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Applied Animal Behaviour Science 141 (2012) 1– 8

Contents lists available at SciVerse ScienceDirect

Applied Animal Behaviour Science

jou rna l h om epa ge: www.elsev ier .com/ locate /applan im

oraging paths through vegetation patches for beef cattle inemi-natural pastures

obert J. Orra,∗, Katherine N. Tozerb, Bruce A. Griffitha, Robert A. Championa,ames E. Cooka, Steven Mark Rutterc

Rothamsted Research, North Wyke, Okehampton, Devon EX20 2SB, UKRuakura Research Centre, East Street, Private Bag 3123, Hamilton 3240, New ZealandHarper Adams University College, Newport, Shropshire TF10 8NB, UK

r t i c l e i n f o

rticle history:ccepted 9 July 2012vailable online 27 July 2012

eywords:oraging pathPSommunity typeegetation heightatch content

a b s t r a c t

Grazing semi-natural grasslands may enhance their wildlife faunal diversity and abundanceby modifying the plant species composition through exploiting the dietary choices of thedomestic livestock, yet diet preferences in these grasslands are often poorly understood.We therefore investigated diet selection of beef cattle in semi-natural pastures, grazed atmoderate stocking (MS) or lenient stocking (LS), using the global positioning system (GPS)to track their foraging paths. This was combined with use of an automatic system to recordforaging behaviour in free-ranging ruminants and measurements of vegetation structure(pasture height and plant functional groups) at a given location. The diet selected by eachanimal at each grazing location was then determined from a vegetation map surveyedusing GPS along with a geographical information system (GIS). Patches containing clovercomprised only 17.5% and 3.1% cover in the MS and LS swards, respectively. Clover wasalso mostly distributed in the sward as singular patches, rather than as larger aggregated

clumps and it appeared that these species were not as strongly selected as has been reportedfor less heterogeneous pastures where they are more prevalent. This study demonstratedthat GPS tracking of livestock foraging paths could be combined with vegetation mappingand automatic grazing behaviour measurements to investigate diet preference in complex,heterogeneous swards.

© 2012 Elsevier B.V. All rights reserved.

. Introduction

Understanding grazing behaviour of livestock in struc-urally complex, biodiverse swards is relatively poorlynderstood (Rook and Tallowin, 2003) compared withpecies-poor swards where it has been shown that swardtate (e.g. Orr et al., 2004) and spatial heterogeneity (e.g.

hampion et al., 2004) can affect grazing behaviour andiet selection. Methodological challenges need to be over-ome if a sound understanding is to be achieved of the role

∗ Corresponding author. Tel.: +44 1837 883546; fax: +44 1837 82139.E-mail address: robert.orr@rothamsted.ac.uk (R.J. Orr).

168-1591/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.applanim.2012.07.003

foraging livestock play in promoting and maintaining floraland faunal biodiversity in semi-natural grasslands (Allenet al., 2011). These landscapes often have a high degree ofspatial heterogeneity (Hofmann and Tallowin, 2004) andprovide rich habitats for invertebrates and birds. The suit-ability of these habitats will be influenced by the stockingintensity of the grazing livestock which, along with live-stock breed (Rook et al., 2004a), is a key managementvariable influencing the structure and composition of pas-tures (Dumont et al., 2007). Generally, a lenient stocking

rate is thought to enhance faunal diversity by improvingthe sward structure and botanical diversity (Tallowin et al.,2005), although in some cases a moderate stocking rate hasalso been shown to increase biodiversity (Adler et al., 2001).

l Behavi

2 R.J. Orr et al. / Applied Anima

In a 3-year study, Dumont et al. (2007) measured diet selec-tion in pastures grazed at moderate or lenient intensity andfound that cattle showed a more pronounced selection forshort patches (<6 cm) at the lenient grazing intensity. Theseauthors suggested that this more-frequent exploitation ofwell-grazed areas of higher nutritional value was likely toreinforce the spatial heterogeneity of vegetation over thegrazing season.

Global positioning system (GPS) technologies (Rutteret al., 1997a) have been used in ecological and behaviouralstudies to track animal movements in grasslands (e.g.Ganskopp and Bohnert, 2009) and enable data collectionon a finer temporal and spatial scale over 24 h than canbe achieved by human observation alone (e.g. Sickel et al.,2004). The grazing animal’s location can then be com-bined with grazing behaviour recordings (which identifyeating bouts) and these data can then be overlaid upon ageographical information system (GIS) map on which thevegetation has been geographically referenced. Here, weexamined whether these techniques could be used to gainan understanding of cattle foraging behaviour. Vegetationpatch contents were measured along with foraging pathsfor beef cattle in order to estimate diet selection among cat-tle grazing semi-natural pastures. These pastures had beenused in the preceding 3 years by Dumont et al. (2007) aspart of a larger, multi-site study.

2. Materials and methods

2.1. Experimental design

The experiment was conducted between 27 June and22 July, 2005 at a site in SW England (50◦46′N, 3◦54′W)as part of a larger study on the ingestion and excretionof nitrogen and phosphorus by grazing beef cattle (Orret al., 2012). Semi-natural swards comprised of species-poor mesotrophic communities with Agrostis stolonifera L.and Holcus lanatus L. present as main components alongwith small (<5%) localised areas of Juncus effusus L. classi-fied as MG6 (Lolium perenne–Cynosurus cristatus) grasslandcommunities under the UK National Vegetation Classifica-tion (Rodwell, 1992) were grazed.

2.2. Treatments

Six groups of beef cattle were stocked at two inten-sities with three replicate paddocks of each intensityfrom 12 May, 2005 using continuous variable stocking.These grazing intensities were designed to either utiliseherbage growth for optimum livestock production (mod-erate stocking, MS) or to increase biodiversity by not fullyutilising herbage growth (lenient stocking, LS). Simmen-tal × Holstein Friesian cattle (2 steers and 2 heifers pergroup) were used, which were born in August 2003. Addi-tional similar spare steers and heifers were available asplot grazers, when required in the put-and-take system,

to maintain the grazing intensities. For the MS and LStreatments, respectively, target compressed sward heights(CSH) measured using a rising platemeter (Ashgrove Pas-toral Products, R.D. 10, Palmerston North, New Zealand)

our Science 141 (2012) 1– 8

were 7–8 cm and 9–11 cm, respectively, based on previousdata for this site (Dumont et al., 2007).

Measurements of animal location and grazing activitywere made to determine foraging paths in tests conductedover 4 consecutive days on the 2 heifers in each group in1.5 ha paddocks which had been surveyed for vegetationcover as detailed below. Measurements were not madeon the steers and these were temporarily moved to adja-cent spare areas, which had the same CSH as the treatmentareas, for each block in sequence.

2.3. Vegetation mapping

The spatial layout of vegetation was mapped onthe MS and LS treatments in each of the 3 blocks insequence on 27 June, 11 July and 18 July, respectively.Sub-metre precision GPS receivers (Trimble® GeoXT, Trim-ble Navigation Limited, Sunnyvale, CA, USA) operatingwith TerraSyncTM software (TerraSync Inc., Burlington,MA, USA) were positioned in each treatment at approx-imately 4 m intervals, using marked tapes as a guide.These were stretched across the paddocks and gradu-ally moved at approximately 4 m intervals to achievea sampling grid. The GPS receivers logged the actualcoordinates at each of 1000 measurement waypoints ineach paddock. A translucent disc was suspended directlybeneath the receiver which defined a target samplingpatch with an area of 36 cm2, intended to be equiva-lent to the bite area of a yearling heifer as measured byRook et al. (2004b). Records were made at each waypointof: community type (grass or rush dominance); vegeta-tion height (tall: ≥6 cm or short: <6 cm) and patch content(predominantly grass; predominantly clover with somegrass; predominantly forb with some grass; or a mix-ture of forbs, clover and grass) and stored in the GPSreceiver.

2.4. Animal measurements

Measurements of animal location and activity com-menced on the MS and LS treatments in each of the 3treatment replicates in sequence on 28 June, 12 July and19 July, respectively. On each of these dates vegetation andanimal measurements were made on a MS replicate and aLS replicate simultaneously. The heifers were trained in thepreceding week to become accustomed to carrying a shoul-der harness (http://www.korecgroup.com/blog/?p=560)onto which the same GPS receivers used for vegeta-tion mapping were secured, enabling their position to berecorded at 25 cm precision once per second. In addi-tion, jaw movements were measured using automaticbehaviour recorders (Rutter et al., 1997b) which mea-sured eating. The measurements were conducted oneach of the 2 heifers in a pair in sequence in three 8-h periods (06.00–14.00 h, 22.00–06.00 h, 14.00–22.00 h)over 48 h. Hence, recordings of grazing behaviour were

interspersed with 8-h periods when no GPS recordingoccurred because the GPS receivers had batteries whichprovided 8 h operating time and required 8 h for recharg-ing.

l Behaviour Science 141 (2012) 1– 8 3

2

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Fig. 1. An example path (•) for a heifer grazing through tall grass (), short grass (�), tall rush ( ) and short rush (�) vegetation com-munities between 06.00 and 22.00 h. Paddock size = 100 m × 150 m, withapproximately 1000 vegetation sample points measured in a grid with4 m spacing. Data is only shown for grazing and not for walking, idling or

R.J. Orr et al. / Applied Anima

.5. Data manipulation

Vegetation data were exported from the GPS receivernto Trimble® (www.trimble.com) GPS Pathfinder Office®

nd differentially corrected using Taunton, UK base sta-ion data from the GB Ordnance Survey active GPS networkwww.ordnancesurvey.co.uk). Data were then importednto ArcGIS (ESRI® 9.1, www.esri.com) and a vegetation

ap of each paddock was created from the data pointssing Thiessen polygons. Boundaries of these polygonsere an equal distance between two adjacent points

nd optimised the allocation of the paddock surface areanto cells approximately similar to the 4 m × 4 m gridquares.

ArcGIS was also used to obtain the proportions of theifferent vegetation components present and the aggrega-ion of clover-dominant, forb-dominant and grass, clovernd forb patches in the LS and MS treatments. The aggre-ation level of vegetation was determined as the numberf adjacent polygons which had at least one side or cor-er in common. For example, an aggregation level of oneenoted that a patch of a specified vegetation type was iso-

ated, whilst an aggregation level of 2 denoted that thereere 2 adjacent patches of the same vegetation type.

The GPS data among the cattle locations were importednto Trimble® GPS Pathfinder Office® software, differ-ntially corrected and imported into Microsoft Excel. Inddition, grazing behaviour data were analysed using

Graze’ software (Rutter, 2000) which differentiates ani-als’ individual jaw movements associated with eating

rom ruminating, drinking, grooming and vocalising. Peri-ds of eating were identified. These were joined intoeals using a 1-min inter-meal criterion. These compiled

ata sets were then imported into Microsoft Excel. Cat-le location and grazing behaviour data for each secondere combined to determine the locations where foraging

ccurred and these data were exported into ArcGIS.The animal data were then merged with the vegetation

ap in ArcGIS to estimate the community type, vegeta-ion height and patch content during foraging. From theseombined data sets the time spent foraging by each heifermong the different vegetation types was determined. Aap of the foraging path for a heifer within the tall grass,

hort grass, tall rush and short rush vegetation communi-ies is given in Fig. 1.

Data for the two heifers in each replicate paddock wereombined. For each 8-h period, total grazing time (min 8 h1), percentage grazing time (min spent grazing in a vegeta-ion class expressed as a percentage of the total time spentrazing for each 8-h period), cumulative grazing time (themount of time (min) spent grazing in each hour expresseds a cumulative percentage) and the diet selection indexere calculated. Diet selection, defined as the proportion

f a sward type in the diet relative to its proportion in theaddock, was quantified by calculating selectivity indexesSi) for each sward component using Jacobs’ (1974) modi-cation of Ivlev’s electivity index:

i = ci − ai

ci + ai − 2ci ai,

other activities.

where ci is the proportion (between 0 and 1) ofcomponent i in the diet, and ai the proportion (between0 and 1) of component i in the paddock. Si varies from −1(totally avoided) to +1 (totally selected). Therefore negativevalues indicate avoidance, positive values indicate prefer-ence and 0 indicates that a vegetation category is grazed inproportion to its availability.

2.6. Statistical analysis

Analyses of the effects of grazing intensity were carriedout using one-way ANOVA in GenStat (www.vsni.co.uk).The percentage cover of vegetation components (commu-nity type and vegetation height) and patch content of cloverdominant, forb dominant and clover, forb and grass patcheswere analysed, along with the effects of stocking intensityon grazing time and the selection index among the dif-ferent vegetation components. As there was little grazing

measured during the night, only cumulative grazing databetween 07.00 and 23.00 h were used.

4 R.J. Orr et al. / Applied Animal Behaviour Science 141 (2012) 1– 8

Table 1Percentage cover predicted from the GPS survey of vegetation community types, vegetation heights and patch contents in semi-natural pastures grazedby beef heifers at moderate or lenient stocking intensity.

Stocking intensity s.e.d. F prob.

Moderate Lenient

Community typeGrass 76.8 91.6 23.5 0.592Rush 23.2 8.4

Vegetation heightTall (≥6 cm) 73.1 88.6 4.67 0.080Short (<6 cm) 26.9 11.4

Community type × vegetation heightTall grass 56.0 80.5 23.4 0.404Short grass 20.8 11.1 0.51 0.003Tall rush 17.1 8.1 18.8 0.677Short rush 6.1 0.3 4.84 0.353

Patch contentGrass 68.2 92.7 11.16 0.159

Clover-dominant 14.8

Forb-dominant 14.3

Grass, clover and forb 2.7

3. Results

Mean compressed sward heights for the MS andLS treatments were 8.1 and 10.3 cm (s.e.d. 0.22; Fprob. = 0.010), respectively, which were close to orwithin the target height ranges (7–8 cm and 9–11 cm,respectively).

3.1. Vegetation cover

Grazing intensity, did not significantly affect (Fprob. = >0.05) the percentage vegetation cover (Table 1) inthe grass or rush community types (F prob. = 0.592). Whilstthere tended to be a higher percentage cover of tall patchesin the LS treatments (88.6% vs. 73.1%), it only approachedsignificance (F prob. = 0.080).

Short grass cover was significantly greater in the MSthan the LS treatment (20.8% vs. 11.1%; F prob. = 0.003),respectively. The percentage cover of potential ‘bites’ clas-

sified as patches containing predominantly grass, clover,forb or a mixture of the three was not significantly differ-ent between the grazing intensities and in both cases, mostpatches contained predominantly grass.

Table 2Aggregation level (number of patches in an aggregate) of clover-dominant, forb-dopastures grazed by beef heifers at moderate or lenient stocking intensity.

Patch content Stocking intensity Aggregation level

1 2 3 4

Clover-dominant Moderate 26.7 8.7 3.3 1.7

Lenient 14.3 2.7 0.7 0.3

Forb-dominant Moderate 18.0 4.7 0 0

Lenient 16.7 4.7 1.3 1.3

Grass, clover and forb Moderate 18.0 4.7 0 0

Lenient 1.3 0 0.3 0

† s.e.d. for grazing intensity × aggregation level means.

2.9 6.72 0.2194.1 3.45 0.0990.2 1.42 0.220

3.2. Patch aggregation

Clover dominant (F prob. = <0.001), forb dominant(F prob. = <0.001) and grass + clover + forb patches (Fprob. = 0.001) occurred predominantly as single patchesin both MS and LS grazing treatments (Table 2). Therewas an interaction between grazing treatment and aggre-gation level for clover dominant (F prob. = 0.040), andgrass + clover + forb patches (F prob. = 0.006). However,there were more clover dominant patches in MS thanLS paddocks when they occurred singularly (n = 1) or asadjacent patches (n = 2). Also more grass + clover + forbsoccurred as single (n = 1) than multiple (n > 1) patches.There was no significant difference between MS and LS pad-docks for clover dominant, or grass + clover + forb patchesat higher levels of aggregation.

3.3. Grazing time

Whilst the cattle spent 545 vs. 427 ± s.e.d. 110.5 min

24 h−1 grazing the MS and LS treatments, respectively, thisdifference was not significant (F prob. = 0.396). There wasalso no significant effect (F prob. = >0.05) of grazing inten-sity on the time spent grazing the different vegetation

minant and a combination of grass, clover and forb patches in semi-natural

s.e.d.† F prob.

5 >5 Grazingintensity

Aggregationlevel

Grazingintensity ×aggregationlevel

1.3 5.32.50 <0.001 <0.001 0.0400.3 0.3

0 04.07 0.644 <0.001 0.9840.3 3.0

0 03.20 0.014 0.001 0.0060 0

R.J. Orr et al. / Applied Animal Behaviour Science 141 (2012) 1– 8 5

Table 3Time (min 24 h −1)† spent grazing different vegetation community types, vegetation heights and patch contents by beef heifers at moderate or lenientstocking intensity in semi-natural pastures.

Stocking intensity s.e.d. F prob.

Moderate Lenient

Community typeGrass 417 400 108.3 0.887Rush 128 27 126.9 0.510

Vegetation heightTall (≥6 cm) 386 350 60.1 0.615Short (<6 cm) 160 77 68.9 0.352

Community type × vegetation heightTall grass 298 324 109.1 0.829Short grass 121 75 35.5 0.326Tall rush 88 26 90.5 0.562Short rush 38 1 37.7 0.431

Patch contentGrass 378 389 59.7 0.878Clover-dominant 85 18 47.6 0.290Forb-dominant 65 19 32.5 0.290Grass, clover and forb 16 1 8.6 0.234

measured during the night.

(225Mrtbia

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Fig. 2. Effect of community type × vegetation height on time (min h−1)

† Data between 07.00 and 23.00 h were used as there was little grazing

Table 3) communities (mean grass 409 vs. rush 77 min4 h−1); vegetation heights (mean tall 368 vs. short 118 min4 h−1); or patch contents (mean grass 384, grass + clover1, grass + forb 42 or grass + clover + forb 9 min 24 h−1).uch less grazing occurred in tall rush (57 min) and short

ush (20 min) than tall or short grass communities overhe 24 h period (Table 3) and the distribution of grazingetween the community types × vegetation heights is shown

n Fig. 2. Grazing time peaked in the morning between 08.00nd 09.00 h and in the evening between 21.00 and 22.00 h.

There was no significant difference between MS and LSaddocks for the time spent grazing grass patches (mean84 min 24 h−1), clover dominant (mean 52 min 24 h−1),orb dominant (mean 42 min 24 h−1) or grass + clover + forbatches (mean 8 min 24 h−1) and the distribution of graz-

ng between the different patch types is shown in Fig. 3here the predominance of grazed grass patches can be

een.

.4. Jacobs’ selection index

Grazing intensity did not significantly (F prob. = >0.05)ffect Jacobs’ selection index (Table 4) for grass (meanndex 0.59) or rush communities and the latter werevoided (mean index −0.62) in relation to their availabil-ty Similarly, grazing intensity did not affect selection forhe two classes of vegetation height; the cattle tended toelect tall patches (mean index 0.03) in preference to shortatches (mean index −0.04).

There was a slight tendency (F prob. = 0.066) for the cat-le grazing the LS treatments to avoid the tall grass patchesn relation to their availability to a greater extent than didhose grazing the MS treatments. The heifers grazing the

S treatments also tended (F prob. = 0.056) to select thehort grass patches more in relation to their availabilityhan those grazing the MS treatments. Overall, tall rushatches were avoided and heifers spent insufficient time

spent grazing in tall grass ( ), short grass (�), tall rush ( ) and shortrush (�) vegetation communities by beef heifers at (a) moderate or (b)lenient stocking intensity.

grazing short rush in the LS treatments to enable selection

to be calculated.

Whilst there was evidence of preference for grazingfrom grass patches more than were available (mean index0.22), the clover- and forb-dominant patches were selected

6 R.J. Orr et al. / Applied Animal Behaviour Science 141 (2012) 1– 8

Table 4Jacobs’ selection index for different vegetation community types, vegetation heights and patch contents by beef heifers grazing semi-natural pastures atmoderate or lenient stocking intensity observed between 06.00 h and 22.00 h.

Stocking intensity s.e.d. F prob.

Moderate Lenient

Community typeGrass 0.46 0.72 0.194 0.231Rush −0.51 −0.74 0.194 0.264

Vegetation heightTall (≥6 cm) −0.02 0.08 0.154 0.545Short (<6 cm) 0.00 −0.08 0.145 0.611

Community type × vegetation heightTall grass −0.49 −0.89 0.190 0.066Short grass 0.47 0.90 0.193 0.056Tall rush −0.86 −0.98 0.071 0.134Short rush† – – – –

Patch contentGrass 0.09 0.34 0.185 0.224Clover-dominant −0.16 −0.52 0.141 0.033Forb-dominant −0.24

Grass, clover and forb −0.40

† There was insufficient grazing on short rush by cattle foraging at lenient inte

Fig. 3. Effect of patch content on time (min h−1) spent grazing grass (), predominantly clover (�), predominantly forb ( ) or a mixture of

ing as leaf/stem ratios changed during rotational grazing

grass + clover + forb (�) patches by beef heifers at (a) moderate or (b)lenient stocking intensity.

less than their availability (mean index −0.34 and −0.35,respectively) as were mixed grass + clover + forb patches

(mean index −0.62). These trends were more stronglyexpressed (Table 4) by heifers grazing the LS treatmentcompared to the MS treatment.

−0.46 0.193 0.293−0.84 0.157 0.023

nsity to enable this index to be calculated.

4. Discussion

4.1. Vegetation cover and patch aggregation

Short grass cover was significantly greater on the MStreatment (20.8%) than the LS treatment (11.1%). Wallis deVries and Daleboudt (1994) suggested grazing cattle wouldbe more likely to select short grass patches due to theirhigher nutritional value.

At this site, patches containing clover and forbs wererare and scattered throughout the sward. Clover and forbcover in the patches in the paddocks used were shownto have declined between 2002 and 2004 (Scimone et al.,2007), especially in the LS treatment, whilst the cover of L.perenne and other grasses increased. By 2005, patches con-taining clover comprised only 17.5% and 3.1% cover in theMS and LS swards, respectively. Clover was found in thesward in singular patches, rather than as larger aggregatedclumps (Table 2). Free-ranging livestock successfully selectfood when it is aggregated (Parsons and Dumont, 2003) yetpreferred vegetation may be selected sparingly because ofthe cost of foraging (Wallis de Vries and Daleboudt, 1994).Given the small amount of clover in these swards and itslack of aggregation, the cost of foraging would probably behigh, resulting in fewer clover and forb dominant patchesbeing selected.

4.2. Time spent grazing

Overall, the heifers spent 485 ± s.e.m. 78.1 min 24 h−1

grazing these semi-natural swards which is somewhatlower than that reported for beef cattle when rotation-ally grazed on fertilised perennial ryegrass swards (530 min24 h−1; Orr et al., 2005). Earlier, Orr et al. (2004) foundthat there was no significant change in time spent eat-

of perennial ryegrass swards.We found the patterns of time spent grazing within

hours (Figs. 2 and 3) were as expected with peaks around

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R.J. Orr et al. / Applied Anima

usk. Cattle are mainly diurnal foragers, with crepusculardawn and dusk) peaks in activity which may have evolveds an anti-predator based behaviour (Rutter, 2006). Cattleend to avoid nocturnal grazing, so need to fill up in the

orning (as their rumen/gut will be emptying) and thenll up again at night (to minimise the need to graze in theark). This pattern of grazing behaviour also reflected likelyhanges in the water soluble carbohydrate and starch con-ent of the constituent grasses and legumes in the swardsuring the photoperiod as a result of the accumulation ofhe products of photosynthesis (Orr et al., 1997).

When grazing, cattle spent a greater amount of timen grass than in a rush community, and so we can inferhey were showing a preference for grass rather than rushpecies. Whilst grass, clover and forbs were present in rushommunities, cattle would need to search for these speciesmong the rush and presumably foraging for them woulde much easier in the grass communities. A greater prefer-nce for grass than rush is further supported by the positiveelection indices for grazing in grass communities and aegative selection indices for grazing in rush communities.revious research from this site found that cattle avoidedush and reproductive grass as plants matured (Dumontt al., 2007).

Heifers grazing the MS treatment tended to spendonger grazing short grass than did those on the LS treat-

ent (Table 3) but as the percentage short grass coveras higher on the MS treatment (Table 1) the selection

ndex for short grass was not as high as that for the heifersrazing the LS treatment (Table 4). Dumont et al. (2007)howed that selection of short vegetation, legumes andorbs were higher and selection of tall grass was lowern the MS than the LS treatment in previous years. Garciat al. (2005) recognised grazing herbivores make a trade-offetween quality and the quantity when it comes to vege-ation intake.

.3. Diurnal patterns in diet selection

The preference of livestock grazing mixed grass/legumewards for a diet containing approximately 70% whitelover (Trifolium repens) and 30% grass (L. perenne), with

higher proportion of clover in the diet in the morning,s well documented (Rutter, 2006). It appeared (Fig. 2)here was increased selection for tall grass and short rushommunities later in the day. This supports the hypoth-sis that livestock show a greater preference for a dietigher in crude fibre as the evening approaches (Rutter,006). Meals high in fibre require longer to digest and itas been hypothesised that this will minimise the need foright-time foraging especially if there is a perceived dan-er of increased predation risk (Rutter, 2006). Dumont et al.1995) found that when ewes were given access to veg-tative or reproductive swards of Dactylis glomerata, thereference for reproductive grass material, which had aigher crude fibre content, increased in the evening. Sim-

larly, a greater grass than clover intake in the eveningould reduce the rate of passage through the gut and lend

upport to this anti-predation hypothesis (Rutter et al.,004).

our Science 141 (2012) 1– 8 7

A relative lack of precision in the scale of the vegeta-tion mapping may explain why there was no detectabledecrease in the time spent grazing clover or forb dominantpatches (Fig. 3) towards the evening. In this study therewas even some avoidance of clover and forbs, which is incontrast to previous research at this site which showedthat livestock selected bites containing legumes and forbs(Dumont et al., 2007).

5. Conclusions

Heifers grazing in complex, heterogeneous semi-natural pastures showed similar trends to those reportedin more-simple L. perenne/T. repens pastures displaying adiurnal pattern for grazing with selection for shorter, moredigestible material, and tending to select material of highercrude fibre content in the evening. However, in these het-erogeneous swards, where the clover and forb cover wasscarce and the level of aggregation of these plants waslow, it appeared that these species were not as stronglyselected as reported in less heterogeneous pastures wherethey were more prevalent. It is possible that the methodused and relatively coarse scale of vegetation mappingmay have been inadequate for preferences for clover andforbs to be detected. Despite this, this study demonstratedhow GPS tracking can be combined with vegetation map-ping and automatic grazing behaviour measurements toinvestigate diet selection in more complex, heterogeneousswards, which is difficult to resource and achieve by man-ual observation.

Acknowledgements

This work was supported by the UK Biotechnology andBiological Sciences Research Council and was carried outin accordance with the welfare standards approved byRothamsted Research, North Wyke’s Ethical Review Proce-dure. Katherine Tozer was supported by the UK StapledonMemorial Trust’s travelling fellowship during this study.

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