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ABSTRACT: Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake.

Increased economic, societal and environmental challenges facing agriculture are leading to a greater focus on effective way to combine grazing and automatic milking systems (AMS). One of the fundamental aspects of robotic milking is cows’ traffic to the AMS. Numerous studies have identified feed provided, either as fresh grass or concentrate supplement, as the main incentive for cows to return to the robot. The aim of this study was to determine the effect of concentrate allocation on voluntary cow traffic from pasture to the robot during the grazing period, to highlight the interactions between grazed pasture and concentrate allocation in terms of substitution rate and the subsequent effect on average milk yield and composition. Thus, 29 grazing cows, milked by a mobile robot, were monitored for the grazing period (4 months). They were assigned to two groups: a low concentrate (LC) group (15 cows) and a high concentrate (HC) group (14 cows) receiving 2 and 4 kg concentrate/cow per day, respectively; two allocations per day of fresh pasture were provided at 0700 and 1600 h. The cows had to go through the AMS to receive the fresh pasture allocation. The effect of concentrate level on robot visitation was calculated by summing milkings, refusals and failed milkings/cow per day. The impact on average daily milk yield and composition was also determined. The interaction between lactation number and month was used as an indicator of pasture availability. Concentrate allocation increased significantly robot visitations in HC (3.60±0.07 visitations/cow per day in HC and 3.10±0.07 visitations/cow per day in LC; P

Manure nitrogen (N) from cattle contributes to nitrous oxide and ammonia emissions and nitrate leaching. Measurement of manure N outputs on dairy farms is laborious, expensive, and impractical at large scales; therefore, models are needed to predict N excreted in urine and feces. Building robust prediction models requires extensive data from animals under different management systems worldwide. Thus, the study objectives were (1) to collate an international database of N excretion in feces and urine based on individual lactating dairy cow data from different continents; (2) to determine the suitability of key variables for predicting fecal, urinary, and total manure N excretion; and (3) to develop robust and reliable N excretion prediction models based on individual data from lactating dairy cows consuming various diets. A raw data set was created based on 5,483 individual cow observations, with 5,420 fecal N excretion and 3,621 urine N excretion measurements collected from 162 in vivo experiments conducted by 22 research institutes mostly located in Europe (n = 14) and North America (n = 5). A sequential approach was taken in developing models with increasing complexity by incrementally adding variables that had a significant individual effect on fecal, urinary, or total manure N excretion. Nitrogen excretion was predicted by fitting linear mixed models including experiment as a random effect. Simple models requiring dry matter intake (DMI) or N intake performed better for predicting fecal N excretion than simple models using diet nutrient composition or milk performance parameters. Simple models based on N intake performed better for urinary and total manure N excretion than those based on DMI, but simple models using milk urea N (MUN) and N intake performed even better for urinary N excretion. The full model predicting fecal N excretion had similar performance to simple models based on DMI but included several independent variables (DMI, diet crude protein content, diet neutral detergent fiber content, milk protein), depending on the location, and had root mean square prediction errors as a fraction of the observed mean values of 19.1% for intercontinental, 19.8% for European, and 17.7% for North American data sets. Complex total manure N excretion models based on N intake and MUN led to prediction errors of about 13.0% to 14.0%, which were comparable to models based on N intake alone. Intercepts and slopes of variables in optimal prediction equations developed on intercontinental, European, and North American bases differed from each other, and therefore region-specific models are preferred to predict N excretion. In conclusion, region-specific models that include information on DMI or N intake and MUN are required for good prediction of fecal, urinary, and total manure N excretion. In absence of intake data, region-specific complex equations using easily and routinely measured variables to predict fecal, urinary, or total manure N excretion may be used, but these equations have lower performance than equations based on intake., (© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

In dairy farms automatic milking systems and grazing, traffic to the robot is the cornerstone of profitability as higher milking frequency enhances milk yield. In this study, we investigated whether shortening the minimum milking interval (MMI), i.e., the required time between two milkings for an animal to get access to the milking unit, coupled with high concentrate allocation, could increase the daily milking frequency (MF, milking/cow/day) and consequently the milk yield of grazing cows. Two groups of cows ( n = 19 and n = 20) belonging to the same herd were discriminated based on concentrate supply (high vs. low: 4 vs. 2 kg/cow/day) and then further divided on the basis of MMI (4 h vs. 6 h) so that four groups were formed (HC4 h-HC6 h-LC4 h and finally LC6 h). Higher concentrate allocation induced a rise in milk yield (MY, kg/cow/day) and allowed to stabilize it in periods of grass shortage but did not influence milking frequency, while shorter MMI (4 h) was correlated with higher MF without effect on MY. A combination of both strategies (4 h and high concentrate) improved the traffic globally to the robot. This result was linked to a reduction of refused milking and, therefore, the decrease in returns to the robot. This strategy could be advised to maximize the system's efficiency during periods of high milk sales. When the economic conditions do not favour the increase in concentrate supply, short MMI could facilitate the traffic and increase the efficiency of returns.

More dairy farms (up to more than one in four in some countries) are equipped with automatic milking systems (AMS) worldwide. Because of the positive impacts of grazing, e.g., on animal welfare or on production costs, numerous researchers have published papers on the combination of AMS with grazing. However, pasture-based AMS usually causes a reduction in milking frequency (MF) compared to indoors systems. The objectives of this meta-analysis were to review publications on the impacts of pasture-based AMS on MF and mitigation strategies. First, data from 43 selected studies were gathered in a dataset including 14 parameters, and on which a Principal Component Analysis (PCA) was performed, leading to the description of four clusters summarizing different management practices. Multiple pairwise comparisons were performed to determine the relationship between the highlighted parameters of MF on milk yield (MY). From these different analyses, the relationship between MF and MY was confirmed, the systems, i.e., Clusters 1 and 2, that experienced the lowest MF also demonstrated the lowest MY/cow per day. In these clusters, grazed grass was an essential component of the cow's diet and low feeding costs compensated MY reduction. The management options described in Clusters 3 and 4 allowed maintenance of MF and MY by complementing the cows' diets with concentrates or partial mixed ration supplied at the AMS feeding bin or provided at barn. The chosen management options were closely linked to the geographical origin of the papers indicating that other factors (e.g., climatic conditions or available grasslands) could be decisional key points for AMS management strategies., Competing Interests: The authors declare no conflict of interest.

Increased economic, societal and environmental challenges facing agriculture are leading to a greater focus on effective way to combine grazing and automatic milking systems (AMS). One of the fundamental aspects of robotic milking is cows' traffic to the AMS. Numerous studies have identified feed provided, either as fresh grass or concentrate supplement, as the main incentive for cows to return to the robot. The aim of this study was to determine the effect of concentrate allocation on voluntary cow traffic from pasture to the robot during the grazing period, to highlight the interactions between grazed pasture and concentrate allocation in terms of substitution rate and the subsequent effect on average milk yield and composition. Thus, 29 grazing cows, milked by a mobile robot, were monitored for the grazing period (4 months). They were assigned to two groups: a low concentrate (LC) group (15 cows) and a high concentrate (HC) group (14 cows) receiving 2 and 4 kg concentrate/cow per day, respectively; two allocations per day of fresh pasture were provided at 0700 and 1600 h. The cows had to go through the AMS to receive the fresh pasture allocation. The effect of concentrate level on robot visitation was calculated by summing milkings, refusals and failed milkings/cow per day. The impact on average daily milk yield and composition was also determined. The interaction between lactation number and month was used as an indicator of pasture availability. Concentrate allocation increased significantly robot visitations in HC (3.60±0.07 visitations/cow per day in HC and 3.10±0.07 visitations/cow per day in LC; P<0.001) while milkings/cow per day were similar in both groups (LC: 2.37±0.02/day and HC: 2.39±0.02/day; Ns). The average daily milk yield over the grazing period was enhanced in HC (22.39±0.22 kg/cow per day in HC and 21.33±0.22 kg/cow per day in LC; P<0.001). However the gain in milk due to higher concentrate supply was limited with regards to the amount of provided concentrates. Milking frequency in HC primiparous compared with LC was increased. In the context of this study, considering high concentrate levels as an incentive for robot visitation might be questioned, as it had no impact on milking frequency and limited impact on average milk yield and composition. By contrast, increased concentrate supply could be targeted specifically to primiparous cows.

Climate change has the potential to impair livestock health, with consequences for animal welfare, productivity, greenhouse gas emissions, and human livelihoods and health. Modelling has an important role in assessing the impacts of climate change on livestock systems and the efficacy of potential adaptation strategies, to support decision making for more efficient, resilient and sustainable production. However, a coherent set of challenges and research priorities for modelling livestock health and pathogens under climate change has not previously been available. To identify such challenges and priorities, researchers from across Europe were engaged in a horizon-scanning study, involving workshop and questionnaire based exercises and focussed literature reviews. Eighteen key challenges were identified and grouped into six categories based on subject-specific and capacity building requirements. Across a number of challenges, the need for inventories relating model types to different applications (e.g. the pathogen species, region, scale of focus and purpose to which they can be applied) was identified, in order to identify gaps in capability in relation to the impacts of climate change on animal health. The need for collaboration and learning across disciplines was highlighted in several challenges, e.g. to better understand and model complex ecological interactions between pathogens, vectors, wildlife hosts and livestock in the context of climate change. Collaboration between socio-economic and biophysical disciplines was seen as important for better engagement with stakeholders and for improved modelling of the costs and benefits of poor livestock health. The need for more comprehensive validation of empirical relationships, for harmonising terminology and measurements, and for building capacity for under-researched nations, systems and health problems indicated the importance of joined up approaches across nations. The challenges and priorities identified can help focus the development of modelling capacity and future research structures in this vital field. Well-funded networks capable of managing the long-term development of shared resources are required in order to create a cohesive modelling community equipped to tackle the complex challenges of climate change., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Nebulizers are a potential source of contamination of the respiratory tract. Nevertheless, only a few clear guidelines regarding cleaning and disinfection of nebulizers are available. This survey aims to analyze the common household methods used by cystic fibrosis (CF) patients in Belgium., Methods: A questionnaire about type of nebulizer, methods, and frequency of cleaning, disinfection, and drying was established by physiotherapists at the Belgian CF centers., Results and Conclusions: The Belgian CF Association sent this questionnaire to all Belgian CF patients. Of the 903 questionnaires distributed, 379 (42%) were returned. Fifty-two, 40 and 8% of patients used a vibrating-mesh nebulizer, jet nebulizer, or ultrasonic device, respectively. Ninety-eight percent of patients cleaned their material and soap was used by 35% of patients. Disinfection was performed by 91% of the patients and was daily performed by 57% of patients. Thermal disinfection (boiling water or sterilizer), a hypochlorite-based solution, or acetic acid solution was used by 44, 29, and 25% of patients, respectively, to disinfect their nebulizers. Thermal disinfection was used by 37 and 53% of patients using a classical or vibrating-mesh nebulizer, respectively. After cleaning or disinfection, 91% of the material was dried. The high percentage of Belgian CF patients disinfecting their nebulizer proves that they seem aware of the hygiene problem. The lack of unanimous guidelines in Belgium at the time of the study perhaps explains the variety of methods used. The arrival of vibrating-mesh nebulizers has led to a change in disinfection methods.

The influence of somatic growth and genetic selection on the whole blood oxygen equilibrium curve (OEC) was measured under standard conditions in double-muscled and dairy calves during their first 3 mo of life. Crossbreed animals were also investigated. Hemoglobin, 2,3-diphosphoglycerate (DPG), Cl, and Pi concentrations were also measured. The percentage of fetal hemoglobin (HbF) was determined. The influence of exogenous Cl, Pi, and pH on the OEC was also assessed. The PO2 at 50% hemoglobin saturation (P50) increased during somatic growth, probably because of the increase in DPG recorded in double-muscled neonates and to the progressive disappearance of HbF in both breeds. The oxygen exchange fraction (OEF%) was used to assess the combined influence of the OEC shift and OEC shape changes on blood oxygen desaturation under standard conditions, when the PO2 decreases within a physiological range. The OEF% showed an increase during the first month, then a stabilization. The effects of Cl, Pi, and pH in Friesian calves were similar as in adult cattle. Double-muscled neonates had a lower P50, OEF% values, and DPG concentrations and higher hemoglobin and Cl concentrations than Friesian neonates. The Pi concentration and the percentage of HbF were similar in both breeds. The pH and the Cl concentration had significantly less effect on the OEC in double-muscled than in Friesian calves. Crossbreed animals exhibited intermediate parameter values, between those recorded for double-muscled and Friesian calves. All differences between breeds progressively disappeared during the first month. These data show that blood function changes markedly in calves during the first month of life and that genetic selection can alter blood function.

Kinetic parameters of parathion and paraoxon uptake were determined in isolated and perfused rabbit and guinea pig lungs. They were related to organophosphate-induced lung cholinesterase inhibition. A single pass procedure was used to perfuse the lungs with an artificial medium perfusate containing paraoxon or parathion. The paraoxon and parathion concentrations were determined in the effluents collected at chosen intervals over an 18-min period beginning at the start of perfusion. Three inflowing concentrations (1 nmol/ml, 10 nmol/ml, and 20 nmol/ml) were tested in guinea pig lungs and one (10 nmol/ml) in rabbit lungs. Cholinesterase activity was determined at time 0 and at the end of the experiment. The lungs abundantly extracted paraoxon and parathion over the perfusion period. The extraction ratio was consistently greater in guinea pig than in rabbit lungs. The uptake velocity varied biexponentially in time, suggesting the existence of two compartments. Initial uptake velocities (A, B) and slopes (alpha and beta) were calculated for both compartments. In guinea pigs, A, B and A + B increased proportionally to the supply rate of paraoxon and parathion while a and b remained constant. No significant difference was observed between parathion and paraoxon uptake kinetics. Parameter B was the only one to differ significantly between the two species (rabbits: 8.19 +/- 1.53 for parathion and 6.85 +/- 1.26 for paraoxon; guinea pigs: 12.75 +/- 0.88 for parathion and 15.02 +/- 3.84 for paraoxon). In the lungs of both species, there was a linear relation between y, the percentage of cholinesterase inhibition induced by either organophosphate, and X, the total amount of drug taken up by the lung tissue (in nmol/g/18 min). The following equations were obtained: y = 0.128 x + 0.979 (R2 = 0.89, p < 0.001 for paraoxon); y = 0.120 x - 6.57 (R2 = 0.82, p < 0.005 for parathion). No difference was observed between the two organophosphates. After treatment with the cytochrome P450 inhibitor piperonyl butoxide, the above relations ceased to apply, but this treatment did not influence the kinetics of paraoxon and parathion uptake. The IC50 value calculated for paraoxon, i.e., the paraoxon concentration required to produce 50% inhibition of lung cholinesterase activity, was similar for guinea pigs (2.22 10(-7) +/- 0.22 M) and rabbits (2.36 10(-7) +/- 0.24 M). In conclusion, the biexponential evolution of the velocity of paraoxon and parathion uptake by the lungs thus demonstrates the presence of two pools. The lower extraction ratios calculated for rabbit lungs reflect the lower initial uptake velocity of the second compartment. In the range of concentrations investigated in guinea pigs, no saturable mechanism could be demonstrated for paraoxon and parathion. Cytochrome P450-related lung metabolic activity, through which parathion is converted to paraoxon, appears as a major step in parathion-induced lung cholinesterase inhibition, although it does not appear to affect parathion toxicokinetics.