J. Krieter

Automation of oestrus detection in dairy cows: a review

Abstract The economic importance of traits like longevity, health and reproduction has increased compared to milk yield in dairy cows. Effective oestrus detection is important for improved reproduction. Commonly, oestrus detection is performed by visual observation, but this is particularly difficult on large dairy farms because of short observation periods during feeding and milking. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible. In many studies different traits have been analysed for utilisation in automatic oestrus detection. The best results were found for detection using pedometers. Results of oestrus detection varied depending on the used threshold value, the number of cows, housing and treatment of cows and the utilised method of time series analysis. The detection rate of most investigations is sufficiently high at 80–90%. Error rates between 17 and 55% and specificities between 96 and 98% indicate a large number of false positive oestrus warnings. The main problem of automatic oestrus detection is to reduce the false positive alerts. In recent years several authors have combined different traits with the objective of improving detection rates. Best multivariate analyses results were found for combinations with activity. Further research should be performed using data from a commercial dairy farm. A comparison of different time series methods and multivariate analysis of traits would be useful.

Static network analysis of a pork supply chain in Northern Germany—Characterisation of the potential spread of infectious diseases via animal movements

Transport of live animals is a major risk factor in the spread of infectious diseases between holdings. The present study analysed the pork supply chain of a producer community in Northern Germany. The structure of trade networks can be characterised by carrying out a network analysis. To identify holdings with a central position in this directed network of pig production, several parameters describing these properties were measured (in-degree, out-degree, ingoing and outgoing infection chain, betweenness centrality and ingoing and outgoing closeness centrality). To obtain the importance of the different holding types (multiplier, farrowing farms, finishing farms and farrow-to-finishing farms) within the pyramidal structure of the pork supply chain, centrality parameters were calculated for the entire network as well as for the individual holding types. Using these centrality parameters, two types of holdings could be identified. In the network studied, finishing and farrow-to-finishing farms were more likely to be infected due to the high number of ingoing trade contacts. Due to the high number of outgoing trade contacts multipliers and farrowing farms had an increased risk to spread a disease to other holdings. However, the results of the centrality parameters degree and infection chain were not always consistent, such that the indirect trade contacts should be taken into consideration to understand the real importance of a holding in spreading or contracting an infection. Furthermore, all calculated parameters showed a highly right-skewed distribution. Networks with such a degree distribution are considered to be highly resistant concerning the random removal of nodes. But by strategic removal of the most central holdings, e.g. by trade restrictions or selective vaccination or culling, the network structure can be changed efficiently and thus decompose into fragments. Such a fragmentation of the trade networks is of particular importance from an epidemiological perspective.

Environmental Impact Assessment--methodology with special emphasis on European pork production.

One of the most discussed topics worldwide is climate change, upon which livestock production is known to have a great environmental impact. There are different methods to measure these environmental impacts, some of which are mentioned in this review. It especially focuses on the method of Life Cycle Assessment (LCA), because it is widely used, of high relevance and good quality. This review highlights a sample of the few published European LCA studies on pork production. These assessments result in an average global warming potential of 3.6 kg CO(2)- eq per kg pork, ranging from 2.6 to 6.3 kg CO(2)- eq per kg pork. Additionally, it illustrates the main limitations of the methodology itself (e.g. data intensiveness, different allocation techniques) and its application in pork production (e.g. limited data availability, use of multiple functional units, varying system boundaries). The missing comparability of various studies arising from a vague standard still represents the main problem in LCA. Therefore, a new standardisation and the development of a more exhaustive database would generate a future trend.

Improving oestrus detection by combination of activity measurements with information about previous oestrus cases

Abstract In this study, the potential benefit of combining the traits activity and period since last oestrus for oestrus detection was investigated. The simultaneous analyses of these traits in a fuzzy logic model should reflect the consideration of the dairy farmer regarding his judging of oestrus warnings. The analyses involved 862 cows, each with one verified oestrus case. Information about previous oestrus or inseminations were available for 373 cows. For comparison only, the trait activity was analysed in a preliminary investigation by an univariate fuzzy logic model. The sensitivity was 91.7% and the error rate was 34.6%. By considering the previous information in a multivariate fuzzy logic model, the sensitivity decreased to 87.9% and the error rate improved to 12.5%. The simultaneous analysis of cows with and without previous information in the oestrus detection model resulted in an increase in error rate to 23.8%, due to the high number of cows without previous information. The obtained results indicate that the information about previous oestrus cases is suitable for multivariate oestrus detection.

Genetic aspects regarding piglet losses and the maternal behaviour of sows. Part 1. Genetic analysis of piglet mortality and fertility traits in pigs.

In spite of the improvement in management and the breeding goal of increasing the number of piglets born alive, piglet mortality is still a substantial problem in pig breeding. The objective of the first part of the study was to estimate genetic parameters for different causes of piglet losses and to investigate the relationship to litter-size traits. Data were collected on a nucleus herd from January till December 2004. Records from 943 German Landrace sows with 1538 pure-bred litters and 13 971 individually weighted piglets were included. Four different causes of piglet losses (LOSS) were evaluated. Additional analysed traits were underweight and runting. Furthermore, the fertility traits number of piglets born alive, born in total and stillborn piglets as well as the individual birth and weaning weights were analysed. The different LOSS were treated as a binary trait and subsequently the heritabilities were estimated using a threshold model. The most important LOSS was crushing under the sow (12.4%). The survival rate and crushing had a heritability of h2 = 0.03. The fertility traits piglets born alive, born in total and stillborn piglets were analysed with a linear model and heritabilities rank from h2 = 0.05 (stillborn) to h2 = 0.10 (born alive). The estimated heritabilities for birth- and weaning weight were both h2 = 0.10. The genetic correlations between number of piglets born alive and each LOSS trait were analysed bivariately. Of all piglets born alive 84.3% survive the lactation period. Survival decreased with increasing litter size (rg = -0.54 up to -0.78) and the probability of being crushed under the sow increased.

Seroprevalence and risk factors associated with seropositivity in sows from 67 herds in north-west Germany infected with Mycoplasma hyopneumoniae.

Risk factors for the spread of Mycoplasma hyopneumoniae in sows have not been studied although vertical transmission from sows to their offspring is considered a significant risk factor in the development of enzootic pneumonia in growers and finishers. Seropositivity for M. hyopneumoniae in sows, as assessed by commercial ELISA, is a possible indicator of infection pressure among sows. The objective of this study was to estimate seroprevalence and associated risk factors of a sow being seropositive for M. hyopneumoniae. A cross-sectional study was carried out in 2578 sows from 67 herds in north-west Germany. Data concerning general herd characteristics, acclimatisation practices, indoor and outside contacts, as well as data describing the immediate local environment were collected during a herd visit via questionnaire. Blood samples were seropositive in 65% of the 2578 sows, and all herds had >/=14% seropositive sows. Data analysis was performed in two steps. First, univariate analysis of predictor variables for the risk of a sow being seropositive for M. hyopneumoniae was performed using chi-square test. Secondly, all variables associated with the risk of a sow being seropositive (P</=0.25) were included in a multivariate model using a generalised linear model. The risk of a sow being seropositive for M. hyopneumoniae was increased in herds with two- or three-site production (OR 1.50), when piglets were not vaccinated against M. hyopneumoniae (OR 1.81), in herds with a 2-week farrowing intervals (OR 1.84) and in herds without all-in/all-out management of the farrowing units (OR 1.37). The lack of an acclimatisation period for replacement boars was also associated with the risk of a sow being seropositive (OR 2.10). The results indicate that M. hyopneumoniae seropositivity is common in sows in north-west Germany and is influenced by various management factors. It is recommended that evaluation of sow herd management should be included in any strategic health plan to control M. hyopneumoniae infection.

Genetic aspects regarding piglet losses and the maternal behaviour of sows. Part 2. Genetic relationship between maternal behaviour in sows and piglet mortality

The aim of the study was to analyse the genetic background of different traits to characterise the maternal behaviour of sows and to evaluate the relationship to different causes of piglet losses - increasing piglet survival due to higher maternal abilities of the sow. A total of 1538 purebred litters from 943 German Landrace sows in the year 2004 were available for data analysis. Around 13 971 individually earmarked piglets were included in the analyses. Maternal abilities were characterised through the sows reaction to the separation from her litter during the first 24 h after farrowing, and on day 21 of lactation, the reaction towards the playback of a piglets distress call and the reaction towards an unknown noise (music). In 1220 of these litters, the sows were also scored for aggressiveness in the group when regrouped before entering the farrowing crates. To describe fertility, the number of piglets born alive, stillborn piglets, number of piglets born in total and the individual birth weight were utilised. Different causes of piglet losses were evaluated as binary traits of the dam with survival rate, different definitions for crushing by the sow, being underweight and runts. The heritability for being aggressive in the group was h2 = 0.32 and for the behaviour traits during lactation, the heritabilities ranged from h2 = 0.06 to 0.14. The genetic correlations showed that more-reactive sows had fewer piglet losses.

Risk factors for Salmonella infection in fattening pigs - an evaluation of blood and meat juice samples.

The main objective of this study was to analyse potential herd‐level factors associated with the detection of Salmonella antibodies in fattening pigs. Two independent datasets, consisting of blood and meat juice samples respectively, were used. Additional information about husbandry, management and hygiene conditions was collected by questionnaire for both datasets. The serological analysis showed that 13.8% of the blood samples and 15.7% of the meat juice samples had to be classified as Salmonella‐positive. Logistic‐regression models were used to assess statistically significant risk factors associated with a positive sample result. The results of the statistical blood sample analysis showed that the application of antibiotics increased the odds ratio (OR) by a factor of 5.21 (P < 0.001) compared to untreated pigs. A fully slatted floor decreased the prevalence of Salmonella as well as the use of protective clothing or the cleaning of the feed tube (ORs 0.35–0.54, P < 0.001). It was shown that a distance of less than 2 km to other swine herds increased the chance of a positive Salmonella result (OR = 3.76, P < 0.001). The statistical analysis of the meat juice samples revealed the importance of feed aspects. The chance of obtaining a positive meat juice sample increased by a factor of 3.52 (P < 0.001) by using granulated feed instead of flour. It also became clear that liquid feeding should be preferred to dry feeding (OR = 0.33, P < 0.001). A comparison of the blood sample analysis to the meat juice model revealed that the latter was less powerful because data structure was less detailed. The expansion of data acquisition might solve these problems and improve the suitability of QS monitoring data for risk factor analyses.

The analysis of simulated sow herd datasets using decision tree technique

Abstract The ability of machine learning techniques, especially the decision tree method, to analyse pig production data was investigated. The C4.5-classification algorithm was used to detect threshold values of management decisions relating to sows’ replacement. In order to generate side-effect-free data, three pig production herds, each on a different performance level, were created using a Monte Carlo simulation. The results of applying the C4.5-algorithm on simulated datasets showed different threshold values within the trees depending on sow herd performance. The evaluation parameters differed due to the adjusted number of instances per class and the dataset size. The sensitivity reached a value of 42.1–72.7%, the kappa value was 44.5–78.1%, and the error rate fluctuated between 6.0 and 31.3%. The overall classification accuracy ranged from 85.8 to 93.4% and the specificity reached a value of 93.7–98.8%. The generated decision trees, visualising the threshold values, varied their number of leaves between 2 and 31 and the number of nodes from 3 to 61.

Characterization of Contact Structures for the Spread of Infectious Diseases in a Pork Supply Chain in Northern Germany by Dynamic Network Analysis of Yearly and Monthly Networks

A major risk factor in the spread of diseases between holdings is the transport of live animals. This study analysed the animal movements of the pork supply chain of a producer group in Northern Germany. The parameters in-degree and out-degree, ingoing and outgoing infection chain, betweenness and ingoing and outgoing closeness were measured using dynamic network analysis to identify holdings with central positions in the network and to characterize the overall network topology. The potential maximum epidemic size was also estimated. All parameters were calculated for three time periods: the 3-yearly network, the yearly and the monthly networks. The yearly and the monthly networks were more fragmented than the 3-yearly network. On average, one-third of the holdings were isolated in the yearly networks and almost three quarters in the monthly networks. This represented an immense reduction in the number of holdings participating in the trade of the monthly networks. The overall network topology showed right-skewed distributions for all calculated centrality parameters indicating that network resilience was high concerning the random removal of holdings. However, for a targeted removal of holdings according to their centrality, a rapid fragmentation of the trade network could be expected. Furthermore, to capture the real importance of holdings for disease transmission, indirect trade contacts (infection chain) should be considered. In contrast to the parameters regarding direct trade contacts (degree), the infection chain parameter did not underestimate the potential risk of disease transmission. This became more obvious, the longer the observed time period was. For all three time periods, the results for the estimation of the potential maximum epidemic size illustrated that the outgoing infection chain should be chosen. It considers the chronological order and the directed nature of the contacts and has no restrictions such as the strongly connected components of a cyclic network.