S1004: Development and Evaluation of TMDL Planning and Assessment Tools and Processes (S273)

(Multistate Research Project)

Status: Inactive/Terminating

S1004: Development and Evaluation of TMDL Planning and Assessment Tools and Processes (S273)

Duration: 10/01/2001 to 09/30/2007

Administrative Advisor(s):


NIFA Reps:


Non-Technical Summary

Statement of Issues and Justification

A Total Maximum Daily Load (TMDL) is the calculated maximum amount of a pollutant that a waterbody can receive and still meet applicable state water quality standards, and an allocation of that amount to the pollutant sources contributing to the water quality impairment. The applicable water quality standards are set by the States, Territories, and Tribes. They identify the uses for each waterbody (drinking water supply, contact recreation, and aquatic life support) and the scientific criteria required to support that use. The calculated TMDL must include a margin of safety to ensure that the waterbody can be used for its designated purposes. The calculation must also account for seasonable variation in water quality. The TMDL program is mandated by section 303 of the Clean Water Act of 1972.



The TMDL program has become a national issue because lawsuits have forced the USEPA to develop rules that require every state to develop and submit TMDL plans for all waterways in the United States that fail to meet state water quality standards. Current public and private costs associated with this effort are estimated to be $1.035 billion for development of TMDL plans, $255 million for additional monitoring to support TMDLs, and $13.5 to $64.5 billion for implementation of TMDL plans over the next fifteen years (USEPA 2001). According to the USEPA, agriculture is the largest source of water quality impairment in the United States. As a consequence, agriculture is the focus of many TMDL studies and the TMDL program may result in the first nationwide regulatory programs for agricultural nonpoint source pollution control if voluntary nonpoint source programs fail to achieve water quality standards. Former Secretary of Agriculture, Dan Glickman, and many others have expressed concern over the TMDL program. Specifically, Glickman stated, "the USDA is concerned about the science being used in assessing and attributing the effects of nonpoint source pollution.



Because of our expertise in agriculture, agricultural economics, water quality monitoring and modeling, agricultural pollution control, and the TMDL planning process, we are in a unique position to evaluate tools being used for TMDL development to insure that they are based on sound science and are used in a sound manner. Similarly, there is a need to develop new tools that are based on the best science available to insure that required water quality improvements are obtained with minimum hardship to the American agricultural community and taxpayers. This work will require a regional/multi-regional effort because of the national scope of the TMDL program and because differences in hydrology and pollutant sources across the country require a variety of TMDL development approaches. In addition, an interdisciplinary team of university scientists, agency personnel, and private sector representatives is required because of the complex water quality, economic, and social issues that must be addressed during TMDL development.


Importance of Modeling and Monitoring in the TMDL Program


The National Research Council (2001) recently completed an assessment of the scientific basis of the TMDL program at the request of Congress (NRC, 2001). Broad conclusions of the NRC committee that are related to the proposed project include:




  • TMDLs should encompass all stressors that determine the condition of a waterbody, not just traditional chemical and physical pollutants.

  • Scientific uncertainty is a reality within the TMDL program and cannot be entirely eliminated. Substantial efforts should be made to reduce this uncertainty. Uncertainty must be explicitly acknowledged both in the models selected to develop TMDLs and in the results generated by those models.

  • Biological criteria should be used in conjunction with physical and chemical criteria to determine whether a waterbody is meeting its designated use.

  • All chemical criteria and some biological criteria should be defined in terms of magnitude, frequency and duration.

  • EPA should selectively promote the development of models that can more effectively link environmental stressors (and control actions) to biological responses.

  • Monitoring and data collection programs need to be coordinated with anticipated water quality and TMDL modeling requirements.



As indicated above, modeling and monitoring are the keys to successfully quantifying the impacts of agricultural nonpoint source pollution for TMDL development in an unbiased and science-based manner. Modelers were once challenged as to whether their models would actually be used to solve real problems. Today, we are being required to use models and modelers must remind users of model limitations. The key question is no longer, "Will models be used?", but rather, "Which model is most appropriate for this application and what is the uncertainty associated with the use of the model for this application?". The proposed regional project provides an opportunity to develop more robust models and to develop guidance that will allow more effective model use for TMDL development over a wide range of conditions.


The Southern Region has a long history of supporting research on water quality protection and watershed modeling. The early projects focused on modeling the hydrologic, sediment, and chemical transport processes to address water quality concerns. More recently, important biological processes have been included to address ecological concerns and we have begun to consider the economic impacts of watershed protection activities on impacted stakeholders. The focus of this project is to build on previous watershed-scale, water quality modeling and monitoring activities and to adapt these techniques and tools to the solution of TMDL development problems. The project will address economic and equity issues as well as pollutant load allocation issues associated with TMDL development in watersheds dominated by agricultural activities.


TMDLs are being developed for a variety of water quality impairments (sediments, pathogens, nutrients, metals, dissolved oxygen, other habitat alterations, temperature, pH, impaired biologic community, pesticides, flow alterations, mercury, organics, noxious aquatic plants, ammonia, etc.). In this project we will focus on the development of TMDL tools for assessing sediment, nutrient, pesticide, pathogen, and ecological impairment related TMDLs.



Economists have made substantial progress in linking economic decision and optimization models with existing hydrologic models. Decisions of farmers have been analyzed for their effects on farm profitability and on water quality. Additional work is needed to link human choices, policy variables, and environmental quality.



Tools are needed to make comprehensive models user-friendly. Recent developments in information technologies, such as expert systems, geographical information systems (GIS) and visualization software, have great potential to simplify the use of models. These technologies can help the user manage large amounts of input data and present the output in an easily understood format. Much of the model complexity can be hidden from the user by the application of appropriate interface software. The user can then apply complex and comprehensive models as easily as simpler models.


Importance of Regional Efforts


The development and evaluation of models and other tools for TMDL development is expensive and time consuming. Since states have similar TMDL modeling needs, a regional project allows development costs to be shared and avoids duplication of efforts. In addition, the expertise required to develop robust planning models that include hydrologic, chemical, biotic, economic, and social components and that are applicable for TMDL development in many regions can best be achieved through a regional project that brings together scientists and stakeholders from a variety of disciplines and experiences.


Comprehensive models are usually written to simulate a wide range of conditions. Different conditions are considered by changing the input parameters. Evaluating the accuracy of the model using data at a single site is risky. Validity at one site does not ensure that the model is valid at a different site because the predictive component of greatest influence could easily change with location. It is therefore of critical importance that models be evaluated with the widest range of possible conditions. This need can best be met through a regional or multi-regional project. Cooperation of scientists on a regional project provides a broad range of conditions from which the models can be tested. The project described herein is actually a multi-region project.


Summary


Changing human activity to protect water quality is expensive. Projected public and private costs of the TMDL program are estimated to be $15 to $66 billion dollars over the next fifteen years. Policies that meet environmental quality goals at a minimum cost are clearly less burdensome to society than inefficient policies. Because agriculture is the leading cause of water quality impairment in the United States, it is essential that TMDL development tools accurately simulate the effects of agricultural activities on water quality. Otherwise, the agricultural community may be forced to implement costly practices with no assurance that desired improvements in water quality will be obtained.


The scope of the project is too broad to be completed with the resources of a single state. In the opinion of the scientists developing the proposal, the objectives can best be achieved through a multi-regional study. Through coordination and cooperation of activities among different states, model development and data collection costs can be shared, and limited public funding can be used more effectively. A multi-regional project is therefore the most cost-effective approach toward expanding and improving watershed models for TMDL development. The strongest advantage of regional cooperation in the modeling effort of this project lies in the evaluation of the models to be improved and/or developed. By sharing models and data among states and regions, the performance and value of the models can best be assessed for a wide variety of conditions.

Related, Current and Previous Work

A CRIS search revealed that there are very few USDA projects (regional and otherwise) that are addressing agriculture and the TMDL program directly. Several Hatch and ARS projects are attempting to develop and or evaluate tools used for TMDL development in agricultural areas, but there is no widespread review like that proposed in this project. There is little, if any, duplication of efforts between the proposed project and the projects identified in the CRIS search. Potentially related projects identified through the CRIS search are summarized in the Appendix.


Models Used or with Potential for Use in TMDL Development


There are hundreds of hydrologic models available for hydrologic and water quality assessment, however, only a few are suitable for TMDL development because of their spatial and temporal scales or their inability to simulate specific pollutants. Shoemaker et al. (1997) selected and reviewed 19 models that they judged to have potential for watershed assessment and TMDL development. Reviewed models that have potential for TMDL development in agricultural watersheds include: AGNPS (http://www.sedlab.olemiss.edu/agnps.html) ANSWERS (Bouraoui and Dillaha, 1999), GWLF (Haith et al., 1992), BASINS (http://www.epa.gov/OST/BASINS/) HSPF (http://water.usgs.gov/software/hspf.html), SWAT (http://www.brc.tamus.edu/swat/), SWRRBWQ (http://www.epa.gov/docs/SWRRB_WINDOWS/), WEPP  Small Watershed Version (sediment only) (http://topsoil.nserl.purdue.edu/nserlweb/weppmain/wepp.html). However, only a few of these models have been tested for their ability to simulate pollutant fate and transport in upland agricultural watersheds and none of the reviewed models abilities to simulate the site-specific effectiveness of agricultural BMPs has been validated extensively. The participants in the proposed regional project have been involved in the development of five of the eight models listed above and are in a unique position to assess and/or enhance their use for TMDL development.

Objectives

  1. Develop, improve, and evaluate watershed models and other approaches for TMDL development and implementation.
  2. Assess potential/likely economic benefits and costs and equity issues associated with TMDL implementation at the watershed and individual landowner scale.
  3. Assess the potential ecological benefits/implications of TMDL implementation at watershed level.

Methods

Objective 1: Develop, improve, and evaluate watershed models and other approaches for TMDL development and implementation.

The ultimate goal of this objective is to improve the ability of TMDL development models to assess the impact of agricultural practices on in-stream water quality. At the first technical committee meeting, the specific needs of the models for TMDL development will be discussed. This discussion will also include establishing techniques for model evaluations. Needs for specific data, data parameters and criteria, and computer-compatible data formats will be mutually developed. As the result of this discussion, model evaluation and development and data collection responsibilities for the participating states/locations will be established. Results will be shared as the research progresses. An objective coordinator will provide overall leadership for the activities of Objective 1. These activities will be summarized to all project participants at the annual technical committee meetings. The objective coordinator will be selected by the participants of Objective 1 at the technical committee meetings and will likely serve a two-year term.

Task 1. Evaluate existing watershed assessment models (AGNPS, ANSWERS, HSPF, SWAT, GWLF, etc.) for their applicability for TMDL development in agricultural watersheds.

Task 2: Develop new and improved systems to integrate existing data sources with models used for TMDL development.

Task 3: Collect and assemble comprehensive databases to facilitate development and evaluation of models used for TMDL development.

Task 4: Develop better guidelines for calibration and estimation of model uncertainty.

Task 5: Extend capability of models for TMDL development.

Objective 2. Assess potential/likely economic benefits and costs and equity issues associated with TMDL implementation at the watershed and individual landowner scale.

Controlling water pollution can follow many courses. Economics has an important, if not vital, role to play in identifying policy strategies that can enhance water quality at least cost to landowners and taxpayers. An economic framework can coordinate policy formulation among different levels of government and help to unify policies across regions. Economics also helps determine the optimal level of water quality protection that balances publics desire for improved water quality and the publics willingness to pay for improved water quality. Society does not benefit from overly stringent or costly water quality goals. Measuring the benefits of water quality protection in economic terms is difficult, since many benefits occur outside the easily observable market conditions. Even where water quality impacts on markets are observed, it can be difficult to ascertain just how water pollution affects the ability of a resource to provide economic goods and services. Nevertheless, information on costs and benefits is essential to developing socially optimal water quality protection policies (Ribaudo et al., 1999). The ultimate goal of this objective is to convey societal costs (benefits, costs, and equity) associated with the current TMDL Program (development and implementation), and societal costs associated with recommended improvements to the overall TMDL Program. Current TMDL development models are used to assess the impact of agricultural practices on in-stream physical, chemical, and to a limited extent, biological water quality. However, data limitations are reducing the efficacy of such tools. In fact, data limitations are generating significant additional costs to water quality management efforts by misdirecting resources to ineffective management alternatives. Economic investigations of this project will closely parallel, and expand upon, the tasks identified in Objective 1; and support the efforts of Objective 3. As in the case of Objective 1, an Objective Coordinator will provide overall leadership for the activities of Objective 2. These activities will be summarized to all project participants at the annual technical committee meetings. The Objective Coordinator will be selected at the technical committee meetings by the participants contributing to Objective 2 and will likely serve a two-year term.

Task 1: Evaluate the costs, benefits, risks, and uncertainty associated with TMDL development modeling applications for three selected watersheds under Objective 1.

Task 2: Develop and evaluate alternative TMDL Implementation Plans for three selected watersheds.

Task 3: Evaluate farm level economics of water quality protection.

Task 4: Quantify ecosystem values for three selected watersheds.

Task 5: Conduct a longitudinal case study of TMDL implementation processes.

Objective 3. Assess the potential ecological benefits/implications of TMDL implementation at watershed level.

Task 1. Develop a better understanding of the physical and hydrologic changes caused in waterbodies in response to TMDL implementation.

Task 2. Define the links between physical and hydrologic changes in waterbodies to impacts on aquatic habitat.

Task 3. Link water chemistry, contaminant loading levels, and stream habitat quality, to aquatic ecosystem health and integrity.

[An indepth discussion of Methods is available in Attachments below.]

Measurement of Progress and Results

Outputs

  • Refereed journal articles and other publications on the applicability of the evaluated models for TMDL development and implementation for the test watersheds and similar watershed conditions.
  • Refereed journal articles and other publications on the economic and social feasibility of TMDL implementation for the test watersheds and similar watersheds.
  • Refereed journal articles and other publications on methods to link physical and chemical models to in-stream ecological impairments.
  • Development of improved model interfaces to facilitate creation of required model input and interpretation of model output.
  • A series of workshops and a concluding conference on TMDL development and implementation in agricultural watersheds.

Outcomes or Projected Impacts

  • An important outcome of the regional project will be increased knowledge concerning the appropriateness of various TMDL development tools for application in agricultural watersheds. In addition, existing TMDL development tools will be enhanced and some new tools may be developed as needed. This outcome will improve the utility of current models used for TMDL development in agricultural watersheds and will incorporate biotic and economic factors into several models that do not currently include them.
  • Another important outcome of the projects is improved software interfaces to aid in the use of watershed assessment and TMDL development models. This outcome will employ advances in information technology (1) to allow data to be entered more easily in models and (2) to aid in the interpretation of simulated results. The former outcome will be accomplished with GIS and the latter using expert systems and visualization techniques. The interface software will allow the models to be friendlier to the user.
  • Another outcome of the project is the collection of data for TMDL model evaluation and for BMP effectiveness assessment. Available data, as appropriate to the research procedures described herein, will be utilized where possible. Because of the importance of model evaluation, especially for a wide range of conditions, the accomplishment of the stated objectives is not possible without additional data collection. To optimize the usefulness of data collection efforts, these activities must be coordinated among different states as will be the responsibility of the Objective 1, Task 3 subcommittee.
  • The overall outcome of the project will be the evaluation and development of watershed models and economic analysis tools that can be used for TMDL development and implementation in agricultural watersheds. The ultimate goal of these applications is to ensure that techniques used for TMDL development and implementation in agricultural watersheds are based on the best science available and proposed TMDLs and their implementation are economically and socially feasible. The ultimate beneficiaries will be the agricultural community, land users, home owners, and other stakeholders who will be impacted by the TMDL program. All stakeholders will benefit from potential water quality improvements and landowners and taxpayers will benefit from the development of TMDL implementation plans that are more economically feasible. The immediate and direct beneficiaries of the project will be agency personnel and consultants involved in TMDL development. Since the regional project provides a vehicle to prevent duplication of activities, it essentially benefits all taxpayers by effectively utilizing limited public funds.

Milestones

(2002): Winter 2002 First project meeting will be held in conjunction with the TMDL Environmental Regulations Conference And Exhibition, March 11-13, 2002, in Fort Worth, Texas. This three-day TMDL conference will familiarize project participants with the TMDL program and different TMDL development approaches. Project participants will meet prior to or after the formal conference to develop strategic plans to accomplish the project objectives.</P> <P>Summer 2002 Identify models to be evaluated and watersheds on which the models will be evaluated. Assemble required databases for the three to four watersheds selected for model evaluation.</P> <P>Fall 2002 Second annual project meeting. Present and discuss preliminary evaluations of tested models and TMDL development approaches. Identify needed improvements in models and model interfaces to improve TMDL development.

(2003): Third annual project meeting. Present and discuss final evaluations of tested models and TMDL development approaches. Present and discuss preliminary development and testing of new and improved models and methods for TMDL development.

(2004): Fourth annual project meeting. Present and discuss final evaluations of tested models and TMDL development approaches. Present and discuss progress towards development and testing of new and improved models and methods for TMDL development. Begin initial planning for 2006 TMDL conference.

(2005): Fifth and final annual project meeting. Final discussion and evaluation of TMDL development models and approaches. Call for papers for TMDL conference.

(2006): Spring 2006 Complete all TMDL development publications and reports</P> <P>Fall 2006 TMDL Development and Implementation in Agricultural Watersheds Conference

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Projected Participation

View Appendix E: Participation

Outreach Plan

The results of this project will be disseminated through the project web page, publications of participants, annual CRIS reports, and the concluding project conference on TMDL Development and Implementation in Agricultural Watersheds.

Organization/Governance

A regional technical committee will be organized upon project approval. Operational procedures to be followed will be according to those outlined in the CSREES Manual for Cooperative Regional Research. The voting members of the regional technical committee will include one representative from each cooperating agricultural experiment station or institution appointed by the director and a representative of each cooperating USDA-ARS or other government unit. The administrative advisor and the CSREES representative will be considered nonvoting members. All voting members of the technical committee will be eligible for office.


The offices of the regional technical committee will be the chair, vice-chair and secretary and will serve as the executive committee. These officers for the first year will be elected at the organizational meeting for the technical committee. In subsequent years the officers will be elected annually and may succeed themselves.


The chair, in consultation with the executive committee, will appoint subcommittees to facilitate the accomplishment of the various research and administrative tasks involving the cooperating institutional representatives. Such tasks may include, but are not limited to, research planning and coordination, development of specific cooperative research procedures, assimilation and analysis of data from contributing scientists, and publication of regional bulletins.


The duties of the technical committee will be to coordinate work activities related to the project. The chair, in accord with the administrative advisor, will notify the technical committee of the time and place of meetings, prepare meeting agendas and preside at meetings of the technical committee and the executive committee. The chair is responsible for preparing the annual progress report and coordinating the preparation of regional reports. The vice-chair assists the chair in all functions. The secretary records the minutes and performs other duties assigned by the technical committee or the administrative advisor.


Annual meetings will be held by the technical committee for the purpose of conducting business related to the project. During each annual technical committee meeting, the subcommittees will report on their progress and identified needs to the entire committee. Considerable time will be devoted to the discussion of these reports.

Literature Cited

Altier, L. S., R. R. Lowrance, R. G. Williams, J. M. Sheridan, D. D. Bosch, R. K. Hubbard, W. C. Mills, and D. L. Thomas. 1993. In: Proc. - Riparian Ecosystems in the Humid US. pp. 373-387. Nat. Assoc. Cons. Districts, Washington, DC.


Baumol W. J. and W. E. Oates 1979. Economics, Environmental Policy, and the Quality of Life Englewood Cliffs: Prentice Hall.


Bellamy, K., J. T. Beebe, and others. 1992. River morphology, sediments, and fish habitats. In Erosion and sediment monitoring programmes in river basins, IAHS Pub. 210:309-315.


Beven, K.J. and A. Binley. 1992 The future of distributed models: Model calibration and uncertainty prediction. Hydrological Processes. 6:279-298.


Bouraoui, F. and T.A. Dillaha. 2000. ANSWERS-2000: Nonpoint Source Nutrient Transport Model. J. of Environmental Engineering, ASCE 126(11):1045-1055.


Costanza, R., R. D'Arge, R. DeGroot, S. Farmer, M. Grasso, B. Hannon, K. Limbur, S. Naeem, R. V. O'Neill, J. Paruelo, R. G. Raskin, P. Sutton, M. van den Belt. "The Value of the World's Ecosystem Services and Natural Capital." Nature May 15, 1997, pp. 253-260.


Dontje, J.H., B.N. Wilson, and J.W. Brown. 1996. Using organizational concepts from ecological studies to model water quality. Presentation at the Annual Meeting of the Institute of Biological Engineering, Phoenix, AZ.


Gupta, V. K., S. Sorooshian, and P. O. Yapo, 1998: Towards improved calibration of hydrologic models: Multiple and non-commensurable measures of information. Water Resources Research, 34, 751763.


Haan, C.T. 1977. Statistical Methods in Hydrology. Iowa State University Press, Ames, IA.


Klepper, O., H. Scholten, and J.P.G. Van De Kamer. 1991. Prediction uncertainty in an ecological model of the Oosterschelde Estuary. Journal of Forecasting. 10:191-209.


Kuczera, G., and E. Parent. 1998. Monte Carlo assessment of parameter uncertainty in conceptual catchment models: The Metropolis algorithm, Journal of Hydrology, 211, 6985.


Legates, D.R. and G.J. McCabe Jr. 1999. Evaluating the use of "goodness-of-fit"


measures in hydrologic and hydroclimatic model validation. Water Resources


Research. 35:233-241.


Haith, D.A., R. Mandel, and R.S. Wu. 1992. GWLF - Generalized watershed loading functions, Ver. 2.0 - User's manual. Dept of Agricultural Engineering, Cornell University, Ithaca, NY.


Leonard, R. A., W. G. Knisel and D. A. Still. 1987. GLEAMS: Ground-water loading effects of agricultural management systems. Trans. of the ASAE 30(5):1403-1418.


Loague, K. M., R. E. Green, and L. E. Mulkey. 1988. Evaluation of mathematical models of solute migration and transformation: an overview and an example. Proceedings of the International Conference and Workshop on the validation of flow and transport models for the unsaturated zone. New Mexico State University, Ruidoso, New Mexico, May 23-26.


NRC. 2001. Assessing the TMDL Approach to Water Quality Management. National Research Council, National Academy Press, Washington, D.C.


Nearing, M.A., M.J.M. Romkens, L.D. Norton, D.E. Stott, F.E. Rhoton, J.M. Laflen, D.C. Flanagan, C.V. Alonso, R.A. Bingner, S.M. Dabney, O.C. Doering, C.H. Huang, K.C. McGregor, A. Simon. 2000. Measurement and models of soil loss rates (letter). Science 290:1300-1301.


Ribaudo, M. O., R. D. Horan, and M.E. Smith. (1999). Economics of Water Quality Protection from Nonpoint Sources: Theory and Practice. Agricultural Economic Report 782. Washington, D.C., U.S. Department of Agriculture, Economic Research Service: 106 pp.


Shoemaker, L., M. Lahlou, M. Bryer, D. Kumar, and K. Kratt. 1997. Compendium of Tools for Watershed Assessment and TMDL Development. EPA-841-B-97-006. USEPA Office of Water, Washington, DC.


Trimble, S.W. and P. Crosson. 2000. U.S. soil erosion rates  myth and reality. Science 289:248-250


USEPA. 1988. Storm Water Management Model, Version 4: User's Manual. Environmental Research Laboratory, Athens, Georgia.


USEPA. 1998. Report of the Federal Advisory Committee on the TMDL Program. U.S. Environmental Protection Agency, Office of the Administrator Washington, DC: EPA-100-R-98-006.


USEPA. 2001. The National Costs of the Total Maximum Daily Load Program (Draft Report). EPA 841-D-01-003. USEPA Office of Water, Washington, DC.


Van Stratten, G. and K.J. Keesman. 1991. Uncertainty propagation and speculation in projective forecasts of environmental change: a lake-eutrophication example. Journal of Forecasting. 10:163-190.


Waters, T.F. 1995. Sediment in streams: sources. biological effects, and control. Bethesda, MD: American Fisheries Society, Monograph 7. 217pp.


Wilmott, C. J. 1981. On the validation of models. Physical Geography 2(2):184-194.


Wood, P. J., and P. D. Armitage. 1997. Biological effects of fine sediment in the lotic environment. Environmental Management 21:203-217.

Attachments

Land Grant Participating States/Institutions

AL, AR, FL, GA, IA, IL, IN, KS, KY, LA, MD, MI, MN, NC, NJ, OK, OR, SC, TN, TX, VA, WV

Non Land Grant Participating States/Institutions

Northeast Region - USDA-ARS, Pasture Systems and Watershed Management Research Unit, University of Illinois at Urbana-Champaign, USDA-ARS/Georgia, USDA-NRCS
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