François Djitie Kouatcho
From
Department of Sciences and Techniques of Sustainable Agriculture, FS, University of Ngaoundéré - CM
In residence at
Avian Biology & Poultry Research (BOA) / Centre INRAE Val de Loire, University of Tours - FR
Host scientists
Agnès Narcy & Sandrine Grasteau
BIOGRAPHY
Francois Djitie Kouatcho holds a PhD in Biotechnology and Animal Production. With a view to contributing to the sustainability of livestock farming systems by reducing the environmental impact of livestock production, he has been working for over 15 years on the impact of livestock farming systems and the use of alternative feed resources in livestock farming in general, with a particular focus on the environment, nutrition, growth and reproduction of poultry.)
He is currently a Research Lecturer and Senior Lecturer in the Department of Science and Technology of Organic Agriculture at the Faculty of Science of the University of Ngaoundéré in Cameroon, where he served between 2017 and 2023 as Head of the Research, Cooperation and Income-Generating Activities Department in one of its establishments (EGCIM). For the past 3 years, he has also been Secretary General of the Cameroon branch of WPSA (World's Poultry Sciences Association). As a researcher and consultant, he has actively contributed to a number of national and international research projects, as well as capacity-building projects for livestock farmers and disadvantaged people. He has experience in the following areas: 1) Teaching and supervision of students and young researchers in Cameroonian universities and abroad;
2) Design, implementation and monitoring of scientific projects, with expertise in laboratory analyses of nutrition, physiology, bone tomography, etc.; 3) Organisational management skills and effective work in large-scale national and international research and development projects; 4) Mentoring of livestock farmers in the adoption of environmentally-friendly livestock rearing practices, through the use of local resources, good practices and respect for animal welfare.
PROJECT
Balance between feed efficiency and bone health in free-range broilers reared under global warming conditions
In order to meet the meat products needs of an ever-growing world population, standard chickens have undergone considerable growth, breast yield, and feed efficiency improvements over the last few decades. Cumulated with intensive production conditions, this selection resulted in birds now reaching a weight of 2 kg in just 5 weeks. Despite this increase in productivity and the success of poultry meat, criticisms on animal welfare and product quality abound. Indeed, fast-growing chickens move around considerably less than slow-growing chickens, lying still for a substantial part of the time during rearing. This results in poor bone mineralisation in birds and thus to musculoskeletal disorders and less robust animals.
The project is part of a drive to improve the sustainability of poultry farming systems to promote the transition towards more agroecological production systems. Feed efficiency, which depends on animal capacity and feed composition, is a key element in these economic and environmental pillars of sustainability. Mastering bone health is a key factor in broiler welfare. However, efficient birds are also those spending less energy on physical activity, which is detrimental to bone health. To adapt systems to this transition by adopting feed and/or selection strategies, it is essential to gain a better understanding of the birds' ability to adapt and the balance between functions.
This project aims to model how feed efficiency and bone tissue robustness interact in summer range conditions, in the context of global warming. Measuring biomarkers of bone health will enable assessment of the impact of access to the open air on efficiency and health parameters under summer conditions and will guide further research on the breeding and feeding strategies best suited to hot weather conditions. The study could suggest new selection criteria of interest for the development of existing prediction models.