Labour profiles and Electronic Identification (EID

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(Aug), post-weaning (Sep), ewe stock draw (Oct) and lamb selection forsales (Oct). For this particular study, labour recording during lambing was deliberately not ...

5th International Symposium forFarming Systems Design7-10 September 2015, Montpellier, France ________________________________________________________________________________________________________________________

Labour profiles and Electronic Identification (EID) technology: assessing different management approaches on extensive sheep farming systems Claire Morgan-Davies∗±1, Nicola Lambe2, Ann McLaren1, Harriet Wishart1, Tony Waterhouse1& Davy McCracken1 1 2

Hill & Mountain Research Centre, Future Farming Systems, Scotland’s Rural College Animal & Veterinary Science, Scotland’s Rural College

Speaker ±

Corresponding author: ([email protected])

1 Introduction Extensive sheep farming systems in marginal areas suffer from climatic and production handicaps (Morgan-Davies et al., 2012). These systems play an important role by providing a source of local skilled labour, even if it is very seasonal(Waterhouse, 1996), and they also have a higher labour requirement in proportion to their total gross margin. The farming population in these areas is also an ageing one, with succession problems and not enough attraction to retain the next generation of farm labour (Madelrieux & Dedieu, 2008). Implementing new technologies on such systems could help rationalise labour requirements and improve farm performance (Olaizola et al., 2008). However, labour data at farm-task level are often not measured, or only assessed on a yearly basis (e.g. Nix, 2014), which does not reflect the seasonal variationof the workload. Assessing labour across the whole sheep production year at task level, and taking into account the variation between different farms, is paramount. This paper presents results from research undertaken on an extensive sheep farm in western Scotland, where yearly labour profiles at task level have been measured and compared for two different sheep managementsystems, one using Electronic Identification (EID) technology, the other following a more conventional approach. Data obtained through questionnaires completed by extensive sheep farmers were also used. 2 Materials and Methods The research was conducted on a 2,200 ha extensive sheep farm in western Scotland. A 900 ewe flock was divided between two systems; with two half-flocks sharing the same pastures, the difference being the use of technology at key handling times to allocate animals to different feeding groups or health treatments. Half of the flock (~450 ewes) was managed using automatic identification, weighing and recording technology (TEC), with each animal identified using Electronic Identification (EID) ear-tags. The other half relied on a more conventional approach (CON), where individuals were identified, weighed and recorded manually. More details of the management systems are available in Umstatter et al. (2013) and Morgan-Davies et al. (2014). Yearly labour profiles were created by measuring the time spent doing each individual task under the two systems. Measurements were carried out using a combination of direct recording (stop-watch and hand-held device for continuous recording (The Observer XT, version 9.0, Noldus Information Technology)) and videos.The individual tasks being measured followed the sheep year calendar and encompassed pre-mating (Nov), post-mating (Jan), scanning/pre-lambing (Mar), marking (Jun), shearing (Jul), weaning (Aug), post-weaning (Sep), ewe stock draw (Oct) and lamb selection forsales (Oct). For this particular study, labour recording during lambing was deliberately not included. Additionaly, a comparision of labour required to do the tasks involved in more typical extensive low-input sheep management systems, with or without automated technology, was carried out. These two additional labour profiles were created using questionnaires with extensive farmers who attended a farm open day. A total of 17 farmers were asked to select the tasks that were routinely carried out on their own farms during the sheep year. Based on the proportions of farmers that selected the different tasks, two more profiles were quantified assuming that the individual measurements were collected using eitherthe CON approach (Trad-CON) or the TEC approach (Trad-TEC). Results (labour in second/animal for each task) were first compiled and scaled up for 100 breeding ewes and 100 lambs, then for a typical large extensive sheep farm, with 1200 ewes and 1000 lambs. 3 Discussion The four labour profiles showed differences during the sheep year (Fig. 1). The most time-consuming periods across the fourprofiles were in June (marking), July (shearing) and October (lamb sales). These periods represented times when all animals were handled (e.g. shearing), or when the tasks needing to be undertaken were numerous (e.g. marking). Selecting lambs for sale was also time-consuming, as animals were handled more than once; generally weighed fortnightly, to check which animals had reached their target weight for sale to an abattoir or market. Conversely, other handling periods, such as October stock draw of the ewes, required minimal handling (just weighing or condition scoring). However, there were differencesbetween the profiles. The labour profiles with technology (TEC and TradTEC) were more labour-efficient than those without (CON andTrad-CON).For 100 breeding ewes, yearly labour use

5th International Symposium forFarming Systems Design7-10 September 2015, Montpellier, France ________________________________________________________________________________________________________________________

was more in CON than TEC by 3 hours 24 minutes. The Trad-TEC profile was also slightly more labour-efficient (2 hours14 minutes) than the Trad-CON one. 90




0 Nov.










Oct. Oct. (ewe) (lamb)


Fig. 1.Yearly labour use (in seconds/animal) for the TEC, CON, Trad-CON and Trad-TEC labour profiles For a large extensive sheep farm with typically 1200 ewes and 1000 lambs, this equated to labour savings of 36 hours 7 minutes for a TEC over a CONprofile and of 23 hours 14 minutes for a Trad-TEC over a Trad-CONprofile. Both TEC profileswere more labour-efficientdue to the automatic weighing, sorting and recording of the animals, compared to the other two profiles where all handlings were done manually. Moreover, the TEC profiles encompassed the selected use of anthelmintic treatment of lambs (Targeted Selective Treatment; Morgan-Davies et al., 2014; Kenyon et al., 2009)in July, August and September. This method, based on individual weight performance of lambs, avoided a blanket treatment approach to anthelmintic use, which, in practice, resulted in less animals being treated, thus saving labour (and anthelmintic costs). Although the Traditional profiles (Trad-TEC and Trad-CON) werecreated based on similar individual task measurements, they were less labour-consuming than the CON and TEC profiles. Indeed, farmers in the sample were undertaking fewertasks at the different time periods.Whilst the CON and TEC profiles were designed to directly compare and benchmark the effect of using technology on an extensive research farm, with a relatively high input management, the Traditional profileswere designed to further represent the inherent variation in husbandry practices within the extensive farmers’ population (Morgan-Davies et al., 2012) and to compare the effect of introducing technology on relatively lower input management sheep farms. 4 Conclusions This research has therefore shown that designing a farming system that incorporates the use of technology could bring potential benefits in terms of labour efficiency, even when the variation in farmers’ practices (high input management or low input management) are taken into account. Provided the initial costs of the associated technology can be met, in addition to potential for improved animal welfare and performance, new technology can help make extensive farming systems more labour efficient and resilient. Acknowledgements. SRUC receives financial support from the Scottish Government’s Rural and Environment Science and Analytical Services Division (RESAS), as part of Programme 1: Environment and Programme 2: Food

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