Rapid turnover of sensor data to genetic evaluation for dairy cows in the cloud.

More and more sensor and automation data are available that enable animal breeders to define novel traits. However, sensor and automation data are often frequently measured differently (e.g., milk yield and different milk components are continuously measured during each milking). These differences are challenging animal breeders to define traits and use the most appropriate analytical models for genetic evaluation and breeding values. Traditionally, the process from raw data to breeding value estimations involves several steps: data curation, trait definition, variance component estimation, genetic evaluation, and validation of the estimated breeding values (EBV). All these steps often take many iterations and several research projects to optimize the final genetic evaluations. To make this entire process-from raw data to validated EBV-more efficient, we combined all these steps in a cloud environment that allows for faster processing and a faster data distribution time. We used real data (including 1,782,373,113 daily milk-yield records of 1,120,550 dairy cows) and a real trait (a resilience trait based on the deviations from expected milk yields) to demonstrate the functioning of this cloud environment. The daily milk-yield records were incorporated into our cloud solution, in which we have set up central binary large object storage. Subsequent steps were all performed in the cloud. The data set was preprocessed in approximately 6 h to obtain the resilience indicator for 352,871 cows in the first 3 lactations. Estimation of genetic parameters (heritabilities and genetic correlations) was performed by splitting the data into 5 subsets in ASReml, and prediction of subsequent EBV was performed on the entire data set using MiXBLUP. Together with the validation of breeding values, this process encompassed 16.5 h. By combining the different steps from preprocessing sensor data to genetic evaluation of new traits in one cloud environment, we generated EBV and validation plots in approximately 1 working day. Moreover, our setup is a flexible design and can be adapted easily to test new, longitudinal sensor-driven traits and compare the performance of these new traits to previous ones.

Accuracies of breeding values for dry matter intake using nongenotyped animals and predictor traits in different lactations.

Given the interest of including dry matter intake (DMI) in the breeding goal, accurate estimated breeding values (EBV) for DMI are needed, preferably for separate lactations. Due to the limited amount of records available on DMI, 2 main approaches have been suggested to compute those EBV: (1) the inclusion of predictor traits, such as fat- and protein-corrected milk (FPCM) and live weight (LW), and (2) the addition of genomic information of animals using what is called genomic prediction. Recently, several methodologies to estimate EBV utilizing genomic information (EBV) have become available. In this study, a new method known as single-step ridge-regression BLUP (SSRR-BLUP) is suggested. The SSRR-BLUP method does not have an imposed limit on the number of genotyped animals, as the commonly used methods do. The objective of this study was to estimate genetic parameters using a relatively large data set with DMI records, as well as compare the accuracies of the EBV for DMI. These accuracies were obtained using 4 different methods: BLUP (using pedigree for all animals with phenotypes), genomic BLUP (GBLUP; only for genotyped animals), single-step GBLUP (SS-GBLUP), and SSRR-BLUP (for genotyped and nongenotyped animals). Records from different lactations, with or without predictor traits (FPCM and LW), were used in the model. Accuracies of EBV for DMI (defined as the correlation between the EBV and pre-adjusted DMI phenotypes divided by the average accuracy of those phenotypes) ranged between 0.21 and 0.38 across methods and scenarios. Accuracies of EBV for DMI using BLUP were the lowest accuracies obtained across methods. Meanwhile, accuracies of EBV for DMI were similar in SS-GBLUP and SSRR-BLUP, and lower for the GBLUP method. Hence, SSRR-BLUP could be used when the number of genotyped animals is large, avoiding the construction of the inverse genomic relationship matrix. Adding information on DMI from different lactations in the reference population gave higher accuracies in comparison when only lactation 1 was included. Finally, no benefit was obtained by adding information on predictor traits to the reference population when DMI was already included. However, in the absence of DMI records, having records on FPCM and LW from different lactations is a good way to obtain EBV with a relatively good accuracy.

Validation of simultaneous deregression of cow and bull breeding values and derivation of appropriate weights.

Training of genomic prediction in dairy cattle may use deregressed proofs (DRP) as phenotypes. In this case, DRP should be estimated breeding values (EBV) corrected for information of relatives included in the data used for genomic prediction, and adjusted for regression to the mean (i.e., their reliability). Deregression is especially important when combining animals with EBV with low reliability, as commonly the case for cows, and high reliability. The objective of this paper, therefore, was to compare the performance of different deregression procedures for data that include both cow and bull EBV, and to develop and test procedures to obtain the appropriate deregressed weights for the DRP. Considered DRP were EBV: without any adjustment, adjusted for information of parents and regression to the mean, or adjusted for information of all relatives and regression to the mean. Considered deregressed weights were weights of initial EBV: without any adjustment, adjusted for information of parents, or adjusted for information of all relatives. The procedures were compared using simulated data based on an existing pedigree with 1,532 bulls and 13,720 cows that were considered to be included in the data used for genomic prediction. For each cow, 1 to 5 records were simulated. For each bull, an additional 50 to 200 daughters with 1 record each were simulated to generate a source of data that was not used for genomic prediction. The simulated trait had either a heritability of 0.05 or 0.3. The validation involved 3 steps: (1) computation of initial EBV and weights, (2) deregression of those EBV and weights, (3) using deregressed EBV and weights to compute final EBV, (4) comparison of the initial and final EBV and weights. The methods developed to compute appropriate weights for the DRP were either very precise and computationally somewhat demanding for larger data sets, or were less precise but computationally trivial due their approximate nature. Adjusting DRP for all relatives, known as matrix deregression, yields by definition final EBV that are identical to the original EBV. Matrix deregression is therefore preferred over other approaches that only correct for information of parents or not performing any deregression at all. It is important to use appropriate weights for the DRP, properly corrected for information of relatives, especially when individual reliabilities of final EBV are computed based on the prediction error variance of the model.

Estimation of genetic parameters and breeding values across challenged environments to select for robust pigs.

Robustness is an important issue in the pig production industry. Since pigs from international breeding organizations have to withstand a variety of environmental challenges, selection of pigs with the inherent ability to sustain their productivity in diverse environments may be an economically feasible approach in the livestock industry. The objective of this study was to estimate genetic parameters and breeding values across different levels of environmental challenge load. The challenge load (CL) was estimated as the reduction in reproductive performance during different weeks of a year using 925,711 farrowing records from farms distributed worldwide. A wide range of levels of challenge, from favorable to unfavorable environments, was observed among farms with high CL values being associated with confirmed situations of unfavorable environment. Genetic parameters and breeding values were estimated in high- and low-challenge environments using a bivariate analysis, as well as across increasing levels of challenge with a random regression model using Legendre polynomials. Although heritability estimates of number of pigs born alive were slightly higher in environments with extreme CL than in those with intermediate levels of CL, the heritabilities of number of piglet losses increased progressively as CL increased. Genetic correlations among environments with different levels of CL suggest that selection in environments with extremes of low or high CL would result in low response to selection. Therefore, selection programs of breeding organizations that are commonly conducted under favorable environments could have low response to selection in commercial farms that have unfavorable environmental conditions. Sows that had experienced high levels of challenge at least once during their productive life were ranked according to their EBV. The selection of pigs using EBV ignoring environmental challenges or on the basis of records from only favorable environments resulted in a sharp decline in productivity as the level of challenge increased. In contrast, selection using the random regression approach resulted in limited change in productivity with increasing levels of challenge. Hence, we demonstrate that the use of a quantitative measure of environmental CL and a random regression approach can be comprehensively combined for genetic selection of pigs with enhanced ability to maintain high productivity in harsh environments.

Estimating challenge load due to disease outbreaks and other challenges using reproduction records of sows.

A method was developed and tested to estimate challenge load due to disease outbreaks and other challenges in sows using reproduction records. The method was based on reproduction records from a farm with known disease outbreaks. It was assumed that the reduction in weekly reproductive output within a farm is proportional to the magnitude of the challenge. As the challenge increases beyond certain threshold, it is manifested as an outbreak. The reproduction records were divided into 3 datasets. The first dataset called the Training dataset consisted of 57,135 reproduction records from 10,901 sows from 1 farm in Canada with several outbreaks of porcine reproductive and respiratory syndrome (PRRS). The known disease status of sows was regressed on the traits number born alive, number of losses as a combination of still birth and mummified piglets, and number of weaned piglets. The regression coefficients from this analysis were then used as weighting factors for derivation of an index measure called challenge load indicator. These weighting factors were derived with i) a two-step approach using residuals or year-week solutions estimated from a previous step, and ii) a single-step approach using the trait values directly. Two types of models were used for each approach: a logistic regression model and a general additive model. The estimates of challenge load indicator were then compared based on their ability to detect PRRS outbreaks in a Test dataset consisting of records from 65,826 sows from 15 farms in the Netherlands. These farms differed from the Canadian farm with respect to PRRS virus strains, severity and frequency of outbreaks. The single-step approach using a general additive model was best and detected 14 out of the 15 outbreaks. This approach was then further validated using the third dataset consisting of reproduction records of 831,855 sows in 431 farms located in different countries in Europe and America. A total of 41 out of 48 outbreaks detected using data analysis were confirmed based on diagnostic information received from the farms. Among these, 30 outbreaks were due to PRRS while 11 were due to other diseases and challenging conditions. The results suggest that proposed method could be useful for estimation of challenge load and detection of challenge phases such as disease outbreaks.

Genetic relationship between boar taint compounds, human nose scores, and reproduction traits in pigs.

A reduction in boar taint, an unpleasant odor arising in pork from some intact males, is desirable if routine castration of piglets needs to be stopped. Commercial slaughter pigs are typically crosses between sire lines mainly selected for finishing traits and dam lines mainly selected for reproduction traits. Previous studies suggest the possibility of reducing boar taint in sire lines due to favorable genetic correlations between boar taint and finishing traits. However, there are indications of unfavorable genetic associations between boar taint and female reproduction traits, but a lack of genetic correlation estimates remain a major roadblock in reducing boar taint in dam lines. This study was conducted to estimate genetic correlations between boar taint traits and female reproduction traits, investigate differences in these genetic relationships among sire and dam lines, and evaluate possible consequences of selection against boar taint in dam lines. The data consisted of 32,549 reproduction records from a Landrace dam line, 23,874 records from a Yorkshire dam line, and 3,745 records from a Pietrain sire line. Androstenone, skatole, and indole were measured on 1,896 carcasses, and human nose scores were recorded on 7,742 carcass samples. In general, the level of boar taint was significantly greater (P < 0.05) in the two dam lines than in the sire line. A majority of genetic correlations of boar taint compounds with reproduction traits were either low or nonsignificant, except for those of skatole and indole, with age at first insemination in dam lines that were -0.32 and -0.46, respectively. Genetic correlations also differed (P < 0.05) between sire and dam lines. The consequences of selection against boar taint in dam lines were evaluated, using selection indexes based on reproduction traits only, boar taint traits only, and both boar taint and reproduction traits. Selection on an index of only reproduction traits increased the number of carcasses with boar taint from 4 to 7.3% in 5 generations. Selection on a combined index reduced carcasses with boar taint from 4 to <0.1% in 19 generations at the cost of a 10% less economic gain in reproduction traits. In markets for intact males, overall economic gain is 78% greater than with selection for reproduction only. Breeding programs for boar taint in commercial production should include boar taint in breeding goals of dam lines as the levels of boar taint and the risk of a further increase are greater.

Adaptive response to Eimeria acervulina in rearing hens is affected by suboptimal incubation temperature and heat exposure in later life.

This study aimed to investigate whether suboptimal incubation (SI) temperature in weeks 1 and 3 of layer embryo incubation affects their development and post-hatch adaptive capacity during infectious challenges, by using Eimeria as a model infection under normal and immediately after more challenging environmental conditions of 72 h heat exposure. Eggs (n = 160 per treatment) were incubated at optimal (OI = 37.8°C continuously) or suboptimal eggshell temperature (36.7°C, 37.8°C and 38.9°C in weeks 1, 2 and 3, respectively). At day 33 of age, half the chickens of each incubation treatment were exposed to 72 h heat (35°C), whereas the other half remained under control conditions (21°C). At day 36 of age, all chickens were inoculated with 1 ml of a phosphate buffer saline solution containing 25 000 sporulated Eimeria acervulina oocysts/ml. The adaptive response to E. acervulina was measured by BW gain and FI from days 0 to 3 post infection (p.i.), days 3 to 5 p.i. and days 5 to 7 p.i., and by oocyst production (days 4 and 7 p.i.) and lesion scores in the duodenum (day 3, 4 and 7 p.i.). Our results demonstrated that SI temperatures in weeks 1 and 3 of incubation resulted in a reduction in yolk-free BW, chick length and navel condition. Moreover, SI temperature appeared to reduce the adaptive capacity to E. acervulina. This was demonstrated by tendencies to lower FI (P = 0.07) and BW gain (P = 0.08), more duodenal lesions (P = 0.09) and higher oocyst production (P = 0.02) after inoculation of E. acervulina. Higher lesion scores and faecal oocyst numbers were especially found when suboptimal incubation was combined with heat exposure preceding the infection. In conclusion, SI layer chickens tend to be less able to cope with an infectious challenge post hatch.

A human nose scoring system for boar taint and its relationship with androstenone and skatole.

A system for sensory evaluation of boar taint was used to evaluate boar taint in fat samples from 6574 entire males. The term "human nose scoring" has been used to describe this system. The samples from each boar were heated with a hot iron and three panelists assigned scores of 0 to 4. The reproducibility of HNS ranged from 0.19 to 0.32 reflecting natural variation in the ability of human beings to detect different odors. The correlations of HNS with androstenone ranged from 0.22 to 0.52, while those with skatole ranged from 0.31 to 0.89, suggesting that skatole is a better predictor of boar taint. Considering (1) the relationship of HNS with the boar taint compounds, (2) the ability of HNS to capture variation not accounted for by the boar taint compounds, (3) low estimation costs and (4) low time requirements, HNS can be used in large scale evaluations of boar taint.

Genetic parameters for androstenone, skatole, indole, and human nose scores as measures of boar taint and their relationship with finishing traits.

The purpose of this study was to evaluate measures of boar (Sus scrofa) taint as potential selection criteria to reduce boar taint so that castration of piglets will become unnecessary. Therefore, genetic parameters of boar taint measures and their genetic correlations with finishing traits were estimated. In particular, the usefulness of a human panel assessing boar taint (human nose score) was compared with chemical assessment of boar taint compounds, androstenone, skatole, and indole. Heritability estimates for androstenone, skatole, and indole were 0.54, 0.41, and 0.33, respectively. The heritability for the human nose score using multiple panelists was 0.12, and ranged from 0.12 to 0.19 for individual panelists. Genetic correlations between scores of panelists were generally high up to unity. The genetic correlations between human nose scores and the boar taint compounds ranged from 0.64 to 0.999. The boar taint compounds and human nose scores had low or favorable genetic correlations with finishing traits. Selection index estimates indicated that the effectiveness of a breeding program based on human nose scores can be comparable to a breeding program based on the boar taint compounds themselves. Human nose scores can thus be used as a cheap and fast alternative for the costly determination of boar taint compounds, needed in breeding pigs without boar taint.

Breeding replacement gilts for organic pig herds.

In this study, breeding structures and commercial sow lines were evaluated by economic and genetic simulation studies for their suitability to provide the Dutch organic pig sector with replacement gilts. Sow and litter performance from over 2000 crossbred sows from 2006 to 2007 were collected on 11 to 14 Dutch organic pig herds, respectively, and compared with conventional herds. Results showed that organic herds had lower farrowing rates (3.6% to 7.5%), more live born piglets per litter (0.4% to 1.2%) and higher preweaning mortality rates (7% to 13%) compared to conventional herds. These results were used to simulate economic performance of various combinations of breeding structures and sow lines under organic conditions, under the assumption of absence of genotype-environment interactions. Sow and litter performance data under organic conditions (total piglets born/litter, stillborn piglets/litter, mortality until weaning, lactation length, interval weaning-oestrus and sow culling rate) and the costprice calculation for the Dutch organic pig sector were used as input for the economic simulation studies. The expected genetic progress was simulated for three potential breeding structures of the organic sector: organic breeding herds producing F1 gilts (OrgBS), a flower breeding system (FlowerBS) and a two-line rotation breeding system (RotBS). In FlowerBS, an organic purebred sow line is bred, using on-farm gilt replacement. The OrgBS with a Yorkshire × Landrace cross had the highest margin per sow place (€779), followed by RotBS with Yorkshire × Landrace cross (€706) and FlowerBS with Yorkshire sow line (€677). In case that an organic purebred sow population of 5000 sows would be available, FlowerBS gave the highest genetic progress in terms of cost price reduction (€3.72/slaughter pig per generation), followed by RotBS and OrgBS (€3.60/slaughter pig per generation). For FlowerBS, additional costs will be involved for maintaining a dedicated breeding programme. In conclusion, OrgBS using conventional genetics is economically the most viable option for the organic pig sector. However, this structure has clear disadvantages in terms of risks with regard to disease transmission and market demand. FlowerBS using a dedicated purebred organic line will only be cost-effective if sow population size is sufficiently large. RotBS might be a viable alternative, especially in combination with artificial insemination (AI) boars that are ranked according to an organic selection index. Regardless of breeding structure, the Yorkshire sow line gave the highest prolificacy and the highest economic returns on organic herds.

Temperature manipulation during layer chick embryogenesis.

The current study investigated the effects of temperature manipulation (TM) during late embryogenesis on temperature preference, response to high environmental temperature, behavior, and performance in young layer chicks. Control (CC) embryos (n = 96) were incubated at 37.8 degrees C eggshell temperature throughout incubation. Thermally manipulated embryos (n = 96) were incubated at 37.8 degrees C eggshell temperature throughout incubation and were exposed to 40 degrees C for 4 h/d from embryonic d 14 to 18 (TM chicks). After hatch, chicks from each treatment were divided into 3 subgroups (n = 32 per group) and were subjected to a temperature preference test at d 1, 7, or 33. One day after the temperature preference test, each subgroup was exposed to 1 thermal challenge for 4 h (d 2, 40 degrees C; d 8, 40 degrees C; or d 34, 35 degrees C). Effects of TM on (fearfulness) behavior of chicks were investigated in a tonic immobility test and during home pen observations. Temperature manipulation decreased incubation time with 7 h (P < 0.0001) and body temperature at hatch with 0.2 degrees C (P = 0.002). The TM chicks preferred a lower ambient temperature in the temperature preference test (P < 0.05) and showed a higher body temperature response than CC chicks to the thermal challenge at d 2 and 8 (P < 0.05). No effects of TM on behavior and performance were observed. Because most TM studies are conducted in broilers, this study is the first attempt to unravel the effects of TM during late embryogenesis on posthatch environmental adaptation in layer chicks. The results demonstrated that effects of our TM on postnatal temperature preference and response to high environmental temperatures are only found until d 8 of age. This may suggest 1 of 3 options: a) the timing or the level, or both, of TM and duration were not at the sensitive period of embryogenesis or not sufficient, or both, respectively; b) the level of the postnatal thermal challenge was not strong enough to induce a hyperthermic response; and c) the postnatal effects of TM in layers are limited in time.

Combining somatic cell count traits for optimal selection against mastitis.

Test-day records of somatic cell counts (SCC) can be used to define alternative traits to decrease genetic susceptibility to clinical mastitis (CM) and subclinical mastitis (SCM). This paper examines which combination of alternative SCC traits can be used best to reduce both CM and SCM and whether direct information on CM is useful in this respect. Genetic correlations between 10 SCC traits and CM and SCM were estimated from 3 independent data sets. The SCC traits with the strongest correlations with CM differed from those with the strongest correlations with SCM. Selection index calculations were made for a breeding goal of 50% CM and 50% SCM resistance using these correlations. They indicated that a combination of 5 SCC traits (SCC early and late in lactation, suspicion of infection based on increased SCC, extent of increased SCC, and presence of a peak pattern in SCC) gave a high accuracy, almost without loss, compared with the full set of 10 SCC traits. The estimated accuracy of this index was 0.91, assuming that the correlations had been estimated without error. To take errors in estimation into account, correlations were resampled from a normal distribution with mean and standard errors as originally estimated. The accuracy of the index calculated with the original correlations was then recalculated using the resampled correlations. The average accuracy based on 50,000 resamplings decreased to 0.81. Use of direct information on CM improved the accuracy (uncorrected for errors in correlations) only slightly, to 0.92.

Early life experiences affect the adaptive capacity of rearing hens during infectious challenges.

This study aimed to investigate whether pre- and early postnatal experiences of rearing hens contribute to the ability to cope with infectious challenges at an older age. In a 2 × 2 factorial arrangement, 352 Lohmann Brown chicks were exposed to either suboptimal or optimized incubation plus hatch conditions, and to cage or enriched rearing from week 0 to 7 of age. After week 7 all rearing conditions were similar until the end of the experiment. The development of adaptive capacity to infectious challenges was investigated by introducing an Eimeria and Infectious Bronchitis (IB) infection on day 53 and day 92, respectively. BW gain and feed intake during the infections, duodenal lesions and amount of positive stained CD4+ T cells, CD8+ T cells and macrophages at day 4 and day 7 after Eimeria infection, as well as the IB antibody titer throughout the experimental period were determined. The results showed a significant interaction between incubation plus hatch and rearing environment. Optimized incubation plus hatch conditions followed by an enriched rearing environment resulted in the least weight loss (P < 0.05) and the highest feed intake (P < 0.01) from day 3 to day 7 after the Eimeria infection (day 56 to 60 of age), compared with all other treatments. In addition, the optimized × enriched chicks had the highest BW gain from day 7 to day 14 after IB infection (day 99 to 106 of age), compared with chicks housed in a cage environment (P < 0.01). Besides the interaction, optimized incubation plus hatch alone resulted in reduced macrophage numbers in the duodenal tissue at day 4 after Eimeria infection, compared with suboptimal incubation plus hatch, whereas the enriched rearing environment stimulated the recovery of intestinal damage caused by Eimeria (P < 0.05) and reduced the production of specific antibodies after IB infection (P < 0.05), compared with the cage environment. In conclusion, this study shows that early life experiences can indeed affect the capacity of rearing hens to cope with an Eimeria and IB infection at an older age, in which performance of chicks is best maintained after optimized incubation plus hatch followed by enriched rearing. This suggests that the development of adaptive capacity to infectious challenges can be influenced with management during a short period in pre- or early postnatal life, but that effects last for a considerable time after cessation of the specific management.

Characterization of distributions of somatic cell counts.

There is more useful information in distributions of somatic cell count (SCC) than is currently used in practice. Analysis of SCC of individual quarters (n = 450,834 quarter records of 133,102 cows) showed that the presence of pathogens did not change the peak of the SCC distribution. Instead, the percentages of observations in the tail changed. Probability density functions of specified sets of up to 5 standard distributions were then fitted on the number of records per class, using a maximum likelihood procedure. Analysis of cow SCC (2 data sets: n = 335,135 test-day records of 41,567 cows on 407 farms and n = 1,665,431 test-day records) showed that a mixture of a normal, a log-normal and an exponential density function (N+LN+E) best described the distribution of SCC. A mixture of 4 normal and an exponential distribution (4N+E) was also a good approximation. For this last mixture, each distribution could be associated with presence or absence of pathogens. The first 2 normal distributions appear to consist of uninfected cows and cows recovering from an infection, the third normal distribution may be associated with minor pathogens, and the fourth normal and the exponential distribution with major pathogens and persistent infections. Estimated percentages of records in each underlying distribution differed between parities, between stages of lactation, and between records with previous records being above or below 100,000 cells/mL. The categorical nature of cow-SCC can be utilized by deriving new traits such as the fraction of cow-SCC records in a lactation that are associated with an infection with a major pathogen.

The use of data from sampling for bacteriology for genetic selection against clinical mastitis.

One breeding objective of Dutch cattle breeders is to improve genetic resistance against clinical and subclinical mastitis. Because of a lack of direct mastitis information, udder health breeding values are based on indirect traits. Inclusion of direct information on clinical mastitis could improve reliability of breeding values. The aim of this study was to investigate whether data from milk samples sent in for bacteriology are potential sources of information for the occurrence of mastitis, which may be used in animal breeding, and if so how this data can be used. Although there are 2 separate flows of milk samples for bacteriology in the Netherlands, it was not considered necessary to account for the origin of the samples. In both flows, the majority of the samples are visually normal and flow-specific traits are highly correlated. Therefore, information from these flows is combined for genetic analysis. Nearly two-thirds of the bacteriology data could be linked to milk recording and pedigree records. Relatively few farmers (<3%) took 5 or more samples for bacteriology between January 1, 2003, and March 31, 2006. Their herds had, on average, greater milk production and lower cell counts than herds for which no samples were taken. However, the range and variation within both groups of herds for these variables was similar and there was a large overlap in sires used within both groups. Whether or not samples were taken for bacteriology turned out to be a potentially useful indicator for clinical mastitis at the cow level, because this trait had a strong positive genetic correlation with clinical mastitis registered by farmers (0.84 or 0.89, depending on the data set) and similar heritability (2%) and genetic variation. Also, genetic correlations of bacteriology with SCC traits were similar to those for farmer-registered clinical mastitis. An important advantage of these bacteriology data is that they are already collected routinely and stored in a central database in the Netherlands; this is not the case for registration of clinical cases. Thus, data from bacteriological culturing can be used for genetic improvement of udder health.

Alternative somatic cell count traits as mastitis indicators for genetic selection.

The aim of this study was to define alternative traits of somatic cell count (SCC) that can be used to decrease genetic susceptibility to clinical and subclinical mastitis (CM and SCM, respectively). Three kinds of SCC traits were evaluated: 1) lactation-averages of SCC, 2) traits derived from the proportion of test-day SCC above 150,000 cells/mL, and 3) patterns of peaks in SCC. Genetic parameters for these SCC traits and their genetic correlation with CM and SCM were estimated; CM and SCM were scored as binary traits. Two data sets (A and B) depending on CM recording were available. After editing, subset A contained 28,688 lactations from 21,673 cows in 394 herds. Subset B contained 56,726 lactations of 30,145 cows in 272 herds. Variance components for sire and permanent animal effects were estimated. Estimated heritabilities for all mastitis traits were around 0.03. Heritabilities for SCC traits ranged from 0.01 for patterns of peaks in SCC to 0.13 for lactation-average SCC. Genetic correlations between SCC traits and CM or SCM ranged from 0.55 to 0.93 for CM and from 0.55 to 0.98 for SCM. High genetic correlations were estimated between CM and SCC averaged over 250 d in milk (0.87), and between SCM and presence of test-day SCC >150,000 cells/mL (0.98) in subset A. In subset B, a high genetic correlation was estimated between CM and an SCC peak with a quick recovery (0.93) and between SCM and SCC averaged between 151 and 400 d (0.95). Partial genetic correlations were calculated to investigate the additional information of the alternative SCC traits, compared with lactation-average SCC. They showed that some traits remain informative for CM and others for SCM. Therefore, use of information from a combination of different SCC traits may be more successful in improving overall udder health than the traditional single SCC measure.

Genetics of the interval from weaning to estrus in first-litter sows: correlated responses.

The objective of this study was to evaluate relationships between rebreeding performance and growth performance (n = 3,777 gilts) and rebreeding performance and reproductive performance (n = 2,242 sows). Our data were from a selection experiment for shorter intervals from weaning to estrus after the first parity (IWE), involving Dutch Landrace pigs, in which a selection line and a control line without selection were maintained for eight generations. Relationships were evaluated before and after transforming IWE to normal interval (NI; IWE < or = 7 d), prolonged interval (PI; IWE > 7 d), and incidence of a prolonged interval (INC). Heritabilities of NI, PI, and INC were .18, .17, and .27. Within-line phenotypic and genetic trends in growth and reproductive performance were not different from zero and did not diverge as a correlated response to the selection applied. Phenotypic correlations between IWE, NI, or PI and growth or reproductive performance were low and ranged from -.14 to .11. Genetic correlations were higher, and, for the majority of traits, the genetic correlations with NI and PI had a different sign. Phenotypic and genetic contrasts between sows with NI and sows with PI different from zero indicated that INC may increase as a correlated response to selection for reproductive performance. Analyses using untransformed IWE or data from populations selected for rebreeding performance may underestimate the correlated response in IWE due to selection on economically important traits.

Genetic relationships among production traits and rebreeding performance.

We studied effects of selection for increased daily gain, reduced backfat, increased number of piglets born alive, and increased 21-d litter weight on interval from weaning to farrowing (IWF) of two commercial populations of purebred Large White (LW) and Landrace (LR) lines with each represented in two farms. The analysis took into account that normal and prolonged intervals could be distinguished. Distributions of IWF were described by a mixture of a normal and an exponential distribution. Observed intervals were transformed to the probability of being a prolonged interval, using the mixed density function. Incidence of normal intervals was less in LR than in LW sows and was least in first-litter sows. Heritability estimates of IWF ranged from zero to .24 across parities and farms. Genetic trend within breed and farm was different from zero (P < .05) for each trait under selection, except backfat on Farm 2. Genetic correlations between the traits under selection and IWF were inconsistent across farms. Differences in estimated breeding value for the traits under selection between sows with a normal and a prolonged interval were significant only on Farm 1 in the Large White breed, when only observed intervals were analyzed. Including culled sows in the analysis as sows with a prolonged interval yielded consistent differences for average daily gain and backfat, such that genetic selection for improved production would increase the liability for a prolonged interval. Culling sows for delayed estrus apparently overcame this problem on Farm 2 but not on Farm 1, on which a trend of increasing incidence of prolonged intervals was observed.