Automatic recording of daily walkover liveweight of dairy cattle at pasture in the first 100 days in milk

Alawneh, J. I.; Stevenson, M.A.; Williamson, N. B.; López‐Villalobos, N.; Otley, T.

doi:10.3168/jds.2010-4002

Cited by 48 publications

(45 citation statements)

References 26 publications

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“…A different approach is to instrument the floor of the robot to detect weight. Alawneh et al (2011) showed that daily variability (SD 17 kg) of weight could be overcome by taking an 8 day rolling mean. The standard deviation of daily live weight (LW) measurements across parities was 17 kg, on average.…”

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confidence: 99%

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection

Mottram

2016

Animal

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Dairy cows are high value farm animals requiring careful management to achieve the best results. Since the advent of robotic and high throughput milking, the traditional few minutes available for individual human attention daily has disappeared and new automated technologies have been applied to improve monitoring of dairy cow production, nutrition, fertility, health and welfare. Cows milked by robots must meet legal requirements to detect healthy milk. This review focuses on emerging technical approaches in those areas of high cost to the farmer (fertility, metabolic disorders, mastitis, lameness and calving). The availability of low cost tri-axial accelerometers and wireless telemetry has allowed accurate models of behaviour to be developed and sometimes combined with rumination activity detected by acoustic sensors to detect oestrus; other measures (milk and skin temperature, electronic noses, milk yield) have been abandoned. In-line biosensors have been developed to detect markers for ovulation, pregnancy, lactose, mastitis and metabolic changes. Wireless telemetry has been applied to develop boluses for monitoring the rumen pH and temperature to detect metabolic disorders. Udder health requires a multisensing approach due to the varying inflammatory responses collectively described as mastitis. Lameness can be detected by walk over weigh cells, but also by various types of video image analysis and speed measurement. Prediction and detection of calving time is an area of active research mostly focused on behavioural change.Keywords: dairy cow, sensors, fertility, precision livestock farming, automation ImplicationsSince the introduction of robotic milking technologies to measure important parameters of cow health and fertility have moved from scientific concepts towards robust techniques routinely used on farms. There is still potential for developing new approaches but the main focus is on applying modelling to data sources that are already available such as tri-axial cow collars, in-line milk sensing and rumen telemetry. IntroductionIn the past century dairy farms have become larger, milk yields have risen, quality requirements have risen, labour has become less available and automated systems for milking and other farm tasks have become common all of these factors have created a need for automated monitoring of health and fertility. The first systems for recording milk yield electronically and allocating feed date back to the 1970s and automatic oestrus behaviour systems from the 1980s. As electronics have become cheaper and new sensing capabilities developed a variety of engineering options were reviewed for all animals by Frost et al. (1997). Mottram (1997) reviewed potential target disease conditions in dairy cows and identified parturition, mastitis and failures in nutrition as the most significant causes of loss. It was suggested that sensing should focus on the most directly measurable indicator of a condition such as rumen pH rather than proxy measures. Since then major developments have taken plac...

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confidence: 99%

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection

Mottram

2016

Animal

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“…Comparing the relative frequency of classified 'physiologically extreme' and 'implausible' milk performances with 1.2% of all observations to the findings of Wiggans et al [5], who identified 1.9% abnormal test day milk records, the same order of magnitude for conspicuous observations can be identified. In other automatic measurements, such as walkover live weight recording, Alawneh et al [9] found a much higher frequency of conspicuous observations. The authors detected a total of 12% outliers, of which they classified 25% as 'biologically implausible' and 75% as 'potentially erroneous'.…”

Section: Classification Of Observations and Descriptive Statisticsmentioning

confidence: 96%

“…However, there are also approaches to further discriminate the source of extreme observations. Alawneh et al [9] for example investigated the daily walkover live weight of cows and differentiated identified outliers into 'biologically implausible' or 'potentially erroneous'. The authors made these classifications based on a smoothed live weight curve, where observations outside the ±4 standard deviation (SD) interval were classified as 'biologically implausible' and observations outside the ±1.96 SD interval (95% confidence interval) as 'potentially erroneous' cases.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Concept for a Multivariate Plausibility Assessment of Simultaneously Recorded Data

Mensching

Zschiesche

Hummel

et al. 2020

Animals

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The aim of this work was to develop an innovative multivariate plausibility assessment (MPA) algorithm in order to differentiate between ‘physiologically normal’, ‘physiologically extreme’ and ‘implausible’ observations in simultaneously recorded data. The underlying concept is based on the fact that different measurable parameters are often physiologically linked. If physiologically extreme observations occur due to disease, incident or hormonal cycles, usually more than one measurable trait is affected. In contrast, extreme values of a single trait are most likely implausible if all other traits show values in a normal range. For demonstration purposes, the MPA was applied on a time series data set which was collected on 100 cows in 10 commercial dairy farms. Continuous measurements comprised climate data, intra-reticular pH and temperature, jaw movement and locomotion behavior. Non-continuous measurements included milk yield, milk components, milk mid-infrared spectra and blood parameters. After the application of the MPA, in particular the pH data showed the most implausible observations with approximately 5% of the measured values. The other traits showed implausible values up to 2.5%. The MPA showed the ability to improve the data quality for downstream analyses by detecting implausible observations and to discover physiologically extreme conditions even within complex data structures. At this stage, the MPA is not a fully developed and validated management tool, but rather corresponds to a basic concept for future works, which can be extended and modified as required.

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“…They compared the dynamic weights with actual static weights, which confirmed the feasibility and effectiveness of the dynamic weighing method in the evaluation of cow weights. Alawneh et al [ 19 ] recorded the daily live weight variations in 463 cows at a dairy farm in northern New Zealand, and the standard deviation of the measured value was 17 kg; they obtained the live weights of cows through autocorrelation analysis, and these weights were used to adjust the cattle feeding plan and detect changes in the bodies of cows. E. Gonzalez-Garcia et al [ 20 ] designed a mobile automatic weighing system based on an environment with outdoor free-range sheep and encouraged the voluntary weighing of sheep combined with stimulations of water and mineral salt intake; this approach not only saved human and material resources but also improved animal productivity and welfare.…”

Section: Introductionmentioning

confidence: 99%

Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform

Ningning

Han

et al. 2020

Sensors

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Weight is an important indicator of the growth and development of dairy cows. The traditional static weighing methods require considerable human and financial resources, and the existing dynamic weighing algorithms do not consider the influence of the cow motion state on the weight curve. In this paper, a dynamic weighing algorithm for cows based on a support vector machine (SVM) and empirical wavelet transform (EWT) is proposed for classification and analysis. First, the dynamic weight curve is obtained by using a weighing device placed along a cow travel corridor. Next, the data are preprocessed through valid signal acquisition, feature extraction, and normalization, and the results are divided into three active degrees during motion for low, medium, and high grade using the SVM algorithm. Finally, a mean filtering algorithm, the EWT algorithm, and a combined periodic continuation-EWT algorithm are used to obtain the dynamic weight values. Weight data were collected for 910 cows, and the experimental results displayed a classification accuracy of 98.6928%. The three algorithms were used to calculate the dynamic weight values for comparison with real values, and the average error rates were 0.1838%, 0.6724%, and 0.9462%. This method can be widely used at farms and expand the current knowledgebase regarding the dynamic weighing of cows.

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Automatic recording of daily walkover liveweight of dairy cattle at pasture in the first 100 days in milk

Cited by 48 publications

References 26 publications

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection

Animal board invited review: precision livestock farming for dairy cows with a focus on oestrus detection

An Innovative Concept for a Multivariate Plausibility Assessment of Simultaneously Recorded Data

Research on a Dynamic Algorithm for Cow Weighing Based on an SVM and Empirical Wavelet Transform

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