Increasing livestock fertility for sustainable agriculture

Brazil is the largest exporter of beef in the world. Livestock supports the economic livelihood and food security of almost 1.3 billion people and contributes 40 percent of the value of agricultural output worldwide. Livestock is also the world’s largest user of land resources, with grazing land and cropland dedicated to the production of feed representing almost 80 percent of all agricultural land.

One factor that greatly affects the economic sustainability of both beef and dairy farming is poor reproductive efficiency associated with embryo mortality. Increasing fertility in cattle will support sustainable agricultural production and reduce environmental impacts worldwide.

Newton-funded researchers from Brazil and the UK are harnessing assisted reproductive technologies to generate robust cattle and avoid embryo mortality. Exposure to environmental stresses can be harmful to offspring health even in the womb. This project has generated a new and exciting in vivo model to understand how the embryo and the mother interact, and how this can go wrong. By understanding communication between the embryo and the maternal environment the researchers can identify interventions to increase successful pregnancy and ensure that offspring are resilient to environmental stresses.

This work has the potential to identify molecules that can be manipulated to enhance pregnancy success or can act as markers of early pregnancy success, helping to transform the sustainable intensification of agriculture in different production systems across the world. If the researchers can implement their findings to help farmers produce more robust cattle that will provide more dairy products and meat, this will have longer term economic and social benefits by increasing the economic viability of small holder farmers.

This project has been a springboard enabling us and our Brazilian collaborators to stay at the cutting edge of science. It has facilitated a new way of working.

Dr Niamh Forde, University Academic Fellow, University of Leeds

This project was shortlisted for the Newton Prize 2018