New project to strengthen Costa Rican dairy sector

Staff from SENRGY recently travelled to Costa Rica to commence a research project funded by the Global Challenges Research Fund, led by Prof. Dave Chadwick, SENRGY.

The dairy farm at CATIE (Tropical Agronomic Research and Education Center) will beone of the models that could be applied to other dairies in Costa Rica. Representatives from SENRGY at Bangor University, CATIE, the National Institute of Innovation and Transfer in Agricultural Technology of Costa Rica (INTA) and the Cooperative Dos Pinos met on 27 and 28 June to start the first steps of the project "Sustainable Futures for the Dairy Sector of Costa Rica: Optimizing Environmental and Economic Results".

Eduardo Somarriba, leader of CATIE's Agriculture, Livestock and Agroforestry Program (PRAGA), said that the project will also involve the Veterinary Medicine School of the National University (UNA) and the School of Industrial Engineering of the University of Costa Rica (UCR) and will establish alliances with other institutions of the cattle sector of Costa Rica.

Somarriba added that this project will provide new data on the emission of ammonia and contamination of soils and water with nitrogen and phosphorus used in dairy production. In addition, a component led by Dave Styles, SENRGY, will study the life cycle of milk production and model the routes and business schemes that could be used to transform current farms into others that balance productivity with environmental aspects.

Rob Brook, SENRGY, mentioned that the actions of the milk sector are key for Costa Rica to achieve its goal of being carbon neutral in 2020 and that this project would greatly support this effort. The project will use as models several dairy farms in Costa Rica with different agroecological and socioeconomic conditions, including CATIE.

The project duration is 30 months and will continue until October 2019. The photograph shows participants in meetings at CATIE, Costa Rica, in which actions were identified to communicate and use efficiently the results and expected learning.

Publication date: 5 July 2017