How much can performance measures explain of the between-cow variation in enteric methane?

Enteric CH4 produced from dairy cows contributes to the emission of greenhouse gases from anthropogenic sources. Recent studies have shown that the selection of lower CH4-emitting cows is possible, but doing so would be simpler if performance measures already recorded on farm could be used, instead of measuring gas emissions from individual cows. These performance measures could be used for selection of low emitting cows. The aim of this analysis was to quantify how much of the between-cow variation in CH4 production can be explained by variation in performance measures. A dataset with 3 experiments and a total of 149 lactating dairy cows with repeated measures was used to estimate the between-cow variation (the variation between cow estimates) for performance and gas measures from GreenFeed (C-Lock, Rapid City, SD). The cow estimates were obtained with a linear mixed model with the diet within period effect as a fixed effect and the cow within experiment as a random effect. The cow estimates for CH4 production were first regressed on the performance and gas measures individually, and then performance and CO2 production measures were grouped in 3 subsets for principal component analysis and principal component regression. The variables that explained most of the between-cow variation in CH4 production were DMI (R2 = 0.44), among the performance measures, and CO2 production (R2 = 0.61), among gas measures. Grouping the measures increased the R2 to 0.53 when only performance measures were used, and to 0.66 when CO2 production was added to the significant performance measures. We found the marginal improvement to be insufficient to justify the use of grouped measures rather than an individual measure because the latter simplifies the model and avoids over-fitting. Investigation of other measures that can be explored to increase explanatory power of between-cow variation in CH4 production is briefly discussed. Finally, the use of residual CH4 as a measure for CH4 efficiency could be considered by using either DMI or CO2 production as the sole predicting variables.

Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs.

BACKGROUND: There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR). RESULTS: The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (- 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (- 0.76 ± 0.06) and residual feed intake (- 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm. CONCLUSIONS: Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs.

Invited review: Disentangling residual feed intake-Insights and approaches to make it more fit for purpose in the modern context.

Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.

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.

Robustness and sensitivity of a blueprint for on-farm estimation of dairy cow energy balance.

Excessive negative energy balance (EB) has been associated with decreased reproductive performance and increased risk of lameness and metabolic diseases. On-farm, automated EB estimates for individual cows would enable dairy farmers to detect excessive negative EB early and act to minimize its extent and duration by altering feeding. Previously, we have shown that EB can be estimated from frequent measurements of body weight (BW) and body condition score (BCS) changes, referred to as EBbody. In this study, we investigated the robustness and sensitivity of the EBbody method to assess its genericity and on-farm applicability. We used 5 data sets with BW of lactating cows (name of data set in parenthesis): 65 Holstein cows in a French feeding trial (INRA); 6 Holstein cows in a British feeding trial (Friggens); 31 Holstein cows and 17 Jersey cows in a Danish feeding trial (DCRC); 140 Holstein cows in a British feeding trial (Scotland's Rural College, SRUC); and 1,592 Holstein cows on 9 Danish farms with milking robots (automatic milking system). We used the INRA and Friggens data sets to develop a dynamic formula to correct BW for increasing residual gut-fill (RGF) during early lactation. With the DCRC data, we tested the effect of smoothing parameters and weighing frequency on EBbody. Also, 2 robustness tests were performed using the SRUC data to test the effect of diet change on BW and the automatic milking system data to test the effect of farm on BW variation. Finally, we combined the results into a blueprint describing different ways to calculate EBbody depending on the purpose and on the availability of BCS. The dynamic RGF adjustment resulted in a lower empty BW during early lactation than that obtained with the previously used constant RGF. The double-exponential smoothing method used to correct for meal-related gut-fill was robust to choice of smoothing parameters. Cows should be weighed at least once every 4 d during early lactation to capture the duration of negative EBbody. Our EBbody method proved robust to diet changes. Finally, although cow BW varied significantly between farms, the quantile regression smoothing of BW did not bias the estimation of weight differences between herds. In conclusion, these results validate the applicability of the EBbody method to estimate EB across a range of farm conditions, and we provided a blueprint that enables the estimation of EBbody for individual cows on-farm using only frequent BW, in combination with BCS when available.

Allometric scaling of the elevation of maternal energy intake during lactation.

BACKGROUND: In most mammals, lactating mothers dramatically increase their food intake after parturition and reach a peak intake rate after a certain time while their offspring continue to grow. A common view, perpetuated by the metabolic theory of ecology, is that the allometric scaling of maternal metabolic rate with body mass limits the changes in energy intake and expenditure. Therefore these potential effects of metabolic scaling should be reflected in the elevation of maternal energy intake during lactation. To test this hypothesis, we collected published data on 24 species (13 domesticated) and established scaling relationships for several characteristics of the patterns of energy intake elevation (amplitude of the elevation, time to peak, and cumulative elevation to peak). RESULTS: A curvilinear allometric scaling relationship with maternal body mass (in double-logarithmic space) was found for the amplitude of maternal energy intake elevation, similarly to what has been observed for scaling relationships of basal metabolic rate in non-breeding mammals. This result indirectly supports the metabolic theory of ecology. However, this curvilinear allometric scaling does not seem to drive the scaling relationships found for the other characteristics of maternal energy intake. Both the duration and shape of the energy intake patterns showed substantial variation independently of species' body mass. CONCLUSIONS: Data available for a few mammals, mostly domesticated, provides little evidence for the hypothesis that a single law of metabolic scaling governs the elevation of maternal energy intake after parturition. Obtaining further food intake data in wild species will be crucial to unravel the general mechanisms underlying variation in this unique adaptation of mammalian females.

Disentangling the relative roles of resource acquisition and allocation on animal feed efficiency: insights from a dairy cow model.

BACKGROUND: Feed efficiency of farm animals has greatly improved through genetic selection for production. Today, we are faced with the limits of our ability to predict the effect of selection on feed efficiency, partly because the relative importance of the components of this complex phenotype changes across environments. Thus, we developed a dairy cow model that incorporates the dynamic interplay between life functions and evaluated its behaviour with a global sensitivity analysis on two definitions of feed efficiency. A key model feature is to consider feed efficiency as the result of two processes, acquisition and allocation of resources. Acquisition encapsulates intake and digestion, and allocation encapsulates partitioning rules between physiological functions. The model generates genetically-driven trajectories of energy acquisition and allocation, with four genetic-scaling parameters controlling these processes. Model sensitivity to these parameters was assessed with a complete factorial design. RESULTS: Acquisition and allocation had contrasting effects on feed efficiency (ratio between energy in milk and energy acquired from the environment). When measured over a lactation period, feed efficiency was increased by increasing allocation to lactation. However, at the lifetime level, efficiency was increased by decreasing allocation to growth and increasing lactation acquisition. While there is a strong linear increase in feed efficiency with more allocation to lactation within a lactation cycle, our results suggest that there is an optimal level of allocation to lactation beyond which increasing allocation to lactation negatively affects lifetime feed efficiency. CONCLUSIONS: We developed a model to predict lactation and lifetime feed efficiency and show that breaking-down feed conversion into acquisition and allocation, and introducing genetically-driven trajectories that control these mechanisms, permitted quantification of their relative roles on feed efficiency. The life stage at which feed efficiency is evaluated appears to be a key aspect for selection. In this model, body reserves are also a key component in the prediction of lifetime feed efficiency since they integrate the feedback of acquisition and allocation on survival and reproduction. This modelling approach provided new insights into the processes that underpin lifetime feed efficiency in dairy cows.

Reproductive robustness differs between generalist and specialist maternal rabbit lines: the role of acquisition and allocation of resources.

BACKGROUND: Farm animals are normally selected under highly controlled, non-limiting conditions to favour the expression of their genetic potential. Selection strategies can also focus on a single trait to favour the most 'specialized' animals. Theoretically, if the environment provides enough resources, the selection strategy should not lead to changes in the interactions between life functions such as reproduction and survival. However, highly 'specialized' farm animals can be required for breeding under conditions that differ largely from selection conditions. The consequence is a degraded ability of 'specialized' animals to sustain reproduction, production and health, which leads to a reduced lifespan. This study was designed to address this issue using maternal rabbit lines. A highly specialized line with respect to numerical productivity at weaning (called V) and a generalist line that originated from females with a long reproductive life (called LP) were used to study the strategies that these lines develop to acquire and use the available resources when housed in different environments. In addition, two generations of line V, generations 16 and 36, were available simultaneously, which contributed to better understand how selection criteria applied in a specific environment changed the interplay between functions related to reproduction and survival. RESULTS: We show that, under constrained conditions, line LP has a greater capacity for resource acquisition than line V, which prevents excessive mobilization of body reserves. However, 20 generations of selection for litter size at weaning did not lead to an increased capacity of nutrient (or resource) acquisition. For the two generations of line V, the partitioning of resources between milk production, body reserves preservation or repletion or foetal growth differed. CONCLUSIONS: Combining foundational and selection criteria with a specific selection environment resulted in female rabbits that had a different capacity to deal with environmental constraints. An increased robustness was considered as an emergent property of combining a multiple trait foundational criterion with a wide range of environmental conditions. Since such a strategy was successful to increase the robustness of female rabbits without impairing their productivity, there is no reason that it should not be applied in other livestock species.

Robustness scores in fattening pigs based on routinely collected phenotypes: determination and genetic parameters.

The objective was to determine operational proxies for robustness based on data collected routinely on farm that allow phenotyping of these traits in fattening pigs, and to estimate their genetic parameters. A total of 7,256 pigs, from two Piétrain paternal lines (Pie and Pie NN), were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2019 to 2021. During the fattening period (from 75 to 150 d of age), individual performance indicators were recorded (growth, backfat, loin depth, feed intake, and feed conversion ratio [FCR]) together with indicators such as insufficient growth, observable defect, symptoms of diseases, and antibiotic and anti-inflammatory injections. These indicators were combined into three categorical robustness scores: R1, R2, and R3. Genetic parameters were estimated using an animal linear model. The robustness score R2 (selectable or not selectable animal) that combined information from status at testing and mortality had the highest heritability estimates of 0.08 ±â€…0.03 for Pie NN line and a value of 0.09 ±â€…0.02 for Pie line, compared with traits R1 and R3. The score R3 that combines information from the score R2 with antibiotic and anti-inflammatory injections presented slightly lower heritability estimates (0.05 ±â€…0.02 to 0.07 ±â€…0.03). Genetic correlations between R2 and R3 were high and favorable (0.93 ±â€…0.04 to 0.95 ±â€…0.03) and R2 and R3 can be considered identical with regard to the confidence interval. These two robustness scores were also highly and favorably genetically correlated with initial body weight and average daily gain, and unfavorably correlated with daily feed intake (ranging from 0.73 ±â€…0.06 to 0.90 ±â€…0.08). Estimates of genetic correlations of R2 and R3 with backfat depth and raw FCR (not standardized between starting and finishing weights) were moderate and unfavorable (0.20 ±â€…0.13 to 0.46 ±â€…0.20). A part of these genetic correlations, that are of low precision due to the number of data available, have to be confirmed on larger datasets. The results showed the interest of using routine phenotypes collected on farm to build simple robustness indicators that can be applied in breeding.

The objective was to determine operational proxies for robustness based on data collected routinely on farm that allow phenotyping of these traits in fattening pigs (from approximately 75 to 150 d of age), and to estimate their genetic parameters. A total of 7,256 pigs, from two Piétrain paternal lines (Pie and Pie NN), were tested. Individual performance indicators were recorded together with indicators such as insufficient growth, observable defects, symptoms of diseases, and antibiotic and anti-inflammatory injections. These indicators were combined into three categorical robustness scores: R1, R2, and R3. The robustness score R2 (selectable or not selectable animal) that combined information from status at testing and mortality had the highest heritability of 0.08 ±â€…0.03 for Pie NN line and a value of 0.09 ±â€…0.02 for Pie line. This robustness score was also highly and favorably genetically correlated with initial body weight and average daily gain, and unfavorably correlated with daily feed intake in both lines (ranging from 0.73 ±â€…0.06 to 0.90 ±â€…0.08). Estimates of genetic correlations of R2 with backfat depth and feed conversion ratio were moderate and unfavorable (0.20 ±â€…0.13 to 0.46 ±â€…0.20). The results showed the interest of using routine phenotypes collected on farm to build simple robustness indicators that can be applied in breeding.

Quantitative milk proteomics--host responses to lipopolysaccharide-mediated inflammation of bovine mammary gland.

Intramammary infusion of lipopolysaccharide (LPS) in cows induces udder inflammation that partly simulates mastitis caused by infection with Gram-negative bacteria. We have used this animal model to characterize the quantitiative response in the milk proteome during the time course before and immediately after the LPS challenge. Milk samples from three healthy cows collected 3 h before the LPS challenge were compared with milk samples collected 4 and 7 h after the LPS challenge, making it possible to describe the inflammatory response of individual cows. Quantitative protein profiles were obtained for 80 milk proteins, of which 49 profiles changed significantly for the three cows during LPS challenge. New information obtained in this study includes the quantified increase of apolipoproteins and other anti-inflammatory proteins in milk, which are important for the cow's ability to balance the immune response, and the upregulation of both complement C3 and C4 indicates that more than one complement pathway could be activated during LPS-induced mastitis. In the future, this analytical approach may provide valuable information about the differences in the ability of individual cows to resist and recover from mastitis.

Lameness Affects Cow Feeding But Not Rumination Behavior as Characterized from Sensor Data.

Using automatic sensor data, this is the first study to characterize individual cow feeding and rumination behavior simultaneously as affected by lameness. A group of mixed-parity, lactating Holstein cows were loose-housed with free access to 24 cubicles and 12 automatic feed stations. Cows were milked three times/day. Fresh feed was delivered once daily. During 24 days with effectively 22 days of data, 13,908 feed station visits and 7,697 rumination events obtained from neck-mounted accelerometers on 16 cows were analyzed. During the same period, cows were locomotion scored on four occasions and categorized as lame (n = 9) or not lame (n = 7) throughout the study. Rumination time, number of rumination events, feeding time, feeding frequency, feeding rate, feed intake, and milk yield were calculated per day, and coefficients of variation were used to estimate variation between and within cows. Based on daily sums, using each characteristic as response, the effects of lameness and stage of lactation were tested in a mixed model. With rumination time as response, each of the four feeding characteristics, milk yield, and lameness were tested in a second mixed model. On a visit basis, effects of feeding duration, lameness, and milk yield on feed intake were tested in a third mixed model. Overall, intra-individual variation was <15% and inter-individual variation was up to 50%. Lameness introduced more inter-individual variation in feeding characteristics (26-50%) compared to non-lame cows (17-29%). Lameness decreased daily feeding time and daily feeding frequency, but increased daily feeding rate. Interestingly, lameness did not affect daily rumination behaviors, fresh matter intake, or milk yield. On a visit basis, a high feeding rate was associated with a higher feed intake, a relationship that was exacerbated in the lame cows. In conclusion, cows can be characterized in particular by their feeding behavior, and lame cows differ from their non-lame pen-mates in terms of fewer feed station visits, faster eating, less time spent feeding, and more variable feeding behavior. Further, daily rumination time was slightly negatively associated with feeding rate, a relationship which calls for more research to quantify rumination efficiency relative to feeding rate.

To what extent do variabilities in hormones, metabolites and energy intake explain variability in milk yield?

The objective of the present paper is to describe the extent to which variability in milk yield can be explained by variability in plasma hormones, metabolites and DE intake. Results from a study including 317 cows and 634 lactations were used. Detailed registration of performance was carried out on these cows and 10,809 plasma samples analyzed for selected hormones and metabolites. Univariate analysis was carried out on energy-corrected milk yield and concentrations of selected plasma hormones (insulin, growth hormone (GH) and triiodothyronine (T3)), metabolites (non-esterified fatty acids (NEFA)), glucose, beta-hydroxybutyrate (BOHB) and urea nitrogen (BUN), and digestible energy intake (DE intake) to estimate between-cow variation through lactations. Partial least square (PLS) models were subsequently run to estimate the extent to which between-cow differences in energy-corrected milk yield could be explained by between-cow differences in hormone concentrations, metabolite concentration or DE intake. The between-cow variability in energy-corrected milk yield and the hormones and metabolites were generally found to be considerable and total variance changed through lactation, particularly for GH, T3, NEFA and BOHB. In this study, the total variance was highest in third lactation cows. When analyzed separately using partial least square models, hormones, metabolites and DE intake accounted for 24, 25 and 26% of the variability in ECM, respectively. Insulin and glucose were the single most important predictors among the selected hormones and metabolites. When including both the hormones and metabolites, the model explained 36% of the between-cow variability in ECM and this figure was increased to 53% if DE intake was also included. The lack of additivity in the variability explained shows that hormones, metabolites and DE intake were correlated illustrating the integration and orchestration of metabolism and intake. Perspectives of the analysis for use in prevention of diseases and reproduction are briefly discussed.

Prediction of the reproductive status of cattle on the basis of milk progesterone measures: model description.

Reproductive management, in particular timely oestrus detection, is important for profitable dairy production. The aim of this study was to develop a biological model to predict reproductive state on the basis of milk progesterone measures. A number of additional inputs were incorporated to make use of other known effectors of reproductive performance that are not reflected in progesterone levels. These are: days from calving, breed, parity, signs of behavioural oestrus, insemination dates, pregnancy determinations, energy status, body fat status, milk urea content and reproductive disorders associated with calving. A dynamic, deterministic model was developed. It is designed to run each time a new trigger input (progesterone, behavioural oestrus, inseminations, pregnancy determinations) occurs using the current and previous values and can run in the absence of the additional inputs. The milk progesterone values are smoothed using an extended Kalman filter before being processed in the biological component of the model. The model predicts the reproductive status of the cow, which can be one of three mutually exclusive states: postpartum anoestrus, oestrus cycling, and potentially pregnant. The other model outputs are all reproductive status specific with the exception of days to next sample (DNS), which is calculated in each model run regardless of reproductive status. DNS is designed to feedback to the sampling system so that the frequency of milk sampling (i.e. progesterone measurement) can be varied according to the predicted likelihood of a future reproductive event, such as onset of oestrus cycling. The other model outputs are: risk of prolonged postpartum anoestrus, risk and type of ovarian cyst, onset of oestrus, likelihood of a potential insemination succeeding, and likelihood of being pregnant (following oestrus). The model was evaluated using three simulated datasets consisting of a timeseries of progesterone values centred on each of the three reproductive statuses and including relevant additional information. Test runs were carried out on the full datasets and then on reduced data. The data reductions were made by using only those values that would have been available if the model days to next sample function was used to control sampling frequency. The sensitivity of the model to noise in the raw progesterone data was examined by adding 1, 2, or 3 residual standard deviations (1.85 ng/ml) random variation to the original data and evaluating model performance. The model was found to be able to readily identify and distinguish reproductive states. A reduction in sampling frequency to 36% of original sample resulted in an average increase in days to detection of oestrus of 0.36. The addition of 1 S.D. noise did not cause additional oestruses to be detected and all oestruses were correctly identified. However, when 2 or 3 S.D. noise were added, the model found on average 1.4 and 3 extra oestruses. It was concluded that reproductive status can be predicted from milk progesterone values using a biological model and that such a model is robust to reductions in sampling frequency number and to a doubling in the random variation in the raw progesterone values. It therefore has the potential to provide the basis for a useful reproductive management tool.

On the Use of a Simple Physical System Analogy to Study Robustness Features in Animal Sciences.

Environmental perturbations can affect the health, welfare, and fitness of animals. Being able to characterize and phenotype adaptive capacity is therefore of growing scientific concern in animal ecology and in animal production sciences. Terms borrowed from physics are commonly used to describe adaptive responses of animals facing an environmental perturbation, but no quantitative characterization of these responses has been made. Modeling the dynamic responses to an acute challenge was used in this study to facilitate the characterization of adaptive capacity and therefore robustness. A simple model based on a spring and damper was developed to simulate the dynamic responses of animals facing an acute challenge. The parameters characterizing the spring and the damper can be interpreted in terms of stiffness and resistance to the change of the system. The model was tested on physiological and behavioral responses of rainbow trout facing an acute confinement challenge. The model has proven to properly fit the different responses measured in this study and to quantitatively describe the different temporal patterns for each statistical individual in the study. It provides therefore a new way to explicitly describe, analyze and compare responses of individuals facing an acute perturbation. This study suggests that such physical models may be usefully applied to characterize robustness in many other biological systems.

A multivariate analysis using physiology and behavior to characterize robustness in two isogenic lines of rainbow trout exposed to a confinement stress.

Robustness is a complex trait difficult to characterize and phenotype. In the present study, two features of robustness in rainbow trout were investigated: sensitivity and resilience to an acute stressor. For that purpose, oxygen consumption, cortisol release, group dispersion and group activity of two isogenic lines of juvenile rainbow trout were followed before and after an environmental challenge. The effect of a 4h confinement protocol (~140kg/m(3)), which is generally considered as a highly stressful challenge, was investigated. Temporal patterns produced by this experiment were analyzed using multivariate statistics on curve characteristics to describe physiological and behavioral adaptive systems for each isogenic line. The two isogenic lines were found to be highly divergent in their corticosteroid reactivity. However, no correlation between physiological and behavioral sensitivity or resilience was observed. Furthermore, the multivariate analysis results indicated two separate and independent fish group coping strategies, i.e. by favoring either behavioral or physiological responses. In addition, considerable intra-line variabilities were observed, suggesting the importance of micro-environment effects on perturbation sensitivities. In this context, cortisol release rate variability was found to be related to the pre-stress social environment, with a strong correlation between pre-stress aggressiveness and cortisol release rate amplitude. Overall, this approach allowed us to extract important characteristics from dynamic data in physiology and behavior to describe components of robustness in two isogenic lines of rainbow trout.

The relationship between postpartum vaginal discharge symptoms and progesterone profile characteristics in lactating dairy cows in Denmark.

In this paper, the effect of clinical symptoms of uterine inflammation on progesterone profile characteristics was quantified in dairy cows. A continuous scale based on visual observation of vaginal discharge (the previously developed D-index) was used to describe the clinical symptoms. Progesterone profiles in milk were used to describe the ovarian cycles, and to determine the distinguishing features of these profiles, a multivariate statistical procedure (principal component analysis) was performed. Significant negative effects of the D-index were seen during the first and second postpartum ovarian cycles. The D-index had a significant effect on the shape of progesterone profiles and the length of the ovarian cycles but it only accounted for a small proportion of the variation in these ovarian cycle features. The D-index was not a significant risk factor for the length of postpartum anovulatory period in the present study.

Progesterone profiles around the time of insemination do not show clear differences between of pregnant and not pregnant dairy cows.

In this study, features of progesterone profiles were examined in relation to the outcome of insemination. Three groups of estrous cycles were analyzed: resulting in pregnancy, not resulting in pregnancy and resulting in lost pregnancy. The aim of the study was to identify a complex of progesterone profile features associated with successful insemination. The features used were (1) from the estrous cycle preceding the artificial insemination: estrus progesterone concentration, post-estrus maximum rate of increase in progesterone, luteal phase peak, pre-estrus maximum rate of decline in progesterone and the length of follicular and luteal phase and (2) from the estrous cycle following insemination: estrus progesterone concentration, post-estrus maximum rate of increase in progesterone and days from estrus to post-estrus maximum rate of increase in progesterone. A discriminant analysis did not reveal clear differences between the groups. However, the analysis correctly classified 75% of true pregnant cows. Conversely, only 60% of not pregnant animals were classified as such by the discriminate analysis. Individual analysis of progesterone profile features in pregnant and not pregnant groups of estrous cycles showed that a shorter follicular phase preceding insemination is associated with proper timing of post-ovulatory luteinisation and therefore is more likely to result in pregnancy.