The Development of Smart Dairy Farm System and Its Application in Nutritional Grouping and Mastitis Prediction

SIMPLE SUMMARY: This study combined Internet of Things technology with dairy farm management to set up a smart dairy farm system (SDFS). All kinds of data in the dairy farm will be intelligently captured by various sensors and transmitted to the SDFS in time for corresponding integration analysis. Nutritional grouping was demonstrated to improve production performance and methane and carbon dioxide emission reduction, which is also a hotspot of concern for the public and scientific research. The information from dairy herd improvement (DHI) analysis was used to predict the incidence of mastitis in dairy cows, which would lead to a new way to predict individual mastitis. By fully interpreting the hidden value of dairy farm data, SDFS could help in the better management of dairy farms and promote the application of intelligent systems in dairy farm production. ABSTRACT: In order to study the smart management of dairy farms, this study combined Internet of Things (IoT) technology and dairy farm daily management to form an intelligent dairy farm sensor network and set up a smart dairy farm system (SDFS), which could provide timely guidance for dairy production. To illustrate the concept and benefits of the SDFS, two application scenarios were sampled: (1) Nutritional grouping (NG): grouping cows according to the nutritional requirements by considering parities, days in lactation, dry matter intake (DMI), metabolic protein (MP), net energy of lactation (NEL), etc. By supplying feed corresponding to nutritional needs, milk production, methane and carbon dioxide emissions were compared with those of the original farm grouping (OG), which was grouped according to lactation stage. (2) Mastitis risk prediction: using the dairy herd improvement (DHI) data of the previous 4 lactation months of the dairy cows, logistic regression analysis was applied to predict dairy cows at risk of mastitis in successive months in order to make suitable measurements in advance. The results showed that compared with OG, NG significantly increased milk production and reduced methane and carbon dioxide emissions of dairy cows (p < 0.05). The predictive value of the mastitis risk assessment model was 0.773, with an accuracy of 89.91%, a specificity of 70.2%, and a sensitivity of 76.3%. By applying the intelligent dairy farm sensor network and establishing an SDFS, through intelligent analysis, full use of dairy farm data would be made to achieve higher milk production of dairy cows, lower greenhouse gas emissions, and predict in advance the occurrence of mastitis of dairy cows.