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Farm level impacts of the Coastal Zone Management Act proposed erosion regulations.

ABSTRACT- It M6(y 1991, the EPA proposed management measures for controlling erosion in the coastal zone regions of the U.S. One proposed management measure for cropland soil erosion and sedimentation would require producers to limit cropland soil erosion to the lesser of T (soil loss tolerance) or that occuring with conservation tillage. This study estimated the, farm level impacts on cropping patterns, soil erosion, and economic returns associated with selected coastal zones complying with this proposed regulation. Three sites were selected for analysis: (1) Texas Coast., (2) Coastal Georgia, and (3) Northern Indiana. The method of analysis was a farm profit maximization program. Farming practices data were incorporated into the models, and the 1987 National Resources Inventory (NRI) data base provided regional hectares of each crop on each of four land types witb different soil erodibility. The results indicate that the Texas Coastal Bend is currently within compliance, thus, there would be little, to no expected impact from the proposed guidelines. For Coastal Georgia and Northern Indiana, row crops on erodible productive land would be expected to shift with hay on less productive land, giving a 25 percent and 43 percent reduction in sheet and rill erosion for the two areas. These shifts in cropping patterns would result in about 3.25 percent and 3.59 percent reduction in net returns for the case farms.

In response to the Coastal Zone Management Act (CZMA) Reauthorization Amendments of 1990, the Environmental Protection Agency (EPA) published the "Proposed Guidance Specifying Management Measures for Sources of Nonpoint Pollution in Coastal Waters" in May 1991. The CZMA regulations will impact 706 coastal counties in 27 states including six around the Great Lakes. States failing to develop implementation programs to meet the regulations could face reductions in federal funding heginning in fiscal year 1996. One proposed EPA management measure for cropland soil erosion and sedimentation would require producers to limit cropland soil erosion to the lesser of T (soil loss tolerance) or that occurring with conservation tillage. Such a regulation could significantly affect cropping patterns, tillage practices, soil erosion, and farm income in the coastal zone drainage basin (CZDB). The objective of this Study was to analyze farm-level impacts of this regulation for selected regions of the CZDB, including (1) Texas Coastal Bend, (2) Coastal Georgia, and (3) Northern Indiana.

Methodology and Procedures

A procedure was developed to determine optimal cropping patterns, tillage practices, soil erosion, and farm income with and without the proposed EPA regulation. First, land was divided into four classifications based on its erodibility. Next, case farms were constructed for each of three study regions. A linear programming (LP) model solved for the mixes of crops and tillage practices to maximize profits. This is referred to as the base scenario. Next, the cropping and tillage alternatives available to the case farm were modified to ensure compliance with the proposed regulation. The LP model again solved for the optimal cropping and tillage practices mix. This is referred to as the EPA scenario. Finally, cropping patterns, tillage practices, soil erosion,and farm income for the two scenarios were compared.

The 1987 National Resources Inventory (87 NRI) provides data on hectares of land by crop and soil class and also indicates the inherent erodibility of the soil and the mix of management practices used. For this evaluation cropland from the 87 NRI was divided into four classes: 1) land in USDA Land Capability Classes III-VIII where the subclass limiter is "w" (w3-8); 2) land not in IIIw-VIIIw with an erosion index (ei) of less than 8 based on the Universal Soil Loss Equation (USLE) factors and T (soil loss tolerance); 3) land not in IIIw-VIIIw with an ei greater than or equal to 8 and less than 20; and 4) land not in IIIw-VIIIw with an ei greater than or equal to 20. The percent of the study area in each crop by land type was calculated. In this way the current situation for each study area was established.

A case farm was constructed for each of the stlidy areas in the CZDB. Each case farm had total hectares equal to the average size farm in its study area based on published statistics (1, 2, 4, 11). The case farms were assumed to contain all four land types in the same proportions as they occurred in their respective study areas. In Coastal Georgia, for example, 81 percent of the cropland hectares are on land with low erodibility; therefore, 81 percent of the cropland hectares of the case farm were assumed to be lands with low erodibility. Because the amount of w3-8 land is very small relative to the land with low erodibility, and because the management of these two types of land is relatively similar, the two cropland class groups were treated as a single decision unit in the Georgia and Indiana case farms. The proportion of total pasture hectares and idle land for the study area were applied to the case farms; idle hectares and pastureland were held fixed for this analysis.

A farm level LP model known as REPFARM (9) was used to determine the optimal mixes of crops and tillage practices for each case farm. REPFARM maximizes net returns to the farm taking into consideration the sequencing of production tasks, time available for each task, and yield reductions if these tasks are delayed. It was developed to provide a flexible model which could be applied in a number of settings (9).

Alternative tillage practices and soil management strategies were identified to enable the case farms to comply with the proposed regulation. The alternatives included conservation tillage (i.e., any system leaving greater than 30 percent of the soil surface covered with residue after planting) contouring, terracing, and two crop rotation options: 1) after 3 years of hay, and 2) after small grains.

Average USLE values I)y crop, management strategy, and land type for the relevant row crops in each study area were calculated. If the USLE of a crop in the base scenario exceeded T, then that crop activity in the LP medel was replaced by an alternative method of production that resulted in a USLE value less than T. If the USLE of a crop in the base scenario was below T but greater than that associated with conservation tillage, then a conservation tillage activity for the crop was added to the LP model and the current tillage activity was deleted. After the crop activities in the base model were modified to meet the proposed EPA standards, the LP model solved for the optimal mixes of crops and tillage practices to maximize profits.

Solutions for the base scenario and EPA scenario were compared for each of the three selected regions. Expected changes in hectares of each crop on each land type, sheet and rill erosion, gross income, variable cost, and net returns were estimated. Changes in gross income hold implications for marketing, processing, storing, and transportation services. Changes in variable cost items hold implications for farm supply industries, and changes in net returns imply expected impact on farm families in the region.

The Texas Coastal Bend, Coastal Georgia, and Northern Indiana were chosen as study areas. These sites were chosen due to the availability of sequencing and field time availability data for these areas. They in no way represent a random selection of three coastal zone areas or an attempt to select areas most heavily or least heavily affected by this proposed legislation.

Data

Secondary data was used to develop a case farm for each region selected. Necessary data included cropland hectares by land type, average size farm, crop budget information, time and labor constraint information, alternative cropping strategies, and soil erosion associated with the cropping strategies. The data were derived from agricultural statistics published by federal and state agencies and from Soil Conservation Service (SCS) surveys and studies.

Crop data on yields, timing constraints, yield impacts due to delays, necessary inputs, and prices of inputs and outputs were collected for each case farm. Crop budgets were provided by extension economists in Texas (3), Georgia (5), and Indiana (10). These budgets provided cost and return data, expected yield, and tillage practice information. Survey data from International Harvester Corporation (6, 7, 8) provided sequencing information, timing constraint data, and yield impacts due to delays for all three regions.

The 87 NRI provided information on land use, cropping patterns, management practice use, and inherent erodibility. Inherent erodibility factors from the 87 NRI were combined with management factors from the Erosion Productivity Index Calculator (EPIC) (13) model to calculate erosion levels associated with the alternative production technologies. EPIC also provided yield differentials by soil class, tillage method, and crop sequence. Tillage and pesticide cost differentials for the alternative technologies were obtained from enterprise budgets in the combined FEDS and SCS budgeting system. Increases in cropping operation time requirements due to contouring and terracing and the annualized cost of installing and maintaining terraces were taken from SCS data.

Results

The implications of enforcing the proposed EPA regulation regarding soil erosion are presented for each of the regions. The NRI data for the study area counties established base conditions. This is followed by the cropping pattern for the base scenario, the EPA scenario, and the changes in costs and returns.

Texas Coastal Bend The CZDB encompasses all or part of 41 counties on the Texas coast. This study examined 10 counties in a region called the Texas Coastal Bend, which includes approximately 0.526 million hectares (1.3 million acres) of cropland that produces primarily sorghum, cotton, and corn. Of this cropland, 26 percent is w3-8 type land, 36 percent is of low erodibility, and 38 percent is of medium erodibility. The low and medium erodibility lands are also the higher producing lands. Grain sorghum, cotton, and corn account for 71 percent of the cropland hectares.

The land in the Texas Coastal Bend is not highly susceptible to sheet and rill erosion. Land in this region is relatively flat and inherently low in erodibility such that average USLE is below T for the 87 NRI current mix of practices for each crop. None of the crops on any of the land types exhibit USLE values greater than T. Conservation tillage is a profitable farming practice for corn, sorghum, and wheat in this are according to the 87 NRI. Conservation tillage is less profitable for cotton, but soil erosion using current tillage systems for cotton is less than it would be for conservation tillage. The proposed soil erosion regulation is expected to have little or no impact on agriculture in the Texas Coastal Bend area. Results are not presented for the case farm because no changes occurred in the solution.

Coastal Georgia. The CZDB encompasses all or part of 33 counties in Coastal Georgia. This includes 0.558 million (1.37 million acres) hectares of crop land that produces primarily corn, soybeans, cotton, wheat, and hay. Of this cropland, 11 percent is w3-8 type land, 81 percent is of low erodibility, five percent is of medium erodibility, and three percent is of high erodibility. The more erodible lands are also the higher producing lands. Corn and soybeans account for 73 percent of the cropland hectares. Hay is a profitable crop, but risk of rain at harvest and low prices in glut years keep the hay hectareage close to five percent of the total (Givan, pers. comm.). Most of the hay is produced on the land with low erodibility leaving the more erodible and productive land for corn and soybean production. Wheat production is very small according to the 87 NRI, but county level data from 1983 to 1989 shows wheat production at close to 10 percent of the harvested hectares in the study area (4).

The average size farm in Coastal Georgia was approximately 136.4 hectares (337 acres) in 1987. A 136.4 hectare case farm was developed with proportions of the four land types equal to the proportions for the study area as a whole (Table 1). Upper limits were set in the LP model such that hay, cotton, ind soybeans could not exceed four percent, four percent, and 43 percent of the total hectareage, respectively. Likewise, lower limits of 10 percent and 21 percent of the total hectareage were set for wheat and corn, respectively. These are the historical percentages of these crops and are believed by the authors to exist for reasons exterior to the LP model. Wheat on land with tow erodibility was assumed produced with conservation tillage due to the increase in yields associated with conservation tillage in this area depicted by the 87 NRI. A profit-maximizing farmer would choose conservation tillage in this situation.

Estimated hectares planted per crop by land type for the base scenario are presented in Table 1. All of the highly erodible land was planted in soybeans, and all of the medium erodible land was planted in cotton. These are two of the most profitable crops and yield more on these soils than on the less erodible land types. The w3-8 land and land with low erodibility produced corn, soybeans, wheat and hay. Estimated gross income and variable cost for the case farm under this solution were $59,322 and $35,944 respectively, leaving 23,388 as returns to fixed cost (Table 2).

[TABULAR DATA OMITTED]

If the proposed regulation is enforced on the case farm, only hay can he grown on the medium and highly erodible land types. This is because no conservation tillage management scheme would reduce soil erosion to T or less for the remaining crops. In this scenario the area in hay was not constrained to four percent of the total but was allowed to increase to 6.9 percent of the total area in order to cover the medium and highly erodible lands. Cotton and soybean areas moved to the land with low erodibility, and wheat area decreased to offset the increase in hectares of hay (Table 1). Estimated gross income and variable cost for the case farm were $59,606 and $37,006, resulting in a 3.4 percent decrease in returns to fixed cost from the base scenario (Table 2).

The results of this case farm are an indication of estimated impacts across the study area as a whole. Cotton, soybean, and wheat sales are estimated to decrease while hay sales are expected to increase throughout the region. The overall effect on agricultural sales for the area is ambiguous, but a definite decrease in drying, storing, and marketing activities of the grains and cotton is expected. Variable costs are estimated to increase over the area. This comes primarily from increases in fertilizer and machinery fuel and repairs. Overall, farmers in Coastal Georgia could expect a 3.37 percent decrease in net returns while impacts on ag-business in the region are ambiguous with decreases in most processing and marketing activities and increases in ag-supply activities.

Northern Indiana. The CZDB encompasses all or part of nine counties in northern Indiana. Three of the counties border Lake Michigan. The counties include approximately 0.648 million hectares (1.6 million acres) of crop land. The primary crops are corn soybeans, hay, and wheat. Of this cropland, six percent is w3-8 type land, 80 percent is of low erodibility, 10 percent is of medium erodibility, and four percent is of high erodibility. The more erodible lands are slightly less productive than the land with low erodibility. Corn and soybeans account for 74 percent of the cropland hectares. This study assumed farmers were using conservation tillage practices on soybean hectares because it is more profitable than conventional tillage. Hay accounts for 14 percent of the total hectares and covers 19 percent of the medium erodible land and 36 percent of the highly erodible land. Wheat production is small according to the 87 NRI.

A 161.9 hectare (400 icre) case farm was developed with proportions of the four land types equal to the proportions for the study area as a whole (Table 3). Upper limits were set such that hay could not exceed 15 percent of the total area, and soybean land must be rotated with some other crop every third year. These practices are common in Northern Indiana.

Application of the LP model provided estimates of hectares planted per crop by land type (Table 3). The percent of farm hectares planted to each crop for the base scenzirio resembles the percent of Study area hectares planted to each crop. The erodible lands were planted in soybeans and corn while 24.3 hectares (60 acres) of hay were planted on the soils with low erodibility. Hay is the most profitable crop and gains a yield increase from the land with low erodibility. Estimated gross income and variable cost for the case farm under this solution were $139,827 and $57,118 respectively leaving $82,709 as returns to fixed cost (Table 4).

[TABULAR DATA OMITTED]

If the proposed regulation is enforced on the case farm, soybeans coulct not be grown on medium and highly erodible land because even with conservation tillage soil loss would exceed T. Corn and soybean production would require the use of conservation tillage on low and w3-8 land types also. Corn on the medium land and wheat on highly erodible land would require terracing in addition to conservation tillage in order for soil loss to be less than T. The profit maximizing solution for this scenario included only hay on the medium and highly erodible land types (Table 3). Some corn area was replaced by an increase in soybean area (Table 3). This is because conservation tillage makes corn production less profitable. Estimated gross income and variable cost for the case farm with EPA proposed regulations were $130,812 and $51,073 resulting in a 3.59 percent decrease in returns to fixed cost as compared to the case (Table 4).

[TABULAR DATA OMITTED]

Total sales and variable cost are estimated to decrease across the study area with sales decreasing more than variable costs. The net effect is a decrease in returns to fixed cost for the farmer, which translates to a decrease in the processing, storing, and marketing activities as well as a decrease in ag-supply activities for the area. Much of the decrease in purchased inputs comes from machinery fuel, lube, and repairs. Agricultural chemicals is the only input estimated to increase. Overall, agriculture in northern Indiana can expect a 3.6 percent decrease in farmer net returns and it decrease in the need for ag related services of many kinds.

Conclusions

The three regions examined in this study give an indication of the expected impact of soil loss regulations such as this one proposed by the EPA. Some areas, like the Texas Coastal Bend, would be virtually unaffected. other areas, like Coastal Georgia and Northern Indiana, would be forced to make adjustments that would translate into reduced ag-business activity and reduced farm income. Only 14 percent and eight percent of the cropland in Northern Indiana and Coastal Georgia have current soil erosion levels greater than T. However, limiting soil loss to T or less on these hectares will results in an approximate reduction in farmer net returns of 3.4 percent. Coastal areas with larger percentages of erodible lands can expect larger reductions in farmer net returns.

Since the completion of this study, the EPA has published the final guidelines for management measures of coastal zone regions. The final guidelines do not require producers to limit cropland soil erosion to the lesser of T or that occurring with conservation tillage. Instead, farmers in the CZDB must apply the erosion component of a Conservation Management System as defined in the Field Office Technical Guide of the SCS, or design and install a combination of management and physical practices to settle the settleable solids and associated pollutants in turnoff delivered from the contributing area (12).

This study focused only on the erosion and sediment control guidelines proposed by the EPA. The CZMA also regulates nutrient, pesticide, and irrigation water management. Any or all of these other regulated activities will impact agriculture in these areas on top of the impacts estimated here.

This study concentrated on case farms that contained all four land types in the same proportions as the study area as a whole. Farms that contain lesser (greater) proportions of the medium and highly erodible lands will face a smaller (larger) change in net returns than depicted here.

Output and input prices have been assumed unaffected by changes in cropping patterns. This assumption could be critical in the Coastal Georgia study area where the area cropped to hay increased to cover all of the medium and highly erodible lands. An increase in the supply of hay would most likely be met with a decrease in the price of hay resulting in lower sales and net returns for the Coastal Georgia farmer than depicted in Table 4.

Due to the investment and management requirement of zero tillage, it was not included as a viable option for meeting the proposed regulation on soil erosion.

REFERENCES CITED

[1.] Barnard, F.L., K.T. McNamara, and J. Falck. 1991. Results of the Indiana farm finance survey for 1991. In: Chris Hurt (ed.) , Purdue Agricultural Economics Report. November. Purdue Univ. Coop. Ext. Serv., West Lafayette, IN. [2.] Bureau of the Census. 1989. 1987 Census of agriculture. Vol. 1, Part 43. U.S. Dept, of Commerce, Washington D.C. [3.] Extension Economist-Management. 1991. Texas crop budgets. Texis Agr. Ext. Serv. Bull.B-1241. College Station, TX. [4.] Georgia Agricultural Statistics Service. 1990. Georgia agricultural facts. Athens, GA. [5.] Givan, W., D. Shurley, and C. Dangerfield. 1990. Crop enterprise cost analysis;. South Georgia 1991. Cooperative Ext. Serv., The Univ. of Georgia College of Agriculture, Athens. Misc. Pub. No. 27-S. [6.] International Harvester. 1979. Pro-Ag: Indiana, Ana Northern 2/3. December. [7.] International Harvester. 1978. Pro-Ag: Texas Costal Bend Area. March. [8.] International Harvester. Pro-Ag: Southern Georgia. No Date. [9.] McCard B.A., and J. Pheasant. 1983. PEP FARM: Documentation of the computer model Station Bulletin No. 409. January. Dept. of Agricultural Economics, Agricultural Experiment Station, Purdue Univ., West Lafayette, IN. [10.] Schulte, A.M., C.L. Dobbins, and D.H. Doster. 1991. Gross margin estimates for crop enterprises. Purdue Research Foundation, Dept. of Agricultural Economics, Purdue Univ., West Lafayette, IN. [11.] Texas Agricultural Statistics Service. 1990. Texas agricultural statistics 1989. Austin, TX. [12.] U.S. EPA. 1993. Guidance specifying management measures for sources of nonpoint pollution in coastal waters. 840-B-92-002, Office of Water, Washington, D.C. [13.] Williams, J. R., C. A. Jones, and P.T. Dyke. 1984. A modeling approach to determining the relationship between erosion and soil productivity. Trans. ASAE. 27:129-144.
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Author:Bryant, Kelly J.; Atwood, J.D.; Lacewell, Ronald D.; Lansford, Vernon D.; McCarl, Bruce A.; Dyke, Pa
Publication:Journal of Soil and Water Conservation
Date:Sep 1, 1993
Words:3790
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