No abstract

Immediate and short-term changes in diet composition can support individualized, real-time interventions in precision dairy production systems, and might increase feed efficiency (FE) of dairy cattle in the short-term. The objective of this study was to determine immediate and short-term effects of changes in diet composition on production parameters of dairy cattle fed varying amounts of top dressed commodities. A 4 × 4 replicated Latin square design was used to evaluate responses of twenty-four Holstein cows fed either no top dress (Control) or increasing amounts of: corn grain (CG), soybean meal (SBM), or chopped mixed grass hay (GH) top dressed on a total mixed ration (TMR) over four, 9-day periods. Throughout each period, top dressed commodities were incrementally increased, providing 0% to 20% of calculated net energy of lactation (NEL) intake. Measured production responses were analyzed for each 9-d period using a mixed-effects model considering two different time ranges. Samples collected from d 3 and 4 and from d 7 and 8 of each period were averaged and used to reflect “immediate” vs. “short-term” responses, respectively. In the immediate response time frame, control fed cows had lower milk yield, milk fat yield, and milk true protein yield than CG and SBM supplemented animals but similar responses to GH supplemented animals. Milk fat and protein percentages were not affected by top dress type in the immediate term. In the short-term response time-frame, GH supplemented animals had lower DMI and milk fat yield than all other groups. Control and GH supplemented cows had lower milk yield than CG and SBM fed cows. In the immediate response time frame, FE of SBM supplemented cows was superior to other groups. In the short-term time frame, FE of GH and SBM groups was improved over the control group. Results suggest that lactating dairy cows show rapid performance responses to small (<20% NEL) changes in dietary composition, which may be leveraged within automated precision feeding systems to optimize efficiency of production. Before this potential can be realized, further research is needed to examine integration of such strategies into automatic feeding systems and downstream impacts on individual animal FE and farm profitability.

Precision livestock farming (PLF) offers a strategic solution to enhance the management capacity of large animal groups, while simultaneously improving profitability, efficiency, and minimizing environmental impacts associated with livestock production systems. Additionally, PLF contributes to optimizing the ability to manage and monitor animal welfare while providing solutions to global grand challenges posed by the growing demand for animal products and ensuring global food security. By enabling a return to the "per animal" approach by harnessing technological advancements, PLF enables cost-effective, individualized care for animals through enhanced monitoring and control capabilities within complex farming systems. Meeting the nutritional requirements of a global population exponentially approaching ten billion people will likely require the density of animal proteins for decades to come. The development and application of digital technologies are critical to facilitate the responsible and sustainable intensification of livestock production over the next several decades to maximize the potential benefits of PLF. Real-time continuous monitoring of each animal is expected to enable more precise and accurate tracking and management of health and well-being. Importantly, the digitalization of agriculture is expected to provide collateral benefits of ensuring auditability in value chains while assuaging concerns associated with labor shortages. Despite notable advances in PLF technology adoption, a number of critical concerns currently limit the viability of these state-of-the-art technologies. The potential benefits of PLF for livestock management systems which are enabled by autonomous continuous monitoring and environmental control can be rapidly enhanced through an Internet of Things (IoT) approach to monitoring and (where appropriate) closed-loop management. In this paper, we analyze the multilayered network of sensors, actuators, communication, networking, and analytics currently used in PLF, focusing on dairy farming as an illustrative example. We explore the current state-of-the-art, identify key shortcomings, and propose potential solutions to bridge the gap between technology and animal agriculture. Additionally, we examine the potential implications of advancements in communication, robotics, and artificial intelligence (AI) on the health, security, and welfare of animals.

Individualized, precision feeding of dairy cattle may contribute to profitable and sustainable dairy production. Feeding strategies targeted at optimizing efficiency of individual cows, rather than groups of animals with similar characteristics, is a logical goal of individualized precision feeding. However, algorithms designed to make feeding recommendations for specific animals are scarce. The objective of this study was to develop and test 2 algorithms designed to improve feed efficiency of individual cows by supplementing total mixed rations (TMR) with varying types and amounts of topdressed feedstuffs. Twenty-four Holstein dairy cows were assigned to 1 of 3 treatment groups as follows: a control group fed a common TMR ad libitum, a group fed individually according to algorithm 1, and a group fed individually according to algorithm 2. Algorithm 1 used a mixed-model approach with feed efficiency as the response variable and automated measurements of production parameters and top-dress type as dependent variables. Cow was treated as a random effect, and cow by top-dress interactions were included if significant. Algorithm 2 grouped cows based on top-dress response efficiency structure using a principal components and k-means clustering. Both algorithms were trained over a 36-d experimental period immediately before testing, and were updated weekly during the 35-d testing period. Production performance responses for dry matter intake (DMI), milk yield, milk fat percentage and yield, milk protein percentage and yield, and feed efficiency were analyzed using a mixed-effects model with fixed effects for feeding algorithm, top dress, week, and the 2-and 3-way interactions among these variables. Milk protein percentage and feed efficiency were significantly affected by the 3-way interaction of top dress, algo-rithm, and week, and DMI tended to be affected by this 3-way interaction. Feeding algorithm did not affect milk yield, milk fat yield, or milk protein yield. However, feeding costs were reduced, and hence milk revenue increased on the algorithm-fed cows. The efficacy of feeding algorithms differed by top dress and time, and largely relied on DMI shifts to modulate feed efficiency. The net result, for the cumulative feeding groups, was that cows in the algorithm 1 and 2 groups earned over $0.45 and $0.70 more per head per day in comparison to cows on the TMR control, respectively. This study yielded 2 candidate approaches for efficiency-focused, individualized feeding recommendations. Refinement of algorithm selection, development, and training approaches are needed to maximize production parameters through individualized feeding.

Background As more patients with appendicitis are treated with antibiotics, factors associated with recurrence may help inform individualized prognostication and decision-making. Methods This cohort study, using data from the Comparison of Outcomes of Antibiotic Drugs and Appendectomy trial, examined patients treated with antibiotics who did not undergo appendicectomy in the first 30 days. Patients who had appendicectomy between 30 days and 1 year were compared with those who did not. Marginalized logistic regression models were used to calculate adjusted risk differences (RDs) to estimate the association between baseline patient factors and the risk of undergoing an appendicectomy between 30 days and 1 year. Results Of 601 patients treated with antibiotics who did not undergo appendicectomy within 30 days (mean age 38.0 years; 217 women (36.1 per cent)), 144 had an appendicectomy and 56 were lost to follow-up between 30 days and 1 year. The estimated rate of appendicectomy between 30 days and 1 year was 28.6 (95 per cent c.i. 25.0 to 32.8) per cent. After adjustment for other factors, nausea, vomiting, or anorexia at baseline presentation was associated with an increased rate of appendicectomy between 30 days and 1 year (adjusted RD 17.52, 95 per cent c.i. 8.64 to 26.40). The presence of an appendicolith (adjusted RD 3.64, −6.08 to 13.36), or an abscess, perforation, or fat stranding on initial imaging (adjusted RD −7.23, −17.41 to 2.95) was not strongly associated with appendicectomy between 30 days and 1 year. Conclusion Most factors commonly associated with appendicitis severity were not strongly associated with an increased risk of undergoing appendicectomy in the longer term after treatment with antibiotics.