CANIF apiaries (Cernay, Bullion, Rochefort).
Extraction of honey and maintenance of the equipment (hives and honey house).


Breeder of Sesamia colonies:
With Laure Kaiser-Arnauld’s team, participation in the monitoring of sesame breeding.

Cette entrée a été publiée le 20 novembre 2020, dans DEEIT, Personnels.

Bonoukpoé, Sokame

Consultant postdoctoral à l’ICIPE dans l’unité de Gestion des données, modélisation et géoinformation

Email: bsokame@icipe.org

Travaux

Responsabilité principale : Mettre en œuvre un projet sur : « Mesure et modélisation des pertes de rendement des cultures dues aux insectes ravageurs dans un contexte de réchauffement climatique ».

Plus précisément :

  • Développer un modèle dynamique des systèmes de la dynamique des populations d’insectes ravageurs dans les agroécosystèmes de culture,
  • Concevoir et réaliser des expériences sur le potentiel de dommages causés par les insectes ravageurs à la plante hôte en fonction de la température et du CO2 en phytotron,
  • Concevoir et mener des expériences sur les pertes de rendement en serre dépendantes des dommages,
  • Développer des modèles avec une modélisation dynamique basée sur les processus (phénologie des insectes ravageurs et modèles de cultures),
  • Tester, évaluer et analyser les modèles pour des prévisions futures:
    • formuler et ajuster les fonctions de dommages représentant la conversion des dommages causés par les insectes en perte de rendement,
    • formuler et ajuster la fonction de perte en convertissant les dommages en pertes économiques,
    • évaluer le modèle développé en comparant les résultats de la simulation aux ensembles de données observées,
    • estimer l’applicabilité du modèle aux nouvelles conditions environnementales en comparant le comportement aux facteurs et processus clés.

Autres responsabilités :

  • Développer une approche méthodologique pour la prévision et la cartographie de l’adaptation phénologique des cultures céréalières tropicales (maïs) en utilisant des essais multi-environnementaux,
  • Formation sur la modélisation des systèmes dynamiques,
  • Superviser/assister les étudiants et les techniciens de premier et de troisième cycle.

Publications

Sokame, B.M., Ntiri, E., Ahuya, P., Torto, B., Le Ru, B.P., Kilalo, D.C., Juma, G., Calatayud, P.-A. (2019).  Caterpillar-induced plant volatiles attract conspecific and heterospecific adults for oviposition within a community of lepidopteran stem borers on maize plants. Chemoecology, 29, 89-101.

Sokame, B.M., Rebaudo, F., Musyoka, B., Obonyo, J., Mailafiya, D.M., Le Ru, B.P., Kilalo, D.C., Juma, G., Calatayud, P.-A. (2019). Carry-over niches for lepidopteran maize stemborers and associated parasitoids during non-cropping season. Insects, 10, 191, doi:10.3390/insects10070191.

Sokame B.M., Rebaudo F., Malusi P., Subramanian S., Killo D.C., Juma G., Calatayud P.-A. (2020). Influence of Temperature on the Interaction for Resource Utilization between Fall Armyworm, Spodoptera frugiperda(Lepidoptera: Noctuidae) and a Community of Lepidopteran Maize Stemborers Larvae. Insects, 11, 73. doi:10.3390/insects11020073.

Sokame B.M., Subramanian S., Kilalo D.C., Juma G., Calatayud P.-A. (2020). Larval dispersal of the invasive fall armyworm, Spodoptera frugiperda, the exotic stemborer Chilo partellus, and indigenous maize stemborers in Africa. Entomologia Experimentalis et Applicata, 168, 322–331.

Sokame B.M., Obonyo J., Sammy E.M., Mohamed S.A.,Subramanian S., Kilalo D.C., Juma G., Calatayud P.-A. (2020). Impact of the exotic fall armyworm on larval parasitoids associated with the lepidopteran maize stemborers in Kenya. BioControl, https://doi.org/10.1007/s10526-020-10059-2

Sokame B.M., Tonnang H.E.Z., Subramanian S., Bruce A.Y., Dubois T., Ekesi S., Calatayud P.-A. 2021. A system dynamic model for pests and natural enemies interactions. Scientific Reports, 11, 1401, https://doi.org/10.1038/s41598-020-79553-y

Cette entrée a été publiée le 22 octobre 2020, dans DEEIT, Personnels.

Ovide, Nuambote

Doctorant

Tel: 01 69 15 49 78

Email: ynuambote@gmail.com

Titre de la thèse (en anglais): Mechanisms of resistance in the African maize germplasm to the fall armyworm, Spodoptera frugiperda (J.E. Smith)

Résumé (en anglais): Maize (Zea mays L.) is the third largest crop in the world after rice and wheat. In Africa, it is the most important food crop in terms of area harvested and alone provides more than 30% of the total calories of the human population in sub-Saharan Africa. The fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), a pest of maize native to the Americas was first reported in West Africa in 2016, is severely threatening food security in sub-Saharan Africa through the loss of tens of millions of tons of maize production each year according to FAO’s 2018 estimates. In the African context where the majority of maize producers are smallholder farmers with limited access to knowledge and adequate inputs to properly manage this new pest, the use of resistant varieties of the host, obtained either through conventional plant breeding methods or through silica induction (a known inducer of resistance in Grasses against pests), is therefore one of the most effective means of control, compatible with other integrated pest management strategies. The first step is to check whether an increase in the silica content of maize disrupts the development of S. frugiperda larvae. While silica induces a significant increase in stem diameter and height of potted maize plants, it has no influence on the development and mortality of S. frugiperda, ruling out the use of silica in maize resistance to this pest. Some resistant maize varieties have been bred and exist in the Americas against S. frugiperda but none are currently available as they are not adapted to the African continent. The other main objective of this thesis is therefore to develop a strategy to control S. frugiperda in Africa by using resistant varieties derived from African maize germplasm. The first results of the work on breeding and genetic improvement of (sub)tropical maize varieties against S. frugiperda, initiated by the International Maize and Wheat Improvement Center in Kenya (CIMMYT) between 2018 and 2019, indicate that five maize lines out of 1303 genotypes tested in greenhouses under artificial infestation have appreciable levels of resistance to S. frugiperda based on leaf and ear damage. After obtaining hybrids from these lines that are potentially resistant to S. frugiperda, this research is divided into three steps: 1) identify the mechanisms of S. frugiperda resistance in lines and hybrids selected for their resistance, 2) check whether these resistant genotypes are avoided by S. frugiperda females for oviposition, 3) and finally identify the chemical compounds responsible for resistance.

Cette entrée a été publiée le 22 octobre 2020, dans DEEIT, Personnels.

Camilo, Patarroyo

PhD Student

Tel: 01 69 15 68 29

Email: ca.patarroyo960@uniandes.edu.co

Title: Environmental demogenetics of potato late blight in Colombia

Abstract: The potato late blight is known for its part in the Irish potato famine during the 19th century. This disease has been extensively studied ever since. However, despite being one of the most well studied plant diseases in the world it remains one of the biggest threats to global food security. The late blight is caused by the Oomycete Phytophthora infestans. This is an hemi-biotrophic pathogen that infects the economically important crops potato (Solanum tuberosum) and tomato (Solanum lycopersicum).
In Colombia due to the prevalence of this disease and the extended use of susceptible potato cultivars, the main control strategy against this disease is the continuous application of fungicides. This constitutes a major problem because the repeated exposure of P. infestans to these fungicides results in the development of acquired resistance, and the effects on the health of the growers and the increased costs of continuously using fungicides during the growth cycle of the crop.
One way of reducing the continuous use of fungicides that has been practiced with some degree of success is the use of simulation and epidemiological models. These models project the proliferation of P. infestans based on environmental conditions such as temperature and relative humidity. This allows the growers to optimize the use of fungicides by applying them exclusively during the most favorable periods for the late blight development instead of a continuous use.
These models however have a few limitations. First, these do not consider the spatial configuration beyond each individual field. This could be an important addition due to the possibility of P. infestans dispersal between fields. Second, these disregard other possible management strategies and conditions that could be informative for the projection of late blight. And third, these are deterministic mechanistic models which have required decades of study to find the response of P. infestans to a variety of environmental conditions. Even though there have been adaptations of these models to tropical conditions, these models have a limited applicability outside the US because of the differences in environmental conditions and the responses of the different lineages present elsewhere.
Our main goal in this work is to develop an integrative model that considers several sources of information including environmental, epidemiological, genetic and spatial within a Bayesian learning framework. The idea is to start with a simple model that can be calibrated through this approach using collected field data. This would allow us to develop both a model for potato late blight in Colombia which would be continually calibrated with newly collected information, and a generic framework that can be used to develop models for lesser known plant pathogens that could be calibrated relying heavily on the collected field data and not previous mechanistic studies.

Cette entrée a été publiée le 2 octobre 2020, dans DEEIT, Personnels.