Water Quality and Farm Management Practices in the McKenzie Brook Watershed

Transcription

1 Water Quality and Farm Management Practices in the McKenzie Brook Watershed Measuring Factors at the Subwatershed Level that Contribute to Phosphorus Loading into Lake Champlain Kate Allman Thomas Epstein Christian Johansen Tariq Mansour Robin Weisselberg Environmental Studies Senior Seminar December 2016

2 Table of Contents Executive Summary 4 List of Acronyms 6 Introduction 7 Principal Investigative Focus 11 The Toolkit 11 Farmer Survey 12 Water Quality Sampling Protocol 12 Geographic Informational Systems (GIS) and Statistical Analysis 13 Methods 14 Farmer Survey 14 Water Quality Sampling Protocol 14 GIS and Statistical Analysis 15 Results and Discussion 23 Farmer Survey 23 GIS and Statistical Analysis 38 Conclusion 46 The Toolkit: Tying it All Together 46 Potential Applications for this Research 47 Recommendations for Further Study 47 Acknowledgements 49 Works Cited 50 Appendices 53 Appendix A.: Farmer Survey 53 Appendix B.: Summary of Farmer Survey Results 59 Appendix C.: Water Sampling Protocol 62 Appendix D: Water Quality Sampling Study Design 65 Appendix E: GIS Methodological Details 68 Appendix F.: Statistical Analysis Methodological Specifics 85 2

3 Executive Summary In 2015, the Vermont Clean Water Act (Act 64) was signed into law with the goal of reducing harmful sediment and nutrient flows into Lake Champlain. The Act established a series of farm and land management practices to control nonpoint source pollution known as Required Agricultural Practices (RAPs). The RAPs require farmers to implement land management practices that prevent excess phosphorus from running into waterways. Currently, a coalition of partners including University of Vermont-Extension, the Natural Resources Conservation Service and the Vermont Department of Environmental Conservation has been working in collaboration with farmers to bring agricultural lands into compliance with the new Act. They focus their efforts in Vermont s four priority watersheds, the most critical subwatersheds for accelerated agricultural conservation practice implementation (NRCS, 2016). Building on the work of a team of Middlebury College Environmental Studies students from the spring of 2016, this project sought to assist in this mission by expanding the knowledge base of conservation practices in action on dairy farms and their efficacy in relation to water quality improvements and landscape characteristics. We developed a toolkit designed to collect and analyze data on farming practices and phosphorus levels in three subwatersheds of the McKenzie Brook watershed (See Figure 1 and Figure 2 in the following section). The McKenzie Brook watershed in Addison County, a highly agricultural county, is one of the four watersheds in Vermont selected for targeted agricultural practices to reduce phosphorus loading in Lake Champlain. The other three watersheds Rock River, Pike River, and St. Albans Bay watersheds are all in Franklin County, which is also a largely agricultural county (USDA/NRCS, 2016 (2)). Our toolkit consists of three components. The first two components of this toolkit, the farmer survey and water sampling protocol, provide a means of collecting data. These data can then be plugged into the third component, a GIS and statistical model, in order to analyze the relationships between a suite of variables and average phosphorus levels at sampling points at the subwatershed level. We created and distributed a survey to 250 farms, providing an avenue by which farmers could report management practices implemented on the ground. Using geographic software, we quantified land characteristics, farm layout and farm practice variables. We then related each of our selected variables to phosphorus levels using statistical software to highlight the impacts of these variables on phosphorus loading in Lake Champlain. We found significant relationships between proximity of farmstead to waterway and phosphorus levels and a statistical trend between cover cropping and phosphorus levels. We also found that forest and slope covaried significantly by subwatershed. The second part of the toolkit sought to improve understanding of what practices will best serve individual farms in reaching target phosphorus loading reductions. We developed a water sampling protocol that will help to provide data on water quality farm by farm, as farmers will be able to sample on their own land instead of samples being taken only by government employees on public land. This will give a much more precise picture of the factors affecting phosphorus loading across the state, as well as providing specific information that can be used to specifically tailor management on a farm by farm basis given its inherent land 3

4 characteristics. The protocol will also collect information on the location and extent of tile drainage systems, a tool that does not currently exist. This protocol will also observe any effects these systems may have on phosphorus loading. While our study was based in three subwatersheds of the McKenzie Brook watershed, in the future the toolkit could be implemented in any watershed, helping to inform agricultural practices and foster informed conversation between all actors in a watershed system. 4

5 List of Acronyms ANR - Agency of Natural Resources BMP - Best Management Practice CWA - Clean Water Act EPA - Environmental Protection Agency GIS - Geographic Information System HSG - Hydrologic Soil Group LFO - Large Farming Operation, more than 700 cows MFO - Medium Farming Operation, cows ND - No Data, or insufficient data for analysis NMP - Nutrient Management Plan NRCS- Natural Resources Conservation Service P - Phosphorus PPM - Parts Per Million RAP - Required Agricultural Practice SFO - Small Farming Operation, 199 or fewer cows TMDL - Total Maximum Daily Load ug/l - Micrograms per Liter USDA- United States Department of Agriculture UVM Extension - University of Vermont Extension VTDEC - Vermont Department of Environmental Conservation 5

6 Introduction One of the great environmental and social challenges of the twenty-first century is the management of nonpoint source pollutants that can lead to impaired water quality (EPA 2004). Many bodies of water in the U.S. and globally suffer from eutrophication caused by runoff from agricultural lands, often leading to the formation of dead zones such as those in the Gulf of Mexico and the Baltic Sea (Brukner; Owen; Rabalais et al. 2002). Unlike point source pollutants, such as carbon dioxide output from a smokestack or sewage spills from a wastewater treatment facility, nonpoint source pollution sources are diffuse. They are essentially invisible and thus difficult to quantify and assess. The modern industrialization of agriculture has led to the widespread use of nutrient fertilizers, particularly of nitrogen and phosphorus. Applying nitrogen and phosphorus fertilizer to cropland allows farmers to improve crop yields and cultivate a single crop on the same plot of land annually. Not all of the nutrients applied to agricultural land are utilized by crops (Altieri 1998). The remaining nutrients stay in the top layer of the soil where they are susceptible to runoff. Precipitation events can cause these nutrients to be washed away from soil and into the local watershed. As these nutrients, specifically phosphorus, accumulate in the system they eventually collect in the larger bodies of water into which these watersheds drain. The prolonged accumulation of these nutrients in a body of water can lead to eutrophication. Bays, gulfs, seas and lakes experience algal blooms when naturally occurring phytoplankton feed off of the elevated nutrients in the water body and their population grows significantly without the usual limitation of nutrient availability. When these algae die, they are broken down by bacteria in the water. This anaerobic process can create a deficiency of dissolved oxygen, which can cause other aquatic species to suffocate, particularly in the areas heavily affected by eutrophication. Light is often unavailable below algae blooms, and the photosynthesis and decomposition creates anoxic or oxygen depleted systems (Sellner et al. 2003). These hypoxic areas in water bodies are referred to as dead zones, ecosystems in which naturally existing organisms can no longer survive in the oxygen deficient environment. In addition to the creation of dead zones, eutrophication leads to toxic algae blooms. The species of algae that thrive in these conditions pose a health hazard to humans and other animals (Sellner et al. 2003). Lake Champlain, which defines Vermont s western border with New York, is currently experiencing both eutrophication and toxic algae blooms (Ghebremicheal and Watzin 2011). Certain toxins in some algae affect the cellular functioning of other organisms, including humans. This can lead to skin irritation, respiratory problems, memory loss, seizures, paralysis, and even death (Sellner et al. 2003). Animals, including pets, and humans are exposed to these toxins and can develop symptoms after swallowing or inhaling water, as well as through simple contact with contaminated water for a period of time, for example, when swimming (EPA 2016). Toxic algae blooms therefore affect the ability of individuals to enjoy the waters of Lake Champlain recreationally. In addition, these algae blooms can cause significant economic effects by necessitating a halt in fishing activity, as 6

7 the toxin can infect humans through consumption of contaminated aquatic species (Hoagland et al. 2002). The effects of toxic algae can be just as extreme on the aquatic organisms living in contaminated water. A wide range of aquatic organisms can be impacted, including invertebrates and vertebrates, and the toxins can lead to a reduction in survivorship, paralysis, reduction in growth and fecundity, biochemical alterations, and behavioral alterations (Ferrão-Filho and Kozlowsky-Suzuki 2011). The Clean Water Act (CWA) of 1972 requires that states establish water quality standards for all water bodies and, when water quality is impaired, set limits on the flow of problematic pollutants. Regulatory efforts to address the problem of impaired water quality in Lake Champlain began following the approval of the Lake Champlain Management Conference plan in 1996 by the states of New York and Vermont as well as the United States Environmental Protection Agency (EPA). In the late 1990s, the Vermont Department of Environmental Conservation (VTDEC) drafted a document that would set a Total Maximum Daily Load (TMDL) of phosphorus loading in the lake (EPA 2016). A TMDL is defined as the sum of individual waste load allocations for point sources, load allocations for nonpoint sources and natural background nutrient flow, plus a margin of safety to account for uncertainty (EPA 2016). The draft TMDL document was circulated for public comment in 2001 and approved by the EPA in 2002 (EPA 2016). Six years later the Conservation Law Foundation filed a lawsuit against the EPA stating that the 2002 TMDL was inconsistent with the CWA and should be made stricter. The Conservation Law Foundation and the EPA signed a settlement agreement by which the EPA would review the TMDL. In that review, the portions of the TMDL addressing the margin of safety and reasonable assurances that nonpoint source reductions would be achieved were found to be inadequate. With guidance from the State of Vermont and the general public, the EPA set a new TMDL for Lake Champlain. Vermont Act 64 was signed into law in 2015 as part of an implementation plan in order to reach the target load reductions for that TMDL (EPA 2016). Act 64 breaks Lake Champlain into twelve segments and, within each of them, identifies the most significant contributors to phosphorus loading. It sets a different TMDL for each segment and specific phosphorus reduction goals for each sector according to their contribution to phosphorus loading and feasibility of reduction measures. Segments of the lake with particularly poor water quality and watersheds that consistently do not meet their TMDL are identified as priority watersheds. Priority watersheds must create and stick to a comprehensive plan for meeting their TMDL by 2032 (EPA 2016). As of 2016, four priority watersheds were identified: McKenzie Brook watershed in Addison County, and Rock River, Pike River, and St. Albans Bay watersheds in Franklin County. While there are many sources that contribute to nutrient loading in the lake, there is significant concern about the levels of phosphorus that come from agriculture throughout Vermont. The EPA considers agricultural land in Vermont to be the largest source of phosphorus runoff into Lake Champlain, with an estimated 41% of the total load into Lake Champlain from Vermont, followed by streambank erosion (20%), developed land (18%), 7

8 forests (16%), and wastewater treatment plants (4%) (EPA, 2015; Lake Champlain Basin Program). The Vermont Agency of Natural Resources (ANR) identified agricultural land to be a sector of high risk for excessive amounts of phosphorus runoff (Vermont Agency of Natural Resources 2008). To address this, Act 64 also set forth a series of required agricultural practices (RAPs) meant to reduce phosphorus runoff from farms. The implementation of the RAPs is intended to help achieve the new TMDL of phosphorus (Act 64). While farms in Vermont were already required to undertake these practices, Act 64 broadened the requirements for small sized farms, requiring implementation of the RAPs on SFOs as well. At the time of writing these new regulations are set to come into effect in January However, little information exists about the actual efficacy of many of the practices being enacted to meet the new TMDL target load reduction for phosphorus. It is important that, as these practices are implemented, researchers are able to effectively observe their impact on phosphorus loading. Additionally it is important to understand to what degree the natural characteristics of the landscape contribute to phosphorus loading rates. A holistic understanding of the impact farming practices and land characteristics have on phosphorus is essential to inform future policy and regulation of agricultural practices. Our project aims to create a framework to collect data and monitor changes in the farming landscape, farming practices, and phosphorus loading for a particular priority watershed: McKenzie Brook, located in Addison County (Figure 1). Further, we hope that our model can assist in determining the relationships and impacts of these features on water quality in Lake Champlain. In this report, we first describe the investigative focus of our project. We then lay out the components of our data collection and analysis toolkit, and explain the process behind the development of this toolkit. Throughout this project, we worked with our community partners Kristin Williams and Jeff Carter from the University of Vermont Extension (UVM Extension), Ethan Swift from the Vermont Department of Environmental Conservation (VTDEC), and George Tucker from the United States Department of Agriculture Natural Resources Conservation Service (USDA/NRCS) to identify our specific study area and develop the direction of our project. 8

9 Figure 1. The McKenzie Brook watershed (highlighted in green) and respective water sampling points (highlighted in orange) correlated with average dissolved phosphorus concentrations collected by VT ANR between 2012 and The vertical green dashed line on the bar graph indicated the accepted in-stream total phosphorus concentration for Class B waters. Source: Homans, Harris, Niles & Raith,

10 Principal Investigative Focus The process of understanding the dynamic relationships between the many factors that contribute to high phosphorus levels in Lake Champlain is complex. In our effort to do so we acknowledge that we must be aware of this complexity and remain mindful of the fact that we are building upon the work of our project partners and other researchers. Our principal questions include: What is the most effective way to collect and analyze data on phosphorus levels, land characteristics and farming practices in the McKenzie Brook watershed at the farm level? In collecting information on farming practices, how can we do so in a manner that is sensitive to farmers individual experiences adopting the RAPs? How can we investigate phosphorus loading at the subwatershed level to better understand the multiple drivers of this complex phenomenon? How can we do this in a way that does infringe on farmers privacy? How can we use a Geographic Informational Systems (GIS) analysis to quantify important factors that may influence phosphorus levels? How can we statistically correlate our selected variables (for variables, see Table 1) with phosphorus concentration data to discover meaningful relationships? It is our hope that the answers to these questions can be explored using our Toolkit. The Toolkit We have compiled a research toolkit that can stand alone or be used in conjunction with other research methods to aide in the understanding of watershed and subwatershed dynamics leading to phosphorus loading. It includes a farmer survey and a water quality sampling protocol designed to be sent out to farmers annually, seasonally or as needed so that they can report on which practices they are currently undertaking on their land and collect water samples from streams on their property. The toolkit also includes a step-by-step GIS and statistical analysis how-to that our community partners can use to make sense of the information collected via the survey and sampling kit. This differs from the Tetra Tech Scenario Tool because it includes a spatial component and can be used to better understand the interactions of many factors that influence water quality. If successfully employed each year, the toolkit could potentially generate a large enough dataset to produce statistically significant correlations between the variables and levels of phosphorus where they exist. 10

11 Farmer Survey In order to better understand management practices on the ground, our partners at UVM Extension recommended the creation of a farmer survey. This survey is comprised of questions regarding the personal experiences of farmers with implementing new management practices. Additionally, it includes a detailed chart of land management practices pulled from the Act 64 RAPs and invites farmers to identify which practices they implemented from (See Appendix A). The information gathered in this survey is intended to be used to better understand how water quality has been influenced by land management practices (i.e. cover cropping) in conjunction with natural land characteristics (i.e. slope or soil type) in a way that is compatible with spatial analysis. In doing so, we collected data for all farms in Addison County willing to participate in our survey, thus adding to the data already collected by our community partners. We hope that this will better inform our analytical models and take steps to bridge gaps in information about practices in place on farms that may be unaccounted for in data currently available to our community partners (For results, see Appendix B or Farmer Survey Results, page 23). Water Quality Sampling Protocol We developed the water sampling protocol to furnish information about phosphorus loading on a subwatershed, farm-by-farm basis as well as in relation to specific land management practices identified as particularly of interest to our community partners. The complete protocol includes both a protocol for bracketed farm sampling to collect data on phosphorus levels above and below individual farms, and a protocol to collect data on the possible effects of tile drains on phosphorus concentrations in agricultural runoff (Appendix C). Tile drainage systems are implemented to reduce saturation in poorly draining soils. Drainage is imperative for crop growth, as saturated soil precludes sufficient aeration for root development (Wright and Sands 2001). These systems are considered an essential practice in many agricultural fields in the Lake Champlain Basin, where water management is a significant concern due to the high percentage of clay soils, which do not drain water easily. However, the excess water from crop fields in the Lake Champlain Basin eventually runs into the lake, along with any nutrients it contains. The Vermont Clean Water Act noted that tile drains may actually increase the phosphorus loading in Lake Champlain by increasing phosphorus concentrations and loading rates from fields with these systems in place (Act 64). However, phosphorus concentrations in subsurface runoff through tile drains may be affected by a wide range of factors, including soil type, field and tile drain system slope, and other land management practices (Sharpley et al. 1993). Using existing sampling protocols and a literature review, this sampling protocol was developed in the hopes of encouraging farmers to collect samples to provide information on the effects of tile drains and phosphorus loading. Ideally, the protocol would be implemented seasonally to collect data across a range of flow and weather conditions. Samples would then 11

12 be analyzed to provide total phosphorus concentration. This information could then inform land management practices to minimize the loss of phosphorus, a vital crop nutrient, from agricultural land. We intend this protocol to be useful in assessing loading rates from tile drains, as well as the comprehensive issue of phosphorus loss for a given parcel of land. With both the section for bracketed sampling and the section for tile drain sampling, any farmer can equally access an avenue to gain information about phosphorus loss from their farm, whether or not they implement tile drainage systems. This would lead to a knowledge base from which farming practices can be adapted to keep nutrients on individual farms based on the characteristics and reality of that farm. In addition, the sampling would help provide reliable data about the location and extent of tile drainage system use in the Lake Champlain Basin, data that do not currently exist. The details surrounding where these samples would be analyzed and sources of any needed funding were not researched as part of this project. Geographic Informational Systems (GIS) and Statistical Analysis To better understand the impact of land use and natural landscape characteristics on phosphorus loading in a given watershed, we created both a GIS and statistical model to measure and analyze these relationships. The purpose of this model is to measure the percent area of each of these variables with phosphorus loading levels so that a statistical analysis can be run to explore the impact of selected features on phosphorus loading at the subwatershed level. Our initial study focuses on three different subwatersheds within the larger McKenzie Brook watershed area. These watersheds are Braisted Brook, Hospital Creek, and Whitney Creek (Figure 2). We chose these specific subwatersheds based on advice from our community partners. Our community partners had collected some farming practice and land characteristic data, some of which is in GIS shapefiles and others in raw acreage, from farms in the McKenzie Brook watershed that they have worked with based on availability of public data and farmer willingness to provide information on private land. They advised us that our selected subwatersheds would provide us with enough variance in data to explore relationships between our variables (see Table 1) and phosphorus loading. By comparing land use, natural landscape, and phosphorus loading data between these subwatersheds over time, our model aims to create a framework to analyze the impact of our selected variables on water quality in Lake Champlain. The data collected from our survey along with our water quality sampling protocol can be plugged into our GIS and statistical model to interrelate our selected features and analyze their relationships with phosphorus loading. We hope that our model will encourage future research and analysis regarding the impacts of landscape characteristics, land use, and farming practices on water quality. 12

13 Figure 2. Our study focused on three subwatersheds of McKenzie Brook: Hospital Creek, Whitney Creek and Braisted Brook are the three subwatersheds. We relate land characteristics, farm layout and farming practices to average phosphorus levels at the VT DEC sampling points located at the mouths of each waterway shown above. Source: Allman, Epstein, Johansen, Mansour & Weisselberg,

14 Methods Farmer Survey We worked in collaboration with our project advisors and our community partners at UVM Extension, NRCS, and the VTDEC to design and distribute a survey that would effectively gather information about implemented management practices and also provide information about regional farmers perceptions surrounding these practices. The survey was made up of two parts. Part one asks a set of questions relating to farmers experience with water quality policy. Questions ranged from whether or not respondents utilize an NMP, to how often water quality factors into decision-making on the farm, to details concerning the most challenging aspects of complying with standards (for full list of questions, see survey in Appendix A). The second part of the survey invites farmers to volunteer information on all changes in land-based management practices they have undertaken from The survey is designed so that it can be carried out annually. Using GIS and statistical software, the information gathered in this survey can be used to better understand the ways in which land characteristics (i.e. slope, soil type), farm landscape characteristics (i.e. average farmstead size, average farmstead proximity to waterway) and farming practices (i.e. no-till farming, cover cropping) influence water quality in Lake Champlain. These data can be aggregated for each question to calculate total acreage (and in some cases other units of measure such as distance to waterway or farmstead size) in our 3 selected subwatersheds. The data collected in our survey can be combined with existing data to update selected variables (see Table 1) and better inform our statistical model. The survey went through 4 iterations. Recommended changes were incorporated into the final draft which was piloted at Monument Farms, Gosliga Farms Inc., and Foster Brother s Farm, before it was distributed in hard copy via mail to 250 farmers within Addison County (see Appendix A). Water Quality Sampling Protocol We developed a sampling protocol to provide a means of quantifying the effects of tile drains using sampling information from drain outlets, as well as bracketed sampling above and below farms to identify the cumulative effect of tile drain runoff on water quality of waterways adjacent to agricultural fields. The bracketed portion of this sampling protocol can also be implemented on farms without tile drains to provide farmers with information regarding the efficacy of the practices that are implemented on their farms. We developed the protocol using a synthesis of existing tile drain sampling protocols as well as ambient sampling protocols. Originally we had hoped to conduct sampling to collect data in order to analyze the effects of various land management practices and land characteristics on phosphorus loading. 14

15 However due to a lack of water flow at the approved monitoring sites, we were unable to do so. Over the course of this project, the sampling section instead focused on the creation of protocols that can be implemented in the future to provide useful information. The focus on tile drains is interesting to both us and our community partners, as data quantifying the effects of these systems on phosphorus loading would inform the future handling and treatment of tile drains. The background information collected for each sample is based on soil and land characteristics, land use practices, and aspects such as weather. In particular, soil type and phosphorus levels, agricultural management practices such as cover cropping and no-till and weather have been found to significantly impact phosphorus concentrations of runoff, especially in tile drain flow. Furthermore, these factors influence the way that water moves through the field (Winter et. al. 1998). Inclusion of these factors in the sampling data will allow for an informed analysis of phosphorus loss concentrations and therefore form an important element of the sampling protocol forms (see Appendix C). For the purposes of this project and the future usefulness of the information collected, sample collection, preservation, handling and analysis shall conform as closely as practicable to methods established by VTDEC (for information on VTDEC sampling methods, please consult Vermont Agency of Natural Resources 2014, and the VTDEC Watershed Management website). The sampling protocol is designed to be introduced by VTDEC or UVM extension through meetings with farmers as well as through practical lab field days held by VTDEC or UVM extension, when interested farmers can learn about the protocol, and the protocol can be practiced to ensure accurate sampling collection. The details surrounding where these samples would be analyzed and sources of any needed funding were not researched as part of this project. GIS and Statistical Analysis The initial phase of developing our analytical model involved collecting data within each selected subwatershed. The variables in our dataset can be broken into three broad categories: land characteristics, farm layout, and farm practices. A complete list of the 24 variables can be found in Table 1 below. Table 1. Variables included in our dataset Category Variable Units Land Characteristics Slope % Acres of subwatershed above significant slope level (8% grade) Land Characteristics Soil Type - Hydrologic Soil Group (HSG) % Acres of subwatershed in each HSG: A, B, C & D 15

16 Land Characteristics Soil Type - Erodibility % Acres of subwatershed consisting of soils that are: highly erodible, potentially highly erodible & not highly erodible Land Characteristics Land Cover % Acres in forest, agriculture, developed, & wetlands Farm Layout Ditch Network Total feet of ditch network within each subwatershed Farm Layout Farmstead - Number Total number of farmsteads within each subwatershed Farm Layout Farmstead - Size % Farmsteads that fall under each classification: SFO, MFO and LFO Farm Layout Farmstead - Proximity to Waterway Average distance in feet between the closest edge of a farmstead to the nearest waterway: stream, ditch & stream or ditch Farm Practices Tile Drains* % Acres of subwatershed that contain subsurface tile drains Farm Practices Continuous Corn % Acres of subwatershed planted in corn (at least 5 years) Farm Practices Continuous Hay / Corn- Hay Rotation % Acres of subwatershed planted as continuous hay (at least 5 years) or in a corn-hay rotation Farm Practices Pasture % Acres of subwatershed kept in grazing pasture Farm Practices Farm Practices Conservation Tillage (No- Till) Conservation Tillage (Low-Till) % Acres of subwatershed where notill practices were used % Acres of subwatershed where lowtill practices were used Farm Practices Cover Crops % Acres of subwatershed where cover crops were planted 16

17 Farm Practices Farm Practices Conservation Crop Rotation Manure Injection - Annual Crops* % Acres of subwatershed where conservation crop rotation was used % Acres of subwatershed where manure injection was used on annual crops Farm Practices Manure Injection - Hay* % Acres of subwatershed where manure injection was used on hay Farm Practices Nutrient Management Plan (NMP) % Acres of subwatershed operating under a NMP Farm Practices Prescribed Grazing* % Acres of subwatershed where prescribed grazing was used on pasture Farm Practices Grassed Waterway % Acres of subwatershed where vegetation is maintained in farm waterways Farm Practices Ditch Buffers* % Acres of subwatershed associated with a 10ft buffer along a ditch Farm Practices Stream Buffers % Acres of subwatershed associated with a 25 ft buffer along a stream Farm Practices Livestock Exclusion from Waterway / Fencing System* % Acres of subwatershed associated with a livestock exclusion/fencing system *There is currently insufficient data on these variables so they are not included in our initial analysis. Land characteristics are qualities of the land that remain constant on a large timescale (>50 years), such as soil type and slope. Farm layout variables describe the farming infrastructure that remains constant in the medium term (approximately years), such as farm size and drainage ditch networks. Finally, farm practices refer to the yearly or seasonal management practices involved in running a farm, which we expect to undergo changes in the future with the implementation of Act 64. Data on phosphorus levels were collected by the Vermont Department of Environmental Conservation (VTDEC) and can be accessed on their website ( This database includes data on phosphorus levels at two sampling points within the Hospital Creek subwatershed and two sampling points within the Whitney Creek subwatershed for the years There are data on phosphorus levels 17

18 at one sampling point in the Braisted Brook subwatershed for Additionally, our community partner at VTDEC provided us with preliminary data collected in all three subwatersheds during A summary of average phosphorus levels can be found in Table 2. In our analysis we only consider data from the sampling points at the mouths of each creek in order to represent the loading of the stream into Lake Champlain (Figure 2). While the use of multiple sampling points could provide insight into important processes such as instream sequestration that could be explored in the future, that analysis was beyond the scope of this study. The sampling points we received data for are shown below (Figure 3). Figure 3. All phosphorus sampling points for which we received data in the three subwatersheds of interest. Points include one upstream sampling point and one sampling point at the mouth of Hospital Creek, one upstream sampling point and one sampling point at 18

19 the mouth of Whitney Creek, and one sampling point at the mouth of Braisted Brook. Source: Allman, Epstein, Johansen, Mansour & Weisselberg, 2016 Table 2. Average levels of total phosphorus (ug/l) at the five VTDEC sampling points in the subwatersheds of Hospital Creek, Whitney Creek, and Braisted Brook (Vermont Department of Environmental Conservation, Watershed Management Division, 2015). Only the sampling points at the mouth of each waterway were used in the final analysis of variables. n reflects number of samples aggregated for each datapoint. Sampling Point *** Hospital Creek Mouth (n=5) (n=2) 309 (n=2) ND ND ND (n=2) Western Tributary to Hospital Creek (n=5) (n=4) 366 (n=3) ND ND ND 342 (n=3) Whitney Creek Mouth ND ND ND (n=5) (n=2) (n=3) (n=3) Whitney Creek Upstream (n=5) (n=4) (n=3) ND ND ND (n=4) Braisted Brook Mouth ND ND ND (n=5*) ND ND (n=6**) *Although there are data for 5 samples, on two dates (8/7 & 9/4) there were 2 samples taken. So this number reflects average P levels for 3 days out of the year. **Although there are data for 6 samples, on two dates (4/5 & 5/3) there were 2 samples taken. So this number reflects average P levels for 4 days out of the year. ***Data from 2016 are preliminary. Samples with average phosphorus data for 10 samples We decided to focus our analysis on the years because dairy farming in the McKenzie Brook watershed is changing, and it will continue to experience significant changes as RAPs are implemented to meet TMDL standards. In our analysis, we therefore had the ability to follow those changes over time, while acknowledging the possibility of decreased accuracy of these data if surveys were filled out based on recall rather than with reference to written records. In our dataset, the land characteristic and farm layout variables should remain constant within each subwatershed, while farm practices will change. Ideally, this would lead to a dataset of seven years for each of the three subwatersheds (n=21). However, currently many of these variables lack data and we only have average phosphorus data for 10 samples: these represent data for four years ( and 2016) in each 19

20 Hospital Creek and Whitney Creek, and two years (2012 and 2016) in Braisted Brook (these samples are marked with in Table 2). The farmer survey and water sampling kit were developed to aid in future collection of data to fill in these information gaps. It will do so by collecting farming practice data from all farmers in Addison County willing to fill out our survey or utilize our water quality sampling kit. In the meantime, we have set up our analysis framework such that those variables can easily be added to the analysis once the data are available. Our community partners at the USDA Natural Resources Conservation Services (NRCS) provided us with an initial data set containing aggregated land use data for each subwatershed. Measured in acres, these variables are represented as the state or degree of implementation for each practice we looked at. These variables include ditch network, farmstead location and size, continuous corn, continuous hay and corn-hay rotation, pasture, conservation tillage, cover cropping and nutrient management plan. Importantly, these data only include farms that received grants from NRCS in order to implement these practices. It does not account for farms that undergo these farming practices on an individual basis without support from NRCS. Again, we hope to resolve this issue by collecting information via our farmer survey. For the variables that were not provided to us by our project partners, which mostly included natural land characteristics such as slope and soil type, we performed a series of GIS operations to isolate each variable (see Appendix E). In calculating slope, for example, we isolated the points for each subwatershed in a percentage map of slope that were considered steep slopes, which were defined to be slopes greater than 8 percent (USDA/NRCS 2015). We then calculated the area (in acres) of the sections of each watershed in which the slope is greater than 8 percent. We normalized our variables in preparation for a statistical analysis by calculating the percent area of each variable, dividing each variable s area by the total area for the subwatershed in which it is measured. A majority of our variables were measured in acres, but there were a few exceptions such as farmstead proximity to waterway, farmstead size, and ditch network (see Table 1). By calculating percent area, as opposed to area in acres, our model accounts for the variance in subwatershed size and shape. Aggregating data on farm practices collected via the farmer survey presents another challenge. Given that farmers often own or rent different fields throughout the county and sometimes even across the bridge in New York, it is possible that survey respondents could own some land within our subwatersheds and some outside of them. We create a procedure for handling such a situation that is exemplified in by our methods for aggregating data on Nutrient Management Plans (NMPs). NRCS provided us with data on NMPs implemented on farms in our subwatersheds that received a grant from them. A NMP is a whole farm plan of practices, however when NRCS records the existence of a NMP they link it to just one field within that farm. For current information on NMP usage in the McKenzie Brook watershed for 2016 in the NRCS system, see Table 3. 20

21 Table 3. Current information on Nutrient Management Plans in McKenzie Brook watershed in Vermont, and in the three subwatersheds of Hospital Creek, Whitney Creek, and Braisted Brook in number of fields and acres recorded in Vermont Natural Resources Conservation Service records. Nutrient Management Plan 2016 (1 year lifespan) Fields Acres McKenzie Brook watershed (Vermont Portion) Total 4 58 Hospital Creek 2 17 Whitney Creek 2 41 Braisted Brook 0 0 The acres in Table 3 represent only the fields linked to the NMP in the NRCS system, however the NMP could actually cover many more fields and acres that are operated by that farm. Our partner at NRCS looked at the 4 fields within McKenzie Brook and worked backwards to figure out which farms own those fields and how many total acres are operated by each farm. Our partner at NRCS calculated the actual NMP acreage total to be This total includes farms that have land in both subwatersheds Whitney and Hospital Creek and includes some land across the lake in New York as well (though still in the McKenzie Brook watershed). Since we cannot know exactly how many acres in each subwatershed are associated with a NMP, we had to make an educated guess at the distribution of the acres. First we divided the total number of acres by 3 and assumed that ⅓ of the acres are in NY and ⅔ of the acres are in VT. We then estimated the distribution of the VT acres between Hospital and Whitney Creeks. To do this we looked at the ratio of the acres reported in Table 3 above (Hospital = 17 acres, Whitney = 41 acres) and multiplied the remaining ⅔ of the total McKenzie Brook NMP acres by those percentages (Hospital = 29.3%, Whitney = 70.7%) to estimate the total acres under a NMP in each of the subwatersheds (Table 4). Table 4. Calculations for distribution of land currently operating under Nutrient Management Plans in the McKenzie Brook watershed. Vermont New York x ⅔ = x ⅓ = x = acres in Hospital Creek x = acres in Whitney Creek 21

22 These acre totals were then normalized as percentages of the total area in each subwatershed just as with all other variables. This methodology provides an example for how to estimate the distribution of farm practices under uncertainty. After normalizing each variable, we then constructed a statistical model that operates under the ideal condition in which data exist for average phosphorus levels and all farm practice variables in all three subwatersheds during each year in consideration. We then plugged our limited data into this model to see if any significant findings existed. For our analysis, variables were initially grouped in a covariance matrix to determine which variables covary and which do not. This aided us in narrowing down the number of variables regressed against phosphorus data such that each variable provided us with unique information. Data percentages for variables deemed the most relevant through the covariance matrix according to the current data were slope, presence of a cover crop, use of no-till, percentage of land in agriculture, and distance from the edge of a field to the nearest waterway. These variables were analyzed in a multiple regression against average phosphorus level for a given subwatershed to discover any correlation between selected variables and phosphorus loading. It is important to note that the emphasis of our research is on the methodology of the statistical analysis, rather than on the results or statistical relationships analyzed. The results of the covariance matrix will likely change with the availability of new, augmented data. 22

23 Results and Discussion Farmer Survey We received responses from 21 of the 250 farms to which we sent our survey. 18 of these responses came from farms that are still in operation. The three that are not in operation had either been sold or the survey respondents were no longer farming. The low response rate was likely due to a combination of variables. Due to the fact that some farmers prefer communication methods other than paper mail, we hope that the survey will distributed via additional mediums (i.e. , an online version, over the phone, and in person) in the future. Our survey was sent out during a busy time of the year the week our surveys were delivered was the week of Thanksgiving, which is also hunting season and the requested time for completion and return of the surveys was one and a half weeks, which is relatively short and may have dissuaded potential respondents who did not complete the form before that time period had ended. It is important to note that none of the three post-operation responses were within our selected subwatersheds so we did not include even the historical management practices portion in our analysis. However, our project partners might want to include these responses in any analyses that extend beyond McKenzie Brook. The vast majority of responses came from SFOs. Two responses included additional hand-written notes, which elaborated on their responses to the survey with regard to their experiences of dealing with water quality. Additionally, respondents provided feedback on the quality of the survey itself. The feedback we received was generally supportive. While some farmers wrote enthusiastically about the practices they were undertaking to maintain water quality and reduce phosphorus loss, others expressed frustration with the thought of government on the farm and current regulatory systems. One farmer included a two page typed letter with the survey. The letter highlights the fact that many of the conservation practices in place on his farm were installed or utilized before It expresses the respondent s view of the survey s ability to acknowledge all of his past efforts, We have a long history of care for the environment that your survey does not capture. In the remainder of the letter he enumerates the various practices undertaken on his farm since It should be noted that this respondent identified himself as a turkey farmer. We address the fact that not all of our respondents were dairy farmers later in this section as well as in the conclusion. 23

24 Question 1: How often does water quality in Vermont lakes and streams factor into your decision-making on the farm? In response to Question 1, 65 % of survey respondents answered that water quality in Vermont lakes and streams factors into their decision-making on the farm very often (Figure 4). Figure 4. Responses to Question 1 of the Farmer Survey, regarding the impact of water quality as a factor in decision-making on farms (See Appendix A for survey, Appendix B for full results). These data are important as they show that improving water quality is indeed a priority for the majority of farmers in our sample and that maintaining water quality is a concern. In the early stages of our project, our community partners expressed to us that the majority of farmers work hard to maintain water quality in their daily farm operations. Responses to Question 1 prove that this is true for a significant majority of farmers who responded to our survey. As RAPs are implemented, it is important to understand the extent to which farmers are participating in efforts to meet TMDL goals. We hope that responses to Question 1 will continue to monitor changes in farmer participation in implementing RAPs and reducing phosphorus loading into Lake Champlain. These data can then be incorporated into conversations regarding the establishment of TMDL goals and the feasibility of accomplishing them. 24

25 Question 2: Are you aware of the upcoming standards for required agricultural practices (RAPs) meant to improve water quality in Vermont? When asked in Question 2 whether or not they were aware of the upcoming standards for required agricultural practices (RAPs) meant to improve water quality in Vermont, 94.4% of respondents were aware of the upcoming RAPs (see Figure 5 below). Figure 5. Responses to Question 2 of the Farmer Survey, regarding farmer awareness of upcoming standards for required agricultural practices (See Appendix A for survey, Appendix B for full results). This result, while somewhat expected, shows that farmers are aware of upcoming changes to policy concerning water quality. Communicating changes in policy to farmers will be increasingly important as RAPs are implemented and TMDL goals are set or adjusted. The fact that over 94% of farmers surveyed were aware of new regulations and standards underscore the effectiveness of efforts to communicate policy changes to farmers. We hope that over time, the data collected from responses to Question 2 can further inform conversations about the effectiveness of communication between policy makers and the farming community regarding changes or adjustments to policy. Question 3: How would you describe your experience with adopting the upcoming standards of water quality management practices on your farm? Responses to Question 3 provided important commentary on farmer s experiences implementing the RAPs. These data indicated that a majority of respondents (64.3%) describe their experiences meeting water quality standards in Vermont as somewhat difficult (see Figure 6 below). 25

26 Figure 6. Responses to Question 3 of the Farmer Survey, regarding farmers experiences adopting upcoming standards for management practices (See Appendix A for survey, Appendix B for full results). Being a dairy farmer in Vermont presents many challenges, particularly in 2016, a year in which milk prices were incredibly low and farmers often paid more to produce their milk than they earned from sales (Mansfield, 2016). This illustrates that the RAPs present an added challenge to the already taxing list of daily tasks on a farm. In order for regulations to be effective, they must also be feasible for farmers to implement. The data collected from responses to Question 3 can be used to analyze the efficacy of efforts to reduce phosphorus loading into Lake Champlain without harming the long-term viability of farms or the economic well-being of Vermont s dairy industry. Our community partners are working very hard to address the concerns of dairy farmers in meeting new standards and facilitating a smooth and economically feasible implementation of RAPs. We hope that responses to Question 3 can be used to share the voices and experiences of more farmers and provide data that can be considered in efforts to support farmers in implementing RAPs going forward. Question 4: How difficult has each aspect of complying with the upcoming state standards been for you? Affordability and time were the responses that indicated the highest degree of difficulty for farmers. For both of these aspects, 80% of respondents marked either somewhat difficult or very difficult (see Figure 7 and Figure 8 below). 26

27 Figure 7. Responses to Question 4 of the Farmer Survey, illustrating the difficulty in complying with standards for reasons of affordability (See Appendix A for survey, Appendix B for full results). Figure 8. Responses to Question 4 of the Farmer Survey, illustrating the difficulty in complying with standards due to a lack of time (See Appendix A for survey, Appendix B for full results). 27

28 As RAPs are implemented going forward, this section of our survey is designed to identify and monitor key challenges posed to farmers in meeting water quality standards. As policy and the economic market undergo changes, so too do challenges posed to Vermont s dairy farmers. We hope that this section of our survey will work to better inform the conversation regarding the effectiveness of RAP implementation and facilitate the incorporation of more farmers voices into policy decisions and adjustments. Questions 5-8: Do you use a nutrient management plan (NMP) to guide your farm operations? If yes, in which years did you follow the plan? How often do you refer to your plan to make direct decisions and updates? Please explain why you did, or did not, choose to follow a NMP. Of the 66.7% of our respondents that used a NMP, a majority (53.8%) referred to it weekly to guide on-farm operations (see Figure 9) Figure 9. Responses to Question 7 of the Farmer Survey, regarding how often farmers following nutrient management plans referred to those plans during the spring season (See Appendix A for survey, Appendix B for full results). These findings underscore the importance of NMPs to farmers that worked individually or with our community partners to create them. Figure 9 shows that 92% of the farms that have created a NMP continue to utilize it each spring (combining weekly and monthly consultation responses). Our community partners work incredibly hard to support farmers in their efforts to design and utilize NMPs effectively. We hope that data collected in Question 7 can be used to monitor the extent to which farmers use and sustain NMPs once they are created. Further, we also hope these data can be useful to our community partners in 28

29 their ongoing efforts to support the creation and use of NMPs among farmers in the McKenzie Brook watershed. A deeper understanding of not only the extent to which NMPs are used over time, but also of reasons that survey respondents did (for example, to build up the soil, and have better crops ) or did not choose to use a NMP (for example, against government on the farm ) can support future efforts to allocate resources most efficiently and improve communication between all parties involved in NMP design and implementation. Question 9-11: How is your farm size categorized? How many acres of your farm do you own? How many acres of your farm do you rent? When asked about the size of their farm in Question 9, a majority (75%) of respondents indicated that their farm was categorized as a small farming operation or SFO for short (see Figure 10 below). Figure 10. Responses to Question 9 of the Farmer Survey, regarding farm size as reported by survey respondents (See Appendix A for survey, Appendix B for full results). This question is particularly relevant as the upcoming standards will have a greater impact on SFOs than previous regulation has. Act 64 presents the first time that smaller farms (SFOs) will be required to follow specific management practices in Vermont. LFOs and MFOs have been required to operate under strict and specific guidelines since 1995 (State of Vermont, 2016). Further, although none of the farms that responded to our survey were located within our selected subwatersheds, responses to Question 9 can be incorporated into future analysis using our GIS and statistical model. Going forward, these data can be used to explore and better understand the relationship between average farmstead size and phosphorus loading rates for a given study area in McKenzie Brook. 29

30 The same is true in regards to responses to Questions 10 and 11 (10: How many acres of your farm do you own?; 11: How many acres of your farm do you rent?) This question is designed to present us with a better picture of the average farm size within our study area. Responses to this section of our survey can improve the accuracy of acreage calculations. By better understanding the percentage of total farmland that is operated by each farm, our GIS model can work more precisely to calculate the extent that certain land management practices are being applied on any given parcel of farmland. These data also help us avoid double counting in land characteristic and farm practice data. Given that certain farms have parcels of land in different subwatersheds within McKenzie Brook, data regarding the amount of acres that farmers operated in different subwatersheds can be useful in preventing future error in our GIS model. Question 12: In the last 5 years, have you completed any farmstead improvement practices to address water quality issues? Of the 34 total farmstead improvements mentioned, the most common farmstead improvement practice among our respondents was manure storage, which received 23.5% of responses (see Figure 11 below). Figure 11. Responses to Question 12 of the Farmer Survey, showing farmstead improvement practices implemented by survey respondents in the last 5 years to address water quality issues, as reported by survey respondents (See Appendix A for survey, Appendix B for full results). 30

31 Some farmers in McKenzie Brook believe that farmstead conservation practices are essential in maintaining water quality. Later in our survey (Question 15) when farmers were asked which management practices they believed to be the most important to maintaining water quality, 2 of the 15 responses indicated that certain farmstead practices listed above are among the most important conservation practices to reduce water contamination. As policy changes change, it is important to monitor which farmstead conservation practices have been the most effective for a majority of farmers in maintaining water quality. We hope that a deeper understanding of which farmstead practices have been the easiest for farmers to utilize and which of these practices are viewed as the most effective in reducing phosphorus loss can further inform conversations regarding the importance and efficacy of farmstead practices in reducing phosphorus loading in the McKenzie Brook watershed. We hope that this information can be applied to facilitate an effective and smooth transition in RAP implementation. Question 13: What method of communication do you prefer from regulators regarding changes in farming policy? When asked in Question 13 which form of communication farmers preferred from regulators regarding farming policy, a majority (14 farmers) indicated that they preferred paper mail (see Figure 12). Figure 12. Responses to Question 13 of the Farmer Survey, illustrating the preferred methods of communication as reported by the survey respondents. We acknowledge the bias that may be included in these results due to the dissemination and collection of our survey through paper mail (See Appendix A for survey, Appendix B for full results). 31

32 We hope that responses to Question 13 can be applied in efforts to expand or improve communication between policy makers and dairy farmers in McKenzie Brook. Responses to Question 2 of our survey indicate that over 94% of dairy farmers in our study area are aware of upcoming regulations. However, this statistic still indicates that there is a population of farmers that are unaware of recent policy changes and out of reach of current communication efforts. The data collected from Question 13, we hope, can be used to expand communications efforts and ensure that policy communication methods are in line with the preferences of dairy farmers. Questions 14: To the best of your knowledge, what brooks, streams or rivers does your farm land drain into? The geographic information collected from Question 14 was used to situate our GIS model and improve its accuracy. It is important to note that none of the farmers we received surveys from worked within our selected subwatersheds. For purposes of farmer confidentiality and privacy, we have not included these data in our report. Question 15: What land management practices were you using between ? Given that none of our survey respondents farmed in our selected subwatersheds, the management practice data could not be plugged into our GIS and Statistical model (as these were designed specifically for Hospital Creek, Whitney Creek, and Braisted Brook). However, we hope that as our study area is expanded to incorporate more subwatersheds within McKenzie Brook (or Vermont as a whole), the data we collected in our survey can be useful for future analysis. For these purposes, we have summarized and analyzed the land management data for 2016, so that our community partners and future students and researchers can use our 2016 results as a starting point in analyzing land characteristic data as the RAPs are implemented over time. A full summary of all data collected from our survey (from ) can be further explored in Appendix B. A summary of survey responses for 2016 can be found in Table 5 below. 32

33 Table 5. Total and average land management practice data (in acres) collected from farmer survey in the McKenzie Brook watershed (2016). Land Management Practice Total (acres) Average (acres per farm) Continuous Corn 707 (n=8) Continuous Hay 2130 (n=11) Hay - Corn Rotation 1715 (n=9) Pasture 780 (n=12) 65 Other (Millet/Winter Rye, Soy) 54 (n=5) 10.8 Reduced Tillage 1119 (n=5) No-Till 900 (n=6) 150 Cover Crops 1819 (n=7) Manure Injection on Annual Crops Manure Injection on Hay Fields 1000 (n=6) (n=4) 10 Prescribed Grazing 680 (n=9) Tile Drainage 525 (n=8) Of the 21 total survey respondents, 9 partially or completely filled out the land management practice chart. The number of respondents varied for different practices or sections of the chart as is shown by the variance in sample size (n) for each row in Table 5 above. Cover Crops and Reduced Tillage were the variables that showed the highest average acreage per farm. We hope that this information can be useful to our community partners in that it provides insight into the extent to which farmers are applying certain practices. These data can be used to deepen future analyses and efforts to understand the feasibility and efficacy of specific land management practices for farmers in McKenzie Brook. In this section of the survey, farmers were also asked to indicate whether or not they had received assistance from our community partners at NRCS and UVM Extension in implementing certain land management practices. The results to this question for a selected group of practices in which farmers had received assistance from our community partners is shown in Table 6 below. 33

34 Table 6. Percent of farmers that received assistance from NRCS or UVM Extension for selected land management practices Land Management Practice Percent of survey respondents that received assistance from NRCS or UVM Extension Reduced Tillage 20% No-Tillage 60% Cover Crops 60% Prescribed Grazing 37.5% Our community partners have shown substantial commitment in supporting farmers in their efforts to meet TMDL goals and implement RAPs. It is important to reiterate that the initial land management practice data that we received from our community partners only accounted for farms that they had collaborated with in the past. Thus, a central objective of our survey was to expand the availability of land management practice data to account for farmers that are implementing certain practices individually or in collaboration with other parties. Table 6 shows that for several variables, there are a substantial amount of farms that implement conservation practices individually or without assistance from our community partners. As the RAPs are implemented and utilized going forward, it is important to consider land management data not only from farms that work in collaboration with UVM Extension or NRCS, but also from farms that are working individually to implement certain practices. At the end of the survey, farmers were asked to list the management practices they perceive as most important for maintaining water quality. The most common response was cover cropping, which accounted for 42% of the practices mentioned (see Figure 13 below). 34

35 Figure 13. Responses to final question of the Farmer Survey, regarding which practices survey respondents considered the most important for maintaining water quality (See Appendix A for survey, Appendix B for full results). Responses listed as other in response to this question ranged from permanent sod, to tile drainage, to hay-corn rotation. Although there was significant variance in responses to this question, overall our findings suggest that cover cropping and grassed waterways were the two practices that farmers were most likely to believe are the most important practices in maintaining water quality. We believe that this information can facilitate ongoing efforts to incorporate farmers voices and opinions into policy decisions and adjustments. By providing a better understanding regarding which land management practices farmers believe to be important, we hope that these data can help future policymakers and regulators allocate resources and efforts most effectively. In different parts of the survey, many farmers expressed strong opinions against government involvement on the farm. One farmer expressed his or her opposition to government involvement simply on principle. Years ago I was told by an old farmer never to let the government in your yard. It has worked very well. Another stated his or her discontent with the usefulness of on-farm visits from state actors. I have had NRCS Engineers out to the farm to determine exact location where the RAPs require action and even they could not give me an answer. A third farmer indicated his or her frustration with the way that regulations change and his or her feeling of being scrutinized by the government. 35

36 It is frustrating since we have been an LFO working with the state to maintain compliance, yet constantly are under scrutiny on a constantly changing concept and comprehension of standards. These types of opinions are extremely valuable and it is important that these farmers voices be heard so that government-farmer relations can be improved. Finally, the results from the very last question on our survey are worth discussing in greater detail. It is interesting that cover cropping was the practice listed most frequently as it happens to be the only farming practice in our statistical analysis found to have a significant impact on phosphorus levels. Beyond the results themselves, this question is essential to a more complete understanding of phosphorus loading. It captures a different type of knowledge that is not necessarily scientific or mathematical like the knowledge produced by our GIS and statistical analysis. The information collected in this question of our survey is rather based on personal experience. For example, one farmer wrote, most of these practices have agronomic value to farmers far greater than the water quality benefits they produce. Mandating them in the name of water quality could result in far worse unintended consequences if not done properly. There are other regulations in the RAPs that I believe are causing [water quality] problems rather than solving them. Additionally, it will be interesting to observe how farmers opinions change over time, how they line up or do not line up with available scientific information, and how these two types of knowledge may influence each other. In reviewing the comments provided to us by the survey respondents, we came to some important realizations about our survey design. Firstly, we wrote our survey with dairy farms specifically in mind since this was the focus of our class. However, there are many other types of farms within Addison County and the upcoming water quality standards are all encompassing of the agricultural sector. In fact, we received one survey from a goat farmer and another from a turkey farmer. We concluded that significant adjustments should be made to the survey so that it is more universal and includes questions pertaining to all types of farms. For example, Question 9 about farm size should not only include the categorical breakdown based on the number of dairy cows on a farm, but also categorical breakdowns for the number of other livestock and acreage of vegetables. Additionally, Question 12 about farmstead improvements should be prefaced by some qualifier like if you operate a dairy farm and similar questions pertaining specifically to other types of farms should be included as well. We also received helpful feedback from farmers during our presentation of our project to the Champlain Valley Farmer Coalition. Based on this feedback, we hope that future iterations of the survey will account for factors such as density of cows as well as temperature or climate. Another important takeaway from the survey responses is that in future iterations, there should be a question included that allows farmers to describe a bit about the history of their farm. Many farmers take pride in their stewardship of the land and have been committed to environmental sustainability throughout their entire career. For example, below Question 6, which asked farmers to indicate which years they followed an NMP in the time period of , one farmer wrote, We have been following our own nutrient management for 40 years. While we would not suggest adding columns to the chart in the 36

37 last question beyond six years into the past since recall issues could affect the accuracy of acres reported, we do believe it is important that farmers be recognized for their historical efforts to maintain water quality. Given the constraints of the survey, we acknowledge that it may not be the best place for a deep engagement with each farmer's history, but adding some flexibility beyond the table s pre-established time-frame could help elucidate the historical depth of BMPs (as well as other types of practices) where applicable. GIS and Statistical Analysis The last component of our toolkit was a GIS and statistical analysis that observes relationships between phosphorus loading and land characteristics, farm layout, and farm practices of our three subwatersheds from We observed variation across all of our variable categories (see Tables 7, 8, and 9) for our three selected subwatersheds in This variation is more significant for certain variables, such as slope and highly erodible soil type, than it is for others, like agricultural land cover or percent acres in no-till farming. For the land characteristic variables in our 2016 dataset, slope, highly erodible soil, and not highly erodible soil showed the most significant variability across our selected subwatersheds with average variances of 9.01%, 14.29%, and 25.92% respectively. This metric is calculated by measuring the percent difference for each variable across our selected subwatersheds. These percent differences are aggregated and then divided by three to calculate the average variability across the subwatersheds. The land characteristic variables that show the least significant variability across our subwatersheds are agricultural land cover, hydrologic soil group B, and hydrologic soil group A, with average variances of 2.65%, 0.58%, and 0% respectively. Further, it should be noted that there are no acres of hydrologic soil group A in all of our three watersheds based on our geographic data. 37

38 Table 7. Percent Area for Land Characteristic Variables in Hospital Creek, Whitney Creek, and Braisted Brook in Hospital Creek (2016) Whitney Creek (2016) Braisted Brook (2016) Slope (% Acres of slopes >8%) Soil Type (% Acres HSG A) Soil Type (% Acres HSG B) Soil Type (% Acres HSG C) Soil Type (% Acres HSG D) Soil Type (% Acres HSG C & D) Soil Type (% Acres highly erodible Soil Type (% acres Not highly erodible Soil Type (% Acres potentially highly erodible) Land Cover (% Acres Forest) Land Cover (% Acres Agriculture) Land Cover (% Acres developed) Land Cover (% Acres wetlands * HSG: Hydrologic Soil Group 38

39 Given that the units for the farm layout variable group (see Table 8 below) differed across variables (i.e. percent area versus total feet), we were unable to compare average variance for our subwatersheds across different variables. However, our analysis of each individual farm layout variable shows significant variance across our three subwatersheds. For example, the average variance in farmstead proximity to either a stream or a ditch is feet. Braisted Brook shows the highest average farmstead distance to either a stream or ditch (1005 ft), while Hospital Creek shows the lowest average distance (2 ft). However, it should be noted that data for only one farmstead is available to us within the Braisted Brook subwatershed. The variability across subwatersheds for our selected farm layout variables can be further explored in Table 8 below. Table 8. Farm Layout Variables. Distribution of layout variables by subwatershed. Hospital Creek (2016) Whitney Creek (2016) Braisted Brook (2016) Ditch Network (Total feet) Total Number of Farmsteads Farmstead Size (%SFO) Farmstead Size (%MFO) Farmstead Size (%LFO) Farmstead Proximity to Waterway (Average distance in feet to stream) Farmstead Proximity to Waterway (Average distance in feet to ditch) Farmstead Proximity to Waterway (Average distance in feet to stream or ditch)

40 The farm practice variables that show the highest average variance across our selected subwatersheds are conservation crop rotation, continuous hay/ corn-hay rotation, and nutrient management plan, with average variances of 9.39%, 28.35%, and 45.71% respectively. It is important to note that the nutrient management plan data are generated from projections based on limited available data. Our methodology for estimating nutrient management plan data are explored in further depth in our methods section above (see page 21). The farm practice variables that show the lowest average variance across our selected watersheds are pasture, no-till farming, and low-till farming, with average variances of 0.87%, 0.73%, and 0.38% respectively. It should be noted that our dataset shows zero acres in low-till farming in Hospital Creek and Braisted Brook, and zero acres in no-till farming in Braisted Brook. The variances for our selected farm practice variables across the three subwatersheds can be further explored in Table 9 on the following page. 40

41 Table 9. Farm Practice Variables. Percent of acreage in each subwatershed currently under each farming practice. Hospital Creek (2016) Whitney Creek (2016) Braisted Brook (2016) Tile Drains (% Acres) Continuous Corn (% Acres) Continuous Hay/ Corn-Hay Rotation (% Acres) Pasture (% Acres) No-Till (% Acres) Low-Till (% Acres) Cover Crops (% Acres) Conservation Crop Rotation (% Acres) Manure Injection (% Acres injected for annual crops) Manure Injection (% Acres injected for hay) Nutrient Management Plan (NMP) (% Acres) ND ND ND ND ND ND ND ND ND

42 Prescribed Grazing (% Acres) Grassed Waterway (% Acres) Ditch Buffers (% Acres) Stream Buffers (% Acres) Livestock Exclusion from Waterway/ Fencing System (% Acres)* ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND *ND: There is insufficient data for these variables as of 2016 We ran a covariance matrix to identify variables that covary and would therefore provide redundant information in analysis. This supported some logical relationships, such as the significant covariance of soil types (All soil types, p<0.001, Covariance). The matrix also revealed significant positive correlations between percent area with a significantly steep slope and percent of forested land (p<0.01, Covariance). Based on the results of the covariance matrix, we chose variables that varied independently to analyze in relation to phosphorus sampling data by subwatershed. The variables chosen in this case were cover cropping, proximity to waterway, percent land in agriculture, and slope. We found a trend of increased acreage in cover cropping correlating with decreased phosphorus levels (Percent acres cover cropped: p=0.098, Linear Regression). There was also a significant negative relationship between proximity to waterway and phosphorus levels, with increased distance of farmstead edge from waterways correlating with lower phosphorus levels (Average feet to closest waterway: p=0.043, Linear Regression). In addition, a trend of lower phosphorus levels in subwatersheds with higher percent of steeply sloped acreage was observed (Slope: p=0.081, Linear Regression). While most of these results are fairly intuitive increasing cover cropping focuses on minimizing erosion by preventing open fields and improving soil stability, and increasing distance from the edge of a field to the nearest waterway minimizes the direct runoff from fields to waterways slope as a variable is more complicated. It seems logical that a steeper slope would lead to more runoff, as water is more likely to travel through the soil and pull nutrients into waterways. However, our analysis showed that in our three study subwatersheds, an increase in the amount of land that is steeply sloped was correlated with a trend in lower phosphorus levels at the sampling points for the subwatershed (Total 42

43 phosphorus by percent land steeply sloped, p=0.081, Regression). Therefore, while the Braisted Brook subwatershed had the most area of steeply sloped land (p<0.001, ANOVA; 33.5% of land in the Braisted Brook subwatershed has a slope greater than 8%), it also had the lowest phosphorus levels (See Table 2 and Table 7). We found that this could be explained by the significant relationship between slope and forested land in the subwatersheds. While all three subwatersheds had similar percentages of land in agriculture, Braisted Brook had significantly more land in forest (p<0.001, ANOVA) and there was a significant positive relationship between land that was in forest and land that was steeply sloped (p<0.001, Regression; See Table 7) Therefore, the steeply sloped land, which we would hypothesize was at great risk of erosion and runoff, is largely the same land that is kept in forest, which stabilizes the soil and also minimizes activities that might increase phosphorus loading from that soil. This co-occurrence is less by design than a reflection of the fact that steep land is more technically difficult to develop, farm, or even clear of forest. Figure 14. The shaded areas in the image on the left show steep slopes around the Braisted Brook waterway. These same steep areas around the waterway are forested, as can been seen in the satellite imagery on the right. 43

44 This finding has important implications. It provides some evidence for the effectiveness of forests in preventing erosion and of riparian buffers around streams in preventing phosphorus from entering waterways. Additionally, it demonstrates the ability of the toolkit to tease apart some of the relationships between our selected variables and phosphorus loading. We know that Braisted Brook has a similar percent area in agriculture as the other two subwatersheds. Although we do not know for certain, if we suppose that the farming practices undertaken in Braisted Brook are also similar to those undertaken in Hospital Creek and Whitney Creek, this finding would suggest that the amount of phosphorus entering Braisted Brook has less to do with on-farm practices and much more to do with the physical land characteristics of the subwatershed. However, mimicry of those natural characteristics in on-farm practices could lead to similar reductions in phosphorus loading. These results reflect the analysis of a very limited dataset. Through future implementation of the farmer survey and water sampling protocol, the relationships between variables will become clearer. In addition, analysis with a greater sample size will provide more reliable results with a likelihood of increased significance. These results are useful and important in informing both on farm practices and policy, as all members of the system will be able to assess efficiency and relative benefit of practices, therefore leading to a system that is more effective and more equitable, as farmers will be able to focus effort and funding on the practices that will reduce phosphorus the most. 44

45 Conclusion The Toolkit: Tying it All Together At the beginning of our project, we had little information about what farming practices were taking place and where within the McKenzie Brook watershed. There was, and still is, a lack of data on phosphorus levels at upstream sampling points and around farms. Additionally, little information is available on the land characteristics of our area of focus. The impetus behind the creation of the toolkit was to address these apparent gaps of knowledge in order to better understand the interplay between land characteristics and management practices and how they affect phosphorus loading. Our goal was to collect as much information in the limited time we had while concurrently setting up a system of collection that can be carried out in the future. Filling in the gaps of information about the system to achieve a comprehensive understanding of subwatershed dynamics and phosphorus loading is a task that will take many years to complete. We developed a three-part toolkit consisting of a farmer survey, a water sampling protocol, and a GIS and statistical analysis how-to. The farmer survey was sent out to 250 farmers in Addison County and received 21 responses. Given our strict time constraints, we decided not to send the water sampling protocol we developed out with the surveys. Instead we summarized VTDEC water quality data at sampling points within our subwatersheds. With GIS software, we analyzed 18 variables using data provided to us by our community partners. Since none of our survey respondents manage land within the three subwatersheds we focused on, we were unable to incorporate any results from the farmer survey into this analysis. We ran a statistical analysis with the limited information available to us on phosphorus, land characteristics, farm layout and farming practices and discovered some interesting trends: the area of land in cover cropping showed a trend of a negative relationship with levels of phosphorus (increased cover cropping correlating with decreased phosphorus levels, p=0.098), an increase in distance of an agricultural field from a waterway was significantly associated with lower levels of phosphorus (p=0.043), increased area of steep slopes showed a trend of decreased phosphorus levels (p=0.081), and area of steep slopes showed a significant positive relationship with area of land in forest (p<0.001). While we did find interesting results from the first implementation of our toolkit that can begin to address the issues surrounding the relationship of phosphorus loading, land characteristics, and farming practices, the main objective of our work was to develop a toolkit that stands alone and can be used by anyone who wishes to efficiently and effectively collect and analyze information on subwatershed dynamics in the future. The results discovered though this initial implementation are evidence that we have achieved this goal. 45

46 Potential Applications for this Research In order for the toolkit to produce significant relationships between selected variables and contribute to the understanding of subwatershed dynamics affecting phosphorus loading in Lake Champlain, we recommend that it be implemented regularly in the future, either annually or biannually. This will enable sufficient data collection to input into our analytical framework and potentially inform future conversations around the issue, perhaps even policy. Observing which on-farm practices are most effective at mitigating phosphorus runoff within the specific geographic context of the McKenzie Brook watershed and discovering the subwatersheds in which farming practices have the most direct impact on phosphorus levels can help prioritize cost share programs to facilitate RAP implementation. Farmers wishing to take action can stand to gain a clearer understanding of where they can invest their time and money most effectively. Although the first implementation of the toolkit focused on three subwatersheds - Hospital Creek, Whitney Creek and Braisted Brook - within one priority watershed, this same framework is intended to be used to analyze the dynamic relationships of factors affecting phosphorus levels in any subwatersheds of McKenzie Brook, other priority watersheds, and even in other areas of the US. Additionally, as the results of farmers water sampling are added to the database of phosphorus levels at upstream points, this analysis can subsequently be carried out at smaller scales to get a more accurate picture of localized subwatershed dynamics. Recommendations for Further Study Our analytical framework will be useful in adding to the conversation about water quality in Lake Champlain and fostering a dialogue between actors, especially with regards to the roles of agriculture (specifically dairy farms) and land characteristics in phosphorus loading from the McKenzie Brook watershed. However, more research is still needed to contribute to a holistic understanding of the issue. In order to ensure that the collection of new information in the future is done in a manner that is efficient and sustainable, it would be advisable to establish some form of a common resource database where information can be aggregated across departments. In addition to continuing the collection of data, we recommend that future researchers look into the effects of weather on phosphorus loading in the face of uncertain weather patterns. Extreme rain events, drought, and changing weather patterns caused by climate change will likely affect the way that phosphorus moves through watersheds, as water dynamics are intrinsically linked with phosphorus loading in waterways. Looking at the relationship between weather and phosphorus levels in waterways was beyond the scope of this study, but may offer insight on another facet of subwatershed dynamics that deserves further exploration. Further, a better understanding of the relationships between hydrologic soil group (HSG), soil P content, proximity to waterways, and the extent of riparian buffers should also be explored in more depth in future studies. 46

47 Finally, we recommend a further exploration into the ways that some of the burden of mitigating phosphorus runoff from farmland can be spread to the final consumer of agricultural products and/or the public at large. Given that the maintenance of a clean lake is beneficial to the public and that compliance with new water quality standards is challenging for farmers, the public could make an impact in the reduction of phosphorus loading from agricultural land by actively supporting farmers. Whether this means increased prices paid by consumers for agricultural products in order to support farmers or other forms of public participation, this presents an interesting social research opportunity with potential for community building and community-based success. 47

48 Acknowledgements David Allen, Assistant Professor in Biology, Middlebury College Mez Baker-Medard, Assistant Professor in Environmental Studies, Middlebury College Jeff Carter, Extension Assistant Professor: Agronomy Specialist Field Crops and Nutrient Management, UVM Extension Bill Hegman, GIS Specialist/Teaching Fellow, Middlebury College Diane Munroe, Coordinator for Community Based Environmental Studies, Middlebury College Kip Potter, Water Quality Specialist, USDA/NRCS Reed Sims, GIS Specialist, USDA/NRCS Ethan Swift, Watershed Coordinator, Watershed Management Division, VTDEC George Tucker, Soil Conservationist, USDA/NRCS Kristin Williams, Agronomy Outreach Professional, UVM Extension 48

49 Works Cited Act No. 64. H June VT LEG # Altieri, M. A., Ecological Impacts of Industrial Agriculture and the Possibilities for Truly Sustainable Farming. Monthly Review, 50(3): Bruckner, Monica. The Gulf of Mexico Dead Zone. Microbial Life Educational Resources. Montana State University. (accessed November 2016). EPA, Health and Ecological Effects. US Environmental Protection Agency. Web, October (accessed December 2016). EPA, Phosphorus TMDL for Vermont Segments of Lake Champlain. United States Environmental Protection Agency. Available from: (accessed November 2016). EPA, National Water Quality Inventory Report to Congress. United States Environmental Protection Agency. Available from: (Accessed December 2016). Ferrão-Filho, A. da S., and B. Kozlowsky-Suzuki, Cyanotoxins: Bioaccumulation and Effects on Aquatic Animals. Marine Drugs, 9(12): Ghebremichael, L. T., and M. C. Watzin, Identifying and Controlling Critical Sources of Farm Phosphorus Imbalances for Vermont Dairy Farms. Agricultural Systems, 104: Hoagland, P., D. M. Anderson, Y. Kaoru, and A. W. White, The Economic Effects of Harmful Algal Blooms in the United States: Estimates, Assessment Issues, and Information Needs. Estuaries and Coasts, 25: Lake Champlain Basin Program, Where does the phosphorus in Lake Champlain come from? (Accessed December 2016). 49

50 Mansfield, Erin, Vermont Dairy Farmers Struggle as Milk Prices in the Northeast Drop. VT Digger. (Accessed December 2016). Owen, James. World s Largest Dead Zone: Suffocating Sea. National Geographic News. 6 March (Accessed November 2016). Rabalais, N. N., R. E. Turner, and W. A. Wiseman Jr., Gulf of Mexico Hypoxia, a.k.a. The Dead Zone. Annual Review of Ecology and Systematics, 33: Sellner, K. G., G. J. Doucette, and G. J. Kirkpatrick, Harmful Algal Blooms: Causes, Impacts, and Detection. Journal of Industrial Microbiology and Biotechnology, 30: Sharpley, A. N., S. C. Chapra, R. Wedepohl, J. T. Sims, T. C. Daniel, and K. R. Reddy, Managing Agricultural Phosphorus for Protection of Surface Waters: Issues and Options. Journal of Environmental Quality, 23(3): State of Vermont, Regulations for Large Farm Operations Web. (Accessed December 2016). USDA/NRCS, 2016 (1). Resource Assessment and Watershed Level Plan for Agriculture in the McKenzie Brook Watershed, Addison County, Vermont. USDA/NRCS, 2016 (2). Vermont NRCS Strategic Watershed Planning Approach. (Accessed December 2016). Vermont Agency of Natural Resources, Progress in Establishing and Implementing the Total Maximum Daily Load (TMDL) Plan for Lake Champlain: Submitted to the Vermont General Assembly in Accordance with Act 43 (2007), Section 4. Vermont Agency of Natural Resources and Vermont Agency of Agriculture, Food, and Markets. Waterbury, Vermont. Vermont Agency of Natural Resources, Vermont Water Quality Standards Environmental Protection Rule Chapter 29(a). Vermont Agency of Natural Resources, Department of Environmental Conservation, Watershed Management Division. Available from: 50

51 Vermont Department of Environmental Conservation, Watershed Management Division, Vermont Integrated Watershed Information System. (Accessed November 2016). Winter, T. C., J. W. Harvey, O. L. Franke, and W. M. Alley, Ground Water and Surface Water, a Single Resource. U. S. Geological Survey Circular Denver, CO. U. S. Government Printing Office. Wright, J., and G. Sands, Planning an Agricultural Subsurface Drainage System. University of Minnesota Extension Service. Agricultural Drainage Publication Series. 51

52 Appendix A.: Farmer Survey Appendices Environmental Studies 401 Middlebury College Middlebury, Vermont Dear Addison County Farmer, We are Middlebury College students working to better understand Vermont s dairy industry. In our capstone environmental studies class, we have been learning about changes in farming practices and are hoping to gain better insight into farmers perspectives. Your participation in this survey is voluntary and your information will be kept confidential. The information collected will not be used for regulatory purposes, but rather to help service providers in their outreach efforts. Accurate responses will assist partners in Addison County to better serve farmers and represent your adoption of practices. This survey should take approximately 25 minutes. You may want to have records on hand in order to answer questions regarding farm management practices. You have the option to provide your name. No names will be used in the final report either way. In our final report, we will use descriptors like Farmer A instead of a farmer s name. Through spatial analysis (i.e. map making), we will use this information to better understand how water quality is influenced by both the natural characteristics of land (i.e. slope or soil type) and land management practices. We will represent farming practices without showing information about each individual farm, but rather present summarized information on a regional scale. Data from our final results will be presented in an anonymous form to community members. Please complete and return this survey by no later than Monday, November 28th, If you have additional questions, or would like a copy of our final product, feel free to contact us (h2oqualitymidd@gmail.com) or our project advisor (dmunroe@middlebury.edu; ). Thank you so much for your participation! I have read and understood the above I consent to participating in this survey 52

53 Farm Name & Address (optional): For each of these questions please mark the answer you feel best describes your experience. 1. How often does water quality in Vermont lakes and streams factor into your decision-making on the farm? Never Infrequently Often Every day 2. Are you aware of the upcoming standards for required agricultural practices (RAPS) meant to improve water quality in Vermont? Yes No 3. If yes, how would you describe your experience with adopting the upcoming standards of water quality management practices at your farm? If no, please continue to question 5. Very easy Easy Somewhat difficult Very difficult 4. How difficult has each aspect of complying with the upcoming state standards been for you? 53

54 Please indicate difficulty (1 = very easy, 2 = easy, 3 = somewhat difficult, 4 = very difficult) Understanding expectations from the state Affordability Time Access to technology (gocrop app, etc.) Other (please specify below) 5. Do you use a nutrient management plan (NMP) to guide your farm operations? If no, skip to question 8. Yes, year created: No 6. In which years did you follow the plan? (please circle)

55 7. How often do you refer to your plan to make direct decisions and updates? Spring... daily weekly monthly Summer... daily weekly monthly Fall... daily weekly monthly Winter... daily weekly monthly 8. Please explain why you did, or did not, choose to follow a NMP? 9. How is your farm size categorized? (check one) LFO (700 + adult dairy cows) MFO ( adult dairy cows) SFO (1-199 adult dairy cows) 10. How many acres of your farm do you own? 11. How many acres of your farm do you rent? 12. In the last 5 years, have you completed any farmstead improvement practices to address water quality issues? (check all that apply) Manure Storage Diverting clean water runoff away from manure sources or stored feed areas Barnyard runoff collection and storage Silage leachate runoff separation or collection and storage Milkhouse/parlor waste collection or storage Other: 13. What method of communication do you prefer from regulators regarding changes in farming policy? (Check all that apply) Paper mail Social media Dairy cooperative communications On farm visits Local coalition meetings UVM Extension Website Other suggestions: 55

56 14. To the best of your knowledge, what brooks, streams or rivers does your farm land drain into? Answers from this question will be used to place your information within a given watershed. 15. What land management practices were you using between ? Please enter your responses on the opposite side of this page. 56

57 Crop System Continuous Corn (5 years or more) Continuous Hay (5 years or more) Hay - Corn Rotation Pasture Other crop(s): In Field Practice Reduced Tillage (spring, no more than 2 tillage passes) No-Till Cover Crops (on annual cropland) Manure Injection on Annual Crops Manure Injection on Hay Fields Prescribed Grazing (paddocks/rotation based upon feed availability) Tile Drainage System Other Practice(s): Please indicate the number of acres you have implemented for each practice Please indicate yes / no whether you have implemented a practice (include feet, if possible) Did you receive assistance from NRCS or UVM Extension to do this practice: (please circle) NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none Field Waterways In-Field Grassed Waterways Ditch Buffer - 10 ft permanent vegetation Stream Buffer - 25 ft permanent vegetation Please indicate yes / no whether you have implemented a practice (include feet, if possible) Livestock Exclusion - Fencing Of the practices listed above, which do you feel are most important for maintaining water quality? 1. NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none NRCS UVM Ext. none

58 Appendix B.: Summary of Farmer Survey Results Question 1: How often does water quality in Vermont lakes and streams factor into your decision-making on the farm? Infrequently: 5%, Often: 65%, Every Day: 30% Question 2: Are you aware of the upcoming standards for required agricultural practices (RAPs) meant to improve water quality in Vermont? No: 5.6%, Yes: 94.4% Question 3: How would you describe your experiences with adopting the upcoming standards of water quality management practices? Very Easy: 7.1%, Easy: 21.4%, Somewhat Difficult: 64.3%, Very Difficult: 7.1% Question 4: How difficult has each aspect of complying with the upcoming state standards been for you? Understanding expectations: Very Easy: 14.3%, Easy: 14.3%, Somewhat Difficult: 64.3%, Very Difficult: 7.1% Affordability: Very Easy: 6.7%, Easy: 20%, Somewhat Difficult: 60%, Very Difficult: 13.3% Time: Very Easy: 6.7%, Easy: 13.3%, Somewhat Difficult: 53.3%, Very Difficult: 26.7% Access to Technology: Very Easy: 23.1%, Easy: 23.1%, Somewhat Difficult: 38.5%, Very Difficult: 15.4% Question 5: Do you use a nutrient management plan (NMP) to guide your farm operations? If no, skip to question 8. No: 33.3%, Yes: 66.7% Question 6: In which years did you follow the plan? Created and followed before 2010: 16.7%, Created and followed from : 83.3% Question 7: How often do you refer to your plan to make direct decisions and updates? 58

59 Never: 7.7%, Monthly: 38.5%, Weekly: 53.8% Question 8: Please explain why you did, or did not, choose to follow a NMP. Responses ranged from Helpful to identify parts of farm that need nutrients to I m against government on the farm to Small farm did not need one. The most common reason for not using an NMP was that their farm was too small to need a NMP. The most common reason for using an NMP was that it helps balance nutrient inputs. Question 9: How is your farm size categorized? SFO: 75%, MFO: 12.5%, LFO: 12.5% Question 10: How many acres of your farm do you own? Total: 11353, Average: Question 11: How many acres of your farm do you rent? Total: 3495, Average: 233 Question 12: In the last 5 years, have you completed any farmstead improvement practices to address water quality issues? Manure Storage: 23.5%, Diverting clean water runoff: 17.6%, Barnyard runoff collection: 11.8%, Sillage leachate runoff separation: 14.7%, Milkhouse/parlor waste collection: 17.6% Question 13: What method of communication do you prefer from regulators regarding changes in farming policy? Paper mail: 31.1%, Social Media: 4.4%, Dairy Cooperative Communications: 6.7%, On- Farm Visits: 17.8%, %, Local Coalition Meetings: 11.1%, UVM Extension Website: 8.9%, Other: 2.2% Question 14 Information from Question 14 was confidential and is therefore not included in our study. Question 15: What land management practices were you using between ? Given that none of our survey respondents farmed in our selected watersheds, we were unable to use this data for our GIS and statistical analysis. However, we have summarized all 59

60 the data collected from our survey for 2016 so that our community partners and future students have recent data from McKenzie Brook to refer to. Continuous Corn (acres): Total = 707, Average = Continuous Hay (acres): Total = 2130, Average = Hay/Corn Rotation (acres): Total = 1715, Average = Pasture (acres): Total = 780, Average = 65 Reduced Tillage (acres): Total = 1119, Average: No Tillage (acres): Total = 900, Average = 150 Cover Crop (acres): Total = 1819, Average = Manure Injection on Annual Crops (acres): Total = 1000, Average = Manure Injection on Hay Fields (acres): Total = 40, Average = 10 Prescribed Grazing (acres): Total = 680, Average = Tile Drainage System (acres): Total = 525, Average = Question 16: Of the practices listen above, which ones do you feel are most important for maintaining water quality? Cover Cropping: 35.7%, Livestock Exclusion: 7.1%, No Till: 10.7%, Grassed Waterways: 14.3%, Ditch and Stream Buffers: 7 60

61 Appendix C.: Water Sampling Protocol Survey Initial Form (Each field) Farm Name?: Field: Tile Drainage system? Yes or No Is tile line older than 1 year? Yes or No System tile or other Tile diameter (inches): Corrugated: Yes or No Tile (or field) slope if known: Soil type: Soil test Phosphorus (ppm): Current Crop: Crop 2015: Crop 2014: Crop 2013: Cover Crop? Yes or No (Circle one) Full Tillage or No Tillage system Immediately adjacent to stream? Yes or No Other pertinent information?: 61

62 Tile Drainage Sample Form Instructions: Rinse sample bottle two times with water from the drain, then fill to 1 or 2 fingers width below the bottle shoulder to allow for expansion when frozen. Freeze sample immediately. Date: Time of collection: How deep is the water in the tile outlet (inches)? Measure the depth of water from bottom of tile just inside outlet. If too low to measure, write trickle. Depth should be measured in the deepest part of the flow, as indicated below. Field operations since last sample date (tillage, manure, fertilizer application): Date Time Operation Have you experienced any thunderstorms in the past 7 days: Yes or No Amount of rain if known: Have there been any noticeable cracks in the field? Yes or No Other information that might be useful: Bracket sample form These samples should be taken at the upper and lower extent of the waterway on your farm. For a graphical explanation, see right. Samples should be taken in flowing water, preferably in a relatively deep area taking care not to disturb the bottom of the stream or waterway. If water is stagnant, please note this 62

63 on the form. Measure water depth after sample collection. If stream or waterway is dry, take no sample but complete form and note that stream or waterway is dry. Rinse sample bottle two times with water from the stream or waterway, then fill to 1 or 2 fingers width below the bottle shoulder to allow for expansion when frozen. Freeze sample immediately. Date: Time of collection: Flow: Flowing or Stagnant or Dry How deep is the stream or waterway at the sample spot (feet or inches)? Approximately how wide is the stream or waterway at the sample spot (feet)? What is the general bottom composition? (Circle one) Overgrown Silt Sand Gravel Small rocks Large rocks Bedrock Field operations at adjacent field since last sample date (please include: tillage, manure, fertilizer application, other?): Date Time Operation Have you experienced any thunderstorms in the past 7 days: Yes or No Amount of rain if known: Have there been any noticeable cracks in the field? Yes or No Other information that might be useful: 63

64 Appendix D: Water Quality Sampling Study Design For the purposes of this project and the future usefulness of the information collected, sample collection, preservation, handling and analysis shall conform as closely as practicable to methods established by VTDEC. Reason for performing given analyses Gather information about impact on phosphorus loading of dairy farming/associated agriculture Lead to informed management of dairy farms and associated agriculture in the face of new TMDL regulations for phosphorus in Lake Champlain Is there a gap between new regulations based on the Lake Champlain BMP Scenario Tool and what farmers are able to achieve what are the barriers/obstacles for farmers in meeting the requirements? How the data will be used To discern effects of dairy farms and land management practices on phosphorus loading in streams leading to Lake Champlain Principal question Is there elevated phosphorus loading from dairy farms? Are phosphorus levels higher in the downstream site of bracketed sampling? Are phosphorus loading levels affected by different land management practices? In what way? Which land management practices appear to be the most effective in lessening phosphorus loading? Decision statement Discern the effect of dairy farming and different land management practices on phosphorus loading in streams leading to Lake Champlain Details/Data sources Water sampling and measurement for phosphorus loading above and below farms (bracketed sampling) Additional subwatershed data from VTDEC Possible new flow data to support loading rate data depending on flow availability due to drought conditions 64

65 Data analysis and consideration using scientific literature; previous data; personal information from farmers, VTDEC, and UVM extension; new data; current subwatershed conditions New data collection will comply with VTDEC protocol in order to allow comparability of new and previous data and future usefulness of new data and consequent discussion and conclusions In the case that sampling is not possible due to unusual weather conditions and extreme drought, previous data will be considered so as to provide for the possibility of future data collection, study, and comparison Geographical area McKenzie Brook watershed, currently Braisted Brook, Hospital Creek, and Whitney Creek subwatersheds Collection All new data will be collected in compliance with current VTDEC protocol Equipment inspection will ensure that streams remain uncontaminated by a) other streams, b) unrelated sites Flow is defined as the volume rate of flow, expressed in cubic feet per second Flow measurements will be replicated and averaged to increase accuracy All water samples will be labeled, stored, and transported as per VTDEC protocol Location, Stream mile or lake sample depth, Collection Date, Collection Time, Tests requested, Quality Assurance indicator (duplicate?) May require storing on ice and/or filtration Sampling for Total Phosphorus? Sample Collection - Grab samples? By hand or using a dip sampler Place mouth of sample container below water surface keeping hands and clothing away from the mouth of the bottle Face upstream to avoid contamination of the sample For duplicates: open sample containers should be submerged side-by-side and collected at the same time for an actual duplicate Filtering Procedure Rinse filter with distilled water 3 times If using a syringe, rinse syringe with ambient water 3 times Fill syringe with ambient sample water and purge filter Filter 65

66 Sources used to inform study design: Welcome to the Watershed Management Division State of Vermont. Agency of Natural resources. Department of Environmental Conservation. n.d. Web. 9 Oct State of Vermont, Agency of Natural Resources. Department of Environmental Conservation. Watershed Management Division. Vermont Water Quality Standards Environmental Protection Rule Chapter 29(a). Effective October 30, Web. 7 Oct State of Vermont, Vermont Agency of Natural Resources. Department of Environmental Conservation. Water Quality Division. Field Methods Manual. Web. 7 Oct American Public Health Association, American Water Works Association. Water Environment Federation. Standard Methods for the Examination of Water and Wastewater. Web. 9 Oct

67 Appendix E: GIS Methodological Details Subwatersheds Raster files for each subwatershed created by ES401 project team last year (Homans, Harris, Niles & Raith, 2016) using CATCHMENT tool and LIDAR data. Rasters converted to shapefiles and adjusted to fit exactly within the Vermont-only McKenzie Brook watershed shapefile. 1. RASTER TO POLYGON a. Input raster = hospital_creek b. Field = subshed c. Output polygon features = hospital_creek.shp 2. UPDATE a. Input features = hospital_creek.shp b. Update features = McKenzieBk_VTonly.shp c. Output feature class = hospital_mb_update.shp 3. Editor: hospital_mb_update.shp a. Select FID 0 (what is outside of McKB) right click delete b. Put original hospital_creek.shp on top c. Select hospital_mb_update.shp cut polygons tool cut along border (careful to click on every vertex) d. Select section of McKB that is not hospital creek right click delete **If there is a tail e. Create features polygon draw polygon that covers McKB tail f. Save edits stop editing 4. Select: hospital_mb_update.shp FID 1 (polygon that covers tail) a. Data export data 67

68 b. Output feature class = tail_cutter.shp 5. ERASE a. Input features = hospital_mb_update.shp b. Erase features = tail_cutter.shp c. Output feature class = hospital_creek_final.shp Red line depicts McKenzie Brook shapefile. Hard blue, green and yellow lines show initial outlines of Hospital Creek, Whitney Creek and Braisted Brook raster files. Filled in blue, green and yellow area depicts adjusted subwatershed shapefiles. Water Quality Sampling Points Water Quality data available at: (Site Search) We created a GoogleMap with the geographic locations of each sampling point, exported the map as a KML file, then imported it into GIS software as a layer file. Average phosphorus levels for each sampling point were calculated separately in an Excel spreadsheet. Then the spreadsheet with average phosphorus levels was converted into a table in the GIS software and finally merged with the sampling points layer attribute table. 1. Google maps: create new map = Sampling_points a. IWIS site search = hospital creek b. For relevant sample point: Google maps copy and paste coordinates into map Sampling_points save point as Hospital Creek Mouth c. collect all sampling points and save under appropriate name d. Export map to KML file 2. KML TO LAYER a. Input: Sampling_points.kml 68

69 b. Output sampling_points.shp 3. Attribute table: sampling_points.shp a. Check to make sure the name column transfered from KML b. Add field (text) = ShortName = HC1 c. Add field (long integer) = LocationID copy and paste from IWIS site 4. IWIS site search = hospital creek a. For corresponding sampling point: Water quality / Chemistry Report viewer save as Excel = WQ_data_HC1 b. Edit excel so that first row contains column headings, remove row that states units of measurement save as WQ_data_HC1 5. EXCEL TO TABLE a. Input: WQ_data_HC.xlsx b. Output: WQ_data_HC c. *Note: Tables saved in geodatabase 6. Excel: Phosphorus Levels a. In a new sheet, calculate the average level of P for each sampling point for each year b. Make sure to set it up as it would appear in an attribute table: sampling point Name and ShortName in left hand column, years as each column heading 7. EXCEL TO TABLE a. Input: Phosphorus_Levels.xlsx b. Output: Avg_P_all 8. sampling_points.shp right click join a. based on field in layer = ShortName b. choose table = Avg_P_all c. based on field in table = ShortName d. after the join, export data sampling_points_hc.shp 69

70 Layer file with all VT DEC water quality sampling points in the McKenzie Brook watershed. Land Characteristics Slope Original data came from a shapefile containing percent slope data for all of Vermont. We reclassified the data to only include steep slopes or slopes greater than 8 percent. We then dissolved this data so that all steep slopes were in the same data layer. By clipping the dissolved data to each of our subwatershed shapefiles, we were then able to calculate the total steep slope area for each of these subwatersheds using the zonal geometry operation. We then divided these total areas of steep slope for each subwatersheds by their respective subwatershed area, which calculated the percentage of each subwatershed that were steep slopes. 1. Model: VT_slopes RECLASSIFY (value >8%=1, value <8%=NoData) a. Input Features: VT_slopes.shp b. Output Features: steep_slopes_only.shp 2. Model: steep_slopes_only DISSOLVE a. Input Features: steep_slopes_only b. Output Features: dissolved_steep_slopes c. Dissolve Field: Value 3. Model: dissolved_steep_slopes CLIP a. Input Value Features: dissolved_steep_slopes b. Clip Features: subwatershed shapefiles (hospital_creek_final, braisted_brook_final, whitney_creek_final) c. Output Features: HC_clipped_slopes, BB_clipped_slopes, WC_clipped_slopes 70

71 4. Model: HC_clipped_slopes, BB_clipped_slopes, WC_clipped_slopes Zonal Geometry a. Open attribute table: Add fields (slope_area, percent_area) b. Zonal Geometry: slope_area Calculate Area (acres) c. Field Calculator: percent_area = slope_area / SW_area (see pg. 64) Left: Red points represent pixels with significant slope (>8%). Right: Final percent area of each subwatershed with a significant slope, Hospital Creek=15.5%, Whitney Creek=18.9%, Braisted Brook=33.5%. Soil Type - Hydrologic Soil Group (HSG) and Erodibility Original data came in the form of a shapefile with soil type information for all of Addison County. We used the DISSOLVE and CLIP tools to aggregate areas first by HSG and then by erodibility. In a new shapefile for each subwatershed we used the field calculator in the attribute table to fill fields with the percent area of each HSG (A-D) and erodibility (highly erodible, potentially highly erodible and not highly erodible. 1. Model: Soil_Type_HSG DISSOLVE a. Input Features = soil_addisoncounty.shp b. Output Features = soil_hydrogroup.shp c. Dissolve_Field = HYDROGROUP 2. Model: Soil_Type_HSG CLIP a. Input Features = soil_hydrogroup.shp b. Clip Feature = hospital_creek_final.shp 71

72 c. Output Feature = soil_hydro_hc.shp 3. Model: Soil_Type_Erosion DISSOLVE a. Input Features = soil_addisoncounty.shp b. Output Features = soil_erosion.shp c. Dissolve_Field = HELCLASS 4. Model: Soil_Type_Erosion CLIP a. Input Features = soil_erosion.shp b. Clip Feature = hospital_creek_final.shp c. Output Feature = soil_erosion_hc.shp 5. Model: Soil_Type DISSOLVE a. Input Features = soil_addisoncounty.shp b. Output Features = soil_hydro_erosion.shp c. Dissolve_Field = HYDROGROUP & HELCLASS 6. Model: Soil_Type CLIP a. Input Features = soil_hydro_erosion.shp b. Clip Feature = hospital_creek_final.shp c. Output Feature = soil_type_hc.shp Model used to dissolve Addison County soil layer by soil type and clip to each subwatershed. 7. Attribute table: soil_hydro_hc.shp a. Add field (double) = Acres b. Acres Calculate Geometry Property = Area, Units = Acres 8. Attribute table: soil_erosion_hc.shp a. Add field (double) = Acres b. Acres Calculate Geometry Property = Area, Units = Acres 9. Editor: soil_hydro_hc.shp a. Select: HYDROGROUP = water right click delete b. Save edits stop editing 10. Editor: soil_erosion_hc.shp a. Select: HELCLASS = water right click delete 72

73 b. Save edits stop editing 11. Select: hospital_creek_final.shp a. Export data soil_hc.shp 12. Attribute table: soil_hc.shp a. Add field (double) = phsg_a b. Open soil_hydro_hc.shp attribute table copy acres in HSG A c. phsg_a Field calculator paste acres in HSG A (DON T CALCULATE YET!) d. Open soil_hydro_hc.shp attribute table Highlight column Acres statistics copy sum e. phsg_a Field calculator paste sum such that final calculation = acres in HSG A / sum * 100 f. Repeat for phsg_b, phsg_c, phsg_d g. Add field (double) = phsg_cd = [phsg_c] + [phsg_d] h. Add field (double) = phe i. Open soil_erosion_hc.shp attribute table copy acres of highly erodible j. phe Field calculator paste acres of highly erodible (DON T CALCULATE YET!) k. Open soil_erosion_hc.shp attribute table Highlight column Acres statistics copy sum l. phe Field calculator paste sum such that final calculation = acres of highly erodible / sum * 100 m. Repeat for helclass NHE (not highly erodible) and PHE (potentially highly erodible), *Note: do not include helclass not rated 73

74 Left: Summary statistics and attribute table for Hospital Creek HSG. Sum of all HSG areas highlighted. Right: Field calculator used to calculate percent area of subwatershed belonging to each HSG. Left: Map depicting the area of each subwatershed belonging to each HSG. Right: Map depicting the area of each subwatershed belonging to each erodibility category. Land Cover In calculating Land Cover, we began with a raster file of Land Cover for all of Vermont. The variables included in the raster file were: Agriculture, Developed, Forest, Wetlands, and 74

75 Pasture. We reclassified each variable to exist independently and created layers of isolated variables. By doing this, we were able to clip each land cover class (Agriculture, Developed, etc.) to each subwatershed shape file. We then used the zonal geometry function to calculate total area for each land cover class in each subwatershed. By dividing these land cover class areas for each subwatershed by that subwatershed s total area, we were able to calculate percent area of our variables. 1. Model: LC_VT.shp RECLASSIFY (e.g. value = Agriculture = 1, value = Developed, Wetlands, Pasture, Forest = 0) a. Input Features: LC_VT.shp b. Output Features: agriculture_only.shp, wetlands_only.shp, developed_only.shp, pasture_only.shp, forest_only.shp 2. Model: CLIP a. Input Features: agriculture_only.shp, wetlands_only.shp, developed_only.shp, pasture_only.shp, forest_only.shp b. Output Features: HC_agriculture.shp, HC_wetlands.shp, HC_developed.shp, HC_pasture.shp, HC_forest.shp, WC_agriculture.shp, WC_wetlands.shp, WC_developed.shp, WC_pasture.shp, WC forest.shp, BB_agriculture.shp, BB_wetlands.shp, BB_developed.shp, BB_pasture.shp, BB_forest.shp 3. Model: ZONAL GEOMETRY, FIELD CALCULATOR a. Add fields: LC_area, percent_area b. Input Features: HC_agriculture.shp, HC_wetlands.shp, HC_developed.shp, HC_pasture.shp, HC_forest.shp, WC_agriculture.shp, WC_wetlands.shp, WC_developed.shp, WC_pasture.shp, WC forest.shp, BB_agriculture.shp, BB_wetlands.shp, BB_developed.shp, BB_pasture.shp, BB_forest.shp c. Zonal Geometry: LC_area Calculate Geometry (Acres) d. Field Calculator: percent_area = LC_area / SW_area (see page 64) 75

76 Left: Each land cover category was converted into percent area of subwatershed: Pasture, Wetlands, Forest, Developed & Agriculture. Right: Example depicting attribute table where total acres of agricultural land in Hospital Creek was converted into a percent of the total area of that subwatershed. Farm Layout Ditch Network Ditch network vector file was provided to us by NRCS (drawn from visual interpretation of satellite imagery, not ground-truthed). We used INTERSECT and the Field Calculator within a newly created shapefile with the form of each subwatershed to fill fields with the total feet of ditches within that subwatershed. 1. Model: Ditch_Network INTERSECT a. Inputs = Ditch_Netwk_McKenzie.shp & hospital_creek_final.shp b. Output = ditch_hc.shp c. repeat for whitney and braisted 2. Select: hospital_creek_final.shp a. Export data ditch_sum_hc.shp 3. Attribute table: ditch_sum_hc.shp a. Add field (long integer) = TotalFeet b. Open ditch_hc.shp attribute table Highlight column LengthFeet statistics copy sum c. TotalFeet Field Calculator paste sum 76

77 Left: Summary statistics and attribute table for Hospital Creek ditch network. Sum representing total feet of ditch network highlighted. Right: Field calculator used to fill fields of new shapefile with total feet of ditch network within Hospital Creek. Map depicting ditch network layer provided to us by NRCS clipped to each subwatershed. 77

78 Farmstead - Number, Size and Proximity to Waterway A shapefile with farmsteads within McKenzie Brook was provided to us by NRCS (drawn from visual interpretation of satellite imagery, not ground-truthed). We decided to include farmsteads that fall on the border of the subwatershed and are only partially within the subwatershed and only farmsteads that are currently operational. We used the INTERSECT and NEAR tools as well as the Field Calculator within a newly created shapefile with the form of each subwatershed to fill fields with the total number of farmsteads, the percent of farmsteads belonging to each size class and the average distance of the closest farmstead edge to the nearest ditch, stream and waterway (ditch or stream, whichever was closest). Strict number of farmsteads 1. Select by Attribute: Farmstead_McKenzie a. Livestock = yes OR Livestock = maybe b. Export data farmstead_in_use.shp 2. Model: Farmsteads_Number INTERSECT a. Input features = farmstead_in_use.shp & hospital_creek_final.shp b. Output feature class = farmstead_hc.chp 3. Select: hospital_creek_final.shp a. Export data farmstead_number_hc.shp 4. Attribute table: farmstead_number_hc.shp a. Add field (short integer) = TotalFarms b. Open farmstead_hc.shp attribute table, look at total number of rows c. TotalFarms Field Calculator total #rows from farmstead_hc.shp 78

79 Left: Hospital Creek farmsteads attribute table. Total number of rows in table represents total number of farmsteads (12). Right: Field calculator to fill attribute table in new shapefile with total number of farmsteads within Hospital Creek (12). Farm size 1. Select: hospital_creek_final.shp a. Export data farmstead_size_hc.shp 2. Attribute table: farmstead_size_hc.shp a. Add field (double) = psfo b. Open farmstead_hc.shp attribute table Select by attribute: Size_Class = SFO look at number of rows selected c. psfo Field Calculator #rows selected as SFO in farmstead_hc.shp / total #rows from farmstead_hc.shp * 100 d. Repeat for MFO & LFO Left: Select by attribute to isolate farms by size (SFO). Right: Field calculator to fill attribute table in new shapefile with percent of farmsteads within Hospital Creek that are SFOs. Proximity to waterway *Note: decision made to use intersected farmstead_hc layer that only considers pieces of farmstead within subwatershed b/c runoff from pieces of farmstead outside subwatershed will go into different waterways. Therefore we will not consider the data in column proxi_strm that comes from parent data b/c it could consider proximity to a steam outside of the subwatershed (see example) 79

80 1. Model: Farmstead_Proximity_Stream INTERSECT a. Input features = vdhcarto_update_l_vt.shp & hospital_creek_final.shp b. Output feature = stream_hc.shp 2. Model: Farmstead_Proximity_Stream NEAR a. Input features = farmstead_hc.shp b. Near features = stream_hc.shp c. Output feature (simply updates farmstead_hc.shp attribute table) d. Note: NEAR produces distances in meters so we need to convert that to feet 3. Attribute table: farmstead_hc.shp a. Add field (short integer) = Prox_Strm b. Prox_Strm field calculator Prox_Strm = [NEAR_DIST] * Model: Farmstead_Proximity_Ditch NEAR a. Input features = farmstead_hc.shp b. Near features = ditch_hc.shp c. Output feature (simply updates farmstead_hc.shp attribute table) 5. Attribute table: farmstead_hc.shp a. Add field (short integer) = Prox_Dtch b. Prox_Dtch field calculator Prox_Dtch = [NEAR_DIST] * Model: Farmstead_Proximity_Waterway NEAR a. Input features = farmstead_hc.shp b. Near features = ditch_hc.shp & stream_hc.shp c. Output feature (simply updates farmstead_hc.shp attribute table) 7. Attribute table: farmstead_hc.shp a. Add field (short integer) = Prox_Ww b. Prox_Ww field calculator Prox_Ww = [NEAR_DIST] * 3.28 c. Note: NEAR produces field NEAR_FC that states whether closest waterway is a stream or a ditch 80

81 8. Select: hospital_creek_final.shp a. Export data farmstead_proximity_hc.shp 9. Attribute table: farmstead_proximity_hc.shp a. Add field (short integer) = AvgProxStr b. Open farmstead_hc.shp attribute table Highlight column Prox_Strm statistics copy mean c. AvgProxStr Field calculator paste mean d. Repeat for AvgProxDch and AvgProxWw Left: Model with intersect and near tools used to calculate farmstead proximity to stream. Right: Model with near tool used to calculate farmstead proximity to waterway (either stream or ditch, whichever was closest). 81

82 Map depicting farmsteads within subwatersheds, categorized by farm size, as well as all waterways (streams and ditches). There is one tiny corner of a SFO in Braisted Brook that is too small to be visible in this image. Farming Practices Our community partner at NRCS provided us with total number of acres under each farming practice aggregated by subwatershed. It is important to note that these acres only include farms that worked with NRCS in some capacity to implement the practice and does not include acres on farms that implemented the practice independently of NRCS. We had hoped to fill in this gap in information via our farmer survey, however none of the respondents farms fall within our selected subwatersheds. Therefore, we simply copy and pasted the aggregated total acres provided to us by NRCS into the attribute table of a shapefile with the form of each subwatershed. We used Zonal Geometry to calculate the total area of the subwatershed and then Field Calculator to divide the total acres under each farming practice by the total subwatershed area to fill a field in the attribute table with percent area of the subwatershed under each practice. There were data for each of the bolded variables below; there were no data for the variables marked with an asterisk. Tile Drains* Continuous Corn Continuous Hay/Corn-Hay Rotation No-Till 82