Högskolan i Skövde

The transportation of animals to slaughterhouses is a major welfare concern. The number of slaughterhouses has decreased over time in Europe due to centralisation. This is expected to increase transport time for animals and as a consequence negatively affect animal welfare. We propose an optimisation model based on a facility location model to perform strategic analysis to improve transportation logistics. The model is tested on the Swedish slaughter transport system. We show that, by strategic planning and redirection of transports while keeping the slaughterhouse capacities as of the originaldata, the potential exists to reduce transport distance by 25% for pigs and 40% for cattle. Furthermore, we demonstrated that approximately 50% of Swedish slaughterhouses can be shut down with a minimal effect on total transport distances. This implies that in terms of the overall welfare picture, the decision of which animals to send where plays a for more significant role than the number of slaughterhouses. In addition, by changing relative weights on distances in the optimisation function the amount of individualtransports with longjourney times can be decreased. We also show results from altered slaughterhouse capacity and geographical location of slaughterhouses. This is the first time an entire country has been analysed in great detail with respect to the location, capacity and number of slaughterhouses. The focus is mainly on the analysis of unique and detailed information of actual animal transports in Sweden and a demonstration of the potential impact redirection of the transports and/ or altering of slaughterhouses can have on animal welfare.

Autocorrelation within ecological time series and synchrony between them may provide insight into the main drivers of observed dynamics. We here present methods that analyse autocorrelation and synchrony in ecological datasets using a spectral approach combined with Bayesian inference. To exemplify, we implement the method on dendrochronological data of the pedunculate oak (Quercus robur). The data consist of 110 years of growth of 10 live trees and seven trees that died during a synchronized oak death in Sweden in c. 2002-2007. We find that the highest posterior density is found for a noise colour of tree growth of gamma approximate to 0.95 (i.e. pink noise) with little difference between trees, suggesting climatic variation as a driving factor. This is further supported by the presence of synchrony, which we estimate based on phase-shift analysis. We conclude that the synchrony is time-scale dependent with higher synchrony at larger time-scales. We further show that there is no difference between the growth patterns of the alive and dead tree groups. This suggests that the trees were driven by the same factors prior to the synchronized death. We argue that this method is a promising approach for linking theoretical models with empirical data.

Financial resources may limit the number of samples that can be collected and analysed in disease surveillance programmes. When the aim of surveillance is disease detection and identification of case herds, a risk-based approach can increase the sensitivity of the surveillance system. In this paper, the association between two network analysis measures, i.e. ‘in-degree’ and ‘ingoing infection chain’, and signs of infection is investigated. It is shown that based on regression analysis of combined data from a recent cross-sectional study for endemic viral infections and network analysis of animal movements, a positive serological result for bovine coronavirus (BCV) and bovine respiratory syncytial virus (BRSV) is significantly associated with the purchase of animals. For BCV, this association was significant also when accounting for herd size and regional cattle density, but not for BRSV. Examples are given for different approaches to include cattle movement data in risk-based surveillance by selecting herds based on network analysis measures. Results show that compared to completely random sampling these approaches increase the number of detected positives, both for BCV and BRSV in our study population. It is concluded that network measures for the relevant time period based on updated databases of animal movements can provide a simple and straight forward tool for risk-based sampling.

This thesis is about animal transports and their effect on animal welfare. Transports are needed in today’s system of livestock farming. Long transports are stressful for animals and infectious diseases can spread via animal transports. With optimization methods transport times can be minimized, but there is a trade-off between short distances for the animals and short distances for the trucks. The risk of disease spread in the transport system and disease occurrence at farms can be studied with models and network analysis.

The animal transport data and the quality of the data in the Swedish national database of cattle and pig transports are investigated in the thesis. The data is analyzed regarding number of transports, number of farms, seasonality, geographical properties, transport distances, network measures of individual farms and network measures of the system. The data can be used as input parameters in epidemic models.

Cattle purchase reports are double reported and we found that there are incorrect and missing reports in the database. The quality is improving over the years i.e. 5% of cattle purchase reports were not correctly double reported in 2006, 3% in 2007 and 1% in 2008. In the reports of births and deaths of cattle we detected date preferences; more cattle births and deaths are reported on the 1st, 10th and 20th each month. This is because when we humans don’t remember the exact number we tend to pick nice numbers (like 1, 10 and 20). This implies that the correct date is not always reported.

Network analysis and network measures are suggested as tools to estimate risk for disease spread in transport systems and risk of disease introduction to individual holdings. Network generation algorithms can be used together with epidemic models to test the ability of network measures to predict disease risks. I have developed, and improved, a network generation algorithm that generates a large variety of structures.

In my thesis I also suggest a method, the good choice heuristic, for generating non-optimal routes. Today coordination of animal transports is neither optimal nor random. In epidemic simulations we need to model routes as close to the actual driven routes as possible and the good choice heuristic can model that. The heuristic is tuned by two parameters and creates coordination of routes from completely random to almost as good as the Clarke and Wright heuristic. I also used the method to make the rough estimate that transport distances for cattle can be reduced by 2-24% with route-coordination optimization of transports-to-slaughter.

Different optimization methods can be used to minimize the transport times for animal-transports in Sweden. For transports-to-slaughter the strategic planning of “which animals to send where” is the first step to optimize. I investigated data from 2008 and found that with strategic planning, given the slaughterhouse capacity, transport distances can be decreased by about 25% for pigs and 40% for cattle. The slaughterhouse capacity and placement are limiting the possibility to minimize transport times for the animals. The transport distances could be decreased by 60% if all animals were sent to the closest slaughterhouse 2008. Small-scale and mobile slaughterhouses have small effect on total transport work (total transport distance for all the animals) but are important for the transport distances of the animals that travel the longest.

Two methods that can help to reduce the distances involved in transportation of cattle to slaughter are presented, route optimization and spatial redistribution of slaughter capacities. In a comparison ot three route optimization techniques we show that RuttOpt is the most effective and that the number of stops on routes can be reduced compared to what is the case today. We also find that transport distances can be reduced by 40 % compared to the real transports of 2008 if animals are sent to the closest available facilities. We believe that the metods highlighted here can help improve the sustainability and animal health in both organic and conventional farming.

Network measures are used to predict the behavior of different systems. To be able to investigate how various structures behave and interact we need a wide range of theoretical networks to explore. Both spatial and non-spatial methods exist for generating networks but they are limited in the ability of producing wide range of network structures. We extend an earlier version of a spatial spectral network algorithm to generate a large variety of networks across almost all the theoretical spectra of the following network measures: average clustering coefficient, degree assortativity, fragmentation index, and mean degree. We compare this extended spatial spectral network-generating algorithm with a non-spatial algorithm regarding their ability to create networks with different structures and network measures. The spatial spectral networkgenerating algorithm can generate networks over a much broader scale than the non-spatial and other known network algorithms. To exemplify the ability to regenerate real networks, we regenerate networks with structures similar to two real Swedish swine transport networks. Results show that the spatial algorithm is an appropriate model with correlation coefficients at 0.99. This novel algorithm can even create negative assortativity and managed to achieve assortativity values that spans over almost the entire theoretical range.

Registration ofcattle and pigmovements is mandatory in Sweden and all registered movements between farms in the years 2006–2008 were investigated using network analysis. The networks were analysed as monthly and yearly networks, separately per species and with the two species together. Measures that have been previously discussed in relation to outbreaks and disease control were calculated; moreover a measure of the ingoing infection chain was constructed.The ingoing infection chain captures in going contacts through other holdings, taking the temporal aspect and sequence of the movements into account. The distribution of the contacts among the holdings was skewed.Many farms had few or no contacts, while others had many, a pattern which has also been described from other countries. The cattle network and the combined network showed a recurring seasonal pattern, while this was not seen in the pig network.The in-degree was not equivalent to the ingoing infection chain; there were holdings with limited direct contacts, but a large number of indirect contacts.The ingoing infection chain could be a useful measure when setting up strategies for disease control and for risk based surveillance as it identifies holdings with many contacts through live animal movements and thus at potentially higher risk for introduction of contagious diseases.

Ecological and epidemiological invasions occur in a spatial context. We investigated how these processes correlate to the distance dependence of spread or dispersal between spatial entities such as habitat patches or epidemiological units. Distance dependence is described by a spatial kernel, characterized by its shape (kurtosis) and width (variance). We also developed a novel method to analyse and generate point-pattern landscapes based on spectral representation. This involves two measures: continuity, which is related to autocorrelation and contrast, which refers to variation in patch density. We also analysed some empirical data where our results are expected to have implications, namely distributions of trees (Quercus and Ulmus) and farms in Sweden. Through a simulation study, we found that kernel shape was not important for predicting the invasion speed in randomly distributed patches. However, the shape may be essential when the distribution of patches deviates from randomness, particularly when the contrast is high. We conclude that the speed of invasions depends on the spatial context and the effect of the spatial kernel is intertwined with the spatial structure. This implies substantial demands on the empirical data, because it requires knowledge of shape and width of the spatial kernel, and spatial structure.

Theoretical exploration of network structure significance requires a range of different networks for comparison. Here, we present a new method to construct networks in a spatial setting that uses spectral methods in combination with a probability distribu-tion function. Nearly all previous algorithms for network construction have assumed randomized distribution of links or a distribution dependent on the degree of the nodes.We relax those assumptions. Our algorithm is capable of creating spectral networks along a gradient from random to highly clustered or diverse networks. Number of nodes and link density are specified from start and the structure is tuned by three parameters (γ, σ, κ). The structure is measured by fragmentation, degree assortativity, clusteringand group betweenness of the networks. The parameter γ regulates the aggregation in the spatial node pattern and σ and κ regulates the probability of link forming.

Background: Livestock movements can affect the spread and control of contagious diseases and new data recording systems enable analysis of these movements. The results can be used for contingency planning, modelling of disease spread and design of disease control programs.

Methods: Data on the Swedish cattle and pig populations during the period July 2005 until June 2006 were obtained from databases held by the Swedish Board of Agriculture. Movements of cattle and pigs were investigated from geographical and temporal perspectives, births and deaths of cattle were investigated from a temporal perspective and the geographical distribution of holdings was also investigated.

Results: Most movements of cattle and pigs were to holdings within 100 km, but movements up to 1200 km occurred. Consequently, the majority of movements occurred within the same county or to adjacent counties. Approximately 54% of the cattle holdings and 45% of the pig holdings did not purchase any live animals. Seasonal variations in births and deaths of cattle were identified, with peaks in spring. Cattle movements peaked in spring and autumn. The maximum number of holdings within a 3 km radius of one holding was 45 for cattle and 23 for pigs, with large variations among counties. Missing data and reporting bias (digit preference) were detected in the data.

Conclusion: The databases are valuable tools in contact tracing. However since movements can be reported up to a week after the event and some data are missing they cannot replace other methods in the acute phase of an outbreak. We identified long distance transports of cattle and pigs, and these findings support an implementation of a total standstill in the country in the case of an outbreak of foot-and-mouth disease. The databases contain valuable information and improvements in data quality would make them even more useful.