Ines Adriaens

Mathematical characterization of the milk progesterone profile as a leg up to individualized monitoring of reproduction status in dairy cows

Reproductive performance is an important factor affecting the profitability of dairy farms. Optimal fertility results are often confined by the time-consuming nature of classical heat detection, the fact that high-producing dairy cows show estrous symptoms shorter and less clearly, and the occurrence of ovarian problems. Todays commercially available solutions for automatic estrus detection include monitoring of activity, temperature and progesterone. The latter has the advantage that, besides estrus, it also allows to detect pregnancy and ovarian problems. Due to the large variation in progesterone profiles, even between cycles within the same cow, the use of general thresholds is suboptimal. To this end, an intelligent and individual interpretation of the progesterone measurements is required. Therefore, an alternative solution is proposed, which takes individual and complete cycle progesterone profiles into account for reproduction monitoring. In this way, profile characteristics can be translated into specific attentions for the farmers, based on individual rather than general guidelines. To enable the use of the profile and cycle characteristics, an appropriate model to describe the milk progesterone profile was developed. The proposed model describes the basal adrenal progesterone production and the growing and regressing cyclic corpus luteum. To identify the most appropriate way to describe the increasing and decreasing part of each cycle, three mathematical candidate functions were evaluated on the increasing and decreasing parts of the progesterone cycle separately: the Hill function, the logistic growth curve and the Gompertz growth curve. These functions differ in the way they describe the sigmoidal shape of each profile. The increasing and decreasing parts of the P4 cycles were described best by the model based on respectively the Hill and Gompertz function. Combining these two functions, a full mathematical model to characterize the progesterone cycle was obtained. It was shown that this approach retains the flexibility to deal with both varying baseline and luteal progesterone values, as well as prolonged or delayed cycles.

Detecting health problems of individual pigs based on their drinking behaviour

Whether cattle grazing in nature reserves in temperate summers ought to be provided with artificial shelter (man-made), in addition to natural shelter (vegetation), is a topic of debate. We have investigated the effect of heat-load on the use of natural versus artificial shelter (with a roof and three walls) by cattle in eight nature reserves in Belgium. GPS collars were used to monitor use of open area, natural and artificial shelter during one or two summers (per 30 min). Cattle location data were coupled to same-time values of climatic ‘heat-stress indices’ calculated from local weather stations’ measurements of air temperature, air humidity, solar radiation and wind speed. Use of open area decreased as heat-load increased. The strength of the effect, and whether the cattle sought natural or artificial shelter, were associated with the amount and spatial distribution of natural shelter in the reserve. When natural shelter was sparse, a more scattered distribution tempered the increased use of shelter with increasing heat-load. If sufficiently available, cattle preferred natural to artificial shelter. When little natural shelter was available, cattle did use the artificial shelter and especially so with increasing heat-load. Microclimatic measurements indicated that solar radiation was blocked by vegetation at least as well as by artificial shelter, and allowed more evaporative cooling. In conclusion, we found no evidence for the added value of additional artificial shelter to protect cattle from heat-load in temperate nature reserves, as long as adequate natural shelter is available.Trabajo presentado al 65th Annual Meeting of the European Federation of Animal Science (EAAP) (Copenhagen, Denmark, 25 al 28 de agosto, 2014).Trabajo presentado al 65th Annual Meeting of the European Federation of Animal Science (EAAP) (Copenhagen, Denmark, 25 al 28 de agosto, 2014).Intramuscular fat content (IMF) influences important qualitative traits of meat as tenderness, juiciness and flavour, and technological characteristics. This trait is difficult to measure in vivo and is not included in the breeding programs, despite its medium heritability. Furthermore, IMF is a complex quantitative trait determined by several biochemical and metabolic processes influencing fat deposition in muscles. Indeed QTL affecting this trait have been already reported and some candidate genes were investigated in the last years, but relevant causative mutations have not been so far detected. Recently, thanks to the sequencing of the porcine genome and to the development of a high throughput genotyping porcine chip, it is possible to perform genome wide association (GWA) studies and put in light markers associated to this trait. With the aim to identify genes and markers associated to IMF we performed GWA using the Illumina PorcineSNP60 BeadChip and 889 Italian Large White pigs included in the Sib Test genetic evaluation program of the Italian Association of Pig Breeders (ANAS). The association analyses were conducted using linear mixed model implemented in GenABEL. The results of GWA analysis allowed to detect seven markers significantly associated with IMF (P-value <10) mapped on chromosomes 1, 3, 8 and 12. A validation study on selected markers was performed using a mixed model procedure of SAS software. Markers identified, once confirmed, could be applied as candidate genes to improve meat quality traits in Italian Large White pigs.The constantly growing yearly demand for meat, dairy products and eggs has important implications for agricultural production methods. Nowadays livestock/crop production is becoming increasingly industrialised worldwide, shifting from extensive, small-scale, subsistence production systems towards more intensive, large-scale, geographicallyconcentrated, specialised and commercially oriented ones. The shift in livestock farming methods from extensive to intensive poses a number of significant challenges for animal welfare, environmental sustainability and food security. The indicators to assess animals health and welfare status, have been increased during the last years, and the importance of this discipline is now widely known worldwide. Thanks to the welfare quality ® protocol the procedure to assess the animal health and welfare has become more and more clear, precise and accurate since the project ended in 2009. Furthermore this procedure to assess animal welfare status is time consuming and requires manpower and accurate planning.Bearing in mind the objectives of this study to investigate the Barbary sheep fertility under our climatic conditions and to evaluate the efficiency of the prostaglandin f2α(pgf2α)injections in induction of fertile estrus in different seasons. For these objectives, some experiments were performed during the period from July,2008 to March, 2009. A total number of 300 Libyan Barbary ewes(3-6 years old, weighing 40-60 kg) was used in this experiment. Ewes were kept in privet farms, fed and managed similarly. Ewes were divided to four season groups(summer, autumn, winter and spring groups). Each season group was divided into treated and control. Animals in the treated groups were injected with double injections of 125 μg of prostaglandin f2α intramuscularly(i/m), 11 days apart. While those in control groups were injected with two injections of 1.0 ml of 0.9% NACL saline solution simultaneously with the treated ewes. At the same day (day 11) rams wearing painted sponges on their briskets regions were introduced for natural mating. Treated groups showed shorter estrus response time than control groups in all seasons(P<0.05). Estrus duration was longer in winter and spring than in summer and autumn (P<0.001),but no difference was found between treated and control groups inside seasons. Treated group showed higher pregnancy rate(P<0.001) in winter season than control group. Percentage of ewes lambed in winter was significantly high (P<0.001) among treated ewes than control (80 vs 38%). Lambing rate differed significantly (P<0.001) among treated groups in all seasons. Data were collected and calculated statistically using SPSS system for percentages, means, standard deviation(mean ± sd) analysis of variance (anova), Chi square and Dunacan’s test were used accordingly. Other values were measured, calculated and analyzed similarly. Adoption of technology in sheep farms of La Mancha, Spain J. Rivas1, C. De Pablos2, J. Perea1, C. Barba1, R. Dios-Palomares1, M. Morantes1 and A. García1 1Universidad de Córdoba, Campus Rabanales, 14014, Córdoba, Spain, 2Universidad Rey Juan Carlos, Paseo de los Artilleros, 28034, Madrid, Spain; pa1gamaa@uco.es Recently in Spain the milk production coming from sheep farms shows higher degrees of specialization based on the adoption of technologies. The aim of this research is to examine the pattern of adoption of technologies in sheep farms of La Mancha. Based on previous researches, from 77 questions, only 38 questions were selected by using qualitative and participatory methods; the chosen variables were grouped into six technology packages (TP): management, feeding, animal health and milk quality, pasture and land use, equipment and facilities, and reproduction and breeding program. The survey was applied to a sample of 157 farms. Using descriptive statistics each TP was characterized and the pattern of adoption was determined. The average of technologies adopted was of 18.4±6.0 (48.3%). TP showing higher degrees of implementation are animal health and milk quality (67.8%), feeding (56.0%) and management (55.7%), but their adoption is not sequential or responds to independent events. This research facilitates the identification of a number of technologies that must be implemented from an organizational strategy point of view. Moreover, all technologies are seeking a dynamic balance system that allows firms migrate to more efficient processes without losing their main attributes. As a technological challenge, an andrologic evaluation of ram, gynecologic evaluation of ewes prior to mating, early detection of non-productive animals are proposed; and a better use of productive records for the decision-making; aspects are recommended. The results of this analysis will have an impact on future research that attempts to improve the use of subproducts, forage reserves and improved rangeland management and hygiene control system, taking the quality milk as an strategic asset, so further research is necessary to assess the impact of each technology on the operating of the mixed system in the Mancha region. Session 40 Poster 20 Session 40 Poster 19The present paper focuses on evaluating the Interobserver Reliability of the Animal Welfare Assessment Protocol for Growing Pigs. The protocol for growing pigs consists of a Qualitative Behaviour Assessment (QBA), direct Behaviour Observations (BO), carried out by instantaneous scan sampling, a Human Animal Relationship Test (HAR) and checks for different Individual Parameters (IP), e.g. presence of tail biting, wounds and bursitis. Three trained observers collected the data by performing 29 combined assessments, which were done at the same time and on the same animals; but they were carried out completely independent of each other. The findings were compared by the calculation of Spearman Rank Correlation Coefficients (RS), Intraclass Correlation Coefficients (ICC), Smallest Detectable Changes (SDC) and Limits of Agreements (LoA). There was no agreement found concerning the adjectives belonging to the QBA (e.g. active: RS: 0.50, ICC: 0.30, SDC: 0.38, LoA: -0.05-0.45; fearful: RS: 0.06, ICC: 0.0, SDC: 0.26, LoA: -0.20-0.30). In contrast, the BO showed good agreement (e.g. social behaviour: RS: 0.45, ICC: 0.50, SDC: 0.09, LoA: -0.09-0.03 use of enrichment material: RS: 0.75, ICC: 0.68, SDC: 0.06, LoA: -0.03-0.03). The rather low agreement of the HAR (RS: 0.38, ICC: 0.54, SDC: 0.34, LoA: -0.40-0.27) can be explained by the fact that the observers entered the pens one after the other to minimise mutual interference which influenced the reaction towards the second intruder. Overall, observers agreed well in the IP, e.g. tail biting (RS: 0.52, ICC: 0.88; SDC: 0.05, LoA: -0.01-0.02) and wounds (RS: 0.43, ICC: 0.59, SDC: 0.10, LoA: -0.09-0.10). The parameter bursitis, however, showed great differences (RS: 0.10, ICC: 0.0, SDC: 0.35, LoA: -0.37-0.40), which can be explained by difficulties in the assessment when the animals moved around quickly or their legs were soiled. In conclusion, the Interobserver Reliability was good in the BO and most individual IP, but not for the parameter bursitis and the QBA.

Sensitivity of heat detection attentions and their relation with the moment of LH surge in dairy cows

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 four different heat detection systems for their ability to detect heat and predict the moment of the LH surge in a single herd during the same study period. This comparison, in which the moment of the LH surge was used as an indicator for ovulation, allowed for the objective evaluation of each system and the potential for optimizing the fertility management on farm. The four systems were traditional visual observation, an activity-based system and two progesterone-based methods: (1) Herd Navigator™ and (2) a novel algorithm which combines a mathematical model and synergistic control. The latter algorithm also allowed to test whether using the mathematical model could improve the relation with the LH surge. First, the systems were compared in terms of sensitivity and positive predictive value for heat detection. Then, the time interval between the attentions and the LH surge was investigated and compared based on their range and SD. Heat attentions based on visual observations had the lowest sensitivity to detect heat (40%), and were noted from 4 hours before until 5 hours after the LH surge (range 9 hours, SD 4 hours), indicating a strong relation. Activity-attentions proved more sensitive (80%). They had the least accurate relation with the moment of the LH surge and were observed from 39 hours before until 8 hours after it (range 47 hours, SD 16 hours). Attentions based on milk progesterone measurements correctly identified all estrous periods. Herd Navigator™ attentions system were followed by the LH surge after 22 to 66 hours (range of 45 hours, SD 11 hours). The model-based approach generated attentions 49 to 81 hours (range 33 hours, SD 11 hours) before the LH surge. As detection of the LH surge was very labor-intensive, only a limited number of potential heat periods could be studied. For some of the methods (e.g. visual observations), the sensitivity restricted the number of cases even more. Nevertheless, the approach ensured an objective comparison between relevant heat detection systems in a commercially representative setting. Accordingly, this study helps to place larger studies linking heat detection and ovulation into perspective and shows the potential of new P4-interpreting algorithms, thereby highlighting the need for further research.Both estrus detection and timely insemination are important factors in optimizing fertility management. The latter is dependent on ovulation time, which is preceded by the LH surge. The performance of an estrus detection system based on activity and based on milk progesterone was evaluated and the timing of the alerts was contrasted against the moment of the LH surge. Activity alerts had a sensitivity of 83% and a positive predictive value of 66%; the LH surge followed average 9.4 ± 16.1 hours later. Using milk progesterone, one can reliably detect luteolysis, which is followed by the LH surge after 62 ± 12 hours.Both heat detection and timely insemination contribute to the optimization of fertility management on farm. In this study, 4 systems were compared for their ability to accurately detect heat and relate to the LH surge preceding ovulation in dairy cows. As the moment of LH surge has been reported to correlate strongly with time of ovulation, the potential to predict the optimal insemination can in this way be evaluated. The systems included were the traditional visual observation of heat, activity attentions and 2 progesterone-based methods. For the latter, it was also investigated whether the prediction of the LH surge could be improved by fitting a longitudinal model to the progesterone data. First, the systems were compared in terms of sensitivity and positive predictive value for heat detection. Then, the time interval between the attentions and the LH surge was investigated. The range on this interval was used as main criterion to evaluate the time-relation between the heat attention and the LH surge. The smaller this range, the better the correlation with the LH surge, and accordingly, ovulation. Heat attentions based on visual observations were noted from 4 hours before until 5 hours after the LH surge (range of 9 hours), indicating a high correlation. However, they also had the lowest sensitivity to detect heat (40%), making it less useful on-farm. Using activity-attentions proved more sensitive (80%), but was less accurate. Moreover, these attentions had the least accurate correlation with the moment of the LH surge and were observed from 39 hours before until 8 hours after it (range of 47 hours). Attentions based on milk progesterone measurements through the detection of luteolysis preceding a follicular phase correctly identified all estrous periods. Alarms generated when the smoothed progesterone level crossed a 5 ng/mL threshold, were followed by the LH surge after 21.6 to 66.4 hours (range of 44.8 hours). The model-based approach performed slightly better with attentions generated 48.8 to 81.2 hours (range 32.9 hours) before the LH surge.

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.

A novel system for on-farm fertility monitoring based on milk progesterone

Timely identification of a cows 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 cows 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.