BMP Cost and Nutrient Management Effectiveness on Typical Beef and Beef-Poultry Farms in Shenandoah County, Virginia

Transcription

1 BMP Cost and Nutrient Management Effectiveness on Typical Beef and Beef-Poultry Farms in Shenandoah County, Virginia Megan F. Dickhans Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Masters of Science In Agricultural and Applied Economics Darrell J. Bosch, Co-Chair James W. Pease, Co-Chair Mary Leigh Wolfe Stephen Kurt Stephenson May 4, 2010 Blacksburg, VA Keywords: BMP, Phosphorus, Nitrogen, Linear Programming, Fencing, Buffer, No-Till, Cover Crop, Nutrient Management

2 BMP Cost and Nutrient Management Effectiveness on Typical Beef and Beef- Poultry Farms in Shenandoah County, Virginia Megan F. Dickhans Abstract This study analyzes the change in whole-farm net revenues and nutrient reduction from the implementation of five best management practices (BMPs) on a typical beef and beef-poultry farm in Shenandoah County. Whole-farm net revenues, resource allocation, nutrient loss reductions, and the cost efficiency of reducing nutrient losses were analyzed to assess which BMPs are the most cost efficient to implement, assuming the baseline scenarios have no voluntarily applied BMPs. The effects of stacking additional BMPs, in combinations of two or more, were also assessed. No-till cropping, winter wheat cover crop, herbaceous riparian buffer, fencing, and P-based NMP were the BMPs that were analyzed. Incentive payments from state and federal governments were incorporated into the cost of BMP adoption. A brief analysis of a farmer s time value of money, with respect to incentive payments, was also conducted. Results indicated that no-till crop management was the most cost efficient BMP, and was the only BMP to increase net revenues for both farm models. Fencing and P-based NMP were the least cost efficient for the beef farm. For the beef-poultry farm, fencing was the least cost efficient. The implications of this study are that farmers that choose to adopt BMP should evaluate both their interests in maintaining (or increasing) farm net revenues along with their interest in improving water quality through the reduction of nutrient losses. There is potential for implementing multiple BMPs, while increasing net revenues from a farm s baseline scenario. For farmers and policy makers, no-till cropping can be a profitable and therefore cost efficient BMP to implement. Incentive payments are intended to encourage the adoption of BMPs by subsidizing a portion of the start-up costs. Policy makers should attempt to make cost-share payments reflect nutrient reduction goals. This can be done by analyzing both the compliance cost to farmers and the nutrient reduction effectiveness of BMPs.

3 Acknowledgements I would very much like to thank my committee members, Dr. Darrell Bosch, Dr. Jim Pease, Dr. Mary Leigh Wolfe, and Dr. Kurt Stephenson, for their constant help, encouragement, and faith in me. I would especially like to thank Dr. Darrell Bosch and Dr. Jim Pease. It has been a wonderful opportunity to work with and learn from them. Their guidance and genuine interest in my work is deeply appreciated and has affected me both personally and professionally. I would also like to thank Dr. Wolfe and her graduate students Sally Walker and Javier Osorio for their help with GLEAMS output. I would like to thank Bobby Clark and Dr. Gordon Groover, with Virginia Cooperative Extension, for their abundance of help with understanding livestock farming in Shenandoah County. I would also like to thank David Faulkner from NRCS for providing me with BMP implementation budget estimates. I would like to thank my wonderful parents, Jill and Chris, for their constant support and motivation throughout this entire process. They have always been there to listen to my long conversations, make me laugh, and express their undying support. I would also like to thank my in-laws, Jenny and Roger, for their excitement, interest, and support in my work. I would like to thank my two beautiful children, Tess and Patrick. Their giggles, songs, dances, and squeals make me grateful each day that they are in my life. Most importantly I would like to thank my loving, supportive, and brilliant husband, Jim. His encouragement above everyone else s has been incredibly motivating. Thank you for all of your help. I love you. iii

4 Table of Contents Chapter 1. Introduction Introduction Background Shenandoah County Agricultural Nonpoint Source Pollution Best Management Practices Problem Statement Objective Methods... 6 Chapter 2. Conceptual Framework Introduction Farm-Level Economic Modeling Introduction Typical Farm Model Concept Whole Farm Economic Modeling Spatial Information Modeling Farm Management Changes Best Management Practice Scenarios Multiple Best Management Practice Implementation Opportunity Costs and Compliance Cost Cost-Share Payments Nutrient Modeling Chapter 3. Empirical Application Introduction Data Linear Programming Model Scenarios Baseline Farm Operation Descriptions Introduction Spatial and Soil Characterization Cropping and Pasture Yields and Nutrient Requirements Cropping and Pasture Economic Modeling Fertilizer iv

5 Labor Beef Poultry Best Management Practice Implementation Introduction Cost Share and Tax Credit Payments Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Cropland Herbaceous Riparian Buffer No-till Cover Crop Phosphorus-Based Nutrient Management Plan Nutrient Losses Compliance Cost and BMP Cost efficiency Chapter 4. Results & Analysis Introduction Beef Farm Scenarios Introduction Scenario 1: Baseline Beef Farm Scenario 2: Beef Farm with Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario 3: Beef Farm with Herbaceous Riparian Buffer Scenario 4: Beef Farm with No-Till Crop Management Scenario 5: Beef Farm with Winter Wheat Cover Crop Scenario 6: Beef Farm with Phosphorus-Based Nutrient Management Plan Beef-Poultry Farm Scenarios Scenario 7: Baseline Beef-Poultry Farm Scenario 8: Beef-Poultry Farm with Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario 9: Beef-Poultry Farm with Herbaceous Riparian Buffer Scenario 10: Beef-Poultry Farm with No-Till Crop Management Scenario 11: Beef-Poultry Farm with Winter Wheat Cover Crop Beef and Beef-Poultry Scenarios Analysis Net Revenue and Compliance Cost Analysis Nutrient Abatement Analysis Discount Rate and Time Value of Money Analysis v

6 4.5. Stacked Best Management Practice Scenarios Introduction Scenario 12: Beef Farm with No-Till and Cover Crop Scenario 13: Beef Farm with No-Till, Cover Crop, and Buffer Scenario 14: Beef Farm with No-Till, Cover Crop, Buffer, and Fencing Scenario 15: Beef Farm with No-Till, Cover Crop, Buffer, Fencing, and P-based Nutrient Management Plan Scenario 16: Beef-Poultry Farm with No-Till and Cover Crop Scenario 17: Beef-Poultry Farm with No-Till, Cover Crop, and Buffer Scenario 18: Beef-Poultry Farm with No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario Analysis Chapter 5. Summary and Conclusion Introduction Summary of Procedures and Results Single Best Management Practice Implementation Discount Rate Multiple Best Management Practice Implementation Conclusion Implications Limitations and Opportunity for Further Research Concluding Remarks References Appendix A. Budgets Appendix B. GLEAMS Results Appendix C. Cost Share payment Calculations Appendix D. Baseline Beef Farm LP Model vi

7 List of Figures Figure 2.1 Stacked Best Management Practice Scenarios Figure 2.2 Components of Compliance Cost Figure 3.1 North Fork of the Shenandoah River Watershed Figure 3.2 Shenandoah County within the Northfork of the Shenandoah River Watershed Figure 3.3 Livestock Operation Concentration Figure 3.4 Hypothetical Model Farm Layout Figure 3.5 Abstract from Virginia Cooperative Extension Beef Cows Spring Calving - Hay Ration Budget Figure 3.6 Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Diagram 51 Figure 3.7 Herbaceous Riparian Buffer Figure 4.1Beef Farm: Net Revenues, Total Nitrogen, and Total Phosphorus of Stacked BMP Scenarios Figure 4.2 Beef-Poultry Farm: Net Revenues, Total Nitrogen, and Total Phosphorus of Stacked BMP Scenarios List of Tables Table 1.1 Impaired Streams in Shenandoah County... 2 Table 3.1 Abstract from the Beef Farm Linear Programming Model Table 3.2 Soil Productivity Group and Crop and Forage Yields Table Shenandoah County Soil Test Summary Abstract Table 3.4 Nutrient Requirements for Crops in Model Farm Table 3.5 P 2 O 5 Removal for Crops Table 3.6 Corn Grain Budget from Virginia Cooperative Extension in 2008 Dollars Table 3.7 Corn Grain and Soybean Production and Sales Tableau Table 3.8 Pasture and Hay Production and Sales Tableau Table 3.9 Crop Requirements and Costs for Fertilizer Tableau Table 3.10 Corn Silage Custom Labor Tableau Table 3.11 Corn Silage Household and Hired Labor Tableau vii

8 Table 3.12 Pasture Requirements for Beef Cattle Feed Rations Tableau Table 3.13 Corn Grain and Silage Requirements for Beef Cattle Ration Options Table 3.14 Stockpiled Fescue Beef Cattle Feeding Schedule Table 3.15 Labor Requirements for Beef Cattle Activities Tableau Table 3.16 Beef Cattle Production and Sales Tableau Table 3.17 Broiler Production Calculations Table 3.18 Poultry Operation Tableau Table 3.19 Baseline Beef-Poultry Farm P-Based Nutrient Management Plan Tableau for Corn Grain and Corn Silage Table 3.20 Fencing and Riparian Buffer Tableau Table 3.21 Off-Stream Watering and Stream Crossing Table 3.22 Herbaceous Riparian Buffer Cost-Share Tableau Table 3.23 No-Till Yields and Nutrient Requirements Tableau Table 3.24 Cover Crop Acreage Rotation Tableau Table 3.25 Cover Crop Labor Tableau Table 3.26 Phosphorus-Based Nutrient Management Plan Scenario Tableau Table 3.27 Beef Farm GLEAMS Scenarios Table 3.28 Beef-Poultry Farm GLEAMS Scenarios Table 4.1 Beef Farm: Baseline Scenario Production and Sales Activity Results Table 4.2 Beef Farm: Baseline Scenario Total Cost, Total Revenues, Net Revenues Table 4.3 Beef Farm: Baseline Scenario Total Nitrogen and Total Phosphorus Losses Table 4.4 Beef Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario Production and Sales Table 4.5 Beef Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario Total Cost, Total Revenues, Net Revenues Differences Table 4.6 Beef Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.7 Beef Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario Reduced TN and Reduced TP Estimates Table 4.8 Beef Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario Reduced TN Cost Efficiency and Reduced TP Cost Efficiency Estimates viii

9 Table 4.9 Beef Farm: Riparian Buffer Scenario Production and Sales Table 4.10 Beef Farm: Riparian Buffer Scenario Total Cost, Total Revenues, Net Revenues Differences Table 4.11 Beef Farm: Riparian Buffer Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.12 Beef Farm: Riparian Buffer Scenario Reduced TN and Reduced TP Estimates Table 4.13 Beef Farm: Riparian Buffer Scenario Reduced TN Cost Efficiency and Reduced TP Cost Efficiency Estimates Table 4.14 Beef Farm: No-Till Scenario Production and Sales Table 4.15 Beef Farm: No-Till Scenario Total Cost, Total Revenues, Net Revenues Differences Table 4.16 Beef Farm: No-Till Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.17 Beef Farm: No-Till Scenario Reduced TN Cost Efficiency and Reduced TP Cost Efficiency Table 4.18 Beef Farm: Cover Crop Scenario Production and Sales Table 4.19 Beef Farm: Cover Crop Scenario Total Cost, Total Revenues, Net Revenues Differences Table 4.20 Beef Farm: Cover Crop Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.21 Beef Farm: Cover Crop Scenario Reduced TN Cost Efficiency and Reduced TP Cost Efficiency Table 4.22 Beef Farm: P-Based NMP Scenario Production and Sales Table 4.23 Beef Farm: P-Based NMP Scenario Total Cost, Total Revenues, Net Revenues Differences Table 4.24 Beef Farm: P-Based NMP Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.25 Beef Farm: P-Based NMP Scenario Reduced TN Cost Efficiency and Reduced TP Cost Efficiency Table 4.26 Beef-Poultry Farm: Baseline Scenario Production and Sales Activity Results Table 4.27 Beef-Poultry Farm: Baseline Scenario Total Cost, Total Revenues, Net Revenues. 81 ix

10 Table 4.28 Beef-Poultry Farm: Baseline Scenario Total Nitrogen and Total Phosphorus Losses Table 4.29 Beef-Poultry Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario Production and Sales Table 4.30 Beef-Poultry: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario Total Cost, Total Revenues, Net Revenues Differences Table 4.31 Beef-Poultry Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing TN and TP Losses and Changes in TN and TP Losses Table 4.32 Beef-Poultry Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario Reduced TN and Reduced TP Estimates Table 4.33 Beef-Poultry Farm: Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Scenario Reduced TN Cost Efficiency and Reduced TP Cost Efficiency Estimates Table 4.34 Beef-Poultry Farm: Riparian Buffer Scenario Production and Sales Table 4.35 Beef-Poultry Farm: Riparian Buffer Scenario Total Cost, Total Revenues, Net Revenues Differences Table 4.36 Beef-Poultry Farm: Riparian Buffer Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.37 Beef-Poultry Farm: Riparian Buffer Scenario Reduced TN and Reduced TP Estimates Table 4.38 Beef-Poultry Farm: Riparian Buffer Scenario Reduced TN Cost Efficiency and Reduced TP Cost Efficiency Estimates Table 4.39 Beef-Poultry Farm: No-Till Scenario Production and Sales Table 4.40 Beef-Poultry Farm: No-Till Scenario Total Cost, Total Revenues, Net Revenues Differences Table 4.41 Beef-Poultry Farm: No-Till Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.42 Beef-Poultry Farm: No-Till Scenario Reduced TN Cost efficiency and Reduced TP Cost Efficiency Table 4.43 Beef-Poultry Farm: Cover Crop Scenario Production and Sales Table 4.44 Beef-Poultry Farm: Cover Crop Scenario Total Cost, Total Revenues, Net Revenues Differences x

11 Table 4.45 Beef-Poultry Farm: Cover Crop Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.46 Beef-Poultry Farm: Cover Crop Scenario Reduced TN Cost efficiency and Reduced TP Cost efficiency Table 4.47 Beef Farm Scenarios: Net Revenue and Compliance Cost Ranking Table 4.48 Beef-Poultry Farm Scenarios: Net Revenue and Compliance Cost Ranking Table 4.49 Beef Farm Scenarios: Reduced TN and Reduced TP Ranking Table 4.50 Beef-Poultry Farm Scenarios: Reduced TN and Reduced TP Ranking Table 4.51 Beef Farm Scenarios: Reduced TN and Reduced TP Cost Efficiencies Table 4.52 Beef-Poultry Farm Scenarios: Reduced TN and Reduced TP Cost Efficiencies Best Management Practices' Annual Net Cost at 5% and 10% Discount Rate Beef Farm BMP Scenarios' Net Revenue Comparison of 5% and 10% Discount Rate Beef-Poultry Farm BMP Scenarios' Net Revenue Comparison of 5% and 10% Discount Rate Table 4.56 Beef Farm: No-Till and Cover Crop Stacked Scenario Production and Sales Table 4.57 Beef Farm: No-Till and Cover Crop Stacked Scenario Changes in Total Cost, Total Revenues, and Net Revenue Table 4.58 Beef Farm: No-Till and Cover Crop Stacked Scenario Percent Changes in TN and TP Losses* Table 4.59 Beef Farm: No-Till and Cover Crop Stacked Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.60 Beef Farm: No-Till, Cover Crop, and Buffer Stacked Scenario Production and Sales Table 4.61 Beef Farm: No-Till, Cover Crop, and Buffer Stacked Scenario Changes in Total Cost, Total Revenue, and Net Revenue Table 4.62 Beef Farm: No-Till, Cover Crop, and Buffer Stacked Scenario Percent Changes in TN and TP Losses Table 4.63 Beef Farm: No-Till, Cover Crop, and Buffer Stacked Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.64 Beef Farm: No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario Production and Sales xi

12 Table 4.65 Beef Farm: No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario Changes in Total Cost, Total Revenues, and Net Revenue Table 4.66 Beef Farm: No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario Percent Changes in TN and TP Losses Table 4.67 No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.68 Beef Farm: No-Till, Cover Crop, Buffer, Fencing, and P-based Nutrient Management Plan Stacked Scenario Production and Sales Table 4.69 Beef Farm: No-Till, Cover Crop, Buffer, Fencing, and P-based Nutrient Management Plan Stacked Scenario Changes in Total Cost, Total Revenues, and Net Revenue Table 4.70 Beef Farm: No-Till, Cover Crop, Buffer, Fencing, and P-based Nutrient Management Plan Stacked Scenario Percent Changes in TN and TP Losses Table 4.71 Beef Farm: No-Till, Cover Crop, Buffer, Fencing, and P-based Nutrient Management Plan Stacked Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.72 Beef-Poultry Farm: No-Till and Cover Crop Stacked Scenario Production and Sales Table 4.73 Beef-Poultry Farm: No-Till and Cover Crop Stacked Scenario Changes in Total Cost, Total Revenue, and Net Revenue Table 4.74 Beef-Poultry Farm: No-Till and Cover Crop Stacked Scenario Percent Changes in TN and TP Losses Table 4.75 Beef-Poultry Farm: No-Till and Cover Crop Stacked Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.76 Beef-Poultry Farm: No-Till, Cover Crop, and Buffer Stacked Scenario Production and Sales Table 4.77 Beef-Poultry Farm: No-Till, Cover Crop, and Buffer Stacked Scenario Changes in Total Cost, Total Revenue, and Net Revenue Table 4.78 Beef-Poultry Farm: No-Till, Cover Crop, and Buffer Stacked Scenario Percent Changes in TN and TP Losses Table 4.79 Beef-Poultry Farm: No-Till, Cover Crop, and Buffer Stacked Scenario TN and TP Losses and Changes in TN and TP Losses xii

13 Table 4.80 Beef-Poultry Farm: No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario Production and Sales Table 4.81 Beef-Poultry Farm: No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario Changes in Total Cost, Total Revenue, and Net Revenue Table 4.82 Beef-Poultry Farm: No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario Percent Changes in TN and TP Losses Table 4.83 Beef-Poultry Farm: No-Till, Cover Crop, Buffer, and Fencing Stacked Scenario TN and TP Losses and Changes in TN and TP Losses Table 4.84 Beef Farm: Total Change and Percent Change in Net Revenue, TN Losses, and TP Losses from the Baseline Scenario to No-till, Cover Crop, Buffer, Fencing, and P-based Nutrient Management Plan Stacked Scenario Table 4.85 Beef-Poultry Farm: Total Change and Percent Change in Net Revenue, TN Losses, and TP Losses from the Baseline Scenario to No-till, Cover Crop, Buffer, and Fencing Stacked Scenario xiii

14 Chapter 1. Introduction 1.1. Introduction Agricultural nonpoint source (NPS) pollution is a major cause of stream impairment within the Chesapeake Bay watershed. There are efforts to reduce agricultural NPS pollution in each of the states within the Chesapeake Bay Watershed (New York, Pennsylvania, Delaware, West Virginia, Maryland, and Virginia). It is estimated that agricultural NPS contributes 35% of nitrogen (N) and 45% of phosphorus (P) loadings to the Bay (Chesapeake Bay Program, 2007a, Chesapeake Bay Program, 2007b). Because further reductions in agricultural NPS pollution need to be made in order to make further improvements in water quality, farm-level economic costs of implementation and the effects BMPs have on whole-farm net revenues, allocation of farm resources, and cost efficiency of nutrient abatement need to be determined Background Shenandoah County The North Fork of the Shenandoah River watershed is a sub-watershed of the Chesapeake Bay watershed. It is a primarily agricultural watershed, containing 2,098 beef cattle farms, 204 cattle feedlots, and 824 poultry farms. Of its 2,606 total miles of streams, miles are impaired (Natural Resources Conservation Service, 2008). Shenandoah County is encompassed by the North Fork of the Shenandoah River watershed and has a strong agricultural economy, ranked fifth in the Commonwealth of Virginia for agricultural revenues (National Agricultural Statistics Service, 2008). Because of its location and agricultural attributes, Shenandoah County was chosen as the location of this study. There are seven streams within Shenandoah County that are impaired and have Total Maximum Daily Load (TMDL) implementation plans. Table 1.1 is the list of streams within the watershed that are reported by the Environmental Protection Agency (EPA) to have TMDLs. 1

15 The table contains the name of the stream, the number of miles impaired within the stream, the impaired use, the cause, and the sources. The initial Department of Environmental Quality (DEQ) listing date is the year in which the stream was reported as impaired. The TMDL date is the year in which a TMDL began. Table 1.1 Impaired Streams in Shenandoah County Water Name Holmans Creek North Fork Shenandoah River Smith Creek Mill Creek Stony Creek Toms Brook Orndorff Spring Branch Impaired Use Cause River (Miles) Recreation Escherichia coli Recreation Fecal Coliform Aquatic Life Benthic- Macroinvertebrate Bioassessments Recreation Escherichia coli Recreation Fecal Coliform Recreation Escherichia coli Recreation Fecal Coliform Aquatic Life Benthic- Macroinvertebrate Bioassessments Recreation Fecal Coliform Aquatic Life Benthic- Macroinvertebrate Bioassessments Recreation Escherichia coli Recreation Fecal Coliform Aquatic Life Aquatic Life Benthic- Macroinvertebrate Bioassessments Benthic- Macroinvertebrate Sources Agriculture, NPS, Wildlife other than Waterfowl Agriculture, NPS, Wildlife other than Waterfowl Initial DEQ Listing Date TMDL Date NPS, Souce Unknown Agriculture, NPS, Wildlife other than Waterfowl Agriculture, NPS, Wildlife other than Waterfowl Agriculture, NPS, Wildlife other than Waterfowl Agriculture, NPS, Wildlife other than Waterfowl Source Unknown Agriculture, NPS, Wildlife other than Waterfowl Source Unknown Agriculture, NPS, Wildlife other than Waterfowl Agriculture, NPS, Wildlife other than Waterfowl Source Unknown Aquaculture (Permitted) Bioassessments Environmental Protection Agency, Internet site: (Accessed July 2, 2010). 2

16 As indicated in the Table 1.1, agricultural activities contribute much of the NPS pollution to streams. The impairment of benthic-macroinverterbrate bioassessments means that the streams are rich with nutrients which cause eutrophication, from high algal growth. Large amounts of algae can remove oxygen from streams, which can kill other aquatic life, such as macroinvertebrates. The presence of macroinvertebrate organisms is not an impairment, but it is used as a gauge to test for eutrophication. Sources of nutrients that promote eutrophication are sediment, animal waste, and fertilizer. Agriculture in the Shenandoah County is primarily livestock and poultry-based. There are no livestock or poultry operations in the county that are large enough to be federally regulated as Concentrated Animal Feeding Operations (CAFOs), which are considered point sources of pollution. Many beef and poultry operations in the region are regulated by the state as Animal Feeding Operations operations that confine animals for at least 45 days in any 12-month period (Virginia Department of Environmental Quality, 2010). Regulated operations are required to implement nutrient management plans for their farms that balance nutrient applications of fertilizer and manure with crop needs Agricultural Nonpoint Source Pollution Agricultural NPS pollution accounts for approximately 35% of all N and 45% of all P deposited into the Chesapeake Bay (Chesapeake Bay Program, 2007a, Chesapeake Bay Program, 2007b). Beef farms contribute to NPS through cattle access to streams, grazing patterns, and manure and urine runoff into streams from spreading collected manure and poultry litter or through manure and urine deposited by livestock on fields adjacent to streams. Poultry farms contribute to NPS by over applying phosphorus-rich poultry litter or by improperly storing litter (Environmental Protection Agency, 2003). Cropping activities contribute to NPS pollution through runoff from tilled soil, from excess fertilization runoff, and from nutrients leaching through soil. Cattle access to streams results in urine and solid manure, both sources of N and P, deposited directly into streams. Over-grazing can lead to compacted soils with decreased vegetation and can increase disturbed soil surfaces, both of which increase soil erosion and runoff of nutrients in 3

17 manure and urine. Over-grazing on fields that neighbor streams and do not contain fenced buffers enables solid manure and sediment to runoff directly into the water. Spreading dry poultry litter on cropland can lead to runoff to streams. Poultry litter is high in P and low in N, which can lead farmers to apply a quantity of poultry litter that meets the N requirements of cropland, but exceeds the P requirements. The excess P may reach streams as soluble runoff or attached to sediment. Poultry litter, however, is an inexpensive way of adding nutrients to crop, hay, and pasture fields. Over application of poultry litter is relatively uncommon on poultry farms because most farms with poultry operations have greater than 20,000 hens (or 11,000 turkeys) and are required to have P-based NMP (Virginia Department of Environmental Quality, 2010). Cropping using conventional tillage can result in sediment and fertilizer runoff from newly tilled land. Excess fertilizer application can lead to nutrient leaching Best Management Practices Best management practices (BMPs) are structures, systems, and procedures that are recognized by federal and state governments to manage operations and in many cases reduce NPS pollution, such as nutrients, sediment, bacteria, and chemicals (Virginia Department of Conservation and Recreation, 2009i, Virginia Department of Environmental Quality, 2009). BMPs are categorized by the land use on which they are applied. The three main categories of BMPs are urban BMPs, forestry BMPs, and agricultural BMPs. In Virginia, the Department of Conservation and Recreation (DCR) is the lead agency in NPS pollution control and is therefore responsible for many of the Commonwealth s policy development, law enforcement, and program coordination for NPS BMPs (Virginia Department of Conservation and Recreation, 2009i). Virginia created the Chesapeake Bay Watershed Nutrient Credit Exchange Program to support further nutrient pollution reduction throughout Virginia s portion of the Chesapeake Bay watershed. This program enables point sources to compensate NPSs for nutrient reductions so that the point sources can increase their nutrient loads beyond their legal limit. The program requires all farms that would like to participate in the program to first implement five BMPs, as applicable to the farm. The BMPs are soil conservation, nutrient management plan (NMP), cover crop, livestock stream exclusion, and riparian buffer instillation (Virginia Department of 4

18 Environmental Quality, 2008). These BMPs form the baseline requirement for NPS pollution reduction before the farm is eligible to be paid point source polluters for further pollution reduction Problem Statement Excess nutrient loading into waterways of the Chesapeake Bay continues to be a point of concern for scientists, policy makers, and stakeholders. Agriculture is the major nonpoint source land use contributor to excess nutrient loading (Chesapeake Bay Program, 2007a, Chesapeake Bay Program, 2007b). Point source pollution regulation has been used as a strategy to improve the Chesapeake Bay watershed water quality. Although addressing point source pollution has helped in reducing pollution levels in streams, water quality problems still remain due to NPS pollution (Heathwaite, et al., 2000). Nonpoint source pollution continues to be a more complex issue than point source pollution because it is difficult or impossible to measure the exact amounts of contributed pollution from a specific source. This makes NPS pollution difficult to manage on social, political, and economic levels. One NPS pollution control strategy is to focus research on sub-watershed and farm-level, like the North Fork of the Shenandoah River watershed, in order to engage local policy makers and stakeholders on a more comprehensive and personal level. This strategy relies on local and individual voluntary action. More research that models and/or measures pollution contributions from NPS pollution sources may initiate greater concern and voluntary action to improve water quality because a greater understanding of individual contributions can be gained. Such research may also help policy makers decide whether additional policies can or should be made to regulate NPS pollution. Economic analysis has taken on a vital role of modeling the economic effects of water quality improvement. On the farm-level, agricultural BMPs affect the entire allocation of resources and, therefore, farm net revenues. Information gained from agricultural BMP economic analysis can help farmers, stakeholders, and policymakers determine the costs and benefits of implementing BMPs. In order to provide a complete analysis of the impacts of BMP integration into farming practices, nutrient reduction also needs to be estimated. Quantifying the pounds of N and P 5

19 reduced on a per-dollar basis will help to determine the cost efficiency of BMPs. This information can be used by policy makers, farmers, and stakeholders in discussing future decisions to improve water quality Objective This study analyzes two typical model farms of Shenandoah County in order to: 1.) Evaluate the effects of BMP implementation on whole-farm net revenues 2.) Evaluate the effects of BMP implementation on resource allocation 3.) Evaluate the N and P losses from the BMP implementation 4.) Evaluate the compliance cost and cost efficiency of BMP implementation The two model farms are a beef farm and a beef-poultry farm. The BMPs that are analyzed are fencing with rotational grazing, off-stream watering, and stream crossing, which is further referred to as fencing; herbaceous riparian buffer; no-till; cover cropping; and phosphorusbased nutrient management plan (P-based NMP). The compliance cost is the costs undertaken by the farmer to implement each BMP, and the cost efficiency is the cost per pound of N or P abated. This definition of cost efficiency does not consider costs to taxpayers of government subsidies or cost share for BMPs. The BMPs were analyzed individually using these objectives, and then additional scenarios were created where the BMPs are combined together, or stacked, one at a time, and are analyzed under the same objectives. These objectives address the need for nutrient reductions within the watershed and the need for information on their effects on wholefarm net revenues 1.5. Methods Two profit-maximizing, linear programs (LPs) were created to represent two livestock farms: a beef farm and a combined beef-poultry farm. The farms are modeled to represent typical farms within Shenandoah County. Expert advice from Virginia Cooperative Extension faculty and staff, county statistics, and other research was used to inform the LP models. A GLEAMS model was used to estimate nutrient losses from the baseline beef and beef-poultry farm, no-till, 6

20 cover crop, and P-based NMP scenarios. Fencing and forested riparian buffer nutrient losses were calculated using Chesapeake Bay Program s estimates (Chesapeake Bay Program, 2006). Two sets of analyses were conducted using the LP models and BMPs. The first analysis compared the results of the baseline scenarios with the results of each of the BMP scenarios to see the effects of BMP implementation on resource allocation, total cost, total revenues, and net revenues. The second analysis ranked BMP scenarios by their net revenues, from greatest revenues to least, and created new scenarios where BMPs were incrementally applied, or stacked. The stacked scenarios combined BMPs in sets of two through five (two through four for the beef-poultry farm), from most profit maximizing to least, so that all BMPs would be implemented in the final scenario. The results were compared with the baseline scenario and the previous scenario. For example, scenario one was the baseline farm, scenario two was compared to the baseline, scenario three was compared to the baseline and scenario two. The nutrient loss reductions and compliance costs were analyzed to determine the cost efficiency of each BMP scenario and stacked BMP scenario. The most cost efficient BMPs were those that require the least amount of money per pound of TN or TP reduced. Chapter 2 discusses the conceptual framework of the typical farm models, BMP scenarios, and nutrient loss modeling. Chapter 3 is the empirical analysis, where the baseline scenarios are and the BMP scenarios described in detail. Example tableaus are given to demonstrate the functioning of the LP models. The information provided to the GLEAMS program is also given. Chapter 4 presents the LP and GLEAMS results of the baseline farms and their BMP scenarios. Results are analyzed and ranked, and nutrient reduction cost efficiencies are calculated and presented. In chapter 5, the summary, conclusions, limitations, and opportunity for further research are presented. 7

21 Chapter 2. Conceptual Framework 2.1. Introduction Two typical-farm whole-farm linear programs were created that model a typical beef and beef-poultry farm in Shenandoah County, Virginia. The scope of the study, farm-level, was chosen because it provides a relatable set of results for farmers, stakeholders, and policy makers in Shenandoah County. Linear programming was used to create a profit-maximizing economic model of farm practices and BMP implementation. Five BMPs were then individually applied to the farms to evaluate the effects of BMPs on farm resource allocation, net revenues, and nutrient losses. The BMPs were then applied in combination with each other in order to evaluate the effects of implementing multiple BMPs on the farm models. This chapter covers the modeling and economic concepts used to conduct this study Farm-Level Economic Modeling Introduction There has been significant research on the type of information gained from different study scales of NPS pollution (e.g. watershed, sub-watershed, county, and farm-level) and the intended audience and usefulness of the information gathered (Bonham, 2003, Feuz and Skold, 1991, Howry, et al., 2008, Weersink, et al., 2002, Wu and Segerson, 1995). Different scales of study provide particular insights into the effects of BMPs on farm net revenues, the environment, stakeholders, and the watershed. Watershed-level and sub-watershed studies on agricultural BMPs provide generalizations among many farms in order to describe the effects of a region. Farm-level research provides a specific perspective on the effects of BMPs a particular real or model farm. Farm-level studies joined with measurements of environmental quality can assist policy makers with understanding both the need for environmental improvement and the means of achieving it (Weersink, et al., 2002). The following sections discuss the type of farm-level economic model that will be created for this study and its general format. 8

22 Typical Farm Model Concept A typical farm model is a real or hypothetical farm that is relatable in its attributes to other farms of a particular nature, such as similar size, production system, region, or any combination thereof and is not necessarily an average of a group of farms attributes (Bonham, 2003, Feuz and Skold, 1991). There are several ways to classify the population of farms from which the typical farm will be modeled, as well as creating the typical farm itself. The population can be determined by using criteria such as location, farm size, types of land use, types of labor, quantity of labor, and economic qualities. Typical farm models can be based on an actual farm that contains many common attributes as other farms within the study s scope, or a hypothetical farm can be created using one or combination of different methods. Statistical measures, such as weighted averages, use mathematics to determine the quality and quantity of the attributes of a farm. Other common methods are using expert advice from those who work in the area, farmers within the scope of the study, and researchers as well as literature to determine common attributes (Feuz and Skold, 1991). This study primarily relied on recommendations from Virginia Cooperative Extension faculty and staff rather than average or aggregated data. When average or aggregated data are used, five biases can occur. The first type of bias is a result of using aggregated data in a given function rather than disaggregated. This is because aggregated data can result in missed nuances of individual farm pollution. The second type of bias occurs when parameters are designed using estimates that are based on aggregate data. The estimated parameters based on aggregate data are likely to differ from estimated parameters based on disaggregated, or true, parameters. The third form of bias comes from the inability to predict pollution per acre or the number of polluting acres in a county. The fourth bias occurs when agricultural activities, such as crops, are averaged over the agricultural land in a county. This practice assumes that the agricultural activity (e.g. crops) is evenly distributed, when in fact it is not. Averaging aggregate data over a county also implies that all acres have the same combination of agricultural activities, not just the same quantities. The final form of bias from aggregated county information occurs when studies attempt to use thresholds. For example, researchers can use thresholds, such as soil types, or depth to water tables, to try to determine areas in a county that are vulnerable to groundwater pollution. The use of thresholds to find vulnerable areas may in fact exclude areas of the county 9

23 that are not considered vulnerable, but are actually polluting heavily (Bonham, 2003, Feuz and Skold, 1991, Wu and Segerson, 1995). A shortcoming of hypothetical typical farm modeling is that the model farm may appear too efficient. The model is designed to choose the most cost efficient allocation of resources, timing, technology, and labor, when, in reality, farmers can often be faced with situations that do not allow them to utilize all of their resources as efficiently as the model would imply (Feuz and Skold, 1991). For example, a farm that produces hay loses a portion of the hay from handling and transporting the hay around the farm. It is not a priority of the typical farm model to be an exact prediction of all farm practices because individual characteristics, such as acreage or exact per acre yield, impact farm net revenues and may vary widely between farms. However, it is important to incorporate estimated inefficiencies, such as estimated hay losses, in order to more accurately represent all applicable farms because of the universality of such inefficiency. The hypothetical typical farm model for this study is focused on a particularly agriculturallydense area of Virginia, Shenandoah County, which is located in the Chesapeake Bay watershed. Shenandoah County is one of the leading agricultural counties in the Commonwealth and is also completely within the North Fork of the Shenandoah River watershed. The county s high levels of agricultural activity are a main source of P and N pollution and therefore the streams and community could benefit from the analysis of agricultural BMPs to improve water quality (Environmental Protection Agency, 2010, Virginia Department of Environmental Quality, 2006). Because it is encompassed by the sub-watershed, information on environmental problems that apply to the sub-watershed can be inferred to also be problems of Shenandoah County. Similarly, the information gathered from this study may also be applicable to other, similar farms in the sub-watershed because of their shared geographical and socio-economic similarities. Besides geographic location, the typical farm model will also be defined by size and type of operation. One model farm will be a small cow-calf operation with spring calving, and the other will be a small cow-calf and poultry operation with spring calving. These criteria should provide narrow enough parameters to create a model farm that is consistent with its peer farms. Kraft and Toolhill created a model farm, based on representative characteristics of farms in the southern Illinois. Their representative model was created to simulate soil losses for different 10

24 tillage, cropping, and livestock activities. Eleven scenarios of the baseline model were created, each constrained by five erosion levels, representing different policy decisions with regards to sediment control. Their research concluded that erosion control could be obtained while maintaining farm returns to management and real property (Kraft and Toohill, 1984). The LP model for this study was defined by research, expert interviews, and judgment. The model coefficients are not primarily averages; however, averages were incorporated if they were seen as relevant. Expert advice from Virginia Cooperative Extension was taken as the primary source of qualitative and quantitative information in order to help develop accurate baseline beef and beef-poultry farm models of the area. The use of expert opinion, rather than averages, allows for a model that is relatable, and though it potentially may be more technically efficient in its production and sales than an actual farm, its structure and results can be understood by other farmers of that area (Bonham, 2003, Feuz and Skold, 1991) Whole Farm Economic Modeling Linear program modeling is one of the most common forms of economic modeling of farms, and is one of the best methods for modeling BMP implementation while maximizing profits (Alford, et al., 2004, Buysse, et al., 2007). It provides the flexibility of estimating changes in farm net revenues when farming practices are altered, without having to commit any actual reallocation of resources, labor, and energy. In its most basic form, linear programming can be mathematically summarized by the following equations: ( ) (2.1) (2.2) The objective function, ( ), for the model farms of this study, is the profit function. The variables represent the decision variable, or farming activities towards which the limited resources are allocated. The availability of resources is indicated by, and the function indicates the amount of resources required by each farming activity. The inequality sign ( ) 11

25 indicates that the supply of any resource must be greater than or equal to its demand by the farming activities. The LP for this study does not include a risk assessment, such as using Minimization of Total Absolute Deviation (MOTAD) or Target MOTAD. Both are programming applications that deal with estimating behavioral changes based on individual risk aversion. Instead, a sensitivity analysis is considered sufficient. This was done for understandability. The use of more complex statistical methods reduces the breadth of readership. Although the introduction, summary, and conclusion would be understandable to most people, the mathematical model would not be as relatable a farmer, policy maker, or stakeholder, for example, could more easily understand the process of composing and running the model without risk assessment, and therefore more likely to gain from the research Spatial Information Spatial information for this study consists of location, soil type, slope, land use, and crop productivity. Spatial information is critical to determining the appropriate application of agricultural BMPs in order to maximize their effectiveness and minimize farm costs (Carpentier, et al., 1998, Howry, et al., 2008, Veith, 2002). Slope and soil type affect soil fertility and therefore land use. A farmer will raise crops on more fertile soils and raise livestock on less fertile. The choice of land use determines the BMPs that can be implemented on the land. For example, rotational grazing for livestock cannot be implemented on cropland. Carpentier et al. (1998) examined the difference in control costs of NPS pollution control between uniformly applied pollution reduction policies and spatially targeted pollution reduction policies. Uniformly applied pollution control policies determined a particular set of BMPs that had to be implemented throughout the study area (dairy farms within the Lower Susquehanna watershed) to meet a watershed goal of 40% N reduction. The study then used a targeting method and applied BMPs using spatial characteristics of each farm so as to minimize cost to both the farmer and tax payer. Targeting consists of discerning which areas are most likely to contribute to NPS pollution, and implementing stricter pollution control in those areas. 12

26 Carpentier et al. concluded that incorporating spatial characterization of farms did reduce the cost of BMP implementation to both the individual farms and the cost to society by reducing compliance and transaction costs. Veith (2002) examined how spatial information could be better utilized to increase cost efficiency of sediment NPS pollution control within a watershed, compared to targeting. Rather than identifying one optimal solution, as done in targeting, Veith s goal was to find multiple near optimal solutions. The multiple near optimal solutions provide more BMP selection to farmers, and therefore increase the probability of adoption, leading to a more cost effective solution at the watershed level. Veith concluded that the optimization procedure that takes into consideration multiple near optimal solutions resulted in a more cost effective BMP placement than the targeting method. Similarly, this study examines a range of BMPs that are considered feasible by Virginia Extension and applies them to the typical farm model in order to assess the most cost effective BMPs. For this study, extension recommendations of BMPs were considered sufficient because of the scale of the research (farm-level). In the case of Veith and Carpentier et al., research was conducted on a watershed level, and therefore computerized analysis of feasible BMPs was both more important and necessary Modeling Farm Management Changes Best Management Practice Scenarios The beef farm s baseline scenario did not include any BMPs, and the beef-poultry farm s baseline scenario incorporates a P-based NMP. There are five BMP scenarios in addition to the beef farm baseline, and four BMP scenarios in addition to the beef-poultry baseline, for a total of 11 LP runs. The BMP scenarios are: 13

27 1. Fencing, rotational grazing, off stream watering, and stream crossing 2. Herbaceous riparian buffer 3. No-till cultivation 4. Small grain cover crop 5. Phosphorus-based nutrient management plan The first BMP scenario encompasses five different BMPs: fencing, rotational grazing, offstream watering, stream crossing, and herbaceous riparian buffer. It is assumed that if fencing is to be used to keep cattle out of streams, then off-stream watering should also be installed, and enough fencing should be purchased to implement a rotational grazing system (Clark, 2009). In order for cattle to have access to all pasture land, stream crossing is necessary for pasture that is divided by the stream. The herbaceous riparian buffer is a result of the stream exclusion fencing (Virginia Department of Conservation and Recreation, 2009e). Research suggests that forage yields of rotationally grazed pasture increase and that the increased vegetation not only leads to greater amounts of available forage, but also prevents sediment transport (Groover, 2006, Howry, et al., 2008, Lyons, et al., 2000, Owens, et al., 1996, Undersander, et al., 2002). This BMP scenario will be referred to as fencing throughout the remainder of the thesis. Herbaceous riparian buffer consists of a 35 foot strip along each bank of a stream so as to separate farming activities from the stream (Virginia Department of Conservation and Recreation, 2009g). The buffer is intended to prevent erosion by catching sediment, and therefore prevent surface runoff of N and P. This BMP is argued to have a wide range of nutrient reduction capabilities that are mostly dependent upon maintenance of the buffer (Easton, et al., 2008). No-till cultivation is, as the name implies, the planting of crops without tillage (e.g. plowing or disking). No-till cultivation does not disturb soil and therefore reduces surface runoff of sediment, which carried nutrients. Small grain cover crops are planted between cropping rotations. In the case of a corn-soy rotation, which is used in this study, the small grain cover crop is planted after corn harvest, and is grown through the late fall till early spring. The purpose of the cover crop is to absorb excess 14

28 nutrients in the soil that may still be present from the previous crop (corn) as well as to reduce sediment loss through erosion (Lal, et al., 1991). Heathwaite et al. (2000) discuss that winter cover cropping has proven to reduce total nitrogen (TN), total phosphorus (TP), nitrate and dissolved phosphorus (DP) in some measured experiments, but only TP in others. Phosphorus-based nutrient management plans are documents that manage the application rates of P to minimize harmful effects to water quality. Soil tests are used to determine the application rate of inorganic P so as not to exceed the crop nutrient needs over the crop rotation (The Virginia General Assembly, 2005, Virginia Department of Conservation and Recreation, 2005). Poultry farms that confine more than 20,000 chickens are required to implement P-based NMPs and register under the Virginia Pollution Abatement General Permit for Poultry Waste Management (Virginia Department of Environmental Quality, 2010). For this reason, the combined beef-poultry farm model has a P-based NMP incorporated into its baseline Multiple Best Management Practice Implementation After modeling the BMPs individually, the BMPs were then stacked in a series of five scenarios (four for the beef-poultry farm) in order to measure the effects of economic and nutrient reduction from implementing all of the BMPs to qualify for the Chesapeake Bay Watershed Nutrient Credit Exchange Program. The stacked scenarios are shown in Figure 2.1. It is assumed that the first BMP that would be implemented on a farm would be the one that was least detrimental to profits. If the farmer wanted to further reduce nutrient losses and proceed closer to the Chesapeake Bay Watershed Nutrient Credit Exchange Program s requirements, he or she would implement the BMP that was the next least detrimental to profits, and so on. 15

29 Figure 2.1 Stacked Best Management Practice Scenarios * The farm will implement the most profit maximizing BMP first and the least profit maximizing BMP last. The BMPs are therefore ranked, starting with 1, from most profit maximizing to least Opportunity Costs and Compliance Cost Once BMPs have been implemented to the baseline farm models, the effects of the BMPs on farming operations, net revenues, and nutrient losses will be determined to assess economic feasibility of each BMP scenario. Opportunity costs and compliance costs are two calculations that will determine economic feasibility. Opportunity cost measures net revenues foregone when choosing a less profitable production activity. For example, the installation of a riparian buffer on cropland takes land out of production. This reduces net yields and therefore creates an opportunity cost: the net revenues foregone to install a riparian buffer. Compliance cost is the loss in net revenues undertaken in order to adhere to a policy or to fulfill a transaction (Bonham, 2003, Carpentier, et al., 1998). Compliance cost can reduce net revenues through direct costs, for example, the actual cost of installing the buffer (such as seed, labor, and time spent on paperwork), as well as through opportunity costs (revenues from crops foregone by installing the 16

30 buffer), as shown in Figure 2.2. It is important to assess both direct costs and opportunity costs as a part of compliance cost in order to have a clear understanding of the economic effects BMP implementation has on the farm model. Figure 2.2 Components of Compliance Cost Compliance Cost Opportunity Cost Direct Cost Cost-Share Payments This study also incorporates cost-share subsidies and a tax credit program when modeling BMPs in order to accurately represent a farmer s likely experience when implementing a BMP. The three cost-share programs that are incorporated into this study are the Conservation Reserve Enhancement Program (CREP), the Conservation Reserve Program (CRP), and the Environmental Quality Incentives Program (EQIP). The Conservation Reserve Enhancement Program is jointly funded by Virginia DCR and the USDA. EQIP and CRP are funded by the USDA (Natural Resources Conservation Service, 2009c, Natural Resources Conservation Service, 2010, Virginia Department of Conservation and Recreation, 2010b, Wossink and Osmond, 2002). CREP incentivizes riparian buffer implementation on lands that are adjacent to water (Virginia Department of Conservation and Recreation, 2010b). CRP promotes BMPs that prevent soil erosion, reduce sediment loss into streams, create wildlife habitat, and enhance forests and wetlands (Natural Resources Conservation Service, 2009c). EQIP promotes reductions of NPS pollution and agricultural point source pollution, conservation of ground and surface water resources, reduction of emissions, reduction in soil erosion and sedimentation, and conservation of habitat for at-risk species (Natural Resources Conservation Service, 2010). 17

31 Because cost-share payments are intended to incentivize farmers to adopt a BMP, it is important to consider how cost-share payments, which are received in the present, affect the farmer s decision to implement and maintain a BMP over the course of one to several years (Wossink and Osmond, 2002). Wossink and Osmond (2002) used publicly accessible information to conduct a study which could help local policy makers and stakeholders identify cost effective N-based BMP programs for the Neuse River Basin in North Carolina. As a part of their modeling process, Wossink and Osmond discussed that cost-share payments are commonly received by farmers who adopt BMPs and therefore are necessary to incorporate in an analysis of cost effective BMPs. In order to make the cost-share payments comparable with one another the net present value (NPV) was calculated. The net present value is the current value of the sum of all future payments (received and paid) over a period of time that is discounted to reflect the time value of money. Cost-share payments can vary widely in their payment schedule, so the NPV enables the net cost of BMPs to be compared because the duration of cost-share payments and of BMPs effectiveness can be converted to an annual basis (Wossink and Osmond, 2002). Wossink and Osmond s study concluded that BMPs had different economic effects in the different regions within the Neuse River Basin and therefore cost-share payments should be adjusted to reflect the success of nutrient reductions on a regional basis. When calculating the present value (PV), the current value of all future received payments up to a particular date, or the NPV can vary greatly based on the discount rate, or the time value of money. The discount rate indicates how much an individual prefers money in the present to money in the future. The greater the discount rate, the more current money is valued compared to future money. The smaller the discount rate, the more closely future money is valued to current money. If the discount rate were zero, future money would be valued the same as current money (Adamowicz, 2009) Nutrient Modeling Nutrient losses from farm land occur most often when cattle are not restricted from stream and therefore deposit solid manure into streams and damage stream banks; fertilizer or sediment from tillage runs off into stream; or nutrients leach through the soil into streams. The primary 18

32 nutrients concerning pollution are N and P. Nitrogen is water soluble and can therefore be leached through the soil quickly. Phosphorus remains in soil until the ground becomes saturated. Once the soil has been saturated, soluble P then leaches through the soil (Heathwaite, et al., 2000). Although BMPs are the preferred method of reducing NPS pollution, there is little ability to provide accurate estimates of their nutrient reducing efficiencies and their costs to farmers (2004, Easton, et al., 2008). Easton, Walter, and Stennhuis argue that few efforts have been made to assess the combined effectiveness of long term monitoring and modeling of NPS pollution before and after BMP implementation. Therefore the true effects of BMPs on water quality are unclear. They state that the most appropriate solution to this absence of information is to combine modeling with monitoring practice. This, however, would be too costly to actually implement. A more economical approach is to rely primarily on modeling nutrient losses (Carpentier, et al., 1998). Nutrient modeling programs offer a more abstract analysis of nutrient transport and impact on streams than monitoring. Much of the difficulty surrounding the research of BMP nutrient-reducing effectiveness is the energy and cost required to run experiments to measure and monitor sites before and after BMP implementation. BMP nutrient reductions are mostly contained in research literature that deals with site-specific experiments. Gitau, Gburek, and Jarrett (2005) collected information on P- based BMP implementation within New York City s watersheds to incorporate into a database. It was an attempt to create a program that could estimate P losses with basic BMP and spatial information (site soil type and slope) by drawing upon past research. A computer program drew upon the database to find similar BMP and spatial information to produce an estimated P loss. Gitau et al. concluded that the tool provided effective estimates of P reductions from BMP implementation, given a site s soil and slope conditions. The tool provided a summary of existing data of BMP implementation of a BMP query as well as alternative BMPs. and offered a faster more affordable estimate than executing an experiment on all BMP implementation sites (Gitau, et al., 2005). However, currently the most reliable source of BMP nutrient reduction for both N and P is the Chesapeake Bay Program s efficiency estimates for different regions within the Chesapeake 19

33 Bay watershed (Chesapeake Bay Program, 2006). Therefore, when the estimates are used, their scope must also be taken into consideration; the many varieties of slope, soil type, weather, farming practices, vegetation, etc. that are a part of any region. This study used Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) to analyze the effects of five BMPs on two hypothetical models that are representative of beef and beef-poultry farms in Shenandoah County, Virginia. GLEAMS estimates edge of field nutrient and pesticide losses. It estimates losses of dissolved nitrate, dissolved ammonium, sediment ammonium, sediment organic N, leached nitrate, runoff phosphate, sediment phosphate, and sediment organic P. Calculation components of GLEAMS are hydrology, erosion, pesticide transport, and nutrient transport. GLEAMS estimates the impact of agricultural management on nutrient and pesticide losses(strickland, 2007). Gerwig et al. (2001) used the GLEAMS model to estimate N and P losses from agricultural fields receiving swine lagoon effluent through a forested riparian buffer. The study applied swine effluent at two application rates of 500 kg N/ha and 1000 kg N/ha. In their analysis Gerwig et al. concluded that the GLEAMS estimations of N losses were reasonable estimation of the observed data, but the P loss estimations showed potential limitations of GLEAMS ability to estimate the differences between the two application rate scenarios. Garnier et al. (1998) used GLEAMS to simulate the effects of animal waste application on different soil types and crops. The purpose of the study was to analyze the environmental effects of disposal of animal waste. The study area, the alluvial plain of the Chiana River, in Tuscany, Italy, contained many livestock farms and contained shallow aquifers, making the groundwater highly vulnerable to pollution. The large study area was divided into homogeneous land units using a GIS, so that the region could be analyzed in GLEAMS. The GLEAMS results indicated that current regulation on manure fertilizer application allowed manure application rates that had excessive nutrient runoff and leaching. Another result from the study was that cropping choices greatly affected nutrient uptake. 20

34 Chapter 3. Empirical Application 3.1. Introduction In order to discover the effects of BMPs on whole farm net revenues, the changes in farming activities when BMPs are applied, and the cost per pound of nitrogen and phosphorus lost, a profit maximizing linear program and a GLEAMS model were used (Knisel and Davis, 1999, Osorio and Walker, 2009). The linear program was created in Microsoft TM Excel TM using the software add-in, Solver TM. This chapter discusses the assumptions made and the processes undertaken to create two typical farm models in a linear program and 11 total scenarios of BMP implementation to the model farms. The two typical farm models are a beef farming operation and a beef-poultry farming operation located in south-central Shenandoah County. The BMPs consist of livestock fencing, herbaceous riparian buffer, no-till cropping, winter wheat cover crop, and P-based nutrient management plan. A scenario was created for each baseline farming operation and then for the application of each of the BMPs (P-based NMP was included as a part of the beef-poultry operation) Data Shenandoah County was used as the location for this analysis because it is situated in the North Fork of the Shenandoah River watershed, a sub-watershed of the Chesapeake Bay watershed, and is the fifth leading county in Virginia agricultural revenue (National Agricultural Statistics Service, 2008). The North Fork of the Shenandoah River watershed has large amounts of agricultural activity, many impaired streams, and most of the stream impairments are, to some extent, a result of agricultural activities (Environmental Protection Agency, 2010, Natural Resources Conservation Service, 2008). Cropping, beef farming, and combined beef-poultry farming are common in Shenandoah County and were therefore chosen to be modeled in this study. The incorporation of cropping activities in the study is also considered important because of application decisions the farms will have to make with regards to fertilizer, and the resulting nutrient absorption and edge of field losses under different BMP implementations. 21

35 Information that was gathered to characterize beef and combined beef-poultry operations in Shenandoah County consisted of both economic and spatial data. Enterprise budgets from the Virginia Cooperative Extension website, custom work budgets, peer-reviewed journal articles, other university research and extension articles, and interviews with Virginia Cooperative Extension faculty and staff were used to create the economic attributes of typical beef and combined beef-poultry farm models. Soil reports from Natural Recourses Conservation Service (NRCS), and Shenandoah County soil test results were used to determine the soil types and slopes of the farms. Soil productivity and nutrient requirements were gathered from DCR publications (Virginia Department of Conservation and Recreation, 2005). Best management practice standards and cost-share specifications were gathered from DCR and an unpublished spreadsheet from the Virginia office of NRCS (Faulkner, 2009). The soil, slope, nutrient requirements, and BMP scenario data were put into a GLEAMS program. The GLEAMS program provided information about the N and P edge of field losses on a kilogram per hectare basis. The GLEAMS output is converted to pounds per acre. The whole farm nutrient losses were then calculated by multiplying the pounds per acre of cropping activity by the number of acres in the corresponding cropping activity. Whole farm nutrient losses from the BMP scenarios were compared to the baseline scenarios to determine the total pounds of nutrients abated. The compliance cost was divided by the abated pounds per acre, in order to determine the per pound cost efficiency of each BMP Linear Programming Model A linear programming model was created to calculate the optimal allocation of resources once a model farm has implemented a BMP while maximizing farm net revenues. The LP model is generalized in Equations 3.1 through 3.7: 22

36 ( ) (3.1) (3.2) Subject to: (3.3) (3.4) (3.5) (3.6) (3.7) Where: The profit maximization function is shown in equation 3.2, where is the summation of all of the selling activities on the farm and are all the purchasing activities on the farm. The constraints are as follows: is the amount of land used in raising crops and livestock subject to availability, ; is the net amount of livestock raised and sold; is the net amount of crops that are raised, fed to livestock, and sold; is the net amount of fertilizer required by and supplied to crops; and is the net amount of household and hired labor on the farm. All resources (b k through b p ) are non-negative, which means that supply can be equal to or greater than demand, but never less than demand. Excel Solver TM was used to create a linear program. Solver is a software program compatible with Microsoft Excel TM. A traditional tableau format was used in this study to set up the LP model. Table 3.1 is an abstract from the beef farm model. Row 1 is the title of each of the objective function variables, Row 2 contains the variables, and Row 3contains the coefficients. 23

37 For the coefficients, the negative signs reflect costs to the farmer and the positive signs reflect revenues. Rows 4 through 7 are constraints to the objective function. They give the parameters in which the model may run the farm. For example, Row 7 is the constraint on crop land. The total cropland (corn grain acres, corn silage acres, and soybean acres) can be less than or equal to 50 acres. Column J contains the inequality signs, and Column K contains the limits of the constraint equation. Within the constraints (Rows 4 through 7), negative numbers are supplies and positive numbers are demands. For example, in Row 4, 150 bushels of corn grain can be produced per acre (Cell C4), corn grain can be purchased on a per bushel basis (Cell F4), and corn grain can be sold on a per bushel basis (Cell H4). The supply of corn grain comes from the production and purchase, and the demand comes from the sale of corn grain. Similarly, in Row 5, 5.9 tons of corn silage are required per head of cattle on a corn silage diet (Cell B5) and 22.5 tons of corn silage can be produced per acre (Cell D5). In this second example, it is clear that the cattle demand the corn silage and the production supplies it. Table 3.1 Abstract from the Beef Farm Linear Programming Model 1 A B C D E F G H I J K Produce Corn Produce Produce Buy Sell Sell Silage Corn Corn Produce Corn Corn Soy Cow Grain Silage Soy Grain Grain (Bu) (Head) (Acre) (Acre) (Acre) (Bu) (Bu) 2 Obj. Fun. Var. X 1 X 2 X 3 X 4 X 5 X 6 X 7 $ Net -$173 -$194 -$207 -$163 -$6 $5 $9 3 Obj. Fun. Coef. Revenue 4 Corn Grain (Bu) $ Corn Silage (Ton) $ Soy (Bu) $ Fred 2-7% Acres $ - 50 Once Solver TM was run, the optimal objective function variables were calculated in Row 2. The resulting objective function variables were then multiplied by their coefficients. The products were then added together (i.e. sum product) to calculate the net revenues (Cell I3). 24

38 Scenarios Two farm models were created: a beef farm and a beef-poultry farm. Each farm had an individual BMP applied to the model, to create a new scenario. There were 11 scenarios run: Scenario 1: Baseline Beef Farm Scenario 2: Beef Farm with Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing (Pasture Only) Scenario 3: Beef Farm with Herbaceous Riparian Buffer (Cropland Only) Scenario 4: Beef Farm with No-Till Crop Management Scenario 5: Beef Farm with Winter Wheat Cover Crop Scenario 6: Beef Farm with Phosphorus-Based Nutrient Management Plan Scenario 7: Baseline Beef-Poultry Farm (P-based NMP) Scenario 8: Beef-Poultry Farm with Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing (Pasture Only) Scenario 9: Beef-Poultry Farm with Herbaceous Riparian Buffer (Cropland Only) Scenario 10: Beef-Poultry Farm with No-Till Crop Management Scenario 11: Beef-Poultry Farm with Winter Wheat Cover Crop After each of the scenarios was run, the net revenues of the BMP scenarios were compared among the two different farm types. The net revenues were ranked from most profitable to least, and then another set of scenarios were run. In the second set of scenarios BMPs were stacked by adding one BMP at a time, from least detrimental to net revenues to most, until all BMPs were implemented. The stacked scenarios were: Scenario 12: Beef Farm with No-Till and Cover Crop Scenario 13: Beef Farm with No-Till, Cover Crop, and Buffer Scenario 14: Beef Farm with No-Till, Cover Crop, Buffer, and Fencing Scenario 15: Beef Farm with No-Till, Cover Crop, Buffer, Fencing, and P-based NMP Scenario 16: Beef-Poultry Farm with No-Till and Cover Crop Scenario 17: Beef-Poultry Farm with No-Till, Cover Crop, and Buffer Scenario 18: Beef-Poultry Farm with No-Till, Cover Crop, Buffer, and Fencing 3.4. Baseline Farm Operation Descriptions Introduction Two model farms were created to represent typical farming operations in Shenandoah County. The first is a small beef operation with a total of 150 farmable acres. Land use consisted of cropland for a corn grain-soybean rotation, pasture, and stockpiled fescue. The 25

39 second farm is a combined beef-poultry operation that consists of the same initial location, attributes, and resources as the beef operation, with the addition of three poultry houses, each housing 20,000 broilers per production cycle. The poultry operation completes approximately 6 production cycles per year (8.67 weeks/cycle). The total annual production of the farm is 360,000 broilers per year. The following sections describe the farms physical characteristics, resources, activities, and requirements. Physical characteristics consist of geographic location, soil type, slope, soil productivity, land use, and layout of the farm. Resources consist of land acreage, labor, and assets. Activities in the farm LP model consist of operation systems (i.e. beef or beef-poultry) and the BMPs that will be implemented. Operation systems include stockpiled fescue-fed beef cattle, hay-fed beef cattle, corn silage-fed beef cattle, poultry, pasture, cropping, labor, and fertilizer activities. They represent choices in the LP model that a farmer would have to make in order to maximize net revenues. Constraints are defined requirements, such as crop nutrient requirements, and relationships between supply and demand of farming actives, for example, the supply of corn grain can be greater than or equal to the demand for corn grain as feed and sales Spatial and Soil Characterization The model farms were located in south-central Shenandoah County, which is a part of the North Fork of the Shenandoah River watershed. Figure 3.1, is a map of the North Fork of the Shenandoah River watershed is a sub-watershed of the Chesapeake Bay watershed within the Commonwealth of Virginia. Figure 3.2 is a map of Shenandoah County within the watershed. 26

40 Figure 3.1 North Fork of the Shenandoah River Watershed (Natural Resources Conservation Service, 2008) Figure 3.2 Shenandoah County within the Northfork of the Shenandoah River Watershed 27

41 Figure 3.3 shows the concentration of unconfined beef operations and confined chickens during a cycle within the North Fork of the Shenandoah River Watershed. This area has the greatest density of unconfined beef and poultry operations in the county (Natural Resources Conservation Service, 2008). In the Map b., poultry cycle is the time it takes to raise a set of chicks to slaughtering age and weight. The circles on each map are in the same location, and indicate the general area in south-central Shenandoah County in which the model farm is located Figure 3.3 Livestock Operation Concentration a.) Unconfined Beef b.) Confined Chickens during a Cycle (Natural Resources Conservation Service, 2008) The farms are composed of a total of 150 acres, including pasture, stockpiled fescue, and cropland. One hundred acres of soil are composed of Frederick and Poplimento silt loams with slopes between 7-15% and 50 acres of soil are composed of Frederick and Poplimento silt loams with slopes between 2-7%. These soil types were chosen based on their frequency within the South-Central region of Shenandoah County. A GIS was used to analyze the State Soil Geographic Database (STATSGO) soil report from NRCS, on Shenandoah County. Soil productivity classes can be different for each crop, and therefore it is possible for a farm to have 28

42 a productivity class of 1 for corn grain, and a productivity class of 2 for hay. The soil productivity class of Frederick and Poplimento silt loams with 2-7% and 7-15% slopes for corn, soy, and pasture is 2, which is defined as having moderate limitations that restrict the choice of crops or that require moderate conservation practices (Natural Resources Conservation Service, 2009b). Figure 3.4 is a diagram of the hypothetical farm. To make measurements and calculations simpler, an arbitrary configuration of a farm, field, and property boundaries was chosen to be rectangular. Figure 3.4 Hypothetical Model Farm Layout As shown in Figure 3.4, the stream runs through the center of the farm, with the slopes coming down, into the stream on both sides (the darker, outer edges being higher than the lighter center). In effect, the land is a mirror image on either side of the stream. The farmed land is 2,178 feet wide by 3,000 feet long. The 100 acre pasture land is 2,178 feet wide by 2000 feet, 29

43 and the 50 acre cropping land is 2,178 feet wide by 1,000 feet long. In the model an acre is measured by strips that are approximately 20 feet by 2178 feet Cropping and Pasture Yields and Nutrient Requirements The model contains two cropping rotation options: corn grain-soybean and corn silagesoybean. All cropping is done on the 50 acre, 2-7% Frederick Poplimento soils, because the slope is more permissive to planting, maintaining, and harvesting the crops. In the baseline scenario, all cropping activities are conventionally tilled with no cover crops or NMPs and there are no buffers. Pasture and hay is located on the 100 acres of 7-15% Frederick Poplimento soil. The land is steeper and less amenable to cultivation, and is therefore more appropriate for livestock and hay production. Native pasture, improved pasture, stockpiled fescue (SPF), and stockpiled fescue, which is hayed once in the spring, are included as forage-raising options in the LP model. As with the cropland, there are no BMPs included in the baseline scenario for the pasture land. Table 3.1 shows the soil productivity group, potential per acre yields of corn grain, corn silage, soybeans, native pasture, pasture, SPF, and SPF hayed once (2005, Clark, 2009). The soil type Frederick Poplimento contains both Frederick soils and Poplimento soils, as the name implies. Neither the 2-7% slope nor the 7-15% slopes were steep enough to reduce soil productivity, so there is also no differentiation between soil productivity types, as seen in Table 3.1. Yields for no-till crop management were not explicitly given in the Nutrient Management Standards and Criteria and so yield estimates from Virginia Cooperative Extension crop budgets were used. 30

44 Table 3.2 Soil Productivity Group and Crop and Forage Yields Corn Grain Corn Silage Soybeans Crop Soil Type Unit Native Pasture Improved Pasture Stockpiled Fescue Stockpiled Fescue Hayed Once Frederick Poplimento 2-7% Frederick Poplimento 2-7% Frederick Poplimento 2-7% Frederick Poplimento 7-15% Frederick Poplimento 7-15% Frederick Poplimento 7-15% Frederick Poplimento 7-15% Soil Productivity Type Yields Bu/Ac IIb 150 Ton/Ac IIb 22.5 Bu/Ac II 40 Grazing Ac/Ac* II 0.5 Grazing Ac/Ac II 1.5 Grazing Ac/Ac 0.6 II Stockpiled Fescue Days 120 Grazing Ac/Ac, (Virginia Department of Conservation and Recreation, 2005) * Grazing acre is a measurement of the amount of forage produced per year 0.1 Stockpiled Fescue Days, II 120 Hay Ton 1.5 Nutrient application to crops and pasture were determined using DCR s Nutrient Management Standards and Criteria (2005). DCR suggests that P application be restricted by using one of several calculation methods. The Soil Test Method uses soil tests for a field and the recommendations for the crop, which is also given in the document. The Environmental Threshold Method uses soil test results, crop P removal rates, and the region in which the field is located (e.g. Ridge and Valley, Middle and Upper Coastal Plain and Piedmont, and Eastern Shore and Lower Coastal Plain). The Phosphorus Index Method uses several calculations to come up with the Phosphorus Index Value, which is used to determine the appropriate P application rates (Virginia Department of Conservation and Recreation, 2005). This study used the Environmental Threshold Method. To determine the typical soil test result for the model farm, the Shenandoah County Soil Test Summaries, from Virginia Tech s Soil Testing Laboratory, were examined for soil nutrients P and potassium (K). In Table 3.2, the Soil Test Summaries data is organized by crop and forage type. Phosphorus and K are rated as being low (L), medium (M), high (H), or 31

45 very high (VH) for each crop. Below each rating is the percentage of samples that tested as L, M, H, or VH. Table Shenandoah County Soil Test Summary Abstract Crop Name No. of Samples P Rating (%) K Rating (%) L M H VH L M H VH Corn Grain Conventional Tillage Corn Silage Conventional Tillage Soybeans Orchardgrass/Fescue -Clover Pasture Stockpiled Tall Fescue Tall Grass - Hay (Maguire, 2009) * No-Till cropping is a part of a BMP scenario and was not used to determine the soil ratings The highlighted cells are the ratings that were the most common for the particular crop. These values were used as the model farm s soil test rating. These values were also used to determine the P removal rates for each crop. Although the P ratings were in both H and VH, all cropping activities were assumed to be rated as H to be compatible with the GLEAMS nutrient modeling. After the P and K ratings were determined, DCR s Nutrient Management Standards and Criteria was used to select the N, P, and K application rate of each of the crops. The application rates are presented in Table 3.3. Nitrogen and K rates were taken from the Nutrient Needs tables and the P quantities were calculated using Table 4-7 entitled, Virginia Nutrient Management Planning P 2 O 5 Removal Table for Grain and Forage Crops (Virginia Department of Conservation and Recreation, 2005). Table 3.4 is a modified version of Table 4-7 from Nutrient Management Standards and Criteria. Further discussion on fertilizer choice and application continues later in the chapter. The K rates for SPF pasture and orchardgrass/fescue-clover pasture, in the Nutrient Needs 32

46 tables, were zero; however, based on Virginia Cooperative Extension faculty recommendations K rates were increased to 40 pounds per acre (Pease, 2010). Table 3.4 Nutrient Requirements for Crops in Model Farm Crop N Lbs/Acre P 2 O 5 Lbs/Acre K Lbs/Acre Corn Grain Corn Silage Soybeans Orchardgrass/Fescue-Clover Pasture Stockpiled Tall Fescue Pasture Stockpiled Fescue Pasture with One Hay Cutting (Tall Grass Hay) Native Pasture Table 3.5 P 2 O 5 Removal for Crops Crop Yield Unit Lbs P 2 O 5 Per Yield Per Acre Yield Per Acre Total Lbs P 2 O 5 per Acre Corn Grain Bushels Corn Silage Tons Soybean Bushels Orchardgrass/Fescue-Clover Pasture Stockpiled Tall Fescue Pasture Stockpiled Fescue Pasture with One Hay Cutting (Tall Fescue Hay) Acre Acre Tons Acre 30 1 Native Pasture Acre Corn grain and corn silage fertilizer rates are based on yields and the carryover N produced by the previous crop of soybeans. Soybeans contribute approximately a half pound of N per acre for every bushel harvested; in this case 20 lbs of N. The soybean contribution is then subtracted from the suggested N rate to determine the total pounds of N to apply per acre. Corn grain, in productivity group IIb, needs 150 lbs of N and corn silage needs 165 lbs. With the soybean rotation included, corn grain for the model farm requires 130 additional lbs of N and corn silage requires 145 lbs. 33

47 Stockpiled fescue with one hay cutting receives two nitrogen applications: 90 lbs in March and then an additional 80 lbs after the hay cutting for the additional stockpiling production. Ninety pounds was the mean of the recommended N application rate ( lbs) for soil productivity group II. The pounds of P were calculated by combining the tons of hay and pasture produced. Approximately 16 lbs of P are removed per ton of hay produced. Because this option of SPF is only hayed once, more P is needed for pasture production. Pasture production removes approximately 30 lbs of P, and so the total P removal per acre was calculated to be 54 lbs/acre (Virginia Department of Conservation and Recreation, 2005). Native pasture receives minimal maintenance. Nitrogen application is suggested only when clover is less than 25% of the pasture stand, and then is only applied once every three to four years (Virginia Department of Conservation and Recreation, 2005). It is assumed in this model that 50 lbs of N will be applied every four years. The P removal rate was estimated to be approximately 30 lbs/acre per year. It is assumed that every four years, 30 lbs/acre of P will be applied with N (Virginia Department of Conservation and Recreation, 2005). Therefore, to annualize the application, 7.5 lbs of P was required in the LP model Cropping and Pasture Economic Modeling Virginia Cooperative Extension crop budgets were used to calculate the costs for producing all crops and pasture for this study. The extension budgets include a complete budget of the operating expenses for each cropping activity. Fertilizer requirements and labor are expenses that were removed from the budgets to be analyzed separately within the LP model. The rest of the costs were averaged into a per acre cost. Table 3.5 is an example of the costs incorporated into the per acre costs. Crop budgets were from 2007 so prices were inflated to be in 2008 dollars ($1 to $1.04 respectively) to be consistent with the Virginia Cooperative Extension beef budgets and hired labor budgets that were used in the study. Inflated costs are found at the bottom of Table

48 Table 3.6 Corn Grain Budget from Virginia Cooperative Extension in 2008 Dollars Unit Quantity Per Acre Cost/Unit Total Per Acre Variable Costs Seed Corn Bag 0.33 $90.00 $29.70 Lime (Prorated) Ton 0.33 $32.50 $10.73 Herbicides Acre 1 $23.49 $23.49 Fuel, Oil, Lube Eq Gallons* 3.63 $2.35 $8.54 Repairs Acre 1 $9.76 $9.76 Crop Insurance Acre 1 $21.85 $21.85 Total Variable Costs $ Fixed Costs Tractor and Machinery Acre 1 $59.96 $59.96 General Overhead Dol. $ % $22.66 Total Fixed Cost $82.62 Total Cost (2007 Dollars) $ Total Cost (2008 Dollars)** $ * Eq Gallons: Equivalent Gallons of Diesel Fuel plus 15% to cover Oil & Lube ** $1.00 in 2007 = $1.04 in 2008 (2010) All of the costs and quantities are taken from the Virginia Cooperative Extension Corn Grain Budget. Tractor machinery; fuel, oil and lube; and repairs are calculated within the budget as estimates of general machinery requirements for pre-harvest and harvest labor. General overhead is a percentage of the total variable cost for the particular crop. Although several of the variable costs are removed from the budget (fertilizer and labor), it is assumed that the specific cost of general overhead would not vary significantly enough to alter this cost estimate. All farm overhead costs were omitted. There is no assumption of farm rent, loans, taxes, or other farm overhead expenses. The addition of such information can add more unique qualities to the model and make it less typical and therefore less relatable to farms of a similar operation system. The LP models a return to fixed and variable costs. To model the production and sales of crops and pasture, yields of each were calculated as well as on-farm demand. Any surplus of crop and hay production, after meeting the demands of the farm, was assumed to be sold. Tables 3.6 and 3.7 are tableaus that generalize some of the activities, relationships, and constraints of the cropping and hay production and sales, using corn grain and soybeans as an example. Table 3.6 is a tableau of corn grain and soybeans production and sales, showing the rotational relationship, as well as how production interacts with on-farm requirements and sales. Table 3.7 is a tableau of pasture and hay production and sales. The 35

49 requirements for fertilizer and labor are discussed below, and are left out of these example tableaus. Table 3.7 Corn Grain and Soybean Production and Sales Tableau A B C D E F G H SPF Beef Livestock (Head) Corn Grain (Acre) Soybeans (Acre) Corn Grain Sales (Ton) Soybean Sales (Bu) 1 2 Obj. Fun. Coef. -$150 -$194 -$163 $6 $9 3 Fred-Pomp 2-7% (Acre) Corn/Soy Rotation 1-1 = 0 5 Corn Grain (Bu) Soybean (Bu) Table 3.8 Pasture and Hay Production and Sales Tableau A B C D E F G H I SPF Beef Livestock (Head) Native Pasture (Acre) Improved Pasture (Acre) SPF + 1 Hay (Acre) SPF (Acre) Hay Sales (Ton) 1 2 Obj. Fun. Coef. -$150 -$5 -$37 -$70 -$47 $147 3 Fred-Pomp 7-15% (Acre) Grazing Acres (Acre) Grass Hay (Ton) SP Fescue Days In Table 3.6, Row 1 is the title of the objective function activities. Row 2 contains the activities coefficients; the negative dollar amounts represent costs, and positive dollar amounts represent revenues. Row 3 is represents the land constraint which allows any combination of cropping acres less than or equal to 50 acres. The Row 4 is a corn grain-soybean rotation. Because this is an annual model, and corn and soybeans are rotated every year, the two crops share the same total cropping acres. For example, if corn grain production used all 50 acres, and the following year soybeans used all 50 acres, then the LP model would bring both years' production to one single year by allotting 25 acres to corn grain and 25 acres to soybeans. Row 5 is the production and sales of corn grain. The inequality sign indicates that there can be more supply than demand for each of the constraints. 36

50 Table 3.7 is very similar to Table 3.6. However, it is important to clarify the hay production calculation. A hay loss is implicitly calculated within the model. In cell F5, SPF with one hay cutting nets 1.1 tons of hay production per acre. A 25% loss is assumed due to transportation and storage of hay. Therefore, the actual production of hay is 1.5 tons per acre, but in the model only 1.1 tons per acre is assumed to be supplied to animal feed or hay sales. Row 6 is the number of days of SPF pasture needed per cow head (92 days), and the amount provided by each acre of SPF pasture that is hayed once and un-hayed SPF pasture Fertilizer As discussed in the cropping and pasture yields and nutrient requirement section, fertilizer requirements were based on soil test summaries, soil productivity, and yields. The fertilizer options for the model farm are poultry litter and commercial, inorganic fertilizer. The crop requirements and prices for the fertilizers are in Table 3.9. It is assumed that if the farmer chooses to use inorganic fertilizer, he or she will be able to purchase the required quantity and combination of inorganic nutrients. Therefore, the costs of the inorganic fertilizer nutrients are given on a per pound basis. Poultry litter cost is the net cost of purchasing and transporting the litter to the farm. 37

51 Table 3.9 Crop Requirements and Costs for Fertilizer Tableau A F G H I J K L M N O P Q Corn Corn Corn Corn Corn Corn Corn Corn Corn Corn Grain Silage Grain Grain Grain Silage Silage Silage Grain Silage Poultry Poultry N P (Ac) (Ac) Litter 2 O 5 K 2 O N P Litter 2 O 5 K 2 O (Lb) (Lb) (Lb) (Lb) (Lb) (Lb) 1 (Ton) (Ton) 2 Obj. Fun. Coef. -$194 -$207 -$18 -$0.44 -$0.33 -$0.27 -$18 -$0.44 -$0.33 -$0.27 Plant Available N (Lbs) (Corn Grain) P 2 O 5 (Lbs) 4 (Corn Grain) K 2 O(Lbs) 5 (Corn Grain) Plant Available N (Lbs) (Corn Silage) P 2 O 5 (Lbs) 7 (Corn Silage) K 2 O(Lbs) 8 (Corn Silage) Row 1 is the title of the objective function coefficients, which are corn grain, corn silage, corn gain poultry litter, corn grain N, corn grain P 2 O 5, corn grain K 2 O, corn silage poultry litter, corn silage N, corn silage P 2 O 5, and corn silage K 2 O. Row 2 is the per acre cost of producing each of the crops and the per ton (pound) cost of fertilizer. Rows 3 through 8 contain the fertilizer requirement for each crop on a pound per acre basis and the nutrient supply of each fertilizer source on a pound of nutrient per ton (pound per pound) of fertilizer purchased. The positive numbers in these rows indicate demand for the nutrients and negative numbers indicate supply for the nutrients. The inequality signs in column P signify that the supply of fertilizer can be greater than or equal to the demand for fertilizer. The demand and supply for nutrients are separated by crop in order to prevent the model from assuming that nutrients are transferable between crops. Separating nutrients by crop type ensures that the appropriate quantities of fertilizer are purchased. In the beef farm model, the farmer may purchase and apply as much poultry litter as desired, and so the inequality sign is present in the P constraint (Cell P4). In the beef-poultry operation, however, a P-based NMP is a part of the baseline scenario and so the P constraint is an equality, which ensures that no excess P can be used. 38

52 Poultry litter is purchased by the ton and the cost of poultry litter and its transportation to the farm is $18. Poultry litter is unlike inorganic fertilizers in that its nutrients, particularly N, are not as readily available to plants. For that reason, the N supply in Table 3.8 is the calculated estimate of the plant-available N (Virginia Department of Conservation and Recreation, 2005). The average pounds of nutrient content per ton of dry broiler litter is as follows: Total Nitrogen: 64.86; NH 4 : 11.48; P 2 O 5 : 52.18; and K 2 O: (Virginia Department of Conservation and Recreation, 2005). Poultry litter was assumed to be dry, not incorporated, and applied in spring. The calculation of the quantity of N that can be utilized by plants is shown in Equations 3.8 and 3.9: (3.8) (3.9) The seasonal purchase and use of fertilizer is aggregated into a one-time purchase in the LP. Although farms may purchase litter and fertilizer more than once a year, for each application of fertilizer, for the model s purposes, this is not a significant factor to analyze, and so it is assumed that only one purchase is made. Application of fertilizer however is measured in number but not specified in timing. For example, SPF acres that are hayed are fertilized once in March and again after hay cutting, given that the grass is no less than three inches tall, in order to minimize runoff Labor Labor for the beef farm consists of part-time household labor, approximately 1000 hours per year, and hired labor. The beef-poultry operation, however, requires significantly more labor, and therefore the household labor will be full-time (2000 hours per year). Hired labor is calculated on a seasonal basis, based on the needs of the farm. In the LP seasons are broken into 39

53 periods based on common activity (e.g. planting, harvesting, breeding, calving, etc): December through March; April through May; June through August; and September through November. Family labor is free, but limited to the amount of total available time, whereas hired labor is paid $10 per hour, but is unlimited in availability. It is also assumed that labor is hired at the beginning of the season and is used as needed throughout that season no further specifications are made about how often or when labor is used. Another type of labor that is used on the farms is custom labor, or labor that is hired to perform a specific task with equipment that the farmer does not own. Custom labor activities that are used on the farms are seeding, tilling, fertilizing, chemical spraying, and harvesting. These activities require equipment that the farmer cannot purchase based on the farm s small size. Tables 3.9 and 3.10 outline the custom labor demand and supply for the farm based on corn silage and other seasonal corn silage household and hired labor, respectively. Table 3.10 Corn Silage Custom Labor Tableau A B C D E F G H I J K Chem.* Complete Corn Chisel Offset Tandem Planting Fert.* Spray Harvest Silage Plow Disk Disk Corn Appl. Appl. in Bag (Ac) (Ac) (Ac) (Hrs) (Ac) (Ac) 1 (Ac) (Ton) 2 Obj. Fun. Coef. -$207 -$17 -$20 -$15 -$16 -$7 -$7 -$7 Chisel Plow 3 (Acre) Offset Disk 4 (Hours) Tandem Disk 5 (Hours) Planting Corn 6 (Acre) Fert. Appl (Acre) Chem. Spray Ap. 8 (Acre) Complete Harvest in Bag (Ton) *Fert. Appl. is fertilizer application; Chem. Spray Appl. is chemical spray application 40

54 Table 3.11 Corn Silage Household and Hired Labor Tableau A B C D E F G H HH Labor* Hired Labor HH Labor Hired Labor Corn Silage Apr-May Apr-May Sep-Nov Sep-Nov (Ac) 1 (Hr) (Hr) (Hr) (Hr) 2 Obj. Fun. Coef. -$207 $0 -$12 $0 -$12 3 Apr-May Labor Hrs Sep-Nov Labor Hrs Total Household Labor 5 Hrs (Apr-May) 1 0 Total Household Labor 6 Hrs (Sept-Nov) 1 0 *HH Labor is Household Labor In both tables, supply of labor can always be greater than demand of labor. Table 3.9 exemplifies how custom labor was model within the LP. Required hours of labor were taken from the Virginia Cooperative Extension farm budgets. Custom labor prices were the average prices in the 2008 Farm Custom-Work Rate Guide for the Shenandoah Valley by Virginia Cooperative Extension. For corn silage, it was assumed that the silage would be stored in bags, and so the harvest in bag, complete custom labor was chosen to represent custom corn silage harvesting. In addition to custom labor, other household or hired labor was included to account for supervision and general maintenance of the crops and fields Beef Three beef systems were used as options for the LP based on different feeding regimes: hay, SPF, and corn silage. The LP was given the option of using any combination of beef cattle feeding regimes in order to maximize net revenues. Each feeding regime had its own combination of pasture, hay, corn grain, corn silage, and labor requirements. Rations and labor requirements were both recommended by Virginia Tech Faculty and Extension (Clark, 2009, Groover, 2009). Feeding requirements and supply for each of the beefing operations is shown in Tables 3.12 and Table 3.12 is the pasture requirements for each of the cattle feeding options and Table 3.13 shows the corn grain and silage feeding options. Stockpiled fescue cattle production 41

55 (column B) requires the most diverse feeding regime: pasture, hay, SPF, and corn grain. The hay cattle production (column C) requires pasture, hay, and corn grain. The corn silage cattle production (column D) only requires pasture and corn silage. Although a corn silage-based feeding regiment can actually incorporate hay, it is assumed in the model that the corn silage season is a typical, good season, and that additional hay fed would be negligible. Table 3.12 Pasture Requirements for Beef Cattle Feed Rations Tableau A B C D E F G H I J Corn SPF Hay Native SPF Hay Silage Pasture SPF Cow Cow Pasture Cutting Cow (Acre) (Acre) (Head) (Head) (Acres) (Acre) 1 (Head) 2 Obj. Fun. Coef. -$150 -$150 -$173 -$5 -$37 -$70 -$47 3 Grazing (Acres) Grass Hay (Ton) Stockpiled Fescue Days Table 3.13 Corn Grain and Silage Requirements for Beef Cattle Ration Options A B C D E F G H SPF Cow (Head) Hay Cow (Head) Corn Silage Cow (Head) Corn Grain (Bu) Corn Silage (Ton) 1 Obj. Fun. Coef. -$150 -$150 -$173 -$194 -$207 2 Corn Grain (Bu) Corn Silage (Ton) Feeding regimes are affected by the calendar schedule and stocking rates a farmer chooses to use. Specifically, factors that are affected by the farm s calendar schedule are when cattle are pastured, when they are fed hay, and the number of acres of pasture per cow. Table 3.14 shows the calendars for each of the feeding options used in the LP model. 42

56 Table 3.14 Stockpiled Fescue Beef Cattle Feeding Schedule SPF Beef Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Hay Beef Corn Silage Beef Grazing Key Fall Stockpiled Fescue Grazing Partial Grazing with Hay Hay Corn Silage The calendars in Table 3.14 help to visualize the feeding requirements of the feeding options for the beef cattle. Based on the SPF calendar, cattle are fed approximately 123 days of pasture, 150 days of hay or partial pasture and hay, and 92 days of SPF. Hay-fed cattle are fed approximately 200 days of pasture and 165 days of hay or partial pasture and hay. Corn silagefed cattle are fed approximately 230 days of pasture and 135 days of corn silage. Cattle labor requirements are based on seasonal demand, and are shown in Table All cattle are calved in the spring; therefore the highest demand for labor is between the months of December and March. Labor for cattle is provided by the household and hired labor. In Table 3.15, labor is summarized from the LP in order to fit within this table. In the LP, both household and hired labor is broken out by seasons in the objective function. 43

57 Table 3.15 Labor Requirements for Beef Cattle Activities Tableau A B C D E F G H Corn Household SPF Cow Hay Cow Silage Hired Labor Labor (Head) (Head) Cow (Hours) (Hours) 1 (Head) 2 Obj. Fun. Coef. -$150 -$150 -$173 $0 -$12 3 Dec-Mar Labor (Hours) Apr-May Labor (Hours) Jun-Aug Labor (Hours) Sep-Nov Labor (Hours) Cattle sale activities are based on the gross receipts from the Virginia Cooperative Extension cattle budgets. An abstract of the gross receipt calculations is shown in Figure 3.5. The circled number is the gross receipts of all cattle sales on a per-cow and bred heifer basis. Table 3.16 is a tableau of the production and sale of the beef cattle. Figure 3.5 Abstract from Virginia Cooperative Extension Beef Cows Spring Calving - Hay Ration Budget Groover, G., Internet site: (Accessed June 3, 2009b). 44

58 Table 3.16 Beef Cattle Production and Sales Tableau A B C D E F G H I Corn Sell Corn SPF Cow Hay Cow Sell SPF Sell Hay Silage Cow Silage Production Production Cow Cow Production Cow (Head) (Head) (Head) (Head) 1 (Head) (Head) 2 Obj. Fun. Coef. -$150 -$150 -$173 $569 $569 $569 3 SpF Cow (Head) Hay Cow (Head) Corn Silage Cow (Head) As is shown in Table 3.16, all beef that are produced are also sold in the LP. This is because total production costs and total gross receipts are averaged over the number of cows and bred heifers. When the LP chooses the total number of cows to produce and sell, it is selecting the total cows and bred heifers. The total cows multiplied by the price per-cow in the LP objective function results in the total gross receipts of cow sales for the farm; it is the aggregate of the combination of heifers, steers, bulls, and cull cows and heifers sold. In Figure 3.5, $28,395 divided by 50 equals $568. This price per cow is the price that is used in the LP. Because costs are also aggregated into a per-cow and bred heifer unit, it is consistent to represent both production and sales of beef cattle in this manner. In order to discern the exact sales of the model farm, the ratios shown in Figure 3.5 can be used; gross receipts will remain the same Poultry Poultry production is based primarily on the number of poultry houses that the farmer owns. Net revenues are also somewhat constant based on the number of pounds produced by the farmer and the contracted price between the farmer and the poultry company. The production of poultry litter is the primary interest of this study and a secondary interest is the allocation of labor, therefore, poultry activities in the LP are focused on these activities. Table 3.17 shows the basic calculations to model the poultry farm. The poultry operation will consist of three houses, each able to hold 20,000 broilers per production cycle. The average broiler weight at the end of the cycle is approximately five pounds and there are approximately 45

59 six cycles per year. The broilers are raised for roughly 47 days, and there are 14 days between sets of broilers. Litter production is calculated per thousand broilers: tons of dry poultry litter per thousand broilers (National Agricultural Statistics Service, 2008). The yearly total for broilers is 360,000 birds (1,800,000 pounds per year), and the total dry litter produced is approximately 300 tons. Table 3.17 Broiler Production Calculations Poultry Production Houses 3 Broilers/House 20,000 Cycles/Year 6 Average Broiler Weight (lbs) 5 Total Broilers/Year 360,000 Total Broiler Lbs/Year 1,800,000 Litter Production/1000 Broilers Dry Litter per Year (Tons/1000 Broilers) Total Dry Litter Tons/Year 300 Broiler Schedule Cycles/ Year 6 Days With Broilers 47 Days Without Broilers 14 Total Days/Cycle 61 Total Days/Year With Broilers 281 Total Days/Year Without Broilers 84 Table 3.18 is a tableau of the poultry activities added to the beef farm baseline scenario in the LP model. Poultry net revenues and litter production are with respect to thousand pounds of poultry, rather than a per-pound basis. The objective function coefficient for poultry in column F is called quasi-net revenue, because the objective function coefficient deducts all operating expenses to maintain the broilers; it does not include the cost of labor or the sale of poultry litter. 46

60 It is assumed that approximately two hours per house are required per day that broilers are on the farm (Vukina, 2010). These labor requirements are divided appropriately among the month groupings (seasons) in the LP. When the broilers are not on the farm, it is assumed that labor will primarily be directed towards the cleanup and removal of the poultry litter. Therefore, 281 days of poultry management labor and 61 days of litter management labor (from Table 3.17) are accounted for in the seasonal labor rows (5 through 8) in Table Table 3.18 Poultry Operation Tableau A B C D E F G H I Litter Management Labor (Ton) Poultry Litter Application (Ton) Household Labor (Hours) Hired Labor (Hours) Poultry (Thousand Lbs of Poultry) Sell Poultry Litter (Ton) 1 Obj. Fun. Coef. -$7.50 $0 $0 -$12 $30 $13.50 Poultry (Lbs/Yr) Poultry Litter 3 (Ton) Poultry Litter Management (Ton) Dec-Mar Labor 5 (Hours) Apr-May Labor 6 (Hours) Jun-Aug Labor 7 (Hours) Sep-Nov Labor 8 (Hours) Another addition to the beef-poultry operation baseline is a P-based NMP. Because the poultry operation raises more than 20,000 chickens, it is required by law to utilize a NMP (Virginia Department of Environmental Quality, 2010). It is therefore assumed that the farmer pays for soil and manure tests. The P limit is modeled by creating equality within the LP model; this is demonstrated in Table 3.19, Rows 3 through 5. As with beef farm model, the fertilizer supply and demand were separated out by crop type in order to prevent the model from assuming that nutrients were transferrable between crops. 47

61 Table 3.19 Baseline Beef-Poultry Farm P-Based Nutrient Management Plan Tableau for Corn Grain and Corn Silage A B C D E F G H I J K L Corn Corn Corn Corn Soy Corn Corn Grain Silage Soy Soy Grain Silage Poultry Grain Silage Poultry Poultry P (Acre) P (Acre) (Acre) Litter 2 O 5 P Litter 2 O 5 Litter 2 O 5 (Lbs) (Lbs) (Lbs) (Ton) 1 (Ton) (Ton) 2 Obj. Fun. Coef. -$194 -$207 -$163 $0 -$0.33 $0 -$0.33 $0 -$0.33 P 2 O 5 (Lbs) = 0 3 (Corn Grain) P 2 O 5 (Lbs) = 0 4 (Corn Silage) P 2 O 5 (Lbs) = 0 5 (Soy) 3.5. Best Management Practice Implementation Introduction This section describes the BMPs that were implemented in the beef and beef-poultry LP models. Each BMP was applied, one at a time, to each of the farm operation LP models. The beef-poultry operation, however, incorporated a P-based NMP as a part of its baseline scenario. After the BMPs were individually implemented and their effects on net revenues were assessed, then the BMPs were stacked, one at a time, in order of least expensive to most. Stacking BMPs represents the order in which a farm may implement BMPs to further reduce N and P losses and possibly qualify for the Chesapeake Bay Watershed Nutrient Credit Exchange Program. Costshare and tax credit programs were also incorporated into the LP models in order to accurately represent the actions a farmer may take to reduce the costs of implementing BMPs Cost Share and Tax Credit Payments Cost share payments were incorporated into the objective function costs in the LP model. Each BMP has a useful life, which is the estimated time span that the BMP is effective. Cover cropping and no-till management have a useful life of one year; NMP has three years; rotational grazing has five; herbaceous riparian buffer, stream crossing, and off-stream watering have ten 48

62 years; and all fencing has 20 years of estimated useful life. The costs for BMPs are annualized over the useful life. Because cost-share payments are paid in full once for most BMPs (the one exception is NMP, which receives $5/acre payments for three years), and the useful lives range from one to 20 years, certain calculations needed to be made in order to make both the cost and cost-share payments more comparable among BMPs (Faulkner, 2009). In order to compensate for the differences in useful life, PV of each BMP payment was calculated. The PV is the current value of all future received payments up to a particular date; in this study it is over the course of 20 years. For all BMPs, except, no-till, rotational grazing, and cover crop, payments consist of both installation and operation and maintenance costs. In the cases of no-till, rotational grazing, and cover crop, payments consist only of installation costs. The equation for PV calculation is: (3.10) is the present value of future payments for the BMP, which includes both implementation and operation and maintenance costs. is the annual cost paid to implement and maintain the BMP, is the real discount rate (5%), and is the number of years (20 years) over which the calculation is being made. The Excel TM function NPV calculated the PV with different payment amounts each year. This function allowed the PV of BMPs that are reinstalled within a shorter time period than 20 years and BMPs with annual operation and maintenance costs to be easily calculated. Incentive payments were also received in different increments and in different time frames, so the present value of incentive payments were also calculated using the same formula (Equation 3.10). After the PV of each BMP was calculated, the cost-share and tax credit payments were subtracted. This represents the payment of the incentives within the first year. The resulting calculation was annualized over 20 years. Equation 3.11 was used to determine the net annualized cost (NAC) of implementing and maintaining each BMP over 20 years (Faulkner, 2009). is the present value and is the present value of the total incentive payments for the BMP. The is the real discount rate (5%), and is the number for years (20 years) over which the calculation is being made. 49

63 ( ( ) ) (3.11) For all BMP scenarios, the incentive payment amounts and calculations can be found in Appendix C. The NAC was the value used as each of the BMPs objective function coefficient. A negative NAC in the objective function represents a loss in the farmers net revenues, whereas a positive NAC in the objective function represents a gain in the farmer s net revenues Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Because the stream for the model farm was placed in the middle of the land, fencing out cattle requires providing off-stream watering and stream crossing so that cattle can have access to water and to all parts of the pasture. Rotational grazing was included in the scenario of fencing because the farmer can maximize cost-share benefits and labor use by purchasing all of the fencing needs and installing all of the fencing at the same time. Rotational grazing generally increases grazing acre yields, which may also influence a farmer to implement rotational grazing while fencing cattle out of streams (Clark, 2009, Groover, 2006). All of the baseline assumptions for this scenario are maintained. Changes were made to the number of usable pasture acres, and the structural additions of fencing, permanent watering troughs, and stream crossing are made. Figure 3.6, is a diagram of the BMPs that are included in this scenario. 50

64 Figure 3.6 Fencing, Rotational Grazing, Off-Stream Watering, and Stream Crossing Diagram Fencing was placed 35 feet from the edge of either side of the stream in order to keep cattle out. The fencing creates an area for a forested buffer that is installed as an additional BMP that will also be modeled in the LP. The fencing consists of four strand high tensile electric (HTE) fencing, which is qualified under the NRCS EQIP program for cost-share funding. Six paddocks are made with three strand HTE cross fencing, which is also qualified for EQIP cost-share funding (Faulkner, 2009). The paddocks are approximately 16.1 acres each. Approximately 2000 feet of one strand HTE was included in the model for the farmer to use for repairs or potentially further subdividing the paddocks (Clark, 2010b, Groover, 2010b). Fencing was calculated in the LP on a linear foot per acre basis: 41 linear feet/acre for the 4 strand fencing along the stream s edge and 44 linear feet/acre for the inner fencing to create six paddocks. Because the riparian buffer removes a total of 70 feet of pasture along the stream, the 51

65 dimensions of an acre of pasture were recalculated as 20.7 feet by 2108 feet (baseline dimensions were 20 feet by 2178 feet). The riparian buffer removes approximately 3.2 acres if the model chooses to maximize pasture acreage, subject to the fencing and buffer constraints. The buffer acreage (3.2 acres) divided by the reduced total acreage (96.8 acres) is the ratio used in the LP model to represent the total amount of buffer installed and total pasture used (0.03 buffer acres/pasture acres). Table 3.20 Fencing and Riparian Buffer Tableau A B C D E F G H I 4 wire 3 wire 1 strand Pasture Rotational HTE* HTE Poly wire Riparian Pasture Grazing Fencing Fencing Fencing Buffer (Acre) (Acre) 1 (Ft) (Ft) (Ft) (Acre) 2 Obj. Fun. Coef. -$0.16 -$0.14 -$0.03 $10 -$12 -$37 4 wire HTE Fencing 3 (LnFt) wire HTE Fencing 4 (LnFt) strand poly wire 5 Fencing Herbaceous Riparian 6 Buffer (Acre) Rotational Grazing 7 (Acre) *HTE stands for High Tinsel Electric Table 3.20 is a tableau of the stream crossing and off-stream watering BMP calculations. The objective function coefficients are in row 2. These are the costs paid or revenues received on a per unit basis. For example, four wire high tinsel electric fencing costs $0.16 per foot. The costs These BMPs are not proportional to acreage and are therefore set in the LP as equalities rather than inequalities. Both receive cost-share benefits, which are calculated within the objective function coefficient. Four watering troughs were chosen because of the pasture layout. In order for cattle to have access to water in each field, without having access to the stream, four troughs are needed (refer to Figure 3.6). The troughs were also selected to be freeze proof (Clark, 2009). Stream crossing costs are measured by square foot, and 100 square feet was 52

66 selected for the model. The incentive payments were calculated into the objective function coefficient. Table 3.21 Off-Stream Watering and Stream Crossing A B C D E 2-Hole Freeze Stream Proof Watering Crossing Trough (SqFt) 1 (Number) 2 Obj. Fun. Coef. -$62 -$ Hole Freeze Proof Watering 1 = 4 3 Trough (Number) 4 Stream Crossing (SqFt) 1 = 100 The addition of rotational grazing within the model only affects pasture yields. Labor was assumed to remain the same overall. Labor requirements for moving cattle from one paddock to another will only reallocate labor, not require more (Groover, 2010). Yields were increased by 25% for all grazing acres (Fiske, 2010, Repair, 2010). Therefore, the same pasture could feed more cattle Cropland Herbaceous Riparian Buffer Herbaceous riparian buffers are vegetative strips that run along the bank of the stream, for a minimum average width of 35 feet (Virginia Department of Conservation and Recreation, 2009g). The riparian buffers in this scenario are only implemented on the cropland, shown in Figure 3.7. Buffer acreage to cropland acreage was calculated using the same ratio that was used for the forested riparian buffer in the fencing BMP (0.03 buffer acres/pasture acres). Costs for the BMP include preparing and planting the buffer acres as well as maintaining it. No fences were included in this scenario because BMP was implemented only on cropland. The NAC for implementing and maintaining the herbaceous riparian buffer, shown in Table 3.22, was a gain in revenue for the farm due to the high cost-share payments. 53

67 Figure 3.7 Herbaceous Riparian Buffer Table 3.22 Herbaceous Riparian Buffer Cost-Share Tableau A B C D E F G Herbaceous Corn Corn Soybeans Riparian Buffer Grain Silage (Acre) (Acre) (Acre) (Acre) 1 2 Cost Coefficient $23 -$194 -$207 -$163 Herbaceous Riparian Buffer (Acre) No-till The no-till BMP requires farms to alter cropping practices so that seeds and fertilizer are not tilled into the soil. For both farming operations, custom labor is hired for planting, fertilizing, 54