A. L. Goetsch, T. A. Gipson, A. R. Askar and R. Puchala

Invited review: Feeding behavior of goats

doi: 10.2527/jas.2009-2332 originally published online Sep 25, 2009; 2010.88:361-373. J Anim Sci

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ABSTRACT: Factors influencing the feeding behav-ior of goats include grazing management practices, type of vegetation and season, breed and stage of production, group size, and properties of diets fed in confinement. Considerable information has been gathered from visu-al observation during daylight. However, tools are now available to characterize the feeding behavior of goats while grazing and while in confinement throughout the day. Global positioning system collars can be used to assess horizontal and vertical distances traveled, up or down position of the head, and movement within pasture or rangeland areas. A commercially available leg activity monitor allows estimation of the number of steps and time spent standing, lying, and moving rap-idly without grazing. However, these measurements do not directly determine grazing. Therefore, prediction equations based on visual observation must be devel-oped. Classification tree analysis is a robust method in developing these equations because the decision tree can be pruned or expanded to provide the best fit. An-

other equipment system determines time spent eating, ruminating, and remaining idle from the pattern of jaw movement. In addition to use of n-alkanes as internal markers to estimate digestibility, their profile can pro-vide an indication of the botanical composition of the selected diet. Automated feeding systems for confined goats permit determinations such as number of feeder visits and meals, eating time, and rate and pattern of feed intake. Heart rate measured while goats are in nor-mal production settings can be used to predict total energy expenditure through multiplication by energy expenditure per heartbeat of individual animals. To partition the activity energy cost, an estimate of ME intake or measures of changes in body energy status and milk energy yield are needed to determine other sources of heat to be subtracted from total energy ex-penditure. These methods create the opportunity to gain a fuller understanding of factors influencing the feeding behavior of goats and the relationships with levels and efficiencies of production.

Key words: activity, behavior, goat

©2010 American Society of Animal Science. All rights reserved. J. Anim. Sci. 2010. 88:361–373 doi:10.2527/jas.2009-2332

INTRODUCTION

Common definitions of behavior are “anything that an organism does involving action and response to stimulation,” “the response of an individual, group, or species to its environment,” and “the way in which something functions or operates” (Merriam-Webster’s Collegiate Dictionary, 1996). Although these definitions are broad, it is well known that animal behavior varies

greatly among domesticated livestock species. However, a commonality is influence of nutrition. For goats, con-sideration of the effects of nutrition on behavior can be categorized into goats in grazing settings and goats in confinement settings. Direct observation methods have been, and are still, useful in both scenarios. Although considerable labor is required with grazing goats, mea-sures are restricted to daylight, and the presence of an observer can modify animal behavior (Barroso et al., 2000; Papachristou et al., 2005; El Aich et al., 2007). Equipment is now available to characterize the behav-ior of goats and other ruminant species throughout 24-h periods, both in confinement and while moving freely.

Study of the behavior of freely moving goats on pas-tures and rangelands is useful for purposes such as determining the most appropriate stocking rate and physiological states and seasons of land use, the need for and types of supplemental feedstuffs, and the desir-ability of monospecies grazing with goats vs. cograz-

Invited review: Feeding behavior of goats1,2

A. L. Goetsch,*3 T. A. Gipson,* A. R. Askar,† and R. Puchala*

*American Institute for Goat Research, Langston University, Langston, OK 73050; and †Animal and Poultry Nutrition Department, Desert Research Center, El Matareya, Cairo, Egypt

1 Presented at the Animal Behavior and Well-Being Symposium titled “Behavior-Nutrition Interactions” during the joint annual meeting in Montreal, Canada, July 12 to 16, 2009.

2 This research was supported by Research Grant No. US-3694-05 R from BARD, the United States-Israel Binational Agricultural Research and Development Fund, and USDA Project Number 05-38814-16353.

3 Corresponding author: goetsch@luresext.eduReceived July 25, 2009.Accepted September 16, 2009.

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ing with cattle, sheep, or both (Lachica and Aguilera, 2005b; Animut and Goetsch, 2008). In addition, recent development of the use of heart rate (HR) measured on free-moving animals to predict total energy expen-diture (EE; Brosh, 2007) has created the opportunity to investigate relationships between the EE of goats, or preferably the activity energy cost (AEC), and feed-ing or grazing behavior, which may lead to means of predicting and minimizing this sizeable loss of energy. Therefore, the objectives are to summarize methods used to characterize the feeding behavior of goats and the effects of nutrition and production practices on goat behavior while grazing and in confinement.

METHODS TO ASSESS FEEDING BEHAVIOR

Methods To Assess Grazing Behavior

A large number of variables are measured with graz-ing goats, commonly categorized as feeding or grazing behaviors. They include time spent eating or grazing, ruminating, resting, standing, walking, in a biped-al stance, and aerially in trees. Other behaviors are horizontal and vertical distances traveled, location and movement within the available land area, number of bites, rate of biting, number of mouth movements in-volved in prehension, chewing before swallowing, and rumination, bite size, instantaneous rate of intake, and diet selection in terms of chemical and botanical com-position, including specific plant parts prehended.

Over the years, many of the variables listed above have been assessed via visual observation and tracking by researchers (Lachica and Aguilera, 2005a,b). Quite detailed procedures have been developed. For example, Agreil and Meuret (2004) described methods to quan-tify the ingestive behavior of sheep and goats. An im-portant component of such procedures is that observ-ers do not disturb animals so that close proximity can be obtained, including the use of land familiar to the group and identification of specific individuals indiffer-ent to observer presence. The behavior of individuals selected (e.g., time spent in ingestion, composition of the selected diet) should be representative of the group. Bites are continuously recorded in real time throughout entire daily grazing periods, with categorization into 41 different types, including plant species or groups of species sharing the same categories, plant parts, and length of leaves prehended. At a later time, each bite is manually simulated to estimate diet composition, bite size, and level of intake.

Direct observation is also used for other behaviors, such as time spent grazing, ruminating, standing, walk-ing, and lying during the daylight period (Barroso et al., 2000; Papachristou et al., 2005; El Aich et al., 2007). Furthermore, animal tracking allows estimation of distances traveled with the use of geographic posi-tioning system (GPS) equipment or geographical infor-mation system software (Ouédrago-Koné et al., 2006),

by recording the number of observer steps and average step distance (Lachica et al., 1997, 1999), or by placing pedometers on animals (Sharma et al., 1998). Distances are now primarily determined with GPS collars on ani-mals, which also can be used to monitor location and movement within grazing areas (Schlecht et al., 2006; 2009). Likewise, equipment systems carried by animals have been used to measure lying, standing, and walking (Champion et al., 1997) and time spent in ingestive and ruminative mastication and numbers of the different patterns of jaw movements or bites via a sensor placed around the mouth based on air pulses (Abijaoudé et al., 1999, 2000) or electrical conductivity sensors (Animut et al., 2005a; Berhan et al., 2005; Patra et al., 2008b). Diet selection has been assessed by esophageal cannulas (Fedele et al., 1993; Raats et al., 1996) or with a ru-minal cannula through collection of ingesta after total digesta evacuation (Patra et al., 2008a,b). Botanical composition of the diet has been estimated by micro-histology of feces (Animut et al., 2005b; Mellado et al., 2005, 2006) and esophageal extrusa (Fedele et al., 1993), near-infrared reflectance spectroscopy analysis of feces (Glasser et al., 2008), and fecal and forage con-centrations of n-alkanes and other plant wax compo-nents (Dove and Mayes, 2005, 2006).

Current Methods To Characterize Grazing Behavior at the American Institute for Goat Research

Grazing Activities and HR. As noted, vari-ous methods are used to study the grazing behavior of goats, many of which have been used at the American Institute for Goat Research (AIGR) over the last de-cade. Currently, the AIGR uses Model 3300SL GPS collars (Lotek Wireless, Newmarket, Ontario, Canada), which log latitude and longitude for 2-dimensional fixes; latitude, longitude, and altitude for 3-dimensional fixes; x-y motion; and head up or head down position. These collars are small (length × width × height: 68 × 48 × 48 mm) and lightweight (200 g) and have the capacity to store more than 41,000 differential locations in the North American Datum 1983 coordinate system. Vari-ous GPS fix schedules can be used, although in AIGR studies, the schedule is set to acquire a fix at the short-est possible interval, which is 5 min. Fixes are down-loaded and corrected postdifferentially by using propri-etary software (N4, Lotek Wireless) and base station files from the Perry, OK, continuously operating refer-ence station (36°16′34.46428″ N, 97°19′17.97610″ W), which is the closest continuously operating reference station to the AIGR (37.4 km straight-line distance). Corrected fixes are then imported into ArcMap 9.3 (ESRI, Redlands, CA). Boundaries of pastures, includ-ing a 7-m external buffer, are constructed as shapefiles by using the coordinate system of the World Geodetic System 1984 Universal Transverse Mercator 14N. The x and y coordinates in meters are calculated for each fix. Only fixes within the boundary and buffer shape-

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files are exported to an Excel (Microsoft Corporation, Redmond, WA) spreadsheet. Distance between con-secutive fixes is calculated using euclidean geometry. Because areas within pastures typically used have not markedly varied in elevation, vertical distance traveled has not been computed.

IceTag activity monitors from IceRobotics Limited (Midlothian, Scotland, UK) are also small (length × width × height: 95 × 85 × 32 mm) and lightweight (170 g) and are attached to a rear leg. These highly accurate 3-axis accelerometers determine movement 16 times per second. The output data of the number of steps and time spent standing, lying, and active are summarized for each minute. Time standing from IceTag monitors encompasses both grazing and nongrazing periods, and active is defined as walking at a relatively fast pace, presumably without grazing. Lying time is solely or predominantly without grazing.

To monitor HR, goats are fitted with Vermed Perfor-mancePlus electrocardiogram electrodes (Vermed, Bel-lows Falls, VT) attached to the chest just behind and slightly below the left elbow and behind the shoulder blade on the right side. Electrodes are secured to the skin with a 5-cm-wide elastic bandage (Henry Schein, Melville, NY) and animal tag cement (Ruscoe, Akron, OH). Electrodes are connected by electrocardiogram snap leads (Bioconnect, San Diego, CA) to T61 coded transmitters (Polar, Lake Success, NY). Human RS400 HR (Polar) monitors with infrared connections to the transmitters are used to collect HR data at 1-min in-tervals. Heart rate data are analyzed using the Polar Precision Performance SW software provided by Polar. To predict EE from HR, the ratio of EE to HR is deter-mined for each animal (Brosh, 2007) with an indirect open-circuit respiration calorimetry system (Sable Sys-tems, Las Vegas, NV) over at least a 24-h period. For relatively short experiments, such as those 12 or 16 wk in length, the ratio is determined once before or after the study. However, for longer trials with animals in different physiological states throughout the year, the ratio is estimated at multiple times.

These 3 equipment systems (i.e., GPS collar, IceTag activity monitor, and HR monitor) are used simultane-ously for at least 2 d. This length is restricted by the number of experimental animals (i.e., 24 to 32 in most AIGR experiments), the number of GPS collars avail-able and the number that researchers can work with at a given time, additional measures at other times, and the desirability of minimizing the length of entire data collection periods. Because GPS collar data are collect-ed at 5-min intervals and the IceTag and HR monitors provide output data at 1-min intervals, data emanat-ing from the 3 pieces of equipment must be matched in 5-min intervals. IceTag and HR data are imported into Excel (Microsoft Corporation) spreadsheets with appropriate macros to consolidate the data into 5-min intervals.

In some studies, goats have also been fitted with Insti-tute of Grassland and Environmental Research (IGER)

grazing behavior monitoring system units (Ultrasound Advice, London, UK). The units consist of a noseband sensor secured around the mouth and a processing unit (1.3 kg) situated on the back. The units are placed on goats for slightly more than 1 d to obtain measures for 24 h. The following day, batteries are charged, and the equipment is reused on other goats every other day. The IGER grazing behavior monitoring units allow es-timation of time spent eating, ruminating, and remain-ing idle, as described by Patra et al. (2008a,b). The IGER system provides somewhat direct estimates of time spent eating or grazing and ruminating based on the pattern of jaw movements. Panels A, B, and C of Figure 1 show typical patterns for eating, ruminating, and remaining idle, respectively. The system software provided differentiates the patterns in most instances. Occasionally, however, areas are marked as unidentified, such as with improper positioning of the noseband, in which case data are deleted or the researcher makes a subjective decision (e.g., Figure 1, panel D).

Data from GPS collars and IceTag monitors do not include direct estimates of time eating or grazing. Thus, equations to predict grazing or eating, walking without grazing, resting while lying, and resting while standing are developed in a calibration study with classification tree analysis (Steinberg, and Colla, 1997) and CART software (Salford Systems, San Diego, CA) analysis. The most recent calibration study at the AIGR in-volved 32 animals with measures during a 6-d period. Animals were equipped with IceTag monitors and GPS collars, and grazing or eating behavior was observed visually. Because supplemental feedstuffs have been provided in some experiments when growing forage was limited, observations of this calibration study included goats provided access to grass hay. The GPS collar and IceTag data were processed in the aforementioned man-ner. Grazing or eating observations were 32% (610) of the 1,882 5-min observations, resting while lying obser-vations were 34% (637), resting while standing obser-vations were 26% (492), and walking observations were 8% (143). The CART software was used to construct classification trees by using the number of steps and time spent standing, lying, and active from the IceTag monitors and head down, x activity, y activity, and dis-tance traveled from the GPS collars as predictor vari-ables and grazing activity as the target variable. Clas-sification success was 77, 81, 56, and 94% for grazing or eating, resting while lying, resting while standing, and walking without grazing or eating. The 47-node clas-sification tree was expressed in a series of if-then state-ments in a SAS program (SAS Inst. Inc., Cary, NC) to predict the behaviors of animals in other experiments, addressing factors such as stocking rate, breed, stage of production, body condition, concentrate supplementa-tion, and length of pasture access.

Forage Digestion, Intake, and Botanical Composition. Most recent AIGR experiments have included the use of total fecal collection bags over at least 4-d periods to estimate fecal output. After ani-

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mals were closely observed, hand-plucked or simulated grazed forage samples were collected, although in other experiments ruminally cannulated goats were used to collect ingesta (Patra et al., 2008a,b). The profile of n-alkanes in individual plant species samples and feces allows estimation of the botanical composition of forage selected. Fecal concentration of individual n-alkanes is corrected for recovery, determined with harvested for-age from the same or similar pastures. Recovery is de-termined in the animals used for grazing in some ex-periments, whereas in others different animals are used. However, in a recent experiment (A. R. Askar, unpub-lished results), there was an interaction between physi-ological state of Boer goats (mature meat goat does, growing wethers, and yearling wethers) and n-alkane length; values were similar among physiological states in recovery of C22 to C26, but were greater for growing wethers compared with mature does and yearling weth-ers in C27 to C31 recovery.

Analysis of n-alkanes at the AIGR follows the proce-dure of Mayes et al. (1986), with the modifications of Ol-iván and Osoro (1999). Botanical composition is based on the least squares optimization procedure (Mayes et al., 1994; Dove and Moore, 1995), using the Solver rou-tine of Excel (Microsoft Corporation) with nonnegative restrictions. Discrimination analysis is used to select suitable n-alkanes for determining botanical composi-tion. The accuracy of using the n-alkane technique to estimate botanical composition depends primarily on the difference in the profile among plant species that allows discrimination between or among plant species and knowledge of the recovery of n-alkanes selected for calculation (Mayes and Dove, 2000; Brosh et al., 2003). An alternative method is free-choice feeding of different plant species available on pasture to goats in metabo-lism crates while collecting total feces excretion. Suit-able internal markers for use in determining botanical composition and digestibility for grazing animals are

Figure 1. Jaw movement pattern of the Institute of Grassland and Environmental Research Grazing Behavior Monitoring Unit for eating (panel A), ruminating (panel B), remaining idle (panel C), and an unknown activity requiring deletion or a subjective decision (panel D). The interval between dashed vertical lines is 1 min.

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based on relationships between values estimated from n-alkanes and ones derived from direct determination of intake and digestion.

Estimation of the AEC. One of the purposes of recent grazing behavior experiments conducted at the AIGR is to evaluate or develop means of predicting the AEC. Grazing behavior measures can be used to predict total EE (Brosh et al., 2004), but partitioning of AEC and EE relative to the ME requirement for maintenance (MEm) would lend itself well to incorpo-ration into nutrient requirement recommendations such as those of Sahlu et al. (2004) and NRC (2007). Vari-ous means by which the AEC of grazing goats, sheep, and cattle are predicted were reviewed by Sahlu et al. (2004) and NRC (2007). Methods are based on vari-ables such as horizontal and vertical distances trav-eled, time spent eating, topography, number of position changes, time spent grazing plus walking, digestibility, or some combination of variables. The energy cost per unit of activity is often based on measures with con-fined animals, such as for locomotion with animals on a treadmill. No method of projection has been evaluated or validated adequately, including that suggested for goats by Sahlu et al. (2004) and recommended by NRC (2007). Nonetheless, this method is based primarily on grazing time, with small adjustments for horizontal dis-tance traveled, forage TDN concentration, and rugged-ness of the terrain.

There are 2 apparent means by which AEC may be “peeled” from estimates of total EE. One is based on ME intake without input of BW other than that of kids with lactating does, and the second is determined from BW or BW and BCS. For the method based on ME intake, recovered energy is determined as the difference between ME intake and EE. For the BW or BW and BCS method, an average energy concentration in tissue gained or lost is assumed or available equations, such as those presented by Ngwa et al. (2007), are used to predict changes in body energy status based on BCS. With assumptions including the MEm of NRC (2007) and milk energy yield for lactating animals based on kid BW and ADG, recovered energy is estimated. Ul-timately, with both methods the various origins of EE (i.e., maintenance, mobilization of tissue energy for maintenance or lactation, and use of dietary energy for tissue gain or milk production) are determined and subtracted from total EE to estimate AEC, which is expressed relative to MEm.

The first method of peeling AEC might seem prefera-ble because of the influence of the gastrointestinal tract digesta mass on BW. However, Beker et al. (2009a,b) used both methods and found that estimation based on BW and an assumed constant concentration of energy in tissue gained or mobilized yielded estimates within the range of values in the literature, whereas this was not true for all values when determined from ME in-take. With methods based on BW or BW and BCS, relatively long periods would minimize the influence on BW of fluctuations in digesta mass.

An alternative approach to that of peeling AEC de-scribed above was recently used by Brosh et al. (2006b) for beef cattle. Several regressions of EE above that while lying were conducted against grazing behaviors, physiological state, stocking rate treatment, hour of the day, and so on. This method yielded energy costs for activities or behaviors such as grazing, standing, and traveling relative to lying that, when multiplied by time spent in the different activities, allowed es-timation of daily AEC. The range of total AEC as a percentage of assumed MEm was low (i.e., 8.5 to 16.5% MEm) compared with many estimates in the literature. In this regard, Patra et al. (2008a,b) observed that EE throughout 24-h periods was greater for goats with free movement than for ones with movement restricted by tethers of 3 or 4.1 m. It was suggested that treatments such as free movement vs. tethering affect the basal met-abolic rate at all times of the day, regardless of specific activities displayed at any one given time. Therefore, a peeling approach in estimating AEC or simultaneously measuring EE of similar restrained or confined animals (Lachica and Aguilera, 2005a) seems warranted to de-termine the absolute magnitude of AEC. Limitations of the latter approach include potential differences in nutrient intake between grazing and confined animals.

Methods of Assessing Feeding Behavior of Goats in Confinement

Regarding grazing experiments, the feeding behavior of goats in confinement has been assessed by visual ob-servation (Dziba et al., 2003a,b; Haddad and Obeidat, 2007; Alonso-Díaz et al., 2008). Equipment systems to determine time spent eating and ruminating based on patterns of jaw movement have been used (Abijaoudé et al., 2000). Fedele et al. (2002) used a feeding cage for individual goats designed to allow simultaneous ad libitum intake of 4 types of concentrate and 2 sources of hay. In some cases, feed containers have been placed on balances to investigate diurnal patterns in feed con-sumption (Abijaoudé et al., 2000). Interest in measur-ing residual feed intake has increased the use of auto-mated feeding systems with cattle. A system developed for swine has been used in research with goats (Gipson et al., 2006, 2007) and in the annual meat goat buck performance test of the AIGR since 2004.

The AIGR currently has 4 MK3 Feed Intake Record-ing Equipment (Osborne Industries Inc., Osborne, KS) feeding system units located in 4 pens. This system allows 1 animal to consume feed at a given time. Feed consumption is monitored throughout the day. The ani-mal is identified on entering the feeding unit and feed is weighed at entry and exit, with measurement of any feed dispensed during feeder occupancy. Measurements are daily feed intake, number and length of feeder visits that can be categorized into meals, feeder occupancy or time spent eating, rate of DMI, and DMI per visit and meal. Although such systems allow for consider-able feeding behavior data to be generated, one limita-

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tion is that they are not conducive to a wide range of diet physical forms, being most suitable for small- to moderate-sized pellets.

The AIGR also has facilities equipped with Calan feeding gates (American Calan Inc., Northwood, NH). Attributes of Calan feeding gates are that individual animal feed intakes can be determined while animals are in a group setting and that any diet physical form can be used. However, considerable labor is required compared with automated feeding systems, and feeding behavior data are not generated without means such as visual observation.

GRAZING MANAGEMENT PRACTICES

Stocking rate is a management decision that can af-fect the behavior of goats in many ways. Animut et al. (2005a) noted linear increases in time spent eating and number of steps as the stocking rate of cograzing goats and sheep increased on grass-forb pastures. In the same study, the selectivity ratio of the most abundant forb, ragweed (Ambrosia artemisiifolia), increased for both species as the stocking rate increased and forage mass decreased (Animut et al., 2005b). As discussed earlier, Sahlu et al. (2004) suggested a relationship between time spent grazing and AEC. This was observed by Animut et al. (2005a), although for goats in the 2 graz-ing seasons, EE was greater for the greatest stocking rate compared with 2 lesser ones. Conversely, Beker et al. (2009a) did not observe effects of stocking rate on grazing time or AEC in Boer and Spanish does when lactating and after weaning when the does were grazed on grass-forb pastures. The factors responsible for these different findings are unclear, although forage mass in the study by Beker et al. (2009a) with both stocking rate treatments was considerably less than in the ex-periment by Animut et al. (2005a,b). Nonetheless, the data reported by Beker et al. (2009a) indicated increas-es in AEC of 5.79 and 5.05% of MEm per hour spent grazing or eating and grazing or eating plus walking, respectively. These values resulted in the prediction of AEC of Angora, Boer, and Spanish goats in a compan-ion experiment by Beker et al. (2009b) with moderate accuracy (R2 = 0.40 to 0.41) and without bias. The energy cost per hour of grazing or eating plus walking was near the 5% proposed by Sahlu et al. (2004) and NRC (2007); however, consideration of neither distance traveled nor digestibility improved prediction. Inclusion of observations of Rambouillet sheep markedly reduced the R2 and resulted in significant prediction bias, indi-cating the need for research specifically addressing the AEC of sheep.

Because stocking rate influences forage mass, it could also modulate differences in response to social behavior or hierarchy. For example, Barroso et al. (2000) deter-mined that with increased pasture forage availability, diets selected by dominant and subordinate goats in a group differed in composition, whereas differences did not exist with low forage availability. In addition,

physical interactions with aggressive behavior should increase with increasing stocking rate, which could af-fect AEC and would be the most significant if nutrient intake were limited by low available forage mass at high stocking rates.

In many areas of the world, goats are sometimes tethered in one area for grazing. Patra et al. (2008a,b) compared forage intake, chemical composition of forage selected, time spent grazing and ruminating, and EE by goats tethered vs. freely moving to evaluate tether-ing as a production practice and also to determine if tethered goats could serve as a model for studying the grazing physiology of free-moving goats. Grass-based pastures with a low to moderate level of forage remov-al from tethered areas were used. Forage intake and composition and time spent grazing and ruminating were similar between tethered goats and ones given free movement, but EE was less for goats tethered through-out the day. Thus, tethered goats could be used to study many grazing behavior characteristics of goats, but could not be used for energy metabolism. In addi-tion, tethering offers the potential to increase the level or efficiency of production by minimizing the AEC. Berhan et al. (2005) made similar determinations for goats given access to a grass-based pasture for 4, 8, or 24 h/d. Rate of ME intake was 26, 19, and 18 kJ/min, although total ME intake was least with 4 h of access (4.6, 6.4, and 5.9 MJ/d for 4, 8, and 24 h, respectively). Total EE was 5.0, 5.1, and 6.2 MJ/d for 4, 8, and 24 h, respectively. Hence, AEC was minimized by restricted pasture access of 8 h compared with 24 h, resulting in greater energy recovery.

VEGETATION CONDITIONS

The types of vegetation present influence the forag-ing position or posture of goats, most notably the use of a bipedal stance and aerial positions. That is, a bipedal stance would not be used without browse or trees pres-ent. Only with climbable trees would goats be aerial (El Aich et al., 2007). How the use of different forag-ing positions affects AEC is unclear, but Dziba et al. (2003b) suggested that AEC increases with increasing foraging height.

A major factor influencing types and levels of veg-etation available for use by goats is season, which thus affects dietary overlap. Generally, dietary over-lap between different ruminant species increases with decreasing forage availability (Celaya et al., 2007), al-though this depends largely on the types of vegetation available. For example, Sanon et al. (2007) noted that cattle, sheep, and goats all decreased feeding activities as time advanced from the rainy to the dry season in a natural pasture in a Sahelian area of Burkina Faso, but goats and sheep made a shift to browsing; brows-ing accounted for only approximately 4% of the feeding activity of cattle in all seasons. Yayneshet et al. (2008) also noted increasing browsing by goats in fallow land of Northern Ethiopia as the quality and quantity of

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understory vegetation declined. Conversely, Brosh et al. (2006a) observed that in some settings, browse can pro-vide large proportions of ME intake by cattle.

In contrast to the findings of Solanki (1994), Papach-ristou (1997) reported a greater rate of biting by goats when available forage was primarily browse vs. non-browse plant species. Similarly, grazing time by goats was longer in a plot of 70% woody and 30% herbaceous plant species compared with one with 90% woody plant species (Decandia et al., 2008), although it was stated that greater selectivity exerted with the plot having 30% herbaceous plant species probably contributed to the difference, in addition to the effect of a disparate rate of biting and intake rate between the 2 plant types. Time spent ruminating was greater for goats in the plot with greater woody plant species.

In addition to investigations targeting daily intake and selectivity, some studies have addressed patterns within a day. For example, in a protected grazing site in India, Solanki (1994) observed that in the first hour of foraging, goats visited many different sites and explored available vegetation, with a diverse array of plants se-lected during that time. Thereafter, in the morning, primarily grasses were consumed. In the afternoon, se-lection changed to favor bushes. The rate of biting was similar between grasses and thorny bushes; however, bite size was twice as great for bushes as for grasses. Conversely, Dumont et al. (1995) observed that with low forage availability, goats were most selective early in the day, consuming relatively little grass and ingest-ing mostly browse. Selectivity was much less at the end of the grazing day, when a greater level of grass was consumed. It was suggested that this strategy mini-mized potential limitations to intake early in the day through excessive ruminal digesta fill, with less impor-tance on fill preceding the overnight period without grazing. Intake rate decreased with advancing day of the experiment as available forage mass decreased.

It is usually assumed that goats explore all potential areas for grazing in a short period of time (i.e., number of days) after being placed in a new grazing area. To address this assumption, in coordination with a dem-onstration of vegetation management with goats (T. A. Gipson, unpublished data), 21 crossbred Boer wethers from the AIGR research farm were fitted with GPS col-lars that recorded a fix every 5 min and were released into a novel 4.6-ha pasture in Oklahoma (35°53′40″ N, 94°45′21″ W) consisting of a wide array of grasses, forbs, and browse plant species. Collar data were downloaded after 1 wk, and 41,744 raw GPS fixes were corrected postdifferentially. Goats spent 62% of the time within 10 m of the fence, which was a cleared area, and 38% of the time was spent within the forested interior. Goats were more active in foraging within the interior pasture area and rested more in the cleared perimeter. In ad-dition, activity was bimodal, with a peak at 0900 h and another at 1400 h. To illustrate how quickly goats explored the entire pasture area and the differences among days in specific pasture areas used, locations at

1, 2, 4, and 6 d after introduction are shown in Figure 2. Conversely, 15 Spanish wethers, also from the AIGR research farm and introduced into the same pasture in the subsequent year, displayed a somewhat different pattern of exploration of the interior pasture area. As shown in Figure 3, some areas were not visited on 1, 2, 4, and 6 d after introduction. Moreover, on none of these days did the Spanish wethers reside in the south-eastern corner area of the pasture interior, which was a preferred location in the preceding season on each day. Factors responsible for such disparities include geno-type, environmental conditions, differences in vegeta-tion conditions, and different plant species preferences, and show the importance of multiple determinations.

CHARACTERISTICS OF GRAZING GOATS

In many areas of the world, relatively large breeds of goats of potentially high productivity have been introduced where previously only small local goats of decreased production potential resided. Research has then ensued to determine how the behavior of intro-duced and local breeds compares in relation to resource availability and use. In this regard, Dziba et al. (2003b) investigated differences between Boer and Nguni goats of South Africa in feeding height with Grewia occiden-talis L. (Tiliaceae) offered in confinement. It was ex-pected that bite size and intake rate would be greater for Boer vs. Nguni goats because of the larger mouth size of Boer goats, but this was not observed. Dry mat-ter intake rate was similar between breeds, although bite rate was greater for Nguni goats. For both breeds, an increase in bite size with increasing feeding height was greater than a decline in bite rate, resulting in an increasing rate of DMI. It was postulated that the increase in intake rate was to compensate for greater EE when feeding at high rather than low heights, but it was also suggested that the large bite size at high heights would result in a reduced nutritive value of ingested DM. Somewhat similarly, in one experiment, Van et al. (2005) observed greater intake by confined goats fed whole foliage hanging from the wall of the pen or tied in a trough compared with chopped foliage of-fered in a trough or with stripped leaves fed in a trough along with tied twigs. Intake of Acacia mangium and Flemingia macrophylla was greatest for hanging foliage and that of Artocarpus heterophyllus was greatest for foliage tied in a trough. It was postulated that a tridi-mensional arrangement or presentation that permitted goats to approach leaves from different angles elicited by hanging foliage on the wall or tying it in a trough was more conducive to high intake than the presenta-tion resulting from chopped foliage or stripped leaves and tied twigs fed in the trough.

The greater influence of bite size than bite rate on rate of DMI by goats was also observed by Dziba et al. (2003a). In this study, Boer and Nguni goats were offered 6 browse plant species in confinement in the

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summer and winter. Bite rate was greater in summer. As observed by Dziba et al. (2003b), intake rate was not different between breeds when scaled by metabolic body size to account for MEm. In agreement with the optimal foraging theory (Belovsky et al., 1999), rate of DMI was related to preferences for different browse plant species. However, in the winter, deciduous species were losing their leaves, thus necessitating a preference for evergreen species high in plant secondary metabo-lites to achieve a relatively high intake rate.

In a rangeland site in the upper Galilee of Israel with appreciable brush encroachment, Boer goats spent 22% of observed daylight time grazing herbaceous plant spe-cies compared with 44% for local Mamber goats (Aha-ron et al., 2007). There were marked differences between breeds in specific plant species browsed, with a greater diversity for Boer goats. Bite rate was slightly less for

Boer than for Mamber goats, although bite mass while browsing was similar. Eating time was greater for Boer than for Mamber goats in the spring and summer sea-sons but was similar in the winter. It was suggested that because Boer goats were less familiar with veg-etation in this region, more time was spent in testing different plant species for postingestive malaise (Forbes and Provenza, 2000).

Beker et al. (2009b) reported a shorter time spent grazing grass-forb pastures by Angora vs. Boer and Spanish goats, and Boer and Spanish goats exhibited similar grazing activities and AEC. The AEC by An-gora goats and Rambouillet sheep was less than that by Boer and Spanish goats (16, 54, 50, and 19% of MEm of confined animals for Angora goats, Boer goats, Spanish goats, and Rambouillet sheep, respectively). Although factors responsible for the differences could not be de-

Figure 2. Locations of 21 crossbred Boer goats in 2008 introduced into a novel 4.6-ha pasture of various grasses, forbs, and browse plant spe-cies on d 1 (panel A), 2 (panel B), 4 (panel C), and 6 (panel D). White dots represent fixes within a cleared fence buffer, and black dots represent fixes within the wooded interior of the pasture. Color version available in the online PDF.

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finitively determined, it was suggested that relatively short times spent grazing for Angora goats and walk-ing without grazing for sheep may have been involved. Conversely, Beker et al. (2009a) found a tendency for a greater time spent grazing or eating by Boer than by Spanish does, which corresponded to greater total EE (13.4 vs. 11.4 MJ/d), although AEC as a percentage of MEm was similar between breeds.

Fedele et al. (1993) compared the grazing behavior and diet selection of Maltese and Rossa Mediterranea goat breeds in southern Italy. Maltese goats were more selective than the Rossa Mediterranea breed, preferred grasses over forbs, and selected a relatively small num-ber of plant species. Rossa Mediterranea goats utilized a broader range of available plant species. It was sug-gested that such differences were related to the imprint-

ing or history of the breeds, with the Rossa Mediter-ranea breed normally in free-range production systems and Maltese goats managed in more controlled grazing settings. In accordance with this explanation, concen-trate supplementation reduced selectivity by Maltese goats but had a minimal effect with Rossa Mediter-ranea goats.

Three goat ecotypes of southeastern Nigeria, the Red Sokoto, the West African dwarf, and their crossbreeds, were studied by Odo et al. (2001). The Red Sokoto con-sumed relatively more dry leaves and standing hay than did the other 2 goat types, which was thought to be related to their origin in northern Nigeria, where forage and vegetation cover are dry during most times of the year. The West African dwarf preferred grazing succu-lent forages. This ecotype grazed most intensively and

Figure 3. Locations of 15 Spanish goats in 2009 introduced into a novel 4.6-ha pasture of various grasses, forbs, and browse plant species on d 1 (panel A), 2 (panel B), 4 (panel C), and 6 (panel D). White dots represent fixes within a cleared fence buffer, and black dots represent fixes within the wooded interior of the pasture. Color version available in the online PDF.

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initiated grazing sooner after morning release than the other ecotypes. The Red Sokoto breed explored furthest and showed the greatest interest in climbing trees.

Mellado et al. (2005) investigated the effects of preg-nancy and lactation on the diet composition and selec-tivity of goats in a desert rangeland of northern Mexico. There were appreciable effects of both pregnancy and lactation on the plant species selected. Browse intake was less and intake of forbs and grasses was greater for pregnant than for nonpregnant goats. Similarly, lactat-ing goats consumed more forbs and fewer shrubs than nonlactating goats. It was postulated that such differ-ences occur in response to the efforts of animals to se-lect diets that satisfy changing nutrient requirements. Conversely, with a similar experimental setting, Mel-lado et al. (2006) did not observe differences in selec-tivity between low- and high-yielding lactating goats. Grazing time was set at 8 h/d for both groups; hence, it was postulated that rate of biting was greater for high-yielding goats and was less than maximal for low-yielding goats.

GOATS IN CONFINEMENT

Diet selectivity in confinement can be considered in terms of selection of particular feedstuffs or parts of feedstuffs in mixed diets or of one or more concentrate and forage feedstuffs offered separately. The ability of goats to select in confinement is well known, in ac-cordance with a reduced rate of intake compared with sheep, thus necessitating careful consideration of the levels of feed offered when determining ad libitum in-take.

Experiments in which choices among feedstuffs are allowed involve the “nutritional wisdom” of goats in selecting a diet meeting nutrient requirements. In an experiment by Fedele et al. (2002), Maltese goats in Italy were offered 6 feedstuffs free choice, alfalfa hay, pasture hay, flaked barley, chickpeas, broad bean grain, and beet pulp, compared with other goats given alfalfa hay free choice and flaked barley at up to 50% of the energy requirement. Before parturition, DMI by free-choice goats increased by 12%, and the increase from pregnancy to lactation was greater for the free-choice than for the traditional treatment (difference of 12.6 vs. 4.6 g/kg BW0.75). Both the dietary ingredients chosen and the chemical composition of the selected diet for animals fed free choice differed among stages of pro-duction. Barley and beet pulp intake decreased during pregnancy and intake of chickpeas, broad beans, and alfalfa hay increased. The CP concentration in the diet increased with advancing pregnancy, the level of starch decreased, and NDF was unchanged. Conversely, dur-ing lactation, the dietary CP concentration decreased by 3 to 4 percentage units, the level of starch increased by 2 to 3 percentage units, and the NDF level again remained constant. Even though the dietary contribu-tion of concentrate was large (i.e., 74 to 85%), no diges-tive disturbances were noted. Likewise, Goetsch et al.

(2003) observed an average dietary concentrate level of 84% when concentrate and wheat hay were offered to young Alpine goats separately and free choice, although the dietary NDF level was appreciably less than those offered by Fedele et al. (2002). Body weight and BCS in the experiment by Fedele et al. (2002) were greater for goats fed free choice vs. traditionally, which may sup-port the observation of nutritional wisdom displayed by goats. However, such comparisons are specific to the particular control treatments imposed. It is possible that the barley-alfalfa diet in this experiment, fed when goats were dry, pregnant, and lactating, may not have been the most appropriate.

Goats are similar to sheep and cattle in the effects of dietary forage level and digestibility on time spent eat-ing and ruminating (i.e., direct relationships; Abijaoudé et al., 2000). However, as noted before, in some cases intake rate is less for goats, with a greater ingestive chewing efficiency (e.g., greater proportion of particles less than 1.0 mm in size in boluses after swallowing; Domingue et al., 1991; Van et al., 2002; Haddad and Obeidat, 2007) compared with sheep. Likewise, Haddad and Obeidat (2007) noted that both eating and rumi-nation times were greater for goats than for sheep with a high-concentrate diet. Also similar to other ruminant species, most feed consumption for confined goats oc-curs in 2 main meals separated by secondary meals (Abijaoudé et al., 2000). In the study by Abijaoudé et al. (2000), the number of secondary meals was greater for the 70 vs. 45% concentrate diet, presumably to min-imize the risk of digestive disturbance. Intake rate dur-ing secondary meals was less than during main meals, possibly because of prior selection of more palatable dietary components or a partially satiated state facili-tating greater selectivity.

Goats are thought to be more social compared with sheep and cattle, although the most appropriate bases of comparison may be difficult to discern. Van et al. (2007) conducted 2 experiments to compare the effects of group size on feed intake and aggressive behavior in goats and sheep. In one experiment, a slight linear increase in total feed intake was observed for both spe-cies as group size increased from 1 to 5 animals per pen. This was explained by increases in competition for feed, socialization, and total available space as the number of animals per pen increased, with the effect on total available space influencing the ability to control microenvironments. Goats displayed more aggressive behavior than sheep, which, for both species, increased with increasing group size. In a second experiment, to-tal intake by goats with a group size of 5 was greater than with 1 animal per pen, although the opposite dif-ference occurred for sheep.

With the automated feeding system of the AIGR de-scribed previously, Gipson et al. (2006) investigated ef-fects of the number of goats per pen or feeding station (6, 8, 10, and 12) on feed intake, growth performance, and feeding behavior. Numbers of feeder visits (17.5, 17.1, 17.9, and 18.7) and meals (8.9, 9.0, 9.3, and 8.9

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for 6, 8, 10, and 12 goats per pen, respectively) were similar among group sizes. However, feeder occupancy or eating time per animal and day (97.8, 73.2, 83.0, and 71.7 min), visit (5.8, 4.4, 5.0, and 3.8 min), and meal (11.2, 8.2, 9.2, and 8.1 min for 6, 8, 10, and 12 animals per pen, respectively) decreased linearly with increasing group size. Rate of DMI relative to feeder oc-cupancy time reached a plateau as group size increased to 8 (14.6, 24.9, 21.5, and 23.1 g/min for 6, 8, 10, and 12 animals per pen, respectively). Daily DMI and ADG changed quadratically as the number of goats per pen increased (1.45, 1.51, 1.60, and 1.37 kg/d of DMI and 156, 167, 181, and 136 g of ADG for 6, 8, 10, and 12 goats/pen, respectively). These results indicate that the intake rate of particular diets is not necessarily maxi-mal and can be markedly affected by environmental and social conditions. There may have been inadequate competition among animals for feeder occupancy time or DMI with the group size of 6, and EE could have been elevated in response to the long time spent eating. With 12 goats per pen, total feeder occupancy time was 14 h. Thus, there was considerable time when the feeder was available, but specific periods may not have been the most preferred in terms of natural behavior. It was postulated that total activity and aggressive be-haviors were greatest among group sizes of 12, and were associated with elevated EE.

Gipson et al. (2007) conducted an experiment with growing meat goats and with the same automated feed-ing system (9 goats per pen) as well as with Calan feeding gates. The 2 feeding systems yielded similar comparisons of different pelletized diets and genotypes. Dietary treatments were diets of approximately 50% concentrate or dehydrated alfalfa meal in pelletized or loose forms. Feeder occupancy time or time spent eat-ing (74, 130, 105, and 132 min/d) for the loose concen-trate diet compensated for a reduced rate of DMI com-pared with the pelletized concentrate diet (24.6, 12.9, 22.0, and 13.7 g/min for pelletized concentrate, loose concentrate, pelletized alfalfa, and loose alfalfa, respec-tively). Increased intake of pelletized alfalfa relative to the pelletized concentrate diet (1.79, 1.67, 2.04, and 1.70 kg/d) resulted in similar ADG (212, 205, 190, and 157 g) and efficiency of BW gain (127, 120, 94, and 94 g/kg for pelletized concentrate, loose concentrate, pel-letized alfalfa, and loose alfalfa, respectively) through a longer time spent eating at a similar rate of DMI. It was concluded that meat goats can markedly vary their feeding behaviors in response to different diet types and forms, but that there may be limits to such changes. For example, the least ADG for loose alfalfa resulted from feeder occupancy time per animal that was great-er than for pelleted alfalfa but that was incompletely compensatory for the decreased rate of DMI. However, with 9 animals per pen, the total length of time the feeder was occupied for diets in the loose form was 20 h/d. This length of time, coupled with that elapsed when animals exited and entered, indicates nearly full-

day feeder use, which would have prevented a greater intake of loose alfalfa meal.

In summary, numerous interactions exist between the behavior and nutrition of goats. These interactions can affect levels and efficiencies of production. Although visual observation techniques are still useful, advances in technology have resulted in the availability of equip-ment systems allowing full-day objective assessments of an array of animal behaviors. Coupling of these meth-odologies with ones to characterize nutrient utilization and energy metabolism should lead to a better under-standing of behavior × nutrition interactions that influ-ence the returns realized from the production of goats.

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