Editorial: Precision livestock farming: a 'per animal' approach using advanced monitoring technologies

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Prediction of Milking Robot Utilization

For the planning of the barn layout, cow traffic and facility locations (such as: cubicles, forage lane, etc.), the farmer has to know the milking robot utilization of his production herd. Therefore, prediction of the milking robot utilization has to be done. The milking robot utilization depends on the cow´s visiting pattern and capacity of the milking robot. The models used for prediction were generalized multiple regression models. Behavioural data were obtained by video observations and electronic measurements. For eleven behavioural variables used in the model from all three experiments, only two (number of cows and sum of milk yields per hour in kilograms) were statistically significant (p ≤ 0.05) and measurable on a commercial farm. A part from the milking capacity, forage feeding routine influenced utilization of the robot. Combined cow traffic used in experiments appeared to be feasible.Izgled staje, kretanje krava, te raspored pojedinih dijelova staje (npr. ležišta, "krmna zabrana"...) ovisi o stupnju iskorištenja robota za strojnu mužnju u postojećem stadu krava. Zbog toga je važno predvidjeti stupanj iskorištenja robota za strojnu mu.nju. On ovisi o redoslijedu posjeta krava robotu i kapacitetu robota za strojnu mužnju. Statistički modeli korišteni za predviđanje su općeniti modeli multiple regresije. Opisni podaci o kravama su prikupljeni pomoću video opreme i elektronskih mjerenja. Od jedanaest varijabli korištenih u statistikom modelu od tri eksperimenta, samo dvije (broj krava i ukupna količina izmuzenog mlijeka (kg/h)) su bile statistički signifikantne (p ≤0.05) i mjerljive na komercijalnoj farmi. Osim kapaciteta strojne mu.nje na stupanj iskorištenja robota za strojnu mužnju utjecao je i vremenski raspored hranjenja na "krmnoj zabrani". Kombinirani način kretanja krava u staji se pokazao izvediv

Prediction of Milking Robot Utilization

For the planning of the barn layout, cow traffic and facility locations (such as: cubicles, forage lane, etc.), the farmer has to know the milking robot utilization of his production herd. Therefore, prediction of the milking robot utilization has to be done. The milking robot utilization depends on the cow´s visiting pattern and capacity of the milking robot. The models used for prediction were generalized multiple regression models. Behavioural data were obtained by video observations and electronic measurements. For eleven behavioural variables used in the model from all three experiments, only two (number of cows and sum of milk yields per hour in kilograms) were statistically significant (p ≤ 0.05) and measurable on a commercial farm. A part from the milking capacity, forage feeding routine influenced utilization of the robot. Combined cow traffic used in experiments appeared to be feasible.Izgled staje, kretanje krava, te raspored pojedinih dijelova staje (npr. ležišta, "krmna zabrana"...) ovisi o stupnju iskorištenja robota za strojnu mužnju u postojećem stadu krava. Zbog toga je važno predvidjeti stupanj iskorištenja robota za strojnu mu.nju. On ovisi o redoslijedu posjeta krava robotu i kapacitetu robota za strojnu mužnju. Statistički modeli korišteni za predviđanje su općeniti modeli multiple regresije. Opisni podaci o kravama su prikupljeni pomoću video opreme i elektronskih mjerenja. Od jedanaest varijabli korištenih u statistikom modelu od tri eksperimenta, samo dvije (broj krava i ukupna količina izmuzenog mlijeka (kg/h)) su bile statistički signifikantne (p ≤0.05) i mjerljive na komercijalnoj farmi. Osim kapaciteta strojne mu.nje na stupanj iskorištenja robota za strojnu mužnju utjecao je i vremenski raspored hranjenja na "krmnoj zabrani". Kombinirani način kretanja krava u staji se pokazao izvediv

Influence of barn climate, body postures and milk yield on the respiration rate of dairy cows

The main objective of this study was to identify the influences of different climatic conditions and cow-related factors on the respiration rate (RR) of lactating dairy cows. Measurements were performed on 84 lactating Holstein Friesian dairy cows (first to eighth lactation) in Brandenburg, Germany. The RR was measured hourly or twice a day with up to three randomly chosen measurement days per week between 0700 h and 1500 h (GMT + 0100 h) by counting right thoracoabdominal movements of the cows. Simultaneously with RR measurements, cow body postures (standing vs. lying) were documented. Cows’ milk yield and days in milk were recorded daily. The ambient temperature and relative humidity of the barn were recorded every 5 min to calculate the current temperature-humidity index (THI). The data were analyzed for interactions between THI and cow-related factors (body postures and daily milk yield) on RR using a repeated measurement linear mixed model. There was a significant effect of the interaction between current THI category and body postures on RR. The RRs of cows in lying posture in the THI < 68, 68 ≤ THI < 72 and 72 ≤ THI < 80 categories (37, 46 and 53 breaths per minute (bpm), respectively) were greater than those of standing cows in the same THI categories (30, 38 and 45 bpm, respectively). For each additional kilogram of milk produced daily, an increase of 0.23±0.19 bpm in RR was observed. Including cow-related factors may help to prevent uncertainties of RR in heat stress predictions. In practical application, these factors should be included when predicting RR to evaluate heat stress on dairy farm

Automatic solution for detection, identification and biomedical monitoring of a cow using remote sensing for optimised treatment of cattle

In this paper we show how a novel photonic remote sensing system assembled on a robotic platform can extract vital biomedical parameters from cattle including their heart beating, breathing and chewing activity. The sensor is based upon a camera and a laser using selfinterference phenomena. The whole system intends to provide an automatic solution for detection, identification and biomedical monitoring of a cow. The detection algorithm is based upon image processing involving probability map construction. The identification algorithms involve well known image pattern recognition techniques. The sensor is used on top of an automated robotic platform in order to support animal decision making. Field tests and computer simulated results are presented

Effect of two cooling frequencies on respiration rate in lactating dairy cows under hot and humid climate conditions

The aim of this study was to evaluate the effects of evaporative cooling at two different frequen-cies per day on the respiration rate (rr) of lactating dairy cows, considering cow-related factors. twenty multiparous israeli holstein dairy cows housed in a naturally ventilated cowshed were di-vided randomly into two treatment groups. the cows of both groups were exposed to 3 or 8 cooling sessions per day (3xcool vs. 8xcool, respectively). the rr was observed hourly, with a maximum of 12 measurements per day. Body posture (standing vs. lying) was simultaneously documented. milk yield was recorded daily. coat color was determined from a digital photograph. the rr of standing and lying cows was lower in the 8xcool group (60.2 and 51.6 breaths per min (bpm), re-spectively) than in the 3xcool group (73.1 and 65.6 bpm, respectively). For each increment of five kilograms of milk produced, rr increased by one bpm, and the rr of cows in early days in milk (dim) was 12.3 bpm higher than that of cows in late dim. in conclusion, eight cooling sessions per day instead of three lead to a rr abatement in heat-stressed cows under hot conditions, and cow-related factors directly impact the rr during heat stress assessmen

Precision livestock farming and animal welfare: contradictions or synergies?

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