Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain.

Feed for dairy cattle has a major impact on profitability and the environmental impact of farms. Sustainable dairy production relies on continued improvement in feed efficiency as a way to reduce costs and nutrient loss from feed. Advances in breeding, feeding and management have led to the dilution of maintenance energy and thus more efficient dairy cattle. Still, many additional opportunities are available to improve individual animal feed efficiency. Sensing technologies such as wearable sensors, image-based and high-throughput phenotyping technologies (e.g., milk testing) are becoming more available on commercial farm. The application of these technologies as indicator traits for feed intake and efficiency related traits would be advantageous to provide additional information to predict and manage feed efficiency. This review focuses on precision livestock technologies and high-throughput phenotyping in use today as well as those that could be developed in the future as possible indicators of feed intake. Several technologies such as milk spectral data, activity, rumen measures, and image-based phenotypes have been associated with feed intake. Future applications will depend on the ability to repeatably measure and calibrate these data across locations, so that they can be integrated for use in predicting and managing feed intake and efficiency on farm.

Quantifying dry matter intake (DMI) in lactating dairy cows is important for determining feed efficiency; however, there are no methods for economically quantifying individual cow DMI on dairy farms where cows are group-fed. Attempts have been made to model DMI using cow factors, milk production, milk infrared spectra, and behavioral sensors with reasonable success. Other data streams are available on the farm that may contribute to DMI predictions. In this study, our objective was to model DMI with multiple linear regression using data from a single point-in-time that can easily be accessed on-farm. Candidate predictor variables included cow descriptors, milk yield and composition, milk fatty acid profile, and production and efficiency predicting transmitting abilities (PTA). Observations of DMI were obtained from 350 cows across 6 cohorts using individual feed bunks. The cow to bunk ratio was 2:1, with an overall bunk occupation rate of 32% throughout the day. The following models were developed sequentially with milk data obtained from a single morning milking and other data from the same day: model B (production, metabolic body weight, body condition score, lactation category, and week of lactation), model BC [model B + fatty acid (FA) content], model BY (model B + FA yield), model BPE (model B + production and efficiency PTA), model BYP (model BY + production PTA), model BYE (model BY + efficiency PTA), and model BYPE (model BY + production and efficiency PTA). Outcome variables predicted in these models were the DMI on the previous day or current day relative to the morning milk sample. The predictions for DMI on the previous day outperformed current day DMI in every model for which they were both determined. Addition of milk FA and PTA as candidate predictor variable types to the models resulted in enhanced predictive ability, with incremental enhancements when combined. The most robust model (BYPE) included cow descriptors, protein and FA yields, and PTA for milk and residual feed intake. Model BYPE described 21 to 32% more of the variation in DMI (based on concordance correlation coefficient) than when other common DMI models were applied to the same data set. Overall, reasonable performance of models including single point-in-time cow descriptors, milk and FA production, and production and efficiency PTA commonly available to dairy farmers through dairy herd improvement programs offer an opportunity for on-farm prediction of DMI, yet further improvement may be possible.

The proceedings of the 12th World Congress on Genetics Applied to Livestock Production provide you with 816 papers representing the leading research in livestock genetics around the globe. This boo...