Search

Simple Summary: Precision livestock farming (PLF) is a concept that has gained significant interest in recent years due to its potential role in developing sustainable livestock production systems. This study aimed to conduct a bibliometric analysis of the PLF literature from 2005 to 2021 to understand the historical routes that influenced the use of technology in livestock farming, highlight future global trends, and identify changes in scientific research on this topic. The analysis of the PLF literature showed that the number of publications has steadily increased over time, with a more than doubled annual growth rate in the last five years. The leading countries in PLF research were the USA, the Netherlands, and Italy, and the top three core journals publishing PLF research were the Journal of Dairy Science, Computers and Electronics in Agriculture, and Animals. The study also highlighted the central role of automated milking systems in driving innovations in the PLF sector and the growing interest in emissions and mitigation as emerging themes for the future. Overall, this study provides insights into the past, present, and future of PLF research. The interest in precision livestock farming (PLF)—a concept discussed for the first time in the early 2000s—has advanced considerably in recent years due to its important role in the development of sustainable livestock production systems. However, a comprehensive bibliometric analysis of the PLF literature is lacking. To address this gap, this study analyzed documents published from 2005 to 2021, aiming to understand the historical influences on technology adoption in livestock farming, identify future global trends, and examine shifts in scientific research on this topic. By using specific search terms in the Web of Science Core Collection, 886 publications were identified and analyzed using the bibliometrix R-package. The analysis revealed that the collection consisted mostly of research articles (74.6%) and reviews (10.4%). The top three core journals were the Journal of Dairy Science, Computers and Electronics in Agriculture, and Animals. Over time, the number of publications has steadily increased, with a higher growth rate in the last five years (29.0%) compared to the initial period (13.7%). Authors and institutions from multiple countries have contributed to the literature, with the USA, the Netherlands, and Italy leading in terms of publication numbers. The analysis also highlighted the growing interest in bovine production systems, emphasizing the importance of behavioral studies in PLF tool development. Automated milking systems were identified as central drivers of innovation in the PLF sector. Emerging themes for the future included "emissions" and "mitigation", indicating a focus on environmental concerns. [ABSTRACT FROM AUTHOR]

Simple Summary: Precision livestock farming, by real time monitoring of dairy cows, has the potential to generate a huge amount of data to be used for farm management purposes, as well as in breeding programs. Daily rumination time (RT) recorded by commercial systems is promising in this context because it may be related to individual milk yield and composition. However, it is necessary to assess the ability of sensor data to be used in a predictive model, but also to evaluate and standardize the correct phenotypes, and how they are related to individual variability rather than from other sources. RT data and milk test day (TD) records collected from 691 cows, monitored for thirteen months, were analyzed for the already mentioned goals and to better characterize the effect of high-, medium- and low-level daily RT on milk yield and composition. Our results showed that "animal" in a farm major contributed to the RT total variability, confirming a possible use in breeding program. The higher RT class reported the best productive performance for milk and each solid yield, in spite of a small reduction in their contents, and appears to be related to a higher degree of saturation in the fatty acid profile. The study aimed to estimate the components of rumination time (RT) variability recorded by a neck collar sensor and the relationship between RT and milk composition. Milk test day (TD) and RT data were collected from 691 cows in three farms. Daily RT data of each animal were averaged for 3, 7, and 10 days preceding the TD date (RTD). Variance component analysis of RTD, considering the effects of farm, cow, parity, TD date, and lactation phase, showed that a farm, followed by a cow, had major contributions to the total variability. The RT10 variable best performed on TD milk yield and quality records across models by a multi-model inference approach and was adopted to study its relationship with milk traits, by linear mixed models, through a 3-level stratification: low (LRT10 ≤ 8 h/day), medium (8 h/day < MRT10 ≤ 9 h/day), and high (HRT10 > 9 h/day) RT. Cows with HRT10 had greater milk, fat, protein, casein, and lactose daily yield, and lower fat, protein, casein contents, and fat to protein ratio compared to MRT10 and LRT10. Higher percentages of saturated fatty acid and lower unsaturated and monounsaturated fatty acid were found in HRT10, with respect to LRT10 and MRT10 observations. [ABSTRACT FROM AUTHOR]