Merino ewe liveweight represents an objective measure shown to have a profound effect on production outcomes and therefore research into technology that provides accurate and timely feedback of ewe liveweight change is warranted. Most sheep in Australia are not individually identified and therefore management of sheep is on a flock or 'mob' basis. Mob-based walk-over weighing (MBWOW) is a remote weighing concept for sheep flocks whereby animal weights are collected as they pass freely over a strategically placed weighing platform. The weights are then collected by the livestock manager, analysed and interpreted to aid nutritional decision making on a whole-flock basis. The hypothesis tested in this study was that data from MBWOW systems is comparable to data from static weighing sessions. At three sites, MBWOW data was collected simultaneously with monthly static weighing sessions. Raw data from MBWOW from each site was manipulated through a series of methodologies that were compared according to their relationship with the static weight data. All forms of MBWOW data showed a significant relationship with static weighing data (P < 0.05). Using a 25% filter (data within 25% of a predetermined central weight kept) and grouping data into 5-day groups strengthens the relationship between MBWOW data and static weighing data. In 1-day groupings, MBWOW data with a 25% filter and subjectively chosen central weight showed the strongest relationship (R² = 0.89) with static weighing data. In 5-day groupings, MBWOW data with a 25% filter and reference weight from a previous weighing event as a central weight showed the strongest relationship (R² = 0.88) to static weighing data. The former MBWOW data manipulation methodology had the least mean numerical difference (±s.d.) between MBWOW data and static weighing data (1.86 ± 0.85 kg), yet the latter had the least mean numerical difference in change-in MBWOW data and change-in static weighing data (1.51 ± 0.39 kg), and as change-in liveweight has the most application to industry, it is recommend as the preferred data manipulation technique. These findings suggest that although MBWOW is not fully congruent with static weighing, a strong relationship (R2 > 0.8) between the two and low mean numerical difference in change-in liveweight indicates that MBWOW has potential to be used to established liveweight profiles for Merino ewes that aid nutritional management.

The ability to monitor average liveweight of a sheep flock provides livestock managers the opportunity to nutritionally manage their flock for higher productivity. Mob-based walk-over weighing (MBWOW) is a remote weighing concept for sheep flocks whereby liveweights are collected as the animals pass freely over a strategically placed weighing platform. We tested the hypothesis that the repeatability and frequency of MBWOW data are sufficient to generate sheep flock average liveweight estimates with a 95% confidence interval (CI) of <2 kg over a 5-day time period. These criteria were considered reasonable, in terms of accuracy and timeliness, for application in a commercial context. Radio frequencyidentifiedWOWdata were obtained from four sheep flocks in south-eastern New South Wales, representing a mix of age and breeds, as sheep traversed a remote weighing platform to and from some form of incentive. The repeatability and frequency of three forms of radio frequency-identified WOW data, being raw (unfiltered), course-filtered (filtered to remove all sheep weights outside the flock weight range) and fine-filtered (filtered to remove all sheep weights outside a 25% range of a recent flock average reference weight), were used in a simulation to test the 95% CI of 1- and 5-dayM BWOW liveweight capture periods (samples). All data-filtering approaches over a 5-day sample generated flock average liveweight estimates with 95% CI of <2 kg, thus meeting the hypothesis criteria. One-day samples generated flock average liveweight estimates with 95% CI of >2 kg and data filtering, although reducing the 95% CI, did not bring it below the hypothesis criteria. Thus, when the appropriate data handling technique is used, MBWOW may provide information suitable for sheep management decisionmaking in a commercial context.

Liveweight is a widely accepted proxy for the energy status of sheep at a particular point in time. Fleece- and conceptus-free ewe liveweight and liveweight change influence the productivity of the ewe and optimisation may increase whole-farm profitability. Despite this, it is uncommon for producers to monitor ewe liveweight regularly and objectively. The current review discusses why ewe liveweight is important, identifies and assesses available technologies for monitoring sheep liveweight, and highlights future research priorities. The common theme in the literature is that while there are options that could possibly be used to monitor the liveweight of sheep in extensive grazing systems, few of them offer realistic solutions, especially in regard to timeliness of data collection. Thermal and stereo imaging, body measurements and plasma hormonal assays are unlikely to be commercially viable, while visual assessment, although widely practised, offers a surprisingly poor indication of sheep liveweight. Alternatively, assessment of body condition (condition scoring) or fat (fat scoring) offers viable methods of assessing sheep energy status; however, like conventional static weighing, they are performed infrequently and therefore contribute little to the day-to-day tactical management of sheep flocks. Walk-over weighing systems offer a feasible alternative for regular monitoring of sheep liveweight. Such systems are fully automated, and may be operated remotely. Currently, there are challenges associated with monitoring the liveweight of individual animals using such systems and hence there is little commercial opportunity for individual animal management. Mob-based walk-over weighing, which generates flock average liveweight estimates, offers greater potential in the short term, although the technology would benefit from further research and development, primarily to increase the frequency and repeatability of liveweight capture.

The association between liveweight and a range of production and economic outcomes has been demonstrated in sheep production systems. Change in ewe liveweight affects her wool production, reproductive performance, survival and lifelong performance of her progeny. Similarly, liveweight in young sheep post-weaning is strongly associated with their survival. This breadth of sheep production parameters with demonstrated association with liveweight suggests that regular liveweight monitoring would provide a robust and versatile tool for managing sheep flocks. Walk-over weighing (WOW) technology has the potential to remotely monitor sheep liveweight either individually or collectively and is commercially available. It functions by collecting liveweight data as sheep voluntarily cross a weighing platform as part of their normal daily routine. The liveweight data is then collected, processed and interpreted by livestock managers to aid nutritional management. Despite the documented benefits of managing ewe liveweight, and the potential of WOW to aid ewe liveweight management, there is a paucity of literature on the subject. This thesis draws on a series of experiments, data analyses and economic models to investigate the factors affecting WOWs potential for commercial application.

Prior exposure of sheep to a novel feed has been shown to expedite the acceptance of that feed later in life. This study was designed to investigate the benefits of early social transmission of feed recognition for productivity and feeding behaviour of sheep in a feedlot. On a research farm near Armidale, Australia, 175 12-week-old Merino × Dorset lambs, together with their dams, were exposed to one of three preweaning treatments: (i) no exposure to feedlot pellets, (ii) offered feedlot pellets on the pasture, or (iii) offered feedlot pellets in feed troughs. The feedlot pellets were offered on two occasions at a rate of 200 g/dam, 1 month before weaning. After weaning, from 18 weeks of age, the lambs were observed for feeding behaviour and their growth during a 50-day feedlot finishing phase. Preweaning exposure to the pellets and the feed delivery system increased the rate of feed acceptance; however, there was no difference in the growth of lambs between the preweaning treatments at the end of the feedlot phase. The difference in percentage of lambs not eating between treatment groups was most pronounced during the first 2 days of the feeding period, with the differences gradually diminishing over the initial week of the feedlot phase. It is considered that differences in feedlot performance due to rate of acceptance of novel feeds are more likely under commercial conditions where pen densities are higher and feed ration transitions may be more rapid.

The present paper covers reproductive performance in an artificial-insemination (AI) program of the Sheep CRC Information Nucleus with 24 699 lambs born at eight locations in southern Australia across five lambings between 2007 and 2011. Results from AI with frozen semen compared well with industry standards for natural mating. Conception rates averaged 72%, and 1.45 lambs were born per ewe pregnant for Merino ewes and 1.67 for crossbreds. Lamb deaths averaged 21% for Merino ewes and 15% for crossbreds and 19%, 22% and 20% for lambs from ewes that were mated to terminal, Merino and maternal sire types, respectively. Net reproductive rates were 82% for Merino ewes and 102% for crossbreds. From 3198 necropsies across 4 years, dystocia and starvation-mismothering accounted for 72% of lamb deaths within 5 days of lambing. Major risk factors for lamb mortality were birth type (single, twin or higher order), birth weight and dam breed. Losses were higher for twin and triplet lambs than for singles and there was greater mortality at relatively lighter and heavier birth weights. We conclude that reproductive rate in this AI program compared favourably with natural mating. Lamb birth weight for optimum survival was in the 4-8-kg range. Crossbred ewes had greater reproductive efficiency than did Merinos.

Sheep liveweight is an indicator of nutritional status, and its measure may be used as an aid to nutritional management. When walk-over weighing (WOW), a remote weighing concept for grazing sheep, is combined with radio frequency identification (RFID), resulting 'RFID-linked WOW' data may enable the liveweight of individual sheep to be tracked over time. We investigated whether RFID-linked WOW data is sufficiently repeatable and frequent to generate individual liveweight estimates with 95% confidence intervals (95% CI) of <2 kg (a sufficient level of error to account for fluctuating gut fill) for a flock within timeframes suitable for management (1-day and 5-day timeframes). Four flocks of sheep were used to generate RFID-linked WOW datasets. RFID-linked WOW data were organised into three groups: raw (unfiltered), coarse filtered (remove all sheep-weights outside the flock's liveweight range), and fine filtered (remove all sheep-weights outside a 25% range of a recent flock average reference liveweight). The repeatability of raw (unfiltered) RFID-linked WOW data was low (0.20), while a coarse (0.46) and fine (0.76) data filter improved repeatability. The 95% CI of raw RFID-linked WOW data was 27 kg, and was decreased by a coarse (11 kg) and fine (6 kg) data filter. Increasing the number of raw, coarse and fine-filtered data points to 190, 30 and 12 sheep-weights, respectively, decreased the 95% CI to <2 kg. The mean cumulative percentage of sheep achieving >11 fine-filtered RFID-linked WOW sheep-weights within a 1-day and 5-day timeframe was 0 and 10%, respectively. The null hypothesis was accepted: RFID-linked WOW data had low repeatability and was unable to generate liveweight estimates with a 95% CI of less than 2 kg within a suitable timeframe. Therefore, at this stage, RFID-linked WOW is not recommended for on-farm decision making of individual sheep.