Global Change Biology (2010) 16, 2901–2911, doi: 10.1111/j.1365-2486.2009.02060.x
Climate change and cattle nutritional stress
J O S E P H M . C R A I N E *, A N D R E W J . E L M O R E w , K . C . O L S O N z, D O U G T O L L E S O N § }
*Division of Biology, Kansas State University, Manhattan, KS 66506, USA, wAppalachian Lab, University of Maryland Center for
Environmental Science, Frostburg, MD 21532, USA, zDepartment of Animal Sciences and Industry, Kansas State University,
Manhattan, KS 66506, USA, §Department of Ecosystem Science and Management, Texas A&M University, College Station,
TX 77840, USA, }School of Natural Resources, The University of Arizona, Cottonwood, AZ 86326, USA
Abstract
Owing to the complex interactions among climate, plants, cattle grazing, and land management practices, the impacts of climate change on cattle have been hard to predict. Predicting
future grassland ecosystem functioning relies on understanding how changes in climate alter
the quantity of forage produced, but also forage quality. Plant protein, which is a function of
plant nitrogen concentrations, and digestible energy limit the performance of herbivores
when in short supply; moreover, deficiencies can be expensive to mitigate. To better understand how changes in temperature and precipitation would affect forage protein and energy
availability, we analyzed over 21 000 measurements of cattle fecal chemistry acquired over
14 years in the continental US. Our analysis of patterns in forage quality among ecologically
defined regions revealed that increasing temperature and declining precipitation decreased
dietary crude protein and digestible organic matter for regions with continental climates.
Within regions, quality also declined with increased temperature; however, the effects of
precipitation were mixed. Any future increases in precipitation would be unlikely to
compensate for the declines in forage quality that accompany projected temperature increases.
As a result, cattle are likely to experience greater nutritional stress in the future. If these
geographic patterns hold as a proxy for future climates, agriculture will require increased
supplemental feeds or the consequence will be a decrease in livestock growth.
Keywords: cattle, climate, digestible organic matter, grazing, protein
Received 13 August 2009 and accepted 29 August 2009
Introduction
Changes in climate have the potential to dramatically
alter grazed ecosystems, yet we have little understanding how climatic warming and altered precipitation
affects cattle (Easterling et al., 2007). Predicting future
grassland ecosystem functioning relies on understanding how changes in climate alter the quantity of forage
produced (Shaw et al., 2002; Huxman et al., 2004) and
also forage quality. Worldwide, o20% of the energy
required by cattle to reach market weight is derived
from cereal crops, while the remainder is derived from
rangeland, pasture, other sources of roughage (Wheeler
et al., 1981; Oltjen & Beckett, 1996). Plant protein, which
is a function of plant nitrogen concentrations (Van
Soest, 1982), and digestible energy limit the performance of grazing cattle when in short supply (Poppi
& McLennan, 1995); moreover, deficiencies are expenCorrespondence: Joseph Craine, tel. 1 1 785 532 3062, fax 1 1 785
532 6653, e-mail: [email protected]
r 2009 Blackwell Publishing Ltd
sive to mitigate. To better understand how climate
change affects cattle, scientists must consider not only
the overall quality of forage but also the associated
phenological changes since the timing of these changes
in quality can ultimately affect animal reproductive
success, as well as seasonal marketing and movement
patterns (Frank et al., 1998).
Predictions of how forage quality will be affected by
changes in temperature and precipitation are varied
and conflicting. Increases in temperature are thought
to favor C4 grasses (Easterling et al., 2007), which are
generally considered to be of lower quality to grazing
animals than C3 species (Ehleringer et al., 2002). Yet,
empirically, C3 and C4 grasses have similar ranges in
quality (Ehleringer et al., 2002; Craine et al., 2005);
moreover, warming during fall or spring favors C3
species and extends the period of high-quality foraging
(Alward et al., 1999; Menzel et al., 2006; Sherry et al.,
2007). Some grassland warming studies have shown
declines in foliar N concentrations (Link et al., 2003;
An et al., 2005), yet the effects of warming on plant
2901
2902 J . M . C R A I N E et al.
communities depend on whether herbivores are present
(Post & Pedersen, 2008). Periods of low precipitation
can reduce plant N concentrations (Hayes, 1985) and
grazing animal biomass generally decreases along gradients of decreasing precipitation (Fritz & Duncan,
1994). Yet on regional scales, plant N concentrations
are thought to increase with decreasing precipitation
which can enhance herbivore nutrition (Breman & de
Wit, 1983; Ellery et al., 1995; Murphy et al., 2002).
The conflicting predictions regarding the effects of
climate on cattle can be difficult to rectify given the
variable bases for prediction. Experiments or responses
of grasslands to interannual variation in climate can
provide first principles from which to predict the functioning of future grasslands. Yet, climate change experiments are generally too small to allow grazing to be
adequately characterized. Furthermore, interannual
variation happens too quickly for plant communities
and ecosystem processes to respond. Land managers
are also unable to adjust their practices to interannual
climate variation in the same way that would occur
with longer periods of climate change. In contrast,
climate gradients are likely to provide useful analogs
for future climates (Rastetter, 1996; Araujo et al., 2005;
Fukami & Wardle, 2005; Menzel et al., 2006). Climate
gradients encompass a large enough scale to incorporate effects of climate on grazing animals as well as
slower processes such as changes in ecosystem properties and management strategies. Despite this potential,
robust datasets that examine how plant quality patterns
change along broad spatial gradients are limited, likely
associated with our inability to remotely sense plant N
concentrations at broad scales and the lack of organized
continental-scale grassland monitoring.
To better understand large-scale geographic relationships between climate and grassland forage quality
patterns, we utilized a continental-scale, long-term
database of cattle fecal chemical composition to test
the influence of climate on the concentration of plant
protein and energy, as well as the timing of peak plant
protein and energy. More specifically, variation within
and among eco-regions in the timing and magnitude of
variation in maximum and minimum crude protein
(CP) and digestible organic matter concentrations
(DOM), indices of the protein and energy, respectively,
available to grazers, are analyzed with respect to mean
annual temperature (MAT) and precipitation (MAP) of
regions or sites. There are few data from which to
derive hypotheses for the patterns. For example, in
native grasslands, plant N concentrations generally
decline with increased precipitation, e.g. (Breman &
de Wit, 1983; Craine et al., 2005). Yet, it is unknown
what relationships will be when managed grasslands
are included.
Materials and methods
Data acquisition
The cattle diet quality values were derived from a
dataset accumulated by the Grazingland Animal Nutrition Lab, a commercial service and research laboratory
of the Ecosystem Science and Management department
at Texas A&M University (Lyons & Stuth, 1992). Since
1993, the GAN Lab has applied near infrared spectroscopy (NIRS) of feces to predict dietary CP and DOM of
grazing livestock and wildlife (Roberts et al., 2004).
Livestock producers and resource managers across the
US collect fresh fecal samples from 5 to 10 animals
generally, and then mail them to GAN Lab fresh or
frozen via carriers providing 2-day delivery. Upon
arrival at the lab, samples are processed using the NIRS
methods of Lyons & Stuth (1992). Briefly this involves
drying fecal material at 60 1C in a forced air oven,
grinding to 1 mm particle size and redrying at 60 1C
before scanning. Spectra (400–2500 nm) were collected
on a Fosss NIRS 6500 scanning monochrometer (Foss
NIR Systems Inc., Silver Spring, MD, USA) with spinning cup attachment. Reference chemistry and chemometrics for NIRS calibration development that link
forage chemistry and fecal spectra were as described
by Showers et al. (2006). Calibration development and
validation involves creation of diet reference chemistry : fecal NIR spectra (D : F) pairs. These D : F pairs
(n 5 620) were derived from spatially and temporally
diverse conditions, primarily from southern, central
and northern Texas, Oklahoma, South Dakota, Nebraska, Montana and Missouri (J. Stuth, unpublished data).
Data workup
Data on CP and DOM were compiled between January 1,
1994 and November 1, 2007. All data points that were
associated with animals that had received supplemental
food such as hay or grain (or were allowed to graze on
alfalfa) were removed from the dataset. As some locations
provided multiple samples from the same herd for a given
date, all data were averaged for each location at each date.
Data are not evenly distributed among years, with some
years having more than 3000 data points, e.g. during the
year 2000 when USDA Natural Resources Conservation
Service provided data on a monthly basis from a large
number of sites, while only 683 points were provided in
1994, which was early in the program’s existence.
As the amount of data from any one location was
generally too low and/or too unevenly spaced in time
to characterize and compare seasonal patterns of diet
quality among individual sites, patterns of CP needed
to be analyzed at the regional level. After finalizing
r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 2901–2911
C L I M AT E C H A N G E A N D C AT T L E N U T R I T I O N A L S T R E S S
inclusion of data points into the dataset, each sample
was then ascribed to one of 57 ecoregions for the
continental US as detailed by World Wildlife Fund
(Olson et al., 2001) (Fig. 1). These regions are derived
in part from previous biogeographic regions, but had
been modified to incorporate finer-scale patterns of
species distributions, which are influenced by climate
as well as other state factors. After mapping samples
onto the ecoregions, we then examined the distribution
of the data over the course of a year. Many regions had
too few data points at some part of the year to determine seasonal curves and data in these regions were
excluded from the analysis of seasonal patterns. For
example, the upper Midwest has too much snow in the
winter for grazing. Of the 209 points in the region, only
10 fell between November 1 and April 1. No attempt
was made to aggregate regions. In all, 21 245 data points
were included in the data set spread among 43 regions
(Fig. 1). Some regions had as many as 3400 points, while
no region had o30.
Among the 43 regions that were deemed to have data
that were sufficiently arrayed across the year in order to
determine some estimates of seasonal patterns of CP,
MAT across regions varied from 4.9 1C (Colorado
Rockies Forests) to 22.1 1C (Tamaulipan mezquital)
(Table 1). MAP varied across regions sixfold from
202 mm yr!1 for the Mojave desert region to
1271 mm yr!1 for the Mississippi Lowland Forests region.
Data analysis
To assess the seasonal patterns of CP, the seasonal time
course of CP was quantified by fitting a spline curve to
the CP data for each region as a function of day of year
2903
with data from all years joined together. To provide
smooth transitions in CP across the end and beginning
of the year, the dataset was replicated twice with day of
year offset by !365 and 1 365 in each replicate and
splines fit across the 3-year period. For each spline fit for
each region, predicted values of CP were saved for each
day of year and the maximum and minimum CP values
described by the curve as well as the dates at which
they occurred were determined for each region. We
tested splines of various l and found that splines with
l 5 106 appeared to best capture a relatively smooth
progression of CP over time, with little qualitative
difference in results from splines with lower l (i.e.,
coefficients that represent more flexible fits). For example, splines with lower l led to greater CPmax, but the
timing and relative magnitude of CPmax among sites
was relatively unchanged.
To determine the rate at which CP increases or
decreases around its peak, from the same set of predicted CP values from the spline fits, we determined CP
60 days before CPmax as well as 60 days after CPmax.
With the these values, we calculated the rate at which
CP increases over the 60 days before CPmax (CPup) as
well as the rate at which CP decreases over the 60 days
after CPmax (CPdown). There was little qualitative differences in the results whether 30 or 60 days were used
(data not shown).
To determine the role of climate in explaining variation in CP among regions, for each of the six variables
determined for the seasonal CP curves for each region
(CPmax, DOY for CPmax, CPmin, DOY for CPmin, CPup,
CPdown) we ran a regression model that include MAT,
MAP, and the interaction between the two. Some regions, such as the Central US hardwood forests region,
Fig. 1 Map showing distribution of data on fecal chemistry. Shaded regions had insufficient data to analyze relationships with climate.
r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 2901–2911
Ecoregion name
Sierra Madre Oriental pine-oak forests
Allegheny Highlands forests
Appalachian mixed mesophytic forests
Appalachian-Blue Ridge forests
Central U.S. hardwood forests
East Central Texas forests
Mississippi lowland forests
Ozark Mountain forests
Southeastern mixed forests
Southern Great Lakes forests
Upper Midwest forest-savanna transition
Arizona Mountains forests
Blue Mountains forests
Colorado Rockies forests
Northern California coastal forests
Piney Woods forests
Sierra Nevada forests
South Central Rockies forests
Southeastern conifer forests
Western Gulf coastal grasslands
California Central Valley grasslands
Central and Southern mixed grasslands
Central forest-grasslands transition
Central tall grasslands
Edwards Plateau savanna
Flint Hills tall grasslands
Montana Valley and Foothill grasslands
Nebraska Sand Hills mixed grasslands
Northern mixed grasslands
Northern short grasslands
Palouse grasslands
Texas blackland prairies
Western short grasslands
California coastal sage and chaparral
California interior chaparral and woodlands
Chihuahuan desert
ID
50303
50401
50402
50403
50404
50405
50409
50412
50413
50414
50415
50503
50505
50511
50519
50523
50527
50528
50529
50701
50801
50803
50804
50805
50806
50807
50808
50809
50810
50811
50813
50814
50815
51201
51202
51303
276–409
805–980
791–1274
769–1340
809–1222
513–891
1021–1377
914–1081
860–1391
615–836
478–755
255–486
292–529
259–471
945–1419
901–1222
368–1193
299–459
985–1355
523–1351
181–841
384–727
554–957
423–769
408–730
679–804
269–419
373–449
341–474
251–492
313–602
636–913
240–564
302–448
234–862
207–400
0.05
0.05
0.06
0.06
0.01
0
0.01
0.01
0.02
0.49
0.10
0.01
0.05
0.08
0.01
0.04
0.05
0.12
0.02
0.01
0.11
0.07
0.32
0.17
0.08
0.05
0.16
0.03
0.14
0.07
0.14
0.02
0.01
0.21
0
0
MAT range MAP range r2
57 14.1–17.1
69
6.6–9.1
661
8.7–15.1
386
8.5–15.8
1278 11.3–15.6
454 17.8–21.6
494 14.9–20.4
248 14.2–16.3
639 11.3–19.6
113
6.8–11
215
4.1–8.1
148
7.4–14.8
145
5–8.1
121 !0.5–8.6
108 10.4–11.8
688 16.4–20
119
5.2–13.8
160
2.5–7.9
499 18.1–23.2
1045 19.4–22.5
141 15.1–17.5
1549
8.8–18.2
1708
10–18.9
212
5.2–11.6
628 17.6–21.7
232 12.1–15.1
388
4.5–7.7
163
8.3–9.6
151
2.8–8.3
3494
3.8–8.7
104
5.1–10.9
255 17.1–20.7
1864
7–18.3
49 13.1–17.6
271
12–17.3
723 11.5–19.4
N
CP
!0.038***
!1.05***
!0.19***
!0.72**
!1.04***
!0.34***
!0.53***
!1.01***
!0.42***
!0.38*
!0.75***
!0.57***
MAT
!0.094***
!0.039**
0.36***
0.14***
0.07*
!0.05**
!0.038*
MAT
MAP
315.51*
0.38 128.24*
0.02
0.05 !6.45***
0.02
!4.39*
0.03
7.31***
0.02
0.03 !9.4*
0.00
0.11
!3.13*
!3.94*
0.13 !15.6**
0.15
0.07
0.02
0.07 !2.29*
0.02
0.07 !10.12***
0.02
0.09
0.04
12.36**
0.01
10.68**
3.76**
0.09
0.07 !3.37***
2.32*
0.07 !2.46***
5.11***
0.09 !12.63***
18.02***
0.02
0.03
0.16 !9.81*** !23.5***
0
0.14 !11.84***
40.8***
0.05 !2.77***
10.42***
0.1
4.35*
0.02
0.02
0.91***
0.07
0.03 !3.33*
!2.75*
0
MAT " MAP r2
0.13*
!0.034***
0.082*** !0.025***
0.22*** !0.027***
!0.031** !0.023*
0.2**
!0.27***
0.16**
!0.075
!0.051***
0.11**
!0.17*
!0.055***
MAP
DOM
!8.43*
!2.08***
1.68*
16.42**
!4.5***
!1.54**
5.93***
!5.22*
!8.58***
!5.49***
1.63*
MAT " MAP
Table 1 Effects of interannual variation in mean annual temperature (MAT) and mean annual precipitation (MAP) on crude protein (CP) and digestible organic matter (DOM)
for individual ecoregions
2904 J . M . C R A I N E et al.
r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 2901–2911
Included are the ranges of MAT and MAP among years for each ecoregion. For both CP and DOM, reported are the coefficients of determination (r2), the slopes of the
relationships between MAT, MAP, and the interaction between MAT and MAP. A negative interaction implies a more negative relationship between MAT and the variable of
interest as MAT increases.
*Significant at Po0.05.
**Significant at Po0.01.
***Significant at Po0.001.
51304
51305
51308
51309
51310
51312
51313
Colorado Plateau shrublands
Great Basin shrub steppe
Mojave desert
Snake-Columbia shrub steppe
Sonoran desert
Tamaulipan mezquital
Wyoming Basin shrub steppe
719
255
33
101
42
235
234
0.8–13.7
4.2–11.5
9.8–17.6
3.8–11.7
17.2–21
21.3–23.5
2–7.5
162–440
96–631
125–292
176–489
187–261
344–594
144–367
0.13 !0.33***
0.1
0.09
0.05
0.27
0.01
0.01
!0.046*
0.07***
!0.05*
0.05
0.07
0.02
0.04
0.67
0.01
0.12
!1.7***
!7.4**
5.34**
!25.69***
C L I M AT E C H A N G E A N D C AT T L E N U T R I T I O N A L S T R E S S
2905
had many data points spread over a large geographic
area. In these regions, to determine how CP is affected
by MAT and MAP within regions, we calculated the
residual deviation of CP for each data point relative to
the master spline fit for each region. A regression model
with MAT, MAP, and the interaction between the two
was used to predict residual CP, which indicates
whether spatial variation in mean annual climate within
a region consistently altered CP. After determining how
climate affected CP among and within regions, these
statistical procedures were repeated for DOM.
In determining the relationships between climate and
forage quality among regions, we analyzed the patterns
among regions of the US exclusive of California and the
Southwest, where the relationships between climate
and quality among regions differed fundamentally
from the rest of the US.
Results
Among ecoregions of the US exclusive of California and
the Southwest, CPmax varied by 57%, from 105 mg g!1 in
the Southeast Conifer Forests region to 165 mg g!1 for
the Upper Midwest forest-savanna transition region
(Fig. 2). The date at which CP was at its maximum
varied by 131 days – from April 10 for the Western Gulf
Coastal Grasslands region to July 1 for the Colorado
Plateau Shrublands region. The rate of increase of CP
over the 60 days before the peak (CPup) averaged
0.35 mg g!1 d!1 over all regions, ranging from
0.085 mg g!1 d for regions like the Tamaulipan mezquital to 0.62 mg g!1 d!1 for regions such as the Central US
Hardwood regions. In general, rapid rates of increases
in CP concentrations were coincident with rapid rates of
declines in CP concentrations: there was a strong positive
correlation between CPup and the rate of decline of CP
over the 60 days after the peak (CPdown; r 5 0.78; Fig. 3).
Minimum CP levels (CPmin) varied among regions
even more on a relative basis than CPmax – nearly
twofold – from 71 mg g!1 for the Western short grasslands region to 128 mg g!1 for the Appalachian Mixed
Mesophytic Forests region. In general, regions with
high CPmax also had high CPmin (r 5 0.81, Po0.001).
The date of CPmin was less constrained than CPmax,
ranging as early as October 24 for the Nebraska Sand
Hills Mixed Grasslands region to February 7 for the
Appalachian Mixed Mesophytic Forests. There was no
relationship between the timings of CPmin and CPmax
among regions (P40.15).
Among regions, maximum and minimum protein
concentrations declined with increasing temperature
(Fig. 4). CPmax decreased with increasing MAT at a rate
of 2.8 mg g!1 1C!1 (Table 2). For example, a site with
MAT of 5 1C would have a CPmax of 155 mg g!1, whereas
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2906 J . M . C R A I N E et al.
Fig. 2 Maps of CPmax (a), CPmin (b), the DOY of CPmax (c), and the DOY of CPmin (d). Ecoregions with color gradations are shown if
there were significant relationships between CP and either MAT or MAP within the ecoregion. Patterns among ecoregions were analyzed
separately for those ecoregions with diagonal lines from those regions without diagonal lines.
Fig. 3 Relationship among ecoregions between rates of increase
in crude protein over the 60 days before peak CP (CPup) and the
rates of decreases in CP over the 60 days after peak CP (CPdown).
Dashed line is 1 : 1, solid line is standardized major axis between
the two metrics. CPup is significantly greater than CPmin
(Po0.001), though slope of the relationship is not significantly
different from 1 (P 5 0.09).
a site with MAT of 20 1C would have a CPmax of
113 mg g!1. CPmin also decreased with increasing MAT
at a rate of 2.0 mg g!1 1C!1. A site with MAT of 5 1C
would have a CPmin of 112 mg g!1 whereas a site with
MAT of 20 1C would have a CPmin of 82 mg g!1.
Although protein concentrations were generally lower
in warmer regions, warmer regions did have a longer
period between maximum and minimum protein concentrations. CPmax occurred earlier in warmer regions
(!1.96 d 1C!1) (Table 2). A region with MAT of 5 1C
would have peak CP on May 25, whereas CP would
peak 30 days earlier in a region with MAT of 20 1C
(April 26). With little effect of MAT on the rate of change
in CP around CPmax, and the timing of CPmin unaffected
by MAT (P 5 0.16; Table 2), the decline in protein with
climate warming could be partially offset by a lengthening of the season of high-quality forage.
Decreases in precipitation could exacerbate increases
in temperature by decreasing forage protein. CPmax
increased with increasing precipitation at rate of
6.0 mg g!1 per 100 mm, while CPmin increased at a rate
of 4.6 mg g!1 per 100 mm (Fig. 4). For example, a site
with MAP of 1000 mm would have CPmax and CPmin of
157 and 114 mg g!1, respectively. A site with MAP of
400 mm of precipitation would have CPmax and CPmin
of 121 and 87 mg g!1, respectively. The effects of precipitation on CPmax and CPmin were stronger for sites
with higher temperature (Table 2) implying that CP in
warm sites would be more sensitive to increases in
precipitation than in cold sites. Variation in MAP did
not affect timing of CPmax or CPmin (Table 2). Neither
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2907
Fig. 4 Relationships among regions between climate [mean annual temperature (MAT), mean annual precipitation (MAP)] and cattle
diet quality. Included are maximum crude protein (a, b), maximum digestible organic matter (DOM; c, d), and the ratio of DOM to CP
(e, f), an index of the availability of energy and protein to cattle.
did variation in MAP affect the rate at which CP
increased before or decreased after CPmax (Table 2).
The pattern of forage quality observed across regions
suggests that a warmer climate would reduce protein
availability to grazing animals. To examine how CP was
altered by temperature and precipitation within regions, we examined the residuals of CP over the year
relative to the spline functions for each region. Within
regions, CP decreased with increasing temperature in
much the same manner that occurred among regions
(Table 1). Of the 38 regions with more than 100 observations, CP decreased significantly with increasing MAT
in 13 regions but in no region did it increase significantly. Although CPmax increased with MAP across
regions, within regions CP increased with MAP as
many times as it decreased (seven each; Table 1). Within
regions, CP declined with increased temperature; however, the effects of precipitation were mixed.
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2908 J . M . C R A I N E et al.
Table 2 Results of regression models that predict magnitude and timing of maxima and minima of crude protein (CP) and
digestible organic matter (DOM), the day of year (DOY) of maxima and minima, as well as the slope at which CP increases up until
its peak as well as after its peak
CPmax (mg g!1)
DOY CPmax
CPmin(mg g!1)
DOY CPmin
CPup(mg g!1 d!1)
CPdown(mg g!1 d!1)
DOMmax
DOY DOMmax
DOMmin
DOY DOMmin
DOMmax:CPmax
DOMmin:CPmin
r2
N
Mean
value
MAT
( 1C!1)
0.73
0.51
0.69
0.13
0.11
0.11
0.71
0.28
0.70
0.29
0.66
0.60
33
29
28
28
33
33
33
29
28
28
33
28
130.7
161.9
92.2
355.8
0.22
0.24
632.0
160
596.6
343.5
4.89
6.7
!2.78
!1.95
!1.97
!1.76
!0.0022
!0.0036
!1.89
0.85
!1.23
!0.08
0.091
0.11
#
#
#
#
#
#
#
#
#
#
#
#
0.39***
0.67**
0.42***
1.21
0.0056
0.0044
0.37***
0.87
0.35**
1.08
0.014***
0.03***
MAP
(100 mm!1)
MAT " MAP
( 1C!1 100 mm!1)
6.03 #
!1.1 #
4.58 #
4.14 #
!0.011 #
0.0000 #
5.61 #
!4.59 #
3.23 #
3.75 #
!0.18 #
!0.26 #
!0.49
!0.09
!0.69
!0.27
0.00
0.0000
!0.52
0.13
!0.67
0.63
0.012
0.037
0.75***
1.3
0.77***
2.22
0.01
0.0001
0.71***
1.63*
0.63***
1.98
0.03***
0.05***
#
#
#
#
#
#
#
#
#
#
#
#
0.12***
0.21
0.13***
0.37
0.00
0.0000
0.12***
0.26
0.10***
0.33
0.004*
0.009***
Models included MAT, MAP, and the interaction between the two, for which a negative interaction would imply that the slope of the
relationship between MAT and the response variable is lowered with increasing precipitation. Reported are also the coefficient of
determination (r2) as well as the number of regions (n) for which data was available.
*Significant at Po0.05.
**Significant at Po0.01.
***Significant at Po0.001.
The effects of climate on DOM were similar to effects
on CP. Among all samples, CP and DOM were positively related (r 5 0.78). Regions that had a high CPmax
also had large maximum DOM (DOMmax; r 5 0.90). The
timing of the maxima were similarly correlated
(r 5 0.71). As such, DOMmax also declined with increasing MAT and decreasing MAP among regions
(!1.72 mg g!1 1C!1 and 5.8 mg g!1 per 100 mm, respectively; Table 2). Minimum DOM concentrations did not
vary with MAT but increased as MAP decreased (Table
2). Within regions, DOM decreased with increasing
MAT almost three times as often as it decreased
(14 vs. 5). Conversely, DOM increased with increasing
MAP nearly as often as DOM decreased (9 vs. 6).
In general, forage quality patterns for California and
Southwest regions did not relate to climate in the same
way as the rest of the US. For example, for some interior
California regions CPmax was 20–30 mg g!1 higher than
expected based on the relationships between CPmax and
climate for the rest of the US. In contrast, CPmax was
20 mg g!1 less than expected for some California and
Southwest forest regions when using the relationships
between CPmax and climate for the rest of the US.
Among the nine California and Southwest regions for
which sufficient data existed to estimate forage quality
patterns, CPmax increased with MAP (P 5 0.01) and
tended to increase with MAT (P 5 0.15). We interpreted
this to indicate the existence of a fundamentally different relationship between forage CP and MAT in the nine
California and Southwest regions than in regions with
more continental climates. The timing of CPmax for the
Chihuahuan desert was more than 60 days later than
expected based on relationships for more continental
climates, whereas for California chaparral regions it
was 60 days earlier than expected.
Discussion
Comparing changes in CP and DOM with climate
suggest that cattle are likely to become more proteinlimited if climates become warmer and drier. The ratio
of forage DOM to CP is a crude index of ruminal
fermentability (Moore et al., 1999); increasing DOM:CP
is indicative of a diet progressing toward protein deficiency. Although both CP and DOM were affected in
similar ways by climate, the declines in DOM with
increasing MAT and decreasing MAP were of lesser
magnitude than the declines in CP. The net effect was to
increase DOM : CP, as MAT increased and MAP decreased (Table 2, Fig. 4).
Using likely projected climate change scenarios for
the end of this century (Christensen et al., 2007), our
climate–quality relationships predict that a 3 1C increase
in temperature, paired with a 100 mm decline in precipitation, would produce a 12.9 mg g!1 decline in peak
CP and a 9.7 mg g!1 decline in DOM. Nutritional models developed for domestic cattle can provide useful
insights on climate-driven changes to animal performance. For example, a decline in average forage DOM
from 670 to 660.3 mg g!1 and a decline in average forage
r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 2901–2911
C L I M AT E C H A N G E A N D C AT T L E N U T R I T I O N A L S T R E S S
CP from 120 to 107.1 mg g!1 would cause body weight
gain to decrease from 0.91 to 0.85 kg d!1 (Subcommittee
on Beef Cattle Nutrition-Committee on Animal Nutrition-National Research Council., 2000). This decline in
forage quality would also be accompanied by a 2.4%
decline in forage intake (Subcommittee on Beef Cattle
Nutrition-Committee on Animal Nutrition-National
Research Council., 2000).
The financial cost of the decline in forage quality can
be estimated by determining the amount of supplemental feed that would be required to balance the decline in
forage quality. Approximately 181 g of soybean meal
(49% CP, 87% DOM, 90% dry matter) would be needed
per day to make up for the difference in performance
(Subcommittee on Beef Cattle Nutrition-Committee on
Animal Nutrition-National Research Council., 2000).
Assuming the value of soybean meal is $0.40 kg!1
(University of Missouri Extension, 2008), it would cost
a livestock producer an extra $0.0724 USD per animal
per day to achieve the performance level associated
with the better-quality forage. As an example of the
magnitude of these costs to producers, in the context of
stocker production in the Flint Hills of Kansas in 2008,
this represents a 4.3% increase in cost (R. Jones, personal communication).
Although examining forage quality across regions
may provide analogs for future climates, some individual regions did not follow the general trend in relationships. These regions may be characterized by
specific environmental features, animal management
practices, or forage management practices that cause
deviations from the trend. For example, the timing of
CPmax was more than 20 days later than expected based
on climate for the Flint Hills. Land managers in the
region often burn pastures annually late in the spring,
favoring the dominance of C4 grasses and potentially
delaying peak quality. Moreover, the Flint Hills region is
typically stocked heavily with domestic herbivores
during the early portion of the grazing season (i.e.,
May 1–July 15) but not the latter half of the grazing
season (i.e., July 16–September 30) (Smith & Owensby,
1978). The effect of this management practice might be
to delay the normal rate of phenological change in the
native C4 forages.
A warmer, drier climate may result in regionally
specific decreases in forage quality. With the current
relationships between climate and plant quality,
changes in precipitation and temperature could offset
effects on plant quality, yet each 1 1C increase in temperature would require an increase in mean annual
precipitation of over 200 mm, far greater than any
predicted increases (Christensen et al., 2007). Extrapolating spatial patterns into the future has the potential
of being decoupled if other factors, such as lags in
2909
management adjustments, or increases in atmospheric
CO2 override climate effects. Projected increases in
atmospheric CO2 would exacerbate declines in plant
N concentrations, leading to further declines in plant
protein although plant production could increase in
water-limited areas (Ainsworth & Long, 2005). For
example, models of cattle production that incorporate
changes in forage quality and quantity under climate
change have predicted that the effects of climate
changes on cattle performance in the Great Plains
would be geographically variable due to regional-specific changes in forage production and quality associated with altered water and nitrogen cycling (Baker
et al., 1993; Hanson et al., 1993). Without a better understanding of the dominant controls over plant protein
and energy concentrations and how such factors such as
grassland management will change in response to climate change as well as economic conditions, it will be
difficult to definitively state whether future forage
quality will follow geographic patterns.
The most likely result of long-term changes in forage
quality will be a move toward livestock classes or
breeds with relatively low nutrient requirements, for
example, mature livestock instead of growing cattle.
Managers would also likely have to alter production
schedules and forage species in a manner consistent
with regional patterns in management. Even after these
adjustments, livestock will likely either gain less weight
or require supplemental feed for weight gains not to
decline. These options are likely to add costs to an
industry with already thin financial margins, no less
increase demands on agriculture to produce the supplements as well as increase the need for fossil energy use.
Declines in forage quality have consequences beyond
the economics of agricultural production. The production of methane from enteric fermentation is a significant portion of the global greenhouse gas emissions
(Johnson & Johnson, 1995). In general, methane production increases per unit of gross energy consumed as diet
quality declines, which would suggest that future declines in forage quality would lead to greater methane
production from cattle (Johnson & Johnson, 1995;
Benchaar et al., 2001). Conversely, the decreases in
voluntary forage intake that accompany declining forage quality mean that less total methane would be
produced during enteric fermentation (Benchaar et al.,
1998; Iqbal et al., 2008). Climate change-driven decreases in forage quality may reduce the contribution
by beef cattle to global warming, barring increases in
the number of cattle or their time on pasture.
More empirical and mechanistic research is required
to understand the nature of geographic variation in
plant quality in order to reduce the uncertainty in
assuming spatial relationships between climate and
r 2009 Blackwell Publishing Ltd, Global Change Biology, 16, 2901–2911
2910 J . M . C R A I N E et al.
forage quality can predict changes in forage quality as
climate changes for a given region. The proximal and
distal causes of lower CP and DOM in warmer and
drier sites is currently unclear. Proximally, seasonal and
geographic patterns in CP and DOM can be driven by
changes in ratios of live to senescent tissues and stems
to leaves, as well as the chemical composition of each
fraction, but their relative importance in generating the
patterns is poorly understood. More distally, the
changes are driven by interactions between climate,
nitrogen cycling, plant carbon gain, and plant species
interactions. Nitrogen availability is thought to be lower
in more mesic unmanaged grasslands compared with
xeric grasslands (Breman & de Wit, 1983; Murphy et al.,
2002; Craine et al., 2005, in press). Yet, for the grasslands
examined here, if anything, nitrogen availability would
be greater in mesic grasslands than xeric grasslands,
which have lower CP than mesic grasslands. The differences between managed and unmanaged grasslands
suggest a predominant role of some aspect of management in determining relationships. With a number of
overlapping potential causes of these patterns at multiple hierarchical levels, it is difficult to speculate on what
might cause forage quality to increase with increasing
MAP and decreasing MAT. For example, assuming that
management practices already optimize plant growth
on a regional scale, climate-driven changes in plant
species composition will be accompanied by lags in
management adaptation. In effect, transitional plant
communities will continue to be managed as if the
previous moisture and temperature regime were still
intact. Inappropriate management of these transitional
plant communities would likely make declines in grazing animal performance worse than that indicated by
forage quality and quantity alone.
Acknowledgements
The research presented here would not have been possible
without the vision and hard work of the late Jerry Stuth and
GAN Lab staff. The authors gratefully acknowledge comments
from anonymous reviewers.
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Supporting Information
Additional Supporting Information may be found in the
online version of this article:
Figure S1. Patterns of dietary crude protein (mg g-1) over the
year for individual ecoregions.
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