Impacts of heat stress on the accuracy of an ear-tag accelerometer for monitoring rumination and eating behavior in dairy-beef cross cattle using an automated gold standard.

Accelerometer-based technologies can be utilized for precision monitoring of feeding behaviors, but limited information is available regarding the impact of varying environmental conditions on sensor performance. The objective of this study was to determine if a commercially available ear-tag sensor (CM; CowManager SensOor, Agis Automatisering BV) could accurately quantify eating and rumination time under heat stress conditions. Data obtained from CM sensors was compared with data collected using an automated gold standard (RW; Rumiwatch System; Itin+Hoch). Automated measurements were obtained from 2 experiments in which cattle were exposed to heat stress conditions. In the principal study (Experiment 1), 3428 h of data were collected from 9 Holstein × Angus steers (470.9 ± 23.9 kg) subjected to either thermoneutral (TN; 21.0°C; 64.0% humidity; temperature-humidity index [THI] = 67; 12- and 12-h light and dark cycle; n = 1714 h), or heat stress conditions (HS; cyclical daily temperatures to mimic diurnal patterns; 0800 - 2000 h: 33.6°C, 40.0% RH, THI: 83.5; 2000 - 0800 h: 23.2°C, 70.0% RH; THI: 70.3; n = 1714 h). Data (n = 719 h) from 6 Holstein x Angus steers (487.9 ± 9.1 kg) were obtained from a subsequent experiment (Experiment 2) to confirm consistency of ear-tag accelerometer performance under elevated THI (HS conditions as described above). In Experiment 1, CM was capable of quantifying rumination time with high accuracy under TN conditions (concordance correlation coefficient [CCC]: 0.75 - 0.81). Overall, agreement between CM and the automated gold standard declined 6 - 7% during HS, which was most apparent later in the day when cattle had been subjected to HS for multiple hours (moderate agreement; CCC: 0.68). Accuracy for rumination time was also only moderate for data collected during Experiment 2 (CCC: 0.55 - 0.61). In contrast, CM reported total eating (eating with the head down + head up while masticating) time with moderate accuracy for TN (CCC: 0.53 - 0.54), only achieved negligible to low accuracy during HS (CCC: 0.39 - 0.44 [Experiment 1] and 0.17 - 0.34 [Experiment 2]). Sensor performance did improve when CM eating time was compared specifically to the time spent with the head down reported by RW; HS still negatively influenced sensor performance, however, with high agreement during TN (CCC: 0.72 - 0.73) but low to moderate agreement during HS (CCC: 0.65 - 0.69 [Experiment 1] and 0.40 - 0.58 [Experiment 2]). Results of this study suggest accuracy of ear-tag accelerometers may be impaired when cattle are subjected to heat stress.

Using Hybrid Nanoplatforms to Combine Traditional Anti-Inflammatory Drug Delivery with RNA-Based Therapeutics for Macrophage Reprograming.

There is growing evidence on the significant role of prolonged inflammation in triggering and progressing of numerous diseases with substantial health and socioeconomic impacts, such as musculoskeletal, cardiovascular and autoimmune disorders, and cancer. Therefore, there is an urgent need to develop therapies that can overcome the main challenges of currently used approaches, such as non-target action, partial modulation of the complex inflammatory pathways, and short-term effects, to effectively manage and resolve chronic inflammatory states. This work investigates the therapeutic synergy of clinically relevant anti-inflammatory agents approaching naïve and classically activated macrophages owing to their central role in inflammation. Aiming at human therapies, a dual-loading nanoplatform reunites molecules with different physico-chemical properties in a single system, seeking to more effectively and comprehensively regulate macrophage functions for precision cell guidance and greater versatility in disease managing. To build this platform, palmitic acid grafted chitosan, superparamagnetic iron oxide nanoparticles, the clinically approved NSAID celecoxib (also known as Celebrex®), and RNA technologies were combined into superparamagnetic polymeric micelles (SPMs). Our findings demonstrated that traditional anti-inflammatory drugs such as celecoxib and microRNA molecules were efficiently delivered by the SPMs, altering the inflammatory profile of naïve (M0φ) and M1-primed macrophages (M1φ) assessed by gene and protein expression. The impact of the dual-loaded SPMs in naïve Mφ is an interesting finding towards the modulation of the initial immune response, reducing the potential for chronic inflammation and promoting tissue healing. Collectively, these encouraging results demonstrate the promise of multi-nanomedicine strategies to enhance the efficacy of therapeutic interventions by offering a fresh approach to more precisely and carefully regulated nanotherapeutics delivery.

Factors associated with intensity of technology adoption and with the adoption of 4 clusters of precision livestock farming technologies in Irish pasture-based dairy systems.

Precision livestock farming (PLF) technologies have been widely promoted as important tools to improve the sustainability of dairy systems due to perceived economic, social, and environmental benefits. However, there is still limited information about the level of adoption of PLF technologies (percentage of farms with a PLF technology) and the factors (farm and farmer characteristics) associated with PLF technology adoption in pasture-based dairy systems. The current research aimed to address this knowledge gap by using a representative survey of Irish pasture-based dairy farms from 2018. First, we established the levels of adoption of 9 PLF technologies (individual cow activity sensors, rising plate meters, automatic washers, automatic cluster removers, automatic calf feeders, automatic parlor feeders, automatic drafting gates, milk meters, and a grassland management decision-support tool) and grouped them into 4 PLF technology clusters according to the level of association with each other and the area of dairy farm management in which they are used. The PLF technology clusters were reproductive management technologies, grass management technologies, milking management technologies, and calf management technologies. Additionally, we classified farms into 3 categories of intensity of technology adoption based on the number of PLF technologies they have adopted (nonadoption, low intensity of adoption, and high intensity of adoption). Second, we determined the factors associated with the intensity of technology adoption and with the adoption of the PLF technology clusters. A multinomial logistic regression model and 4 logistic regressions were used to determine the factors associated with intensity of adoption (low and high intensity of adoption compared with nonadoption) and with the adoption of the 4 PLF technology clusters, respectively. Adoption levels varied depending on PLF technology, with the most adopted PLF technologies being those related to the milking process (e.g., automatic parlor feeders and milk meters). The results of the multinomial logistic regression suggest that herd size, proportion of hired labor, agricultural education, and discussion group membership were positively associated with a high intensity of adoption, whereas age of farmer and number of household members were negatively associated with high intensity of adoption. However, when analyzing PLF technology clusters, the magnitude and direction of the influence of the factors in technology adoption varied depending on the PLF technology cluster being investigated. By identifying the PLF technologies in which pasture-based dairy farmers are investing more and by detecting potential drivers and barriers for the adoption of PLF technologies, the current study could allow PLF technology companies, practitioners, and researchers to develop and target strategies that improve future adoption of PLF technologies in pasture-based dairy settings.

Precision technologies for the management of reproduction in dairy cows.

Precision livestock farming (PLF) utilizes information and communication technology (ICT) to continuously monitor, control, and enhance the productivity, reproduction, health, welfare, and environmental impact of livestock. Technological advancements have facilitated the seamless flow of information from animals to humans, enabling practical decision-making processes concerning health, reproduction management, and calving surveillance. With the increasing population of livestock per farm, it has become impractical for farmers to individually track every animal within these large groups. Historically, cattle management decisions heavily relied on human observation, judgment, and experience. However, it is impossible for a single individual to gather reliable audio-visual monitoring data round the clock. Presently, dairy cows exhibit subtler indicators of estrus, resulting in a substantial chance of missing an estrus cycle. Furthermore, calving complications sometimes go unnoticed on farms, resulting in a higher number of culled cattle. In addition, an increasing number of crossbred cows experience delayed return to estrus after calving due to low body condition scores (BCS). The decline in BCS during the dry period is associated with a reduced likelihood of pregnancy following the first and second postpartum inseminations. Precision technologies enable the monitoring and tracking of an individual cow's physiological behavior and reproductive parameters, thereby optimizing management practices and farm performance. Despite the exploration of various technologies, there are still some common challenges that need to be addressed, including battery lifespan, transmission range, specificity and sensitivity, storage capacity, and economic affordability. Nonetheless, the demand for these tools from farmers and researchers is growing, and the implementation of PLF in grazing systems can yield positive outcomes in terms of animal reproductive welfare and labor optimization. This review primarily focuses on the different aspects of reproduction management in dairy using sensors, automated cameras, and various computer software.

Effect of a targeted reproductive management program based on automated detection of estrus during the voluntary waiting period on reproductive performance of lactating dairy cows.

The objective of this experiment was to evaluate the effect on reproductive performance of a targeted reproductive management (TRM) program for first postpartum insemination (AI) that prioritized AI at detected estrus (AIE) by providing different intervals for estrus detection based on records of automated estrus alerts (AEA) during the voluntary waiting period (VWP). A secondary objective was to evaluate the association between occurrence of AEA during the VWP and reproductive performance. Lactating Holstein cows (n = 1,260) fitted with neck behavior monitoring sensors for detection of estrus were randomly assigned to a program that used all-timed AI (TAI) for first service (ALL-TAI; n = 632) or a TRM program that prioritized AIE and used TAI only for cows not detected in estrus (TP-AIE; n = 628). Cows in the ALL-TAI treatment received TAI at 76 ± 3 days in milk (DIM) after a Double-Ovsynch protocol. Cows in the TP-AIE treatment were eligible for AIE for 30 ± 3 or 16 ± 3 d after a 49 d VWP if at least one (n = 346) or no (n = 233) AEA were recorded from 15 to 49 DIM. Cows not AIE received TAI after an Ovsynch protocol with progesterone supplementation at 90 ± 3 or 76 ± 3 DIM if the cow had or did not have AEA during the VWP, respectively. Data were analyzed by logistic and Cox's proportional hazard regression. In the TP-AIE treatment, 69.3 % of cows received AIE and more cows with (83.3 %) than without (45.0 %) AEA during the VWP received AIE. Cows in the TP-AIE (69.0 ± 0.7 d) treatment had fewer days from calving to first AI than cows in the ALL-TAI (75.7 ± 0.8 d) treatment. The proportion of cows pregnant by 150 DIM (ALL-TAI = 59.1 % and TP-AIE = 56.0 %) and the hazard ratio (HR) for time to pregnancy (1.0 [95 % confidence interval: 0.9, 1.2]) did not differ between treatments and median days to pregnancy were 102 and 107 for the ALL-TAI and TP-AIE treatments, respectively. Overall, the ALL-TAI (42.3 %) treatment had more first service pregnancies per AI (P/AI) than the TP-AIE (29.0 %) treatment. Cows with AEA during the VWP had greater P/AI (42.5 % vs. 28.9 %), proportion of cows pregnant by 150 DIM (67.4 % vs. 47.0 %), and HR for time to pregnancy (1.6 [1.4, 1.9]) than cows without AEA during the VWP. We conclude that a TRM program that prioritized AIE based on AEA during the VWP led to a similar pregnancy rate and proportion of cows pregnant by mid-lactation than a program that used all-TAI with extended VWP despite fewer P/AI to first service. Also, expression of estrus during the VWP was associated with improved reproductive performance. Thus, AEA during the VWP could be used as a predictor of reproductive potential for TRM of lactating dairy cows.

Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle.

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.