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Seasonal space-time dynamics of cattle behavior and mobility among Maasai pastoralists in semi-arid Kenya

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Seasonal space-time dynamics of cattle behavior and mobility among Maasai pastoralists in semi-arid Kenya
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  Seasonal space-time dynamics of cattle behavior and mobility among Maasaipastoralists in semi-arid Kenya B. Butt a , b , * a Department of Geography, University of Wisconsin - Madison, 160 Science Hall, 550 N. Park Street, Madison, WI 53706, USA b People, Livestock and Environment Theme, International Livestock Research Institute, PO Box 30709, Nairobi 0100, Kenya a r t i c l e i n f o  Article history: Received 26 April 2008Received in revised form16 September 2009Accepted 17 September 2009Available online 23 October 2009 Keywords: BehaviorCattleCultural ecologyGPSHerd mobilityPastoralismSeasonalityTrackingZebu a b s t r a c t This study was conducted to understand how seasonality in drylands influences the space–timedynamics of cattle behavior and mobility among pastoralist managed Zebu cattle. The study relies on theuse of handmade collars holding global positioning system (GPS) units to document the spatially andtemporally explicit patterns of cattle mobility, field based herd-follows to document cattle behavior, andkey informant interviews to document the role of pastoral herding strategies in explaining thesepatterns. Data were analyzed as a function of seasonality, distance from household, time of day, and landcover zone. During the dry season, there was an unexpectedly high frequency of grazing/walking cattlebehavior. This pattern is attributed to ‘tracking’ strategies of Maasai pastoralists resulting in movement toniche grazing areas. During the wet season a bimodal distribution of grazing behavior can be attributedto milking strategies. The study concludes that simple, low cost GPS collars are an effective and easilyreplicable method to help understand the spatial and temporal dynamics of cattle behavior and mobility,and patterns of cattle mobility are related to seasonal constraints. Differences between different cattlebehaviors can be partly explained by cultural herding practices of Maasai pastoralists.   2009 Elsevier Ltd. All rights reserved. 1. Introduction Pastoralism is considered the most important and sustainablelivelihoodsystem in the world’s arid and semi-arid lands(Sandford,1983). More than half of the world’s pastoralists are in Africa (Reidetal.,2008),andwithinpartsofEastAfrica,wherearidandsemi-aridlands encompass up to 60% of the land area, pastoralism is thedominant livelihood system (Bourn and Blench, 1999). Pastoralistswho reside within arid lands employ a range of adaptive strategiesthat facilitate mobility and avert risk in the face of spatial andtemporalvariabilityofenvironmentalresources(Behnkeetal.,1993;Scoones, 1994a; Niamir-Fuller, 1998). These strategies include, butarenotlimitedto,thepoolingoflaborresources(Sieff,1997;Turner,1999), continuous tracking of resources (Niamir-Fuller, 1999), accessing key resource areas (Scoones, 1991; Ngugi and Conant,2008), and the use of culturally designated grazing reserves (Ruttanand Mulder, 1999). Pastoral livestock micro-mobility, in particular,has been used to infer how pastoralists cope with environmentalvariability by documenting seasonal patterns of forage intake andenergyexpenditure(Coppocketal.,1986;WesternandFinch,1986).Geographically oriented social scientific research on pastoralcoping strategies has traditionally focused on understanding thesocial, political, economic and ecological processes of access to, andutilization of, resources in arid and semi-arid lands (Bassett andZimmerer, 2003). This body of research typically suffers from inat-tention to the non-human agents that make these processespossible (that is, the type of livestock species itself) and a lack of understanding of the varied ethological aspects of livestock. Alter-natively, natural science researchers have suggested that focusingon animal behavior allows for greater understanding of how foragemaximizationandenergyexpenditureminimizationofcattlecanbeachieved (Vavra and Ganskopp, 1998). This may be done either bydocumenting the time dedicated to grazing activities, which can beused as a proxy for forage intake (Coppolillo, 2000; Schlecht et al.,2004), or by measuring the distance traveled by livestock froma pastoral household, which is an indicator of energy expenditureand how far away adequate forage and water resources are located(Western and Finch, 1986; Semenye, 1987; Coppolillo, 2000).However, this type of research is often devoid of a context-specificset of socio-ecological and cultural pastoral herding strategies. *  Correspondence to: Department of Geography, University of Wisconsin -Madison,160 Science Hall, 550 N. Park Street, Madison, WI 53706, USA. Tel.: þ 1608262 2138; fax:  þ 1 608 265 3991. E-mail addresses:  bilal@geography.wisc.edu, b.butt@cgiar.org Contents lists available at ScienceDirect  Journal of Arid Environments journal homepage: www.elsevier.com/locate/jaridenv 0140-1963/$ – see front matter    2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.jaridenv.2009.09.025  Journal of Arid Environments 74 (2010) 403–413  Pastoralistsarehighlydependentonresourceavailability,whichis driven by precipitation (Ellis and Galvin, 1994). Precipitation inEast Africa is spatially and temporally heterogeneous, and isa function of the north–south movement of the Inter-TropicalConvergence Zone (ITCZ) (Norton-Griffiths et al., 1975). Inter-seasonalprecipitationisoneofthemainfactorsinfluencingherdingstrategies, which are manifested through cattle mobility andbehavior in various ways. During the wet season, precipitationincreases soil moisture, stimulates dormant seeds, and enhancesgrowth of perennial grasses (O’Connor and Everson,1998). Herdersarethoughttorespondtoincreasedresourceavailabilitybykeepingcattle as close to the household as possible, which reduces theenergyexpended bycattle, increases forageintake, and reduces theamount of labor required for herding (Coppolillo, 2000). Suchastrategywouldbeillustratedbya higherfrequencyofgrazinganda lower frequency of walking behaviors. During the dry season thistrend is expected to reverse, as forage resources are likely todecrease in both quality and quantity. Consequently, herders directcattletoforageavailablespacesfurtherfromthehouseholdandthefrequency of walking behavior is theorized to be higher whilegrazing behavior is lower, resulting in higher energy expenditureand lower forage intake (De Boer and Prins, 1989; Samuels et al.,2007). At the same time, pastoralists are known to rely on a keysocio-ecological strategy known as tracking. Niamir-Fuller andTurner (1999: 37) suggest that tracking occurs when, ‘‘ . herdersscout and track ecologicalvariability, both spatiallyand temporally,by constant monitoring, and adjust(ing) the behavior of theiranimals.’’ This strategy ensures that pastoralists are able to accessniche areas and take advantage of ecological heterogeneity on thelandscape (Behnke et al.,1993; Niamir-Fuller, 1998). However, fewempirical studies have sought to document the explicit spatial,temporal, and behavioral manifestations of tracking in traditionalpastoral herding practices, and the extent to which these practicesdiffer under seasonal constraints (Niamir-Fuller,1999).The purpose of this paper is therefore to address previousshortcomingsofresearchonpastoralcopingstrategiesbyprovidinga more robust geographical and behavioral interpretation of pastorallivestockmicro-mobility. This is donethrough a casestudyofMaasaipastoralistsandtheircattleinEastAfrica,andis executedby testing two hypotheses that postulate how relationshipsbetween cattle mobility and behavior vary seasonally.H 1  The total distance and duration of cattle mobility in the wetseason is less than during the dry season.H 2a  The frequency of grazing behavior is greater than walkingbehavior in the wet season.H 2b  The frequency of walking behavior is greater than grazingbehavior in the dry season.These hypotheses are empirically evaluated by combining fieldbasedtechniquesdocumentingcattlebehaviorwithexplicitspace–time tracking of cattle mobility, and supplemented by key infor-mantinterviewswithherdersforapastoralareainsouthernKenya.These data allow for more accurate assessments on the degreeto which inter-seasonal variability influences the grazing strategiesof pastoralists. These datawould also provide a unique opportunityto understand how pastoral livelihood systems and herding strat-egies are likely to be affected by: (1) climate change, given thatcurrent projections indicate that the climate in East Africa willwitness greater variability both within and between seasons(Christensen et al.,2007;McSweeneyetal., 2007); and, (2)pastoralsedentarization,giventhattrendsindicatefurtherindividualizationof tenure and reduced livestock mobility (Rutten,1992; Fernandez-Gimenez and Le Febre, 2006). Fig.1.  Study site in national and local contexts. Map 1: Kenya, showing the study region in south western Kenyawithin the Narok district of Rift Valley Province. Map 2: the MaasaiMara National Reserve (MMNR) along the Kenya–Tanzania border. Map 3: the study site, showing the Talek village center, perennial rivers, seasonal streams, all Maasai householdsand 100 m contour intervals. B. Butt / Journal of Arid Environments 74 (2010) 403–413 404  2. Materials and methods ThesiteselectedforthisstudyistheTalekarea(700 km 2 ),locatedinNarokdistrict,Kenya(1  26 0 S,35  12 0 E;Fig.1),immediatelynorthoftheborderwiththeMaasaiMaraNationalReserve.TheTalekareawas selected because it is: (1) biophysically representative of thelarger Serengeti–Mara ecosystem that characterizes a wide expanseofaridandsemi-aridlandsinsouthernKenyaandnorthernTanzania(Sinclair and Arcese, 1995); (2) socio-culturally and economicallyrepresentative of areas where traditional pastoral herding occurswidelyinEastAfrica(Homewoodetal.,2009);and(3)ithasbeenthesite of long-term geographical research by the author.  2.1. Study site: physical environment  There is a single long dry season between May and August/September.Themajorityofprecipitationfallsduringthe‘shortrains’(between November and January), and ‘long rains’ (between MarchandMay)(Norton-Griffithsetal.,1975).Thelong-termmeanannualrainfall is approximately 742.6 mm, and the total precipitationrecorded during the 12 month study period was 740.5 mm. Foragequantity and net primary productivity (NPP) are strongly correlatedwith precipitation in this region of East Africa (Deshmukh, 1984;McNaughton,1985).Remotesensinganalyses,throughtheuseoftheNormalized Differential Vegetation Index (NDVI), have shown thatthereisagradientofNPPthatincreasesasdistancesouthandwestof the majority of households in the Talek area increases. NDVI ishighestinsidetheMaasaiMaraNationalReserveonthePosseplains(Butt, 2007).Altitudewithinthestudyarearangesfrom1501to1985 mabovesealevelandthelandscapecanbebroadlydividedintothreezones:the upland zone to the north dominated by a tsetse fly ( Glossina spp.) infested  Acacia  shrubland comprising  Acacia geradii ,  A. dre- panolobium ,and  A.xanthopholea ;thelowlandgrasslandzonetothesouth dominated by red oats grass ( Themeda triandra ), thatch grass( Hyparrhenia  spp.), sweet pitted grass ( Bothriochloa insculpta ) and Pennisetum  spp.; and the riverine woodland zone in the middle,whichisdominatedby Crotondichogamus , Grewiabicolor  and Cordiamonoica shrubs,whichline thenumerousseasonalstreams,knownlocally as luggas. There is only one perennial river, the Talek River,which is also the delineating feature for the border of the MaasaiMara National Reserve across much of the study area.  2.2. Study site: socio-cultural environment  The Maasai of the Talek area are semi-nomadic; they shift theirherds and form temporary livestock enclosures during times of droughtandalsomaintainsemi-permanenthomebases(Buttetal.,2009). At the centerof the study site is a small village center whichhas numerous small general purpose merchandise shops and bars.Datawere collected from seven Maasai households. The householdis comprised of a married man and his wife or wives, their associ-ated unmarried and married children, and other dependents.Characteristics of the household are: the locus of cattle ownership;an autonomous decision making unit; the people attached to thehouseholdarehighlymobileand flexible,occasionallysplittingandmoving elsewhere or joining other households in order to securebetter grazing and water access, buffer effects of droughts, andmaintain food security within the household (Bekure et al., 1991).Households were selected based on representativeness and vari-abilityinherdingstrategies,herdsize/composition,andwillingnesstoparticipateinthestudy.Thenumberofhouseholdswasrestrictedto seven because logistical constraints limited the number of households that could be reached before the cattle left in themorning(especiallyduetomuddyroadsinthewetseason).Alargernumberofhouseholdswouldhavelimitedtheabilityoftheresearchteam to conduct complimentary studies on herding strategies thatwere occurring at the same time.  2.3. Study animals The main cattle breed in the study site is the East African Zebu( Bos taurus indicus ). A typical herd contains approximately 150individuals with a majority of heifers and a smaller percentage of bulls (Lamprey and Reid, 2004). The average wither height of Zebucattle is between 110 and 140cm and the average weight rangesbetween 275 and 445 kg (DAGRIS, 2006). Cattle diet is broad, butcoarse grasses such as  Themeda  spp. and  Pennisetum  spp. are morepalatable. The total numbers of livestock within the region aredifficult to estimate because of the transient nature of householdsandtheirlivestock.However,datafromaerialsurveysbetween1980and2000overtheMMNRandtheKoyakeGroupRanch(northoftheMMNR) reveal that cattle numbers within the study area havefluctuated between 6000 and 55,000 (Broten and Said, 1995); thevariation in cattle numbers can be attributed to drought losses andlarge-scale livestock movements (Lamprey and Reid, 2004).  2.4. Herding and tracking strategies An understanding of how seasonality influences cattle behaviorand mobility needs to be situated within the cultural herdingpractices of Maasai pastoralists. Pastoral herding is a complexcontextual process that incorporates biophysical, social, economic,political,andculturaldimensions(Bekureetal.,1991;Bassett,1994),and is difficult to distil down to a few key variables. As a result, keyinformantinterviewswereemployedaspartofprovenparticipatoryrural appraisal techniques for pastoralists in sub-Saharan Africa(Waters-Bayer and Bayer,1994) in order to better understand howtracking strategies of pastoralists influenced cattle mobility andbehavior. Key informant interviews were conducted with herdersbefore cattle departed in the mornings and once again when theyreturnedintheevening.Interviewswerealsoconductedwithheadsof households and other elders in order to gain greater depth andclarity on specific components of herding. Maasai research assis-tants (RAs) administered semi-structured questions that wereconducted in local languages (Maa and Swahili). These questionsfocused on the labor requirements, cultural practices, and socio-environmental strategies that influence the herding process.  2.5. Cattle behavior  Cattle behavior was assigned using sevenpossible identificationcodes: sitting, standing, drinking water, walking, grazing, mixedgrazingandwalking(abbreviatedasgrazing/walking),andrunning.Behavior protocols were adapted from previous studies of Zebucattle in Kenya (Semenye,1987; Bekure et al.,1991). Descriptions of the protocol used to code cattle behaviors are listed in Table 1.Behavioral observations were based on one animal per herd perhousehold. Datawere collected from each of the seven households.The study animal was the same as the animal that the GPS wasattached to (see Section 2.6). A single RA made visual observationsevery 10 min, beginning when cattle were released from theenclosures in the morning, and ending when they returned in theevening. Behavior observations were conducted by three locallyrecruited Maasai RAs who were trained to observe and recorddiscrete categories of cattle behavior (Table 1) while walkingalongside the herd for the duration of the grazing day (herd-follows).EachindividualRAobservedcattlebehaviorfromthesamehousehold (i.e. no two RAs made behavior observations from thesame herd). Herd-follows were conducted every 2 days during the B. Butt / Journal of Arid Environments 74 (2010) 403–413  405  dry season and every day during the wet season. The RAssynchronized their watches with the navigation clock on the GPSreceiver.Observationsweremadefor10 satthestartofeach10 mininterval. In total, 46 herd-follows each were conducted during thewet and dry seasons, and resulted in 6286 direct observationsthroughout the study period. Approximately 3202 individualobservations were made during the dry season and 3084 observa-tions during the wet season. Dry season observations were madebetween mid-September 2005 and mid-January 2006, while wetseason observations were made between May and June 2006.  2.6. Cattle mobility Global positioning system (GPS) units were used to collectspatialandtemporaldata.ThiswasdonebyfirstconstructingaGPScollar to house the GPS unit. A canvas sleeve (the collar) was fabri-cated to protect and hold the GPS unit. Each GPS was then placedinto a 6 by 6 inch ziplock bag to protect the unit from water (bothfrom rain and high river crossings) and dust. A cow bell was thenused to mount the GPS collar onto an individual bull within eachherd.Cowbellsare locally manufacturedfromold rubbertires,ironrods and metal sheets. Cultural practices of the Maasai dictate thatonly bulls are adorned with cow bells. The GPS units were thenattached to the cow bells using Velcro straps and duct tape(SupplementaryAppendix1).TheGPSwasplacedonthebullbeforethe cattle left their enclosures and removed when the cattlereturned. In total, six identical Garmin   eTrex Legend 12 channelGPS units (at a costof  < US$120 each and weighing less than 250 g)were used to track cattle mobility from seven households. The GPSreceiver was powered by two rechargeable AA 2500 mAh batteriesthattypicallylastedbetween15and18 h.TheGPSwasprogrammedto collect the northing, easting, altitude, and universal time, every30 s. The resulting path traveled by cattle during the course of thegrazing day and documented by the GPS is known as the ‘grazingorbit’(Grandinetal.,1991;BurnSilveretal.,2003;Buttetal.,2009).Eachorbitwasreferencedtoauniversaltransversemercator(UTM)grid,zone36south,ARC1960datum.Theseriesofdatapointsfromeach orbit were then downloaded from the GPS and stored as uni-code text files using OziExplorer   software each evening usinga laptop. The northing and easting locations were then extracted tomatchthetimeatwhichtheRAsmadethebehaviorobservations.Tocontrol for variations within the landscape, distance from house-hold was normalized bycalculating the hypotenuse distance of theobservation point from the household of srcin.  2.7. Potential sources of error  PotentialsourcesoferrorintheGPSdatainclude:(1)atmosphericconditionssuchasheavycloudcoverorheavyrainfall(Solheimetal.,1999); (2) local environmental effects such as the scattering of thesatellite signals when cattle were under dense canopy (Liu, 2002);and (3) in-situ effects, such as the power on the GPS inadvertentlyswitchingoff(Kawamuraetal.,2005),poorplacementoftheGPSonthe cow bell, and the effect of covering the GPS units for protection.To limit these sources of error, each grazing orbit was graphicallydisplayedwithinageographicalinformationsystem,andincompletegrazing orbits and grazing orbits with excessive spatial scatteringwere discarded. Field trials were conducted at the beginning of thestudy period (August 2005) to determine the effects of the canvascasing on the accuracy of the GPS units. The canvas housing did notsignificantly reduce the accuracy of the GPS receiver. Units with thehousingwerecomparabletoreceiverswithoutthecanvas(standarddeviations of 15m). GPS collars were placed on multiple cattlewithinthesameherdtodetermineifthegrazingorbitofasinglebullwas representative of the orbitof the entire herd. The grazingorbitswere found to be within a standard deviation of 30 m from eachother. This source of error was accepted because: (1) the typicalwidthofamediumsizedherdis50m;and(2)behaviorobservationswere extracted every 10 min.  2.8. Data sets Cumulatively these data produced two data sets from whichstatistical analyses were conducted. The first data set encompassedall 6286 observations, which contained the individual  x  and  y locations of each observation point, the time of observation, thedistance from household, cattle behavior that corresponded tothe household of srcin, one of the seven behavior codes, and theseason. The second data set contained information on the mobilitypatternssummarizedattheleveloftheorbit.Thisdatasetconsistedof informationon the dayof observation, the start time(the timeatwhich cattle left their enclosures), the end time (the time at whichcattle re-entered their enclosures), the duration of grazing, themaximum herd to household radius (the furthest hypotenusedistance between the household of srcin and the location furthestfrom the household), and the total distance traveled by cattle (thesumof the distancesbetweensuccessivecattle observationpoints).  2.9. Data analysis Behaviorobservationsweregroupedbyseason(wetanddry)andsummarized every 0.5km between 0km and 6km from the house-hold. Similarly data were pooled by time of day and summarizedevery 2h between 0600 and 2000 h. Any data documented after2000 hwerenotedasaseparatecategoryof‘beyond2000 h’.Pearsoncorrelations, difference of means and non-parametric chi-squaredstatistical tests were used to analyze cattle mobility and behaviordata. All tests were considered statistically significant at  a ¼ 0.05.Statistical analyses were conducted using SPSS   and Stata 7/SE  (SPSS, 2005; StataCorp, 2005) software packages, while spatialanalysis was undertaken using the animal home range extension inArcGIS  (Hooge and Eichenlaub, 2000). Qualitative data on herdingand tracking narratives were analyzed by transcribing and codingresponsestothecorrespondingorbitforeachhouseholdandseason. 3. Results  3.1. Observed seasonal cattle mobility patterns Cattlemobilitypatternsvariedsignificantlybetweenthewetanddry seasons (Table 2). The average daily herd to household radiusduring the dry season was 1.80 km greater than in the wet season,whiletheaveragedailytotaldistancetraveledwas2.76 kmgreaterinthe dry season than the wet season. The duration of grazing wasmarginally but significantly different (approximately 26 min longerinthedryseason)betweenthetwoseasons.Therewasnodifference  Table 1 Behavior codes and protocol for observation of behavior. The protocol applies tothebehavior of the bull with the GPS collar.Behavior ProtocolStanding Standing motionless with its head raisedSitting Sitting with all four legs on the groundRunning Running for 10 consecutive secondsWalking Walkingcontinuouslyforall10 softheobservationperiod(headraised)Grazing/walkingBoth grazing and walking (read raised and lowered)Grazing Grazing continuously for 10 s (head lowered)DrinkingwaterDrinking water at a water source B. Butt / Journal of Arid Environments 74 (2010) 403–413 406  between the times at which cattle were led out of their enclosuresduring either the wet or dry season. However, the time at whichcattlereturnedtotheirenclosureswasslightlylater,butsignificantlydifferentduringthedryseason.Herdtohouseholdradiuswashighlycorrelated with herd distance traveled during both the wet season( r  p ¼ 0.83,  p < 0.0001) and dry season ( r  p ¼ 0.90,  p < 0.0001).Durationofherdingwasnotsignificantlycorrelatedwitheitherherddistance traveled (wet season  r  p ¼ 0.12,  p ¼ 0.39, dry season r  p ¼ 0.26,  p ¼ 0.0718) or herd to household radius (wet season r  p ¼ 0.03,  p ¼ 0.80, dry season  r  p ¼ 0.13,  p ¼ 0.36).  3.2. Relationships between distance from household and time of day Aseasonalrelationshipwasfoundwhentimeofdaywasplottedagainst distance from household (Fig. 2). For the wet season, a highnumber of observationpoints were located closer tothe householdbetween 0600 and 1030 h, indicating that cattle were within closeproximity to the household. Distance from the household slowlyincreased concomitantly between 1030 and 1400 h. Distance fromhousehold then decreased between 1400 and 1800 h, as cattletraveled back to their enclosures. During the dry season, cattlespent a short period of time near the household between 0630 and0800 h. Cattle then traveled to their furthest point from theirhousehold until approximately 1430 h before returning rapidlyback to their enclosures between 1830 and 1930 h.  3.3. Observed seasonal cattle behavior patterns The results of the data analysis suggest that there is also a statis-tically significant relationship between the frequency of cattlebehavior and seasonality ( c 2(7) ¼ 386.462,  p < 0.0001). The domi-nantcattlebehaviorobservedduringthedryseasonisgrazing,whichencompasses 42% of the total observations, followed by grazing/walking (23%), walking (23%), and standing (8%), while sitting,drinkingwater,andrunningcumulativelyaccountforapproximately5.5% of total observations (Table 3). During the wet season, grazingencompasses 54% of the total observations, followed by walking(18%), standing (12%), and grazing/walking (7%), while sitting,drinking water, and running cumulatively contribute to approxi-mately 4%of the total observations.However, furtherexamination isneeded to understand the seasonal spatio-temporal dimensions of cattle behavior. This is done in the following sections by evaluatinghowthedifferentcattlebehaviorsvaryasafunctionofdistancefromthe household, time of day, and land cover zone.  3.3.1. Seasonal cattle behavior and distance from household Thefrequencyofobservationsmadeasafunctionofdistancefromthe household is seasonally different ( c 2(6) ¼ 376.097,  p < 0.0001).During the dry season the distribution is bimodal, whereby thefrequency of observations is high between the household and lessthan 0.5 km away, and then decreases between 0.5 and 1.0km fromthe household (Fig. 3). Beyond this point the frequency of observa-tions increases until 4 km away, and decreases rapidly between 4.0and 6.5 km from the household. Conversely, during the wet season,the frequency distribution of observations is unimodal, peaking atlessthan0.5kmfromthehousehold.Thefrequenciesofobservationsaresimilarbetween0.5and2.5kmfromthehousehold.Thenumberof observations then decreases rapidly between 2.5km and 4.5km,with very few observations between 4.5 and 5.5km, and no obser-vations beyond 5.5km.The most common types of behavior near the household( < 0.5 km) during the dry season are standing still and walking  Table 2 Observed cattle mobility patterns. Herd to household radius (HR) is the distance (inkm) between the household of srcin and the point furthest from the householdwhile navigating the grazing orbit. Herd distance traveled (HD) is the total distance(in km) traversed during the course of the grazing orbit. The start time (ST) is thetime of day when cattle are released from their enclosures. The end time (ET) is thetime when cattle re-enter their enclosures. Total duration (TD) (in h) is the differ-ence between the start and end times.Variable Wet,  n ¼ 46(mean  SD)Dry,  n ¼ 46(mean  SD)Dry  wet d.f. ¼ 90(difference) t p HR 2.78  0.94 4.59  1.09 1.81 8.52  < 0.0001HD 8.01  2.21 10.76  2.04 2.76 6.21  < 0.0001ST 7.42  0.63 7.36  0.51   0.06   0.50 0.6914ET 18.43  0.31 18.79  0.45 0.36 4.53  < 0.0001TD 11.01  0.74 11.43  0.77 0.42 2.70 0.0041 Fig. 2.  Distance from household plotted against time of day for the dry season and wet season. The last series of points between 18:00 and 21:00 h in the dry season demonstrateshow one household moved their cattle from their permanent household to a temporary encampment to cope with the oncoming drought, which began in mid-January 2006. B. Butt / Journal of Arid Environments 74 (2010) 403–413  407
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