ReviewJDS communications2023
Precision technologies to improve dairy grazing systems.
Review in JDS communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 20 citations in OpenAlex.
- Bovine Leukemia Virus as a One Health Challenge in Nutrition, Food Security, and Public Health: A Narrative Review.The Journal of nutrition · 2026Review
- ASAS-NANP SYMPOSIUM: mathematical modeling in animal nutrition: the importance of animal science flight simulators to enhance the competitiveness and sustainability of livestock production.Journal of animal science · 2026Review
- In-Line Monitoring of Milk Lactose for Evaluating Metabolic and Physiological Status in Early-Lactation Dairy Cows.Life (Basel, Switzerland) · 2025Article
- ASAS-NANP symposium: mathematical modeling in animal nutrition: application of modeling innovations to support satellite remote sensing for sustainable grazing cattle management.Journal of animal science · 2025Review
- Internet of Things (IoT): Sensors Application in Dairy Cattle Farming.Animals : an open access journal from MDPI · 2024Review
- Accounting for minimum data required to train a machine learning model to accurately monitor Australian dairy pastures using remote sensing.Scientific reports · 2024Article
- Health Treatment Cost of Holsteins in Eight High-Performance Herds.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pasture-based dairy herds continue to grow around the world as demand increases for sustainable farming practices. Grazing dairy farmers may benefit from the utilization of precision dairy technologies because these technologies have the potential to improve animal welfare, increase farm efficiency, and reduce costs. Precision dairy technologies have provided novel information about activity, rumination, and grazing behavior of various breeds in pasture-based systems. Previous research with wearable technologies has indicated that rumination, eating, and no activity have moderate to high correlations (r = 0.65 to 0.88) with visual observation; however, activity may be difficult to record in grazing herds. However, many grazing dairy farmers around the world are using activity monitors with generally positive success. Grazing is a complex behavior to define because cows may walk to an area and stop to eat or continuously walk and take bites of grass from the pasture. Wearable technologies can detect whether a cow is grazing with reasonable accuracy. However, the challenge is to determine pasture intake as bite rate and bite size because these can vary as the pasture is grazed to a low residual height. Nevertheless, grazing behavior data collected with wearable technologies was highly correlated (r = 0.92 to 0.95) with visual observations. Grazing is a behavior that should continue to be explored, especially with precision dairy technologies. As healthy and productive pastures are integral to grazing systems, accurate forage biomass measurements can improve efficiency and production of pastured dairy cows. However, few farms use technology to determine forage availability. Therefore, using dairy technologies to monitor forage dry matter from pasture may provide a potential benefit for grazing-based dairy farms. Current satellite technology with the normalized difference vegetation index and electronic rising plate meters may provide new technologies for farms to monitor forage biomass and fine-tune grazing within pastures. In the future, pasture-based dairy farms may rely on virtual fencing, drones to detect animal health issues and forage availability, and autonomous vehicles to move cattle and to detect weeds on pasture.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.