Researchers at the University of California, Davis have developed a controller that applies environmental data to optimizing operations of livestock cooling equipment.
Heat stress in dairy cows causes multiple detrimental effects. It reduces milk production, decreases reproductive efficiency and can cause premature deaths of the cows. These concerns have led to increased interest in developing improved methods for identifying and addressing conditions likely to produce such heat stress. Current methods of controlling bovine heat stress include water spraying, as well as fans to increase air circulation. Existing controllers for fans and sprayers are based primarily on temperature indications, with some technologies also incorporating humidity measurements. However, the computational basis behind many of the algorithms built into these controllers remain unsophisticated (with many field programmed based on subjective human observations). Thus, there is a need for a sophisticated controller that can optimize the operation of cooling equipment based on real-time changes in ambient environmental factors.
Researchers at the University of California, Davis have developed a controller that adjusts water rates and fan conditions based on environmental data fed to a predictive heat transfer model. The model assesses animal heat transfer rates based on correlations in the model that have been specifically developed for livestock. This technology can be applied either to currently-existing cooling systems or integrated into the development of new systems. Moreover, this technology - developed and implemented originally for dairy cows - can also be adapted to optimize cooling processes for other livestock.
Dairy cows, Livestock heat stress, Controllers, Energy savings, Algorithm, Water usage, Cooling system