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The impact of 316L stainless steel discolouration on chloride induced pitting corrosion was quantified. Several degrees of discolouration were produces by chemical passivation, simulated rouging or anodic polarization. Discolouration was characterized by spectral analysis in the visible and NIR range (400-1000 nm). Pitting corrosion resistance was quantified by pitting potential measurements. The spectra were used to indicate whether pitting or transpassive behaviour would occur using chemometric classification techniques with 88% accuracy. In addition, pitting potentials were predicted combining chemometrics and survival analysis techniques with a 90 mV RMS error in cross-validation. XPS analysis linked pitting resistance with the Cr/Fe balance. • 316L surface discolouration can be quantified by Vis-NIR spectral measurements. • The occurrence of pitting was predicted by application of chemometric classification. • The pitting potential was predicted by application of chemometric survival analysis. • Transpassive outcomes are included by combining chemometric and survival regression. [ABSTRACT FROM AUTHOR]

The progesterone (P4) monitoring algorithm using synergistic control (PMASC) uses luteal dynamics to identify fertility events in dairy cows. This algorithm employs a combination of mathematical functions describing the increasing and decreasing P4 concentrations during the development and regression of the corpus luteum and a statistical control chart that allows identification of luteolysis. The mathematical model combines sigmoidal functions from which the cycle characteristics can be calculated. Both the moment at which luteolysis is detected and confirmed by PMASC, as well as the model features themselves, can be used to inform the farmer on the fertility status of the cows. [ABSTRACT FROM AUTHOR]

Both the sensitivity of an estrus detection system and the consistency of alarms relative to ovulation determine its value for a farmer. The objective of this study was to compare an activity-based system and a milk progesterone–based system for their ability to detect estrus reliably, and to investigate how their alerts are linked to the time of the LH surge preceding ovulation. The study was conducted on an experimental research farm in Flanders, Belgium. The activity alerts were generated by a commercial activity meter (ActoFIT, DeLaval, Tumba, Sweden), and milk progesterone was measured using a commercial ELISA kit. Sensitivity and positive predictive value of both systems were calculated based on 35 estrus periods over 43 d. Blood samples were taken for determination of the LH surge, and the intervals between timing of the alerts and the LH surge were investigated based on their range and standard deviation (SD). Activity alerts had a sensitivity of 80% and a positive predictive value of 65.9%. Alerts were detected from 39 h before until 8 h after the LH surge (range: 47 h, SD: 16 h). Alerts based on milk progesterone were obtained from a recently developed monitoring algorithm using a mathematical model and synergistic control. All estruses were correctly identified by this algorithm, and the LH surge followed, on average, 62 h later. Using the mathematical model, model-based indicators for the estimation of ovulation time can be calculated. Depending on which modelbased indicator was used, ranges of 33 to 35 h and SD of about 11 h were obtained. Because detection of the LH surge was very labor intensive, only a limited number of potential estrus periods could be studied. [ABSTRACT FROM AUTHOR]

Timely identification of a cow's reproduction status is essential to minimize fertility-related losses on dairy farms. This includes optimal estrus detection, pregnancy diagnosis, and the timely recognition of early embryonic death and ovarian problems. On-farm milk progesterone (P4) analysis can indicate all of these fertility events simultaneously. However, milk P4 measurements are subject to a large variability both in terms of measurement errors and absolute values between cycles. The objective of this paper is to present a newly developed methodology for detecting luteolysis preceding estrus and give an indication of its on-farm use. The innovative monitoring system presented is based on milk P4 using the principles of synergistic control. Instead of using filtering techniques and fixed thresholds, the present system employs an individually on-line updated model to describe the P4 profile, combined with a statistical process control chart to identify the cow's fertility status. The inputs for the latter are the residuals of the on-line updated model, corrected for the concentration-dependent variability that is typical for milk P4 measurements. To show its possible use, the system was validated on the P4 profiles of 38 dairy cows. The positive predictive value for luteolysis followed by estrus was 100%, meaning that the monitoring system picked up all estrous periods identified by the experts. Pregnancy or embryonic mortality was characterized by the absence or detection of luteolysis following an insemination, respectively. For 13 cows, no luteolysis was detected by the system within the 25 to 32 d after insemination, indicating pregnancy, which was confirmed later by rectal palpation. It was also shown that the system is able to cope with deviating P4 profiles having prolonged follicular or luteal phases, which may suggest the occurrence of cysts. Future research is recommended for optimizing sampling frequency, predicting the optimal insemination window, and establishing rules to detect problems based on deviating P4 patterns. [ABSTRACT FROM AUTHOR]

Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments. [ABSTRACT FROM AUTHOR]

As lameness detection in dairy cattle using visual locomotion scoring is cumbersome and subjective, research efforts are dedicated to develop automatic lameness detection systems. ‘Tender hoof placement’ and the distribution of the body weight over the four legs are possible lameness indicators, but no research exists on how to derive from automatically measured gait characteristics. This study aims to derive new variables related to the (i) landing, full weight bearing and lifting phases of a stance time and the (ii) time spent on combinations of legs during the different phases of the gait cycle from cow gait recordings on a dedicated pressure mat, known as the Gaitwise. Data of 9 non-lame, 11 mildly lame and 12 severely lame cows were gathered. For all measurements, each variable was calculated per leg or combination of legs, after which the group means of each variable were compared between the three lameness statuses using a one-way ANOVA analysis. Landing and lifting variables indicated that the proportion of time for hoof placement and hoof lifting during the total stance time was longer in lame cows, and that the proportion of full weight bearing time was shorter. Lame cows were thus more careful to place and retract the hind feet in the case of a hind-lame leg. Support time variables indicated that lame cows increased the percentage triple support time (i.e time spent with three feet on the ground during walking) and lowered the percentage double support (i.e. time spent with two legs on the ground). Also, double support combinations on the same side of the body were preferred above diagonal combinations. The newly defined gait variables indeed reflect tenderness of hoof placement and body weight distribution and hence seem useful for discriminating between non-lame, mildly lame and severely lame cows. However, several of these interesting variables may have to be combined to obtain automatic lameness detection with sufficient accuracy. [ABSTRACT FROM AUTHOR]

The study of animal feeding behaviour is of interest to understand feeding, to investigate the effect of treatments and conditions or to predict illness. This paper reviews the different steps to undertake when studying animal feeding behaviour, with illustrations for group-housed pigs. First, one must be aware of the mechanisms that control feeding and the various influences that can change feeding behaviour. Satiety is shown to largely influence free feeding ( ad libitum and without an operant condition) in animals, but ‘free’ feeding seems a very fragile process, given the many factors that can influence feeding behaviour. Second, a measurement method must be chosen that is compatible with the goal of the research. Several measurement methods exist, which lead to different experimental set-ups and measurement data. Sensors are available for lab conditions, for research on group-housed pigs and also for on-farm use. Most of these methods result in a record of feeding visits. However, these feeding visits are often found to be clustered into meals. Thus, the third step is to choose which unit of feeding behaviour to use for analysis. Depending on the situation, either meals, feeding visits, other raw data, or a combination thereof can be suitable. Meals are more appropriate for analysing short-term feeding behaviour, but this may not be true for disease detection. Further research is therefore needed. To cluster visits into meals, an appropriate analysis method has to be selected. The last part of this paper provides a review and discussion of the existing methods for meal determination. A variety of methods exist, with the most recent methods based on the influence of satiety on feeding. More thorough validation of the recent methods, including validation from a behavioural point of view and uniformity in the applied methods is therefore necessary. [ABSTRACT FROM AUTHOR]