Movement Behaviour of Traditionally Managed Cattle in the Eastern Province of Zambia Captured Using Two-Dimensional Motion Sensors

Two-dimensional motion sensors use electronic accelerometers to record the lying, standing and walking activity of cattle. Movement behaviour data collected automatically using these sensors over prolonged periods of time could be of use to stakeholders making management and disease control decisions in rural sub-Saharan Africa leading to potential improvements in animal health and production. Motion sensors were used in this study with the aim of monitoring and quantifying the movement behaviour of traditionally managed Angoni cattle in Petauke District in the Eastern Province of Zambia. This study was designed to assess whether motion sensors were suitable for use on traditionally managed cattle in two veterinary camps in Petauke District in the Eastern Province of Zambia. In each veterinary camp, twenty cattle were selected for study. Each animal had a motion sensor placed on its hind leg to continuously measure and record its movement behaviour over a two week period. Analysing the sensor data using principal components analysis (PCA) revealed that the majority of variability in behaviour among studied cattle could be attributed to their behaviour at night and in the morning. The behaviour at night was markedly different between veterinary camps; while differences in the morning appeared to reflect varying behaviour across all animals. The study results validate the use of such motion sensors in the chosen setting and highlight the importance of appropriate data summarisation techniques to adequately describe and compare animal movement behaviours if association to other factors, such as location, breed or health status are to be assessed

Scatter plot of animals in PC1 and PC3 space.

<p>Each point located in PC1 and PC3 space, represents individual cattle from the Kasero (blue) and Makale (orange) veterinary camps of Petauke District in the Eastern Province of Zambia. The dotted line represents a classification line predicted from a logistic regression model that best separates the animals from the two vet camps. PC1 and PC3 accounted for 51.0% and 9.0% of the total variance in the data.</p

Scatter plot illustrating the location of each animal in PC1 and PC2 space.

<p>Annotations of plots represent each of 40 individual cattle from the Kasero (blue) and Makale (orange) veterinary camps of Petauke District in the Eastern Province of Zambia. PC1 and PC2 accounted for 51.0% and 14.1% of the total variance in the data.</p

Loading value for each behaviour across 24 hours.

<p>The loading of each cattle behaviour variable, active (green hollow circles), lying (blue diamonds), and standing (red crosses) across 0:00 to 23:00 hours, on [A] PC1 and [B] PC2. These figures indicate that much of the heterogeneity in cattle behaviour during the night and day time is accounted for by PC1 and PC2, respectively.</p

Proportion of unaccounted variance against the number of PC included.

<p>Scree plot showing the proportion of variance that is unaccounted for against the number of Principal Components included. The first four components derived from Principal Component Analysis account for over 80% of the variance in the original data.</p

Correlation between steps and percentage of time involved in active behaviour in a given hour.

<p>Scatter plot illustrating the association between the number of steps taken and the proportion of time spent in active behaviour for 40 cattle between December 2006 and January 2007 in the Kasero and Makale veterinary camps of Petauke District in the Eastern Province of Zambia. [A] Scatter plot and fitted linear trend (Pearson’s correlation coefficient = 0.994) based on all 24 hours (<i>n</i> = 12,745). [B] Scatter plots for only those hours where cattle spent 50% or more time in standing (shown in blue circles) or lying (red circles) behaviour. (Note that the scales on the x and y-axis in [B] are halved compared to those in [A] because the maximum proportion of time that can be spent in active behaviour is 50%.)</p

Intraclass correlation coefficients within animal on the first four PCs.

<p>Intraclass correlation coefficients for PC scores over the studied period within individual animal were calculated separately in each of two vet camps. Moderate to high ICC values for the first four PCs indicate that there would be little benefit in employing daily behaviour variables, at the cost of increasing the total variance in the data. All ICC values were significantly different from 0 (<i>p</i> < 0.001).</p><p>Intraclass correlation coefficients within animal on the first four PCs.</p

Diagnosis and genotyping of African swine fever viruses from 2015 outbreaks in Zambia

In early 2015, a highly fatal haemorrhagic disease of domestic pigs resembling African swine fever (ASF) occurred in North Western, Copperbelt, and Lusaka provinces of Zambia. Molecular diagnosis by polymerase chain reaction targeting specific amplification of p72 (B646L) gene of ASF virus (ASFV) was conducted. Fourteen out of 16 domestic pigs from the affected provinces were found to be positive for ASFV. Phylogenetic analyses based on part of the p72 and the complete p54 (E183L) genes revealed that all the ASFVs detected belonged to genotypes I and Id, respectively. Additionally, epidemiological data suggest that the same ASFV spread from Lusaka to other provinces possibly through uncontrolled and/or illegal pig movements. Although the origin of the ASFV that caused outbreaks in domestic pigs in Zambia could not be ascertained, it appears likely that the virus may have emerged from within the country or region, probably from a sylvatic cycle. It is recommended that surveillance of ASF, strict biosecurity, and quarantine measures be imposed in order to prevent further spread and emergence of new ASF outbreaks in Zambia. Keywords: African swine fever; Asfarviridae; Molecular epidemiology; Zambi

The Epidemiology of African Swine Fever in "Nonendemic" Regions of Zambia (1989-2015) : Implications for Disease Prevention and Control.

African swine fever (ASF) is a highly contagious and deadly viral hemorrhagic disease of swine. In Zambia, ASF was first reported in 1912 in Eastern Province and is currently believed to be endemic in that province only. Strict quarantine measures implemented at the Luangwa River Bridge, the only surface outlet from Eastern Province, appeared to be successful in restricting the disease. However, in 1989, an outbreak occurred for the first time outside the endemic province. Sporadic outbreaks have since occurred almost throughout the country. These events have brought into acute focus our limited understanding of the epidemiology of ASF in Zambia. Here, we review the epidemiology of the disease in areas considered nonendemic from 1989 to 2015. Comprehensive sequence analysis conducted on genetic data of ASF viruses (ASFVs) detected in domestic pigs revealed that p72 genotypes I, II, VIII and XIV have been involved in causing ASF outbreaks in swine during the study period. With the exception of the 1989 outbreak, we found no concrete evidence of dissemination of ASFVs from Eastern Province to other parts of the country. Our analyses revealed a complex epidemiology of the disease with a possibility of sylvatic cycle involvement. Trade and/or movement of pigs and their products, both within and across international borders, appear to have been the major factor in ASFV dissemination. Since ASFVs with the potential to cause countrywide and possibly regional outbreaks, could emerge from "nonendemic regions", the current ASF control policy in Zambia requires a dramatic shift to ensure a more sustainable pig industry