SYMPOSIA
Interrelationships among Forage Nutritive Value and Quantity and Individual Animal Performance Lynn E. Sollenberger* and Eric S. Vanzant
ABSTRACT Relationships of forage nutritive value (NVAL) and quantity with individual animal performance on pasture have long been evaluated, but there have been few attempts to describe the specific roles or relative importance of NVAL and quantity in determining animal response. The objective of this review was to more clearly define these roles based on a comprehensive assessment of pastureland literature and the application of a meta-analysis. It is clear that quantity and NVAL interact. When pastures present a wide range in forage mass, 60 to 90% of variation in average daily gain (ADG) can be explained by quantity, but there may be no relationship of NVAL with ADG. If pastures present only high forage mass, there may be no relationship between quantity and ADG, but NVAL may explain more than 50% of variation in ADG. The meta-analysis and a review of studies that evaluated forages of differing NVAL across a range of forage mass provided evidence that NVAL (i) sets the upper limit for ADG, (ii) determines the slope of the regression of ADG on stocking rate (SR), and (iii) establishes the forage mass at which ADG plateaus. In contrast, forage quantity determines the proportion of potential ADG that is achieved and is the primary driver for direction of the ADG response (negative) to increasing SR. Thus, the literature supports a conclusion of interaction among forage NVAL and quantity in affecting individual animal performance on pasture, but it suggests that the roles of each are quite well defined.
L.E. Sollenberger, Univ. of Florida, Gainesville, FL, 32611-0500; E.S. Vanzant, Univ. of Kentucky, Lexington, KY 40546-0215. Received 13 July 2010. *Corresponding author (
[email protected]). Abbreviations: ADG, average daily gain; IVDMD, in vitro dry matter disappearance; MAX ADG, presumptive maximum average daily gain; NVAL, nutritive value; SLOPE, slope of average daily gain on stocking rate; SR, stocking rate; Y-INTERCEPT ADG, y-intercept of the regression of average daily gain on stocking rate.
T
he nature of the relationships of forage NVAL and quantity with individual animal performance on pasture has been evaluated and discussed for many years (Mott, 1960; Riewe, 1961; Petersen et al., 1965; Duble et al., 1971; Hart, 1978, 1993; Guerrero et al., 1984; Wu and Rykiel, 1986). For example, it is known that increasing forage mass, starting from low levels of mass, is associated with greater bite weight and intake that lead to greater animal performance (Sollenberger and Burns, 2001). Likewise, it has been shown that at a similar level of forage mass, increasing forage NVAL is associated with greater animal performance (Guerrero et al., 1984). Thus, the general nature of animal responses to forage NVAL and quantity has been characterized. The relative importance and specific roles of forage NVAL and forage quantity in determining individual animal performance on grazed pastureland have received much less attention. Mott (1959) said, “It is important to know...whether the nutritive value of a unit of forage or the rate at which the forage is consumed is the factor exerting the greater influence on animal performance.” Guerrero et al. (1984) observed that “the relationship between animal performance and the amount and digestibility of available forage is of particular interest to the grazier.” In one of Published in Crop Sci. 51:420–432 (2011). doi: 10.2135/cropsci2010.07.0408 Published online 29 Dec. 2010. © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.
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the most direct statements regarding the relative importance and roles of NVAL and quantity, Burns et al. (1989) suggested that experimenters utilizing a range of fi xed SR should expect higher individual animal performance on pastures with greater NVAL when SR is low but lower performance on higher NVAL pastures when SR is high. They went on to suggest that individual animal performance is likely to be less responsive to SR when forage is of low NVAL. Most studies that provide insight into these relationships were not designed specifically to explain the role of forage NVAL or quantity in affecting animal performance. Instead, treatments of interest, such as SR (Hernández Garay et al., 2004), sward canopy height (Newman et al., 2002), or pasture fertilization (da Lima et al., 1999), were imposed and found to cause variation in forage quantity and NVAL and animal performance. Subsequently, relationships of these forage traits and animal performance were explored as a means of explaining the mechanisms for the effects of treatments on animal performance. Although numerous authors have related forage NVAL or quantity to individual animal performance, there have been relatively few studies where: (i) the objective was to elucidate the relative importance of these two factors and (ii) the treatment structure was such that they could be described conclusively. Defining the impact of these factors on animal performance is important and could contribute significantly to building predictive models. The overall objective of this review was to more clearly define the role and relative importance of forage NVAL and quantity in determining individual animal performance from (i) a comprehensive review of the pastureland literature and (ii) application of a meta-analysis. The preponderance of the literature suitable for addressing this question involved studies where grazing intensity was the treatment imposed. Thus, our focus is on the relative contributions of forage NVAL and quantity to individual animal performance when grazing intensity is varied. The review is organized around three specific questions: (i) How does grazing intensity affect forage NVAL and quantity in pastures? (ii) How does animal performance respond to these changes? and (iii) Have the specific roles of forage NVAL and quantity in influencing animal performance been defined, and are there interactions? After addressing these questions from the review of literature, results from application of the meta-analysis will be presented. The goal of this analysis is to identify the primary determinants of the variation in the slope of the average daily gain (ADG) response to increasing SR across a large number of pastureland studies.
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FORAGE NUTRITIVE VALUE AND QUANTITY EFFECTS ON ANIMAL RESPONSE How Does Grazing Intensity Affect Forage Nutritive Value and Quantity in Pastures? Among the many factors that affect forage quantity and NVAL on pasture are species and cultivar selection (Casler, 2000; Wilkins and Humphreys, 2003), defoliation frequency (Vendramini et al., 2008; Liu et al., 2011), grazing method (Bertelsen et al., 1993; Stewart et al., 2005), and fertilization (da Lima et al., 1999; Hernández Garay et al., 2004). A management strategy that has been shown to have a disproportionately large impact on forage and associated animal responses is grazing intensity (Sollenberger and Newman, 2007). Measures of grazing intensity include SR, forage mass, canopy height, forage allowance, and grazing pressure (Forage and Grazing Terminology Committee, 1992). Forage Quantity The literature is clear that among grazing strategies, grazing intensity plays the most prominent and consistent role in determining forage mass and allowance on pasture (Burns et al., 2002; Hernández Garay et al., 2004). In a recent literature review, it was found that forage mass decreased with increasing grazing intensity in 29 of 31 (94%) studies in which it was reported (Fig. 1), and in most cases the response to increasing SR or decreasing sward height was linear (Sollenberger et al., 2009). Forage allowance decreased with increasing grazing intensity in eight of nine studies (89%; Fig. 1). Burns et al. (1989) concluded that the predictable and highly significant relationship of individual animal performance with grazing intensity is due to the profound effect of grazing intensity on forage mass and allowance. Forage Nutritive Value Nutritive value is defined as the chemical composition, digestibility, and nature of digested products of forage (Mott and Moore, 1985) and is often expressed using crude protein, in vitro dry matter (or organic matter) disappearance (IVDMD), neutral detergent fiber, acid detergent fiber, and/ or lignin concentrations. The NVAL response to increasing grazing intensity is less consistent than the response of forage mass or allowance. In a literature review (Sollenberger et al., 2009), nearly all studies (40 of 41; 98%) that measured NVAL responses to grazing intensity reported either no effect (13 of 41; 32%) or a positive effect (27 of 41; 66%) of increasing grazing intensity on NVAL (Fig. 1). The positive effect of grazing intensity on NVAL occurs because as sward canopies are grazed intensively over an extended period of time, average maturity of regrowth decreases and the leaf proportion of forage mass is greater due to shorter
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intervals between animal visits to individual patches (Roth et al., 1990; Newman et al., 2002; Hernández Garay et al., 2004; Dubeux et al., 2006). In evaluating the question of what management strategies effect changes in forage quantity and NVAL, the consensus of the literature is that grazing intensity has a very strong effect on these responses (Sollenberger et al., 2009). From the standpoint of animal performance, increasing grazing intensity appears to create an environment in which the effects of forage quantity and NVAL are in competition. Specifically, decreases in forage allowance would, at some point, be expected to decrease individual animal performance, whereas the concomitant effects on NVAL would, by themselves, be expected to increase individual animal performance. How these effects combine to influence animal performance is the subject of the remainder of this review.
How Does Individual Animal Performance Respond to Changes in Forage Quantity and Nutritive Value? Forage Quantity Across a wide range of forage mass or allowance, individual animal performance is usually quite well explained by pasture quantity. The pattern of response often shows linear increases in ADG to increasing forage quantity when quantity is low, but as forage quantity reaches greater levels the ADG response typically plateaus. Except for a linear response of ADG to forage mass in Year 2, this was the response observed to both forage mass and forage allowance by Hernández Garay et al. (2004; Fig. 2 and 3) and to forage allowance by McCartor and Rouquette (1977; Fig. 4). These data indicate that when considered across a wide range in forage mass or allowance a high proportion of the variation in ADG (60–90%) is explained by forage quantity. It should be noted, however, that there may be no detectable relationship between forage quantity and ADG if animal response is evaluated only at greater levels of forage mass and allowance. The forage mass at which quantity no longer affects ADG will differ for different forages (Guerrero et al., 1984), but it likely will occur when animals have opportunity for selection and ad libitum intake. Forage Nutritive Value Published data can be found that support a variety of conclusions regarding the effect of forage NVAL on individual animal performance. Forage IVDMD explained 74% of the variation in ADG of cattle grazing pearl millet [Pennisetum glaucum (L.) R. Br.] “if data from heavily stocked paddocks were not used” (McCartor and Rouquette, 1977). For the data included in their equation, each 10 g kg−1 increase of IVDMD resulted in an increase in ADG of 0.026 kg. For five C4 grass species, IVDMD explained 56% of the 422
Figure 1. The effect of grazing intensity on forage mass, allowance, and nutritive value described in terms of the percentage of studies for which the response for low grazing intensity is > , = , or < the response for high grazing intensity. Number of experiments reporting data is indicated in parentheses. Higher and lower refer to grazing intensity (i.e., higher or lower stocking rate).
variation in ADG “when forage availability was not a limiting factor” (Duble et al., 1971). For data in their equation, each 10 g kg−1 increase of IVDMD increased ADG by 0.029 kg. In contrast, across a wide range of stocking rates (2.5–7.5 weanling bulls ha−1) on stargrass (Cynodon nlemfuensis Vanderyst) pastures, the relationships between animal performance and several measures of NVAL were not significant (Hernández Garay et al., 2004). Thus forage NVAL may explain more than 50% of variation in ADG when quantity is not limiting. Across a wide range in quantity, including low forage mass, there may be no detectable relationship between NVAL and ADG because of the overriding influence of forage quantity.
Are the Roles Defined For Forage Nutritive Value and Forage Quantity in Determining Animal Response? In the existing literature, there has been little emphasis on ascertaining the relative importance or the specific roles of forage quantity and NVAL in determining animal response. Our review shows a primary focus of researchers has been on establishing the existence or absence of relationship between forage characteristics and individual animal performance. In most cases this was done to explain the mechanism of animal performance response to a pasture management treatment, most often grazing intensity. When a single forage species was evaluated over a wide range of grazing intensities, the most common outcome was that individual animal performance was significantly and positively related to forage quantity and negatively related or unrelated to forage NVAL (Guerrero et al., 1984; Hernández Garay et al., 2004; Tables 1 and 2). This speaks to the importance of forage quantity in affecting animal response, but it does not elucidate i) the role of NVAL, if any, ii) the relative importance of quantity and NVAL,
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Figure 4. Average daily gain response to forage allowance (kilograms of forage mass per kilogram of animal live weight) of yearling cattle grazing pearl millet pastures. Adapted from McCartor and Rouquette (1977).
Figure 2. Weanling bull average daily gain (ADG) response to herbage mass (kilograms herbage dry matter [DM] per hectare) of stargrass pasture during 2 yr (Year 1 above and Year 2 below). Each point represents an experimental unit mean. Adapted from Hernández Garay et al. (2004).
Figure 3. Weanling bull average daily gain (ADG) response to herbage allowance (kilograms of herbage mass per kilogram of animal live weight [LW]) of stargrass pasture during 2 yr (Year 1 above and Year 2 below). Each point represents an experimental unit mean. Adapted from Hernández Garay et al. (2004). DM, dry matter.
or iii) whether there is interaction between them. There are at least two studies reported in the literature that aid in CROP SCIENCE, VOL. 51, MARCH– APRIL 2011
clarifying the roles of forage quantity and NVAL in affecting animal response to grazing intensity (Duble et al., 1971; Guerrero et al., 1984), and these studies will be discussed. Five warm-season grasses were continuously stocked at Overton, TX, for 180 d during 2 yr at four SR per grass type (Duble et al., 1971). The stated objective was “to establish relationships between laboratory measurements and animal performance and to identify characteristics of forage limiting animal performance.” Forage mass and IVDMD of the pastures and ADG of yearling F1 Brahman-Hereford heifers were measured on each experimental unit every 28 d. Data from each 28-d period were sorted based on IVDMD of the forage. The IVDMD categories were 600 g kg−1. For all data within a given IVDMD category, ADG was regressed on forage mass. These authors observed that as forage IVDMD decreased, maximum ADG decreased and greater forage mass was required in order for animals to achieve maximum ADG (Fig. 5). In a second study, five bermudagrass [Cynodon dactylon (L.) Pers.] experimental lines and cultivars were continuously stocked at College Station, TX, at four SR (Guerrero et al., 1984). Stated objectives were “to describe mathematically the relationship of animal performance to available bermudagrass forage and its digestibility.” Forage mass and IVDMD on pasture and ADG of Santa Gertrudis steers were measured on each experimental unit every 28 d during ~150 d in each of 3 yr. A measure termed “available forage” (grams forage dry matter per kilogram of animal body weight per day) was calculated by the authors for each 28-d period. Because available forage is not a preferred term (Forage and Grazing Terminology Committee, 1992), in this review the term forage allowance is used as a proxy for available forage, even though when properly expressed forage allowance
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Table 1. Effect of stocking rate of weanling bulls (average weight of 205 kg at start of trial) on measures of forage quantity, nutritive value, and bull average daily gain on stargrass pastures in Jamaica. Data are means across 2 yr, three N fertilization levels, and two replicates (n = 12) and are adapted from Hernández Garay et al. (2004). Stocking rate head ha
−1
2.5 5.0 7.5 Polynomial contrast¶
Pregraze forage mass −1
Forage allowance†
Crude protein
−1
Mg ha 6.6 4.5 2.7 Linear
−1
kg kg 7.6 2.7 1.2 Linear, quadratic
g kg 134 140 151 Linear
IVDMD‡ −1
g kg 586 593 599 Linear
NDF§
Average daily gain
g kg−1 774 762 749 Linear
kg d−1 0.68 0.54 0.31 Linear, quadratic
†
Kilograms of forage per kilogram of animal live weight. IVDMD, in vitro dry matter disappearance. § NDF, neutral detergent fiber. ¶ Linear or quadratic effect of stocking rate. ‡
Table 2. Forage allowance and in vitro dry matter disappearance (IVDMD) and cattle average daily gain responses to stocking rate (average weight of 219 kg at start of trial) on ‘Coastal’ bermudagrass pastures. Data are adapted from Guerrero et al. (1984) and were not compared statistically in the original source. Stocking rate head ha−1 5.2 7.8 9.6 12.1
Forage allowance
IVDMD†
Average daily gain
g kg−1 d−1 162 83 60 24
g kg−1 497 505 520 568
kg d−1 0.63 0.61 0.47 0.30
†
IVDMD, in vitro dry matter disappearance.
does not include a unit of time (Sollenberger et al., 2005). Data from the 28-d periods were sorted based on IVDMD of the forage, and categories were 600 g kg−1 (high). For all data within a given IVDMD category, ADG was fitted to an asymptotic function of forage allowance. For high, medium, and low IVDMD forages, approximate asymptotic ADG values were 0.94, 0.74, and 0.31 kg, respectively (Fig. 6). Forage allowances of 68 (high IVDMD), 83 (medium IVDMD), and 89 g forage kg−1 body weight d−1 (low IVDMD) were needed to achieve the asymptotic ADG value. Based on the studies with C4 grasses by Duble et al. (1971) and Guerrero et al. (1984), it can be concluded that NVAL (in this case described by IVDMD) sets the
Figure 5. Relationship between yearling F1 Brahman-Hereford heifer average daily gain and forage mass for forages of varying in vitro dry matter digestion (DMD). Adapted from Duble et al. (1971).
upper limit on individual animal performance. In addition, these studies show that NVAL determines the forage mass at which ADG plateaus, with forages of greater NVAL requiring less forage quantity to reach maximum ADG. These studies also support a conclusion that forage quantity determines the proportion of the potential ADG response that is attained, but the upper limit of potential ADG response is set by NVAL. Thus, these studies provide strong evidence of interaction of forage NVAL and quantity in affecting individual animal response. The concept of NVAL setting the upper limit on individual animal performance fits with the definition of
Figure 6. Relationship between cattle average daily gain and forage allowance (kilograms of forage dry matter per kilogram of animal body weight [BW] per day) for forages of varying in vitro dry matter digestion (DMD). Adapted from Guerrero et al. (1984).
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forage quality (Mott and Moore, 1985). They indicated that forage quality equals animal performance when animals have genetic potential for gain, forage is the sole source of energy and protein, and forage quantity is not limiting intake and ADG. Maximum ADG achieved in the studies by Duble et al. (1971) and Guerrero et al. (1984) can be considered to have occurred at the forage mass or allowance where quantity was no longer limiting intake and ADG and the quality potential of the forage was fully expressed (Fig. 5 and 6). Increasing forage quantity above this level had no or very limited effect on ADG. Thus, forage quantity has a major effect on ADG, but only in the range in which quantity is limiting. In contrast, NVAL affected ADG across a wide range of forage mass or allowance (Fig. 5 and 6). These experiments with C4 grasses refute the conclusion, often based on experiments evaluating a single forage (e.g., Tables 1 and 2), that forage quantity is the only important driver of ADG response. Such a conclusion is inaccurate and too simplistic when considered in the context of studies designed to evaluate the interaction of NVAL and quantity in affecting ADG (e.g., Duble et al., 1971; Guerrero et al., 1984).
Meta-Analysis Background and Basis for Assumptions There is a large body of published literature that describes the effect of grazing intensity on individual animal performance. These experiments derive from a wide range of environments and include many different forage species, stocking rates, sward canopy characteristics, and types and classes of livestock. There is merit to developing and analyzing a large data set including all of the studies, but there is also potential for confounding that may make it difficult to draw meaningful and well-substantiated conclusions. Meta-analysis is a tool for assessing relationships across a group of studies; therefore, meta-analysis was applied to a large collection of U.S. pastureland data to broaden the database for drawing conclusions about the role of forage quantity and NVAL in animal response. The data included were obtained from nonrangeland U.S. studies published in refereed journals over the last 48 yr. Two of the studies (Gerrish, 2000; Vanzant, 2010) do not appear in the refereed scientific literature, but they were included to provide data from underrepresented geographical regions and because all of the essential data were available in these reports. The majority of the studies utilized growing beef cattle and reported rates of gain as affected by grazing intensity. As indicated earlier, there is broad recognition and agreement that increasing grazing intensity, typically measured as SR, results in a decrease in individual animal performance. The specific nature of this decrease has been the subject of considerable discussion (Hart, 1993) and has been described as linear (e.g., Hart, 1978), curvilinear with a CROP SCIENCE, VOL. 51, MARCH– APRIL 2011
concave response surface (e.g., Mott, 1960), or curvilinear with a convex response surface (e.g., Petersen et al., 1965). Even if the linear model is an oversimplification of the true biology of the association, it appears to adequately describe the response across all but the lowest SR in the majority of studies reported. Thus, in this review, we have summarized various studies with respect to the parameters of a threshold model in which ADG is relatively unaffected at low SR and declines in a linear fashion with increasing SR. Because of the wide variation in individual animal weights used in various studies, describing SR in terms of numbers of animals per unit area is of limited value for comparisons across studies. Thus, we have described SR in terms of kilograms live weight per hectare based on live weight at the beginning of the grazing season to eliminate confounding of SR with responses to SR. Specifically, because ADG is a function of SR, use of average weights (a function of initial weight, ADG, and length of grazing season) would necessarily be confounded with SR. Additionally, and for the same reasons, the approach chosen is much more conducive to practical application and management implementation. It is well recognized that energy requirements and intake are more closely related to metabolic body weight (weight0.75) than to body weight, per se. This adjustment is particularly important when making comparisons across wide ranges of body weight, as would be the case when comparing across animal species or between young, growing animals and mature animals. Thus, the influence of SR was evaluated both as a function of body weight and metabolic body weight. Procedure for Meta-Analysis To provide a response criterion that could be quantitatively analyzed, ADG was regressed on SR for each individual study to supply both a y-intercept and a slope value. When no effect of grazing intensity was detected for a given study, the response was completely characterized by the y-intercept value that essentially describes the average of ADG across grazing intensities. In studies in which the data described a threshold model (i.e., no effect of grazing intensity at low SR with a decrease in ADG at greater SR), the slope was calculated only from the portion of the curve in which ADG decreased in response to increasing SR. Though in some studies ADG was unaffected by SR, in the majority of studies it declined with increasing grazing intensity (Table 3). To help identify factors that influence the ADG response to grazing intensity, a multiple regression approach was used to evaluate the influence of several factors on the slope of the ADG to SR response curve. For this regression, only data from studies with growing cattle were utilized. Observations were comprised of data at the fi nest level reported in the paper. Specifically, some reports found and reported differences among years; others
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Morgantown, WV State College, PA
Kentucky bluegrass–white clover Orchardgrass–Kentucky bluegrass– smooth bromegrass Rye Rye–annual ryegrass Oat Orchardgrass–Kentucky bluegrass
Ona, FL El Reno, OK
Atrapaspalum Old world bluestem Booneville, AR Linneus, MO
Eatonton, GA
Alfalfa
E(+) tall fescue E(–) tall fescue, orchardgrass–Kentucky bluegrass–red clover–birdsfoot trefoil
Shorter, AL
Johnsongrass
State College, PA
Headland, AL
Prairie, MS
Winfield, AL
Bermudagrass–subterranean clover
‘Coastal’ bermudagrass–rye–annual ryegrass
Morgantown, WV Ona, FL
Kentucky bluegrass–white clover Bahiagrass–carpon desmodium– aeschynomene–phasey-bean
Ona, FL
Stargrass
College Station, TX
Overton, TX
Pearl millet
Bermudagrass
Davis, CA Davis, CA
Location
Orchardgrass–ladino clover Orchardgrass–ladino clover
Forage species†
Bovine Bovine
Bovine Bovine
Bovine
Bovine (dairy) Bovine
Bovine Bovine (dairy) Bovine
Bovine
Bovine
Bovine Bovine
Bovine
Bovine
Bovine
Bovine Bovine
Species
Growing Growing
Growing Growing
Growing
Growing
Lactating
Growing
Growing Lactating
Growing
Growing Reprod Growing Growing Reprod Growing
Growing Growing
Growing
Growing
Growing
Growing Growing
Physiological status
Study description‡
Yr 1 Yr 2
Yr 1 Yr 2 Yr 1 Yr 2 Yr 3
Summer Yr 1 Spring Yr 2 Summer Yr 2 A - Steers Yr 1 A - Cows Yr 1 A - Calves Yr 1 A - Steers Yr 2 A - Cows Yr 2 A - Calves Yr 2 Spring Yr 1 Summer Yr 1 Spring Yr 2 Summer Yr 2
A - Light steers A - Heavy steers Yr 1 Yr 2 C – UF-5 C - UF-4 C – McCaleb C - Coastal C - Callie C - S-54 C - SS-16 C - S-16
Table 3. Effect of grazing intensity on animal performance responses.
268 250
239 227
272
222
204
320 580
250*
285 264 227 279 334 462 140 334 462 140
219
240
279 244 316 240
kg
Avg. initial weight High SR
1918 2300 1532 1444 1767 2630 2925 2655 1600 1346
1009 1262 1009
505 757 666
1152 1443 732 672 712 710 735 1085 800 336
3228 3294 4266 2546 2724 3480 3324 3396 2650 2781 2190 2475 1949 1425 1320 1135 2093 2484 4253 4253 2484 4253 4253 1246 1125 1654 1117 1600 2262
931 1098 1422 1709 895 1800 1800 1800 1139 1270 854 942 788 855 528 454 837 1224 1485 1485 1224 1485 1485 750 860 750 670 960 1450
––––kg live weight ha−1––––
Low SR§
CTS CTS ROT
ROT CTS
CTS
CTS
ROT
CTS
CTS ROT
CTS
CTS
CTS CTS
CTS
ROT
CTS
ROT ROT
Slope
0.42 1.43 1.14 1.19 1.15 0.80 0.83 1.04 0.79 1.00 0.86
0.000 −0.580 −0.524 −0.221 −0.297 −0.130 −0.170 −0.202 0.000 −0.438 −0.304
−0.523 −0.369 −0.280
−0.179 −0.222 −0.044 0.000 −0.667 0.000 0.000 −0.759 −0.756 −0.160 −0.491 −0.594
0.00 1.10 0.30 0.56 1.90 0.17 0.58 1.69 1.22 1.19 0.87 1.57
1.29 1.37 1.30
−0.190 −0.170 −0.120 −0.635 −0.572 −0.145 −0.223 −0.132 −0.328 −0.340 −0.426 −0.367 −0.392 −0.526 −0.244
g d−1 (kg live weight ha−1)−1
1.04 0.95 0.69 2.03 1.83 0.759 0.904 0.664 1.17 1.32 1.23 1.23 1.24 1.04 0.79
kg d−1
Grazing method¶ Intercept
(cont’d)
Aiken and Piper, 1999 Gerrish, 2000
Kalmbacher et al., 1997 Coleman and Forbes, 1998
Bates et al., 1996
Rankins and Bransby, 1995
Fales et al., 1995
Kouka et al., 1994
Bryan and Prigge, 1994 Holden et al., 1994
Fairbrother et al., 1992
Aiken and Bransby, 1992
Bryan and Prigge, 1990 Aiken et al., 1991
Guerrero et al., 1984
Adjei et al., 1980
McCartor and Rouquette, 1977
Hull et al., 1965
Hull et al., 1961
Reference
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Caprine Bovine
Langston, OK Hope, AR Booneville, AR Stephenville, TX Versailles, KY
Lake Placid, FL
Dallisgrass–common bermudagrass E(+) tall fescue
Arrowleaf and crimson clover–burr and button medic–annual ryegrass–Bromus spp. E(+) tall fescue
Bahiagrass
Raleigh, NC Linneus, MO
Steeles Tavern, VA
Bermudagrass Stockpiled tall fescue
E(+) tall fescue, clover
Bovine
A - With implant A - Without implant
631
A - Unsupplemented A - Supplemented
Lactating Growing Growing Reproductive or growing Reproductive or growing
Growing
A - Does A - Kids
NR
43 12.5 277
212
530
243 245 285 308
25
231 279
345 511 506 21
239 232 237 265 141 265 160 248 435
kg
Avg. initial weight
Yr 1 Yr 2 Yr 3 Yr 4 A - Light steers Yr 1 A - Heavy steers Yr 1 A - Light steers Yr 2 A - Heavy steers Yr 2
Study description‡
Yr 1 Yr 2 Yr 3 Yr 4 Reproductive or F - Summer pasture growing F - Winter pasture Yr 1 Reproductive Yr 2 Growing
Growing
Growing Growing
Growing
Growing Lactating
Lactating
Growing
Growing
Growing
Physiological status High SR
312.5
2564.1 1674
2933 5110 3795 420
1088
2796 2436 1872 2094 840
4460
562
2790
0.91 ha cow–1 0.71 ha cow–1
3102
262
1461
557 2507 646 3068 656 3002 580 2845 1.35 ha cow–1 0.58 ha cow–1 2.16 ha cow–1 0.93 ha cow–1 445.2 630.7
125
854.7 837
2035 3832 2530 210
652
1410 1230 1256 1404 392
––––kg live weight ha−1––––
Low SR§
ROT
CTS ROT
ROT
ROT ROT CTS
CTS
CTS
CTS
CTS CTS
ROT
ROT
CTS
CTS
CTS
CTS
ROT
0.13 0.11 1.37
1.54
1.08 0.92 1.38 0.37 0.88
0.97 1.28 0.77 0.16
0.07
1.17
1.12 1.31 0.85 1.54 1.00 1.48 0.85 0.78 0.53
kg d−1
Grazing method¶ Intercept Slope
−0.335 −0.130 −0.218
−0.363
−0.219 −0.205 −0.269 0.000 0.000
−0.224 −0.358 0.000 −0.225
−0.067
−0.247
−0.335 −0.299 0.000 −0.391 0.000 −0.500 −0.300 0.000 0.000
g d−1 (kg live weight ha−1)−1
Scaglia et al., 2008
Burns and Fisher, 2008 Curtis et al., 2008
Stuedemann and Franzluebbers, 2007 Yiakoulaki et al., 2007
Driskill et al., 2007
Arthington et al., 2007
Vanzant, 2010
Muir, 2006
Gunter et al., 2005 Aiken et al., 2006
Animut et al., 2005
Newman et al., 2002 Fike et al., 2003
Bargo et al., 2002
Valencia et al., 2001
Ackerman et al., 2001
Schlegel et al., 2000
Reference
E(+) = endophyte-infected; E(–) = endophyte-free. For species not previously identified, the scientific names are as follows: aeschynomene (Aeschynomene americana L.); alfalfa (Medicago sativa L.); annual ryegrass (Lolium multiflorum Lam.); arrowleaf clover (Trifolium vesiculosum Savi); atrapaspalum (Paspalum atratum Swallen); bahiagrass (Paspalum notatum Flüggé); birdsfoot trefoil (Lotus corniculatus L.); burr medic (Medicago polymorpha L.); button medic [Medicago orbicularis (L.) Bartal.], carpon desmodium [Desmodium heterocarpon (L.) DC.]; clover (Trifolium spp.); crimson clover (Trifolium incarnatum L.); dallisgrass (Paspalum dilatatum Poir.); johnsongrass [Sorghum halepense (L.) Pers.]; Kentucky bluegrass (Poa pratensis L.); ladino clover (Trifolium repens L.); limpograss [Hemarthria altissima (Poir.) Stapf & C.E. Hubb.]; oat (Avena sativa L.); old world bluestem (Bothriochloa spp.); orchardgrass (Dactylis glomerata L.); phasey bean [Macroptilium lathyroides (L.) Urb.]; red clover (Trifolium pretense L.); rhizoma peanut (Arachis glabrata Benth.); rye (Secale cereale L.); smooth bromegrass (Bromus inermis Leyss.); subterranean clover (Trifolium subterraneum L.); tall fescue [Schedonorus arundinaceus (Schreb.) Dumort = Lolium arundinaceum (Schreb.) Darbysh.]; white clover (Trifolium repens L.). ‡ A = animal description; C = forage cultivar or germplasm description; F = forage type; and Yr = year number. § SR, stocking rate. ¶ ROT, rotational stocking; CTS, continuous stocking.
†
Caprine
Langston, OK Bovine Bovine
Bovine
Farmington, GA
‘Coastal’ bermudagrass with inorganic N, crimson clover, or broiler litter Bermudagrass– johnsongrass
Bovine
Ames, IA
Stockpiled E(–) tall fescue–red clover
Bovine
Bovine (dairy) Bovine Bovine (dairy) Ovine or caprine Bovine Bovine
State College, PA Gainesville, FL Gainesville, FL
Bovine
Brooksville, FL
Rhizoma peanut–bahiagrass– bermudagrass Smooth bromegrass–orchardgrass– Kentucky bluegrass Limpograss ‘Tifton 85’ bermudagrass Rhizoma peanut Bermudagrass–johnsongrass–forbs
Bovine
Bovine
Species
Stillwater, OK
East Lansing, MI
Location
Old world bluestem
Alfalfa
Forage species†
Table 3. Continued.
reported differences among forage treatments, grazing strategies, etc. Thus, 58 observations were included in the regression analysis from 26 independent reports (Table 3). It seems reasonable to assume that much of the variation in the slope of the ADG response to increasing grazing intensity would be related to the average forage mass across the grazing season. However, since forage mass is affected by grazing intensity, it is necessary to derive a construct that represents the theoretical average forage mass in the absence of grazing pressure. Thus, for studies in which the data were available (n = 26 of the total 58 observations) grazing intensity effects on average seasonal forage mass were removed by calculating the theoretical forage mass that would have been available at a SR of 0 kg live weight ha−1. This was accomplished by regressing measured forage mass (averaged across sampling dates during the grazing season) against SR and determining the y-intercept of this relationship for each study. This “zero-grazing” average forage mass was then used in the multiple regression models, described later, to predict the slope of the ADG response to SR. Some researchers have demonstrated a clear relationship between forage allowance (kilograms forage mass per kilogram animal live weight) and animal performance (McCartor and Rouquette, 1977; Sollenberger et al., 2005). For purposes of modeling the grazing intensity effect, however, forage allowance could not be used because it is, by definition, dependent on grazing intensity. A variable that could be viewed as an integrated measure of forage quantity and NVAL is the y-intercept of the regression of average daily gain on stocking rate (Y-INTERCEPT ADG). In studies in which there was no detectable effect of SR on ADG (i.e., slope = 0), this response would represent the average ADG across all SR. Given the threshold model, however, it is apparent that the y-intercept determined from the linear portion of the curve would tend to overestimate the true “maximum” ADG on a given forage base. Thus, in addition to the Y-INTERCEPT ADG, we also calculated a presumptive maximum average daily gain (MAX ADG) based on the threshold model. In studies in which sufficiently low SR were used to detect the threshold, this MAX ADG represented the average response across the threshold. For studies in which only the linearly declining portion of the curve was evident from the data, the MAX ADG was presumed to be the ADG at the lowest SR used in the study. Although this approach likely underestimated the true maximum ADG in some studies, it is likely that the error would be smaller than that introduced using the y-intercept approach. For 21 of 58 observations, sufficient data were available to calculate average monthly precipitation across the duration of the grazing season. While we recognized that inclusion of rainfall across some period before the start of the grazing season may improve the relationship with forage growth, and thus, with animal gains, such data were not 428
available for most studies and were therefore not used. Furthermore, we recognized the importance of the temporal component in attempting to evaluate potential influences of precipitation (e.g., a single 7-cm precipitation event is not the functional equivalent of 7 cm of precipitation received over a 1-wk period). However, we were constrained by the availability of precipitation data as reported in the literature. Potential effects of geographical location were accounted for by including latitude and longitude measures in the multiple regression. The presence of grass (37 observations), legume (seven studies), or combined grass–legume stands (14 studies) were coded using dummy variables, as was the C-assimilation pathway of the dominant forage (C3 = 24 observations; C4 = 34 observations). Additionally, the multiple regression included terms for the length of the grazing period (in days), average initial weight of the grazing cattle, and the low and high SR (kilograms live weight per hectare) used in each study. In addition to the nontransformed independent variables, we also evaluated various transformations. These were generated using SAS Enterprise Miner (v. 4.3) (SAS Institute, 2004). For each independent variable, two transformations were assessed, one to maximize the normality of the distribution of the independent variable and the second to maximize the correlation with the slope of average daily gain on stocking rate (SLOPE). Transformations of the variables generally had no appreciable influence on the fit of the model; thus, except where noted, the model data reported refer to the fit with nontransformed input variables. Models were fit using the stepwise method of SAS (v. 9.1) PROC REG (SAS Institute, 2008) with significance levels for variable entry and retention both set at p = 0.15. Because incomplete data were available for precipitation and “zero-grazing” forage mass, relationships between these variables and SLOPE were initially analyzed in single regression models. Neither “zero grazing” forage mass nor transformations of it was associated with SLOPE (p ≥ 0.26). Likewise, neither monthly average precipitation nor one of its transformations was associated with SLOPE (p ≥ 0.34), although when precipitation was transformed to provide the highest correlation with SLOPE, the relationship (p = 0.10) explained 21% of the variation in the SLOPE response. However, when precipitation was included in multivariate analysis, it did not enter the model (either transformed or not), presumably because the indirect effects of precipitation on forage quantity were better captured in other variables. It is somewhat surprising that neither estimates of forage mass nor precipitation had much detectable influence on the slope of the ADG response to increasing SR. This can be partly explained by the limited amount of data available for these measures, the large variation associated with measures of forage mass, and the assumptions that were necessary to calculate our estimate of “zero-grazing” forage mass.
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Results of Meta-Analysis Ultimately, a four-variable model was derived using all 58 observations that accounted for 69% of the variation in SLOPE (Table 4; Fig. 7). Most of the variation in the slope of the ADG response was attributable to differences in the Y-INTERCEPT ADG. When both Y-INTERCEPT ADG and MAX ADG were included in the model, MAX ADG, though significant, accounted for only about 13% of the variation in SLOPE, compared with 56% for Y-INTERCEPT ADG. Because these two variables were ostensibly estimates of the same parameter (the Pearson correlation coefficient between the two variables was 0.78), MAX ADG was eliminated from subsequent models. Thus, from this data set, the strongest predictor of the slope of the ADG response to increasing SR was the estimate of ADG at a theoretical zero SR. The greater the estimated gain of cattle at low SR, the more rapid the decrease in ADG with increasing SR. This relationship was not only evident across the entirety of the data set but was also evident within most individual reports. Of the 26 independent reports in this analysis, 16 contained multiple (i.e., two or more) observations, detailing differences among various years, forage species or cultivars, implant strategies, initial steer weights, or grazing strategies. Numerically, 13 of 16 demonstrated more negative slopes when values for the y-intercept were greater. The other three showed essentially no effect of Y-INTERCEPT ADG on SLOPE. Responses from a couple of these studies can elucidate some underlying mechanisms behind this relationship. Specifically it appears that higher growth potential of individual animals (i.e., greater Y-INTERCEPT ADG) results in greater grazing pressure at an equivalent live weight. Aiken et al. (2006) evaluated the influence of implanting cattle on production responses to increasing SR. As expected, implanted cattle had greater ADG than nonimplanted cattle. Additionally, they reported decreases in ADG with increasing SR for implanted but not for nonimplanted steers. Increased ADG, in this case as a consequence of a growth-promoting implant, would be expected to result in increased forage demand for each kilogram of animal live weight, thus explaining the more negative SLOPE response with increasing Y-INTERCEPT ADG. Earlier work by Hull et al. (1965) demonstrated that across a 4-yr period, lighter steers gained more than heavy steers with a greater negative impact of increasing SR on ADG in the lighter, more rapidly growing steers. Comparing three cultivars of stargrass in a 2-yr study, Adjei et al. (1980) reported a similar negative association between Y-INTERCEPT ADG and SLOPE. Applying this mechanistic explanation to our analysis suggests that greater NVAL forages, capable of sustaining greater levels of ADG, are associated with greater forage intake per unit of live weight and thus with steeper SLOPE. This is in keeping with an abundance of data indicating that CROP SCIENCE, VOL. 51, MARCH– APRIL 2011
forage intake increases with increasing forage NVAL. Thus, any factor that leads to greater forage NVAL and intake will increase the rate at which forage is consumed in response to increasing SR and ultimately accelerate the rate of decrease in ADG with increasing SR. This supports the observations of Burns et al. (1989) that ADG is likely to be less responsive to SR when pasture NVAL is low. There are occasional reports of improvements in animal performance with increased grazing intensity (Bryan and Prigge, 1994; Fike et al., 2003; Burns and Fisher, 2008). In general, such improvements are attributed to increased diet nutritive value. Decreasing fiber and lignin concentrations, and subsequent effects on digestibility, and changes in protein concentration and degradability can moderate the effect of grazing intensity on animal performance and likely have influence on the slope of the ADG response. However, within the range of SR typically studied, the overwhelming influence of grazing intensity on animal performance appears to be mediated through decreased intake consequent to decreases in forage mass. Much smaller but significant portions of the variation in SLOPE were explained by variables that coded for the presence of grass and/or legumes. The only “all legume” studies included in this analysis were studies with alfalfa (Medicago sativa L.), but a number of legume species were present in mixed grass–legume associations. Thus, the coefficients associated with these indicator variables show little difference in the effect of grazing intensity on ADG from alfalfa as compared with “grass-only” pastures but that a greater influence existed in mixed grass-legume stands. The nature of the relationship between grazing intensity and animal performance is likely more complex in stands containing mixtures of species than in monoculture stands because of variable effects of grazing intensity on the growth, persistence, physiological maturity, and nutrient composition of different species. This result should be viewed with caution because the number of studies was small, and it may be an artifact of these particular experiments. A small, but significant source of variation was accounted for by differences in latitude. According to this model, at northern latitudes, an increase in SR has a somewhat reduced effect on ADG than a similar increase in SR at southern latitudes. The sites of the studies in this analysis ranged from 27.4 to 42.7° N latitude. The regression model indicates that, all other things being equal, a SR increase of 1000 kg live weight ha−1 accounts for a 0.13 kg d−1 smaller decrease in ADG in East Lansing, MI, than it would in Ona, FL. Based on expected regional differences in forage NVAL due to use of C3 vs. C4 forages, the opposite effect may be anticipated (i.e., greater NVAL in Michigan than Florida, thus a more rapid decline in ADG in Michigan). Thus the latitude effects are not well understood. It was interesting to note that length of grazing season did not affect SLOPE. Others have reported a significant
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association between length of the grazing season and the magnitude of the SR effect (Gerrish, 2000). Gain responses to SR are more evident late in the grazing season, as forage quantity becomes more limiting. However, such effects would be largely negated by experiments in which variable SR were used to maintain consistent sward characteristics, as was the case in about 36% of the observations in these data.
SUMMARY AND CONCLUSIONS
Table 4. Significant terms in the multivariate regression of factors affecting the slope (g d−1) of the average daily gain (ADG) response to increasing stocking rate (SR; kg initial live weight ha−1). Variable
Estimate ± SE
Intercept 0.0725 ± 0.1665 y-intercept gain† −0.4424 ± 0.0420 Legume‡ −0.1725 ± 0.0384 Grass§ −0.1275 ± 0.0567 0.0085 ± 0.0042 Latitude¶
p>F 0.67
