Forecasting the onset of depression with limited baseline data only: A comparison of a person-specific and a multilevel modeling based exponentially weighted moving average approach.

The onset of depressive episodes is preceded by changes in mean levels of affective experiences, which can be detected using the exponentially weighted moving average procedure on experience sampling method (ESM) data. Applying the exponentially weighted moving average procedure requires sufficient baseline data from the person under study in healthy times, which is needed to calculate a control limit for monitoring incoming ESM data. It is, however, not trivial to obtain sufficient baseline data from a single person. We therefore investigate whether historical ESM data from healthy individuals can help establish an adequate control limit for the person under study via multilevel modeling. Specifically, we focus on the case in which there is very little baseline data available of the person under study (i.e., up to 7 days). This multilevel approach is compared with the traditional, person-specific approach, where estimates are obtained using the person's available baseline data. Predictive performance in terms of Matthews correlation coefficient did not differ much between the approaches; however, the multilevel approach was more sensitive at detecting mean changes. This implies that for low-cost and nonharmful interventions, the multilevel approach may prove particularly beneficial. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Allantoic Fluid-Based qPCR for Early Onset In Ovo Sexing.

The day-old male chick culling remains a welfare issue in the poultry industry. Several governments have prohibited this practice, pushing hatcheries to seek alternatives. Although different solutions exist for solving this problem, sex determination during the embryo's incubation (in ovo sexing) is considered the most suitable one among the consumers and industry. However, to be industrialized, in ovo sexing technologies must meet several requirements: compatibility with all egg colors and early developmental stages while maintaining a high hatchability rate and accuracy at low cost and high throughput. To meet these requirements, we studied the use of the sexual genes HINTW (female-specific) and DMRT-1 (both sexes) at incubation days 6-9. By utilizing the quantitative polymerase chain reaction in allantoic fluid (AF) samples, our study confirmed female-specific HINTW detection on all days without any significant detrimental effects on embryo development. We achieved 95% sexing accuracy using the HINTW cycle threshold (Ct) alone and 100% accuracy rate when using Δλ values (difference between the HINTW and DMRT-1 Ct). In conclusion, the developed assay can provide information about AF as a sample for in ovo sexing and open new industrial possibilities for faster and cheaper assays.

Noninvasive Assessment of Chicken Egg Fertility during Incubation Using HSSE-GC-MS VOC Profiling.

Volatile organic compounds (VOCs) carry crucial information about chicken egg fertility. Assessing the fertility before incubation holds immense potential for poultry industry efficiency. Our study used headspace sorptive extraction-gas chromatography-mass spectrometry to analyze egg VOCs before and during the initial 12 incubation days. A total of 162 VOCs were identified. Hexanal was significantly higher in unfertilized eggs, whereas compounds such as propan-2-ol, propan-2-one, and carboxylic acids were higher in fertilized eggs. Furthermore, the obtained multiple logistic regression model outperformed the partial least-squares-discriminant analysis (PLS-DA) model, demonstrating lower complexity and superior performance. Fertile eggs were accurately identified in the validation set in 68-75% of the cases during the initial 4 days, to 85 and 100% on days 6 and 8. Finally, hierarchical cluster analysis in fertilized eggs revealed the clustering of VOCs of the same chemical class, indicative of their shared biochemical origin. This suggests a promising direction for future research aimed at understanding the biological information embedded in VOCs and their relationship to biochemical processes during embryo development.

Non-destructive egg breed separation using advanced VOC analytical techniques HSSE-GC-MS, PTR-TOF-MS, and SIFT-MS: Assessment of performance and systems' complementarity.

Over the past decade, advanced analytical techniques have been utilized to examine volatile organic compounds (VOCs) in eggs. These VOCs offer valuable insights into factors such as freshness, fertility, the presence of cracks, embryo sex, and breed. In our study, we assessed three mass spectrometry-based systems (headspace sorptive extraction gas chromatography-mass spectrometry; HSSE-GC-MS, proton transfer reaction time-of-flight-mass spectrometry; PTR-TOF-MS; and selected ion flow tube mass spectrometry; SIFT-MS) to analyze and identify VOCs present in intact hatching eggs from three distinct breeds (Dekalb white layer, Shaver brown layer, and Ross 308 broiler). The eggs were sampled on incubation days 2 and 8, to identify VOCs that distinguish breeds irrespective of incubation day. VOC measurements were conducted on 15 eggs per breed by placing them together with PDMS-coated stir bars inside inert Teflon® air sampling bags. After an accumulation period of 2 h, the headspace was analyzed using PTR-TOF-MS and SIFT-MS, while the VOCs adsorbed onto the stir bars were analyzed using GC-MS for additional compound identification. Partial least squares discriminant analysis (PLS-DA) models were constructed for breed differentiation, and variable selection was performed. As a result, 111 VOCs were identified using HSSE-GC-MS, with alcohols and esters being the most abundant. The PLS-DA models demonstrated the efficacy of breed discrimination, with the HSSE-GC-MS and the PTR-TOF-MS exhibiting the highest balanced accuracy of 95.5 % using a reduced set of 11 VOCs and 5 product ions, respectively. The SIFT-MS model had a balanced accuracy of 92.8 % with a reduced set of 11 product ions. Furthermore, complementarity was observed between HSSE-GC-MS, which primarily selected higher molecular weight VOCs, and PTR-TOF-MS and SIFT-MS. A higher correlation was found for compound abundances between the HSSE-GC-MS and the PTR-TOF-MS relative to the SIFT-MS, indicating that the PTR-TOF-MS was better suited to quantify specific compounds identified by the HSSE-GC-MS. Finally, the findings support the presence of VOCs originating from both synthetic and natural sources, highlighting the ability of the VOC analysis systems to non-destructively perform quality control and reveal differences in management practices or biological information encoded in eggs.

Real-time detection of mean and variance changes in experience sampling data: A comparison of existing and novel statistical process control approaches.

Retrospective analyses of experience sampling (ESM) data have shown that changes in mean and variance levels may serve as early warning signs of an imminent depression. Detecting such early warning signs prospectively would pave the way for timely intervention and prevention. The exponentially weighted moving average (EWMA) procedure seems a promising method to scan ESM data for the presence of mean changes in real-time. Based on simulation and empirical studies, computing and monitoring day averages using EWMA works particularly well. We therefore expand this idea to the detection of variance changes and propose to use EWMA to prospectively scan for mean changes in day variability statistics (i.e., s 2 , s , ln( s )). When both mean and variance changes are of interest, the multivariate extension of EWMA (MEWMA) can be applied to both the day averages and a day statistic of variability. We evaluate these novel approaches to detecting variance changes by comparing them to EWMA-type procedures that have been specifically developed to detect a combination of mean and variance changes in the raw data: EWMA- S 2 , EWMA-ln( S 2 ), and EWMA- X ¯ - S 2 . We ran a simulation study to examine the performance of the two approaches in detecting mean, variance, or both types of changes. The results indicate that monitoring day statistics using (M)EWMA works well and outperforms EWMA- S 2 and EWMA-ln( S 2 ); the performance difference with EWMA- X ¯ - S 2 is smaller but notable. Based on the results, we provide recommendations on which statistic of variability to monitor based on the type of change (i.e., variance increase or decrease) one expects.

Association between management practices and estimated mastitis incidence and milk losses on robotic dairy farms.

This study aims to describe the relation between farm-level management factors and estimated farm-level mastitis incidence and milk loss traits (MIMLT) at dairy farms with automated milking systems. In this observational study, 43 commercial dairy farms in Belgium and the Netherlands were included and 148 'management and udder health related variables' were obtained during a farm visit through a farm audit and survey. The MIMLT were estimated from milk yield data. Quarter-level milk yield perturbations that were caused by presumable mastitis cases (PMC) were selected based on quarter-level milk yield and electrical conductivity. On average, 57.6 ± 5.4% of the identified milk yield perturbations complied with our criteria. From these PMC, 3 farm-level MIMLT were calculated over a one-year period around the farm visit date: (1) the 'average number of PMC per cow per year', (2) the 'absolute milk loss per cow per day', calculated as the farm-level sum of all milk losses during PMC in one year, divided by the average number of lactating cows and the number of days, and (3) the 'relative milk loss', calculated as the farm-level sum of milk losses during PMC in one year, divided by the estimated total production in the absence of PMC. The 'average number of PMC per cow per year' was on average 1.81 ± 0.47. The PMC caused an average milk loss of 0.77 ± 0.26 kg per lactating cow per day, which corresponded to an average production loss of 2.38 ± 0.82% of the expected production in the absence of PMC. We performed a principal component regression (PCR) analysis to link the 3 MIMLT to the 'management and udder health related variables', whilst reducing the multicollinearity and the number of dimensions. The first principal component was mainly related to 'milking system brand, maintenance and settings'. The second component mainly linked to average productivity and somatic cell counts, whereas the third component mainly contained variables linked with mastitis management, treatment, and biosecurity. The 3 PCR models had R² ranging from 0.46 (for absolute milk loss per cow per day) to 0.57 (for relative milk loss). For all models, the second PC had the largest effect size. This analysis raises awareness of the impact of management factors on a factual basis and provides handles to take management actions to improve udder health.

Monitoring cadmium concentrations in cacao: inter-laboratory variation and the effect of sample size on variability among ready-for-sale beans.

Since the implementation of new EU limits on cadmium (Cd) in cacao-derived products, reliable measurements of the Cd concentration in cacao samples have become even more important. This study was set up to analyse the robustness of the measured Cd concentrations in cacao as affected by sampling strategy and by the laboratory receiving these samples. Six different homogenised cacao liquor samples were sent to 25 laboratories, mainly located in Latin America. On average, only 76% of the laboratories reported acceptable results per sample using internationally accepted criteria. More unreliable data was obtained when Atomic Absorption Spectroscopy (AAS) rather than Inductively Coupled Plasma (ICP) instruments were used or where concentrations were outside the calibration range. Subsequently, four commercial lots in Ecuadorian warehouses were sampled to identify the variation among beans, bags and replicate chemical analyses of ground samples. Simulations indicate that a composite sample should be made from at least 10 bags on a pallet and at least 60 beans should be ground prior to analysis to obtain an acceptable CV below 15%. This study shows that current Cd analyses in cacao on the market are neither sufficiently accurate nor precise and that more control on laboratory certifications is needed for reliable screening of Cd in cacao.

Towards automatic insect monitoring on witloof chicory fields using sticky plate image analysis.

Context: Sticky trap catches of agricultural pests can be employed for early hotspot detection, identification, and estimation of pest presence in greenhouses or in the field. However, manual procedures to produce and analyze catch results require substantial time and effort. As a result, much research has gone into creating efficient techniques for remotely monitoring possible infestations. A considerable number of these studies use Artificial Intelligence (AI) to analyze the acquired data and focus on performance metrics for various model architectures. Less emphasis, however, was devoted to the testing of the trained models to investigate how well they would perform under practical, in-field conditions. Objective: In this study, we showcase an automatic and reliable computational method for monitoring insects in witloof chicory fields, while shifting the focus to the challenges of compiling and using a realistic insect image dataset that contains insects with common taxonomy levels. Methods: To achieve this, we collected, imaged, and annotated 731 sticky plates - containing 74,616 bounding boxes - to train a YOLOv5 object detection model, concentrating on two pest insects (chicory leaf-miners and wooly aphids) and their two predatory counterparts (ichneumon wasps and grass flies). To better understand the object detection model's actual field performance, it was validated in a practical manner by splitting our image data on the sticky plate level. Results and conclusions: According to experimental findings, the average mAP score for all dataset classes was 0.76. For both pest species and their corresponding predators, high mAP values of 0.73 and 0.86 were obtained. Additionally, the model accurately forecasted the presence of pests when presented with unseen sticky plate images from the test set. Significance: The findings of this research clarify the feasibility of AI-powered pest monitoring in the field for real-world applications and provide opportunities for implementing pest monitoring in witloof chicory fields with minimal human intervention.

Trends in in ovo sexing technologies: insights and interpretation from papers and patents.

Numerous researchers and institutions have been developing in ovo sexing technologies to improve animal welfare by identifying male embryos in an early embryonic stage and disposing of them before pain perception. This review gives a complete overview of the technological approaches reported in papers and patents by performing a thorough search using Web of Science and Patstat/Espacenet databases for papers and patents, respectively. Based on a total of 49 papers and 115 patent families reported until May 2023 worldwide, 11 technology categories were defined: 6 non-optical and 5 optical techniques. Every category was described for its characteristics while assessing its potential for application. Next, the dynamics of the publications of in ovo sexing techniques in both paper and patent fields were described through growth curves, and the interest or actual status was visualized using the number of paper citations and the actual legal status of the patents. When comparing the reported technologies in papers to those in patents, scientific gaps were observed, as some of the patented technologies were not reported in the scientific literature, e.g., ion mobility and mass spectrometry approaches. Generally, more diverse approaches in all categories were found in patents, although they do require more scientific evidence through papers or industrial adoption to prove their robustness. Moreover, although there is a recent trend for non-invasive techniques, invasive methods like analyzing DNA through PCR or hormones through immunosensing are still being reported (and might continue to be) in papers and patents. It was also observed that none of the technologies complies with all the industry requirements, although 5 companies already entered the market. On the one hand, more research and harmony between consumers, industry, and governments is necessary. On the other hand, close monitoring of the market performance of the currently available techniques will offer valuable insights into the potential and expectations of in ovo sexing techniques in the poultry industry.

Non-Destructive Eggshell Strength Assessment Using Hertz Contact Theory Part I: Theory and Applicability.

In the egg industry, fast and highly reliable quality measurements are crucial. This study presents a novel method based on Hertz contact theory that allows for non-destructive determination of eggshell strength. The goal of the study was to evaluate the material strength (Young's Modulus) and structural strength (stiffness) of eggshells. To this end, an experimental setup was constructed to measure the collision of an eggshell with a small steel ball, which was recorded using a laser vibrometer. The study analyzed a sample of 120 eggs and found a correlation of 0.85 between the traditional static stiffness measured during quasi-static compression tests and the stiffness obtained from the Hertz contact theory. The results show that Hertz contact theory is valid for small steel spheres impacting eggshells, while a sensitivity analysis indicated that the most important factor in determining the strength of the eggshell is the contact duration between the egg and the impactor. These results open up the possibility of grading eggs based on their shell strength in a non-destructive manner.

Non-Destructive Eggshell Strength Assessment Using Hertz Contact Theory-Part II: Implementation and Validation.

Eggshell strength is a critical quality factor for consumption eggs as it affects the probability of breakage in practice. In this study, a fast and low-cost methodology for the non-destructive determination of eggshell strength is presented. The method utilized a small steel ball to impact the egg and a microphone to analyse the impact characteristics. Hertz contact theory was applied to relate the measured impact characteristics to the local stiffness of the eggshell. Therefore, a total of 150 eggs were studied on which eight consecutive measurements per egg were taken around the equator at equidistant places. The results showed a strong correlation of 0.93 between the traditional static stiffness measured during quasi-static compression tests and the average stiffness obtained from the new methodology. This paves the way towards fast, low-cost and non-destructive in-line shell strength measurements to reduce the number of cracked eggs reaching the consumer.

Optical Identification of Fruitfly Species Based on Their Wingbeats Using Convolutional Neural Networks.

The spotted wing Drosophila (SWD), Drosophila suzukii, is a significant invasive pest of berries and soft-skinned fruits that causes major economic losses in fruit production worldwide. Automatic identification and monitoring strategies would allow to detect the emergence of this pest in an early stage and minimize its impact. The small size of Drosophila suzukii and similar flying insects makes it difficult to identify them using camera systems. Therefore, an optical sensor recording wingbeats was investigated in this study. We trained convolutional neural network (CNN) classifiers to distinguish D. suzukii insects from one of their closest relatives, Drosophila Melanogaster, based on their wingbeat patterns recorded by the optical sensor. Apart from the original wingbeat time signals, we modeled their frequency (power spectral density) and time-frequency (spectrogram) representations. A strict validation procedure was followed to estimate the models' performance in field-conditions. First, we validated each model on wingbeat data that was collected under the same conditions using different insect populations to train and test them. Next, we evaluated their robustness on a second independent dataset which was acquired under more variable environmental conditions. The best performing model, named "InceptionFly," was trained on wingbeat time signals. It was able to discriminate between our two target insects with a balanced accuracy of 92.1% on the test set and 91.7% on the second independent dataset. This paves the way towards early, automated detection of D. suzukii infestation in fruit orchards.

In ovo sexing of eggs from brown breeds with a gender-specific color using visible-near-infrared spectroscopy: effect of incubation day and measurement configuration.

The culling of day-old male chicks is an animal welfare issue within the laying hen industry that raises substantial ethical concern. Alternative methods are sought to pre-select males during embryonic development. This method is called in ovo sexing and allows more humane male culling. A robust and non-invasive in ovo color sexing technique was developed and validated in this research. To this end, visible-near-infrared (vis-NIR) point spectroscopy was used, which has advantages over state-of-the-art hyperspectral imaging in terms of accuracy and cost. Two independent experiments were each conducted on a batch of 600 Isa Brown eggs. These eggs were individually illuminated on d 8 to 14, and d 18 of incubation by a halogen lamp and the signal was measured in the vis-NIR range from 300 to 1,145 nm. Next, optimal preprocessing strategies were applied and partial least squares discriminant analysis (PLS-DA) models were built and further optimized after performing a forward interval partial least squares (FiPLS) for variable selection. Results demonstrated that d 12 is too early for vis-NIR in ovo sexing, resulting in a prediction accuracy of 86.49%. However, after 13 d of incubation, an accuracy of 97.78% was obtained, increasing to 99.52% on d 14. Furthermore, these accuracies were higher than earlier reported percentages from hyperspectral imaging and successful sexing was expedited from d 14 to d 13. Moreover, prediction improvement up to 99.05% was obtained on d 13 by correcting for the variability in eggshell properties using d 0 eggshell corrections. Applying the method on d 18 resulted in a lower accuracy of 94.62% due to stronger light attenuation by the growing embryos. Finally, a reduced spectral range of 749 to 861 nm was found to be sufficient for correct classification of 98.46% of the eggs. This paves the way for high-throughput and cost-efficient usage of smaller and cheaper spectrophotometers in commercial hatcheries.

Detecting mean changes in experience sampling data in real time: A comparison of univariate and multivariate statistical process control methods.

Detecting early warning signals of developing mood disorders in continuously collected affective experience sampling (ESM) data would pave the way for timely intervention and prevention of a mood disorder from occurring or to mitigate its severity. However, there is an urgent need for online statistical methods tailored to the specifics of ESM data. Statistical process control (SPC) procedures, originally developed for monitoring industrial processes, seem promising tools. However, affective ESM data violate major assumptions of the SPC procedures: The observations are not independent across time, often skewed distributed, and characterized by missingness. Therefore, evaluating SPC performance on simulated data with typical ESM features is a crucial step. In this article, we didactically introduce six univariate and multivariate SPC procedures: Shewhart, Hotelling's T², EWMA, MEWMA, CUSUM and MCUSUM. Their behavior is illustrated on publicly available affective ESM data of a patient that relapsed into depression. To deal with the missingness, autocorrelation, and skewness in these data, we compute and monitor the day averages rather than the individual measurement occasions. Moreover, we apply all procedures on simulated data with typical affective ESM features, and evaluate their performance at detecting small to moderate mean changes. The simulation results indicate that the (M)EWMA and (M)CUSUM procedures clearly outperform the Shewhart and Hotelling's T² procedures and support using day averages rather than the original data. Based on these results, we provide some recommendations for optimizing SPC performance when monitoring ESM data as well as a wide range of directions for future research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Comparison of the Technological Application Potential of Functional Ingredients for the Meat Industry Based upon a Novel Fast Screening Tool.

The application potential of functional ingredients for the meat industry is often assessed through different measuring tools, thereby making comparisons difficult. The aim of this study was to create valuable information about the performance of functional ingredients based upon standardized and comparable data gathered through a newly developed screening tool. Therefore, 25 ingredients, selected from different techno-functional classes, were characterized at 2 different dosages by means of the screening methodology. The tool itself consisted of a lean meat model and fatty liver-based system, representative of the finely minced and/or emulsified charcuterie market. A total of 23 different parameters were measured through both model systems, providing information concerning water and fat binding capacity, emulsification, and texture and structure formation. Through cluster analysis, the ingredients were assigned to groups, each with their own specific properties. The screening tool provided good descriptive and distinctive power concerning ingredient functionalities and offers the industry a clear overview of their application characteristics.

Tunnel entries and a killing agent uncover the importance of fly retention in Drosophila suzukii traps.

BACKGROUND: Spotted wing drosophila (SWD), Drosophila suzukii (Matsumura) (Diptera: Drosophilidae), is a pest of stone and small fruits causing considerable economic losses. Current management strategies rely primarily on calendar-based spraying, owing to the poor relationship between monitoring data and damage levels, and the lack of success of mass-trapping tools. The aim of this study was to evaluate different trap models for SWD, with an emphasis on their fly-retention capacity. To this end, we examined and quantified the added value of two fly-retaining trap features; tunnel entries to impede escape and an insecticide-coated inner surface as a killing agent. RESULTS: An insecticide-coated inner surface resulted in significantly higher trap retention after 24 h in the laboratory (4.9- to 7.4-fold greater, depending on trap type) compared to a noncoated trap. Trapping efficacy was significantly improved in field trials by such a killing agent in the trap (1.2- to 4.5-fold greater). Tunnel entries significantly improved trap retention in the laboratory and field (by 1.5-fold). CONCLUSION: The outcomes of this study clearly reveal the substantial impact of the fly-retention capacity of SWD traps on their overall capture performances. It was demonstrated for the first time that an insecticide-coated inner surface as a killing agent significantly improves trap efficacy for SWD. This finding can readily be implemented in any trap model to improve monitoring and mass trapping of SWD. Also tunnel entries were shown to have a significant influence on the fly retention and, hence, substantially enhance trapping efficacy.

Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows: Timing of the alerts and robustness against missing values.

Automated monitoring of fertility in dairy cows using milk progesterone is based on the accurate and timely identification of luteolysis. In this way, well-adapted insemination advice can be provided to the farmer to further optimize fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test data set of progesterone time-series that fully covers the desired variability in progesterone profiles is needed. Further, the data should be measured with a high frequency to allow rapid onset events, such as luteolysis, to be precisely determined. Collecting this type of data would require a lot of time, effort, and budget. In the absence of such data, an alternative was developed using simulated progesterone profiles for multiple cows and lactations, in which the different fertility statuses were represented. To these, relevant variability in terms of cycle characteristics and measurement error was added, resulting in a large cost-efficient data set of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modeling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, 2 progesterone monitoring tools were compared: a multiprocess Kalman filter combined with a fixed threshold on the smoothed progesterone values to detect luteolysis, and a progesterone monitoring algorithm using synergistic control, PMASC, which uses a mathematical model based on the luteal dynamics and a statistical control chart to detect luteolysis. The timing of the alerts and the robustness against missing values of both algorithms were investigated using 2 different sampling schemes: one sample per cow every 8 h versus 1 sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 h earlier compared with the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when 1 sample per day was used, the Kalman filter gave its alerts on average 24 h later, and the variability in timing of the alerts compared with simulated luteolysis increased with 22%. Accordingly, we postulate that implementation of the PMASC system could improve the consistency of luteolysis detection on farm and lower the analysis costs compared with the current state of the art.

Short communication: Validation of a novel milk progesterone-based tool to monitor luteolysis in dairy cows using cost-effective, on-farm measured data.

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.

Short communication: Sensitivity of estrus alerts and relationship with timing of the luteinizing hormone surge.

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 model-based 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.

Method for short-term prediction of milk yield at the quarter level to improve udder health monitoring.

Udder health problems are often associated with milk losses. These losses are different between quarters, as infected quarters are affected both by systemic and pathogen-specific local effects, whereas noninfected quarters are only subject to systemic effects. To gain insight in these losses and the milk yield dynamics during disease, it is essential to have a reliable reference for quarter-level milk yield in an unperturbed state, mimicking its potential yield. We developed a novel methodology to predict this quarter milk yield per milking session, using an historical data set of 504 lactations collected on a test farm by an automated milking system from DeLaval (Tumba, Sweden). Using a linear mixed model framework in which covariates associated with the linearized Wood model and the milking interval are included, we were able to describe quarter-level yield per milking session with a proportional error below 10%. Applying this model enables us to predict the milk yield of individual quarters 1 to 50 d ahead with a mean prediction error ranging between 8 and 20%, depending on the amount of historical data available to estimate the random effect covariates for the predicted lactation. The developed methodology was illustrated using 2 examples for which quarter-level milk losses are calculated during clinical mastitis. These showed that the quarter-level mixed model allows us to gain insight in quarter lactation dynamics and enables to calculate milk losses in different situations.