DELINEATING AGRICULTURE IN THE TAR-PAMLICO · PDF fileDELINEATING AGRICULTURE IN THE TAR-PAMLICO RIVER BASIN ... Tar-Pam Basin and count units selected ... Large quantities

DELINEATING AGRICULTURE IN THE TAR-PAMLICO RIVER BASIN

For Final Report for the

Sampling Analysis: Delineating Agriculture in the Tar-Pamlico River Basin

Submitted January 31, 2006 to the NC Department of Environment and Natural Resources (NCDENR),

Division of Water Quality

Funding Period 2-28-2003 to 1-23-2006

Principal Investigator: Deanna L. Osmond

Co-Principal Investigator: Keith Cassel NC State University

Department of Soil Science

with

Kathy Neas (Cooperator) U.S. Department of Agriculture (USDA),

National Agricultural Statistics Service (NASS)

ACKNOWLEDGMENTS

This project was funded under an EPA Section 319 Grant. Cooperators on this project were: NCDA&CS – Craig Hayes and Bob Murphy NC DENR - Division of Water Quality: Rich Gannon USDA-NRCS: Roger Hansard A special thanks to USDA-NRCS for helping to develop soil loss information, to Roberta Miller-Haraway and Rob Austin (NC State University, Soil Science Department) for printing the maps and providing the soil shape files, and Steve Pratt (Understanding Systems, Inc) for determining map units. Dr. Amy Johnson produced the information on PLAT ratings and N losses from NCANAT. Her help was invaluable. Last, but not least, this project could not have been completed without the field enumerators who collected the data. Without all these people, and the funding provided through USEPA as a pass through to NCDENR, this project would not have been possible.

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TABLE OF CONTENTS LIST OF TABLES, FIGURES, AND APPENDICES .................................................................. iv EXECUTIVE SUMMARY ........................................................................................................... vi DELIVERABLES........................................................................................................................ viii INTRODUCTION .......................................................................................................................... 1 PURPOSE AND GOALS............................................................................................................... 1 METHODOLOGY ......................................................................................................................... 2

Area Frame Sampling ................................................................................................................. 2 Determining the Census Blocks to Map ................................................................................. 2

Determining the Sampling Segments.......................................................................................... 4 Data Processing....................................................................................................................... 6

RESULTS ....................................................................................................................................... 8 Agronomic and BMP Results ..................................................................................................... 8

Land Use ................................................................................................................................. 8 Drainage Directions ................................................................................................................ 8 Cover Crops ............................................................................................................................ 9 Conservation Tillage............................................................................................................. 11 Water Control Structures ...................................................................................................... 14 Riparian buffers .................................................................................................................... 14 Nitrogen and Phosphorus Rates ............................................................................................ 17 Soil Test P and P Fertilization .............................................................................................. 31 Slope and Soil Loss............................................................................................................... 34

North Carolina Agricultural Nutrient Assessment Tool Results .............................................. 36 PLAT Results........................................................................................................................ 36 NLEW................................................................................................................................... 42 Field-scale NLEW to Aggregate-scale NLEW Comparisons for Crops, Nitrogen Rates, and BMPs ........................................................................................ 45 Field-scale NLEW to Aggregate-scale NLEW Comparisons for Crops, Nitrogen Rates, and BMPs ........................................................................................ 45

OUTCOMES AND CONCLUSIONS.......................................................................................... 48 BUDGET* .................................................................................................................................... 49 APPENDICES .............................................................................................................................. 51

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LIST OF TABLES, FIGURES, AND APPENDICES

TABLES Table 1. Information on Segments That Were Not Enumerated. .............................................. 8 Table 2. Drainage Directions. ..................................................................................................... 9 Table 3. Acres and Percentage of Cover Crops by County. ..................................................... 11 Table 4. Acres and Percentage of Conservation Tillage by County ........................................ 12 Table 5. Acres and Percentage of Water Control Structures by County .................................. 14 Table 6. Number of Acres and Percentage of this Area with no Buffers by

Drainage Direction and County. .......................................................................................... 15 Table 7. Number of Fields, Affected Acres and Percentage of Buffers by Buffer Type......... 15 Table 8. Minimum, Maximum and Mean Buffer Width of Shrub/Tree .................................. 16 Table 9. Minimum, Maximum and Mean Buffer Width of Tree/Shrub and

Vegetated Buffer Types. ...................................................................................................... 17 Table 10 a. Nitrogen Rates 2004.............................................................................................. 18 Table 10 b. Phosphorus Rates 2004.......................................................................................... 18 Table 10 c. Potassium Rates 2004 ............................................................................................ 18 Table 10 d. Nitrogen Rates 2004 ............................................................................................. 19 Table 10 e. Phosphorus Rates 2004 ......................................................................................... 19 Table 10 f. Potassium Rates 2004............................................................................................ 19 Table 10 g. Nitrogen Rates 2004 ............................................................................................. 20 Table 10 h. Phosphorus Rates 2004......................................................................................... 20 Table 10 i. Potassium Rates 2004............................................................................................ 20 Table 10 j. Nitrogen Rates 2004 .............................................................................................. 21 Table 10 k. Phosphorus Rates 2004......................................................................................... 21 Table 10 l. Potassium Rates 2004............................................................................................ 21 Table 10 m. Nitrogen Rates 2004 ............................................................................................ 22 Table 10 n. Phosphorus Rates 2004......................................................................................... 22 Table 10 o. Potassium Rates 2004 ........................................................................................... 22 Table 10 p. Nitrogen Rates 2004 ............................................................................................. 23 Table 10 q. Phosphorus Rates 2004......................................................................................... 23 Table 10 r. Potassium Rates 2004............................................................................................ 23 Table 10 s. Nitrogen Rates 2004.............................................................................................. 24 Table 10 t. Phosphorus Rates 2004.......................................................................................... 24 Table 10 u. Potassium Rates 2004 ........................................................................................... 24 Table 10 v. Nitrogen Rates 2004 ............................................................................................. 25 Table 10 w. Phosphorus Rates 2004........................................................................................ 25 Table 10 x. Potassium Rates 2004 ........................................................................................... 25 Table 10 y. Nitrogen Rates 2004 ............................................................................................. 26 Table 10 z. Phosphorus Rates 2004 ......................................................................................... 26 Table 10 aa. Potassium Rates 2004.......................................................................................... 26 Table 10 bb. Nitrogen Rates 2004 ........................................................................................... 27 Table 10 cc. Phosphorus Rates 2004 ....................................................................................... 27 Table10 dd. Potassium Rates 2004 .......................................................................................... 27 Table 10 ee. Nitrogen Rates 2004............................................................................................ 28 Table 10 ff. Phosphorus Rates 2004 ........................................................................................ 28

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Table 10 gg. Potassium Rates 2004 ......................................................................................... 28 Table 10 hh. Nitrogen Rates 2004 ........................................................................................... 29 Table 10 ii. Phosphorus Rates 2004......................................................................................... 29 Table 10 jj. Potassium Rates 2004........................................................................................... 29 Table 11. Number of Fields by Crop with Identical Fertilizer Application

Rate for the Same Producer ................................................................................................. 30 Table 12. Number of Fields with Different Fertilizer Rates by Crop...................................... 30 Table 13. Mean and Weighted Mean Soil Test P-Index by County ........................................ 31 Table 14. P Fertilization Rates for Soils Testing High and Very High (P-Index >60)

and Soils Testing Low and Medium (< 60) ......................................................................... 32 Table 15. Minimum, Maximum and Weighted Means for Field Slope and

Soil Erosion by County........................................................................................................ 35 Table 16. Minimum, Maximum and Weighted Means for Receiving Slope Width................ 35 Table 17. PLAT Risk Assessment by County. ........................................................................ 37 Table 18. N Losses by County as Calculated in NLEW.......................................................... 42 Table 19. N Losses by County as Calculated in NLEW.......................................................... 43 Table 20. NLEW N Rates and Survey Nitrogen Rates by Crop and County for 2004............ 45 Table 21. Survey delineated BMPs vs. NLEW-Aggregate Reported BMPs. .......................... 47

FIGURES Figure 1. Tar-Pam Basin and count units selected..................................................................... 4 Figure 2. Frequency of total PLAT Index Values.................................................................... 38 Figure 3. Frequency of PLAT Index Values for Soil Erosion ................................................. 39 Figure 4. Frequency of PLAT Index Values for Soluble P Runoff Losses ............................. 40 Figure 5. Frequency of PLAT ratings for Applied Sources of P ............................................. 41

APPENDICES Appendix 1: Public Information On The Survey Released From NCDA&CS -

Statistics Division ................................................................................................................ 51 Appendix 2: Tar-Pamlico Best Management Practices Survey Interviewer’s Guide............... 52 Appendix 3: Tar-Pamlico Best Management Practices Survey................................................ 66 Appendix 4: Soil Loss By Region And Cropping System........................................................ 73 Appendix 5: Additional Data .................................................................................................... 74

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EXECUTIVE SUMMARY Under the Tar-Pam Rules, the Basin Oversight Committee (BOC) is tasked with submitting agricultural information on a yearly basis. Unfortunately, some of this information, such as fertilizer rates, is based on best professional judgment; there are no fertilizer-use statistics that are reliable. In addition, best management practices (BMPs) are only captured if they are cost shared. To obtain a better estimate of agricultural practices, this one-time statistically valid area sampling frame was applied to agricultural fields in the Tar-Pamlico River Basin in order to collect an agricultural baseline of cropping systems, soil types and currently used best management practices. Using a valid statistical sampling technique, random census blocks were selected. The number of maps selected per county in the Tar-Pamlico basin were based on how much of the county was within the river basin boundaries, as well as the amount of agriculture. Counties sampled consisted of Beaufort, Edgecombe, Franklin, Granville, Halifax, Hyde, Martin, Nash, Pamlico, Pitt, Washington, and Wilson. Warren and Vance were not sampled due to the lack of soils information, while Dare, Person and Tyrrell had insufficient land area within the basin. Enumerators employed by the North Carolina Department of Agriculture and Consumer Service (NCDA&CS), Division of Statistics administered the survey. The data set consisted of 1,156 records. We collected information on a wide variety of agricultural characteristics, including number of acres in development, wildlife, and CREP/CRP. Other data collected consisted of county, field size (ac), current crop, fertilizer applications (amount and type), tillage type, cover crop use, presence of different types of buffers, buffer widths, acreage affected by the buffers, presence of water control structures, acres affected by the water control structure, field slope, receiving slope length, and presence of other BMPs (sediment basin or pond). In two counties (Franklin and Granville), slope length was also determined in order to calculate soil loss. In all other counties, soil loss was based on table values, determined by USDA-NRCS, using physiographic region and cropping system. The survey instrument is attached at the end of this report. After the information was collected, the data were then transformed in order to report on them. Most counties had low use of cover crops; Halifax and Martin counties were the exception with 25% or greater of the agricultural land planted in winter cereal cover crops. Conservation tillage (CT) was used in most counties at a rate of 50% or more. Water control structures (WCS) consisted of more than 25% of the agricultural acreage in Washington and Pamlico counties, while Beaufort and Halifax had lower levels of WCS use. Five counties (Beaufort, Granville, Nash, Pitt, and Wilson) had the greatest amount of agricultural land affected by tree/shrub buffers, while five (Edgecombe, Halifax, Martin, Pamlico and Washington) had more acres affected vegetated buffers. Only one county, Franklin, had most the acres affected by mixed buffers (tree and vegetated). No mixed buffer types (tree and vegetated) were enumerated in Hyde, Martin, Pitt or Wilson counties. Upper basin counties, such as Franklin and Granville, had the majority of their field acres buffered with over 92% of the agricultural lands next to buffers.

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In many counties for many crops, nitrogen (N) fertilizer rates were below the expected rates based on realistic yield expectations. Some crops such as pasture, peanuts, and cover crops are rarely fertilized. The majority of farmers use the same fertilizer plan with a particular crop, regardless of soil test recommendations or differences in yield goal. Mostly, however, this did not translate into excess fertilization of crops. Average soil test phosphorus (P) levels were very high in seven counties, high in four counties, and medium in one county. Over 2/3 of the fields did not need P applications; yet P application rates were identical (27.5 lb ac-1) regardless of whether the soil need P or not. Large quantities of P are still being applied to tobacco. Soil erosion was modest, ranging from an average of about 1 t ac-1 to approximately 8 t ac-1. Slope ranges were 0 to about 3 percent, with slopes increasing in the piedmont physiographic region. Average receiving slopes were either 10-19 feet or 20-29 feet; these results suggest that there is limited capacity of receiving slopes to slow soil erosion, thus reducing P. The field data from this sample was used in the North Carolina Agricultural Nutrient Assessment Tool (NCANAT). Phosphorus Loss Assessment Tool (PLAT) analysis showed that potential off-site loss of P from agricultural fields in the Tar-Pamlico River Basin is very low. Likewise, when data were analyzed in the Nitrogen Loss Assessment Tool (NLEW), the N losses were low because fertilizer N application rates were often lower than the recommended N rates. In summary, when all the data are combined, it appears that producers in the Tar-Pamlico River Basin are minimizing environmental impact of nutrient and soil losses from agricultural fields. Best management practices are being used, including buffers, water control structures, cover crops, and conservation tillage. Nutrient inputs generally are below recommended levels. The only area where we believe producers could improve management is by following soil test reports and reducing P fertilization.

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DELIVERABLES

1. Area sampling frame will be randomly selected using the algorithm used in the Neuse River Basin survey. Sample size will be ~400 samples (~ 7 fields per sample or 2800 fields total), with a CV of 4%. • The final sample was 1400 fields because 136 selected segments were not agricultural, no

samples were selected in Person County because the land area was so small, and two counties had no soils data and many of our analysis would have been incomplete.

2. Orthoquod maps will be printed with the selected area outlined. Some maps will require further segmentation of the area. Segmentation must be conducted by hand. Accompanying soil maps will also be printed. Orthoquod maps will be laminated. • This was completed.

3. Survey document will be redesigned to account for the need for phosphorus information. • This was completed (see Appendix 2).

4. Field survey will be administered to producers. Information includes cropping and fertilizer history. Fields will be surveyed for slope length (Granville and Franklin county only, slope, receiving slope length, BMPs, and soil samples will be taken when the operator gives permission. Soil test P results will be provided by NCDAC&S. Information will be collected on approximately 2,800 fields within the Tar-Pamlico River Basin. Complete PLAT analysis will only be conducted on ¼ to 1/3 of the fields due to the increased data necessary to run PLAT. This will represent in the neighborhood of 28,000 acres. • All but 9 of these samples were used in PLAT and all but 65 were used in NLEW. The

number of useable records were much greater than the anticipated 25 to 33% anticipated, representing more fields but fewer acres than anticipated.

5. Survey data will be entered into a SAS database and analyzed by county. There will be over 20 pieces of information entered for each field. We will be able to determine crop acreage, fertilizer rates for crops, soil series, appropriate N rates, current level of BMP use, % of agricultural land buffered, soil test P status of the soils, etc. Although some of this information is available as distinct components of other surveys, the complete set of analysis that we will perform is a function of the field-scale sampling that we will be conducting. Similarities and contrasts in the data will be noted with the Neuse River Basin. All or portions of the data will be run through NLEW and PLAT. • This was completed (see results).

6. Final Report documenting project accomplishments.

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INTRODUCTION Under the Tar-Pam Rules, the Tar-Pam BOC is charged with determining a baseline for N losses from 1991. To calculate the N losses and change in N losses due to BMP implementation, a tracking and reporting field-scale tool was developed (NLEW), to accommodate this task (Osmond et al., 2001). Under the Tar-Pam Rules, N-reduction plans could be at the subwatershed level, county level, or any other arrangement that the stakeholders chose. Stakeholders chose to account for N losses at the county level. There are 17 counties in the Tar-Pam Basin (from the upper basin to the lower basin - Person, Granville, Vance, Warren, Franklin, Nash, Halifax, Edgecombe, Martin, Wilson, Pitt, Hyde, Washington, Beaufort, Tyrrell, Pamlico, and Dare). We were unable to sample five of these counties because either the area within the basin was too small or no samples were selected (e.g. Person, Tyrrell), no agricultural samples were selected (Dare), or no soil map information was available (Warren and Vance). Unlike the Neuse Rules, in the Tar-Pamlico basin P also had to be considered. The Phosphorus Technical Accounting Committee determined that agricultural activities had not increased from the 1991 baseline (Johnson and Osmond, 2005). Data were collected in this survey for use in the PLAT to determine potential P loss in 2004 (NC PLAT Committee, 2005). There was no way to determine potential loss in 1991 as data must be collected in real time. Under the Tar-Pam Rules, the BOC had to establish a baseline that recreated many agricultural historical events. This information would be collected yearly and then compared from one year to the next to determine reductions in N from agricultural producers. Unfortunately, fertilizer rates that are used in this accounting are best professional judgment; there are no fertilizer-use statistics that are reliable. In addition, BMPs are only captured if they are cost shared. To obtain a better estimate of practices, this one-time sampling was designed to better assess agricultural conditions.

PURPOSE AND GOALS The goal of this proposal is to apply a statistically valid area sampling frame to agricultural fields in the Tar-Pamlico River Basin in order to collect an agricultural baseline of cropping systems, soil types and currently used best management practices. This work is critical if the mandated 1991 baseline information is to be obtained. Specific objectives are: 1) collect field-scale agricultural data set to quantify current agricultural practices and provide

baseline buffer information for 1991. 2) use collected data in NCANAT (PLAT & NLEW combined) to determine N and P losses in

the Tar-Pamlico Basin. 3) compare field-scale NLEW to aggregate-scale NLEW to confirm 1991 estimated baseline. 4) utilize the data collected in the state-supported nutrient tracking tools, NCANAT, to assess

potential BMP implementation at a county level scale.

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METHODOLOGY In order to obtain statistically defensible data to meet the two objectives, we have done the following: 1) Determine the sample units through area sampling, which is a proven and defensible

sampling technique. It is well tested and is used by USDA-NASS and others who need data on agricultural fields. From judgments of field to field variation in N runoff and of the number of fields to put in each sampled area, a sample size of approximately 250 areas was deemed adequate to give a sampling CV (coefficient of variation) of 4%. These sampling areas were randomly selected and segmented for enumerators to collect data. Maps were printed with the census block outlined and road names printed on the map. Census blocks were then segmented by hand.

2) Conduct the survey

The field survey was designed and pre-tested. The survey was used to collect relevant information on all fields within the selected sample segment. The data were collected by the National Association of State Departments of Agriculture under the supervision of USDA-NASS enumerators.

3) Analyze data by: • County • Crop • Crop acres • % Crop acres • N rate • P rate • BMP

4) Determine N and P losses using the field-scale version of NCANAT. Run the data through

the field-scale NCANAT tool to determine N and P losses and then compare the aggregated NLEW (baseline) results to the field-scale results (survey). Changes between field-scale and aggregate can only be accomplished for N. Determine if there needs to be an adjustment in the baseline NLEW calculations.

Area Frame Sampling Determining the Census Blocks to Map In an area frame sample, the collection of N sampling units is determined clerically and then selected randomly (Monroe and Finkner, 1959). Data acquisition for frame construction is the first step in the process. Tiger data were obtained from the U. S. Census Bureau for each county in the Tar-Pam River Basin. The census blocks were then extracted from the Tiger files and merged into a single consistent data table. Census blocks were used to define the areas from which samples would be randomly accessed. Efforts were taken to ensure that data were not resorted in order to maintain the data in its serpentine order as created by the Census Bureau.

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Once those data were sorted, a rural filter was applied to the data. For a census block to remain on the list, it had to have an area of 10 acres or more and had to contain more acres than persons to be considered for sampling. Then the Tar-Pam River Basin filter was applied. For census blocks to remain on the frame, only census blocks that were entirely or to some extent within the Tar-Pam basin were used. Each of the listed census blocks had a specific acreage associated with it. Listed census blocks then had their areas (in acres) accumulated. For all valid census blocks, the accumulated area of the census block equaled the area of all proceeding blocks plus the area of that block. A convenient sample unit size was then selected. This is an iterative step where trial sample sizes can be experimented with to get reasonable sample unit sizes. For the purposes of this survey, the sample size was determined to be 300 acres. From this, a provisional total population size (N) was calculated (N= total area / sample size). The total population size (N) is then readjusted to a convenient integer that must be divisible by 5 to allow for the five replicate subsamples. A census block could itself become a sampling unit if the area of the census block was around 300 acres. If the census block area was less than 300, it may be combined with the next or previous census block or blocks to make the count unit, which is the technical term to describe the census block, or blocks, that contains the sampling units. The area sampling calculations with cumulative areas and cumulative sampling units define all of these units (Finkner and Monroe). To randomly select the 600 sampling units, a set of random number seeds were determined for each of the five subsamples from a random number generator and then applied to the cumulative sampling units and those sampling units that were hit became the areas to be sampled. Sample selections for the Tar-Pam area sample have been made in 5 replicate subsamples, each covering all counties in the watershed and each consisting of 120 sampling units for a total of 600 sampling units (Fig. 1). Each selected cumulative sampling unit that qualified was marked for the plotting of its containing count unit. Once these census blocks were determined, plots were batched for printing. The count units were outlined on 1993 land cover digital orthographic quarter quadrangles (DOQQ). The census blocks of the count unit was plotted as an aerial view outlined in blue with its associated assigned number of sampling units. The apparent number of segments selected is displayed in Figure 1. Because most soils data is now electronically available, soil mapping units were associated with the sampled field through the use of a latitude/longitude reading obtained for each field through hand-held global positioning equipment. The soil mapping unit information was electronically layered into the fields selected for sampling in order to determine the predominant soil series. This saved both time and money and did not require the printing of soil maps.

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Figure 1. Tar-Pam Basin and count units selected.

Determining the Sampling Segments The selected census blocks count unit was segmented into the number of assigned sampling units

gments into which the count unit should be divided.

x

oundaries such as lines through the middle of woods are acceptable and sometimes necessary oads or

r

he ASU number was designated on the maps. When there were two to four ASU numbers, the

(ASUs). The ASU dictates the number of seThe cumulative sampling units was calculated as the Cumulative_Acres/(Total_Acres/Adjusted_N ). The ASU was calculated as (cumulative sampling unit)x – (cumulative sampling unit) x-1, where - 1 = the preceding cumulative sample unit of x. Assigned sampling units are numbers of potential sampling units and their boundaries mustallocate all fields in the census block count unit to one and only one segment. Natural boundaries, such as roads, streams and property lines, are useful boundaries. Imaginary bbut less useful than natural boundaries. Imaginary boundaries may also be extensions of rpoint-to-point lines or similar ones, so long as the enumerator will have an objective basis fodeciding whether a field is in or out of a segment. Tboundaries were all delineated in one pass. During segmentation, we tried to ensure that the amount of cultivated land (land in fields) was about equal over the segments. However, since

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field distribution was generally irregular, especially in heavily forested areas, this was not alwaypossible. With five or more ASUs, the count units were divided into two, three or four parts before drawing in all segment boundaries. Segments were numbered and then selected fromrandom number table. The selected segment was outlined in red. Enumeration of the Sam

s

a

pling Segments public service campaign to inform area producers about the survey began a few months before

of

izer

er of the training, the enumerators had hands-on field training. The enumerators were ught to recognize the different types of BMPs and to determine the area affected by them.

ed to uffers and controlled drainage.

ze

nce

g slope ngth, and presence of other BMPs (sediment basin or pond). In two counties (Franklin and

rticipate. All fields within the selected segment were enumerated if they were urrently agricultural lands, including idle fields. If there were no fields within the segment, the

s

t have soil samples available, enumerators asked producers if they could take d

d

NRCS. Four regions of the coastal plain were recognized: Tidewater region, lower coastal plain,

Athe survey. The public information used for this campaign can be found in Appendix 1. Enumerators were trained by NC State and NCDA&CS personnel before they collected any the information. This one-day training involved both classroom discussion of the sampling strategy and survey instrument, as well as a field exercise The Tar-Pam Rules, BMPs, fertilinformation collection and sample surveys were explained to the enumerators. During the remaindta Training books were developed for the enumerators and used during the training session (Appendix 2). These books could also be referred to during the actual survey. The booklet included a wide range of information ranging from the reason the survey was being performpictures of b The data survey instrument allowed collection of the following information: county, field si(ac), previous crop, current crop, fertilizer applications for previous crop (amount and type), fertilizer applications for current crop (amount and type), tillage type, cover crop use, preseof different types of buffers, buffer widths, acreage affected by the buffers, presence of water control structures, acres affected by the water control structure, field slope, receivinleGranville), slope length was also determined. The survey instrument is attached at the end of this report (Appendix 3). Enumerators visited all segments to collect the necessary information. The agricultural community was extremely helpful with this survey, in that we only had only a few individuals decline to pacenumerator made note of this and continued. This was especially important since the land coverage data that we were using was from 1993. Fields that had become fallow were noted asuch. If producers did nosoil samples. About ¼ of the producers had soil samples from which the enumerators couldetermine the soil test P-Index amount; on the other ¾ of the fields they pulled soil samples ansent them to the NCDA&CS laboratory for analysis. Sample results were sent back to the producers. Erosion losses in the flatter coastal plain were estimated using tables developed by USDA-

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middle coastal plain, and upper coastal plain (Appendix 4). Soil loss was a function of the physiographic region and cropping system employed. For Franklin and Granville counties, soil

ss was calculated using RUSLE as both slope and slope length were determined.

ee

. . Many of these records

ere then subdivided if two crops (winter cereal and summer crop) were produced on the same

a useful format. We had to parate fertilizer applications into winter crops and summer crops. To determine the N and P

plied

dd each N or P addition to determine the total N r P applied to each crop. Sometimes nutrients were applied only once, but in some instances,

se most of the fertilizer is applied in the roceeding calendar year to when the crop is planted. In some fields two crops were produced

were using no-till techniques. If farmers used no-till for cotton, for example, and there was no winter cereal cover crop, this field was considered to be conventional tillage. If, however, a cover crop was grown, then this was noted as conservation tillage. In addition, to be considered a winter cereal cover crop, no N fertilizer can be applied to that crop. Some producers told us they were producing a winter cereal cover crop, but if this crop was fertilized with N, it had to be disqualified as such. Numerous transformations had to be made with the BMP data in order to characterize the different buffer types and the acreage that they affected. Vegetation characteristics and widths were determined; tree or shrub buffer were considered the same type and vegetative buffers were separated and considered to be grass buffers.

lo Each field survey was reviewed by three individuals: the enumerator’s supervisor, an employof NCDA&CS and the principle investigator of the project. Any potential irregularities discovered in the review were addressed with the enumerator to explain or revisit the sample siteThe data set consisted of 1,156 records, each record representing a fieldwfield. Soil maps were placed under the DOQQs in order to determine the predominant soil series of thefield. Once the quality of each survey was assured, all data was entered into a SAS database (SAS, 1985). Data Processing It took considerable effort and time to transform the data intoseamount per application for each record, we had to first determine the amount of N and P apat each application. We used fertilize application rate, N and P analysis of the nutrient material,and material type (e.g. liquid fertilizer, pounds fertilizer, or pounds elemental N). For crops with multiple applications of N or P, we then had to aothere were five applications of nutrients over the course of a growing season. When a winter cereal crop was grown in 2003 and it was fertilized, we considered the crop to be produced in the next year (in this case 2004), becaupfor a calendar year so the records were separated and stored as two unique records. Because of the double cropping, there are more data records than sample records. We had to be very careful about collecting tillage and cover crop information. To be consideredconservation tillage, 30% residue had to be left on the surface. Many farmers believed that they were using conservation tillage if they

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To accommodate different slope directions nstrument allowed for three separate ater flow directions per field, and thus three different types of possible buffers and/or ditch

drainage systems of two or more directions were the same, then ey were added into one BMP type for that field.

, the survey iwsystems. If buffers or controlledth The NCANAT was used to determine N and P losses. For the NCANAT program to run it musthave a minimum data set in order to process the data. If a minimum data set did not exist, the record was discarded. Each record (field) that contained the minimum data set was then run through the field version of NCANAT. Only 9 records could not be processed in PLAT and 65 in NLEW. (NLEW cannot run pasture conditions.) Nitrogen losses based on NCANAT results were summed for county and crop.

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RESULTS Agronomic and BMP Results Land Use Many segments (136) that were selected during the random selection were disqualified because

t (Table 1). The number of disqualified acres represented over es. Most of ents were visited to ensure that they were not in agriculture. In

addition, many other fields were idle, a few had been converted to development and some were in conservation reserve programs such as CRP or CREP. For fields that were converted to other

the first number is the number of fields and the second number is the acreage r example, in Franklin County 12 fields were found to be idle representing 98 acres.

he idle fields w ound in th er part of the basin in the Piedm egion in the o River Basin

1. Information o gments Th ere Not Enume d. Total

A

no agricultural fields were presen2,500 acr these segm

land uses,involved. FoMany of t ere f e upp ont rTar-Pamlic . Table n Se at W rateCounty*

g Acreage in Tar-Pam

Basin**

s sampled

NumbeFields – A

in Developm

Number Segments Not

in Agriculture

(#fields – acres)

Wildlif(# fields

acres) CRP fields –acres)

No Access or Refusal

141,620 4 1 – 10 35 1 – c 1 3 -- 3

e 109,890 2 .9 7 1 – 15 a ranklin 31,950 759.6 2 8 12 – 98 ac 1 – 15 ac ranville 8,600 333.5 7 6 – 113 ac 2 – 4 ac 1 – 4 ac

61,800 1,232.6 12 9 – 76 ac 3 – 23 ac 3 – 89 ac 1

2 – 40 ac 1 – 10 ac ashington 14,820 430.0 4

he number of drainage directions within each field is presented in Table 2. The maximum umber of drainage directions was 3 and the minimum 1. Counties near the coast (Beaufort,

mlico, Washington and even Wilson) had on average only 1 drainage direction. ranklin County had the most number of drainage directions (average of 2), whereas dgecombe, Granville, Halifax, Martin, Nash and Pitt had average drainage directions closer to 1 ange of 1.1-1.3).

Acre r of cres

ent

Idle e CREP/ –

(#

Beaufort Dare

1,956. ac 30 a0

Edgecomb ,146 c FGHalifax Hyde 49,000 1,207.4 37 1 – 15 ac Martin 18, 250 493.3 3 1 – 20 acNash 61,600 1,231.2 5 – 15+ ac 5 6 – 42 ac 1 – 15 ac 1 Pamlico 1,975 104.2 1 - 25 ac 5 1 – 5 ac Pitt 70,760 1,501.3 1 – 10 ac 9 WWilson 14,940 317.1 1 1 – 5 ac * Counties not sampled: Person (1,680); Vance (5760 acres); Warren (10,230 acres) ** 2003 total ag acreage In all, 1,156 fields were selected representing 11,714 acres. The average field size ranged from less than 1 to 205 acres with a mean of 10.1 acres per field and a standard deviation of 12.8 acres. Drainage Directions TnHyde, PaFE(r

8

Drainage direction is important because edge-of-field BMPs (e.g. controlled drainage structures, riparian buffers) may change if there is more than one drainage direction. In addition, since slope may vary with each drainage direction, soil erosion may differ. Sampling fields with fewer drainage directions is easier. It was not unexpected that number of drainage directions decreased closer to the coast where the landscape is flat. Table 2. Drainage Directions. County Number

of Fields Minimum Drainage Directions

Maximum Drainage Directions

Mean

Beaufort 237 1.0 2.0 1.0 Edgecombe 146 1.0 3.0 1.3 Franklin 100 1.0 3.0 2.0 Granville 49 1.0 3.0 1.1 Halifax 107 1.0 3.0 1.2 Hyde 57 1.0 1.0 1.0 Martin 58 1.0 3.0 1.3 Nash 137 1.0 3.0 1.2 Pamlico 40 1.0 1.0 1.0 Pitt 126 1.0 2.0 1.1 Washington 34 1.0 1.0 1.0 Wilson 62 1.0 2.0 1.0 Cover Crops Winter cereal cover crops are one of several BMPs that can reduce both N and P. Although producers answered a question about cover crop use, we did not use their answer, but rather selected cover crops based on N fertilizer rates. To qualify as a winter cereal cover crop, no N fertilizer can be applied to wheat, oats, rye or triticale. Table 3 lists the acres of the different cover crop types by county and the percentage of the county acreage that these practices comprised for the 1999/2000 winter season. Two counties (all lower coastal plain) did not use a cover crop. Further in the upper coastal plain and piedmont, wheat was the predominant winter cereal cover crop used by producers in the Tar-Pamlico River Basin; the exception was Franklin County where rye cover crop use was greater than wheat. The lowest county acreage in the basin for cover crop use was Beaufort (0.1 %) and the greatest was Halifax (59.0 %), followed by Franklin (57.5 %), Nash (39.1 %), Martin (24.7%), Edgecombe (15.6 %), and Wilson (11.6 %). Washington, Granville, Pitt and Washington had fewer than 10% of the total agricultural acres in cover crops. Producers did not fertilize cover crops with either phosphorus or potassium.

9

10

Table 3. Acres and Percentage of Cover Crops by County. County A Wh

Cover Acres

Rcres sampled

eat % of l

TotaAcres

ye % ToCover

Acres

of tal

s

esToAcres

eaufort 4 2.0 0. 0.0 .0 0.0gecombe 2, 334.6 0.0 0.0 0 0.0

ranklin 24.5 48.0 6.3 .0 0.0ranville 3.4 0. 0.0 .0 0.0alifax 726.8 0.0 0.0 0 0.0yde 0.0 0.0 0.0 .0 0.0artin 121.9 0. 0.0 .0 0.0ash 2 226. 0. 0.0 .7 4.2

0.0 0 0.0 .0 0.0 81. 0. 0.0 .0 0.0

ashington 0 27.0 0. 0.0 0.0ilson 1 36.9 0.0 0.0 0.0

onservation Tillage or a field to CT, a 30 due co st be ained. If producers selected onservation y ha ropping m that w d produce th 30% cover, we

re using CT but did not have a ropping sys ould pro the mini cover, such as cotton following cotton without winter cove that field w ignate nvent tillage.

ps, s sweet potat tobacco vegeta re almost always produced under onal (Table 4). T emainin ps wer times produced under

onventional e and someti s CT. F ample, in three counties all of the corn was roduced usi gecomb nklin, a artin), s in two counties all corn was roduced und tional t Pamlic Wilson). Of the field crops, cotton was the ast likely to ced using CT; six counties had no conservation tillage of cotton and the ighest rate o 52% of the acreage in Halifax County.

ll crop acre ummed in n coun ine the total ercent of all ds under CT (CT acres/Total acres = ). Pamlico and Wilson County

ashington County used CT on 97.5% of the crop acreage. The next e o as Halifax ( ), follo by Bea (57.0%), M rtin (56.7%),

ranville (51.4%), Franklin (45 Pitt (40 , Hyde %), Edgeco be (30.1%), and ash (24.8% e of CT r s crop rotations, topography, and acceptance of this ractice.

cluded in T is a column T in NLd by lico tech and p d in th 4 Tar-Pam

the Enviro al Managem ommiss MC). CT funded hrough cost-share rograms are ar-Pa accoun report. The CT funded through cost-share are

ilson County) than tha practiced by farmers. The greatest discrepancy as in Wash unty; th y show at 97.5anagement e cost ccoun entifie y 5.8% of C usage.

Acres

OatCoverAcr

% of tal

B 1,956. 0.1 0 00.Ed

F146.9 759.6

15.63.20 0

G 333.5 1.0 0 0HH

1,232.6 1,207.4

59.00.0

0.0

M 493.3 24.7 0 0N 1,231. 7 18.4 0

.020

Pamlico 10Pitt

4.2 1,501.3

0.00 5.4

000

W 430. 6.3 0 0.0W 317. 11.6 0.0 CF be under % resi ver mu maintcdesignated these fields as CT. If

tillage and the d a c, however, producers said they we

syste oul e

c tem that w duce muma r crop, as des d as co ional Some croconventi

such a tillage

oes, he r

and g cro

bles wee some

c tillag mes a or exp ng CT (Ed e, Fra nd M whereap er conven illage ( o and leh

be produf CT was

A s were s a give ty as were acreage of CT to determp ag lan % CThad no CT, whereas Whighest us f CT w 62.4% wed ufort aG .1%), .8%) (39.6 mN ). The us eflectp In able 4 (% C EW), which displays the percentage of CT identifie Tar-Pam nicians ublishe e 200 lico accounting report to nment ent C ion (E Only tpmuch lower (excep

used in the Tt for W

mlico ting t

w ington Co is surve ed th % of the fields were under CT m , whereas th -share a ting id d onl T

11

Table 4. Ac ntag onserv Tillag County. ounty Total Acres CT % % CT in NLEW eaufort C 6

C 7P 0 0 S s 7 6 8 T W 4 3 T 2 1 5 4

mb

C 1 2 2

C 2 21 10 O 10 P 2 R 5 8 1 S 4 2 5 S otato T 0 0 W 3 2 8 T 25 75 3 7.

ranklin C 7 10 R 1 S 1 1 10

S 343.7 133.7 38.9 T 6 0 0 V 0 0 W 1 8 5 T 2 4 1O 3 3 8 S s 5 5 9 T 6 0 0 W 4 3T 1 8 5 2.

alifax C 6 31 5 C 4 2 5 C P 1 2 2

res and Perce e of C ation e byC Crop Acres CT B otton 05.0 0.0 0.0 orn 489.0 366.8 5.0 eanuts 12.0 .0 .0 oybean 81.6 28.5 0.4 obacco 39.8 0.0 0.0 heat 05.7 34.2 82.4 otal 333.1 329.5 7.0 2.4 Edgecoe

otton 067.6 13.6 0.0

orn 19.2 9.2 0.0 ats 4.5 4.5 0.0 eanuts 37.5 0.0 0.0 ye 0.7 .7 7.2 oybean 87.9 46.9 0.6 weet P 9.0 0.0 0.0 obacco 94.3 .0 .0 heat 44.6 77.6 0.6 otal 15.3 6.9 0.1 0 F orn 7.0 .0 0.0 ye 48.0 48.0 00.0 orghum

oybean 9.6 9.6 0.0

obacco 8.2 .0 .0 egetable 8.5 .0 .0 heat 45.4 0.5 5.4 otal 640.4 88.8 5.1 2.5 Granville ats 6.7 0.6 3.3 oybean 6.8 3.6 4.4 obacco 7.3 .0 .0 heat 12.0 .6 8.3 otal 72.8 8.8 1.4 7 H otton 18.2 9.5 1.7 orn 0.3 3.3 7.8 orn Silage 28.4 0.0 0.0 eanuts 42.7 9.2 0.5

12

S s 2 1 5 S otato T o 6 W 7 7 9 T 1 1 10.8

yde C 6C 1 8 5 S s 3 36 10 W 4 4 10 T 12 49 3 13.3

artin C 2 10 4 C 5 5 0 P s 7 0 0Sorghum 28.0 28.0 0.0

94.2 58.0 61.6

Oats 20.7 3.5 16.9

s .7 56.8 SPo

7 0.0

To 0.0

Peanuts 57.8 0.0 0.0 Soybeans 761.0 421.9 55.4

oybean 04.1 04.0 1.0 weet P 33.8 0.0 0.0 obacc 3.1 0.0 0.0 heat 67.8 08.3 2.3 otal 898.4 184.3 62.4 H otton 93.9 0.0 0.0 orn 47.5 7.5 9.3 oybean 66.0 6.0 0.0 heat 0.0 0.0 0.0 otal 47.4 3.5 9.6 M otton 26.6 0.7 4.4 orn 0.2 0.2 .0 eanut 0.9 .0 .0 Soybeans Tobacco 20.4 0.0 0.0 Wheat 121.9 110.0 90.2 Total 612.2 346.9 56.7 NA Nash Cotton 314.1 0.0 0.0 Corn 83.3 42.8 51.4

P s .0 0.0 eanutSoybean

20.1 410

0233.3

weet tatoes

3.0 0.0

bacco 84.6 0.0 Vegetables 103.9 0.0 0.0 Wheat 226.7 52.1 23.0 Total 1337.1 331.7 24.8 1.0

25.9 0.0 0.0 Pamlico Cotton Corn 14.0 0.0 0.0 Soybeans 64.3 0.0 0.0 Total 104.2 0.0 0.0 NA Pitt Cotton 309.2 74.0 23.9 Corn 129.7 7.5 5.8

13

Tobacco 169.1 0.0 0.0 Wheat 225.0 171.0 76.0 Total 1651.8 674.4 40.8 NA Washington

Corn 64.5 61.5 95.3

Soybeans 365.5 351.5 96.2 Wheat 241.5 241.5 100.0 Total 671.5 654.5 97.5 5.8 Wilson Cotton 112.8 0.0 0.0 Corn 38.2 0.0 0.0 Sweet Potato 51.3 0.0 0.0

57.8 0.0

Tot 4 0.0 0.0 2

ntrol St res control s s ar to contro shallo oundwate depths eral slop than 1% ng t ing boa place lashboard isers a ove low t

dwater tab rop, rying il for planting. After planting, the boards are ed to incr the of the gro dwat le. Gene lly, bo and P educe

use o e st s. Th ber of acres affected by W d the nt of land ted BMP are presented in Table 5.

le 5. Acres erce of W Contr ructures by County.

most of the agricultural creage affected was in either Pamlico or Washington Counties. Approximately 62% of all acres

in the Tar-Pamlico River Basin is unsuit S.

Riparian buffers Several buffer types are recognized under the Tar-Pa es n or in

or ater ised o at leas feet 0 fe f veg , 2) 30-foot or greater vegetative buffers, 3) 20 to 29 foot- buff or 4) 29 ee or ub b R buf ata wn

9. In ation collected was buffer vegetation type (tree, shrub vegeta e), bund num of c ac t flow roug uff

Soybeans 57.0 0.0 0.0 Tobacco 0.0 Wheat 36.9 0.0 0.0 al 35 3.1 Water

Co ructu

Water tructure e used l w gr r , gen ly when es are less . Duri he spr rds d in f r re rem d to al he groun le to d thus d up soreinstall ease depth un er tab ra th N are r d through the

ralf thes ructure e num CS an perce

agricultu s affec by this

abT and P ntage ater ol St County

Acres sampled

Acres Affected by WCS

% of Total Acres

Beaufort 1956.4 70.7 3.6Halifax 1232.6 0.5 0.04Pamlico 104.2 64.3 61.7Washington 430.0 117.0 27.2 Water control structures were only identified in four counties andawere affected by WCS in Pamlico and 27% in Washington County. The topography of mostcounties able for WC

m Rul and givecompr

credit f reduc g N. of These buffer types are as follows: 1) 50-foot buffer gre f t 30

trees and 2ve

et o etationvegetati er, 20- to -foot tr shr uffers. iparian fer d are sho in Tables 6- form or tiv ffer width, a ber ropped res tha th h the b er.

14

We are confide hat t fer very id. Ea MP was viewed ainst

OQQ m by t fes – on rom USDA-NASS and the other f C Sity. Re rkabl e h alm no changes in buffer placement and pe or

tche The e te some fields that were no longer farmlarly in dly u zing es. N data was collec n thes ields bu hang

use can b nd i le 1.

72 (see

onversely, counties that have buffers present next to streams, such as Edgecombe, ranklin and Granville (, have few streams that are not buffered.

d Percentage of this Area with no Buffers by Drainage n and County.

Direction A irection B Directio Total

nt t he buf data is sol ch B record re ag the 1993 D

versaps wo pro sionals e f rom N tate

Uni ma

y, thern erators did loca

ad been ost ty drainage di s. um ed, particu rapi rbani counti o ted o e f t c es in field e fou n Tab Agricultural land in counties close to the estuary use drainage cannels to move water. Most ofthese counties have little vegetation next to the drainage ditches and therefore few buffers. Thus counties such as Beaufort, Hyde, and Pamlico have a large amount of area without buffers – to 98% (Table 7). These counties, however, have the greatest amount of controlled drainage Table 6). CF Table 6. Number of Acres anDirectio

D n C County # o A cres res % # of A % Acres f Acres % cres # of A % Ac # of Acres Acres cres

Beaufort 7 12 6 72.06 1396.8 1.40 .9 0.6 0.00 1409.7 Edgecombe 7 3.37 87.3 4.07 15 7.44 2.4 0.00 9.7 Franklin 1 1.38 13.2 1.74 16.8 1 4 5.33 0.5 2.2 0.5 Granville 2 7.2 0.00 2 .29 4.3 9 0.00 4.3 7Halifax 46 3.73 0.00 0 3.96 0.0 46 Hyde 119 98.92 0.00 0 119 98.92 4.4 0.0 4.4 Martin 15 30.91 14 2.84 .00 166 .75 2.5 0 .5 33Nash 20 16.8 61.8 5.02 9.9 0 27 24.00 6.9 0 0.8 8.6 Pamlico 6 61.7 0.00 0 6 61.71 4.3 1 0.0 4.3 Pitt 84 56.60 47.8 3.18 0 89 59.78 9.7 0.0 7.5 Washington 1 45.58 0.00 0 45.58 96 0.0 196 Wilson 19 60.67 14.1 4.45 20 65.12 2.4 0.00 6.5All 440 37.62 251.1 2.14 26.7 4 39.99 6.2 0.23 684 Five counties(Beaufort, Granville, Nash, Pitt, and Wilson) had the greatest amount of agricultural land affected by tree/shrub buffers, while five (Edgecombe, Halifax, Martin, Pamlicoand Washington) had more buffers affected vegetated buffers (Table 7). Only one county, Franklin, had most the acres affected by mixed buff

ers (tree and vegetated) (Table 7). No mixed uffer types (tree and vegetated) were enumerated in Hyde, Martin, Pitt or Wilson counties. pper basin counties, such as Franklin and Granville, had the majority of their field acres

buffered with over 92% of the agricultural lands next to buffers. Table 7. Number of Fields, Affected Acres and Percentage of Buffers by Buffer Type.

Buffer Type

bU

Shrub/Tree Vegetated Shrub/Tree + Vegetated County

# Fields Acres % Total Acres



Beaufort 37 259.1 13.39 11 63.4 12.63 6 31.2 1.59 Edgecombe 29 202.5 13.39 64 737.2 48.83 55 648.9 30.22 Franklin 60 202.5 34.29 65 272.1 19.02 71 315.4 41.52

15

Granville 36 198 68.58 1 0.8 22.07 1 7 2.10 Halifax 32 312.6 28.48 30 196.3 57.95 21 140.2 12.08 Hyde 3 13 1.08 0.00 0 0 0.00 0 0 Martin 19 12 0 0.00 8.9 31.08 14 37 35.21 0 Nash 6 51 1 95.1 16.81 5 7.3 45.10 29 189 2.70 31 1Pamlico 4 12 11.52 1 2 1.92 1 2 24.86 Pitt 3 1 41 0 0 0.00 6 28 .8 23. 1 3 16.81 Washington 7 5 7 2 28 28.14 6.51 8 19.7 2 28 Wilson 18 .6 3 0 1.39 0.00 88 31.6 0 0 0 All 354 3 5 2 1528.8 25.16 8 136 11.82 301. 5.2 18 18 7.8

eeShrub/trranged fr

buffers ran from a th of 1 fo o 3,500 , while ve ated buffe idths om 1 foot 1,000 feet (Table 8). In general, the mean buffer width met the Tar-Pamlico

buffer width of feet; only Hyde, Pamlico, Washington and Wilson counties had buffer widths than 5 et. Three of these counties had considerable controlled

e 8. Minimum, Ma imum and Mean Buffer Width of Shrub/Tree Buffer T es.

Buffer Type

ged wid ot t feet get r w

requiredaverage

50less 0 fe

drainage. Tabl xor Vegetated yp

Shrub/Tree Vegetated County

Minimum Maximum (ft)

Mean Widths (ft)

Minimum Widths (ft)

Maximum Widths (ft)

Mean Widths (ft) Widths (ft) Widths

Beaufort 6 150 50 4 30 14 Edgecombe 10 600 158 3 400 15 Franklin 5 1400 199 5 1000 85 Granville 20 1000 160 5 200 80 Halifax 10 500 100 3 100 20 Hyde 20 20 20 Martin 5 1055 79 5 250 20 Nash 10 3500 500 5 100 35 Pamlico 30 30 30 30 30 30 Pitt 1 600 130 3 75 11 Washington 5 5 5 1 10 9 Wilson 1 160 45 20 75 48 Buffer widths in mixed buffers (tree and vegetative) were measured for both the tree/shrub portion and the vegetated portion (Table 9). Buffer widths ranged from a minimum of 5 feet for

e tree/shrub portion of the buffer and a maximum of 3200 feet. The vegetative portion of the ixed buffer had width ranges from 2 to 500 feet.

thm

16

Table 9. Minimum, Maximum and Mean Buffer Width of Tree/Shrub and Vegetated uffer Types.

Mixed Buffer Type B

Tree/Shru veb Vegetati County

Min Width

n Widths Widths Widths

n Widths s

Max Widths

Mea Min Max Mea

Beaufort 8 0 10 100 4 4 40Edgecombe 5 0 50 600 10 3 100Franklin 5 5 60 3200 100 5 00Granville 50 0 20 50 5 20 20Halifax 5 0 15 100 7 5 30Hyde Martin Nash 10 400 50 5 100 15 Pamlico 15 50 50 50 50 50 Pitt Washington 5 20 18 2 3 3 Wilson Nitrogen and Pho rus R

age N, P otass ) rates w sorted by county and crop and presented as of acres o rticula surveyed tal acres of the crop, number of fields surveyed

erage N, P rates 10 a-ii) utrient data ere dete ed by ng all relevant , including that cou ot be run rough NC AT becau of missing information.

organic waste as fertilizer. One field applied septage and the tes wo . Therefore,

ts comm ia ra The survey demonstrates that there were few instances of fertilizer over-application for any of

trients (Tab a en is still ing applied o soybeans but at low rates. orus is sti g app fairly lar mounts to bacco. Pastures, cover crops and are usually being ed with er N or P.

spho ates The aver and p ium (K ere number f a pa r crop , toand av and K (Table . N w rmin usirecords those ld n th AN se Only two of the selected fields used

pulle d uld noother the nutrient data presented represen

t manure an we co

-ii). Nitro

t determineerc

nutrient ral fertilizer

for these ttes.

fields

the nuph

le 10ll

g be tPhos bein lied in

fege ae

topeanuts not rtiliz ith

17

Beaufort County Table 10 a. Nitrogen Rates 2004 Crop Fiel

Samt

CouCro

P Basin

bers

um Rate

mte ate

d Acres pled

To al nty p Acres

NumField

in T

of MinimN

MaximuN Ra

Mean N R

Wheat 403.7 28,615 56 18 4.1 1113 1 Corn 489.0 44,329 59 57 204 149 Peanuts 12 267 1 0 0 .0 0 Soybe 781. 61,013 117 0 12 2 an 6 Tobacco 39.8 2,750 4 87 180 111 C 605.0 21,146 28 0 103 60 otton Pasture 21.5 -- 6 30 30 30 Fruits -- 2 100 100 15.5 00 Table 10 b. Phosphorus Rates 2004

Acres Sampled

Total County

cTP

Number of Fields

Minimum P Rate

Maximum P Rate

Mean P Rate

Crop Field

Crop A res in

Basin Wheat 403.7 56 0 48 9 28,615 Corn 489.0 44,329 59 0 143 64 Peanuts 12.0 267 1 0 0 0 Soybea 781. 61,013 117 0 92 n 6 8 Tobacco 39.8 2,750 4 47 180 84 Cotton 605 21,146 28 0 100 45 .0 Pasture 21 -- 6 0 0 .5 0 F 5.5 -- 2 40 40 40 ruits Table 10 c. Potassium Rates 2004 Crop Field Acres

pled tal

ounty Crop Acres in

Number of Fiel

Minimum K Ra

Maximum K Rat

Mean K Rate Sam

ToC ds te e

TP Basin Wheat 403.7 28,615 56 0 144 29 Corn 489 44,329 59 0 160 100 Peanuts 0 12.0 267 1 0 0 Soybean 781.6 117 0 144 13 61,013 Tobacco 39.8 2,750 4 140 540 253 Cotton 605. 21,146 28 0 114 45 0 Pasture 21 -- 6 0 0 .5 0 Fruits 5 -- 2 40 40 .5 40

18

Edgecombe County Table 10 d. Nitrogen Rates 2004 Crop Field Acres

Sampled Total County Crop Acres in TP Basin

Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Oats 4.5 317* 1 12 12 12 Rye 5 7 70.7 -- 4 9 79 9

Wheat 110.0 4,455 1 3 13 13 Corn 2 9,2 17 119.2 07 18 172 40

Peanuts 237.5 11,563 10 0 0 0 Soybean 48 34,1 37.9 55 4 0 15 4

Sweet Potato 9.0 3,861 1 48 48 48 Tobacco 4,094.3 29 13 0 84 62

Cotton 1067.6 45,540 69 0 116 72 Pasture 27.4 -- 1 79 79 79

Wildlife 4.0 -- 1 0 0 0 *Last data 2002

10 e. Phosph R 4Field Acres Sampl

Total CounCrop AcrTP Basin

Number oFields

MinimumRate

MaximumRate

Mean P RTable orus ates 200 Crop

ed ty

es in f P P ate

Oats 4.5 317* 1 0 0 0 Rye 50.7 -- 4 0 0 0

Wheat 10.0 4,455 1 25 25 25 Corn 219.2 9,207 17 13 28 18

Peanuts 23 11,567.5 3 10 0 0 0 Soybean 487.9 34,155 34 0 38 14

Sweet Potato 9.0 3,861 1 15 15 15 Tobacco 94.3 4,029 13 0 156 53

Cotton 0 1067.6 45,54 69 0 80 14 Pasture .4 -- 1 0 0 0 27


f. Potassiu tes 2004 Field Acres Sampled

Total CouCrop Acres in TP Basin

Number Fields

Minimum KRate

MaximumRate

Mean K R

Table 10 m RaCrop nty of K ate

Oats 314.5 7* 1 0 0 0 Rye 50.7 -- 4 0 0 0

Wheat 10.0 4,455 1 75 75 75 Corn 219.2 9,207 17 63 114 92

Peanuts 237.5 11,563 10 0 100 39 Soybean 487.9 34,155 34 0 120 65

Sweet Potato 9.0 3,861 1 36 36 36 Tobacco 94.3 4,029 13 0 252 155

Cotton 1067.6 45,540 69 0 188 99 Pasture 27.4 -- 1 0 0 0


19

Franklin County Table 10 g. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Wheat 1 4, 10 20 120.9 365 30 97 Corn 7.0 1,170 2 0 37.5 5 Other Hay 2 1 2 114.3 0,350 19 0 02 00 Sorghum 19.6 -- 2 3 14 80 8 9 Soybeans 3 143.7 7,550 44 0 50 7 Vegetables 9 9 98.5 -- 1 7 7 7 Tobacco 68.2 3,150 12 21 94 73 Pasture 79.0 -- 19 0 840 51 Table 10 h. P us RCrop Field Acres

Samplety

Crop Acres TP Basin

Number of Fields

Minimum P Rate

Maximum P Rate

Mean P Rate hosphor ates 2004

Total Cound in

Wheat 120.9 4,365 10 0 38 7 Corn 7.0 1,170 2 13 13 136 6 6 Other Hay 2 114.3 0,350 19 0 120 58 Sorghum 4 219.6 -- 2 0 0 0 Soybeans 343.7 17,550 44 0 70 12 Vegetables 8.5 -- 1 54 54 54 Tobacco 68.2 3,150 12 12 136 81 Pasture 79.0 - - 19 0 770 42

10 i. Potassiu tes 2004 Field Acres Sample

Total CounCrop AcreTP Basin

Number Fields

Minimum KRate

MaximumRate

Mean K R

Table m RaCrop

d ty

s in of K ate

Wheat 120.9 4,365 10 0 150 34 Corn 7.0 1,170 2 180 180 180 Other Hay 214.3 10,350 19 0 120 60 Sorghum 19.6 -- 2 0 50 25 Soybeans 343.7 17,550 44 0 420 46 Vegetables 8.5 -- 1 135 135 135 Tobacco 68.2 3,150 12 32 232 168 Pasture 79.0 -- 19 0 30 2

20

Granville County Table 10 j. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Oats 36.7 108 5 53 80 74 W 1,24heat 8.6 7 2 53 53 53 Other Ha 12 y 40.2 4,472 0 78 35 Soybe 17 0 0 0 ans 56.8 1,376 Tobacco 6 767.3 1,909 10 8 78 4 P 1asture 30.0 -- 4 0 0 0 Table Phosph Rates 200

Acres Sampled

Total County Crop Acres in

Number oFields

Minimum P Rate

Maximum PRate

Mean P Rat 10 k. orus 4

Crop Field f e

TP Basin Oats 36.7 108 5 0 30 16 Wheat 6 7 2 30 30 30 8. 1,24Other Hay 40.2 4,472 12 0 39 10 Soybea 17 0 0 0 ns 56.8 1,376 Tobacco 67.3 1,90 10 63 104 87 9 Pasture 130.0 -- 4 0 0 0 Table 10 l. Potassium Rates 2004 Crop Field Acres

SampleTotal CounCrop AcreTP Basin

Number oFields

Minimum Rate

MaximumRate

Mean K Rd

ts in

y f K K ate

Oats 3 1 4 26.7 08 5 0 2 3 Wheat 8.6 1,247 2 30 30 30 Other Hay 40.2 4,472 12 0 39 10 Soybeans 6 56.8 1,37 17 0 0 0 Tobacco .3 9 10 126 190 162 67 1,90Pasture 130.0 -- 4 0 0 0

21

Halifax County Table 10 m. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Wheat 41.0 2,580 2 62 62 62 Corn silage 28.4 0 5 42 115 57

Corn 6 140.3 2,580 8 5 135 05 Peanuts 142.7 7,464 8 0 18 2

Soybean 204.1 14,280 28 0 22.5 8 Sweet Potato 33.8 240 4 46 46 46

Tobacco 63.1 1,137 9 0 84 64 Cotton 0 618.2 34,80 44 20 105 73 Pasture 0 1 0 0 0 7. --

10 n. Phospho Rates 200

Field Acres Sample

Total CouCrop AcrTP Basin

Number Fields

Minimum P Rate

MaximumRate

Mean P RaTable rus 4 Crop

d nty es in

of P te


Corn 0 40.3 2,58 8 0 54 19 Peanuts 7 4 8 0 54 7 142. 7,46

Soybean 204.1 ,280 28 0 54 13 14Sweet Potato 33.8 240 4 60 60 60

Tobacco 63.1 1,137 9 0 84 47 Cotton 618.2 34,800 44 0 54 25 Pasture 7.0 -- 1 0 0 0

Table 10 o. Potassium Rates 2004 Crop Field Acres


Number of Fields

Minimum K Rate

Maximum K Rate

Mean K Rate


Corn 40.3 2,580 8 60 122 91 Peanuts 142.7 7,464 8 0 108 39

Soybean 204.1 14,280 28 0 500 103 Sweet Potato 33.8 240 4 196 196 196

Tobacco 63.1 1,137 9 0 252 140 Cotton 618.2 34,800 44 0 393 91 Pasture 7.0 -- 1 0 0 0

22

Hyde County Table 10 p. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Wheat 40.0 4,830 1 350 350 350 Corn 1 1 11 74 17 147.5 8,970 .1 3.1 46 Soybeans 3 17,9 166.0 90 3 0 140 11 Cotton 6 6 193.9 9,800 34 5 00 88 Table 10 q. Phospho Rates 2004

rop Field Acres Total County Number of Fields

Minimum P Rate

Maximum P Rate

Mean P Rate rus

CSampled Crop Acres in

TP Basin Wheat 0 40 4,83 1 0 0 0 Corn 147.5 0 11 7 100 77 18,97Soybeans 1 13 0 0 0 366 7,990 Cotton 6 34 93.9 9,800 0 65 49 Table 10 r. Potassiu ates 2004

Field Acres Sampl

Total CountCrop AcreTP Basin

Number oFields

Minimum Rate

MaximumRate

Mean K Rm R

Crop ed

y s in

f K K ate

Wheat 40 4,830 1 120 120 120 Corn 147.5 18,970 11 2 150 47 Soybeans 366 17,990 13 0 200 15 Cotton 0 693.9 9,80 34 60 80 76

23

Martin Table 10 s. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Corn 1 1 150.2 1,100 5 43 48 45 Peanuts 70.9 3,103 7 0 21 11 Sor 2 13 13 13ghum 8.0 60 2 7 7 7 Soybe 10 2 1an 94.2 3,750 8 3 6 Tobacco 3 620.4 776 6 0 89 5 C 2 2 1otton 26.6 9,700 5 0 10 93 Wi 2ldlife 3.0 -- 3 2.5 22.5 23 Table hosph ates 200

Acres Sampled

Total County Crop Acres in

Number oFields

Minimum P Rate

MaximumRate

Mean P Ra 10 t. P orus R 4

Crop Field f P te

TP Basin Corn 50.2 1,100 5 35 36 36 Peanuts .9 3 7 0 30 13 70 3,10Sorghum 28.0 60 2 18 18 18 Soybea 10 n 94.2 3,750 0 30 21 Tobacco 168 92 20.4 776 6 30 Cot 22 25 6 3ton 6.6 9,700 0 0 6 Wildli 2 2 2fe 3.0 -- 3 3 3 3 Table 10 u. Potassium Rates 2004 Crop Field Acres

SampleTotal CountyCrop AcreTP Basin

Number oFields

Minimum KRate

MaximumRate

Mean K Rd

s in

f K ate

Corn 1,1 10 10 1050.2 00 5 5 8 7 Peanuts 70.9 3,103 7 0 114 72 Sorghum 28.0 60 2 119 119 119 Soybean 0 94.2 3,75 10 68 135 100 Tobacco .4 6 6 90 207 158 20 77Cotton 226.6 9,700 25 0 152 113 Wildlif 1 1 1e 3.0 -- 3 35 35 35

24

Nash County Table 10 v. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Wheat 3,12.5 120 2 48 48 48 Corn 12 15 1383.3 1,920 10 1 2 1

Other Hay 5,23.5 00 1 0 0 0 Peanuts 20.1 2,492 3 0 0 0

Soybeans 410.7 24,960 59 0 25 4 Sweet Potato 73.0 4,800 9 30 75 66

Vegetables -- 103.9 9 30 132 109 Tobacco .6 6 13 60 99 82 84 4,99

Cotton 314.1 ,720 29 0 94 64 14Pasture 4 55.0 -- 0 80 20

Table 10 w. Phosphorus Rates 2004

Number of Fields

Minimum P Rate

Maximum P Rate

Mean P Rate Crop Field Acres Sampled

Total CountyCrop Acres in TP Basin

Wheat .5 0 2 15 15 15 12 3,12Corn 83.3 1,920 10 17 28 25

Other Hay 5, 1 0 0 0 3.5 200 Peanuts 3 0 0 0 20.1 2,492

Soybeans 4 24,9 59 0 25 5 10.7 60 Sweet Potato 73.0 4,800 9 0 50 19

Vegetables 103.9 -- 9 0 63 21 Tobacco 84.6 4,996 13 0 80 53

Cotton 314.1 14,720 29 0 25 14 Pasture 55.0 -- 4 0 0 0

Table 10 x. Potassium Rates 2004 Crop Field Acres


Number of Fields

Minimum K Rate

Maximum K Rate

Mean K Rate

Wheat 12.5 3,120 2 90 90 90 Corn 83.3 1,920 10 60 148 125

Other Hay 3.5 5,200 1 0 0 0 Peanuts 20.1 2,492 3 0 0 0

Soybeans 410.7 24,960 59 0 126 28 Sweet Potato 73.0 4,800 9 48 200 149

Vegetables 103.9 -- 9 75 135 102 Tobacco 84.6 4,996 13 60 222 166

Cotton 314.1 14,720 29 0 188 107 Pasture 55.0 -- 4 0 0 0

25

Pamlico County Table 10 y. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Corn 1 114.0 490 5 76 176 76 Soybeans 64.3 1005 31 0 0 0 Nuts 25.9 -- 4 0 0 0 Table hosph Rates 200Crop Field Acres


Number oFields

Minimum P Rate

Maximum PRate

Mean P Rat 10 z. P orus 4

f e

Corn 14.0 490 93 93 93 Soybeans 64.3 1005 0 0 0 Cotton 25.9 -- 0 0 0 Table 10 aa. Potassium Rates 2004 Crop Field Acres

SamplTotal CounCrop AcreTP Basin

Number oFields

Minimum KRate

MaximumRate

Mean K Red

ty s in

f K ate

Corn 6 6 14.0 490 0 60 0 Soyb 6eans 64.3 1005 0 60 60 Cotton 25.9 -- 0 0 0

26

Pitt County Table 10 bb. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Wheat 124.0 6,032 5 90 100 92 Corn 1 10 29.7 9,222 44 159 115

Peanuts 57.8 2,816 9 0 0 0 Soybeans 761.0 32,712 61 0 44 4 Tobacco 169.1 2,004 19 0 103 73

Cotton 0 309.2 20,10 22 15 146 79 Pasture 0 1 0 0 0 5. --

Wildlife 7.5 -- 1 0 0 0 Sod 62.0 -- 3 8 8 8

Table 10 cc. Phosp Rates 200

rop Field Acres Total County Number of Fields

Minimum P Rate

Maximum P Rate

Mean P Rate horus 4

CSampled Crop Acres in

TP Basin Wheat 0 2 5 0 45 36 124. 6,03

Corn 129.7 9,222 10 0 40 22 Peanuts 57.8 2,816 9 0 0 0

Soybeans 7 3 661.0 2,712 1 0 36 5 Tobacco 169.1 2,004 19 0 82 51

Cotton 309.2 20,100 22 0 30 22 Pasture 5.0 -- 1 0 0 0

Wildlife 7.5 -- 1 0 0 0 Sod 62.0 -- 3 0 0 0

Table10 dd. Potassium Rates 2004 Crop Field Acres


Number of Fields

Minimum K Rate

Maximum K Rate

Mean K Rate

Wheat 124.0 6,032 5 10 100 28 Corn 129.7 9,222 10 30 136 92

Peanuts 57.8 2,816 9 0 0 00 Soybeans 761.0 32,712 61 0 112 27 Tobacco 169.1 2,004 19 0 210 155

Cotton 309.2 20,100 22 22 140 83 Pasture 5.0 -- 1 0 0 0

Wildlife 7.5 -- 1 0 0 0 Sod 62.0 -- 3 7 7 7

27

Washington County Table 10 ee. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

W 2 1heat 14.5 2,432 7 98 100 99 Corn 64.5 4,750 6 9 1 10 60 30

Soybeans 3 5, 165.5 852 28 0 60 19

Table Phosp Rates 200Crop Field Acres Total County

Number of Fields

Minimum P Rate

Maximum P Rate

Mean P Rate 10 ff. horus 4

Sampled Crop Acres inTP Basin

Wheat 2 214.5 2,43 17 40 46 43 Corn 64.5 4,750 6 0 60 50 Soybeans 3 28 6 365.5 5,852 0 0 1 Table 10 gg. Potass ates 2004

Field Acres Sample

Total CountCrop AcreTP Basin

Number Fields

Minimum Rate

MaximumRate

Mean K Rium R

Crop d

y s in

of K K ate

Wheat 214.5 2,432 17 60 90 76 Corn 64.5 4,750 6 0 120 93 Soybeans 365.5 5,852 28 0 120 55

28

Wilson County Table 10 hh. Nitrogen Rates 2004 Crop Field Acres


Number of Fields

Minimum N Rate

Maximum N Rate

Mean N Rate

Corn 38.2 1,062 9 18 126 90 Soybeans 57.0 6,678 13 0 72 24

Sweet Potato 51.3 634 11 50 63 61 Tobacco 57.8 1,064 8 79 192 124

Cotton 2 82 112.8 4,698 1 38 119

10 ii. Phosphorus R 2004 Field Acres Sampled

T County Crop Acres in TP Basin

Minimum P Rate

MaximRate

Mean P Rate Table atesCrop otal Number of

Fieldsum P

Wheat 11.9 1,044 0 0 0 1 Corn 38.2 1,062 35 270 192 9 Soybeans 57.0 6,678 0 72 24 13 Sweet Potato 51.3 634 25 30 29 11 Tobacco 57.8 1,064 8 40 80 54 Cotton 112.8 4,698 21 1 36 20 Table 10 jj. Potassium Rates 2004 Crop Field Acres


Number of Fields

Minimum K Rate

Maximum K Rate

Mean K Rate

Wheat 11.9 1,044 1 0 0 0 Corn 38.2 1,062 9 0 105 35 Soybeans 57.0 6,678 13 0 83 9 Sweet Potato 51.3 634 11 145 150 149 Tobacco 57.8 1,064 8 126 222 156 Cotton 112.8 698 21 4, 82 150 103

29

Information for crop fertilization was derived from 227 producers. Of these farmers, 54 had only to

e one field sampled, and, thus, one fertilizer formulation. There were 173 producers that had 231 fields enumerated and fertilizer rates reported. This allowed us to determine if the samfertilization was used for a given crop on multiple fields. We found that 197 comparisons had exactly the same fertilizer rates by crop regardless of the soil test P or soil mapping unit (Table 11). Table 11. Number of Fields by Crop with Identical Fertilizer Application Rate for the Same Producer. Crop

Number of Fields with Identical Fertilization

Crop

Number of Fields with Identical Fertilization

Corn 26 Oats 2 Cotton 38 Wheat 24 Soybeans 63 Rye 1 Peanuts 8 Bermuda – hay 1 Sweet Potatoes 6 Bermuda – pasture 1 Tobacco 18 Fescue – hay 4 Sorghum 1 Fescue - pasture 6 Trates. Tafields of

here were 28 ltiple f s did no ilar fertilizer ble 12 se ren or n ce r had 12 corn, d with the same formulation and 1 with a different fertilizer

n. In t rd cell corn, lds ha same izer formulation and then the ee f each h iffere rtilize me from each other; in all this producer

fields w 4 differ fertil lans. Most often, the rate difference was N, but it was P or K. Most of these differences in fertilization were very subtle.

12. Numb f Field fferent Fertilizer Rates by Crop. Cr

producers with m shows the

11 fertilize

u occur

ieldces. F

in the same crop thatthe first

t have simll, the produce example, i

formulatio he thi for 2 fie d the fertilremaining thr ields ad d nt fe r regihad 5 corn ith ent izer poccasionally Table er o s with Di

op Corn

Cotto

Soybeans

Wheat acco

n ge

ue - ture

Fescue - Hay n

Tob

CorSila

FescPas

Number of Fields with Different Fertilizer Rates 11-1 9-4 3-1 5-1 2-1 4-1 1-1 8-1 2-1 4-2 3 -1 -1 -1 2-1 2

2-1-1-1 2-1 2-2 2-1 2-1 5-1-1 1-1 1-1 3-3 4-1-1 3-1 1-1 2-1 1-1 2-1

30

Soil Test P and P Fertilization The mean and weighted mean for soil test P is presented in Table 13. We had P data for the majority of the fields sampled. Soil test data was either from farmer records or from soil sampling during this enumeration. The mean and weighted soil test P values are very similar.

eighted soil s ist value and then sums a these v a is the ivided

Usin oil tes us m r than ju In this wa if one rge O 1 ti led, have m an s P

100, reflecting very high soil test ratings; these soils do not need additional P. Soil test P values of the counties were high, which again indicates that generally P doe ot n to b

d. The m oil n W hin Cou was m ium atin at th soils l ilizer

DA&C ata ar he d eve d in ur ble . F ouny high s test es, ile si ountie ave h rati one the ties il test v in t edi nge. Pitt a s has hig oil test P va , d by eith ash tin il

13. Mean d W ed an S Test ndex ou

test le s are e 6 op ot n addi l P t as ter.o those h so levels above 60 and those less than or equal to 60 to determine if P

ation rates are dif (Ta 14) amount of P fertilizer used on very igh or medium/low test soils s the e – 26 lb P2O

however, almost twice as many acres testing high ver h as um w.

Soil Test P-Index

Wteof acres.

mean takes each soil test value, multiplie t by the number of acres for that soil ll

ighted salues. This summed v

t P mean giveslue

ore infon d

mation by the total number

st the mean g the wevalue. y, la f the 2 coun es samp 7 e oil test levels above

for four s n eed e applie ean s test P i as gton nty ed , indic g th ese stilneed P fert s.

CMean N S d is similP lu

to t ata d lope sthis vey (Ta 13) ive cc n

ties hhave ver

ooil va wh x c s h igh ngs. N

h of ou ad

mean sfollowe

alueser N

he m, Mar

um ra, or W

lway the est s luesson.

eTabl an eight Me oil P-I by C nty.

ilWhen so

fields intvelwit

abovil test

0 r, c s ndo e ed tiona excep s rta We divided

fertilizhigh/h

ferent soil

ble P

. T wa

he averagesam and

5 per acre. There are, mediy hig or lo

County

N berof Fields

in Ma WeightedMean

an D S an 4)

um M

x Me NC A&C( 0Me 20

Beaufort 210 2 36 100 04 872 1 1 Edgecombe 4 3 351 8 101 14 2 111 11Franklin 95 28 71 7 87 87 2 Granville 1 20 0 647 1 8 85 9 9 Halifax 105 19 27 76 76 79 5 Hyde 57 75 78 25 42 55Martin 47 3 281 6 112 1 127 12Nash 134 20 374 30 108 1 27 1Pamlico 54 19 12 40 4 114 1 -- Pitt 92 0 288 29 12 1 36 1 43 Washington 7 103 85 34 1 47 47Wilson 61 8 33 6 109 2 2 149 13ALL 1066 374 5 -- 1 99 10

31

Table 14. P Fertilization Rates for Soils Testing High and Very High (P-Index >60) and Soils Testing Low and Medium (< 60). [Yellow is used to highlight similar lb P2O5 per acre rates (within 5 lb P2O5 per acre) for the same crop and county regardless of soil test values. Blue indicates rates that are greater than 5 lb P2O5 per acre.]

High and Very High Soil Test P (>60) Low and Medium Soil Test P (< 60) #

Fields #

Acres P Fertilization (lbs P205/ac)

# Fields

# Acres

P Fertilization (lbs P205/ac)

County/ Crop Min Max Mean Min Max Mean

Beaufort Wheat 44 273.4 0 48 5 11 46.3 0 48 26.2 Corn 43 317.9 0 143 65 15 75.1 22 96 62 Soybeans 81 463.4 0 92 9 35 234.2 0 60 6 Tobacco 3 26.6 47 180 97 NA* NA NA NA NA Cotton 13 177.5 21 70 66 12 323.5 0 100 22 Pasture 1 5 0 0 0 5 16.5 0 0 0 Fruits NA NA NA NA NA 2 5.5 40 40 40

Edgecombe Oats 1 4.5 0 0 0 NA NA NA NA NA Rye 4 50.7 0 0 0 NA NA NA NA NA Wheat 1 10 25 25 25 NA NA NA NA NA Corn 13 131.2 13 18 18 4 88 15 18 17 Peanuts 7 153.8 0 0 0 2 77.7 0 0 0 Soybeans 26 314.6 0 38 9 8 173.3 0 38 28 Tobacco 13 94.3 0 156 53 NA NA NA NA NA Cotton 58 899.4 0 80 14 11 168.2 0 21 13 Sweet potatoes

NA NA NA NA NA 1 9.0 15 15 15

Pasture 1 27.4 0 0 0 NA NA NA NA NA Franklin

Wheat 5 62 0 0 0 3 31.2 0 12 4.0 Corn NA NA NA NA NA 2 7 136 136 136 Other hay 13 140.5 0 120 76 6 73.8 0 75 19.9 Sorghum 1 11.0 40 40 40 NA NA NA NA NA Soybeans 27 212.5 0 70 11 16 130.2 0 57 10 Vegetables NA NA NA NA NA 1 8.5 54 54 54 Tobacco 8 45 12 136 89 2 7.0 30 96 63 Pasture 5 22.8 0 15 5 14 56.4 0 770 55

Granville Oats 2 21.1 0 30 15 3 15.6 0 24 16 Wheat 2 8.6 30 30 30 NA NA NA NA NA Other hay 5 16.1 0 69 8 7 24.1 0 39 11 Soybeans 15 49.8 0 0 0 2 7.0 0 0 0 Tobacco 4 17.8 63 96 80 4 13.5 84 104 95 Pasture 4 130 0 0 0 NA NA NA NA NA

32

Halifax Wheat 2 41 36 36 36 NA NA NA NA NA Corn silage 4 21.0 25 25 25 1 7.4 25 25 25 Corn 4 24.3 15 54 27 4 16.0 0 21 10 Peanuts 4 49.5 0 54 14 3 68.2 0 0 0 Soybeans 16 140.0 0 54 15 10 58.2 0 36 7 Sweet potatoes

2 18.5 60 60 60 2 15.3 60 60 60

Tobacco 5 32.0 48 84 55 4 31.1 0 48 36 Cotton 23 324.0 0 54 32 20 286.2 0 50 16 Pasture 1 7 0 0 0 NA NA NA NA NA

Hyde Wheat NA NA NA NA NA 1 40.0 0 0 0 Corn 6 39.5 28 100 73 5 108.0 7 100 81 Soybeans 5 41.0 0 0 0 8 325.0 0 0 0 Cottons 11 44.2 60 65 61 22 589.7 0 60 44

Martin Corn 1 6.7 35 35 35 1 4.5 35 35 35 Peanuts 6 63.4 0 30 16 1 7.5 0 0 0 Soybeans 6 37.0 21 25 22 1 4.5 23 23 23 Tobacco 4 16.5 48 168 110 1 1.9 82 82 82 Cotton 24 219.7 0 60 35 1 6.9 50 50 50 Wildlife NA NA NA NA NA 1 1 23 23 23

Nash Wheat 2 12.5 15 15 15 NA NA NA NA NA Corn 9 64.1 17 28 25 1 19.2 17 17 17 Other hay 1 3.5 0 0 0 NA NA NA NA NA Peanuts 3 20.1 0 0 0 NA NA NA NA NA Soybeans 55 395.0 0 25 5 2 13 0 3 1 Sweet potatoes

9 73.0 0 50 19 NA NA NA NA NA

Vegetables 9 103.9 0 63 21 NA NA NA NA NA Tobacco 13 84.6 0 80 53 NA NA NA NA NA Cotton 2 272.1 0 25 15 2 42.0 0 17 9 Pasture 3 51 0 0 0 NA NA NA NA NA

Pamlico Corn 3 8.0 93 93 93 2 6.0 93 93 93 Soybeans 21 46.3 0 0 0 10 18 0 0 0 Nuts 3 20.5 0 0 0 1 5.4 0 0 0

Pitt Wheat 20 124 0 45 36 NA NA NA NA NA Corn 7 80.1 15 40 27 1 7.5 30 30 30 Peanuts 8 52.8 0 0 0 1 5.0 0 0 0 Soybeans 49 658.0 0 36 5 2 15.6 0 10 5 Tobacco 15 151.6 0 82 51 NA NA NA NA NA

33

Cotton 4 53.1 0 30 7 4 31.4 0 30 23 Wildlife 1 7.5 0 0 0 NA NA NA NA NA

Washington Wheat 1 12.5 40 40 40 16 202.0 40 46 43 Corn 1 3.0 0 0 0 5 61.5 60 60 60 Soybeans 3 51.5 0 50 27 25 314.0 0 60 32

Wilson Corn 9 38.2 35 270 192 NA NA NA NA NA Soybeans 10 48.7 0 72 17 3 8.3 0 72 48 Sweet potatoes

11 51.3 25 30 29 NA NA NA NA NA

Tobacco 7 54.3 40 80 56 NA NA NA NA NA Cotton 18 94.7 1 36 21 3 18.1 10 24 15

All Counties All Crops 762 6827.8 0 270 26 296 3594.9 0 770 26 * NA= no crop was sampled in the soil test range In most counties the rate of P fertilizer was generally greater for tobacco on high/very high soil test P soils than on fields testing low or medium; this is just the opposite of what farmers should be doing. Often the rate of P fertilization was very similar for a crop within a given county regardless of soil test P. This implies that fertilizer application is prescriptive rather than based on soil test P recommendations. Some crops, such as pasture and peanuts, were rarely fertilized with P. Other crops, such as corn, tobacco and sweet potatoes received higher P fertilization rates. Slope and Soil Loss Predictably, counties in the lower coastal plain had much lower slopes than counties in the piedmont (Table 15). Franklin and Granville had the greatest slopes. In the coastal plain region, soil erosion loss was based on physiographic region in the coastal plain, crop and cropping system. These erosion rates were calculated using tables developed from RUSLE by USDA-NRCS. In the piedmont region, soil erosion was calculated using RUSLE. Interpretation of erosion rates must be viewed with care since we used two methodologies to determine soil loss. Thus, the differences in soil loss should be viewed as relative loss rates. In general, most counties displayed generally low rates except for Martin and Pitt counties. The order of soil loss was Washington = Pamlico (1.2 t ac-1) < Franklin = Granville (1.4 t ac-1) < Hyde (1.7 t ac-1) < Halifax (3.0 t ac-1) < Edgecombe = Nash (3.7 t ac-1) < Wilson (5.1 t ac-1) < Pitt (7.8 t ac-1) < Martin (8.5 t ac-1). Counties such as Washington, Pamlico and Hyde with mostly flat fields had low soil loss; Granville and Franklin, although they had some of the most sloping land, demonstrated low average soil loss due to large amounts of land in pasture or hayland, because conservation tillage was used on most crops except tobacco, and due to actual calculations of soil loss rather than table values.

34

Table 15. Minimum, Maximum and Weighted Means for Field Slope and Soil Erosion by County.

Field Slope (%) Soil Erosion (t/ac)

County

Number of Fields

Min Max Weighted Mean

Min Max Weighted Mean

Beaufort 217 0 2.0 0.1 0.1 12.6 2.0 Edgecombe 146 0 4.0 1.0 0.4 7.8 3.7 Franklin 100 1.0 8.2 3.4 0.1 11.6 1.4 Granville 49 1.0 6.0 3.1 0.1 10.0 1.4 Halifax 109 0 3.0 1.2 0.4 7.8 3.0 Hyde 58 0 1.0 0.3 0.1 4.7 1.7 Martin 58 0 0 0 0.5 12.6 8.5 Nash 136 0 8.0 1.7 0.4 7.8 3.7 Pamlico 36 1.0 1.0 1.0 1.2 1.2 1.2 Pitt 126 0 3.0 0.7 0.5 12.6 7.8 Washington 34 0 1.0 0.9 0.1 3.2 1.2 Wilson 62 0 5.0 1.3 1.4 7.8 5.1 ALL 1131 0 8.2 1.1 0.1 12.6 3.5 Receiving slopes are the slopes within the field that allow deposition of eroded materials. A receiving slope of 0-9 feet would indicate no receiving slope. Receiving slopes ranged from a minimum of 0-9 to a maximum of 20-20 feet (Table 16). Weighted means were very similar between counties; two counties (Beaufort and Franklin) had narrow mean receiving slopes (10-19 feet), whereas the remaining counties have weighted average receiving slopes of 20-29 feet. Table 16. Minimum, Maximum and Weighted Means for Receiving Slope Width

Field Slope (%)

County

Number of

Fields Minimum

(ft) Maximum

(ft) Weighted Mean (ft)

Beaufort 217 0-9 20-29 10-19 Edgecombe 146 0-9 20-29 20-29 Franklin 100 0-9 20-29 10-19 Granville 49 20-29 20-29 20-29 Halifax 109 15-25 20-29 20-29 Hyde 58 0-9 20-29 20-29 Martin 58 20-29 20-29 20-29 Nash 136 0-9 20-29 20-29 Pamlico 36 20-29 20-29 20-29 Pitt 126 20-29 20-29 20-29 Washington 34 20-29 20-29 20-29 Wilson 62 10-19 20-29 20-29 ALL 1131 0-9 20-29 10-19

35

North Carolina Agricultural Nutrient Assessment Tool Results The NCANAT contains the P (PLAT) and N (NLEW) accounting tools. The majority of all fields were analyzed in NCANAT. The PLAT provides a P risk assessment for agricultural fields, while NLEW develops N field loss information. PLAT Results There are four potential loss pathways in PLAT: erosion (I), soluble P in overland flow (II), leaching (III), and loss of applied P (IV). A rating is associated with each loss pathway as well as a total loss. In the program, these losses are calculated in lbs per acre, but then transformed to a rating. Ratings between 0 and 25 are considered low for P loss, whereas ratings of 26-50 are medium. High (51-100) and very high (>100) loss potential suggest a need to change management. Distribution of PLAT ratings are low for most counties (Table 17), suggesting that the risk for P losses from agricultural fields is minimal. Rating indices range from a low of 3 in Franklin County to a high of 21.5 in Martin County; all of these ratings, however, are considered low risk of P loss. The two most sensitive parameters in PLAT are hydrology and soil test P. Although the mean soil test P in the Tar-Pamlico Basin is high or very high for agronomic crops (Table 14), the values are still low enough that losses of P from fields are low. In addition, it appears that erosion is being well controlled (Table 15) thus reducing erosion losses in counties with greater slope changes.

36

Table 17. PLAT Risk Assessment by County. Loss Pathway Units of P Loss I II III IV Total

Beaufort lbs per acre 2.2 7.6 0 1.5 11.3

rating index 1 5 0 0 7

Edgecombe lbs per acre 3.7 8.4 0 0.5 12.6


Franklin lbs per acre 0.7 3.3 0 1.1 5.1


Granville lbs per acre 0.9 7 0 0.5 8.5


Halifax lbs per acre 1.7 7.4 0 1.2 10.4

rating index 1 7 0 0 8.5

Hyde lbs per acre 1.8 20.7 0 3.2 25.8

rating index 1 11.5 0 3 21

Martin lbs per acre 10.2 10.7 0 2.2 23

rating index 9 11 0 2 21.5

Nash lbs per acre 3.8 7.5 0 0.3 11.6


Pamlico lbs per acre 1.7 8.1 0 0 9.8


Pitt lbs per acre 10.8 12.6 0 0.6 24


Washington lbs per acre 1.2 9.1 0 2.4 12.7


Wilson lbs per acre 7.5 12.4 0 1.1 21


37

The four figures presented below give the distribution of P losses for total loss and for the three loss pathways calculated (Figure 2-5). (We were unable to calculate leaching losses as we were unable to collect deep soil samples.) As can be seen from these figures, P losses distribute near the y axis, which indicates very low potential P losses from agricultural fields.

Total P Loss

0

50

100

150

200

250

0 10 20 30 40 50 60 70 80 90 100

110

120

PLAT Index value

Freq

uenc

y

Figure 2. Frequency of total PLAT Index Values.

38

Erosion P Loss

0

100

200

300

400

500

600

700

0 10 20 30 40 50 60 70 80 90 100

110

120

PLAT Index value

Freq

uenc

y

Figure 3. Frequency of PLAT Index Values for Soil Erosion.

39

Runoff P Loss

0

50

100

150

200

250

300

0 10 20 30 40 50 60 70 80 90 100 110120

PLAT Index value

Freq

uenc

y

Figure 4. Frequency of PLAT Index Values for Soluble P Runoff Losses.

40

Applied Source P Loss

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80 90 100 110 120

PLAT Index value

Freq

uenc

y

Figure 5. Frequency of PLAT ratings for Applied Sources of P.

41

NLEW The NLEW tool estimates relative N losses. No crop is 100% efficient in using all the fertilizer N provided, and thus, there will always be some losses of N. Table 18 gives an overall N loss by county for the crops that we surveyed. In half of the counties (Beaufort, Edgecombe, Franklin, Granville, Halifax, and Nash) the amount of fertilizer N was less than the crops needed based on realistic yield expectations (Table 18). When N applied was greater than N needed (Hyde, Martin, Pamlico, Pitt, Washington, and Wilson), the amount of excess N was generally quite small, the exception being Hyde County. Table 18. N Losses by County as Calculated in NLEW County N applied N Needed Excess N N Lost Beaufort Mean 598.8 615.6 107.6 299.1 Median 300.0 360.0 0.0 99.0 Edgecombe Mean 1069.5 1078.9 134.3 471.8 Median 503.5 670.2 10.2 198.8 Franklin Mean 585.5 692.6 118.4 236.1 Median 207.8 263.6 0.0 68.7 Granville Mean 295.5 498.6 0.5 97.4 Median 96.6 278.4 0.0 7.6 Halifax Mean 629.3 624.2 102.5 220.0 Median 429.8 463.3 0.0 99.4 Hyde Mean 2231.7 1151.1 1091.9 1666.3 Median 544.5 406.7 120.1 320.2 Martin Mean 725.5 564.1 177.7 378.1 Median 602.0 390.5 100.0 300.7 Nash Mean 441.4 491.4 43.3 187.9 Median 147.0 190.2 0.0 59.1 Pamlico Mean 68.4 33.4 35.0 17.3 Median 0.0 278.6 0.0 0.0 Pitt Mean 638.7 616.7 147.4 349.6 Median 110.0 0.0 0.0 60.0 Washington Mean 696.5 676.3 150.2 438.6 Median 350.0 0.0 0.0 340.0 Wilson Mean 394.7 389.5 86.2 228.8 Median 250.0 310.0 8.5 149.7

42

Table 19 shows N losses as a function of county and crop. Nitrogen applied is a function of the rate and number of acres. Generally, excess N was applied to corn, cotton, and soybeans whereas insufficient N was applied to hay, cereal crops. Patterns of N use, however, varied based on county. Table 19. N Losses by County as Calculated in NLEW

County Crop N Applied N Needed Excess N N Lost

% of Applied N

LostBeaufort Corn 1294 1201 151 593 45.8 Cotton 1091 1080 404 660 60.5 Soybean, DC 38 32 6 19 50.5 Soybean, FS 22 0 22 14 63.3 Tobacco 914 880 69 289 31.6 Winter wheat 566 797 45 276 48.7 Edgecombe Corn 2242 2055 441 1008 45.0 Cotton 1365 1391 121 590 43.2 Oats 54 491 0 10 19.3 Rye 953 1504 0 500 52.5 Soybean, DC 55 0 55 53 95.7 Soybean, FS 69 0 69 57 83.0 Sweet potato 432 450 0 432 40.0 Tobacco 575 717 0 165 28.8 Winter wheat 130 1200 0 72 55.0 Franklin Corn 652 464 188 241 36.9 Cucumber 825 1020 0 403 48.9 Hay 1062 1867 69 241 22.7 Sorghum 806 910 194 406 50.3 Soybean, DC 104 74 31 60 57.3 Soybean, FS 162 0 162 125 77.3 Tobacco 465 453 84 179 38.5 Winter wheat 1258 1170 187 628 50.0 Granville Hay 106 612 0 9 8.1 Oats 580 739 0 241 41.6 Soybean, DC 0 0 0 0 NA Soybean, FS 0 0 0 0 NA Tobacco 773 985 2 262 33.9 Winter wheat 228 416 0 38 16.7 Halifax Corn 554 676 7 150 27.1 Cotton 1080 1034 178 353 32.7 Soybean, FS 59 0 59 41 69.1 Sweet potato 376 409 0 352 93.4 Tobacco 383 593 0 133 34.6 Winter wheat 1271 1886 0 245 19.3

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Hyde Corn 2141 2016 184 1193 55.7 Cotton 1915 1213 702 1299 67.8 Soybean, DC 0 0 0 0 0 Soybean, FS 2392 0 2392 2392 100.0 Winter wheat 14000 4560 9440 11948 85.3 Martin Corn 1195 1085 117 542 45.3 Cotton 942 697 247 479 50.9 Sorghum 1918 1527 391 1155 60.2 Soybean, FS 153 0 153 153 99.9 Tobacco 198 290 4 72 36.2 Nash Corn 1054 1061 43 298 28.3 Cotton 741 869 31 292 39.4 Cucumber 965 1064 75 347 36.0 Soybean, DC 47 0 47 18 38.3 Soybean, FS 26 17 25 15 56.7 Sweet potato 685 475 214 499 72.8 Tobacco 543 687 18 178 32.7 Winter wheat 156 755 0 30 19.3 Pamlico Corn 493 240 252 125 25.3 Soybean, FS 0 0 0 0 NA Pitt Corn 1949 1944 156 824 42.3 Cotton 1087 826 426 692 63.6 Soybean, DC 0 0 0 0 NA Soybean, FS 99 0 99 79 80.0 Tobacco 756 1093 45 369 48.8 Winter wheat 2292 2827 0 1261 55.0 Washington Corn 1420 1484 139 899 63.3 Soybean, DC 295 0 295 295 100.0 Soybean, FS 170 0 170 105 62.0 Winter wheat 1119 1418 0 595 53.2 Wilson Corn 308 607 0 185 60.0 Cotton 509 437 97 259 50.9 Soybean, DC 0 0 0 0 NA Soybean, FS 121 2 121 121 100.0 Sweet potato 279 243 36 169 60.5 Tobacco 874 792 172 502 57.5 Winter wheat 0 1624 0 0 NA

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Field-scale NLEW to Aggregate-scale NLEW Comparisons for Crops, Nitrogen Rates, and BMPs Nitrogen fertilizer rates from this survey are compared to the N rates provided by county staff for the NLEW accounting in 2004 (Table 20). If the difference is negative, then the NLEW rate is greater than the surveyed N rate. Positive difference values mean that surveyed N rates are greater than NLEW rates. Three counties (Beaufort, Hyde and Wilson) have higher surveyed N rate usage than NLEW N rate estimates, whereas five counties (Edgecombe, Granville, Halifax, Nash and Pitt) have lower surveyed rates. Franklin, Martin and Washington counties had half of the crops over-fertilized and half the crops under-fertilized. Although soybeans are almost always over-fertilized, the amounts are relatively low. Soybeans, however, often account for considerable acreage. Table 20. NLEW N Rates and Survey Nitrogen Rates by Crop and County for 2004.

County Crop

N Applied ( lab/ac) -

2004 Survey

N Applied (lb N/ac) –

NLEW 2004 Difference

(-/+)Beaufort Corn 149 185 - 36 Cotton 103 75 + 28 Soybean, DC 2 0 + 2 Soybean, FS 2 0 + 2 Tobacco 111 75 + 36 Winter wheat 111 75 + 36 Pasture 30 100 - 70 Edgecombe Corn 140 150 - 10 Cotton 72 78 - 6 Oats 12 100 - 88 Rye 79 0 + 79 Soybean, DC 0 12 - 12 Soybean, FS 0 2 - 2 Sweet potato 48 80 - 32 Tobacco 62 80 - 18 Winter wheat 13 80 - 67 Peanuts 0 8 + 8 Pasture 79 108 - 29 Franklin Corn 5 130 - 125 Cucumber 73 150 - 77 Hay 100 198 - 98 Sorghum 89 0 + 89 Soybean, DC 7 0 + 7 Soybean, FS 7 0 + 7 Tobacco 73 80 - 7 Winter wheat 97 80 + 17

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Granville Hay 35 50 - 15 Oats 74 0 + 74 Soybean, DC 0 0 --- Soybean, FS 0 0 --- Tobacco 74 80 - 6 Winter wheat 53 80 - 27 Halifax Corn 105 170 - 65 Cotton 73 80 - 7 Soybean, FS 8 0 + 8 Sweet potato 46 No value -- Tobacco 64 70 - 6 Winter wheat 62 70 - 8 Hyde Corn 146 185 - 39 Cotton -- 60 -- Soybean, DC 11 0 + 11 Soybean, FS 11 0 + 11 Winter wheat 350 No value -- Martin Corn 145 150 - 5 Cotton 93 80 + 13 Sorghum 137 No value -- Soybean, FS 16 0 + 16 Wheat No value 100 -- Tobacco 65 80 - 15 Nash Corn 131 180 - 49 Cotton 64 80 - 16 Cucumber 109 92 + 17 Soybean, DC 4 8 - 4 Soybean, FS 4 0 + 4 Sweet potato 66 75 - 9 Tobacco 82 75 + 7 Winter wheat 48 100 - 52 Pamlico Corn 176 No value Soybean, FS 0 No value Pitt Corn 115 175 - 60 Cotton 79 95 - 16 Soybean, DC 4 10 - 6 Soybean, FS 4 0 + 4 Tobacco 73 90 - 17 Winter wheat 92 130 - 38

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Washington Corn 130 185 - 55 Soybean, DC 19 0 + 19 Soybean, FS 19 0 + 19 Winter wheat 99 110 - 11 Wilson Corn 126 165 - 39 Cotton 119 73 + 46 Soybean, DC 24 7 + 17 Soybean, FS 24 0 + 24 Sweet potato 61 78 - 17 Tobacco 124 78 + 46 The only BMP that could be compared between the survey and information developed for the tar-Pamlico aggregated NLEW was cover crop information. In most instances the % acreage of cover crops was much greater for NLEW estimates than the surveyed information (Table 21). For instance, Halifax reported 105% of all acres were planted into cover crops, whereas the survey found that approximately 60% of all acreage had winter cover crops planted. Some counties (Edgecombe, Franklin, Nash, and Wilson) had more cover crops surveyed than reported through NLEW. Table 21. Survey delineated BMPs vs. NLEW-Aggregate Reported BMPs. County Cover Crops –

NLEW 2004 (acres)

Total Acreage in Basin (acres)

% Cover Crops– NLEW 2004

Cover Crops - 2004 Survey

(acres)

Cover Crops - 2004 Survey

(%) Beaufort 141,620 0.0 2 0.1 Edgecombe 4,895 109,890 4.5 334.6 15.6 Franklin 1,907 31,950 6.0 72.5 9.5 Granville 2,878 8,600 33.5 3.4 1 Halifax 65,000 61,800 105.2 726.8 59 Hyde 49,000 0.0 0 0 Martin 11,244 18, 250 61.6 121.9 24.7 Nash 10,100 61,600 16.4 247.4 22.6 Pamlico 1,975 0.0 0 0 Pitt 3,828 70,760 5.4 81 5.4 Washington 3,825 14,820 25.8 27 6.3 Wilson 14,940 0.0 36.9 11.6

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OUTCOMES AND CONCLUSIONS

The area frame sampling technique did an excellent job of allowing proper selection of fields within the Tar-Pam River Basin. Agricultural information that is not generally collected through USDA agricultural surveys was gathered. This information demonstrates much more detail about current agricultural practices than normally obtained. In addition, the data was used in agricultural accounting tools in order that we could generalize about potential nonpoint source pollution losses from agricultural activities. In general, this survey demonstrated that agricultural producers are minimizing soil erosion through CT and cover crops. Also, potential P losses as determined through the use of PLAT are low, in part because soil test P is not excessive from an environmental standpoint. Over half of the counties surveyed, however, had very high soil test P levels. Farmers could reduce P fertilization, particularly for tobacco and cotton, and still produce high crop yields. Nitrogen fertilizer rates are often less than recommended, except for some corn and cotton fields. In some instances, farmers are still applying N to soybeans, which is not needed. Nitrogen and P reducing BMPs are used throughout the basin. Many fields are naturally buffered with trees, vegetation (grass and forbs) or a combination, especially in the piedmont region of the basin. A few counties could use additional buffers. Some counties, such as Hyde and Pamlico, which have few buffers, have considerable acres affected by controlled drainage. When the data collected in this survey were compared to data collected in the counties for the annual report, we found that typically, fertilizer rates reported annually were greater than the fertilizer rates found in this survey. Conversely, this survey found greater use of CT than reported. This is not surprising, however, since only cost-shared practices are reported. In some counties the use of cover crops were over reported, while in others they were under reported. The data collected through this survey gave us a statistically valid sample of agricultural activities. The types of data collected are rarely sampled anywhere in the United States; thus this unique data set allows us a better view of agricultural activities that impact water quality. The data lead us to believe that although work still remains to be done, the producers are doing a good job of implementing BMPs and moderating fertilizer use.

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BUDGET Description

Budget ($)

Spent ($)

Salaries 25,000.00 24,585.11 Fringe 6,375.00 6,502.49 Total Personnel Costs 31,375.00 31,087.60 Supplies and Materials 4,800.00 4,794.99 Travel 3,000.00 3,274.45 Current Services 193,402.00 193,401.66 Fixed Charges 0 18.30 Total Direct Costs 232,577.00 232,57.00 Total Indirect Costs 23,258.00 23,258.00 Total Costs 255,835.00 255,835.00 .

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REFERENCES Monroe, J. and A.L. Finkner, 1959. Handbook of Area Sampling. Chilton Co., Philadelphia, New York. Johnson, A.M. and D.L. Osmond. 2005. Accounting Method for Tracking Relative Changes in Agricultural Phosphorus Loading into the Tar-Pamlico. With concurrence and consent of the Phosphorus Technical Advisory Committee. October 21, 2005. The N.C. PLAT Committee. 2005. North Carolina Phosphorus Loss Assessment: I. Model Description and II. Scientific Basis and Supporting Literature. North Carolina Agricultural Research Service Technical Bulletin 323, North Carolina State University, Raleigh, NC. Osmond, D.L., L. Xu, N.N. Ranells, S.C. Hodges, R.Hansard, and S.H. Pratt. 2001. Nitrogen Loss Estimation Worksheet (NLEW): An Agricultural Nitrogen Loading Reduction Tracking Tool. In Optimizing Nitrogen Management in Food and Energy Production and Environmental Protection: Proceedings of the 2nd International Nitrogen Conference on Science and Policy. Scientific World:1. SAS. 1985. SAS/STAT guide for personal computers, Version 6 Edition. SAS Institute Inc., Cary, NC. 373 pp.

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APPENDICES Appendix 1: Public Information On The Survey Released From NCDA&CS - Statistics Division

For more information, contact: North Carolina Agricultural Statistics Service

1-800-437-8451

Study Will Validate Best Management Practices in the Tar-Pamlico River Basin

The Tar-Pam Best Management Practices Survey will be conducted by the North Carolina Agricultural Statistics Service for North Carolina State University. This survey will collect information concerning cover crops, fertilizer rates, soil fertility, controlled drainage, riparian buffers, and other production information from producers in the Tar-Pamlico River Basin. What do farmers have to do in the Tar-Pamlico River Basin drainage area to meet the Tar-Pam Rules? In 2001, the North Carolina Environmental Management Commission passed a rule that affects all farmers in the Neuse River Basin. (Rules can be found at http://h2o.enr.state.nc.us/nps/tarpam.htm). The goal of the Rules is to reduce nitrogen loss into the Tar-Pamlico River by 30% when the rules end in 2006. Agricultural producers must either use mandatory best management practices on all of their acres or join a Local Area Committee. Most farmers have already joined their Local Area Committee. In order to use the Local Area Committee option, however, the agricultural sector must utilize a nitrogen accounting tool to track changes in nitrogen from the use of best management practices. To track the use of best management practices and their reduction of nitrogen, an accounting tool has been developed by a committee from North Carolina State University and other state agencies. Reporting by the Local Area Committees, during the fall of 2003, indicated that the 30% N reduction goal has been met. This survey will help to validate the information acquired. North Carolina farm operators in the study will be contacted by interviewers to collect the information from August 2004 through January 2005. Individual operator information will be confidential. Published results will be available in local extension offices in the summer of 2001, or on the internet at http://www.soil.ncsu.edu.

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Appendix 2: Tar-Pamlico Best Management Practices Survey Interviewer’s Guide

I. Tar-Pamlico Best Management Practices Survey General The Tar-Pamlico Best Management Practices Survey will be conducted by the North Carolina Agricultural Statistics Service for North Carolina State University. This survey will collect information concerning fertilizer and manure use, controlled drainage, and riparian buffers from producers in the Tar-Pamlico River Basin.

The Tar-Pamlico River basin is the fourth largest river basin in North Carolina, encompassing 5,578 square miles in 16 counties. The Tar-Pamlico River basin is relatively undeveloped with agricultural land use comprising about 34%. In 2001, the North Carolina Environmental Management Commission passed a rule that affects all farmers in the Tar Pamlico River Basin. (Rules can be found at http://h2o.enr.state.nc.us/nps/tarpam.htm). The goal of the Rules is to reduce nitrogen loss into the Tar-Pamlico River by 30% and to have no net increase of phosphorus when the rules end in 2006. The dilemma with reducing nitrogen loss by 30% is that there is no concrete data available to define how much nitrogen is currently entering the Tar-Pamlico River from farming enterprises. Survey Purpose The purpose of the survey is to provide data that will benchmark management practices of Tar-Pamlico River basin farmers and to provide information for a tool that tracks nitrogen and phosphorus loss from agriculture. The accounting tools that tract the progress of nitrogen and phosphorus in the Tar-Pamlico River basin will be used to determine nitrogen and phosphorus losses. The data that we collect will accomplish this goal objectively while keeping individual farmers’ reports confidential. If another agency collected the information, it would be public record and available for anyone to review. Future surveys will track the progress from the information that is collected in this survey.

se of the Tar-Pamlico Best Management Practices Survey At the present time, no baseline information exists on the extent of best management practices in the Tar-Pamlico Basin. This survey will establish that baseline. The information will also be used in two agricultural BMP tracking and accounting tools. The use of the nitrogen tool (NLEW) is mandatory and tracks N reduction: a 30% N reduction is required. The phosphorus tool (PLAT) is only required for animal operators.

U

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Reasons to Participate It is important for agricultural producers to participate in this survey to validate independently baseline agricultural practices and nitrogen and phosphorus reductions. Information will be used in summary form only and all personal information will be kept STRICTLY CONFIDENTIAL. II. Terms and Definitions Most of the terms used in this survey can be found in your Interviewer’s Manual. Terms that are relevant to this survey are:

• Acre • Crop • Cover Crop • Fertilizer • Fertilization • Field Diagram • Growing Season • Manure • No-till • Refusal • Sampling Frame • Sampling Unit • Segment Boundary • Strip-till • Yield

Additional Terms or information: Artificial Drainage: Drainage in a field or parts of a field, either by tile drains that are buried under a field or deep drainage ditches. Water deep in the field (~ 2.5 to 3.5 feet) drain to outlets. If the ditch is brightly colored (not gray) then the ditch is a surface water drainage devise. Commercial Fertilizer: Plant nutrients that are not organic. Conservation Tillage: Tillage method that will leave a minimum of 30% of the soil surface covered by residue following planting. Controlled Drainage: (see Appendix 1) After water control structures are added to drainage ditches, the water table level can be controlled. This control of the water table level is referred to as controlled drainage. Conventional Tillage: Tillage that leaves less than 30% vegetative material on the surface.

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Cover Crop: Crop grown in the fall and winter to protect the soil from wind and water erosion. Cover crops are usually small grain crops that are either killed with herbicides or plowed down prior to spring planting. Drainage Direction: The direction that either surface or ground water flows from a field into the nearest ditch, stream, river or lake. Field: A continuous area of land devoted to annual or perennial crop use including field buffers. This includes fields that are hay or pasture. Excludes wasteland, woods, and farmstead. Field Slope: The percent slope of the field, measured from the highest point of the field to the lowest point of the field. Hydrologic Condition: Hydrologic condition is based on factors that affect infiltration and

runoff, including density and percent canopy of vegetation, amount of year round cover, amount of grass or close seeded legumes in rotation, percent of surface residue cover, and surface roughness. • Cropland choices are Good or Poor. A poor condition is a finely prepared seedbed, not

drilled, with a low plant population, and not in rotation with a sod. A good condition is rough seedbed, high plant population, and in rotation with sod, high residue-producing crop, or conservation tillage.

• Pasture choices are Good, Fair, or Poor. A poor condition is over-stocked, under fertilized, low year-round plant population and poor plant condition. A good condition is properly stocked, adequate nutrient management, and a full plant population (nearly 100% cover). A fair condition is represented by factors less than "Good" and better than "Poor", and is determined at the planner's discretion.

Pond: Ponds are located in depressional areas. There is no stream flowing into the pond. The pond may have a spillway exiting the pond. Receiving Slope: The receiving slope is the concave slope extending from the base of the RUSLE slope to the field edge or to a source of concentrated runoff flow in a defined channel. Riparian Buffer: Vegetation growing next to a stream. The vegetation can be either trees, grasses or shrubs. Sample Segment: For this survey, the sample segment is defined as the area on the aerial photo that is outlined in black with a circled number identifier. This term is used so that it is not confused with our regular area frame work. In this survey, we will not be accounting for the total acreage within the boundary. Sediment Basin: Sediment basins are ponds within streams. There is a stream into and out of the pond. Slope Length: The length of the slope, from the highest to lowest point.

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Vegetative Buffer: (see Appendix 1) Vegetation growing next to a stream, river, pond or lake that is not trees. The vegetation may be a pure grass stand or a mix of grasses and other types of plants, such as shrubs or weeds. Water Control Structure: (see Appendix 1) A structure placed in a ditch that allows the water table level to be controlled by inserting wooden boards in the structure. These boards run perpendicular to the flow of the water. III. Survey Design The 2004 Tar-Pamlico Best Management Practices Survey will use an area frame sample only. The area frame photos and segment selection were done by NCSU. The area photos were taken in 1993. Road identification has been overlaid on the area photos for ease in locating the sample segment. A “road map” is also on the back of the aerial photo. The sample segment was randomly selected from a larger Census block. This block was divided, numbered, and then the sample segment selection was made. The sample segment is the area outlined in black with a circled number. The circled number will also be listed on the left side of the photo. Questionnaires will be completed for each field within the sample segment. We are only interested in cultivated, hay, or pasture fields in the sample segment. A total of 272 sample segments will be in the sample. Because we do not know the operators within the sample segments, we could not coordinate this survey with any of our ongoing surveys. Keep this in mind, if an operator indicates that he was recently contacted. Since our Agricultural Resource Management Phase II Survey, Vegetable Chemical Use Survey, and Conservation Effects and Assessment Survey all ask for fertilizers applied, the operator may have been interviewed for one of our other surveys.

Tar-Pamlico River Counts by County 2004

County Sample Segment

Count

County

Sample Segment

Count

Beaufort 36

Hyde 32

Dare 1

Nash 28

Edgecombe 33

Pamlico 6

Franklin 30

Pitt 27

Granville 19

Wilson 7

Halifax 35

Washington 8

Total 272

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IV. Survey Time Frame Data collection will be September 17 through December 31, 2004. If there are any problems meeting this schedule, please contact your supervisor. All time and mileage should be charged to project code 402. IV. Completing the Questionnaire Identification Step 1: Locate the sample segment identified on the envelope label. Using the aerial photo or county map (located on the back of the photo), identify any landmarks (roads, streams, etc.) to orient the map to the sample segment. The sample segment is outlined on the photo with a circled number. Drive around the segment, if possible, and locate the boundaries of the sample segment. Remember the sample segment may have changed significantly from the photo. Step 2: Locate an operator for any of the fields within the sample segment. Determine how many of the field(s) for which he/she is the operator. Using the red China marker pencil, draw the field boundaries in red on the photo and number the field beginning with 1. All fields in the sample segment have to be drawn in red and numbered. Do not account for any of the non-agricultural acres. Step 3: Ideally, you and the farmer should go to the field so that you can draw the field with the drainage direction. Practically, you may have to go by yourself to the field, you may not be able to get to the field, or the farmer may not allow you to go to the field. Make notes as to how the field was drawn – with or without the farmer, with or without entering the field. After you have located the farmer, and drawn and numbered his/her field(s) in red, complete one questionnaire per field. You need to draw the field on the back of the questionnaire and indicate drainage direction, streams, buffers, etc. Step 4: Continue to locate all of the farmers operating agricultural fields (i.e. cultivated fields, hay, or pasture) in the sample segment and complete the needed questionnaires. In some sample segments, one farmer may operate all or most of the fields. In this case, one farmer will complete a questionnaire for each field; thus, you will have several questionnaires all completed by one farmer. Other sample segments may have several farmers operating within the segment boundaries. In that situation, you will need to contact each farmer to complete the needed questionnaire(s). Note: When numbering the fields within the segment, use each field number ONE TIME ONLY. For example, if you have 2 operators in the segment and each have 2 fields, farmer1 will have fields 1 and 2, and farmer2 will have fields 3 and 4.

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Identification Section Copy the county code and the sample number from the envelope to the questionnaire. Write the field number from the photo on the questionnaire. Complete the name, address, and telephone number section at the top. There will be one questionnaire for each field that is drawn on the photo. You will also be using your GPS unit to record the latitude and longitude. The procedure is to walk into the field 10 paces, turn on the GPS unit and wait for the latitude and longitude to appear. Copy the latitude/longitude to the questionnaire. The format of the latitude/longitude is degrees, minutes, and seconds. (xx.xx.xx) The GPS unit will automatically display in this format. After the completion of the sample segment, make sure that the number of questionnaires match the number of fields drawn. Question 1 Record the number of acres in the field to tenths of an acre. For example, if the field is 2 acres, you record 2.0. The total field acres are important because yields and application rates are all on a per acre basis. *Enumerator Note Draw the field on the back of the questionnaire in the Afield diagram@ section. Indicate the crest (highest point) of the field with a solid line, and the drainage direction with arrows from the crest line. Draw and label the nearest ditch, stream or river, water control structures, and the vegetative buffers (grass, trees, or shrubs) on the diagram. You are not responsible for a perfect drawing, but a sketch of the field is necessary in analyzing the data you have collected. See Appendix 3 for examples. Question 2 The table pertains to crops grown for the 2004 crop year (i.e. planted in the fall of 2003 or spring of 2004). Small grains, such as wheat or oats, planted in the fall of 2003 would be for the 2004 crop year. It is important to know the crops planted and yields per acre. Each crop receives different amounts of nitrogen, so the crop determines the amount of nitrogen that is applied. Column 1 - Indicates which planting season (fall or spring) Column 2 - Use the crop table in Appendix 2 for the crop code in column 2.

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Fall of 2003: If there was a small grain planted in the fall 2003, circle one of the crops listed (wheat, oats, rye, barley, triticale) and record the appropriate code in box 211. If small grains were not planted, leave box 211 blank. Spring/Summer of 2004: Record the crop name on the top line and the crop code in box 219.

Column 3 - Record the month planted. Write the name of the month on the top line and the number in box 212 (i.e. 1 for Jan, 2 for Feb, etc). Column 4 - Cover crops can be used to reduce nitrogen movement. It is important to know the length of the cover crop on the land. Check if the small grain was a cover crop or not, then record the corresponding number in box 213. A cover crop CANNOT receive fertilizer. If a farmer says that the crop was a cover crop and they have applied fertilizer, DO NOT check the cover crop box. Column 5 – Conservation tillage reduces soil loss and energy use. Check if conservation tillage was used or not. Record the corresponding number in box 215 or 223. (Example: Conservation tillage must leave 30% cover on the surface. For instance, a cotton farmer who uses minimum tillage with cotton crops year-after-year is NOT conservation tillage. A farmer who plants a cover crop in the fall and cotton in the summer does have conservation tillage. A farmer could lightly disk corn and still meet the 30% cover.) Column 6 - Record the date of harvest. If not harvested, dash the box. You may record the month, day, year (xx/xx/xx) on the top line, but the bottom line is to record the Julian date (boxes 216 and 224). Column 7 - Record the date the cover crop was killed. If crop was harvested, dash the box. You may record the month, day, year (xx/xx/xx) on the top line, but the bottom line is to record the Julian date (boxes 217 and 225). Question 3 The table in question 3 is to record commercial fertilizers that were applied to the particular field during the 2004 crop year (i.e. fall planting through 2004). The fertilizers should include custom applied fertilizer, including fertilizers applied in the fall of 2003. If there were fertilizers applied, check Ayes,@ record 1 in box 301 and complete the fertilizer table for the fertilizers applied. Use one line per application. If additional lines are needed, please record on a blank sheet of paper with the sample segment and field number recorded at the top of each additional sheet. If there were no fertilizers applied, check Ano,@ record 3 in box 301 and go to Question 4. Column 1 - Record the crop name.

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Column 2 - Record the crop code. See Appendix 2 for a list of crop codes. If the crop is not listed, write the crop name and leave the crop code blank. Columns 3 - Enter the percentage analysis of nitrogen, phosphate, and potash OR the actual pounds of nutrients per acre. If the total percentage (i.e. nitrogen+phosphate+potash) is greater than 85, verify if it is the actual nutrients). Column 4 - Record the quantity of the material applied per acre. If actual nutrients were recorded in columns 3 - 5, leave column 6 empty. Column 5 - Record the unit of the material reported in columns 3 - 5 (1 for pounds, 12 for gallons, 15 for tons, and 19 if actual nutrients). Column 6 – Enter the month of the application. The codes for this column are located at the bottom of the table. Column 7 – Record how the fertilizer was applied. The codes for this column are located at the bottom of the table. Question 4: The table in question 4 is to record manure that was applied to the particular field during the 2004 crop year (i.e. fall planting through 2004). The manure should exclude commercially prepared manure. If manure was applied, check Ayes,@ record 1 in box 401 and complete the manure table for all applications of manure. Use one line per application. If additional lines are needed, please record on a blank sheet of paper with the sample segment and field number recorded at the top of each additional sheet. If there was no manure applied, check Ano,@ record 3 in box 401 and go to Question 5. Column 1 - Record the crop name. Column 2 - Record the crop code. See Appendix 2 for a list of crop codes. If the crop is not listed, write the crop name and leave the crop code blank. Columns 3 - Enter the actual pounds of nutrients applied per material code. (For example, chicken litter would be 75 lb N and 80 lb P2O5 per ton of litter). Column 4 - Record the quantity of the material applied per acre. If actual nutrients were recorded in columns 3 - 5, leave column 6 empty. Column 5 - Record the unit of the material reported in columns 3 - 5 (1 for pounds, 12 for gallons, 15 for tons, and 19 if actual nutrients).

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Column 6 – Enter the month of the application. The codes for this column are located at the bottom of the table. Column 7 – Record how the fertilizer was applied. The codes for this column are located at the bottom of the table. Column 8 – Enter the code for the major source of manure. If the respondent can not (or will not) select one from the list, consider the response a refusal. “Ref” should be written to the side of the box. You have to choose one of the manures in the list. If “other” is recorded, note the type of manure. Question 5: If a soil test has been completed in the last 2 years AND the farmer has the copy of the test, you will not have to take a soil sample of the field.

1. Soil test completed in last 2 years and farmer has results. a. Copy the P-index from the Soil Test Report. The P-index will be P-I on the

report. See Appendix 5 for a sample of a Soil Test Report. b. Following the flow of the questionnaire, question 5 will be “yes,” box 801

will be 1, and go to question 5a. c. Question 5a will be “yes,” box 802 will be 1, and go to question 5b. d. Question 5b will be the value of P-I from the farmer’s soil report. Go to

question 6.

2. Soil test NOT completed in last 2 years. a. On the Soil Test Report, the “Sample No.” will be the same as the “sample

identification” on the Soil Sample Information Sheet that was completed at the time of the interview.

b. Following the flow of the questionnaire, question 5 will be “no” and box 801 will be 3. Go to question 5c.

c. Question 5c - Ask the farmer for permission to collect a soil sample after harvest, and to write his name and address on the Soil Sample Information Sheet that is sent to NCDA.

i. If the farmer does NOT give permission to both collect the soil sample AND give his name to the soil lab, box 804 is 3 then go to question 6.

ii. If the farmer does give permission to both collect the soil sample AND give his name to the soil lab, box 804 is 1. Some information about the field will need to be recorded on the Soil Sample Information Sheet. You will record it now for use later.

1. Record a “Farm ID.” This is how the farmer knows the field. For example, “Jones Farm” or “Back40”, etc. This will be on the Soil Test Report.

2. Record a “Sample ID.” This is how the farmer identifies the particular field. This should be 6 or less number and letter combination. For example, “FLD1” or “1F”, etc.

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3. Lime applied – If lime has been applied to the field in the last 12 months, record the tons per acre to tenths AND the 2-digit month and year. For example the farmer applied ½ ton of lime per acre in December 2003…you record “.5 t/ac” and “mo12 yr2003.”

3. Soil test completed in last 2 years and farmer does NOT have results.

a. On the Soil Test Report, the “Sample No.” will be the same as the “sample identification” on the Soil Sample Information Sheet that was completed at the time of the interview.

b. Following the flow of the questionnaire, question 5 will be “yes” and box 801 will be 1. Go to question 5a.

c. Question 5a will be “no,” box 802 will be 3, and go to question 5c. d. Question 5c - Ask the farmer for permission to collect a soil sample after

harvest, and to write his name and address on the Soil Sample Information Sheet that is sent to NCDA.

i. If the farmer does NOT give permission to both collect the soil sample AND give his name to the soil lab, box 804 is 3 then go to question 6.

ii. If the farmer does give permission to both collect the soil sample AND give his name to the soil lab, box 804 is 1. Some information about the field will need to be recorded on the Soil Sample Information Sheet. You will record it now for use later.

1. Record a “Farm ID.” This is how the farmer knows the field. For example, “Jones Farm” or “Back40”, etc. This will be on the Soil Test Report.

2. Record a “Sample ID.” This is how the farmer identifies the particular field. This should be 6 or less number and letter combination. For example, “FLD1” or “1F”, etc.

3. Lime applied – If lime has been applied to the field in the last 12 months, record the tons per acre to tenths AND the 2-digit month and year. For example the farmer applied ½ ton of lime per acre in December 2003…you record “.5 t/ac” and “mo12 yr2003.”

Question 6:

water control structure may drain an entire field or a portion of the field. To determine the effectiveness of a water control structure, it is important to know how much of the field is affected by the water control structure.

ecord the percentage of the field OR the number of acres to tenths, but do not record both.

uestion 7: Record number of acres to tenths that are served by artificial drainage (either tile drains or ditches). Record the distance between drains – this is the “Spacing” in feet. Then record the

A

R

Q

61

depth of the drain in inches. Ditch depths can be determined visually. Tile drain depth will have to be determined by asking the farmer. Question 8: Drainage directions are important for determining how ground and surface water flow. While

ost fields will probably only have one drainage direction, there is space to record 3 drainage directions. You will need to take the time needed to properly determine the number of drainage directions. One drainage direction - complete Table A and go to question 12. Two drainage directions - complete Tables A and B then go to question 12. Three drainage directions - complete Tables A, B, and C then go to question 12. If the slopes, receiving slopes, slope length and edge-of-field practices are the same for the drainage directions, then note the number of drainage directions but only go to Table A. Completing Tables A, B, and C:

m

Questions 8, 9, and/or 10 - Vegetation type and width affects the effectiveness of buffers in reducing pollutants. If there is no buffer, check “none.” Check one or two type of buffer (shrub/tree or vegetative), and record the distance in feet to the nearest 5 foot increment for each buffer. ( For example, sometimes you will have a tree buffer next to the stream (example distance is 30 ft and then a grass buffer next to the tree buffer (example distance is 20 ft). In this case you will need to mark both buffers. a - Because of field topography, the entire field may not drain to an outlet (ditch, stream, river). Therefore it is important to know how much of the field drains to an outlet. Only ask if either (or both) of the buffers checked in the previous question. If either of the buffers was 20 feet or greater, ask how much of the field was affected by the vegetative buffer. Record as a percentage of the field. b – Field slope is recorded as a percentage. This is determined using the clinometer. c – Slope Length (for Granville and Franklin counties only) – recorded in feet. c(d) – Receiving slope – check one of the distance categories. d(e) – Determine if there is a sediment basin in the drainage area. Check yes or no. e(f) – Determine if there is a pond in the drainage area. Check yes or no.

62

Question 12 If the field is under conservation tillage (recorded in question 2) OR if the field is hay, dash box 811 and 812. If conventional tillage was checked in question 2, choose if the hydrologic condition is good or poor. If the field is pasture (recorded in question 2), choose if the hydrologic condition is good, fair, or poor. Question 13 Do not ask the farmer this question. It will be recorded in the office using soil maps. This will be very useful once the crop and fertilizer/manure application information is obtained, because newly developed N fertilizer management is determined by the predominant soil type of a field. Conclusion Synthesized results will be available on the Internet at http://www.soil.ncsu.edu. No individual farm or field data will show up. Only county-level information. Record the name of the respondent, their phone number (if different than on the front), and the date of the interview. Respondent code box can be 1, 2, or, 3; 1 = operator, manager, or partner, 2 = spouse, and 3 = other (such as another relative, etc). Response code can be 2, 3, or, 7; 2 = telephone, 3 = interview, and 7 = refusal. Most interviews will be A3. However, you may obtain most information from one respondent concerning what is planted, but have to call the operator to obtain the fertilizer information. Make note if there is more than one respondent. Enumerator code is your 3-digit code. Evaluation will be completed by the office. V. Problems and Special Situations Sample Segment does not have any agricultural fields If the sample segment has no agricultural fields, there will not be any questionnaires for the sample segment. Write notes on the front of the envelope as to the situation, i.e. all forest, entire area developed etc. Before you decide that there are no fields in the boundary, investigate the area thoroughly.

63

Sample Segment is different than the photo Make any notes on the front of the envelope concerning how a sample segment has changed. The aerial photographs were taken in 1993 and significant changes could have occurred. If there are no agricultural fields in the sample segment, do not complete any questionnaires and make notes of the situation on the envelope. The sample segment map can be marked using the red China marker pencil to indicate changes. Refusal Prepare a questionnaire with the field number on the questionnaire and (if possible) obtain the farmer=s name and what crops are in the field. If the field can be observed, record as much information about the field as possible. Inaccessible Given the length of the data collection period, there should be very few inaccessibles. Someone who is not available in October may be available in November or December. If an operator is truly inaccessible for the entire 3-month period, prepare a questionnaire with the field number on the questionnaire and (if possible) obtain the farmer=s name and what crops are in the field. If the field can be observed, record as much information about the field as possible. Write notes about the situation. Smaller Ditches in Field When there are smaller ditches (controlled drainage or not) in the field AND the entire area is the same crop, this area can be counted as one field. However, if each of the areas between the ditches is treated individually by the farmer, the areas between the ditches have been counted as separate fields. Soil Sampling Soil samples will be obtained from the field if:

- farmer has not completed a soil test in the last 2 years; - farmer has completed a soil test in the last 2 years but does not have the results; - permission is obtained from the respondent

o IMPORTANT----the respondent’s name will be used as the person granting permission to enter the field for the soil sample and to attach their name to the sample so they can get a result. This is very important since it concerns confidentiality for the soil test only!

When to take soil sample:

- Samples from pasture or hay fields can be taken anytime. - Samples from cultivated fields have to be taken after harvest.

64

Identification information: - Do NOT contact the farmer for the soil sample information….it is all on the

questionnaire. - Copy all of the information from the questionnaire to the Soil Sample Information

sheet prior to mailing the questionnaire to the office. - Copy the name, address, and phone from the questionnaire to the Soil Sample

Information sheet. (do not complete email address for respondent) - Copy the Farm ID from page 2 of the questionnaire to the Farm ID # box on the Soil

Sample Information sheet. - Copy the Sample ID from page 2 of the questionnaire to the Sample Identification

column on the Soil Sample Information sheet. - If lime was applied in the last 12 months to the field, copy the lime information from

page 2 of the questionnaire to the Lime column on the Soil Sample Information sheet. - Copy the crop from question 2 page 1 of the questionnaire to the First Crop column.

The back of the Soil Sample Information sheet contains a list of codes used for this crop. Note…if double-cropped the code is 018.

- Do not complete Second Crop column. Samples to the lab:

- Samples will be collected by your supervisor and brought to the office. - Do NOT send any samples directly to the soil lab!

65

Appendix 3: Tar-Pamlico Best Management Practices Survey

Proje 40 Tar-Pamlico River Basin Best Management Practices Survey How

2

2003

Spring/ Summer

2004

1 Cover Crop - Cwith herbicides o2 Conservation T 2. Were com

(Include c

ct
2004
m n the field?

any acres are i

rk the cresith the veg

on this field

this ver ? 1

1 3

Yes 1 No 3

*Enumerator e sample field on the back diagram. Mathe nearest ditch, stream, or river, water, control structures, along wdrainage directions are the same, do not separate. 1. Now I would like to ask some questions about the crops planted

Planting Season

Crop and

Crop Code

Month Planted

(1=Jan,…12=Dec)

Wasa CoCrop

note: Draw th

(Circle one.) Wheat, Oats, Rye, Barley, Trticale

Yes No

Fall of

Latitude Longitude

County

Sample

Field

Name of Operator

Address

City , NC Zip

Telephone
211 212 213 2
Crop Y N

219 2

rop grown in the fall and winter to protect the soil from wind and water erosion. r plowed down prior to spring planting. illage - Tillage method that will leave a minimum of 30% of the soil surface covere

mercial fertilizers applied to this field for the 2004 crop year? ustom applied fertilizer.)

(Fall of 2003 through the Summer of 2004.) Yes -1

t of the field and the drainage directions. Indicate etative buffers on the diagram. If factors of the

in the fall of 2003 and spring and summer of 2004.

Was Conservation

Tillage Used? 2

Date of Harvest

(mm/dd/yy)

Date Cover Crop Killed (mm/dd/yy)

Purpose of survey - This survey is designed to help farmers add more flexibility to the Best Management Practices (BMPs) required in the Tar-Pamlico river basin. The program will develop a baseline of nitrogen and phosphorus use, track nitrogen and phosphorus reduction and demonstrate what type of BMPs farmers currently utilize. By law, your response will remain confidential. It will only be used in combination with other reports to summarize the results for the Tar-Pamlico river basin. The results of this survey will be available at the end of 2005.

112 . Acres

15 216 217

es o

23

Cover

d by r

(co

1 3

224 225

crops are usually small grain crops that are either killed

esidue following planting.

ntinue) 301

66

67

No -3 (go to item 4)

3 Materials Used (Enter percentage analysis or actual pounds of plant nutrients applied per acre.)

1 Crop

2 Crop Code

N Nitrogen

P2O5

Phosphate K2O

Potash

4 Quantity Applied Per Acre

(leave blank if actual nutrients were reported)

5 Used Material Code 1 Pounds 12 Gallons 15 Tons 19 Pounds of actual nutrients

6 Month

of Application[Enter Code.]

7 How was

this applied?

[Enter Code.]

310 311 312 313 314 315 316 317

320 321 322 323 324 325 326 327

330 331 332 333 334 335 336 337

340 341 342 343 344 345 346 347

350 351 352 353 354 355 356 357

360 361 362 363 364 365 366 367

370 371 372 373 374 375 376 377

Codes for Column 6 1 January 6 June 11 November 2 February 7 July 12 December 3 March 8 August 4 April 9 September 5 May 10 October

Codes for Column 7 1 Injected 2 Incorporated within 48 hours 3 Incorporated between 48 hrs and 4 weeks 4 Incorporated between 4 weeks and 3 months 5 Surface Application

68

3. Was manure applied to this field for the 2004 crop year? (Fall of 2003 through the Summer of 2004.) Yes -1 (continue)


3 Materials Used (Enter percentage analysis or actual pounds of plant nutrients applied per acre.)

1 Crop

2 Crop Code

N Nitrogen

P2O5

Phosphate

4 Quantity Applied Per Acre

(leave blank if actual nutrients were reported)

5 Used Material Code

1 Pounds 12 Gallons 15 Tons 19 Pounds of actual nutrients

6 Month

of Application [Enter Code.]

7 How was

this applied?

[Enter Code.]

8 Major

Source of Manure

[Enter Code.]

410 411 412 414 415 416 417 418

420 421 422 424 425 426 427 428

430 431 432 434 435 436 437 438

440 441 442 444 445 446 447 448

450 451 452 454 455 456 457 458

460 461 462 464 465 466 467 468

470 471 472 474 475 476 477 478

4. Has a soil test been completed on this field in the last 2 years?

Yes -1 (go to 5a) No -3 (go to 5c)

a. Do you have a copy of the soil test available? Yes -1 (continue)


b. Copy the P-index from latest soil test (go to 6) c. With your permission, I will collect soil samples from this field after harvest. Your name will be

given to the NCDA soil lab so that the results of the free soil test can be sent to you. Will that be all right? Yes -1

No -3

Farm ID Sample ID

Line (if applied in last 12 months)

Codes for Column 6 1 January 7 July 2 February 8 August 3 March 9 September 4 April 10 October 5 May 11 November 6 June 12 December

Codes for Column 7 1 Injected 2 Incorporated within 48 hours 3 Incorporated between 48 hours and 4 weeks 4 Incorporated between 4 weeks and 3 months5 Surface Application (liquid or dry broadcast)

Codes for Column 8 1 Broiler – house litter 7 Hogs – lagoon liquid 2 Broiler – stockpiled litter 8 Hogs – lagoon sludge 3 Dairy – lagoon liquid 9 Turkey – house litter 4 Dairy – lagoon sludge 10 Turkey – stockpiled litter5 Dairy – scraped manure 11 Other 6 Poultry layer – lagoon liquid

401

801

802

803

804

811

. t/ac

mo yr 200

69

5. How much of the field is affected by a water control structure? OR

6. Acres served by artificial drainage in the whole field

a. Spacing (feet)

b. Depth (inches)

7. How many drainage directions are in the field? 1 (Complete Table A) 2 (Complete Tables A+B) 3 (Complete Tables A+B+C)

TABLE A – First Drainage Direction in Field 8. What type(s) of buffer is between this field and the nearest ditch, stream or river?

(Record the distance in feet using 5 foot increments.) none shrub/tree buffer vegetative buffer (not shrub/tree)

a. How much of the field is affected by either the vegetative and/or shrub/tree buffer? b. Field Slope c. Receiving Slope width (in field) (Check one.)

0-9 feet (1) 30-49 feet (4) 150-199 feet (7) 10-19 feet (2) 50-99 feet (5) 200 – 299 feet (8) 20-29 feet (3) 100-149 feet (6) 300 or more feet (9)

d. Is there a sediment basin in the drainage area? Yes -1

No -3

e. Is there a pond in the drainage area? Yes -1

No -3

OFFICE USE SL = 516

805 %

806 . Acres

807 . Acres

808 ft

809 in

810

501

502 ft

503 ft

504 %

505 %

507

508

509

70

TABLE B – Second Drainage Direction in Field

9. What type(s) of buffer is between this field and the nearest ditch, stream or river?


a. How much of the field is affected by either the vegetative and/or shrub/tree buffer?

b. Field Slope

c. Receiving Slope width (in field) (Check one.) 0-9 feet (1) 30-49 feet (4) 150-199 feet (7) 10-19 feet (2) 50-99 feet (5) 200 – 299 feet (8) 20-29 feet (3) 100-149 feet (6) 300 or more feet (9)


No -3


No -3

OFFICE USE SL = 616

601

602 ft

603 ft

604 %

605 %

607

608

609

71

TABLE C – Third Drainage Direction in Field

10. What type(s) of buffer is between this field and the nearest ditch, stream or river?


a. How much of the field is affected by either the vegetative and/or shrub/tree buffer?

b. Field Slope

c. Receiving Slope width (in field) (Check one.) 0-9 feet (1) 30-49 feet (4) 150-199 feet (7) 10-19 feet (2) 50-99 feet (5) 200 – 299 feet (8) 20-29 feet (3) 100-149 feet (6) 300 or more feet (9)


No -3


No -3

OFFICE USE SL = 716

11. What is the hydrologic condition?

(If the field is under conservation tillage or hay, do not answer.) Conventional tillage good poor Pasture good fair poor

12. Predominant soil mapping unit for this field. OFFICE USE

13. Results of the survey will be available at www.soil.ncsu.edu.

Respondent’s name Phone ( ) - Date

701

702 ft

703 ft

704 %

705 %

707

708

709

811

812

813

72

N FIELD DIAGRAM

Respondent Response Code Enum. Eval. 1-Op/Mgr/Ptr 2-Sp 3-Other

101 2-Tel 3-Int 7-Ref

910 098 100

S/E

Appendix 4

: Soil Loss By Region And Cropping System

Region Color Crop Soil Loss Cover Crop

Soil Loss No-till

Soil Loss Factors C C

0.37660.39750.3437

0.23610.3437

0.3786

0.1793

0.2391

0.3388

0.3587

0.3847

0.2323

0.3412

0.3992

0.3676

0.3875

0.2583

0.0695

0.3378

At Coastal Uplands Lt purple Tobacco 12.6 Cover Crop 10 No-till 6.8 33.456At Coastal Uplands Lt purple Vegetable crops 13.3 Cover Crop 11.9 33.456At Coastal Uplands Lt purple Peanuts 11.5 No-till 4.2 33.456

At Coastal Uplands Lt purple Corn, wheat or soybeans 7.9

No-till, LT 4.3 33.456

At Coastal Uplands Lt purple Cotton 11.5 Cover Crop 8.3 No-till 5.4 33.456

Tidewater Uplands Dark blue Vegetable crops 1.9 Cover Crop 1.7 5.0184

Tidewater Uplands Dark blue Peanuts 0.9 Cover Crop 0.7 5.0184

Tidewater Uplands Dark blue

Corn, wheat or soybeans 1.2

No-till, NT 0.1 5.0184

Tidewater Uplands Dark blue Cotton 1.7 Cover Crop 1.5 No-till 0.2 5.0184

Tidewater Uplands Dark blue Tobacco 1.8 Cover Crop 1.2 5.0184

Tidewater Organic Light blue Vegetable crops 5.3 Cover Crop 4.8 13.776

Tidewater Organic Light blue

Corn, wheat or soybeans 3.2 13.776

Tidewater Organic Light blue Cotton 4.7 Cover Crop 4.1 13.776

Tidewater Organic Light blue Potatoes 5.5 Cover Crop 4.8 13.776

So Roll CP Dark green Tobacco 7.4 Cover Crop 6 20.13

So Roll CP Dark green Vegetable crops 7.8 Cover Crop 7 20.13

So Roll CP Dark green Peanuts 5.2 Cover Crop 4.4 No-till, 2.5 20.13

So Roll CP Dark green

Corn, wheat or soybeans 1.4

No-till NT 0.4 20.13

So Roll CP Dark green Cotton 6.8 Cover Crop 6

No-till NT 0.6 20.13

Appendix 5: Additional Data Table A5a. Average Receiving Slope by Direction.

Direction A Direction B Direction C County # Min Max Mean # Min Max Mean # Min Max Mean

Beaufort 217 0-9 30-49 10-19 10 0-9 0-9 0-9 Edgecombe 146 0-9 50-99 10-19 41 0-9 30-49 10-19 6 0-9 0-9 0-9

Franklin 100 0-9 +300 10-19 65 0-9 100-149 10-19 31 0-9 30-49 10-19 Granville 49 0-9 0-9 0-9 4 0-9 0-9 0-9 1 0-9 0-9 0-9

Halifax 109 0-9 100-149 0-9 24 0-9 0-9 0-9 1 0-9 0-9 0-9 Hyde 58 0-9 0-9 0-9

Martin 58 0-9 0-9 0-9 16 0-9 0-9 0-9 3 0-9 0-9 0-9 Nash 136 0-9 0-9 0-9 23 0-9 20-29 10-19 4 0-9 20-29 10-19

Pamlico 40 0-9 0-9 0-9 Pitt 126 0-9 0-9 0-9 11 0-9 0-9 0-9

Washington 34 0-9 0-9 0-9 Wilson 62 0-9 20-29 10-19 8 0-9 0-9 0-9

All 1135 0-9 +300 10-19 292 0-9 100-149 10-19 46 0-9 30-49 10-19

Table A5b. Type of Buffer, Number of Fields Enumerated, Buffer Acres, and Minimum, Maximum and Mean Width by County for Direction A

Shrub/Tree Buffer Vegetated Buffer Field Characteristics Buffer Width (feet) Field Characteristics Buffer Width (feet)

County

# Field

Affected Acres

% Acre

Min Max Mean # Field Affected Acres

% Acres

Min Max Mean

Beaufort 36 258.1 13 0 150 60 20 213.4 11 4 30 15Edgecombe 21 194.5 9 10 500 102 72 759.2 35 3 400 16Franklin 37 161.1 21 10 1400 226 22 67.9 9 10 1000 87Granville 33 195.0 58 20 1000 141 12 72.8 22 20 200 82Halifax 26 306.6 25 10 500 165 65 590.6 51 3 100 18Hyde 3 13.0 1 20 20 20 -- -- 0 -- -- 0Martin 16 125.1 25 5 1055 79 24 136.7 28 5 250 21Nash 62 513.2 42 10 3500 508 19 127.9 11 5 100 21Pamlico 4 12.0 12 30 30 30 4 25.9 25 30 30 30Pitt 31 276.8 18 1 600 135 20 249.4 17 3 75 11Washington 7 85.0 20 5 5 5 10 121.0 28 1 10 9Wilson 15 85.6 27 10 160 49 3 4.4 1 20 75 48All 291 226.0 19 1 3500 203 271 2369.2 20 1 1000 26

Table A5c. Combined Shrub/Tree and Vegetated Buffer: Number of Fields Enumerated, Buffer Acres, and Minimum, Maximum and Mean Width by County for Direction A

Field Characteristics Shrub Buffer (feet) Vegetated Buffer (feet) County # Field

Affected Acres

% Acres

Min Max Mean Min Max Mean

Beaufort 5 29.7 0 8 100 43 4 40 11Edgecombe 42 448.1 21 10 500 75 3 100 16Franklin 37 195.5 26 5010 3200 210 5 480 64Granville 0 0 Halifax 15 114.3 9 10 100 80 5 25 14Hyde 0 0 Martin 0 0 Nash 24 169.5 14 50 400 111 5 100 29Pamlico 1 2.0 2 15 50 50 50 50 50Pitt 0 0 Washington 2 28.0 7 5 20 18 2 3 3Wilson 0 0 All 126 987.1 8 5 3200 120 2 480 32

Table A5d. Type of Buffer, Number of Fields Enumerated, Buffer Acres, and Minimum, Maximum and Mean Width by County for Direction B.

Shrub Buffer Vegetated Buffer Field Characteristics Buffer Width (feet) Field Characteristics Buffer Width (feet)

County

# Field

Affected Acres

% Acres Min Max Mean # Field Affected Acres

% Acres Min Max Mean

Beaufort 1 3.8 0 25 25 25 6 33.7 2 4 0 12Edgecombe 8 92.9 4 30 600 216 20 281.5 13 3 30 10Franklin 22 80.0 11 5 1400 164 15 40.4 5 5 650 68Granville 3 33.7 10 50 400 183 1 0.8 0 5 5 5Halifax 6 24.0 2 10 200 78 13 80.6 7 5 100 22Hyde 0 0 0 0 Martin 2 26.4 5 5 10 8 12 36.4 7 5 30 9Nash 2 8.2 1 10 100 55 4 13.5 1 10 100 45Pamlico 0 0 0 0 Pitt 5 74.7 5 50 100 60 1 3.0 0 3 3 3Washington 0 0 0 0 Wilson 3 14.7 5 15 40 32 0 0 All 52 358.4 3 5 1400 133 72 489.9 4 3 650 26

Table A5e. Combined Shrub/Tree and Vegetated Buffer: Number of Fields Enumerated, Buffer Acres, and Minimum, Maximum and Mean Width by County for Direction B.

Field Characteristics Shrub Buffer (feet) Vegetated Buffer (feet) County # Field Acres % Acres Min Max Mean Min Max Mean

Beaufort 1 1.5 0 8 8 8 4 4 4Edgecombe 9 157.5 7 5 100 49 5 10 9Franklin 26 87.1 11 5 1000 81 3 500 61Granville 0 0 Halifax 6 25.9 2 5 100 68 5 30 16Hyde 0 0 Martin 0 0 Nash 6 20.8 2 10 100 63 5 15 8Pamlico 0 0 Pitt 0 0 Washington 0 0 Wilson 0 0 All 48 292.8 2 5 1000 70 3 500 38

Table A5f. Type of Buffer, Number of Fields Enumerated, Buffer Acres, and Minimum, Maximum and Mean Width by County for Direction C.

Shrub Buffer Vegetated Buffer Field Characteristics Buffer Width (feet) Field Characteristics Buffer Width (feet)

County

# Field Acres % Acres Min Max Mean # Field Acres % Acres Min Max Mean Beaufort

Edgecombe 2 7.6 <1 5 5 5Franklin 1 19.4 3 25 1400 199 13 36.2 4 5 100 34Granville Halifax 2 1.4 <1 25 5 5Hyde Martin 1 1.8 <1 5 5 5 2 0.6 <1 5 5 5Nash 1 2.1 <1 100 100 100 1 6.0 <1 10 10 10Pamlico Pitt Washington Wilson All 11 23.3 <1 1400 173 20 51.8 4 5 100 26

Table A5g. Combined Shrub/Tree and VegetateBuffer Acres, and Minimum, Maximum and Mean Width by CountCounty

Beaufort EdgecomFranGranvHalifaxHyMartinNash PamlicoPitt WasWAll

d Buffer: Number of Fields Enumerated, y for Direction C.

Field Characteristics Shrub Buffer (feet) Vegetated Buffer (feet) # Field Acres %

Acres Min Max Mean Min Max Mean

be 4 43.3 2 10 600 173 5 10 9

klin 8 32.8 4 5 60 28 5 100 31ille 1 7.0 2 50 50 50 20 20 20

de

1 4.8 0 100 100 100 10 10 10

hington ilson

14 87.9 1 5 600 76 5 100 22

Table A5h. Mapping Unit, Crop, and Fertilizer Rates

MUNAME FieldAcres CROP_NLEW

Number

FallN

FallP

FallK

SpringN

SpringP

SpringK

Norfolk, Georgeville, And Faceville Soils, 2 To 8 Percent Slopes 3.5

Common Bermudagrass (Hay) 1 0 0 0 0 0 0

Norfolk Loamy Sand, 2 To 6 Percent Slopes 4 Common Bermudagrass (Pasture) 1 0 0 0 80 0 0

Wagram Loamy Sand, 0 To 6 Percent Slopes 27.4 Common Bermudagrass (Pasture) 1 0 0 0 78 0 0

Augusta Fine Sandy Loam 5 Common Bermudagrass (Pasture) 6 0 0 0 30 0 0

Gritney Fine Sandy Loam, 2 To 6 Percent Slopes 7 Common Bermudagrass (Pasture) 5 0 0 0 0 0 0

Craven Fine Sandy Loam, 0 To 1 Percent Slopes 2 Corn (Grain) 2 0 0 0 204 80 40 Leon Sand 3 Corn (Grain) 5 0 0 0 176 93 60 Augusta Fine Sandy Loam 3 Corn (Grain) 6 0 0 0 175 35 90 Roanoke Fine Sandy Loam 1.9 Corn (Grain) 10 0 0 0 174 77 160 Ponzer Muck, 0 To 2 Percent Slopes, Rarely Flooded 2.5 Corn (Grain) 1 0 0 0 173 67 2 Wickham Sandy Loam, 0 To 4 Percent Slopes 23 Corn (Grain) 3 0 0 0 172 15 90 Wedowee Sandy Loam, 2 To 6 Percent Slopes 2.5 Corn (Grain) 8 0 0 0 170 136 180 Roanoke Fine Sandy Loam 12 Corn (Grain) 9 0 0 0 161 74 48 Belhaven Muck 12.5 Corn (Grain) 3 0 0 0 160 60 120 Wagram Loamy Sand, 0 To 6 Percent Slopes 1.5 Corn (Grain) 1 0 0 0 159 24 136 Belhaven Muck, 0 To 2 Percent Slopes, Rarely Flooded 60 Corn (Grain) 1 0 0 0 157 7 150 Norfolk Loamy Sand, 0 To 2 Percent Slopes 6.6 Corn (Grain) 7 0 0 0 152 18 108 Craven Fine Sandy Loam, 0 To 1 Percent Slopes 16.2 Corn (Grain) 7 0 0 0 151 96 120 Craven Fine Sandy Loam, 0 To 1 Percent Slopes 21.5 Corn (Grain) 1 0 0 0 150 0 120 Craven Fine Sandy Loam, 0 To 1 Percent Slopes 6.7 Corn (Grain) 1 0 0 0 148 35 105 Norfolk Loamy Sand, 2 To 6 Percent Slopes 6 Corn (Grain) 2 0 0 0 146 15 75 Exum Fine Sandy Loam, 0 To 1 Percent Slopes 40 Corn (Grain) 5 0 0 0 143 21 126 Roanoke Fine Sandy Loam 5.5 Corn (Grain) 5 0 0 0 142 77 160 Tomotley Fine Sandy Loam 4.6 Corn (Grain) 1 0 0 0 142 77 0 Tomotley Fine Sandy Loam 4.3 Corn (Grain) 5 0 0 0 142 77 160 Foreston Loamy Sand, 0 To 2 Percent Slopes 1.3 Corn (Grain) 2 0 0 0 140 27 114 Rains Fine Sandy Loam 19.5 Corn (Grain) 3 0 0 0 139 22 112 Norfolk Loamy Sand, 2 To 6 Percent Slopes 12 Corn (Grain) 2 0 0 0 137 13 63 Roanoke Fine Sandy Loam 96 Corn (Grain) 3 0 0 0 135 30 90 Norfolk Loamy Sand, 0 To 2 Percent Slopes 12 Corn (Grain) 6 0 0 0 128 28 148 Rains Fine Sandy Loam 11 Corn (Grain) 1 0 0 0 127 28 88 Exum Very Fine Sandy Loam, 0 To 2 Percent Slopes 2.3 Corn (Grain) 6 0 0 0 126 270 0 Lynchburg Fine Sandy Loam 13.6 Corn (Grain) 3 0 0 0 122 0 88 Norfolk Loamy Sand, 0 To 2 Percent Slopes 4 Corn (Grain) 5 0 0 0 121 21 60 Gritney Fine Sandy Loam, 2 To 6 Percent Slopes 5 Corn (Grain) 2 0 0 0 120 0 60 Dothan Loamy Sand, 0 To 2 Percent Slopes 10 Corn (Grain) 2 0 0 0 118 54 108 Emporia Fine Sandy Loam, 2 To 6 Percent Slopes 6 Corn (Grain) 1 0 0 0 111 25 75

Bladen Fine Sandy Loam 7.5 Corn (Grain) 2 0 0 0 105 30 90 Lynchburg Fine Sandy Loam 5.1 Corn (Grain) 1 0 0 0 105 15 90 Portsmouth Loam 8 Corn (Grain) 1 0 0 0 100 40 40 Ponzer Muck, 0 To 2 Percent Slopes, Rarely Flooded 2 Corn (Grain) 2 0 0 0 90 72 2 Belhaven Muck 12.2 Corn (Grain) 2 0 0 0 90 60 100 Roanoke Fine Sandy Loam 5.2 Corn (Grain) 1 0 0 0 89 142 160 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 6 Corn (Grain) 1 0 0 0 86 17 105 Ponzer Muck, 0 To 2 Percent Slopes, Rarely Flooded 2 Corn (Grain) 1 0 0 0 74 28 2 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 4 Corn (Grain) 1 0 0 0 72 21 122 Emporia-Wedowee Complex, 2 To 6 Percent Slopes 1 Corn (Grain) 1 0 0 0 65 0 105 State Sandy Loam, 0 To 3 Percent Slopes 15.8 Corn (Grain) 4 0 0 0 57 21 12 Lenoir Loam, 0 To 1 Percent Slopes 8 Corn (Grain) 1 0 0 0 44 35 105 Goldsboro Sandy Loam, 0 To 2 Percent Slopes 5.2 Corn (Grain) 3 0 0 0 18 35 105 Norfolk Loamy Sand, 0 To 2 Percent Slopes 15 Corn (Grain) 1 0 0 0 18 18 88 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 4.8 Corn (Silage) 1 0 0 0 115 25 98 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 7 Corn (Silage) 4 0 0 0 42 25 98 Coxville Fine Sandy Loam 12.2 Cotton 4 0 0 0 146 30 60 Goldsboro Sandy Loam, 0 To 2 Percent Slopes 1.7 Cotton 3 0 0 0 119 1 109 Norfolk Loamy Sand, 0 To 2 Percent Slopes 14.8 Cotton 3 0 0 0 116 18 108 Foreston Loamy Fine Sand 16.6 Cotton 6 0 0 0 110 37 108 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 3.9 Cotton 1 0 0 0 105 23 93 Bonneau Loamy Sand, 0 To 6 Percent Slopes 38 Cotton 14 0 0 0 103 15 75 Tarboro Loamy Sand, 0 To 3 Percent Slopes 10 Cotton 1 0 0 0 101 21 100 Ponzer Muck, 0 To 2 Percent Slopes, Rarely Flooded 60 Cotton 7 0 0 0 100 40 70 Bonneau Loamy Sand, 0 To 6 Percent Slopes 22 Cotton 2 0 0 0 99 30 90 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 8 Cotton 3 0 0 0 98 36 108 Exum Silt Loam, 0 To 2 Percent Slopes 8.2 Cotton 5 0 0 0 97 21 100 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 3.9 Cotton 1 0 0 0 94 32 95 Norfolk Loamy Sand, 2 To 6 Percent Slopes 4 Cotton 1 0 0 0 94 10 50 Goldsboro Sandy Loam, 0 To 2 Percent Slopes 4.9 Cotton 7 0 0 0 94 36 100 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 7 Cotton 2 0 0 0 94 60 120 Rains Fine Sandy Loam, 0 To 1 Percent Slopes 17.1 Cotton 2 0 0 0 92 32 102 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 3 Cotton 5 0 0 0 91 28 152 Lenoir Fine Sandy Loam, Thin Solum Variant, 0 To 3 Percent Slopes 2.5 Cotton 3 0 0 0 91 0 140 Lenoir Loam 6.9 Cotton 1 0 0 0 90 50 150 Tomotley Fine Sandy Loam 35 Cotton 1 0 0 0 90 100 0 Norfolk Loamy Sand, 2 To 6 Percent Slopes 25.5 Cotton 1 0 0 0 90 80 0 Acredale Silt Loam, 0 To 2 Percent Slopes, Rarely Flooded 2.4 Cotton 22 0 0 0 90 65 80 Gritney Fine Sandy Loam, 2 To 6 Percent Slopes 12 Cotton 5 0 0 0 88 46 0 Craven Fine Sandy Loam, 1 To 4 Percent Slopes 7 Cotton 1 0 0 0 88 60 120 Coxville Fine Sandy Loam 7.8 Cotton 5 0 0 0 88 25 75 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 9 Cotton 9 0 0 0 86 17 105 Norfolk Loamy Sand, 0 To 2 Percent Slopes 8.2 Cotton 1 0 0 0 86 17 120

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Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 13 Cotton 8 0 0 0 86 0 126 Noboco Fine Sandy Loam, 0 To 2 Percent Slopes 24 Cotton 1 0 0 0 86 15 60 Aycock Very Fine Sandy Loam, 0 To 2 Percent Slopes 5 Cotton 1 0 0 0 84 0 84 Aycock Very Fine Sandy Loam, 0 To 2 Percent Slopes 3 Cotton 11 0 0 0 84 0 80 Norfolk Loamy Sand, 0 To 2 Percent Slopes 8 Cotton 2 0 0 0 83 17 150 Wickham Sandy Loam, 0 To 4 Percent Slopes 36.8 Cotton 9 0 0 0 81 17 60 Norfolk Loamy Sand, 2 To 6 Percent Slopes 5.1 Cotton 5 0 0 0 80 0 42 Lynchburg Fine Sandy Loam 23 Cotton 2 0 0 0 79 40 120 Norfolk Loamy Sand, 2 To 6 Percent Slopes 5.6 Cotton 2 0 0 0 75 17 100 Aycock Fine Sandy Loam, 0 To 1 Percent Slopes 25 Cotton 4 0 0 0 74 27 81 Wagram Loamy Sand, 0 To 6 Percent Slopes 19 Cotton 5 0 0 0 74 21 100 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 8 Cotton 1 0 0 0 73 23 115 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 15 Cotton 1 0 0 0 73 23 90 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 19 Cotton 1 0 0 0 73 23 115 Norfolk Loamy Sand, 0 To 2 Percent Slopes 10.3 Cotton 1 0 0 0 73 17 92 Rains Fine Sandy Loam 14.5 Cotton 2 0 0 0 72 20 60 Norfolk Loamy Sand, 0 To 2 Percent Slopes 13.5 Cotton 1 0 0 0 72 20 0 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 7 Cotton 7 0 0 0 71 19 58 State Fine Sandy Loam, 0 To 2 Percent Slopes 7.9 Cotton 2 0 0 0 70 0 60 Conetoe Loamy Sand, 0 To 4 Percent Slopes 82 Cotton 2 0 0 0 69 0 120 Aycock Very Fine Sandy Loam, 0 To 2 Percent Slopes 14.5 Cotton 1 0 0 0 69 19 6 Lenoir Loam 20 Cotton 1 0 0 0 68 21 114 Aycock Very Fine Sandy Loam, 2 To 6 Percent Slopes 8 Cotton 3 0 0 0 67 25 160 Lenoir Loam 50 Cotton 3 0 0 0 66 48 96 Noboco Fine Sandy Loam, 0 To 2 Percent Slopes 34.6 Cotton 4 0 0 0 65 0 60 Rains Fine Sandy Loam 40 Cotton 2 0 0 0 65 0 114 Lynchburg Fine Sandy Loam 35 Cotton 1 0 0 0 65 0 90 Rains Sandy Loam 1.1 Cotton 1 0 0 0 65 17 100 Scuppernong Muck, 0 To 2 Percent Slopes, Rarely Flooded 13 Cotton 2 0 0 0 65 0 72 Scuppernong Muck, 0 To 2 Percent Slopes, Rarely Flooded 34 Cotton 3 0 0 0 65 25 60 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 15 Cotton 2 0 0 0 63 54 108 Aycock Very Fine Sandy Loam, 2 To 6 Percent Slopes 15 Cotton 3 0 0 0 61 20 100 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 9.7 Cotton 3 0 0 0 60 17 0 Bonneau Loamy Sand, 0 To 4 Percent Slopes 19 Cotton 4 0 0 0 59 23 115 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 4 Cotton 5 0 0 0 56 25 188 Bonneau Loamy Sand, 0 To 4 Percent Slopes 10 Cotton 5 0 0 0 56 25 125 Wagram Loamy Sand, 0 To 6 Percent Slopes 6 Cotton 1 0 0 0 55 2 100 Marlboro Loamy Sand, 0 To 2 Percent Slopes 4.9 Cotton 4 0 0 0 54 10 82 Norfolk Loamy Sand, 0 To 2 Percent Slopes 19.7 Cotton 1 0 0 0 50 25 98 Conetoe Loamy Sand, 0 To 4 Percent Slopes 41 Cotton 1 0 0 0 48 0 102 Dothan Loamy Sand, 2 To 6 Percent Slopes 13.8 Cotton 1 0 0 0 45 30 90 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 14 Cotton 3 0 0 0 40 45 120

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Emporia Fine Sandy Loam, 2 To 6 Percent Slopes 16.6 Cotton 2 0 0 0 40 30 290 Rains Sandy Loam 6.5 Cotton 1 0 0 0 38 24 120 Roanoke Fine Sandy Loam 18 Cotton 1 0 0 0 37 70 60 Altavista Fine Sandy Loam, 0 To 3 Percent Slopes, Rarely Flooded 1.6 Cotton 1 0 0 0 35 0 60 Norfolk Loamy Sand, 0 To 2 Percent Slopes 3.5 Cotton 6 0 0 0 32 0 188 Emporia Fine Sandy Loam, 2 To 6 Percent Slopes 15.2 Cotton 1 0 0 0 30 23 393 Norfolk Loamy Sand, 2 To 6 Percent Slopes 1.5 Cotton 1 0 0 0 28 0 170 Aycock Very Fine Sandy Loam, 2 To 6 Percent Slopes 8.1 Cotton 2 0 0 0 27 18 63 Exum Fine Sandy Loam, 1 To 6 Percent Slopes 18 Cotton 1 0 0 0 22 0 22 Emporia Fine Sandy Loam, 2 To 6 Percent Slopes 6.7 Cotton 1 0 0 0 20 50 50 Exum Fine Sandy Loam, 0 To 1 Percent Slopes 25 Cotton 1 0 0 0 19 30 89 Wagram Loamy Sand, 0 To 6 Percent Slopes 4 Cotton 3 0 0 0 15 30 90 Roanoke Fine Sandy Loam 20 Cotton 9 0 0 0 5 10 15 Pantego Loam 100 Cotton 5 0 0 0 0 0 0 Wedowee Sandy Loam, 2 To 6 Percent Slopes 4 Fescue (Hay) 4 0 0 0 202 70 70 Cecil Sandy Loam, 2 To 6 Percent Slopes 11 Fescue (Hay) 1 0 0 0 150 0 0 Wedowee Sandy Loam, 6 To 10 Percent Slopes 6.5 Fescue (Hay) 1 0 0 0 148 40 50 Wedowee Sandy Loam, 2 To 6 Percent Slopes 20 Fescue (Hay) 5 0 0 0 120 120 120 Wedowee Sandy Loam, 2 To 6 Percent Slopes 3.8 Fescue (Hay) 1 0 0 0 80 75 95 Enon Loam, 2 To 6 Percent Slopes 4.5 Fescue (Hay) 3 0 0 0 78 39 39 Wedowee Sandy Loam, 2 To 6 Percent Slopes 9.5 Fescue (Hay) 1 0 0 0 70 70 70 Cecil Sandy Loam, 2 To 6 Percent Slopes 12 Fescue (Hay) 9 0 0 0 23 32 38 Pacolet Clay Loam, 6 To 10 Percent Slopes, Eroded 16 Fescue (Hay) 1 0 0 0 13 13 13 Wedowee Sandy Loam, 6 To 10 Percent Slopes 8 Fescue (Hay) 5 0 0 0 0 0 0 Wedowee Sandy Loam, 6 To 10 Percent Slopes 7 Fescue (Pasture) 1 0 0 0 840 770 0 Wedowee Sandy Loam, 2 To 6 Percent Slopes 12 Fescue (Pasture) 1 0 0 0 60 15 30 Wedowee Sandy Loam, 2 To 6 Percent Slopes 0.8 Fescue (Pasture) 3 0 0 0 30 0 0 Wedowee Sandy Loam, 2 To 6 Percent Slopes 6 Fescue (Pasture) 18 0 0 0 0 0 0 Exum Very Fine Sandy Loam, 0 To 2 Percent Slopes 4.5 Oats 1 12 0 0 0 0 0 Helena Sandy Loam, 2 To 6 Percent Slopes 6.1 Oats 1 52.5 30 30 0 0 0 Vance Sandy Loam, 2 To 6 Percent Slopes 9 Oats 2 80 0 0 0 0 0 Herndaon Silt Loam, 2 To 6 Percent Slopes 4.1 Oats 2 78 24 42 0 0 0 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 10 Peanuts 3 0 0 0 21 21 114 Fuquay Sand, 0 To 4 Percent Slopes 4.5 Peanuts 1 0 0 0 18 54 108 Norfolk Loamy Fine Sand, 0 To 2 Percent Slopes 10 Peanuts 1 0 0 0 15 30 90 Craven Fine Sandy Loam, 0 To 1 Percent Slopes 12 Peanuts 1 0 0 0 0 0 0 Wickham Sandy Loam, 0 To 4 Percent Slopes 6 Peanuts 1 0 0 0 0 0 48 Norfolk Loamy Sand, 2 To 6 Percent Slopes 33 Peanuts 23 0 0 0 0 0 0 Altavista Fine Sandy Loam, 0 To 3 Percent Slopes 57.9 Peanuts 3 0 0 0 0 0 100 Altavista Fine Sandy Loam, 0 To 3 Percent Slopes 8 Peanuts 2 0 0 0 0 0 48 Johns Fine Sandy Loam 6 Peanuts 2 0 0 0 0 0 96 Foreston Loamy Fine Sand 16.4 Peanuts 1 0 0 0 0 0 75 Rains Fine Sandy Loam 8.7 Rye 4 78.5 0 0 6 14 54

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Cecil Clay Loam, 2 To 6 Percent Slopes, Eroded 11 Sorghum (Grain) 1 0 0 0 148 40 50 Lynchburg Fine Sandy Loam 12 Sorghum (Grain) 2 0 0 0 137 18 119 Wasda Muck, 0 To 2 Percent Slopes, Rarely Flooded 205 Soybeans (Full Season) 1 0 0 0 140 0 200 Exum Very Fine Sandy Loam, 0 To 2 Percent Slopes 3.4 Soybeans (Full Season) 4 0 0 0 72 72 0 Wake-Saw-Wedowee Complex, 2 To 8 Percent Slopes, Rocky 1 Soybeans (Full Season) 1 0 0 0 50 60 120 Aycock Fine Sandy Loam, 1 To 6 Percent Slopes 50 Soybeans (Full Season) 1 0 0 0 44 0 112 Wedowee Sandy Loam, 2 To 6 Percent Slopes 24 Soybeans (Full Season) 4 0 0 0 36 70 133 Norfolk Loamy Sand, 2 To 6 Percent Slopes 15 Soybeans (Full Season) 4 0 0 0 25 25 125 Craven Fine Sandy Loam, 1 To 4 Percent Slopes 2.5 Soybeans (Full Season) 1 0 0 0 23 23 135 Gritney Fine Sandy Loam, 2 To 6 Percent Slopes 15 Soybeans (Full Season) 1 0 0 0 23 0 23 Emporia-Wedowee Complex, 2 To 6 Percent Slopes 21 Soybeans (Full Season) 1 0 0 0 21 54 108 Appling Loamy Sand, 2 To 6 Percent Slopes 4.5 Soybeans (Full Season) 2 0 0 0 20 25 38 Emporia-Wedowee Complex, 2 To 6 Percent Slopes 7 Soybeans (Full Season) 4 0 0 0 18 54 108 Cape Fear Loam 7 Soybeans (Full Season) 9 0 0 0 15 40 60 Wedowee Sandy Loam, 6 To 10 Percent Slopes 7.5 Soybeans (Full Season) 1 0 0 0 14 24 72 Wedowee Sandy Loam, 2 To 6 Percent Slopes 20 Soybeans (Full Season) 7 0 0 0 13 25 75 Craven Fine Sandy Loam, 0 To 1 Percent Slopes 3 Soybeans (Full Season) 3 0 0 0 12 60 144 Pacolet Sandy Loam, 2 To 6 Percent Slopes 8 Soybeans (Full Season) 6 0 0 0 12 36 72 Lenoir Loam 2 Soybeans (Full Season) 5 0 0 0 12 30 80 Norfolk Loamy Sand, 0 To 2 Percent Slopes 4.5 Soybeans (Full Season) 1 0 0 0 12 12 36 Ocilla Loamy Fine Sand, 0 To 4 Percent Slopes 7 Soybeans (Full Season) 6 0 0 0 12 12 72 Norfolk Loamy Sand, 2 To 6 Percent Slopes 1.5 Soybeans (Full Season) 3 0 0 0 11 11 53 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 2.5 Soybeans (Full Season) 1 0 0 0 10 30 60 Wedowee Sandy Loam, 2 To 6 Percent Slopes 2 Soybeans (Full Season) 1 0 0 0 10 28 75 Norfolk Loamy Sand, 0 To 2 Percent Slopes 11.6 Soybeans (Full Season) 1 0 0 0 10 23 115 Altavista Fine Sandy Loam, 0 To 2 Percent Slopes 22 Soybeans (Full Season) 10 0 0 0 10 20 60 Lenoir Loam, 0 To 1 Percent Slopes 11 Soybeans (Full Season) 1 0 0 0 10 10 50 Noboco Fine Sandy Loam, 0 To 2 Percent Slopes 2.5 Soybeans (Full Season) 7 0 0 0 10 0 50 Wagram Loamy Sand, 0 To 6 Percent Slopes 7 Soybeans (Full Season) 5 0 0 0 8 17 99 Vance Sandy Loam, 2 To 6 Percent Slopes 4 Soybeans (Full Season) 1 0 0 0 8 22 90 Dogue Fine Sandy Loam, 0 To 3 Percent Slopes 13.5 Soybeans (Full Season) 1 0 0 0 8 18 90 Lynchburg Fine Sandy Loam, 0 To 2 Percent Slopes 9 Soybeans (Full Season) 2 0 0 0 8 18 92 Rains Fine Sandy Loam 2.5 Soybeans (Full Season) 1 0 0 0 8 23 45 Craven Fine Sandy Loam, 1 To 4 Percent Slopes 18 Soybeans (Full Season) 2 0 0 0 8 23 68 Norfolk Loamy Sand, 2 To 6 Percent Slopes 7.8 Soybeans (Full Season) 3 0 0 0 8 8 30 Norfolk Loamy Sand, 2 To 6 Percent Slopes 13.5 Soybeans (Full Season) 2 0 0 0 8 8 93 Goldsboro Sandy Loam, 0 To 2 Percent Slopes 10.8 Soybeans (Full Season) 1 0 0 0 7 14 83 Wahee Fine Sandy Loam 13.1 Soybeans (Full Season) 4 0 0 0 6 92 0 Norfolk, Georgeville, And Faceville Soils, 2 To 8 Percent Slopes 3.9 Soybeans (Full Season) 3 0 0 0 6 12 76 Wedowee Sandy Loam, 2 To 6 Percent Slopes 16 Soybeans (Full Season) 1 0 0 0 5 10 30 Lenoir Loam 5 Soybeans (Full Season) 4 0 0 0 5 14 27 Norfolk Loamy Sand, 2 To 6 Percent Slopes 13 Soybeans (Full Season) 3 0 0 0 3 3 13

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Norfolk Loamy Sand, 2 To 6 Percent Slopes 5 Soybeans (Full Season) 2 0 0 0 3 3 0 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 2 Soybeans (Full Season) 12 0 0 0 3 3 13 Lenoir Loam, 0 To 1 Percent Slopes 12 Soybeans (Full Season) 1 0 0 0 0 0 1 Norfolk Loamy Sand, 2 To 6 Percent Slopes 50.2 Soybeans (Full Season) 7 0 0 0 0 38 75 Lenoir Loam 4 Soybeans (Full Season) 1 0 0 0 0 14 27 Norfolk Loamy Sand, 2 To 6 Percent Slopes 3 Soybeans (Full Season) 1 0 0 0 0 13 63 Norfolk, Georgeville, And Faceville Soils, 2 To 8 Percent Slopes 8.3 Soybeans (Full Season) 2 0 0 0 0 7 126 Lenoir Loam 2 Soybeans (Full Season) 151 0 0 0 0 0 0 Portsmouth Fine Sandy Loam 9 Soybeans (Full Season) 4 0 0 0 0 0 120 Wedowee Sandy Loam, 2 To 6 Percent Slopes 7 Soybeans (Full Season) 1 0 0 0 0 0 420 Gritney Sandy Clay Loam, 2 To 6 Percent Slopes, Eroded 7.5 Soybeans (Full Season) 4 0 0 0 0 0 90 Emporia Fine Sandy Loam, 2 To 6 Percent Slopes 14.3 Soybeans (Full Season) 4 0 0 0 0 0 500 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 4.1 Soybeans (Full Season) 1 0 0 0 0 0 72 Faceville Loamy Sand, 1 To 6 Percent Slopes 3 Soybeans (Full Season) 1 0 0 0 0 0 114 Yonges Loamy Fine Sand 2 Soybeans (Full Season) 1 32 0 0 0 0 60 Exum Fine Sandy Loam, 0 To 1 Percent Slopes 12 Soybeans (Full Season) 1 0 0 0 0 0 72 Exum Fine Sandy Loam, 0 To 1 Percent Slopes 15 Soybeans (Full Season) 1 0 0 0 0 0 75 Faceville Loamy Sand, 1 To 6 Percent Slopes 8 Sweet Potatoes 2 0 0 0 75 50 200 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 21 Sweet Potatoes 1 0 0 0 73 0 150 Norfolk Loamy Sand, 2 To 6 Percent Slopes 2 Sweet Potatoes 4 0 0 0 68 0 48 Norfolk Loamy Sand, 0 To 2 Percent Slopes 2 Sweet Potatoes 9 0 0 0 66 36 162 Norfolk Loamy Sand, 0 To 2 Percent Slopes 20 Sweet Potatoes 2 0 0 0 50 25 145 Portsmouth Fine Sandy Loam 9 Sweet Potatoes 1 0 0 0 48 15 36 Emporia Fine Sandy Loam, 0 To 2 Percent Slopes 6.2 Sweet Potatoes 4 0 0 0 46 60 196 Norfolk Loamy Sand, 2 To 6 Percent Slopes 2 Sweet Potatoes 1 0 0 0 30 36 162 Goldsboro Sandy Loam, 0 To 2 Percent Slopes 3.5 Tobacco (Flue Cured) 2 0 0 0 192 42 126 Lenoir Loam 1 Tobacco (Flue Cured) 1 0 0 0 180 180 540 Wagram Loamy Sand, 0 To 6 Percent Slopes 72 Tobacco (Flue Cured) 1 0 0 0 103 70 210 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 1 Tobacco (Flue Cured) 4 0 0 0 99 80 222 Coxville Fine Sandy Loam 3 Tobacco (Flue Cured) 2 0 0 0 96 70 210 Wedowee Sandy Loam, 2 To 6 Percent Slopes 4 Tobacco (Flue Cured) 2 0 0 0 94 136 172 Wake-Saw-Wedowee Complex, 2 To 8 Percent Slopes, Rocky 14.2 Tobacco (Flue Cured) 2 0 0 0 90 70 180 Lynchburg Fine Sandy Loam 2.7 Tobacco (Flue Cured) 2 0 0 0 89 168 207 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 12.3 Tobacco (Flue Cured) 2 0 0 0 88 47 140 Lynchburg Fine Sandy Loam 13.3 Tobacco (Flue Cured) 1 0 0 0 87 63 189 Norfolk Sandy Loam, 1 To 6 Percent Slopes 8.6 Tobacco (Flue Cured) 1 0 0 0 86 66 198 Conetoe Loamy Sand, 0 To 4 Percent Slopes 5.1 Tobacco (Flue Cured) 1 0 0 0 84 156 252 Emporia-Wedowee Complex, 2 To 6 Percent Slopes 1 Tobacco (Flue Cured) 1 0 0 0 84 84 252 Norfolk Loamy Sand, 0 To 2 Percent Slopes 4.7 Tobacco (Flue Cured) 1 0 0 0 84 60 180 Lynchburg Fine Sandy Loam 4.5 Tobacco (Flue Cured) 1 0 0 0 84 60 180 Norfolk Loamy Sand, 2 To 6 Percent Slopes 2.5 Tobacco (Flue Cured) 1 0 0 0 84 40 60 Rains Fine Sandy Loam 1 Tobacco (Flue Cured) 1 0 0 0 84 64 213

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Alaga Loamy Sand, Banded Substratum, 0 To 6 Percent Slopes 6 Tobacco (Flue Cured) 2 0 0 0 83 40 120 Vance Sandy Loam, 2 To 6 Percent Slopes 14 Tobacco (Flue Cured) 4 0 0 0 83 99 210 Norfolk, Georgeville, And Faceville Soils, 2 To 8 Percent Slopes 1.2 Tobacco (Flue Cured) 1 0 0 0 81 77 189 Exum Fine Sandy Loam, 0 To 1 Percent Slopes 4 Tobacco (Flue Cured) 1 0 0 0 81 66 183 Wagram Loamy Sand, 0 To 6 Percent Slopes 1 Tobacco (Flue Cured) 1 0 0 0 80 48 96 Norfolk Loamy Sand, 0 To 2 Percent Slopes 11 Tobacco (Flue Cured) 3 0 0 0 80 40 148 Norfolk Loamy Sand, 0 To 2 Percent Slopes 14.1 Tobacco (Flue Cured) 3 0 0 0 80 62 162 Marlboro Loamy Sand, 0 To 2 Percent Slopes 5 Tobacco (Flue Cured) 1 0 0 0 79 40 162 Norfolk, Georgeville, And Faceville Soils, 2 To 8 Percent Slopes 4 Tobacco (Flue Cured) 1 0 0 0 79 56 189 Herndon Silt Loam, 2 To 6 Percent Slopes 4.5 Tobacco (Flue Cured) 4 0 0 0 78 96 172 Goldsboro Sandy Loam, 0 To 1 Percent Slopes 2 Tobacco (Flue Cured) 1 0 0 0 78 54 163 Wagram Loamy Sand, 0 To 6 Percent Slopes 2.5 Tobacco (Flue Cured) 2 0 0 0 76 49 143 Lenoir Fine Sandy Loam, Thin Solum Variant, 0 To 3 Percent Slopes 15.6 Tobacco (Flue Cured) 1 0 0 0 75 48 128 Wedowee Sandy Loam, 2 To 6 Percent Slopes 4.5 Tobacco (Flue Cured) 1 0 0 0 74 96 172 Helena Sandy Loam, 2 To 6 Percent Slopes 3.4 Tobacco (Flue Cured) 2 0 0 0 73 63 190 Cecil Sandy Loam, 2 To 6 Percent Slopes 2 Tobacco (Flue Cured) 1 0 0 0 72 84 126 Norfolk Loamy Sand, 0 To 2 Percent Slopes 3 Tobacco (Flue Cured) 1 0 0 0 72 72 216 Pactolus Loamy Sand 25 Tobacco (Flue Cured) 2 0 0 0 72 40 157 Vance Sandy Loam, 2 To 6 Percent Slopes 1.5 Tobacco (Flue Cured) 1 0 0 0 72 104 177 Emporia-Wedowee Complex, 2 To 6 Percent Slopes 6.5 Tobacco (Flue Cured) 7 0 0 0 71 48 144 Norfolk Loamy Sand, 2 To 6 Percent Slopes 5 Tobacco (Flue Cured) 2 0 0 0 70 70 210 Faceville Loamy Sand, 1 To 6 Percent Slopes 12 Tobacco (Flue Cured) 1 0 0 0 70 40 150 Cecil Sandy Loam, 2 To 6 Percent Slopes 35 Tobacco (Flue Cured) 2 0 0 0 68 84 126 Norfolk Loamy Sand, 2 To 6 Percent Slopes 6 Tobacco (Flue Cured) 3 0 0 0 68 45 160 Goldsboro Fine Sandy Loam, 0 To 2 Percent Slopes 1.9 Tobacco (Flue Cured) 1 0 0 0 65 82 140 Lynchburg Fine Sandy Loam 6 Tobacco (Flue Cured) 1 0 0 0 64 0 192 Bonneau Loamy Sand, 0 To 6 Percent Slopes 3.5 Tobacco (Flue Cured) 1 0 0 0 63 48 144 Craven Fine Sandy Loam, 0 To 1 Percent Slopes 12.2 Tobacco (Flue Cured) 1 0 0 0 62 82 186 Norfolk Loamy Sand, 2 To 6 Percent Slopes 25 Tobacco (Flue Cured) 1 0 0 0 60 60 180 Norfolk Loamy Sand, 0 To 2 Percent Slopes 10 Tobacco (Flue Cured) 1 0 0 0 57 48 144 Aycock Very Fine Sandy Loam, 2 To 6 Percent Slopes 2.5 Tobacco (Flue Cured) 3 0 0 0 56 45 146 Norfolk Loamy Fine Sand, 2 To 6 Percent Slopes 5.3 Tobacco (Flue Cured) 1 0 0 0 54 54 162 Exum Fine Sandy Loam, 0 To 1 Percent Slopes 4 Tobacco (Flue Cured) 2 0 0 0 52 42 126 Cecil Sandy Loam, 2 To 6 Percent Slopes 2.5 Tobacco (Flue Cured) 2 0 0 0 43 30 104 Lenoir Loam 2 Tobacco (Flue Cured) 1 0 0 0 30 30 90 Wedowee Sandy Loam, 2 To 6 Percent Slopes 9 Tobacco (Flue Cured) 1 0 0 0 21 12 32 Aycock Very Fine Sandy Loam, 0 To 2 Percent Slopes 4 Tobacco (Flue Cured) 3 0 0 0 0 0 0 Belhaven Muck 14 Wheat 1 100 40 90 160 60 120 Wedowee Sandy Loam, 2 To 6 Percent Slopes 8.6 Wheat 1 130 37.5 150 30 0 0 Roper Muck 14 Wheat 5 100 40 90 25 50 95 Belhaven Muck 13.6 Wheat 2 0 0 0 25 50 95

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Belhaven Muck 12.5 Wheat 3 100 40 90 25 50 95 Norfolk Loamy Sand, 2 To 6 Percent Slopes 2.5 Wheat 1 47.7 15 90 15 21 81 State Sandy Loam, 0 To 3 Percent Slopes 17.8 Wheat 3 49.2 29.7 99.9 0 0 0 Roanoke Fine Sandy Loam 84 Wheat 1 18 36 108 0 0 0 Portsmouth Loam 40 Wheat 1 23 17.9 68.9 0 0 0 Portsmouth Loam 2 Wheat 4 0 0 0 0 0 0 Portsmouth Loam 3 Wheat 9 100 0 0 0 0 0 Portsmouth Loam 2.1 Wheat 12 134 0 0 0 0 0 Tomotley Fine Sandy Loam 6.2 Wheat 8 131 0 0 0 0 0 Tomotley Fine Sandy Loam 2.1 Wheat 14 100 0 0 0 0 0 Rains Fine Sandy Loam 8.6 Wheat 8 133 48 144 0 0 0 Portsmouth Fine Sandy Loam 10 Wheat 1 12.5 25 75 0 0 0 Vance Sandy Loam, 2 To 6 Percent Slopes 15 Wheat 8 100 0 0 0 0 0 Wake-Saw-Wedowee Complex, 2 To 8 Percent Slopes, Rocky 19.1 Wheat 1 115 20 120 0 0 0 Cecil Clay Loam, 2 To 6 Percent Slopes, Eroded 0.8 Wheat 1 20 12 72 0 0 0 Helena Sandy Loam, 2 To 6 Percent Slopes 4.6 Wheat 2 52.5 30 30 0 0 0 Fuquay Sand, 0 To 4 Percent Slopes 15 Wheat 2 62 36 72 0 0 0 Hydeland Silt Loam, 0 To 2 Percent Slopes, Rarely Flooded 40 Wheat 1 350 0 120 0 0 0 Exum Fine Sandy Loam, 0 To 1 Percent Slopes 55 Wheat 4 90 45 10 0 0 0 Exum Fine Sandy Loam, 0 To 1 Percent Slopes 30 Wheat 2 100 0 100 0 0 0 Pettigrew Muck 12.5 Wheat 8 98 46 60 0 0 0 Norfolk Loamy Sand, 0 To 2 Percent Slopes 15.2 2 0 0 0 132 63 126 Norfolk Loamy Sand, 2 To 6 Percent Slopes 6 5 0 0 0 130 0 91 Lynchburg Fine Sandy Loam 2 2 0 0 0 100 40 40 Vance Sandy Loam, 2 To 6 Percent Slopes 8.5 1 0 0 0 97 54 135 Norfolk Loamy Sand, 2 To 6 Percent Slopes 33 1 0 0 0 35 60 135 Norfolk Loamy Sand, 0 To 2 Percent Slopes 15.4 1 0 0 0 30 0 75 Craven Fine Sandy Loam, 1 To 4 Percent Slopes 1 3 0 0 0 23 23 135 Leaf Silt Loam 6 3 0 0 0 8 0 7 Lenoir Loam 2 2 0 0 0 0 0 0 Appling Loamy Sand, 2 To 6 Percent Slopes 7 1 0 0 0 0 0 420 0 12 0 0 0 0 0 0

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DELINEATING AGRICULTURE IN THE TAR-PAMLICO · PDF fileDELINEATING AGRICULTURE IN THE TAR-PAMLICO RIVER BASIN ... Tar-Pam Basin and count units selected ... Large quantities