Effects of range management on the lower Rio Grande watershed

Journal of Arid Environments (1998) 40: 217]233 Article No. ae980436

Effects of range management on the lower Rio Grande watershed

Gary McBryde Texas A & M University-Kingsville, Department of Agronomy and Resource Sciences, Campus Box 156, Kingsville, TX 78363, U.S.A. (Received 2 February 1998, accepted 22 June 1998) The SPUR-91 model was used to simulate the effect of alternate stock densities on properties of the lower Rio Grande watershed. The output data was then used as input data into an income maximizing model to measure economic effects of alternate management on two representative ranches in Texas and Mexico. Each ranch had a management choice defined over four stocking densities combined with two alternate deer leasing systems. Deer lease income was inversely related to cattle density. Each management choice had a different outcome on firm revenue, and runoff and sediment mean and standard deviations. The economic model recorded the watershed data related to the management choice that maximized ranch incomes under nine scenarios. SPUR-91 results showed greatest runoff under heavy stocking (7.9 cm yeary1 ) and least under light stocking (7.1 cm yeary1 ). Sediments and soil evaporation showed similar correlations. Plant transpiration had an inverse correlation, and deep percolation was non-existent. Nine scenarios were generated with the economic model: (1) no deer leasing, (2]5) alternate deer lease prices, (6) a base, present condition, (7]8) imposed runoff limits with and without deer leasing, and (9) imposed runoff increases. Without deer leasing, net revenues were $8.54 hay1 and $6.08 hay1 for the Texas and Mexico ranches, respectively. Under the base scenario, Texas revenue increased $3.03 hay1 , and Mexico, without appreciable deer leasing, held steady. Deer leasing in Texas acts as an economic incentive to lower cattle stocking; while in Mexico, because production choices are limited, cattle overstocking is more prevalent. The effect on the economics of land management to increase water supplies is reversed in the two countries. Texas ranchers would give up deer leasing and increase cattle stocking to increase runoff, whereas Mexican ranchers would give up the opportunity to develop deer leasing, as they are already overgrazing. The cost of compensation paid to ranchers to yield these options so that urban residents will have marginally more water are favourable. An appeal to social values concerning overgrazing, however, suggests such a policy is irrational at a deeper level than economic values. q 1998 Academic Press Keywords: range; livestock; wildlife; watershed; grazing; economics 0140]1963r98r020217 q 17 $30.00r0

q 1998 Academic Press

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G. McBRYDE

Introduction The lower Rio Grande region of south Texas and north-eastern Mexico has a rapidly growing population coupled with a limited water supply. In 1990, on the Texas side the population was approximately 703,000 and is expected to double in less than 20 years. Demographic trends show an increasingly urbanized population dependent on international trade, which is moving away from an agricultural economy (Schmandt & Mu, 1992). Meeting the water needs of the population, 97% of the water supply enters as instream flows from upriver or local drainage into the Rio Grande from the immediate watershed (Schmandt, 1993). Extreme variation in regional precipitation (Le Houerou ´ & Norwine, 1988) makes supply uncertain and combines with increased demand to create conditions that can lead to severe water shortages. To the extent that water supply control is feasible, it centers on the local watershed and primarily reservoir management. Several reservoirs recently completed in Mexico, however, have created an overcapacity in surface water storage limiting the benefit of any additional reservoirs (Hansen, 1997). Hence, the land surface in the local watershed presents one of the few remaining potential options for water supply management. The lower Rio Grande watershed occupies 11.2 million ha, predominately in Mexico (88%). The Texas side has no perennial tributary flowing into the Rio Grande; in Mexico there are four, the Rio Pesqueria, San Juan, Sabinas, and Salado. All have been observed to go dry during prolonged droughts in the last 50 years (Hanselka et al., 1996). Watershed topography consists of rolling hills with caliche outcrops or gravelly ridges and small valleys holding alluvial soils. In the southern reach of the watershed low (1000 m) mountains exist. Prolonged frost free periods ()300 days), a relatively low average precipitation (57.9 cm yeary1 ) with a high standard deviation (18.01 cm yeary1 ), and high summer temperatures (June, July, and August have means near 308C) support a semi-arid vegetation dominated by drought deciduous brush (Jahrsdoerfer & Leslie, 1988). Edaphic variations in the region create a series of mixed brush-grass communities that define 32 ecological sites, 17 in Texas (Pendelton & Carter, 1974; Nelle, 1982) and 15 in Mexico (COTECOCA, 1973a,b ). Each ecological site is a geographic area that has the potential to respond similarly to equal treatments (NRC, 1994). Soil and climate constraints make livestock grazing the dominant (90%) land use. Historically, Spaniards brought domestic livestock into the region in 1584 (Jordan, 1993). Early on sheep and goats were more important than today, for example, sheep in 1880 outnumbered cattle by almost 3:1 (Lehman, 1969). Today sheep are virtually absent and goats are restricted to small numbers (TASS, 1991). Management of white-tailed deer ( Odocoileus virginianus ) for the sale of hunting leases has a significant effect on land use in south Texas, with hunter expenditures in 1991 reaching $310 million in the area (USFWS, 1993). Season leases allowing hunters access to land and game for several months of the year and package leases allowing several days of access are the most common lease arrangements (Steinbach et al., 1987). Land-owners providing lease access to larger antlered bucks command higher lease rates proportional to hunter expectations of taking a trophy class animal. Nesbitt et al. (1997) describes deer antler measurements for trophy determination using the widely accepted Boone and Crockett scoring method. Altogether the water demand]supply relation has three factors that are focusing attention on how range management affects water supply in the lower Rio Grande region: (1) a growing population that is experiencing more frequent water rationing, (2) a limited ability to increase supply control with additional reservoirs, and (3) a dominant land use of grazing. In order to address how range management, in particular stocking densities, effect water supply and the economics of the potential effect, a study

RANGE MANAGEMENT ON THE LOWER RIO GRANDE WATERSHED

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was conducted using the SPUR-91, Simulation of Production and Utilization of Rangelands (Carlson & Thurow, 1992) computer model to provide input data into an economic model of two representative ranches, one from Texas and the other from Mexico.

Background The SPUR model had its inception in 1980 with work initiated by USDA-ARS in Boise, Idaho (Wight & Skiles, 1987). An upgrade effort focusing on incorporating features that would allow for more accurate modeling of woody species created SPUR-91 (Carlson & Thurow, 1992). The model has two landscape-scale versions, one for field level research and the other for basin level work. Both versions have four major modules: climate, soil hydrology, plant, and animal. A smaller economic module records herd cost-price data. Each module requires site specific input data to generate simulations of, for example, water runoff, sediments, plant biomass by species groups, and transpiration spanning multiple years in time increments as small as a day. At its most basic level each module consists of multiple difference equations that update variable levels, such as runoff, at each time step. The stage of development of each module and its linkage to the others varies. For example, the soil hydrology and plant modules are directly dependent on the climate module, whereas the animal module is independent of the climate module. The climate and hydrologic module share a developmental history with the Erosion Productivity Impact Calculator (EPIC), a crop model also developed by USDA-ARS (Williams et al., 1983). While both models share many features, SPUR has a larger scope of simulation. In particular, the added trophic level of livestock creates a need to integrate livestock effects back into the system. Also, the system modeled includes a multiple species plant community. Because the smallest landscape unit of interest is typically an ecological site, and by definition different sites have the potential to support different plant compositions, the transferability of SPUR from one site to another without recalibrating the plant module is limited. When research focuses on economic issues, typically the entire range ecosystem is equally important, as are multiple sites. While most of the use of SPUR-91 and alternate versions has related to work in developing model features, less work has used the model in applications. Hanson & Baker (1993) provide an application studying climate change effects. A significant amount of independent work, however, has investigated economic aspects of watershed management. In Texas, as early as 1967, Rechenthin & Smith discussed how invading brush on range results in lower surface water yields. The Texas State Soil and Water Conservation Board (1991) reviewed studies that have shown interactions between water yields, precipitation level, brush density, and slope in order for brush management to effectively influence yield rates. Griffin & McCarl (1989) assessed the economics of investing in brush management to increase water yields. Interacting with brush density management and water yields is the link between brush density and stocking rates (Blackburn, 1984; Thurow et al., 1986). In the Tampaulian biotic province, overstocking leads to dense brush clusters with bare ground interspaces. Per cent brush canopy increases with proximity to the Gulf of Mexico and the more mesic ecological sites. Weltz & Blackburn (1995) conducted runoff studies for 2 years on bare soil, grass interspaces, and in brush clusters in south Texas and found rates to be 32% greater for bare ground compared to the other surface covers. Runoff under brush was only slightly more than grass and statistically insignificant.

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G. McBRYDE

Economic studies of the optimal stocking rate are limited and linkages with the effect on water yield are rare. Early studies of stocking decisions, such as that of Sharp & Boykin (1967) using multi-period linear programming, found that the time value of money had a strong influence on how ranch decisions effect profits. Their findings were supported by subsequent studies, for example Kim & Yanigida (1981), Pope & McBryde (1984), and Bernardo (1989). Karp & Pope (1984) outlined the economic incentives that exist to encourage ranchers to overstock, converting natural resources into capital resources. More recently studies have tended to look less at the economic efficiency of the absolute stocking level and have examined how the timing of destocking under drought contributes to forage declines (Torrell et al., 1991; Huffaker & Wilen, 1991).

Methods and data The field version of SPUR-91 was used to simulate the results for two reasons. In part it requires less soil hydrologic input data, e.g. stream flow properties. Primarily, however, limitations in the link between the animal and plant components of SPUR require extensive calibration of plant physiology parameters to simulate alternate stock densities and there exists limited modeling literature to guide this type of parameterization. Given that the field version requires less of this type parameterization its use over the basin model seemed more appropriate. Input soil and hydrologic data for the field version of SPUR-91 were based on a 40.5 ha representative field composed of four ecological sites. These sites varied from bottomland drainage to upland sites. All sites were from the Brennan-McAllen Soil Association (Thompson et al., 1972) within Texas. Hydrologic input data was not available for soils in Mexico. The representative field modeled represents the approximate percentage of sites within this association. Of the four slopes, the upland site had the greatest slope at 10%. Within the watershed in both Texas and Mexico cow]calf operations are the dominant operation (Hanselka et al., 1991; Garcia-Vega, 1995). Within the model land management choices consisted of stocking a cow]calf operation at four densities. Recommended (or light) stocking was set at 9.7 ha cowy1 , moderate at 8.1, moderate heavy at 6.5, and heavy at 4.8 ha cowy1 , approximately double the recommended rate. Much of Mexico is estimated to be carrying 4.8 ha cowy1 , whereas Texas has densities closer to 8.1 ha cowy1 (COTECOCA, 1978; Hanselka et al., 1996). Current stocking densities suggest that to the extent possible any added water from additional stocking would be generated from the Texas side. Data parameters in the hydrology module that were varied based on stocking density 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 have greater residue cover and soil porosity values. Six generic plant groups were idealized for the simulation. These groups were: three groups of grasses based on association with good, fair, and poor ecological conditions (NRC, 1994), two woody species groups based on moderate and low palatability to cattle, and a group representing forbs. Actual input plant data for model parameterization requires 36 variable parameters per plant group. Each plant group then potentially requires 36 different parameter values. Determining the values to use is part of the validation process, which consists of comparative tests of the reasonableness of SPUR output values. Validation in the plant and hydrology modules was based on 15-year simulations. The method was to initially estimate parameter values from

RANGE MANAGEMENT ON THE LOWER RIO GRANDE WATERSHED

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Table 1. Hydrologic input data parameters varied to simulate alternate stock densities

Stocking density (ha cowy1 ) 9.7 8.1 6.5 4.8

Sites*

SPUR-91 variables

1]4 1]4 1]2 3]4

FLDC (unit less) GR (unit less) SM0 (decimal fraction) SM0 (decimal fraction)

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

*1 s Brennan Soil Series; 2 s McAllen Soil Series; 3 s Ramadero Soil Series; 4 s Zapata Soil Series.

existing literature for each of the six plant groups. These values were then averaged across all plant groups to obtain a single parameter value for each of the 36 input parameters. In essence, this created six identical plant groups. The six identical plant groups were used to validate that the model could respond to wet and dry weather, absolute biomass production values, and litter accumulation and decomposition rates. After the model was validated to predict reasonable values of biomass production over 15-year simulations, the plant parameter data for each plant group was altered to simulate plant responses that suggested plant community compositions under light to heavy stocking. This was done in an iterative process requiring multiple simulations to achieve a final unique set of 36 input parameters for each of the six plant groups. Three plant input parameters in each group played a key role in achieving the simulation of community dynamics under alternate grazing pressures. These variables were biomass to leaf area conversion (P16), root respiration (P24), and maximum leaf area (CRIT1). The final community species compositions were sensitive to extremely small changes in these values (for P24 " 0.0002). Important, but not as sensitive, were maximum and optimum plant activity temperatures (P3 and P4) and the Julian day that senescence begins (CRIT8). Data simulated from the SPUR-91 model were used in a mathematical programming model which maximized ranch net income adapted from Thompson & Thore (1992). A general mathematical representation of the programming model is: Maximize: K

NR s

J

I

Ý Ý Ý

ri jk x i jk

( Eqn 1 )

ks1 js1 is1

subject to equations (2]5): a i jk x i jk y bi jk yi jk F 0 I J K

Ý yi k y w k F 0 K

( Eqn 2 ) ( Eqn 3 )

i

Ý y yi k q z i k q vi k F 0 K

( Eqn 4 )

i

Ý c l k x i js1 y u k F 0 L K

( Eqn 5 )

i

The objective function (Eqn 1) is to find the values of output activity x that maximizes total net revenues, NR. The function is the sum of K s 2 ranch net revenues, with each ranch 4048.5 ha, one in Texas and another in Mexico. Each ranch earns net revenue of r per each unit of output activity x. There are J s 3 activities per

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G. McBRYDE

ranch: calf production, season deer hunt leasing, and package deer hunt leasing. The inner summation, i g { 1, 2, 3, 4} , partitions the range of possible stocking densities into four segments, only one of which can be chosen in solution. The second equation set contains 24 equations. The production coefficient a represents calf production cowy1 kgy1 when j s 1, or the identity coefficient for the package and season deer leasing system activities when j s 2 and 3. The corresponding constraint coefficient b represents the maximum cows stocked or the gross Boone and Crockett score depending on the level of j. The variable y whose value is found in the solution is a binary integer variable, only one of whose value in solution is yi s 1, with the others 0, enforced by equation set (3). The choice yi s 1 in the solution selects one of the four cow stocking densities. The third equation set contains two equations. The constraint coefficient w is found in solution and is a binary integer variable that binds the optimal yi s 1 in solution. Note if w s 0, then no stocking would take place. The fourth equation set contains two equations, one for each ranch. The unknown binary integer variables z and v found in solution indicate the choice of either season or package deer hunting lease systems. The final set of equations are accounting equations that measure mean and standard deviation of water runoff and mean and standard deviation of sediments for the activities that optimize NR in solution. These are represented by the production coefficient c, with index l g { 1, 2, 3, 4} . All values of c were found using the SPUR-91 model. The variable u, whose value is found in solution, counts the respective total levels of water runoff and sediments. There are a total of eight accounting equations, four per ranch. The model was programmed and solved using the GAMS software MIP (mixed integer programming) option (GAMS, 1997). Besides data generated by SPUR-91, the other coefficients requiring data for the optimization model were r, a, and b. The values of r were found by solving for the maximum net revenue of four separate sub-problems. The general functional form of all was similar, which was: maximize NR s R y C, where NR is net revenue, R is revenue, and C is cost for producing good x. In all cases R was a linear relation between dollars and output and C was quadratic between dollars and output (Fig. 1). Solving each of the sub-problems for maximum net revenue, it was necessary first to restrict output levels to one of the four stocking densities and solve the sub-problem. The process was repeated for each of the four stocking densities. The solution process was done using an iterative program accurate to the nearest $0.50 ranchy1 . The non-linear property of the cost function has an increasing rate of cost increase as output

Figure 1. Relation between the input (stocking rate) and the output (calf production) for the south Texas representative ranch. ( } - - } ) s TC Mx; (????? ) s TR Mx; ( }}} ) s TR Tx; ( ] ] ] ) s TC Tx.

RANGE MANAGEMENT ON THE LOWER RIO GRANDE WATERSHED

223

Figure 2. Supply curves for calf production output (1000 kg) for the representative ranches in Texas (l) and Mexico (B).

increases. This reflects the underlying production technology of diminishing incremental yields with each added cow after the recommended stocking rate. There were two net revenue functions related to cow]calf production, one for south Texas and the other for Mexico. The cow]calf functions were estimated using least-squares regression from data generated using the cow]calf budget generator (McGrann et al., 1989). These data were engineered with input data from the Texas Agricultural Extension Service (1997) and the Zambrano Ranch in Mexico (Zambrano, 1995). Tietenberg (1996) provides background on the method. The cost function estimated for the cow]calf operation is less land opportunity costs. McGrann et al. (1996) estimates that for a typical calf operation net revenue would be slightly less than total costs. The inability of cattle production to cover all costs explains the widespread interest in deer leasing in Texas. Comparing the calf supply curves derived from the estimated cow]calf cost curves (Fig. 2), the U.S. curve has contracted and gotten relatively more elastic over expected price ranges, indicating at a given price Texas producers supply less beef but respond with larger output changes to a calf price change. The difference in supply characteristics reflect that on average in south Texas deer leasing opportunities act to reduce cattle stocking rates. The other two net revenue functions were for season and package hunting deer leases in south Texas. These functions were derived by integrating marginal revenue and marginal cost curves from McBryde (1996). The functions obtained indicated a relation between lease revenue and expense dollars and an expected gross Boone and Crockett score a hunter would anticipate for a particular deer lease. Lacking sufficient data for statistical analysis of Mexican deer leases, south Texas net revenue deer lease functions were utilized for Mexico and parameterized in the final model for alternate lease prices to examine price effects. A basis for this approach is that many hunters in Mexico are from south Texas and ranches in south Texas with deer hunting leases are acting as models for many Mexican ranches (Cox, 1994). In the case of calf production, the coefficient a was linked to the cost curves through the budget generation process to the stocking density. The number itself is the

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G. McBRYDE

reciprocal of the total output obtained for the respective stocking densities. In the case of deer sales the value was one for both types of hunting lease systems. The constraint coefficient b was the stocking density divided into the ranch size for the cow]calf activity. For the deer leasing activities it was the expected number of deer given a stocking density of cattle. A competitive relation between cattle densities and deer densities and deer quality measured by gross Boone and Crockett scores was defined based on Beasom (1994) and Merrill (1957).

Results Simulated precipitation all fell as rain with a mean of 57.8 cm yeary1 and a standard deviation of 17.6 cm yeary1 . 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 was dry]average]wet]dry. Patterns of vegetative composition under the four stocking densities showed considerable variation. Light stocking (Fig. 3) and moderate stocking showed the greatest diversity of species groups. Also, plant composition under the light grazing showed a pattern of grasses dominating in biomass until the 1982 growing season. Interestingly, brush gained a competitive advantage from a wet rainfall cycle followed by an average rainfall cycle. Typically, it is thought deep rooted woody species have a competitive advantage over grass in drought and gain canopy cover coming out of the drought. The model indicated that with overgrazing brush has an absolute advantage on both ends of the wet]dry spectrum. Additionally, between any two stock densities, the least variation was shown when comparing light stocking}slightly more forb biomass in wet years}to moderate. Heavy stocking (Fig. 4), when contrasted to light stocking (Fig. 3), 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 preferred browse, and then forbs. Under the moderate heavy stocking with above average rainfall at the peak of the growing season, live unpreferred browse comprises an average of near 2000 kg hay1 and the forbs contribute

Figure 3. Simulated plant community compositions for year 7 of a 15-year simulation under light stocking as measured by live biomass (kg hay1 ) every 2 weeks for six idealized plant groups: (`) s grasses associated with good range condition; (B) s grasses associated with fair range condition; (^) s grasses associated with poor range condition; (I) s browse preferred by cattle; (e) browse not preferred by cattle; (=) s forbs.

RANGE MANAGEMENT ON THE LOWER RIO GRANDE WATERSHED

225

an average of roughly 560 kg hay1 . Also, as stocking pressure increases, the resiliency of the vegetation is reduced. Comparing results between light stocking and moderate heavy stocking the vegetation takes almost a year longer for the range to respond to wetter annual weather patterns. When comparing moderate heavy stocking to heavy stocking, there is a virtual elimination of the forb biomass. Heavy stocking (double the recommended rate, the current situation in Mexico) created virtually a 100% brush community with the highest level of live biomass of unpreferred browse compared to other stocking rates. Runoff was greatest, averaging 7.9 cm yeary1 , under the heavy stocking and least, . 7 1 cm yeary1 , under the light stocking (Table 2). This is an 11% increase compared to the 32% that Weltz & Blackburn (1995) found between bare ground and vegetated areas in south Texas. Confounding the comparison between that study and the simulation results are two factors. First, the study examined homogeneous units

Table 2. Effect of stocking density averaged over 15 years for simulated hydrologic variables for south Texas as modeled by SPUR-91

Hydrologic effect Runoff (cm) Average Standard deviation Sediments (kg h I1 ) Average Standard deviation Plant transpiration (cm) Average Standard deviation

9.7

Stocking density (ha cowy1 ) 8.1 6.5

4.8

7.1 11.8

7.4 12.0

7.8 12.6

7.9 12.8

132.2 228.6

141.2 230.8

197.2 331.6

242.0 405.6

22.3 25.8

21.1 23.2

16.5 19.3

14.8 17.4

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G. McBRYDE

whereas simulation results were modeled for heterogeneous landscape units. Second, woody perennial species tend to increase with increased grazing pressure. In part, because of the effect of woody species, Weltz & Blackburn concluded (p. 52) ‘Increasing water yields, in south Texas through vegetation manipulation is marginal . . . ’, suggesting the simulated runoff results are within reason. Sediment rankings showed a similar influence based on stocking density. Sediments averaged 242.0 kg hay1 for heavy stocking and had a low at 132.2 kg hay1 under light stocking. Plant transpiration correlated with total biomass production and was highest with light stocking, averaging 22.3 cm yeary1 , and lowest, 14.8 cm, under heavy stocking. The two other fates of water in SPUR-91 are soil evaporation and deep percolation. Soil evaporation increased with stocking density. Deep percolation was virtually non-existent in every year under all stocking densities. Turning to the economic component, eight scenarios were generated with the economic model. The first five scenarios explored effects from no deer leasing and then four alternate deer prices. The sixth scenario was considered to be the base or present situation. The final three examined effects when runoff was limited with and without deer leasing and the profit maximizing choices if a runoff increase was forced (Tables 3 and 4). The first scenario without deer leasing had net revenue values of $8.54 hay1 and $6.08 hay1 in Texas and Mexico. As mentioned earlier this value does not include land opportunity costs. Land opportunity costs in Mexico are unclear, but limited evidence suggests that cow]calf operators are near breakeven including land costs. The base scenario, which includes deer leasing in Texas but not in Mexico, increased Texas ranch revenue by $3.06 hay1 , a value estimated to be slightly above the state average revenue for deer leasing. The season leasing system was selected as optimum in this scenario and ranchers would supply bucks that average 130 in gross Boone and Crockett scores. Increasing the deer lease price (note: causality between deer score and lease rates is likely simultaneous, but for exposition prices are discussed as causing score) caused Texas ranchers to receive larger net revenues but no essential change in

Table 3. Economic optimizing solution results for the Texas ranch model

Scenario

Net Stock Season Package Runoff Runoff Sediment Sediment revenue rate lease lease average S.D. average S.D. ($ hy1 ) (ha cowy1 ) (GBC) (GBC) (cm) (cm) (kg hy1 ) (kg hy1 )

1. No deer 8.54 2. Deer: low price 11.60 3. Deer: mid price 12.89 4. Deer: high price 14.02 5. Deer: trophy 35.85 6. Base 11.60 7. Limit runoff, no deer 11.60 8. Limit runoff with deer 11.60 9. Increase runoff 5.33

8.1

0

0

7.3

11.8

134.4

224.1

8.1

130

0

7.3

11.8

134.4

224.1

8.1

130

0

7.3

11.8

134.4

224.1

8.1

0

130

7.3

11.8

134.4

224.1

9.7 8.1

0 130

170 0

7.0 7.3

12.3 11.8

134.4 134.4

224.1 224.1

8.1

130

0

7.3

11.8

134.4

224.1

8.1

130

0

7.3

11.8

134.4

224.1

4.8

100

0

7.9

12.7

224.1

358.5

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227

Table 4. Economic optimizing solution results for the Mexico ranch model

Scenario

Net Stock Season Package Runoff Runoff Sediment Sediment Revenue rate lease lease average S.D. average S.D. ($ hy1 ) (ha cowy1 ) (GBC) (GBC) (cm) (cm) (kg hy1 ) (kg hy1 )

1. No deer 6.08 2. Deer: low price 9.03 3. Deer: mid price 10.22 4. Deer: high price 11.17 5. Deer: trophy 16.74 6. Base 6.08 7. Limit runoff, no deer 5.84 8. Limit runoff with deer 5.90 9. Increase runoff 6.08

6.5

0

0

7.8

12.6

224.1

336.1

8.1

120

0

7.3

11.9

134.4

224.1

8.1

120

0

7.3

11.9

134.4

224. 1

8.1

120

0

7.3

11.9

134.4

224.1

8.1 6.5

120 0

0 0

7.3 7.8

11.9 11.9

134.4 197.2

224.1 336.1

8.1

0

0

7.3

11.9

134.4

224.1

6.5

90

0

7.3

11.9

179.3

313.7

6.5

0

0

7.8

11.9

201.7

336.1

operation occurred until the high price was reached, then the ranchers were expected to supply bucks scoring at least 150 and the deer leasing system switched to the package hunt. In general, at scores around 140]150 the economics will favor the package leasing system. When Texas ranchers can provide trophy quality animals (better than or equal to 170) revenues jump dramatically. Few ranches earn these revenues but those that do generate enormous attention and discussion by other ranchers about how to raise trophy class bucks. Because doe hunting is illegal in Mexico, antler management is thwarted as is deer leasing, which has a detrimental effect on ranch revenue (Martinez et al., 1997). Exactly how much so is difficult to say, but assuming modest prices and antlers scoring less than Texas averages, the revenue effect is $2.95 hay1 . This increase, however, would be significant and helps explain the high interest Mexican ranchers have in deer management (ANGADI, 1997). The scenario for trophy deer in Mexico did not assume that it would be broadly feasible to raise such animals, although it is certainly possible to do so, and is occurring on individual ranches. The assumption is that trophy deer management in Mexico does not presently exist to an extent to effect watershed characteristics or prices significantly. In Texas runoff averaged 7.3 cm across all but two scenarios: the trophy deer scenario where it dropped by 0.3 cm, and when runoff was enforced in the model it increased by 0.6 cm. The trend is the same in Mexico; deer leasing creates less runoff. Indirectly, in order to support better quality deer, there is a trend to reduce stocking densities, which in turn lowers runoffs. Comparing no deer to the lowest price scenario, runoff dropped 0.5 cm. Standard deviations in runoff follow the same pattern for the same reasons. Average sediments in both countries change dramatically with stocking density changes. In Mexico, sediments drop almost one-half to economic incentives to lower stocking rates with the price break points that induce deer management. Or, conversely in Texas, sediments increase two-fold when higher runoff is enforced. Enforcing

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increased runoff reveals an important aspect about long-term land tenure in the two countries. In Texas, comparing average sediments, they are the same between the no-deer scenario and the base scenario, which has deer leasing, but they increase when runoff increases are enforced. Comparing the situation in Mexico, all three scenarios show approximately similar results. Why do the results differ between Texas and Mexico? In Texas, alternate production options with deer leasing create opportunity costs that have been incorporated into the calf supply curve and, in order to be competitive over the long-term, ranchers have been forced to adopt deer leasing as a complimentary production activity. However, in Mexico institutional arrangements have prevented the adoption of deer leasing on a large scale. Consequently the cow]calf operation in Mexico is the only production option and competition cannot act to actually lower stocking rates due to deer management options.

Discussion and conclusion Four criteria were used to assess the validity of SPUR-91 for providing input data into an economic study of range watershed management. The first was based on whether the model could accurately simulate plant compositional changes resulting from alternate stock densities. Simulation experiments showed that the linkage between the plant and animal module was inadequate to predict directly plant community dynamics resulting from the amount of livestock grazing. None the less, plant compositional differences under alternate grazing and stocking pressures were modeled by altering input data parameters in the plant and hydrology modules. To simulate animal effects through the plant module requires considerable modeling effort. There is often simply a lack of information about data parameterization. The range in reported data for any one parameter is often so large that it is difficult to select the appropriate value, raising the question as to whether the parameter should be static input or in a more complete model calculated endogenous to the model and updated dynamically. The second criterion 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. The third criterion 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 by altering stocking rates and livestock preferences in the model. Due to this it was determined to be more accurate to determine appropriate stocking rates exogenous to SPUR-91 by using other data sources. Strengthening this linkage will be important to future economic studies. The last criterion was to determine if hydrologic properties could be distinguished for semi-arid lands under alternate stocking densities. 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 watershed hydrologic properties. Undoubtedly, there remains much work in refining SPUR-91 module linkages and the mathematical representation of biophysical interactions on semi-arid land. Despite this, the differences in hydrologic results obtained in the study suggest SPUR-91 can, and indeed did, play a contributing role in a regional economic watershed study. What do the economic results mean at the watershed scale? Two errors are common when extrapolating from firm to regional scales; slippage and the fallacy of composition. Slippage occurs because seldom are the practices being extrapolated adopted 100%. The fallacy of composition is that the region is not as homogeneous, as the representative firm and regional effects can shift values one way or the other. Assuming the deviations

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are normally distributed, slippage weighs heaviest and the results tend to be overestimates. Recognizing these limitations, extrapolations can be used to provide insight into the significance of the results at different scales. Because the watershed characteristic results are simulated as point estimates on sites prior to the water reaching flowing channels, it is necessary to scale these results based on the loss of runoff that ultimately fails to reach a reservoir. A loss factor was calculated from the average water flow volume of the four perennial rivers in the Mexico catchment divided by the average point runoff calculated from SPUR-91 for the base scenario extrapolated to the watershed. In effect this forces total mean 15-year simulated point runoff to equal the total mean watershed river flow amount. The loss value was 0.057, indicating the extreme evaporation in the region. Based on the adjusted runoff values, the gain in runoff water in Texas from overgrazing would amount to 4.6 million m 3. Comparing this value to the annual evaporation at Falcon reservoir in the watershed, its point loss is 2.88 m yeary1 and with a lake surface of 388 million m 2 , this yields 1117 million m 3 of water evaporating annually. So the gain from overgrazing on the Texas side is less than half a per cent of what is lost to evaporation annually in Falcon reservoir. Even though the amount is relatively small it is interesting to ask what the cost of the water would be to ranchers on the Texas side from the loss of deer lease revenue going from the base scenario to the increase runoff scenario. The value is $2000 kmy2 my1 . At this level the value of the gained water is too expensive for alternate agricultural uses to compensate the ranchers’ lost income, for example irrigation users. But, agricultural users are not those willing to pay the highest price; typically it is the municipal users. Placing the value on a comparable household unit it is $0.002 ly1 . This value, however, is not the full cost of the water because society, in particular deer hunters, would also lose value from such a policy. Prorating total hunter expenditures of $310 million (note: this value includes transportation, equipment, hunting license, and lodging costs as well as lease costs) in south Texas to the watershed, the cost of the gained water is still low at approximately $0.01 ly1 . Certainly other costs should be added to this value, such as the increased sedimentation, the loss of long-term productivity, and decreased biodiversity. But these values are either occurring over such long time horizons or are outside present market valuations. Either way, they are exceedingly difficult to quantify in an unequivocal manner and more importantly the extensive nature of arid and semi-arid lands is driving the relatively low cost result and would likely continue with additional costs added. Given the cost of the added water is within a range that urban water users would be willing to pay to semi-arid land users as compensation for their giving up deer leasing from overgrazing, it is worth asking if the price alone should justify a policy of overgrazing to increase water for urban areas, and if not why not? Before considering the question it is worth while to recall the hectares in the Texas catchment are relatively small (12%) compared to the portion in Mexico. But, the situation is different in Mexico. Overgrazing has resulted in all the potential gains in runoff being realized. Now the question is not can we get additional water from overgrazing, but should Mexico maintain a relatively cheap (somewhere between the Mexican ranchers costs of $0.001 ly1 and the combined Texas supplier and consumer cost of $0.01 ly1 ) source of water. Mexico appears to be moving toward altering deer harvest laws to facilitate deer leasing development. What do the results say about that policy course? Comparing both countries situations brings us back to a fundamental question. Should resources on arid and semi-arid land, an extensive margin where resources will be valued less, be exported to urban areas to support the development of the intensive margin? Economists rely on a benefit-cost calculus to guide rational decision-making, but there is concern within the discipline that certain classes of problems are less amenable to this analysis than others. Robinson (1962) stated: ‘The first essential for economists, arguing amongst themselves, is to . . . combat, not foster, the ideology which pretends

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that values which can be measured in terms of money are the only ones that ought to count.’ (p. 147). Considering what values ought to count is difficult, but insight can be gleaned by what values have counted in the past. Livestock were introduced in 1584 to the watershed, but widespread and continuous range use did not begin until the early 1700s under land grants from the Queen of Spain. At this time the watershed was united under the Spanish flag. Cattle have always been important as domestic livestock, but the trend in range use paralleled the growth of the larger economy of which ranches are a part (Lehman, 1969; Van Young, 1981; Jordan, 1993). Three distinct stages mark this evolution. The first being use of livestock for subsistence with ranches or haciendas being self-sustaining economies. In this stage cattle, sheep, and goats were all uniformly important. With the second stage, commerce between ranches and cities evolved. As the larger economy developed, ranches became more dependent and started to raise cattle and sheep for cash. With machinery becoming relatively less expensive compared to labor, cattle displaced sheep. In the interim, south Texas became a part of the U.S. economy and ranch evolution in Texas accelerated into recreational management with deer leasing, particularly after World War II. The evolution in ranch management progressed to the extent that major ranches in south Texas now report earning more revenue from wildlife than cattle (DeYoung, 1996). Simultaneous with this changing pattern of land management, USDA, through various agencies, heavily promoted soil and water conservation and developed stocking guidelines that discouraged overgrazing. While Mexico has similar institutions, finances and cultural differences certainly played a role to keep stocking densities relatively high. The contrast can be seen in infrared satellite imagery of the international border where vegetation differences visually demarcate the border as predominately red vs. blue areas on photographs. Without implicating U.S. policy as irrational Karp & Pope (1984) economically appraised ranch overgrazing as: ‘ . . . taking advantage of the good times while they last is rational.’ (p. 445). Yet now in hindsight a factor that greatly facilitated the economic development of semi-arid lands in parallel with the larger economy in Texas, and in particular deer leasing, was conservative cattle stocking. Conserving the resource was, and is equivalent to maintaining option values or flexibility for future development. Intrinsic to the mission of the historic U.S. Soil Conservation Service was the assignment of high levels to option values. Land conservation was a socially accepted norm that emanated from the Great Depression and dust bowl disasters, which transcended a firm level economic calculus (Brewster, 1961). Vatn & Bromley (1995) underscore how institutions guided by social norms make decisions over the environment, particularly regarding conservation. A social norm that appears to have preceded conservation is a notion of protecting natural resource reserves for progress in an uncertain future. The point being made is that historically decisions were made regarding natural resources based on social norms. These norms, independent of individual firm values, then generated economic values that guided firms. The present study suggests that the economic rationalization of conservation decisions should not pretend to effectively separate social norms from existing individual firm economic values to arrive at conservation prescriptions. Further applied economic studies examining land conservation issues should consider the link between market rationalizations and actions that rationalize social norms such as natural resource enhancement for social progress. Practically, we need to decide what kind of values are important and then strive to make those values economic, rather than the other direction. In the case at hand, exporting water at the cost of biodiversity and productivity would diminish the potential for land use to develop towards recreational usage along with the demands of a changing society, contradicting a social norm of progress. To embrace progress, land managers will need to look to the larger society and find complimentary activities. Supporting non-game wildlife and using land as storage banks for greenhouse gases, possibly by becoming involved with

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transfer credits to ameliorate global warming, are two areas that come to mind. The idea that markets cannot value all natural resources completely, and to ask social institutions to build norms that guide resource allocation supported by economic information highlights positive aspects of the political process. To pretend otherwise would deny our role as citizens and ignore values we hold meaningful when building our lives, our society, and our relation to the natural world. The financial assistance of the Cooperative States Research Service, U.S. Department of Agriculture, agreement 94-37314-1226, is gratefully acknowledged.

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