Context. Lamb loss and dyctocia are two major challenges in extensive farming systems. While visual observation can be impractical due to the large sizes of paddocks, number of animals and high labour cost, wearable sensors can be used to monitor the behaviour of ewes as there might be changes in their activities prior to lambing. This provides sufficient time for the farm manager to nurse those ewes that are at risk of dyctocia. Aim. The objective of this study was to determine whether the behaviour of a pregnant ewe could predict the time of parturition. Methods. Two separate trials were conducted: the first trial (T1), with 32 ewes, included human/video observations, and the second trial (T2), with 165 ewes, conducted with no humans present, to emulate real extensive farming settings. The ewes were fitted with tri-axial accelerometer sensors by means of halters. Three-dimensional movement data were collected for a period of at least 7 and 14 days in T1 and T2 respectively. The sensor units were retrieved, and their data downloaded using ActiGraph software. Ewe behaviour was determined through support vector machine learning (SVM) algorithm, including licking, grazing, rumination, walking, and idling. The behaviours of ewes predicted by analysis of sensor data were compared with behaviours determined using visual observation (video recordings), with time synchronisation to validate the results. Deep learning and neural-network algorithms were used to predict lambing time. Key results. The concordance percentages between visual observation and sensor data were 90 ± 11, 81 ± 15, 95 ± 10, 96 ± 6, and 93 ± 8% ± s.d. for grazing, licking, rumination, idling, and walking respectively. The deep-learning model predicted the time of lambing with 90% confidence via a quantile regression method, which can be interpereted as 90% prediction intervals, and shows that the time of lambing can be predicted with reasonable confidence approximately 240 h before the actual lambing events. Conclusion. It was possible to predict the time of parturition up to 10 days before lambing. Implications. The behaviour of ewes around lambing time has a direct effect on the survival of the lambs and therefore plays an important part in animal management. This knowledge could improve the productivity of sheep and considerably decrease lamb mortality rates.

The objectives of this study were to validate the application of Bluetooth technology to determine maternal pedigree and to determine ewe-lamb spatial relationships in extensive farming systems. A total of 35 first-cross Merino ewes (Merino × Border Leicester and East Friesian) and 23 of their lambs aged 1 to 3 wk were fitted with activity monitors equipped with Bluetooth (BT) technology (ActiGraph wGT3X-BT) by means of halters and collars, respectively. The BT devices on lambs were programmed to receive wireless signals once every minute from nearby BT units on ewes, which were programmed as beacons sending BT signals 4 times every second. Ewes and lambs fitted with sensors were dispatched into the paddocks, and after 10 d, the sensor units were retrieved and the BT signals received by lambs were downloaded using the ActiGraph software. The maternal pedigree of the lambs was determined as the ewe from which the lamb received the most BT signals. The distance between the lamb receiving the signal and the ewe sending the signal was estimated from the strength of BT signal received. The pedigree determined by BT was compared with the pedigree determined by DNA profiling and verification. The results showed that the accuracy of maternal pedigree determined by BT signals reached 100% within the first 15 min of returning animals to pasture of ewes and lambs fitted with sensors. Maternal signals (counts/d) received by 1-, 2-, and 3-wk-old lambs were 617 ± 102, 603 ± 54, and 498 ± 36, respectively, and the corresponding nonmaternal signals received were 140 ± 27, 106 ± 30, and 155 ± 39, respectively. Maternal signals received during the dark period were significantly higher than the maternal signals received during the light period ( < 0.05). Maternal signals received during the light period by 3-wk-old lambs were significantly lower when compared with those received by 1- and 2-wk-old lambs. Over 90% of the BT signals received from within 2 m of the lamb were from its mother. The maternal BT signals expressed as a portion of total BT signals decreased with increasing distance from the lamb. The results show that BT wireless networking is a fast and reliable method for the determination of maternal pedigree of lambs in extensive farming systems. In addition, wireless BT technology is also useful in determining mother-offspring spatial relationships.

Limited research has suggested that higher lambing densities increase interference from foreign ewes at lambing which disrupts the ewe-lamb bond and compromises lamb survival. This may be particularly evident in mobs of twin-bearing ewes compared to single-bearing ewes because a greater number of lambs are born per day. Therefore, we hypothesised that; (i) decreasing the mob size of ewes at lambing has a greater impact on the survival of twin-born lambs than single-born lambs; (ii) the relationship between mob size and lamb survival can be explained by differences in the rate of interaction with foreign ewes and lambs at lambing; and (iii) ewes will utilise a limited area of the paddock at lambing and thus lambing density will be defined by the distribution of ewes in the paddock rather than the paddock area. Merino ewes were allocated into a 2×2 factorial combination of ewe pregnancy status (single- or twin-bearing) and mob size (high (n=130 ewes) or low (n=50 ewes)) on day 140 from the start of joining. Each treatment had two replicates excepting the low mob size for twins which had a third replicate. Ewes lambed at a stocking rate of 11 ewes/ha. Feed-on-offer during lambing exceeded 2400 kg dry matter (DM)/ha. Ewe-lamb behaviour was observed and dead lambs were autopsied over 11 days during the peak of lambing. The distribution of ewes in each paddock was recorded every 2 h during daylight hours by counting the number of ewes occupying 2500 m2 grids. The proportion of ewes and their newborn progeny which interacted with foreign ewes at lambing did not differ between the high and low mob sizes for single- (24.9% v. 20.8%) or twin-bearing ewes (14.3% v. 19.6%; P=0.74). Similarly, interaction with foreign lambs did not differ between the high and low mob sizes for single- (14.5% v. 25.2%) and twin-bearing ewes (34.5% v. 26.4%; P=0.44). The distribution of ewes within the paddock did not differ between treatments (P=0.95). On average, single-bearing ewes which lambed at the high and low mob sizes occupied 34% and 36% of the paddock during daylight hours, and the corresponding values for twin-bearing ewes were 40% and 43%. Survival of twin-born lambs was lower than single-born lambs (75.3% v. 87.9%; P&lt;0.01), however, lamb survival was not influenced by mob size regardless of birth type. These results suggest that higher mob sizes may not compromise lamb survival when feed-on-offer during lambing exceeds 2400 kg DM/ha.

Context. Monitoring the behaviour of animals can provide early warning signs of disease or indicate loss of appetite. Also, an understanding of the variation in behaviours among animals and their distributions is essential for meaningful statistical inference. Therefore, quantifying the variation of behaviours is of both biological and statistical interest. Aim. The objectives of this study were to determine the distributions and quantify the variation among animals with respect to the times spent grazing, ruminating, idling, walking, and licking. Methods. The activities of 147 (male = 67, female = 80) Merino lambs at 10-11 months of age on a commercial farm in Edenhope, Victoria, Australia were recorded for 26 days, using ActiGraph accelerometer sensors attached to the left side of the sheep's muzzle. The male and female sheep were kept in separate paddocks. A Support Vector Machine algorithm was used to differentiate sheep behaviour into six categories: grazing, ruminating, idling, walking, licking, and other activities. The distributions of behaviours were analysed using energy statistics-based tests and Generalised Additive Models for Location, Scale, and Shape (GAMLSS). Different distributions were compared using Akaike Information Criterion (AIC) values. Key results. Among the distributions that were considered, we found that times spent ruminating in both male and female sheep populations as well as idling in male sheep were best described by the skew exponential type 2 distribution. Grazing, walking and licking behaviours were best described by the Box-Cox t distribution. The distribution of time spent grazing was symmetrical and unimodal in males, and adequately modelled by a normal distribution, but the distribution in females had a prominent left skew. Also, we found that females typically grazed for a longer time than males. However, males spent more time ruminating than grazing. Conclusions. The time spent by the animal in each activity varied during the day. Within each population, the variation among animals in the time spent grazing was best described by a Box-Cox t distribution. Implications. This study has enhanced our understanding of grazing behaviour and will facilitate more appropriate analyses of the causes of variation among animals in grazing behaviour.

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