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A Bioeconomic Model of Farm Management Practices and Environmental Effluents in the Western Lake Erie Basin.

ABSTRACT: Agricultural production has been identified as a major source of groundwater and sediment pollution in the western Lake Erie Basin of Ohio. In order to anticipate the effects of potential conservation policies, we employed a bioeconomic model to examine the relationship between alternative tillage practices and environmental and economic impacts in the western Lake Erie Basin of Ohio. Our results indicated that the effects of conservation tillage were mixed. Favorable impacts for the region's environment and economy were: a.) soil erosion decreased and future soil productivity improved, b.) several potential pollutants (e.g., sediment, organic nitrogen, and total phosphorus loadings) decreased, and c.) farm profitability improved. But these positive effects were somewhat offset by unintended consequences such as: (a) some potential pollutants (e.g., nitrates and herbicides) increased, (b) crop mix and input usage changed, and (c) average farm size increased resulting in fewer farms in the region.

Keywords: Conservation tillage, economic analysis, environmental impacts, Lake Erie Basin

The contribution that agriculture makes to water quality problems in Lake Erie has been a focus of policymakers' concern for the past three decades. During the 1970s, it was recognized that Lake Erie is more susceptible to sediment and nutrient loads than the other Great Lakes (International Joint Commission 1974). A number of factors, such as industrial centers surrounding the lake, intensive crop production, and the shallow lake depth, combine to make the basin prone to accumulate high concentrations of sediment, phosphorus, nitrates, pesticides, and other pollutants. Furthermore, unit area exports of phosphorous and nitrates within northwestern Ohio tributaries to Lake Erie are high relative to other agricultural watersheds (Baker 1993), while herbicide concentrations are high relative to many midwestern U.S. rivers (Solomon et al. 1996).

Congress mandated the design and development of a demonstration wastewater management program for the rehabilitation and environmental repair of Lake Erie in Sections 108(d) and 108(e)) of the Federal Water Quality Act Amendments of 1972 (United States Congress 1972). At the time this legislation was enacted, it was assumed that excessive phosphorus loadings were the principal cause of accelerated eutrophication in Lake Erie (Forster et al. 1985). Since that time, progress has been made toward achieving the phosphorus load reduction called for in the 1978 Great Lakes Water Quality Agreement between the United States and Canada. Lake Erie tributary loading data reveal that concentrations of total and soluble phosphorus declined during the 1975 to 1990 period (Richards and Baker 1993).

Since the 1970s, however, concern has grown over pollutants other than phosphorus. Of special concern in the Lake Erie Basin are nitrate concentrations in rivers; pesticide levels in lakes, rivers, and drinking water supplies; and sediment deposition in drainage ditches and harbors (Great Lakes Commission 1996). Research indicates that for most of the Lake Erie tributaries, concentrations of nitrates have increased and concentrations of sediment have remained relatively unchanged in the 1975 to 1990 period (Richards and Baker 1993).

Changes in concentrations of phosphorus, nitrates, pesticides, and sediment relate directly to farming practices (crops, rotations, tillage methods, fertilizer and chemical applications, the timing of field activities, etc.). To reduce pollutant concentrations, a policy that encourages increased use of conservation-oriented farming practices (e.g., conservation tillage) typically has been adopted. The quantitative relationship between farming practices and reduced pollution is not well understood, however, and is made more difficult because of confounding factors, such as soil type, terrain, and weather patterns. Before encouraging or requiring farmers to adopt particular conservation practices, therefore, the relationships between farming practices and the economic performance and pollutant discharges of farms should be investigated.

To explore these relationships, we developed a bioeconomic model to associate farming practices with production, profits, pollutants, and farm size. Two geographic sites are modeled to simulate representative farms on flat (i.e., Hoytville soils) and sloping (i.e., Blount-Glynwood-Pewamo soil association) farmland typical of the western Lake Erie region of Ohio. On each of these geographic sites, performance of representative farms is assessed under alternative tillage technologies. Then, various scenarios of conservation tillage adoption in a region are compared by extrapolating results of the analysis of representative farms.

Bioeconomic Model

Our bioeconomic model features two components. First, the Erosion Productivity Impact Calculator (EPIC) is used to simulate crop yields and pollution parameters. Second, using production results from the EPIC model, a farm-level integer programming model is used to simulate profit maximizing farming practices (i.e., crop rotations, level of fertilizer and pesticide inputs, and farm size) on representative farms.

EPIC is designed to simulate biophysical processes and the interaction of cropping systems over long periods of time, during which changes in the environment occur at a relatively slow rate. Using EPIC with predefined management practices, a wide range of soils, climates, and crops can be simulated in an efficient, convenient manner. The EPIC model contains the following 10 major biophysical components: weather, hydrology, erosion, nutrient cycling, pesticide fate, soil temperature, tillage, crop growth, and crop and soil management (Sharpley and Williams 1990).

Our research plan first simulated outcomes for representative farms under alternative farming practices. Second, we compared alternative tillage systems on the basis of their profitability and longterm sustainability. The third stage of our research consisted of an examination of alternative conservation tillage adoption scenarios and estimates of their impacts on crop production, farm profitability, environmental quality and farm size. Finally, where possible, we verified the results of this research by comparing them with empirical water quality and farm level data collected from the Lake Erie Basin.

Many studies have examined environmental, and economic consequences of alternative policy or farming practice scenarios in the Great Lakes region. For example, Braden et al. (1991) used Lake Michigan case studies to examine the effects of alternative farming practices on fish habitat. In that study, a bioeconomic model was used to predict impacts on fish habitat. Representative farm analysis is often used to evaluate one or a few typical farms in the area of concern (Ellis et al. 1991 and Wossink et al. 1992). This study extends this line of inquiry by incorporating EPIC, a widely used model of biophysical processes, to examine multiple environmental parameters. Unlike other studies that examine the relationship between farming practices and economic impacts, this study explicitly considered the consequences of alternative farming practice scenarios on-farm size.

The Simulation Model

Farm-level Model. As previously stated, our ultimate objective was to create representative regional aggregations that represent conditions on two predominant soil associations in the western Lake Erie Basin. To achieve results that are typical of various cropping scenarios in the regions, we first used the bioeconomic model to simulate results for representative farms. Crop yields and environmental impact parameters generated by EPIC were used as inputs in a farm-level integer programming model that maximizes returns above total costs for each representative farm. Other inputs into the integer programming model included costs of various farming operations (e.g., plowing, disking, planting, cultivating, spraying, and harvesting), which are computed through budget analysis. Variable and fixed costs for machinery were calculated using "Ohio Farm Machinery Economic Costs Estimates for 1994" (Lines 1994). Other input costs were based on estimates by Althauser (1996), crop budgets published by Ohio State Universi ty Extension (1995), and survey results from Stout et al. (1992). Also, machinery capacity estimates, time requirements of various farming operations, and seasonal time constraints (e.g., hours of field time available during particular weeks of the growing season) were inputs to the integer programming model, based on estimates by Althauser (1996).

For each representative farm, the object of the model was to maximize returns above total costs across all available crop rotations and input levels. Crop rotations, input levels, and crop area, both for individual crops and for the total of all crops on the farm, were determined endogenously by the model. On both the Hoytville and Blount-GlynwoodPewamo sites, three representative farms were created, each using either a conventional, mulch, or no-till tillage system.

Data generated by the biophysical model (EPIC), including crop yields and environmental impact parameters, were used as inputs into the farm-level integer programming model. Associated farm management practice costs were computed through budget analysis and entered into the integer program as accounting rows. Finally, management practices were constrained by their use of time during the growing season. Algebraic formulation of the model was presented in Smith (1997) but not shown here for the sake of brevity.

Regional Aggregation Model. Once optimal rotations, crop areas, and input levels for each representative farm were determined by the integer programming model, the results were used in a regional aggregation of representative farms. These analyses allowed us to compare the structural, economic, and environmental impacts brought on by alternative levels of conservation tillage adoption. A hypothetical region was constructed by assuming a particular land area of a particular soil. Then, various tillage scenarios were created by specifying proportions of the region using alternative tillage systems. Structural impacts were measured by changes in the number and average size of farms making up the region, as well as by changes in crop production. Economic impacts were measured by changes in the region s revenues and costs. Finally, environmental impacts were measured by changes in individual pollution parameters as well as by changes in an environmental index, which is simply the average percentage change for all pollution parameters.

We simulated four regional tillage scenarios on hypothetical units of 100,000 cropland acres (40,500 hectares). The four scenarios, shown in Table 1, depict alternative rates of conservation tillage adoption. In each of these four scenarios, the three tillage systems were used in varying proportions ranging from predominantly conventional tillage (Scenario 1) to predominantly no-till (Scenario 4).

Model Parameterization. The EPIC model requires six sets of parameters representing soil type, weather patterns, cropping practices, tillage practices, chemical and fertilizer inputs and environmental parameters. By varying the parameters in the model, we were able to simulate the environmental impacts under the various regional and individual. farm scenarios. We describe below the sources of the model parameters.

Soils. One soil group and one association of three soils were chosen, one to represent the center of the basin and the other the undulating reaches of the basin, as characterized by the county Soil Survey (United States Department of Agriculture 1984). Hoyrville soil, found in the center of the basin, is represented by flat ([less than] 2% slope), glacial tilled soil, which is very poorly drained. Permeability is low in the subsoil and therefore subject to ponding, but it has a very high natural fertility suitable for crops if properly drained. Hoytville soil comprises 16% of the western Lake Erie Basin. The BlountGlynwood-Pewamo association (0 to slope), which is found in the boundaries of the basin and accounts for 33% of the basin's soils, is moderately well drained, somewhat poorly dralned, and very poorly drained depending on the predominant soil of the grouping. Tile drainage is used on Hoyrville soil and Blount-GlynwoodPewamo associations.

Weather Generator WXGEN. The EPIC model requires a long series of daily weather data for precipitation, air temperature, solar radiation, wind speeds, and direction. In order to complete a long run simulation, predictions of daily weather are required. The WXGEN weather generator was developed for the purpose of providing daily weather data based on statistical characteristics of actual weather for a given location (Richardson and Nicks). In this study, the Toledo, Ohio weather station was the closest weather station to the study region, and its historic weather data were used in the EPIC model.

Rotation and Cropping. Western Lake Erie Basin cropland agriculture is dominated by three crops (corn, soybeans, wheat) in four rotations (continuous corn, corn/soybean, soybean/wheat, corn/soybean/wheat). For purposes of our simulation, we assumed that corn/soybean and soybean/wheat rotations are two year rotations with no cover crop planted between the designated crops. In the soybean/wheat rotation, we assumed that winter wheat planting immediately follows soybean harvest. The corn/soybean/wheat rotation was simulated as a three year cycle, with no cover crops planted between the designated crops. We used EPIC to generate annual estimates of production and pollution over a 50 year time period to capture the long term effects of management practices.

Tillage Practices. Conventional tillage, mulch tillage, and no-till management practices were simulated to predict the impact tillage has on crop yields and the environment. For purposes of the EPIC model, a set of farming practices was specified to represent a particular tillage system. Soil preparation activities involving conventional field operations for corn, soybean, and wheat consisted of moldboard plowing, disking, and field cultivating before planting. A rotary hoe was used to reduce weeds in corn and soybean rotations. For corn and soybeans, plowing presumably occurred in the fall.

Mulch tillage is characterized as having more than 30% of the soil surface covered by residue from the previous crop. In our model, we assumed that this was achieved by using a chisel plow and a field cultivator prior to planting and a rotary hoe operation after planting.

No-till eliminates perturbation of the soil surface with a tillage operation. Instead, pesticides are used to control weeds and a specialized no-till drill is used to plant crops directly in the previous year's post harvest debris.

Fertilizer and Chemical Inputs. Fertilizer and chemical inputs were partitioned into three levels of application: high, medium, and low. High input levels were based on maximum recommended application rates. Medium (75%) and low (50%) input levels were simple fractions of the recommended level. For example, if the maximum input level of nitrogen in a corn rotation was 91 kg (200 lb), then the medium application was 68 kg (150 lb) and the low input was 45 kg (100 lb). In the simulation, chemical nitrogen and phosphorus were applied at planting, followed by an anhydrous application in a corn rotation. Fertilizer was added in the spring in a wheat rotation and at planting in a soybean rotation.

Herbicide inputs were also applied in a three tiered application following the 100% high, 75% medium, 50% low standard. Herbicide levels for conventional and conservation tillage used the same application rate. No-till herbicide application was substantially higher with the addition of a "knock down" herbicide instead of a tillage operation.

Environmental Impact Parameters. Five environmental parameters were chosen for their relevance to Lake Erie Basin water quality concerns. The parameters measured the effluents that each management system delivered to the edge of the field on a per hectare basis. Summation of these results would not be directly inferred as quantities of effluents entering the water system. Rather, results were representative of effluents leaving the field and approximate losses of resources to the farmer.

In general, the five parameters measured soil erosion, organic nitrogen loss, nitrate loss, phosphorus loss, and pesticide runoff on an average annual basis over 50 years of production. Soil erosion was measured by the USLE (Universal Soil Loss Equation) or soil lost from water erosion in units of tons per hectare. Nitrogen was measured in two ways. YON measured organic loss of nitrogen in sediment and YNO3 measured nitrate loss in surface runoff, both in units of kilograms per hectare. Phosphorus loss was measured by YP, which measured phosphorus loss with sediment in units of kilograms per hectare. Pesticides, as a group, were measured by PSRO, or pesticides in surface runoff in units of grams per hectare.

Simulation Results

The results presented here illustrate the outcomes for regions that are characterized by Hoytville soil and a Blount-Glynwood-Pewamo soil association.

Hoytville Soils. The conventional-tillage-dominated scenario (50% conventional, 35% mulch, and 15% no-till) exemplified tillage practices in the western Lake Erie Basin during the early 1990s. In Table 2, we illustrate the impact of the three alternative tillage scenarios on a region's average annual returns, costs, and farm size as compared to those of the conventional-dominated scenario. Percentage values represent the increase or decrease in a parameter relative to its value in the conventional-tillage-dominated scenario.

As mulch-till and no-till usage increased in the Hoytville region, profits improved for a variety of reasons. First, optimal wheat production decreased and corn production increased in the three alternative conservation-dominated tillage scenarios. The cause of this substitution was the reduced spring field time require ments for corn and soybeans when the conventional tillage system is replaced by conservation tillage. In the three alternative scenarios, wheat production was reduced by 13 to 41% compared to the conventional tillage scenario.

Second, in the mulch-till and no-till-dominated scenarios, labor and operating machinery costs decreased. In part, this was because labor and machinery costs were linked essentially to the management system's efficiency in field time utilization. Two to 5% decreases in labor costs were realized on a regional scale. The same was true for operating machinery costs, which were 6 to 12% lower for the region in the alternative scenarios.

A large decrease in aggregate fixed machinery cost occurred for the region as tillage shifted from conventional to the three alternative scenarios, especially no-till. First, at the individual farm-level, no-till fixed costs were less than conventional and conservation fixed costs. Second, the number of farms in a region decreased by about 10% in the no-till-dominated scenario. In short, fixed costs decreased in the three alternative scenarios because of less expensive implements and the ability of those implements to farm more area at the same cost.

Several input costs remained the same or increased slightly from the base scenario to the alternative scenarios. Nitrogen fertilizer use increased in the three alternative scenarios because of increased corn production. Transportation, seed, insurance, and interest (other costs) also increased slightly in the alternative scenarios because of increased corn production; however, the magnitude of these changes was not large.

The only cost to substantially increase when moving toward the conservation and no-till systems was herbicide. In the fourth scenario, where no-till usage comprised 50% of the total, herbicide costs increased by 18% compared to the base scenario.

The effects of alternative tillage scenarios on the environmental parameters are depicted in Table 3 for the Hoytville region. In general, measures linked to soil, such as soil erosion (USLE), organic nitrogen (YON), and phosphorus lost in sediment (YP) decreased as conservation tillage and no-till technology reduced the amount of soil particles leaving the field. Alternatively, those measures related to water leaving the field in runoff, such as herbicide (PSRO) and nitrates (YNO3), increased. The average of these environmental pollution measures provided an overall index to compare scenarios, weighing all pollution measures equally. Scenario two, three, and four provided an overall decrease in the environmental index by 9, 15, and 20% in the Hoytville region.

Blount-Glynwood-Pewamo Soils. The Blount-Glynwood-Pewamo region was analyzed using the same methodology as the Hoytville region. Once again, the base scenario represented the conventional-dominated system or current state of agricultural production in the western Lake Erie Basin. In Table 4, the three alternative scenarios (even scenario, mulch scenario, and no-till scenario) are compared against the conventional scenario.

Returns above total costs improved in the Blount-Glynwood-Pewamo soil association region in scenarios where mulch tillage and no-till systems made up a greater percentage of the cropped area in the region. However, the percentage improvement in net returns was more modest than in the Hoytville region, primarily because wheat was less prevalent in the Blount-Glynwood-Pewamo region. In the base scenario, about one-fifth of the Hoytville region cropland was in wheat, compared to one-ninth in the Blount-Glynwood-Pewamo region. Moving from the conventional tillage scenario to alternative tillage scenarios in the Blount-Glynwood-Pewamo region resulted in a relatively small shift in corn and wheat production, and changes in the region's revenues and costs were due mainly to yield and cost differences among tillage systems. In the Hoytville region, changes among scenarios were attributable to differences among tillage systems as well as to shifts in crop production.

Changes in revenue, costs, and farm size among scenarios were of approximately the same order of magnitude in the two regions. When compared to the base scenario, increased conservation tillage resulted in reductions in labor and machinery costs, increases in herbicide use, and increases in farm size with corresponding decreases in numbers of farms in a region.

As with the Hoytville location, when more land was devoted to no-till and conservation systems, there was a decrease in soil erosion (USLE), organic nitrogen (YON) and phosphorus in sediment (YP) lost at the edge of the field, while effluents of herbicides (PSRO) and nitrates (YON3) carried in water runoff increased (Table 5). These results reflect the fact that with conservation tillage systems more herbicides are placed on the field to control weeds. When the topsoil is not disturbed or turned, as with no-till, and to a lesser degree in conservation systems, fewer soil particles have the opportunity to become dislodged and leave the field via erosion. Phosphorus and nitrogen particles are also less likely to be lost to erosion in the conservation and no-till systems. Overall, the resulting index closely matched the Hoytville index, with average reductions in pollutants carried with the soil of 10%, 15%, and 19% in the three scenarios.

Western Lake Erie Basin Region Impacts. The results presented in the previous two sections demonstrate the changes in economic and environmental performance that might occur as farms in two separate soil regions move from conventional to conservation tillage systems. Here, we discuss the impact that conservation tillage adoption can have on the overall region's farm economy and environment of the western Lake Erie Basin. As demonstrated by the results of the model simulation, farms at both the Hoytville and Blount-Glynwood-Pewamo locations were optimally managed under a no-till system and were more profitable than the same land farmed conventionally. We found that conservation tillage systems improved regional net returns substantially because of three factors: reduced labor and machinery inputs, crop enterprise mix shifts, and improved scale economies.

By using herbicides and a no-till planter to achieve the same function as the slower and more costly conventional tillage equipment, the no-till system generally exhibited the most cost efficient use of machinery and labor on both the farm-level and the average hectare level. In addition, on a per hectare basis, the fixed costs associated with no-till were 47% lower (Hoytville) and 60% lower (Blount-Glynwwod-Pewamo) than for the conventional systems. Herbicide was the only input in the no-till system that was more costly at the farm-level and on a per hectare basis. However, avoided investment in tillage implements resulted in cost savings that outweighed the additional herbicide expense, leaving the total costs on an average per hectare basis the lowest for no-till.

Time available for field work played a major role in the selection of no-till as the optimal system. Under no-till management, field time in spring was used almost exclusively to plant corn and soybeans, without having to perform any of the seedbed preparation that conventional and conservation tillage require. This allowed more cropland to be planted in both the Hoyrville location and in the Blount-Glynwood-Pewamo location under the no-till system. In the Hoyrville location, increased field time enabled the farm producer to expand cropland by 34% over what would be possible with conventional methods. In the Blount-Glynwood-Pewamo location, the difference in farm size from no-till to conventional was even greater, with a 60% increase in area planted, due to increased efficiency in field time.

The reduced labor and machinery inputs associated with mulch tillage and no-till systems had the overall effect of lowering production costs. Cost reductions enjoyed under conservation and no-till systems were achieved because a farm operator reduced the amount of machine time expended on any particular hectare. Herbicide costs presented an opposing trend when moving to conservation tillage systems. However, herbicide cost increases were more than offset by the labor and machinery cost reductions resulting from reduced tillage operations. Compared to the base scenario, the scenarios using more conservation tillage resulted in cost reductions of 1 to 2% in the Hoytville region and 3 to 4% in the Blount-Glynwood-Pewamo region.

The second feature of those scenarios with greater adoption of conservation tillage was that the crop enterprise mix changes. In the base scenario, the spring and fall time requirements for conventionally tilled corn and soybeans allowed wheat to be profitably grown on some cropland in both regions. Wheat production declined sharply with the use of mulch tillage or no-till systems.

Third, the technological transformation toward mulch tillage and no-till translated into larger and fewer farms in the designated regions. Economies of scale caused costs per unit of output to be reduced in the conservation tillage scenarios. Increases in farm size caused by shifts from the conventional scenario to mulch tillage and no-till scenarios reflected observed trends in the basin. Examination of farm-level data from Stout et al. (1992) lends credibility to the results of the simulation in that Lake Erie Basin farms in the sample using predominantly ([less than] 50%) conservation tillage are larger than the predominantly conventional tillage farms.

Lake Erie Basin commercial crop farms using conventional tillage average 217 ha; mulch tillage farms average 323 ha; and no-till farms, 516 ha. If these farm sizes are used to compute regional average farm sizes comparable to those in the four simulated scenarios, the average farm size in the base scenario would be 300 ha. The scenario in which conventional, mulch, and conservation tillage were evenly distributed would result in an 18% increase in farm size, compared to the 11% projected in Table 4; the mulch tillage scenario would result in a 15% increase, compared to the 9% increase in Table 4; and the no-till scenario, a 35% increase, compared to 20% in Table 4.

The projected environmental impact trends at the individual farm-level were consistent across the two study areas. Pollutant levels related to soil leaving the field were lower for conservation and no-till systems and higher for conventional systems. Phosphorus lost in sediment (YP) decreased from an average of more than 9 kg [ha.sup.-1] in conventional management to less than 2 kg [ha.sup.-1] in conservation and no-till systems. Organic nitrogen lost to the edge of the field (YON) followed a similar trend, decreasing from a range of 52 to 56 kg [ha.sup.-1] in the conventional system to a range of 10 and 11 kg [ha.sup.-1] under conservation and no-till management. Although soil erosion (USLE) values were not high, in general they follow the same trends as phosphorus and organic nitrogen. Conventional systems lost the most soil, with values approaching 2.5 t [ha.sup.-1], while conservation and no-till systems lost the least, with average values of 1.2 t [ha.sup.-1]

A reverse trend was evident for effluents associated with water runoff. Nitrates lost in water runoff (YON3) were lowest under conventional systems, with an average of 6 kg [ha.sup.-1]. Conservation and no-till systems were higher, with average values of 6.6 kg to 8.4 kg [ha.sup.-1]. Herbicides leaving the field in water runoff (PSRO) showed only slight increases under conservation and no-till practices on the Hoytville location. The conventional system's average herbicide runoff was 53 g [ha.sup.-1], whereas conservation and no-till systems had values of 57 and 54, respectfully. In the Blount-Glynwood-Pewamo location, the same results were evident, with the optimal conventional system averaging more than 42 g [ha.sup.-1] while the no-till system averaged more than 44 g [ha.sup.-1].

The effects of conservation tillage systems on pollution patameters were mixed. Nitrates increased substantially and herbicides increased slightly in both regions as conservation and no-till systems become predominant. However, adoption of conservation tillage systems produced large decreases in soil erosion and organic nitrogen and phosphorus losses. The impact of conservation tillage adoption, as measured by our rather simple environmental index, was favorable for the environment due to relatively large decreases in soil erosion, organic nitrogen losses, and phosphorus losses.

Our results are consistent with observed trends in the Lake Erie Basin. Tributary loading data in the basin reveal that a statistically significant reduction in total phosphorus loading and a significant increase in nitrate concentrations occurred between 1975 and 1990 (Richards and Baker). These water quality changes probably were attributable, for the most part, to changes in tillage practices. At the beginning of this period, few farmers used conservation tillage, and its adoption was expanding slowly. By the 1990s, however, major increases in adoption occurred; conservation tillage technologies were used on 51% of the corn and soybean cropland by 1994.

Implications

Our research suggests that adoption of conservation tillage technologies can have far-reaching consequences for a region's agricultural economy and environment. First, soil erosion decreased and future soil productivity was enhanced. Second, potential pollutants that are directly related to soil erosion, such as sediment, organic nitrogen, and total phosphorus loadings, also decreased. Third, farm profitability improved. These effects have positive implications for a region's farmers, its environment, and the long term sustainability of its agriculture.

But these positive effects can be somewhat offset by some unintended consequences of conservation tillage adoption. First, some pollution parameters may increase (e.g., nitrates and herbicides). Second, a region's crop mix and input usage may change. In our study, wheat production decreased at the expense of row crops. Third, labor usage and input purchases may decrease, which implies that agriculture's contribution to a region's economy also might decrease. Finally, average farm size may increase, resulting in fewer farms in the region.

D. Lynn Forster is Professor and Eric C. Smith is a former Graduate research Associate in the Department of Agricultural, Environmental and Development Economics at Ohio State University. Diane Hite is Assistant Professor in the Department of Agricultural Economics at Mississippi State University.

Acknowledgements:

This study was supported by funds provided by the United States Department of Agriculture for a project titled, "Agricultural Pollution Prevention in Lake Erie Basin: Analysis and Design." We are grateful to David Baker and Frank Calhoun, project directors, for their assistance and comments on this study.

REFERENCES CITED

Althauser, C.E. 1996. An economic evaluation of various more-sustainable farming systems for cash grain farmers in northwest Ohio. Master's thesis, Ohio State University. 1996.

Baker, David B. 1993. The Lake Erie agroecosystem program: water quality assessments. Agriculture, Ecosystems and the Environment 46:197-215.

Braden, J.B., R.S. Larson, and E.E. Herricks. 1991. Impact targets versus discharge standards in agricultural pollution management. American Journal of Agricultural Economics 73:388-597.

Ellis, J.R, D.W. Hughes, and W.R Butcher. 1991. Economic modeling of farm production and conservation decisions in response to alternative resource and environmental policies. Northeastern Journal of Agricultural and Environmental Policies 20:98-108.

Forster, D.L., T.J. Logan, S.M. Yaksich, and J.R. Adams. 1985. An accelerated implementation program for reducing the diffuse source phosphorus load to Lake Erie. Journal of Soil and Water Conservation 40:136-141.

Great Lakes Commission. April 1996. An action agenda for Great Lakes Basin agriculture. Ann Arbor, Michigan.

International Joint Commission. 1974. Great Lakes Quality Agreement.

Lines, Allan E. 1994. Ohio farm machinery economic cost estimates for 1994. Department of Agricultural, Environmental, and Development Economics, The Ohio State University, ESO 2131.

Ohio State University Extension. 1995. Ohio crop enterprise budgets. The Ohio State University.

Richards, R., P. and D.B. Baker. 1993. Trends in nutrient and suspended sediment concentrations in Lake Erie tributaries, 1975-2000. Journal of Great Lakes Research 19:200-211.

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Sharpley, A.N., and J. R. Williams (eds). 1990. EPIC--Erosion/productivity impact calculator: 1 Model documentation. USDA Technical Bull. No. 1768.

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Smith, E.C. 1997. Farm management practices and environmental efflents in the western lake Erie Basin of Ohio: An economic optimization of farming systems. Master's thesis, The Ohio State University.

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Wossink, G.A.A., T.J. De Koeijer, and J.A. Renkema. 1992. Environmental-economic policy assessment: A farm economic approach. Agricultural Systems 39:421-438.
                        Regional Tillage Scenarios
             Conventional   Unifom   Mulch-Till  No-Till
              Scenario 1  Scenario 2 Scenario 3 Scenario 4
                  %           %          %          %
Conventional      50         33.3        25         15
Mulch-Till        35         33.3        50         35
No-Till           15         33.3        25         50
                     Economic comparison of scenarios,
                             Hoytville region.
                                                       Change from
                                     Conventional Conventional Scenario
                                       Scenario           Even
                                       Average/         Scenario
                                        Acres               %
                                          $
Total revenue                            269                2
Variable costs            Nitrogen        20                2
                          Phosphorus      10               -4
                          Herbicide       14                9
                          Labor            7               -2
                          Machinery       10               -6
                          Othercosts      58                2
                          Land cost       75                0
Fixed costs               Machinery       40               -8
Total costs                              234                0
Returns above             total cost      34               22
Average farm size (acres)              1,515                5
                           Mulch   No-till
                          Scenario Scenario
                              %        %
Total revenue                 0        5
Variable costs                0        3
                             -4       -8
                              3       18
                             -5       -4
                             -9      -12
                              0        4
                             -1        1
Fixed costs                  -7      -15
Total costs                  -2       -1
Returns above                16       43
Average farm size (acres)     6       10
                  Environmental comparison of scenarios,
                             Hoytville region.
                                Change from
              Conventional Conventional Scenario
                Scenario           Even           Mulch   No-till
                Average/         Scenario        Scenario Scenario
                 Acres               %              %        %
USLE    tons      0.70              -8            -17       -17
YNO3    kg        5.70               2              5         5
YON     kg       27.73             -21            -33       -44
YP      kg        4.89             -21            -33       -45
PSRO    grams    22.15               1              0         2
Average                             -9            -15       -20
                    Economic comparisons of scenarios,
                      Blount-Glynwood-Pewamo region.
                                                        Change from
                                      Conventional Conventional Scenario
                                        Scenario           Even
                                        Average/         Scenario
                                         Acres               %
                                           $
Total revenue                             311               -1
Variable costs            Nitrogen         24               -1
                          Phosphorus       10               -2
                          Herbicide        17                4
                          Labor             7               -5
                          Machinery        11               -9
                          Other costs      63               -1
                          Land cost        75               -1
Fixed costs               Machinery        45               12
Total costs                               252               -3
Returns above             total cost       59                8
Average farm size (acres)               1,370               11
                           Mulch   No-till
                          Scenario Scenario
                             %         %
Total revenue               -1         0
Variable costs              -1         0
                            -2        -2
                             1        10
                            -6        -8
                            -9       -15
                            -1         0
                            -1        -1
Fixed costs                 -9       -21
Total costs                 -3        -4
Returns above                9        19
Average farm size (acres)    9        20
                  Environmental comparison of scenarios,
                       Blount-Glywood-Pewamo region.
                           Change from Conventional
              Conventional         Scenario
                Scenario             Even              Mulch   No-till
                Average/           Scenario           Scenario Scenario
                 Acres                %                  %        %
USLE    tons      0.73               -10                -14      -19
YNO3    kg        5.73                 6                  6       14
YON     kg       30.63               -23                -33      -47
YP      kg        5.17               -23                -33      -46
PSRO    grams    16.96                 0                 -1        2
Average                              -10                -15      -19
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Article Details
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Author:Forster, D. Lynn; Smith, Eric C.; Hite, Diane
Publication:Journal of Soil and Water Conservation
Article Type:Statistical Data Included
Geographic Code:1USA
Date:Mar 22, 2000
Words:5766
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