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Modeling Mixed Brush Rangeland to
Determine Economic Characteristics
of Managing a Mexico-U.S. Watershed
Gary L. McBryde
C. Wayne Hanselka
which there exist data to validate SPUR-91 for the climatic
conditions holding for the larger region. Located in the
Tampaulian biotic province, the study area is classified as a
mixed thorn-brush savanna with dominant climax grasses
ofbluestem and bristlegrass (J ahrsdoerfer and Leslie 1988).
SPUR has five modules climate, soil-hydrology, plant,
animal, and economic that were designed to study biophysical processes at the landscape scale (Wight and Skiles
1987). The original SPUR model had its inception with the
Agricultural Research Service in 1980 and in part drew from
existing bio-physical models. Sub-models adopted into SPUR
included existing climate and soil hydrology modules. The
climate module was incorporated into SPUR-91 with minimal alterations. The soil-hydrology and plant modules were
modified from crop system models for short-grass prairies
with distinct winter-summer growing seasons. The 1991
upgrade version was a cooperative task undertaken by the
Natural Resource Conservation Service and the TexasAgricultural Experiment Station. Upgrade efforts focused on
incorporating into the plant and hydrology modules features
that would allow for more accurate modeling of woody
species on rangeland. Key modifications were the addition of
increased soil profiles and root growing depths for woody
plant species (Carlson and Thurow 1992).
Abstract-Range along the Texas-Mexico border was modeled
using SPUR-91. Stocking at 12 acres per animal unit with cattle
and goats induced the largest runoff and sediments, averaging 3.20
inches per year and 0.11 tons per acre. Lighter stocking, 25 acres
per animal unit, also with cattle and goats induced the least runoff
and sediments averaging 2.88 inches per year and 0.06 tons per
acre. Plant transpiration rates were largest with the light combination stocking averaging 11.56 inches per year and smallest with
heavy stocking, 5.69 inches per year. Overall, stocking rate was
more influential on hydrologic properties than livestock types.
The lower Rio Grande region of south Texas and Mexico,
which has a growing population, faces a condition where the
demand for water is quickly exceeding available supplies
(Schmandt 1993). Recent drought conditions in Mexico have
brought the problem of water scarcity to the attention of
residents of the region (Texas AgriNews 1995). More often
alternate methods ofincreasingwater supplies that include
range management are being discussed (Griffin and McCarl
1989). Previous range hydrology studies have examined
local conditions, but few range studies have attempted to
address the issue of whether range management would be
feasible to regulate regional water supplies (Thurow and
others 1986; Weltz and Blackburn 1995).
Integral to making regional watershed management assessment studies would be field data that frequently does
not exist, in particular for the mountainous areas in northern Mexico that drain into the lower Rio Grande. Several
computer models were reviewed (Spaeth 1993; Botkin 1993;
Srinivasan and Arnold 1994) as part of an economic study to
address regional range watershed management in the lower
Rio Grande area. Based on the review, the 1991 upgrade of
the Simulation of Production and Utilization of Rangelands
(SPUR-91) model was selected for additional assessment.
The study area for the initial assessment includes rangeland that sheds water into the lower Rio Grande surrounding Garcias Creek in Starr County, Texas. This is an area for
Objectives
In general the present study was conducted to evaluate
those components of SPUR-91 that would be applicable for
providing simulated input data into an economic study of
rangeland watersheds along the lower Rio Grande in both
Texas and Mexico. More specifically, the first objective in the
validation process was to determine if S PUR-91 could model
plant community dynamics on a mixed brush rangeland
with growing seasons determined by water availability
rather than temperature. One aspect of this involved determining if the model was capable of reflecting plant community compositional differences over the course of several
years under alternate grazing pressures. Related to the
verification of plant growth, plant-plant competition, and
plant-animal interactions was the second objective of validating rates of litter decomposition. A third objective was
the determination of the applicability of the animal and
economic modules. Last, it was necessary to determine if the
model would predict different hydrologic properties for the
same range under a different state of plant succession and
livestock management.
In: Barrow, Jerry R.; McArthur, E. Durant; Sosebee, Ronald E.; Tausch,
Robin J., comps. 1996. Proceedings: shrubland ecosystem dynamics in a
changing environment; 1995 May 23-25; Las Cruces, NM. Gen. Tech. Rep.
INT-GTR-338. Ogden, UT: U.S. Department of Agriculture, Forest Service,
Intermountain Research Station.
Gary L. McBryde is Associate Professor ofAgricultural Economics at Texas
A&M University-Kingsville, Department of Agronomy and Resource
Sciences, Campus Box 156, Kingsville, TX 78363. C. Wayne Hanselka is
Professor and Extension Range Management Specialist, Texas A&M
University College Station, TX 77843.
This material is based upon work supported by the Cooperative
State Research Service, U.S. Department of Agriculture, under agreement
No. 94-37314-1226.
119
Methods
decomposition rates. Accurate direct simulations, however,
of plant-animal interactions were not assumed as a condition for useful hydrologic simulations. In particular, altering
animal preferences for plants and stocking rates in the
animal module and inducing realistic plant community
responses was a tested validation check.
Additional working assumptions to achieve the simulation results were based on soil and hydrologic data from a
100 acre representative field composed of four range sites.
These sites varied from bottomland drainage sites to upland
sites. All sites were from the Brennan-McAllen Soil Association (USDA 1972) and represent the approximate percentage of sites within this association. No site had a slope
greater than 10 percent. Land management choices consisted of stocking cattle alone and stocking goats in combination with cattle. Light stocking was set at 25 acres per
animal unit and heavy at 12 acres per animal unit. When
goats were grazed in combination with cattle the composition was a ratio of 80 cattle to 10 goats on an animal unit
basis. Data parameters in the hydrology module that were
varied based on land management choices were the modified
universal soil loss equation cover parameter (FLDC), a
mulch cover factor (GR), and the top two soil layer porosity
(SMO) values (table 1). When adjusting parameters the
trend was to make lower stocked range and combination
grazed range have greater residue cover and soil porosity
values.
Additionally, six generic plant groups were idealized for
the simulation. These groups were: three groups of grasses
based on association with good, fair, and poor range conditions, two woody species groups based on moderate and low
palatability to goats, and last a group representing forbs.
Actual input plant data for model parameterization requires
36 data per plant group. Validation parameterization of the
plant and hydrology module was based on 15-year simulations. The method was to initially estimate parameter values from existing literature for each of the six plant groups.
These values were then averaged across all plant groups to
obtain one parameter value. In essence this created six
identical plant groups. The average plant group was used to
validate that the model could respond to wet and dry weather ,
absolute biomass production values, and litter decomposition rates.
Mer the model was validated to predict reasonable values of biomass production averaged over 15-year simulations, the unaveraged plant parameter data was used as a
target to shift the averaged parameter data toward. This
was done in an iterative process with the final result generating responses that suggested plant community compositions under light and heavy stocking. Indirect simulation of
plant responses to stocking pressure was done through the
plant and hydrology module rather than the animal module.
Rather than using the animal module for simulating differences between cattle and goats on rangeland hydrology, the
simulation results were also achieved by altering plant and
hydrology module data parameters.
Three plant parameters played a key role in achieving the
simulation results. These variable were biomass to leaf area
conversion (P16), root respiration (P24), and maximum leaf
area (CRITl). The final community species compositions
were sensitive to extremely small changes in these values
--------------------------------
Shannon (1975) describes the calibration and validation of
simulation models as two separate tasks. Calibration was
described as the task of verifying that simulation model
output quantitatively matches an existing set of data. The
data being similar, but not identical, to the situation for
which the model will be applied for predictive purposes.
Validation, on the other hand, was described as a qualitative
assessment that determines how applicable a model will be
for the simulation of events for which predictions are desired. In particular, can the results from a model simulation
be rationally interpreted. The use of SPUR-91 for the economic study involved some calibration but primarily validation checks. For example, one phase ofthe testing ofSPUR-91
required a routine calibration check of the simulated 10 day
average temperatures from the climate module, CLIMGEN,
against existing temperature averages. Widespread use of
CLIMGEN and its previous documentation in other models
dictated simple graphical comparisons for the present study
(Carlson and Thurow 1992).
Contrasted to the case of weather data and the climate
module, limitations in detailed plant growth data and the
behavior of the plant growth model exist. These data limitations precluded exhaustive calibration tests of the plant and
hydrology modules and justified a more heuristic series of
validation checks. Also, population growth and water shortages in the Lower Rio-Grande region are triggering decisions on water use that will likely affect land management.
Rather than wait for additional data and fail to contribute to
the policy making process, the approach adopted is to utilize
existing knowledge and add qualified advice into the policy
formulation process (Dinar and Lochman 1994; Musser and
Tew 1987). While recognizing existing shortfalls in data,
limitations in calibration checks, or the validity of a SPUR-91
module for a particular task, adopting sensitivity analysis of
key variables in the economic analysis can be used to assist
in characterizing the economic stability of a decision relative
to data obtained from SPUR-91. Also, the economic analysis
can playa role in defining future range hydrology research
agenda items.
Given these considerations, several working assumptions
were adopted. These were assumptions not subject to direct
validation checks. Foremost it was assumed that the climate
and hydrology models would be essentially correct if qualitative accurate plant compositions could be simulated for
the study area. This included validating organic matter
Table 1-Hydrology data parameters varied for alternate land
management choices.
Sites SPUR-91 variables
1-4
1-4
1-2
3-4
FLOC (unit less)
GR (unit less)
SMO (decimal fraction)
SMO (decimal fraction)
Land management choiceHsgt
Hsng
Lsng
Lsgt
0.13
0.60
0.40
0.42
0.11
0.50
0.41
0.45
0.07
0.40
0.41
0.50
0.08
0.30
0.43
0.52
ALand management choices: Hsgt, heavy stocking with goats and cattle; Hsng,
stocking with cattle only; Lsgt, light stocking with goats and cattle; Lsng,
hght stocking with cattle only. Sites: 1, Brennan Soil Series; 2, McAllen Soil Series;
3, Ramadero Soil Series; 4, Zapata Soil Series.
~eavy
120
(for P24 ± 0.00002). Important, but not as sensitive were
maximum and optimum plant activity temperatures (P3
and P4) and the Julian day that senescence begins (CRIT8).
preferred browse, and then forbs. Under the heavy stoc~ng
with above average rainfall at the peak of the grOWIng
season, live unpreferred browse comprises an average of
near 1,800 pounds per acre and the forbs contribute an
average of roughly 500 pounds per acre. Also, with the
increased stocking pressure the resiliency of the vegetation
is reduced.
Comparing results between light stocking with cattle only
(fig. 2) and heavy stocking with goats and cattle (fig. 3) the
vegetation takes almost an additional year longer (interval
127 versus interval 150) for the range to respond to wetter
annual weather patterns. When comparing heavy stocking
with cattle to the combination stocking management choices,
the addition of goats essentially eliminated the forb biomass
(figs. 3 and 4). Heavy stocking with cattle and goats created
a virtually solid woody brush community that had an increased level of live biomass of unpreferred browse compared to other management choices.
Runoff was greatest averaging 3.204 inches per year
under the heavy stocking with goats and least, 2.883 inches
per year, under the light stocking with goats (table 2).
Interestingly the light stocking with cattle alone had the
highest standard deviation in runoff followed closely by the
heavy stocking with a combination of livestock. Sediment
rankings showed a similar influence based on stocking rate
and livestock choices. Sediments averaged 0.111 tons per
acre for heavy stocking with goats and had a low at 0.062
tons per acre with light stocking and a combination of
livestock. Plant transpiration correlated with total biomass
production and was highest with light stockin~ with go~ts
averaging 11.563 inches and the lowest, 5.695 Inches, WIth
heavy stocking.
Results _ _ _ _ _ _ _ _ _ __
Simulated rainfall (all precipitation fell as rain) averaged
22.57 inches per year compared to an actual average of
22.59. The 15 year simulated weather pattern was dry the
first 5 years, the next 4 years were approximately average,
then there were 3 more wet years, and then the 15 years
ended with 3 dry years. The general pattern is dry-averagewet-dry (fig. 1).
Patterns of vegetative composition under the four management treatments showed considerable variation. The
general trend was for light stocking either with a combination or only cattle to show the greatest diversity of species
groups (fig. 2). Also, plant composition under the light
grazing showed a pattern of grasses dominating in biomass
until the 1982 growing season (the 300th 15-day simulation
interval). Hence, brush gained a competitive advantage
from a wet rainfall cycle followed by an average rainfall
cycle. Additionally, between any two management choices
the least variation was shown when comparing light stocking with cattle-slightly more forb biomass in wet years-to
light stocking with a combination of cattle and goats.
Heavy stocking with only cattle (fig. 3) when contrasted to
light stocking with only cattle (fig. 2) shows a marked
reduction in grass biomass from all condition groups. Essentially, the only species groups remaining are unpreferred
browse, which dominates in biomass, followed by the
70
60
50
•
average
m 40
.s::
(oJ
..5 30
A
sirnJlated
U)
20
10
0
0
'"
0')
-'"
0')
N
'"
0')
o
Ln
'"
(X)
0')
0')
Year
Figure 1-Simulated 15-year precipitation versus annual precipitation for Rio Grande City, Texas.
121
N
(X)
0')
('Y")
(X)
0')
v
(X)
0')
•
gg
o
pg --m-- pb
fg
----to
3500
3000
2500
CJ
2000
"""""
:9
1500
a::I
1000
500
o
15-day intervals
Figure 2-Simulated plant community compositions under light stocking with only cattle over a
15-year period measured in pounds per acre every 15-days for six idealized plant groups: grasses
associated with good range condition (gg), grasses associated with fair range condition (fg) ,
grasses associated with poor range condition (pg), browse preferred by goats (pb), browse not
preferred by goats (ub) and forbs (fo).
•
gg
II
fg
•
pg.--G-pb - - - u b
3500
3000
2500
u 2000
a::s
~
..Q
1500
1000
500
o
15-day intervals
Figure ~imulated plant community compositions under heavy stocking with only cattle
over a 15-year period measured in pounds per acre every 15-days for six idealized plant
groups: grasses associated with good range condition (gg), grasses associated with fair
range condition (fg), grasses associated with poor range condition (pg), browse preferred
by goats (pb), browse not preferred by goats (ub) and forbs (fo).
122
----to
•
•
gg
•
fg
~pb
pg
to
ub
4000
3500
3000
u 2500
~ 2000
:e
1500
1000
500
0
or-
~
,....
('I")
Lt')
Lt')
,....
(Y')
or-
en
~
0
,....
N
LO
V
(Y')
to
or-
to
~
~
,....
N
15-day intervals
or-
or-
0r-
Lt')
,....
en
,....
('I")
N
N
N
N
('I")
in
(Y')
to
0
in
C"")
C\J
V
('I")
('I")
Figure 4-Simulated plant community compositions under heavy stocking with cattle and goats
over a 15-year period measured in pounds per acre every 15-days for six idealized plant groups:
grasses associated with good range condition (gg), grasses associated with fair range condition
(fg), grasses associated with poor range condition (pg), browse preferred by goats (pb), browse
not preferred by goats (ub) and forbs (fo).
simply a lack of information about data parameterization.
The range in reported data for anyone parameter is often so
large that it is difficult to select the appropriate value for the
present conditions. Also, there is the issue of a limited
capability to model plant-plant competition. SPUR-91 plant
parameters are limited to the physiological parameters of
the idealized plant groups. This has two repercussions.
First, the parameter may be affecting outcomes in the model
that would not be observed in reality. Hence, the actual
parameters values used may not correspond directly to
empirical values. Second, the lack of a coherent theory of
plant community dynamics in the plant module or SPUR-91
model as a whole imposes constraints on what can be
modeled.
Results from this study suggest efforts to include more
detailed dynamics or greater plant diversity would require
increasingly large amounts of modeling time and large doses
of counter-empirical (and likely non-intuitive) parameterization. Both suggest SPUR-91 is at a limit on modeling both
plant community diversity and dynamics until additional
conceptual elements are incorporated into the model. These
elements would be processes operating at an ecological scale
larger than plant physiological processes.
The second objective was to verify decomposition rates of
organic matter. These appear adequate as non-living organic matter quantities tend to cycle with seasons in response to overall weather conditions rather than accumulate or vanish altogether.
The third objective dealt with assessing the applicability
of the animal and economic model. As noted earlier, the poor
linkage between the animal and plant modules made it
infeasible to reflect vegetation composition changes byaltering stocking rates and livestock preferences in the model.
Table 2-Effect of four land management choices on hydrologic
characteristics for south Texas as modeled by SPUR-91.
Hydrologic effect
Runoff (in.) avg.
Runoff std. dey.
Sediments (tons/ac.) avg.
Sediments std. dey.
Plant transpiration (in.) avg.
Plant trans. std. dey.
Land management choiceLsgt
Lsng
Hsng
Hsgt
2.883
4.579
0.062
0.102
11.5631
10.307
3.079
4.897
0.063
0.104
1.329
9.925
3.007
4.639
0.088
0.142
8.463
8.044
3.204
4.868
0.111
0.175
5.695
5.263
aHsgt, heavy stocking with goats and cattle; Hsng, heavy stocking with cattle
only; Lsgt, light stocking with goats and cattle; Lsng, light stocking with cattle only.
Conclusions _________
Four objectives were established to assess the feasibility
ofSPUR-91 for providing input data into an economic study
of range watershed management. The first objective was to
determine if the model could accurately simulate plant
compositional changes resulting from alternate land management choices. Four managelDp.nt choices were used to
make this determination. Simulation experiments showed
that the linkage between the plant and animal module was
inadequate to predict directly plant community dynamics
resulting from the type and amount of livestock grazing
interactions. Nonetheless, plant compositional differences
under alternate grazing and stocking pressures were modeled by altering input data parameters in the plant and
hydrology modules.
The plant module has two limitations that require considerable modeling effort to circumvent. The first is often
123
Griffin, R C; B. A McCarl. 1989. Brushland Management for
increased water yield in Texas. Texas Water Res. Bull. 25:175-186.
Jahrsdoerfer, Sonja E.; David M. Leslie, Jr. 1988. Tampaulian
brushland of the lower Rio Grande Valley of south Texas: descriptions, human impacts and management options. U.S. Dept. of
Interior, Fish and Wildlife Svc. Bio. Rpt. 88(36) Albuquerque,
NM 87103.
Musser, Wesley N.; Bernard V. Tew. 1984. Use of biophysical
simulation in production economics. Southern J. of Agri. Econ.
16(1):77-86.
Schmandt, Jurgen. 1993. Water and development: the Rio Grande!
Rio Bravo. Lyndon B. Johnson School of Public Affairs PRP
1992193. Univ. of'Texas at Austin, Austin, TX.
Shannon, Robert E. 1975. Systems simulation: the art and science.
Englewood Cliffs, NJ. Prentice Hall. 387 p.
Spaeth, Kenneth E. 1993. Erosion prediction in the Bad River
Basin, South Dakota, using SPUR-91. USDA-SCS Tech. Note.
Northwest Watershed Research Center, Boise, ID.
Srinivasan, R; J. G. Arnold. 1994. Integration of a basin-scale water
quality model with GIS. Water Resources Bulletin. Vol. 30, No.3.
Texas Agri News. 1995. Texas Water Supply in Jeopardy. McAllen,
TX. 4(10): 1.
Thurow, T. L.; W. H. Blackburn; C. A Taylor, Jr. 1986. Hydrologic
characteristics of vegetation types as affected by livestock grazing systems, Edwards Plateau, Texas. J. of Range Management.
39(6): 505-509.
United States Department of Agriculture, Soil Conservation Service. 1972. Soil Survey of Starr County, Texas.
Weltz, MarkA; Wilbert H. Blackburn. 1995. Water budget for south
Texas rangelands. J. of Range Management. 48(1):45-52.
Wight, J. R; J. W. Skiles. 1987. Simulation of production and utilization of rangelands; documentation and user's guide. USDA
Agri. Research Svc. ARS-63.
Due to this it was determined to be more accurate to determine appropriate stocking rates exogenous to SPUR-91 by
using other data sources. This outcome yields the output
from the economic module invalid for purposes in the larger
economic study.
The fourth objective was to determine if hydrologic properties could be distinguished for rangeland under alternate
land management. The model performs this task well if a
commitment is made to learn how plant parameter changes
will induce vegetation composition changes, which will act
indirectly on rangeland hydrologic properties. Undoubtedly,
there remains much work in refining SPUR-91 module
linkages and the mathematical representation ofbio-physical interactions on rangeland. Despite this, the differences
in hydrologic results obtained in the study suggest SPUR-91
has a definite role to play in contributing input data into
regional watershed studies.
References
------------------------------
Botkin, Daniel B. 1993. Forest dynamics: an ecological model. New
York, NY. Oxford Univ. Press. 309 p.
Carlson, D. H.; T. L. Thurow. 1992. SPUR-91: Simulation ofproduction and utilization of rangelands, workbook and user guide.
Texas Agri. Exp. Sta. Misc. Publ. 1743, College Station, TX.
Dinar, Ariel; E. Tusah Lochman. 1994. Water quantity/quality
management and conflict resolution: institutions, processes, and
economic analysis. Praeger, Westport, CN. 515 p.
124