Position:
Oil palm geneticist (Yaoundé, Cameroun, 2015-current)
Oil palm geneticist (Montpellier, France, 2011-2015)
Oil palm breeder/Geneticist (Pobè, Benin, 2006-2011)
Researches:
My researches primarily focus on breeding methodologies in order to maximize the genetic gain. I am mostly interested in quantitative genetics, genomics and statistics applied to oil palm and rubber tree breeding.
I am based at the African Center of Excellence in Information and Communication Technologies (CETIC), a training and research center created within the National Advanced School of Engineering of the University of Yaoundé 1 and funded by the World Bank.
NEWS:
Congratulation to Achille Nyouma for his travel grant to the Plant and Animal Genome 2020 (San Diego, CA).
From mass selection to genomic selection: one century of breeding for quantitative yield components of oil palm (Elaeis guineensis Jacq.) (first online 2019, Aug. 15 in Tree Genetics & Genomes)
Within-family genomic selection in rubber tree (Hevea brasiliensis) increases genetic gain for rubber production (published 2019, June 27 in Industrial Crops and Products)
***PhD studies (2011 - 2014): Factors controling the efficiency of genomic selection in oil palm (Elaeis guineensis)
Summary:
Agricultural production must increase at an unprecedented
rate to meet the strong growth expected in food demand. Genomic selection (GS)
could contribute to reaching this goal by allowing selection of individuals on
their sole genotype, making breeding more efficient. Breeding for yield in oil
palm, the first oil crop in the world, is currently based on hybrid production
by reciprocal recurrent selection. The integration of GS to this scheme would
have major repercussions. My thesis aimed to assess the potential of GS to
predict hybrid combining abilities in parental populations (Deli and group B).
Data from the last breeding cycle were used to
obtain the first empirical estimate of GS accuracy. Despite the small
populations available to calibrate the genomic model, the study showed that with
candidates related to the training population (sibs, progenies), the accuracy
was sufficient to make a pre-selection in the group B on some yield components.
In addition, simulations over four generations showed that the accuracy of several
GS strategies (especially when training the model only in the first generation
using hybrid genotypes) was high enough for non progeny tested individuals to
allow selecting among them on their genotype. This resulted in an increase of
more than 50% of annual genetic gain compared to traditional breeding. A faster
increase in inbreeding was also demonstrated, but this could be limited by conventional
methods of inbreeding management. Finally, the experimental and simulated data
indicated that GS could reduce the average generation interval and increase the
selection intensity, vastly speeding up the genetic progress for oil palm yield. The results led to suggest a recurrent reciprocal genomic selection scheme for oil palm.
[quantitative genetics, computer simulation, oil palm breeding, genomic selection]
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