Final Report

Accounting Method for Tracking Relative Changes in Agricultural Phosphorus Loading to the Tar-Pamlico River

October 21, 2005

Prepared by Amy M. Johnson and Deanna L. Osmond

Soil Science Department NC State University

With Concurrence and Consent of the Phosphorus Technical Advisory Committee (PTAC)

PTAC Committee Members: NCDA&CS, David Hardy;

NCDENR – Div. Water Quality, Rich Gannon, and Keith Larick; NCDENR-Div. Soil and Water, Steve Coffey, Vernon Cox, Natalie Jones;

NC Farm Bureau Federation, Anne Coan; NC Environmental Defense, Joe Rudek;

NC State University, Amy Johnson, Gene Kamprath, and Deanna Osmond; USDA-NRCS, Lane Price

This work was funded through a USEPA 319 (h) Grant as a pass through grant with NCDENR, Division of Water Quality

TABLE OF CONTENTS EXECUTIVE SUMMARY ................................................................................................ ii

INTRODUCTION .............................................................................................................. 2

METHODOLOGY ............................................................................................................. 3

Data Used........................................................................................................................ 5

RESULTS ......................................................................................................................... 14

Land Use ....................................................................................................................... 14

Manure Production........................................................................................................ 21

Soil Test P ..................................................................................................................... 25

Fertilizer P..................................................................................................................... 28

ACCOUNTING for P ....................................................................................................... 30

SUMMARY AND CONCLUSIONS ............................................................................... 33

REFERENCES ................................................................................................................. 37

APPENDIX....................................................................................................................... 39

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EXECUTIVE SUMMARY The Tar-Pamlico Agricultural Rule requires the development of a methodology

that accounts for phosphorus losses and gains from agricultural activities in the basin. The Rule recognizes potential challenges associated with this objective, and calls for the Basin Oversight Committee (BOC) to form a phosphorus technical advisory committee (PTAC) to evaluate this issue and provide recommendations to the BOC. The following report compiles the findings of the PTAC and conveys its recommendations to the BOC.

Foremost, the PTAC has determined that a quantitative accounting of watershed

or county-scale agricultural phosphorus loading is not achievable due to the complexity of phosphorus behavior and transport within a watershed, the lack of suitable data required to adequately quantify the various mechanisms of phosphorus loss and retention within watersheds of the basin, and the problem with not being able to capture agricultural conditions as they existed in 1991. In addition, modeling phosphorus loss under agricultural conditions is highly inaccurate. The PTAC has instead evaluated, to the limits of available data, trends in land use and management related to agricultural activity in the basin from 1991 to the present. In addition, it has developed recommendations for qualitatively tracking relative changes in practices that either increase or decrease the risk of phosphorus loss from agricultural lands in the basin on an annual basis. The committee believes this approach to be the best alternative toward meeting the spirit of the Rule.

Overall, the acreage of cropland in the basin (#1 in the table below) has

decreased, although only slightly. The number of livestock and poultry (2) experienced an increase between 1991 and 2003, although the greatest increase occurred in the mid-1990s, after which populations declined. This change in livestock distribution and numbers increased total phosphorus excreted by 10%. There is no accurate means to calculate fertilizer phosphorus inputs into the basin for the baseline or any subsequent years. Nor is it possible to retroactively establish fertilizer P management changes that may have occurred across the basin since 1991. Thus, while excreted phosphorus inputs to the basin increased, cropland acres decreased, and the effect of these factors on total P inputs or losses is uncertain. Looking forward, the addition of a new 4.75 million bird layer facility in Hyde County would add approximately 2 million pounds of phosphorus to the basin and may contribute to an increased localized risk of phosphorus loss to surface waters if the waste is not managed well and distributed widely.

The large increase of land uses and conservation practices that reduce or prevent

the loss of phosphorus from agricultural land (3-8) is expected to provide significant reductions in phosphorus loss from affected lands, and supports the conclusion that the contribution of phosphorus to the basin from agricultural land has not increased. Although a trend analysis of phosphorus index in soil samples analyzed by NCDA for the entire 13-year period (9) indicates an increase, detailed analysis suggests that soil test P peaked approximately in 1995/1996 and has declined slightly or remained constant from 1996 to 2004. Furthermore, this analysis is confounded by several factors. These include some changes expected to increase soil test values (mandatory sampling of animal waste application fields, shallower soil sampling for no-till fields) and others with an uncertain

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effect (change in laboratory methodology, advent of precision soil sampling, voluntary nature of most soil sample submission). While effects of the latter may be better quantified in the future, the first two factors clearly increased the number of samples submitted, likely affected the average value of those samples in differing ways, but added two sources of unquantifiable variability that further mask any real trend in phosphorus content of soils in the basin. Unfortunately, the only significant data set for soil phosphorus in the basin, produced by this voluntary NCDA service, is not well-suited to answering the trend question.

In summary, phosphorus inputs are either not increasing or increasing only

slightly, while the use of conservation practices to prevent phosphorus loss has increased substantially, thus reducing the off-site movement of phosphorus from agricultural fields into receiving waters. Although we cannot know with absolute certainty, we would conclude from this relative change assessment that no substantial increases in phosphorus loading from agricultural activities have occurred in the Tar-Pamlico River Basin relative to 1991 levels. This conclusion is corroborated by water quality data showing a significant net decrease in phosphorus loading to the estuary, estimated to be 33% between 1991 and 2002.

Data in Table E1 depicts what the committee sees as the most useful sources of

data for this qualitative approach and it is our recommendation that Table E1 act as a guide for the assessment of increases or decreases in potential P loss in future years.

It is essential to recognize that sources of error and uncertainty associated with the available data are numerous. The PTAC made every effort to identify these sources and to quantify those that could be quantified, as reflected in this report. Most of these sources were not directly quantifiable; the sources that are quantifiable have error and uncertainty associated with their measures. To some degree, these uncertainties may call into question the reliability of the conclusions offered here. It is, however, the best data available.

We note several sources of major uncertainty. The reliance on historical county-level data for several of the sources is problematic because of the lack of reliable, complete, and unbiased datasets. In addition, data from almost all sources were reported on a county scale, thereby requiring data to be aggregated in order to account for the portion of each county contained within the boundaries of the basin. This necessitates an assumption that crops and livestock are uniformly distributed throughout each county, an assumption that introduces a potentially large amount of error. Another problematic matter is that the best indicator of phosphorus loss potential is soil test phosphorus data; because this dataset represents accumulated producer records from a voluntary service provided by the NCDA&CS Soil Testing Division, it reflects several important sources of uncertainty and may inaccurately reflect average soil phosphorus levels. While some of these factors have been examined in this report, many more bias the available data in ways that cannot realistically be quantified. It is important that the BOC understand the limitations of all of the data compiled for this report in considering management implications.

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Table E1. Relative changes in land use and management parameters and their effect on P loss risk.

Parameter affecting

changes in P

Data source

1991

(Baseline)

2003

% change 1991-2003

P loss risk +/-

1. Agricultural land, (acres)

NASS AgStats 660,405 632,289 -4.3 -

2. Animal waste P produced, (lbs P yr-

1)

2004 NC Ag. Chem. Manual / NASS

AgStats

13,597,734 14,971,251 +10 +

3. Conservation Reserve Program / Wetlands Reserve Program, (acres)†

Conservation Tech. & Information Center

19,254 23,107 +20 -

4. Cropland conversion to forest- or grass-land, (acres)

NC Dept. Environ. & Natural Resources

651 7,109 +992 -

5. Conservation tillage, (acres)†

Conservation Tech. & Information Center

70,783 317,489 +349 -

6. Vegetated buffers, (affected acres)

Farm Service Agency / Local

Advisory Comm.

97,760 169,706 +74 -

7. Water control structure, (affected acres)

Farm Service Agency / Local

Advisory Comm.

52,865 85,072 +61 -

8. Scavenger crop, (affected acres)

Farm Service Agency / Local

Advisory Comm.

21,522 103,057 +379 -

9. Soil test P, median (mg kg-1)

NC Dept. Ag. & Consumer Services

83 92 +11 +

†Data are from 2002; after 1998 CTIC only reports data in even years

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INTRODUCTION One major goal of the Tar-Pamlico Agriculture Rule, 15A NCAC 2B .0256, is to account for changes in water quality with respect to phosphorus (P) contributed from agricultural activities in the Tar-Pamlico River Basin. The Basin Oversight Committee established a P technical advisory committee (PTAC) pursuant to Subparagraph (f)(2)(C) to “monitor advances in scientific understanding related to P loading, evaluate the need for additional management action to meet the P loading goal, and report its findings to the BOC.” This report attempts to answer the question of whether the level of P attributed to agricultural activities has increased since 1991. In order to do so, we examined various aspects of agricultural land use and management and documented the changes that have occurred during the period of 1991-2003. Phosphorus behavior in the agricultural landscape is fundamentally different than that of nitrogen. Much of soil P is bound to fine particles and immobile, while nitrogen is much more soluble and therefore more mobile in the soil solution. While the great majority of nitrogen movement in agricultural settings is through subsurface flow that may ultimately reach surface waters, non-point source P loss from agricultural land can occur through several pathways: soil erosion carrying particulate P, surface runoff carrying P in a dissolved form, P in both particulate and dissolved form that is leached into groundwater through drain pipes, and direct loss of manure and fertilizer that is surface applied. The complexity of the movement and solubility of P that occurs in the landscape make its delivery to surface waters highly site-specific. This site-specific and highly variable delivery makes it difficult, if not impossible, to approximate P loading from agricultural activities on a large scale in the manner that nitrogen can be. The delivery of P to surface waters is dependent on many field-specific factors. These include: vegetative cover and condition, soil texture, landscape position, management history, timing and method of animal waste application, and distance of P source to stream networks; all of which can change spatially and/or temporally. Because P is attached to soil particulate matter, its loss is also affected by the residual P content of soil, tillage practices that retain soil, and other erosion-controlling best management practices (BMPs) that do not affect nitrogen. While some of these factors are quantifiable, the site-specificity of their effects means that the large-scale spatial assumptions, which are defensible with nitrogen accounting, are not so with P. Other methods of accounting for changes in P loss from agriculture in the Tar-Pamlico Basin were considered by the PTAC and found inadequate. For example, examination of available water quality data does not allow for quantification of the P contribution specifically from agricultural lands due to the many other sources of P in the watershed. Export coefficients can be used to estimate the P load delivered to a water body by assigning different weighting factors to different land uses and managements. Export coefficients must be derived from literature sources or through field experiments in order to determine the rate of P loss from each identifiable source under a wide variety of conditions. This method lacks the specificity needed to incorporate spatial and temporal changes in the factors affecting P movement. Additionally, export coefficients cannot account for the field-specific differences in capabilities of soil and vegetation to act as attenuation agents (utilizing or storing P) as soluble or particulate P moves over the soil surface. An example of this is an agricultural field that receives nutrients from a

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hydrologically-linked, upslope landscape. Export coefficients do not account for such field-to-field attenuation. A mass balance or P budget is another potential accounting method. The weakness of such an approach is that there is no way to account for the highly site-specific and variable losses of P occurring on each field in the basin. A mass balance approach would require more intensive study on a field scale where complete records from an operation can be collected and some of the more difficult to measure components can be determined. But again, P losses from runoff and erosion are difficult to extrapolate to a watershed scale because of the high field to field temporal and spatial variability.

Predictive models can be used to estimate P losses in runoff, erosion or leaching under a highly specific set of conditions. However, such models must be calibrated individually to each watershed, are very data-intensive, and require significant expertise and user resources. All of these factors make such models ill-suited to the Tar-Pamlico’s basin-scale regulatory effort. Because of the set of challenges described here by the complex nature of P movement, the PTAC has determined that the only viable option for predicting changes from 1991 to 2003 was to examine all available historical data to estimate relative changes in certain parameters that affect P inputs and losses. There is a high level of uncertainty associated with making quantitative estimates of P additions/losses from the landscape based on historical data. Recognizing this, we constrain this analysis to that of the general patterns of P source additions/removals associated with human activity. Thus our overall objective was to determine net decreases or increases in P loss by examining patterns in land use and agricultural practices within the stated time period. The methodology we have developed will allow for a qualitative accounting of relative changes in agriculture’s potential contribution of P to surface waters of the Tar-Pamlico River Basin subsequent to the baseline year of 1991. We also identified parameters that can be tracked annually as indicators of trends or lack thereof.

METHODOLOGY All available information was sought addressing agricultural P in the basin for the period of record. This included types and acreage of agricultural land use, fertilizer and animal waste inputs, storage of P by soils, P removal by crops and prevalence of P-conserving BMPs. Patterns in land use and cropping practices were obtained from published historical data from Agriculture Censuses and sampling surveys collected from federal and state agencies. The most comprehensive and reliable source of county-level agricultural statistics, which we relied on the most heavily, was the National Agricultural Statistics Service (NASS). County crop and livestock information is contained in two independent sets of data collected and published by NASS. The Census of Agriculture (AgCensus) is conducted every five years and is intended as a complete accounting of crops and livestock produced on all farms for the AgCensus year. County data (referred to in this report as AgStats data) reporting crop production and livestock inventories are published annually by NASS and are based on reports from a sample of farms. Other sources of data used to track changes in P included land use data from the Natural Resources Inventory (NRI) collected by the USDA - Natural Resources

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Conservation Service (NRCS), conservation tillage as well as other land use data from the Conservation Technology Information Center (CTIC) and Farm Bill Program participation BMP data from the USDA - Farm Service Agency (FSA). Soil test P data was obtained from a database maintained by the North Carolina Department of Agriculture and Consumer Services (NCDA&CS), Soil Testing Division. Table 1. Summary of data sources used in the present study.

Data Source Data Items Frequency, Scale Census of Agriculture (USDA-NASS)

Individual Crop Acreage Livestock Inventories

Other Land Use Acreage BMPs – CRP, WRP Acreage

5-year, County

Agricultural Statistics (USDA-NASS/NCDA)

Individual Crop Acreage Livestock Inventories

Annual, County

Natural Resources Inventory (USDA-NRCS)

Land Use Acreage 5-year, 8-digit HU

Conservation Tillage (CTIC)

Conservation Tillage Acreage Land Use Acreage

Annual, County

LACs (USDA-FSA, NC ACSP)

BMPs - WCS, Buffers, Scavenger Crop

Annual, County

Soil Test P (NCDA) Soil Test P Index Annual, County All data, with the exception of NRI data, were reported on a county level. In order to aggregate to a watershed scale, we calculated the proportion of land in each county falling within the boundaries of the Tar-Pamlico River Basin County. The percent of land area in the basin for each county was estimated by the North Carolina Center for Geographic Information and Analysis and is shown in Table 2. For the purposes of this study, it was assumed that each data item, e.g. hog inventory, was uniformly distributed within a county. Adjusted county-level quantities were summed to obtain a river basin total for each item. The time period of interest was 1991 through 2004. In instances where data were available on an annual time scale, year-to-year trends were examined as well as the percent change of the data item over the project period. Certain data were only available irregularly or once every five years. Table 2. County land area within Tar-Pamlico River Basin boundaries.

County Percent in basin Beaufort 97 Edgecombe 99 Franklin 90 Granville 43 Halifax 60 Hyde 70 Martin 25 Nash 80 Person 8 Pitt 58 Vance 48 Warren 62 Washington 19 Wilson 18

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Franklin

Granville

Person Vance

NashEdgecombe

WarrenHalifax

Pitt

Washington

Wilson

Martin

BeaufortHyde

Franklin

Granville

Person Vance

NashEdgecombe

WarrenHalifax

Pitt

Washington

Wilson

Martin

BeaufortHyde

We examined changes in land use and farming practices in the basin as indicators of agricultural P dynamics. Therefore, the following data were obtained: • Changes in crop acreage and types of crops grown as an indicator of land usage, P

inputs through fertilization, and crop removal of P. • Changes in animal inventories and manure production as an indicator of manure P

input. • Changes in land use as a gauge of the relative potential for P loss. For example,

Correll et al. (1992) found that a predominantly cropland watershed discharged eight times as much phosphate as a forested watershed. Additionally, they reported that differences in land use accounted for 73% of the variability in P discharge to surface water in a Coastal Plain estuary.

• Implementation of certain P-conserving best management practices (BMPs) as another relative indicator of P loss potential.

• Water quality data were used to indirectly corroborate our findings and give a relative indication of P loading in the basin (NCDENR, 2003).

Data Used

NASS Annual Agricultural Statistics (AgStats), 1991-2003 Because AgStats is an annual source of county level data, it provides the best

depiction of temporal trends in crop acreage and livestock inventory. The only other source of county data, the AgCensus, is less current since it is only conducted once every five years. Basing predictions of relative changes on annual data points, rather than only two or three points in time, better captures the variability in the data.

AgStats methodology. Mail surveys, telephone interviews, face-to-face interviews, and field observations are used to gather information for NASS annual surveys. Most of the estimates are based on data collected from a sample of a given population from which inferences can be made about the whole population. This probability-based survey technique – i.e. the probability of including a particular operation in the sample - has several advantages over the AgCensus; it takes less time, costs less, can be more accurate due to fewer reporting and handling errors, and the results do not depend on relationships to other sets of data.

Two different sampling techniques are used to survey the sample population. Area frame sampling uses satellite imagery, aerial photographs and maps to divide the entire state land area into small segments, each about 1 square mile. A random selection is performed on these land segments to obtain an “area frame sample.” Enumerators then visit each segment and record all information about agricultural activity within the segment. Area frames are often classified into broad land-use categories to make sampling easier. The land segments classified as intensively cultivated are sampled at a greater frequency (approximately 1 in 125) than other land segments. This technique provides continuous coverage of all agricultural activity and prevents omission or duplication of farms.

The second sampling technique used is the “list frame sample,” which is used for commodities that are typically concentrated within a relatively small area. Samples are drawn from a list frame consisting of producers grouped by size and type of operation. While area frame sampling requires face-to-face interviews, list frame surveys cost less

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because the data can be collected largely by phone or mail. The majority of agricultural information is gathered using list frame sampling, while the area frame sample is used to collect information from those not on the list frame sample.

In June of each year (June Agricultural Survey), farmers are visited by enumerators to collect data on crop acreage, livestock inventories, and issues related to farm economy at the state level. This midyear survey collects data on crop acreage, livestock inventories, number of farms and land on farms, and other estimates. Farmers on list samples are also surveyed during this time through telephone interviews.

A subsample of farmers from the June survey is selected to provide monthly crop yield projections. Enumerators gather data on crop density, crop conditions and fruit per plant in order to project yield. Enumerators will visit the same sample plots several times during the growing season, including harvest.

Other surveys are conducted throughout the year using extensive list samples to obtain estimates of crop and livestock production and fall grain crop acreage.

An additional mailing is sent to a county sample of farm operators (approximately 15% of farms) and the collected information acts to supplement the state sample data. At the county level, NASS’s Agricultural Statistics are the most accurate and up-to-date. Most county estimates are prepared from surveys mailed to a large sample. Estimates are periodically revised if, for example, harvested acres are significantly different from production forecasts.

The following reported items were collected from AgStats: • Harvested cropland, which presumably includes all crops and hay. • Acres harvested and production of individual crops, including: barley for grain, corn

for grain, corn for silage, cotton, all hay, oats for grain, peanuts, potatoes, sorghum for grain, soybeans for beans, sweet potatoes, tobacco, and winter wheat for grain.

• Livestock and poultry inventories, including: broilers, all cattle, all chicken, hogs and pigs, and turkeys.

Error in AgStats data. No published data are available to apply sampling errors directly to NASS survey data since the official estimates represent a composite of information from multiple sources. Therefore, confidence intervals cannot be determined. On a national level, data for planted acreage of selected crops from the 2004 area frame survey only had the following relative standard errors:

Crop Relative standard error

Barley 6.0% Corn 1.1%

Cotton 2.8% Sorghum 6.3% Soybean 1.1%

Winter wheat 1.8%

The above data are for the entire country but typically errors for major crops at the state level are generally between 1.0 and 6.0%.

Advantages of AgStats data. Is released on an annual basis; represents the most accurate and current estimates of county level data; data is cheaper and easier to collect then AgCensus data; no missing data values due to disclosure issues as in AgCensus; data is comparable from year to year.

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Disadvantages of AgStats data. Data are not as comprehensive as AgCensus; for example, doesn’t include data on vegetables and orchards, different categories of cropland, pastureland, or land enrolled in federal programs; no indicators of data reliability are available; because it is only a sample, it does not represent a complete count of farms. Census of Agriculture (AgCensus) 1992, 1997, 2002

The Census of Agriculture is the most comprehensive source of data on agricultural land use and production. It is also arguably the most documented and widely used. Specific definitions are given for each data item reported and statistical errors are discussed.

AgCensus methodology. The method used by AgCensus is a mailout/mailback methodology, supplemented with follow-ups by mail, telephone, and personal enumeration. Inventories of livestock and poultry are measured as of December 31st of the census year. Crop and livestock production and sales and expense data are for the calendar year for which the production year overlaps the calendar year.

Although the AgCensus is purported to represent a complete count of all farm operators, estimates for certain data items are based on a sample of farm operators rather than a full enumeration. Two different report forms, a long and short form, are mailed to farm operators. All operators receive forms including “complete count” items. In addition, a sample of operators receive forms with complete count items as well as additional “sample count” questions related to the usage of fertilizers and chemicals, farm production expenditures, value of machinery and equipment, and value of land and buildings. Sample forms are sent to all farms that are expected to have large total value of agricultural products sold or large acreage, and that are in a county with less than 100 farms. Mail list records are systematically sampled at a rate of 1 in 2 in counties containing 100 to 199 farms, 1 in 4 for counties containing 200, and so on as shown in the table below:

Number of farms in county Rate of operators receiving long forms

100-199 farms 1 in 2 200-299 farms 1 in 4 300-399 farms 1 in 6

400 or more farms 1 in 8

The following data from AgCensus were collected: • Harvested cropland. • Cropland used only for pasture or grazing. • Other cropland, including: idle cropland used for cover crops or soil improvement but

not harvested and not pastured or grazed, cropland on which all crops failed or were abandoned, and cropland in cultivated summer fallow.

• Pastureland and rangeland, other than pastured cropland and woodland. • Land enrolled in Conservation Reserve or Wetlands Reserve Program. • Acres harvested and production of individual crops, including: barley for grain, corn

for grain, corn for silage, cotton, all hay, oats for grain, peanuts, potatoes, sorghum for grain, soybeans for beans, sweet potatoes, tobacco, and winter wheat for grain.

• Harvested vegetables.

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• Land in orchards. • Livestock inventories, including: broilers, all cattle, layers and pullets, hogs and pigs,

and turkeys. • Cropland and pastureland and rangeland fertilized. • Fertilizer purchased.

AgCensus methodology specific to the current report. Any tabulated item that identifies data reported by a respondent or allows a respondent’s data to be accurately estimated or derived, was suppressed. The number of farms reporting an item, however, is not considered confidential information and is provided even though other information may be withheld. The general guideline for determining disclosure is less than three reports for the item or one operation with more than 60% of the total.

The following protocol was applied for obtaining data for items not disclosed. When the number of an item was not disclosed, the value for number of farms was often still included so this information was used by obtaining the ratio of item number to the value for number of farms from the next most recent AgCensus. This ratio was then multiplied by the value for number of farms for the AgCensus of interest to estimate a value for the unknown item. If the farm value for the next most recent AgCensus was not available, the same ratio described above was taken for the AgCensus previous to the AgCensus of interest. For example, if the 1997 value for number of broilers was not available due to disclosure issues but the value for number of farms was 5, and the number of broilers and broiler farms for the same county in 2002 was reported as 15,000 and 7, (15,000 / 7) * 5 = number of broilers in 1997 equals 10,714.

Occasionally, values were not available for the 1992, 1997 or 2002 AgCensus. In this case, the value was left blank. Usually, with an item where disclosure was an issue, only one or two farms were involved, so the item number value is potentially small. However, with a data item such as broilers, one farm can have a large amount of broilers, so that not counting them can seriously bias the data.

On at least one occasion, when the acres of a harvested crop were reported but the production value was not, the yield from an adjacent county for that crop was used to determine the amount of crop production.

Error in AgCensus data. Many types of error are involved with the data collected in the AgCensus. A census that relies on mail lists has to contend with both sampling and non-sampling error. Sampling error is the error associated with the fact that only a sample of the true population is surveyed, as in the case of the sample count items. The complete count items are collected from every farm operator on the mail list, which is assumed to be complete. In reality, the AgCensus mail list is known to be incomplete and repetitious. The error associated with this phenomenon is called “coverage error” and consists of both undercoverage and overcoverage. Undercoverage, which generally dominates overall coverage error in the AgCensus, occurs when a farm operation is not on the AgCensus mail list or, less commonly, when a farm on the list is incorrectly classified as a non-farm. Overcoverage occurs when a list farm is actually a non-farm or when a farm is represented more than once on the AgCensus mail list.

An additional source of non-sampling error is referred to as “nonresponse error” and is the error due to those farm operations that do not respond, despite numerous attempts to contact them. Besides this whole-list noresponse, item nonresponse also occurs, which is nonresponse to a particular question or questions on an otherwise

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completed report form. In order to account for item nonresponse, information reported from another farm with similar characteristics is used to impute for the missing data item. For example, if a farm operation reported acres of corn harvested but not bushels of corn harvested, the bushels of corn harvested from another reporting farm with similar characteristics and an acceptable yield are used.

Variability in the estimates of complete count items (short report forms) is due only to nonresponse and coverage estimation procedures. Variability in the estimates of sample count items (long report forms), however, is due to nonresponse and coverage adjustments and the error associated with sampling only a subset of the actual population. Therefore, sample count item estimates tend to have greater variability than the complete count item estimates.

Because the AgCensus is not able to collect all information from every possible farm operation, adjustments are necessary to produce totals that represent the entire population. Statistical weighting procedures are used to account for whole farm nonresponse and sample data collection. The weights of the responding farms and/or sampled farms are adjusted to account for farms that do not respond and/or farms that are not in the sample subset. A separate evaluation of AgCensus coverage is conducted to provide estimates of the completeness of AgCensus farm counts. In 2002, an improved methodology was used to estimate coverage that used an area frame sample. Within the sampled land segment, farms that do not occur on the mail list are counted, giving an estimate of undercoverage. Based on the area frame sample, initial weights that account for nonresponse were further adjusted to compensate for undercoverage. Thereby, each value was given a fully-adjusted weight. Totals reported in the 2002 AgCensus are fully-adjusted values down to the county level. It is important to note that this procedure for estimating coverage was utilized for the 2002 AgCensus only and that values published in the 1992 and 1997 AgCensuses are not adjusted for coverage in this manner. Therefore, comparisons between the AgCensuses are not possible. However, data published in the 1997 AgCensus were re-weighted for coverage according to the same methodology employed in the 2002 AgCensus to allow for comparability between the 2002 and 1997 AgCensuses. Unfortunately, no adjustments were made to published data in the 1992 (or earlier) AgCensus, so comparisons between data from the 1992 AgCensus and the 1997 and 2002 AgCensuses will be suspect at best. In the current report, we attempted to adjust 1992 AgCensus totals using the ratio of 1997 adjusted values to 1997 non-adjusted values. However, it’s important to note that it is impossible to obtain accurate adjusted totals for the 1992 AgCensus and the comparability to values reported in the 1997 and 2002 AgCensuses is highly questionable, even after applying the above ratio.

Reliability estimates, or standard errors, were reported at the county level for all items in the 1992 and 1997 AgCensuses, but these error estimates do not account for coverage error. Therefore, the full extent of nonsampling error is unknown and cannot be entirely accounted for. County level estimates of error for selected data items in the 2002 AgCensus incorporate error associated with coverage and are, therefore, much higher than reported standard errors for 1992 and 1997 AgCensus data.

Relative indications of the effect of coverage and nonresponse adjustments are given for selected items at the state level in the1992 and 1997 AgCensuses through reporting the percent of state totals contributed by nonresponse and coverage estimations.

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In general, coverage adjustments contributed approximately 20-30% to the AgCensus aggregates, while about 5-10% was attributed to nonresponse adjustments.

Advantages of AgCensus data. It is a more complete dataset; indicators of error are available; it represents a complete survey for most items.

Disadvantages of AgCensus data. It is released only once every five years; county level data is not as accurate as AgStats data; comparability between censuses can be an issue; some item values not reported due to disclosure issues. Definitions of AgCensus data items.

Acres and quantity harvested (individual crops): Obtained for land on which crops were harvested or hay was cut, and land in orchards, fruit trees and nuts. Acreage of land in nurseries and greenhouses was not obtained. • For crops: if two or more crops were harvested from the same land during the year,

the acres were counted for each crop. Therefore, the summation of “acres harvested” for all individual crops exceeds the “acres of harvested cropland”.

• For hay: when more than one cutting of hay was taken from the same land, the acres were counted only once but the quantity harvested includes all cuttings. Acreage cut for both dry hay and haylage, silage, or greenchop was reported for each crop. For inter-planted crops or “skip-row” crops, acres were reported according to the portion of the field occupied by each crop. If a crop was inter-planted in an orchard or vineyard and harvested, then the entire orchard or vineyard acreage was reported under the appropriate fruit crop and the estimate of inter-planted crop acreage was reported under the appropriate crop.

• If a crop was planted but not harvested, the acres were not reported as harvested (for individual crop) and were reported in the appropriate cropland item – “cropland used only for pasture or grazing,” “cropland on which all crops failed or were abandoned,” etc. This does not apply to orchards; all land in bearing and nonbearing fruit and nut orchards and vineyards was reported as cropland harvested regardless of whether the crop was harvested or failed. Abandoned orchards were reported as “cropland idle” but not as harvested orchard acreage, and the abandoned orchard crop acres were not reported.

• Crops that were only hogged or grazed were reported as “cropland used only for pasture or grazing.” Crop residue left in the field after harvest and later hogged or grazed was not reported as “cropland used only for pasture or grazing”, but reported as “cropland harvested” for that individual crop.

• Harvested quantities were not obtained for fruits and nuts, berries and vegetables but only harvested acreage.

• Acres of land in nurseries and greenhouses are not included in this report. It is assumed that these acreages are of minor importance; the highest acreage (acres in the open) in any one county (portion of county within Tar-Pam basin boundaries) was 489 and the sum of “acres in the open” of nursery, greenhouse, floriculture and sod was 1281 acres. It is important to note this acreage is included in the “harvested acreage” category; another reason why the summation of individual crop acreages is not equal to acreage of “harvested cropland”.

Cropland used for cover crops or soil-improvement but not harvested and not pastured or grazed: In 2002, cropland used for cover crops or soil-improvement

10

was combined with cropland idle, while in 1992 and 1997 these two categories were reported separately.

Cropland used only for pasture or grazing: Includes land used only for pasture or grazing that could have been used for crops without additional improvement. Also included were acres of crops hogged or grazed but not harvested prior to grazing. However, cropland that was pastured before or after crops were harvested was included as “harvested cropland” rather than cropland used for pasture or grazing.

Harvested cropland: This category includes land from which crops were harvested and hay was cut, land used to grow short-rotation woody crops (counted only in 2002), land in orchards, Christmas trees (counted only in 1997 and 2002), vineyards, nurseries and greenhouses. Land from which two or more crops were harvested was counted only once. Therefore, acreage of “harvested cropland” may be significantly less than the summation of harvested acreage for individual crops; acres for each crop were counted if two or more crops were harvested from the same land during the year.

Hay, all: Includes land in alfalfa, other tame hay, small grain hay and wild hay as well as land used for all haylage, grass silage, and greenchop. If both dry hay and haylage, grass silage, or greenchop were cut from the same acreage, the harvested acreage was counted only once. Likewise, if multiple cuttings of dry hay or haylage were taken from the same field, the acreage was reported as harvested acres in the appropriated category only, but the production from all cuttings was combined in the corresponding quantity harvested. Straw acreage and production is excluded. Quantities of harvested haylage, grass silage, and greenchop are reported in green tons but were converted to dry tons based on 13% moisture. All dry hay was reported in dry tons.

Land enrolled in the Conservation Reserve or Wetland Reserve Programs: For the 1997 and 2002 AgCensuses, operations with land enrolled in the CRP or WRP were counted as farms if they received $1,000 or more in government payments even if they had no sales and otherwise lacked the potential to have $1,000 or more in sales. In 1992, CRP and WRP acreage were only included as land in farms where the definition of a AgCensus farm was met, i.e. $1,000 or more of agricultural products were produced or sold during the AgCensus year. Operations which placed all of there cropland in CRP or WRP and did not otherwise meet the farm definition based on sales or other criteria for potential sales were not included as farms in the AgCensus tabulations.

Land in orchards: Includes land in bearing and nonbearing fruit trees, citrus or other groves, vineyards, and nut trees of all ages, including land on which all fruit crops failed. Abandoned orchards were not included here, but were included in the “cropland, idle”.

Other cropland: Includes cropland not harvested and not grazed that was used for cover crops or soil-improvement and land in cultivated summer fallow and idle cropland.

Vegetables harvested: Represents the summation of the acres of individual vegetables harvested. When more than one vegetable crop was harvested from the same acreage, acres were counted for each crop. Quantities of vegetables harvested were not reported.

Woodland, total: Includes natural or planted woodlots or timber tracts, cutover and deforested land with young growth, which has or will have value for wood products and woodland pastured. Land covered by sagebrush or mesquite was not included in this

11

category, nor was land planted for Christmas tree production and short-rotation woody crops and land in tapped maple trees. Livestock and Poultry:

Cattle and calves: Includes beef cows and heifers that have calved, milk cows and heifers that have calved, heifers and heifer calves, steers, steer calves, bulls, and bull calves.

Hogs and pigs: Includes hogs and pigs used or to be used for breeding and any other hogs and pigs.

Layers 20 weeks old and older: Includes layer hens in molt and other layer hens and pullets 20 weeks old and older. Data are comparable for all three AgCensuses.

Pullets for laying flock replacement: Includes “pullet chicks and pullets less than 13 weeks old” and “layers and pullets 13 weeks old and older but less than 20 weeks old”. In 1992 and 1997, these two categories were separated and, thus, had to be added together to be comparable to the 2002 AgCensus. NRI Data

The Natural Resources Inventory is a longitudinal survey performed by the Natural Resources Conservation Service to analyze trends in the condition of natural resources related to land use and conservation practices. Data collected include estimates of soil erosion and land cover. The benefit of these data was that they were consistently collected every five years from the same land, thus providing a database for trend analyses to be performed. However, limited funding has resulted in lapses in data collection. Therefore, data suitable for the purposes of this report are only available for 1992 and 1997. Another benefit of these data is they are collected at a watershed scale so no aggregation to county level is necessary.

The following definitions are provided to allow for comparisons between NRI data and other data sets.

Cultivated cropland: Includes areas used for the production of adapted crops for harvest; it is comprised of land in row crops or close-grown (small grains) crops and other cultivated cropland, for example, hayland or pastureland that is in a rotation with row or close-grown crops. It does not include permanent hayland or horticultural cropland.

Forest land: Includes land that is at least 10 percent stocked by single-stemmed woody species of any size that will be at least 4 meters tall at maturity. It also includes land bearing evidence of natural regeneration of tree cover (cut over forest or abandoned farmland) and not currently developed for non-forest use. The minimum area for classification as forest land is 1 acre, and the area must be at least 100 feet wide.

Pastureland: Includes land managed primarily for the production of introduced forage plants for livestock grazing. Pastureland cover may consist of a single species in a pure stand, a grass mixture, or a grass-legume mixture. Management usually consists of cultural treatments: fertilization, weed control, reseeding or renovation, and control of grazing. For the NRI, it includes land that has a vegetative cover of grasses, legumes, and/or forbs, regardless of whether or not it is being grazed by livestock.

12

CTIC Data The Conservation Technology and Information Center is a clearinghouse for data

related to agriculture and conservation of soil and water. County-level data on agricultural acreage under conservation tillage as well as other land uses are provided. No indication of the quality of the data provided by CTIC is available. FSA Data

Baseline data on county cropland acreage and the use of conservation practices were collected by Local Advisory Committees (LACs) in each county. In general, these data were obtained from the Farm Service Agency and adjusted as needed by the LACs. No estimates of reliability are available as the data represent best estimates based on the judgment of the LACs. Soil Test P Data

The Agronomic Division of the NCDA&CS is the only source for historical data showing the relative magnitudes of residual soil P, or that P that is stored in the soil. Soil test P (STP) was originally developed to predict plant response to soil P and was never intended to be used as a predictor of environmental losses of P. However, losses of P through erosion, runoff and subsurface drainage can often be related to STP, and, as stated earlier, it has been shown in numerous studies to be the largest ‘compartment’ of P in the environment. However, there are concerns in using this data set for our purposes. One major weakness is the lack of comparability between the pre-1996 and post-1996 years. This is due to the fact that the methodology used to measure STP changed from absorption spectroscopy (colorimetry) to Inductively Coupled Plasma Emission Spectroscopy (ICP). Because there is a lack of studies examining the correlation between colorimetry and ICP results for all soil types, it is difficult to compare our two data sets.

In studies from Iowa where soils are predominantly mollisols that contain relatively high levels of organic matter, Mallarino (2003) found measurements with ICP yielded slightly higher values of STP than colorimetric measurement. It is generally believed that ICP is better able to measure certain organic P compounds in soil and this may explain the results of the Iowa study, considering the greater organic matter content of mollisols when compared to the highly weathered, low organic matter soils that comprise the majority of North Carolina’s soils. A more recent study performed in Kentucky, which generally has soils more similar to North Carolina, found very little difference between the two methods (Sikora, et al., 2005). Based on this study, we chose not to adjust our pre-1996 STP data to post-1996 data according to any reported regression equations available in the literature. However, it is important to note that soil samples submitted from certain areas of the Tar-Pamlico River Basin that fall within the Tidewater Region of North Carolina (Beaufort, Hyde and Washington counties) have substantial amounts of organic matter.

Another concern with the use of this database is that there is no way to aggregate the data, which is reported by county, to a watershed scale. The spatial distribution of the samples taken is not known and soil test P can vary greatly across field scales. In addition, numerous sources of bias exist in the collection of the soil samples. No way to account for temporal changes in the submission of soil samples exists; for example, large animal operations were required to submit soil samples starting in 1995/1996. Samples

13

taken from no-till fields are only taken to a 4-inch depth where P may be concentrated as compared to an 8-inch soil depth. These and many other sources of bias call in to question the reliability of this data set. However, because it provides the best available indication of regional soil test P trends, it cannot be ignored. Any conclusions drawn from patterns in the data must be made with these caveats in mind.

RESULTS

Land Use Data for the 1991/1992 to 2002/2003 time interval suggest a small decrease in

estimates of harvested cropland according to most data sources; the exception being data collected by the AgCensus (Table 3a). The reasons for this increase in the AgCensus data set are not known for certain but may be due to differences in coverage adjustments applied to AgCensus data of various years. The data from 1997 were adjusted to account for the change in methodology that occurred in 2002, but no such adjustment was applied to the 1992 published data. Therefore, an “on-the-fly” adjustment was made to the 1992 data based on the ratio of revised 1997 data to original 1997 data for each data item. As a result, we have little confidence in the AgCensus data for 1992. Because of the lack of comparability between 1992 and 2002 estimates, we cannot draw conclusions over the 10-year period. However, when examining the change in AgCensus-reported cropland acreage from 1997 to 2002, the two collection years for which data were adjusted to account for the change in methodology, the results are more similar to AgStats, CTIC and FSA estimates. Note that a downward trend is indicated, although smaller than the trend shown from the other data sources between 1997 and 2003.

Two categories of data from AgCensus and AgStats are used for comparison in Table 3a; the summation of individual crop acreages (‘all crops’ in Table 3a) and an overall ‘harvested acreage’ estimate. The crops that are included in the summation for AgCensus data are: barley for grain, corn for grain, cotton, oats for grain, peanuts for nuts, potatoes, rye for grain, sorghum for grain, soybeans for beans, sweet potatoes, tobacco, winter wheat for grain, all hay, land used for vegetables and fruit/nut orchards. The summation of AgStats crop data does not include acreage for vegetables, orchards, or rye. ‘Harvested cropland’ in the AgCensus includes all crops, all harvested hay, vegetables and orchards, Christmas trees (except in 1992) and nurseries/greenhouses. This information is not published for the AgStats data but it is assumed that NASS defines crop categories for AgStats and AgCensus similarly. Differences between the summation of individual crops and harvested cropland are to be expected since the ‘harvested cropland’ category only counts acreage once, despite the fact that more than one crop may have been grown on the same land. This is presumably the reason that the summation of harvested acres of individual crops is larger than the acreage of ‘harvested cropland’ and can be quite large; for example, as in Fig. 1a in the year 1993. Although the summation data overestimates land in actual production, it is a better source of data in terms of amounts of P fertilizer applied and P removed through crop harvest.

14

Table 3a. Acres of harvested cropland and the percent change from 1991/1992 to 2002/2003.

Year Earliest

available† 1997 Most recent available†

Change from earliest to most

recent year§

Data Source Acres (± margin of error, where available‡) --- % ---

NASS AgStats, (harvested cropland) 628,009 652,110 600,900 -4.3

NASS AgStats, all crops (harvested)# 660,405 736,818 632,289 -4.3

Census of Agriculture, (harvested cropland)§ 584,883 618,141 615,557 +5.2

Census of Agriculture, all crops (harvested)# 650,799 703,474 668,142 +2.7

Natural Resources Inventory, (Cultivated cropland)¶

885,000 (±57,000)

815,000 (±55,200) -- -8

(±0.5) Conservation Technology Information Center, (planted acres)

681,534 760,079 659,287 -3.3

Farm Service Agency as collected by LACs, (harvested cropland)

826,588 -- 741,692 -10.3

†Values in bold represent data that are only available at 5-year intervals; therefore, the earliest year for which data is available is 1992 and the most recent year for which data are available is 2002. All other data sources have data available from 1991 and 2003. ‡Margins of error are based on 95% confidence intervals. §Percent change = [(most recent year available – earliest year available) / earliest year available] * 100. ¶See text for definition. #Value represents the summation of acreages of all individual crops.

NASS AgStats data may not be the most rigorous in terms of coverage but are

reported on an annual basis and remain the most consistent and current data available at the county level. Therefore, trends can be examined over a longer period of time rather than just relying on two or three values to base conclusions (Fig. 1a). Examining annual AgStats data for ‘harvested cropland’ shows that very little change occurred between 1991 and 2003. An overall downward trend between 1991 and 2003 is revealed by the summation data (R2 = 0.34).

Data from NRI are also collected on a five-year interval and no data are available past 1997 at this time. Confidence intervals are included with this data, however, allowing us to make conclusions with a greater degree of certainty. Even though mean values between 1992 and 1997 show a decrease in acreage, essentially no significant difference in crop acreage is estimated due to the relatively large margins of errors.

15

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

Acr

es o

f cro

plan

d

Harvested croplandSummation of crops

Fig. 1a. Annual harvested cropland acreage and summation of acreages of individual crops in the Tar-Pamlico River Basin as estimated by NASS AgStats.

Data from CTIC, which are also reported annually (until 2002 after which time data are reported every two years) follow the same general trends as the other data sources, if AgCensus data from 1992 are ignored. Data collected from FSA estimates higher acreages of harvested cropland as compared to AgStats, AgCensus, and CTIC, but also shows an overall decrease in crop acreage from 1991 and 2003. Both the NRI and FSA data sets are much larger than the other sources (Table 3a). A possible reason for this is that neither of these data sources is based on county data that is aggregated up to a river basin scale. Unlike AgStats, AgCensus and CTIC data, estimates of crop acreages from NRI and FSA data are collected directly on a river basin scale and, thus, avoid the assumption of uniform distribution. The fact that these values (NRI and FSA) are larger suggests that our assumption of uniform distribution of crop acreage throughout the counties involved may be incorrect. Once land use data from satellite imagery becomes available, we will have the capability to better estimate of farmland occurring within the basin versus outside the basin. This will allow a better technique for aggregating annual county data, such as AgStats, to a river basin scale.

Whether cropland acreage is actually decreasing once errors are accounted for is unknown, but Table 3a, along with the annual trends depicted in Fig. 1a seem to clearly indicate that at least no major increase in cropland acreage occurred over the period of interest.

Figure 1b and Table 3b separate the harvested cropland acreage into three components to allow a closer look at patterns in land use; acreage in row crops, small grains and hay.

16

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

Year

Har

vest

ed A

cres

Row cropsSmall grainsAll hay

Fig. 1b. Annual harvested acreage of three different crop groups in the Tar- Pamlico River Basin as estimated by NASS AgStats. Table 3b. Changes in cropland acreage in the Tar-Pamlico River Basin between 1992 and 2002, as estimated by NASS AgStats and AgCensus.

Year 1992 1997 2002 Source

-------------- Harvested acres -------------

% change from 1992-2002

AgStats Row crops 569,150 579,749 536,692 -5.7 Small grains 120,559 126,207 67,268 -44.2 Hay 18,708 30,863 35,351 +89.0 Census of Agriculture

Row crops 526,648 549,234 556,730 +5.7 Small grains 95,928 115,066 69,288 -27.8 Hay 20,708 26,772 33,941 +63.9

The NASS AgStats data presented in Table 3b are for the years 1992 and 2002

instead of 1991 and 2003 as in Table 3a, in order to allow direct comparisons to AgCensus data. No drastic differences between the two data sources are evident. From both sources of data, it is concluded that a decrease of acreage in small grains (R2 = 0.51) and an increase of acreage grown to hay (R2 = 0.91) occurred over time. Acreage in row crops is more difficult to interpret. Temporally, AgStats data indicate a slight decrease in row crop acreage (R2 = 0.13) while AgCensus data suggest the opposite. These are

17

relatively small amounts (±5.7%) and if error were taken into account, these amounts would most likely not be significantly different from each other.

Table 3c shows the acreages of additional land uses that are generally considered conservative with respect to potential P loss relative to other land uses. With the exception of cropland used for pasture, it appears that the acreage of pastureland has increased within the time period. Cropland used for pasture is narrowly defined in AgCensus and this may account for the disagreement with other sources of pastureland estimates. AgCensus data include rotation pasture and grazing land “that could have been used for crops without additional improvements” while NRI pastureland is “land that has a vegetative cover of grasses, legumes and/or forbs, regardless of whether or not it is being grazed”. It is unclear what CTIC defines as ‘permanent pasture’.

The Conservation Reserve Program and the Wetland Reserve Program are both programs that take marginal lands out of agricultural production and provide financial assistance to restore them to either wetland and wildlife habitat or to establish protective covers. From 1992 to 1997 CRP acreage increased by 13% (NRI data), 41% (CTIC data from 1991 to 2004) and 57% (AgCensus data from 1992 to 2002). Once again, the margins of error for NRI data are too large to see any real difference. However, it appears as if more land is being taken out of production through these two programs even though the overall acreages are relatively small. Table 3c. Annual acreage of additional land uses that may act to reduce in-field loss of particulate P. Data from AgCensus and NRI are every five years, while CTIC data is annual.

Earliest year

available† 1997

Most recent year

available† Land Use and Source

Acres (± margin of error, where available‡)

% change from earliest to most

recent year

Permanent pasture, CTIC 3,649 2,568 24,164 +562

Cropland used for pasture, AgCensus 42,266 50,613 31,867 -25

Pastureland, NRI 83,500 (±12,900)

120,600 (±19,800) -- +44

(±7)¶ CRP/WRP land, AgCensus 14,705 19,9758 23,107 +57

CRP, CTIC 19,254 17,860 27,109 +41

CRP, NRI 13,600 (±200)

15,400 (±2,000) -- +13

(±10)¶ Cropland conversion, NCDENR#

651 3,203 7,884 +1111

†Values in bold represent data that are only available at 5-year intervals; therefore, the earliest year for which data is available is 1992 and the most recent year for which data are available is 2002. All other data sources have data available from 1991 and 2003 except for CTIC data, for which 2004 is the most recent year that data is available. ‡Margins of error are based on 95% confidence intervals. §This category encompasses cropland that is idle or used for cover crops or soil-improvement but not harvested and not pastured or grazed. ¶Represents the difference between the data from year 1992 to 1997. #Assumed that no cropland was converted prior to 1991. Subsequent years are cumulative acres since 1991; most recent data year available is 2004.

18

Cropland conversion data are compiled from the Agricultural Cost Share Program and indicates a large increase in acreage of cropland being taken out of farm production. Subsequent land uses are unknown, but since the rule only accounts for agricultural land, any decreases in land used for agricultural can be viewed as a positive. Figure 1c shows the cumulative acreage of land that has been converted since 1991 as well as the year-to-year variation in conversion. More agricultural land is being converted to grassland than woodland, and overall acreage of land being converted is relatively small compared to other beneficial land uses shown in Table 3c.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Con

vert

ed a

cres

Trees, cumulativeGrass, cumulativeTreesGrass

4,437 acres

3,447 acres

Fig. 1c. Cumulative and yearly acreage of conversion of cropland to grass and trees in the Tar-Pamlico River Basin reported as Cropland Conversion BMPs by ACSP.

Data in Table 3d and 3e show the relative change between 1991 and 2003 of effected acres of various in-field and edge-of-field BMPs that reduce the loss of P. The only source of conservation tillage data is the CTIC, which shows a drastic increase during the time period of interest. Although this value seems extreme, conservation tillage has increased nationally during the past ten years, so our assumption of an increase in the Tar-Pamlico River Basin during the time period of interest appears valid. Across the Southeast region it is estimated that a 94% increase in acreage under conservation tillage has occurred in the past 10 years. The reference for the conservation tillage acres is http://www.ctic.purdue.edu/Core4/CT/ctsurvey/2002/RegionalSynopses.html.

Data estimating changes in acreages under cover crops are erratic. Cover crop data from AgCensus are questionable because, again, adjustments were required to make data comparable between AgCensus years. In addition, land in cover crops was combined with idle land in the 2002 AgCensus, so estimates are based on ratios of idle land to cover

19

crop land from other AgCensus years. The extreme increase for cover crop estimates in 2003 reported by the LACs, is suspicious. It is possible that those collecting data for the LACs used a very broad definition of cover crops. Given the positive relationship between cover crops and conservation tillage, it is likely that cover crop acres have increased; however, better data are needed to draw conclusions on trends in cover crop usage.

Table 3d. Annual affected acres of in-field BMPs for Tar-Pamlico River Basin.

1991 2003/2004 Land use ------------------Acres---------------

% Change

Conservation tillage† 70,783 358,889 +407 Cover crops‡ 21,522 103,057 +379 Cover crops§ 25,187 12,298 -51

† Data are from CTIC. ‡Data are from FSA and LACs. §Data are from 1992 and 2002 AgCensus. Table 3e. Annual affected acres of edge-of-field BMPs for Tar-Pamlico River Basin based on data collected by the LACs.

1991 2003 Change BMP ---------------------Acres --------------------

% Change

WCS 52,865 85,072 +32,207 +61 20’ – veg 6,236 28,592 +22,356 +358 20’ – tree 16,500 43,240 +26,740 +162 30 ‘ – veg 12,617 34,192 +21,575 +171 50’ buffer 62,407 63,682 +1,275 +2

The use of water control structures (WCS) and buffers appears to have increased

significantly (Table 3e). However, no other data sources exist for comparison. Because we have no information on location of in-field and edge-of-field BMPs relative to vulnerable areas of the field or their management and condition, their efficacy in reducing particulate or dissolved P is uncertain, as is the relative quantity of P loss that they may be preventing. But certainly the increase of acres affected by these BMPs represents a potential reduction in P loss from agricultural land within the basin. It seems safe to conclude that the conditions for a significant increase in P loss through erosion and runoff from 1991 to 2003 do not exist because of an increase in BMPs (conservation tillage, cover crops, buffers and WCSs), even though we do not know the exact magnitude of increases or their effects.

This section has presented data quantifying acres affected by various P-reducing land uses and BMPs without inferring P reductions. The highly field-specific nature of phosphorus movement described in the Introduction would make such calculations unachievable in any defensible manner. A coarse, partial P reduction estimate for a subset of BMPs implemented in the basin is provided here to give a sense of the scale of potential reductions and limitations tied to such estimates. The North Carolina Agricultural Cost Share Program tracks BMPs by Hydrologic Unit Areas, and since1997 has generated estimates of P reduced by ACSP-funded cropland BMPs. The program estimated the cumulative reduction in P loss since 1997 to be approximately 1.0 million lb P/yr. This is a partial value for BMP implementation for several reasons: it does not capture 1992-1996 implementation, it includes only cropland BMPs and does not

20

estimate P savings from livestock BMPs, and it addresses only ACSP BMPs and therefore does not capture federally assisted BMPs and those installed without government assistance. Beyond the limited set of BMPs addressed, the estimate is incomplete in ways that may underestimate P savings, and inaccurate in at least one respect that can overestimate P savings. It accounts for only soil loss reduction and then multiplies erosion rates by an average STP. This calculation is very crude and does not account for all P loss pathways (leaching, dissolved P and source P). On the non-conservative side, the calculation does not account for in-field redeposition, which can be a significant factor. Manure Production

Relatively speaking, livestock and poultry constitute a major flow of P within the agricultural landscape. A number of studies using P budgets have concluded that animal waste represents the largest input of P in the agricultural ecosystem, accounting for between 35 and 62% of P inputs (Correll et al., 1992; Jaworksi et al., 1992; McMahon and Woodside, 1997).

Manure P production was determined using annual county livestock inventories from AgStats and aggregating to a watershed scale. Animal production, whether swine, poultry, or cattle is much lower in the Tar-Pamlico River Basin than either the Neuse or the Cape Fear River Basins (Appendices, Figure 1A and 2B). Aggregation carried the assumption that livestock and poultry were uniformly distributed throughout each county. County livestock inventories were multiplied by estimates of annual per-animal waste production and nutrient content and then summed for all livestock types to estimate total manure nutrient generation. It was assumed all animal feed is imported into the basin and that all waste is land applied. The amount of P produced per animal was estimated as the amount of manure + urine P (on a dry weight basis) voided by the animal. This estimate comes from the American Society of Agricultural Engineering Standard D284.2 (ASAE, 2005).

Table 4a shows animal data from AgStats, indicating annual inventories as well as the number of animal units. Animal data from AgCensus are presented in the appendix. Although the data agree with AgStats data, very little confidence is placed in the animal data reported in the AgCensus. For instance, a large number of data entries are missing in the AgCensus data due to disclosure issues. Normally, data are withheld if only one or two farms are involved or if one operation has >60% of the total value. This is more apt to happen with broiler data because a single operation tends to have a large number of broilers, sometimes in the hundreds of thousands. Another issue with the AgCensus data is, again, the lack of comparability between AgCensuses.

Data from AgStats indicate that both cattle and layer inventories decreased between 1991 and 2003 while the opposite is true of hogs and pigs (Table 4a). Broiler inventories show a slight increase, overall, during this time period. However, when the percent change between 1997 and 2003 is calculated, a 37% decrease in broilers is seen. This illustrates the importance in analyzing as many data points as possible when examining temporal data for trends. Using only two or three points in time can lead to erroneous results. When annual trends of animal units are examined (Fig. 2a), broiler populations are shown to have increased sharply between 1992, when broiler numbers

21

were first recorded, and 1996. This increase is followed by a decrease and a gradual leveling off of broiler inventories between 1999 and 2003. Table 4a. Changes in livestock inventories and animal units (AUs) in the Tar-Pamlico River Basin estimated from NASS AgStats data.

1991 1997 2003 Change Animal type # animals - AUs - # animals - AUs - # animals - AUs - --- % --- All cattle 52,586 52,586 59,296 59,296 40,312 40,312 -23.3 Hogs and pigs 341,654 46,169 599,621 81,030 505,920 68,368 +48.1

Broilers 20,837,740 41,675 35,613,000 71,226 22,367,000 44,734 +7.3 Layers 3,777,580 15,110 2,103,900 8,416 1,522,500 6,210 -59.7

† % change between 1991 and 2003.

A decrease in chicken (layers) inventory is evident from both data sources. However, a planned addition of 4,750,000 layer chickens to the basin as a result of the Rose Acres Egg Farm in Hyde County represents 19,000 new animal units – an increase of more than three times the basinwide 2003 layer total. The potential significance of this addition is discussed at the end of this section.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Year

Ani

mal

Uni

ts

BroilersLayers

Fig. 2a. Annual number of animal units for poultry in the Tar-Pamlico River Basin as estimated from AgStats data.

An overall decrease in cattle numbers and an increase in hog numbers are evident in Table 3a. Examining the trends annually, however, shows that the increase in hogs is actually more moderate than appears from an examination of the percent change from

22

1991 to 2003 (Fig. 2b). In fact, hog inventories have been experiencing a slight decline after having reached a maximum around 1997, most likely due to the moratorium on expanding or new swine facilities enacted during this year.

Relative changes in manure P produced by livestock and poultry in the Tar-Pamlico River Basin follow very closely those of animal inventories (Table 4b). Once again, the percent change of two individual years is not indicative of the trends in animal waste P from livestock and poultry. For example, if swine data for the year 1994 are used as the starting year instead of 1991, the result is a 5% decrease rather than the large increase (48.1%) shown in Table 4a. Therefore, it is important to examine annual changes in the data in order to glean any apparent overall trends.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Ani

mal

Uni

ts

SwineCattle

Fig. 2b. Annual number of animal units for livestock in the Tar-Pamlico River Basin as estimated by AgStats. Table 4b. Amount of P produced from livestock and poultry in the Tar-Pamlico River Basin.

Lbs P produced Year Livestock Type

1991 2003 % change

Broilers 5,542,839† 5,949,622 +7.3 Layers 1,516,698 623,329 -58.9 Swine 4,676,389 6,924,780 +48.1 Cattle 1,861,807 1,423,521 -20.9 †Earliest year for which broiler data was collected is 1992.

23

With this in mind, Fig. 2c presents a much clearer summary of what is occurring with respect to animal-generated P being introduced to the river basin. Overall, broilers and swine represent the largest contributors of animal waste P to the basin, two to four times greater than cattle or layers. Within animal types, the amount of P produced by swine has remained relatively constant, perhaps slightly decreasing after an initial period of increasing waste P production between 1991 and 1997. Layer and cattle production of manure P have been generally decreasing over time by small increments each year, while manure P production from broilers increased for several years and then decreased after peaking in 1996. Figure 2c underscores the important point that the number of animals is not necessarily a good indicator of P inputs to the basin, rather the amount of animal units and relative P excreted per animal unit allow comparison of basin P contributions across animal types. When total P from all animal types are added, the P amount produced in 1992 (the first year that all data can be compiled) is slightly less (13.6 M lb P) than the amount produced in 2003 (15.0 M lb P). This is an increase of 1.3 M lbs P. This assumes that the P content of the animal waste has remained the same, a doubtful assumption. The North Carolina Department of Agriculture and Consummer Services, Animal Waste Analysis Laboratory had been measuring P concentration in animal waste for many years and, within the past five years, there has been a decline in the P concentration. Thus the 10% increase in animal-waste P is a worst-case calculation. Both animal numbers and total manure-P peaked in 1997, declining substantially since the mid-1990s.

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003Year

Lbs

anim

al w

aste

P p

rodu

ced/

yr

Layers SwineCattleBroilers

Fig. 2c. Annual amounts of P produced in waste of different animal types in the Tar-Pamlico River Basin.

24

The permitted Rose Acres egg laying facility currently under construction in Hyde County would add just under 2 million lbs of P per year through animal waste to the river basin. It is important to note that the increase may potentially represent a problem if the waste is not managed well and distributed widely. Many of the soils in the immediate vicinity of this farm have a significant amount of organic matter. The behavior of animal waste P added to these types of soils has not been studied extensively and it is unclear what the overall effect of the new facility to the surrounding environment will be. Soil Test P

Soil test (Mehlich-3 P) values were obtained for over 790,000 samples from fiscal years 1990 to 2004 that correspond to approximately mid-1989 – mid-2004 (numbers of samples from individual counties and years are shown in the appendix in Table A3 and Fig. A3). As discussed earlier, because of the nature of this data set, the data depicted in Figures 3a and 3b do not necessarily reveal real trends in the magnitude of residual P in the basin. Therefore, these data have to be examined for what they are, trends in STP from farms submitting soil samples for testing. We have no idea if this sample of farms is biased toward high-P soils or low-P soils. It is also important to keep in mind that these data have not been aggregated for the river basin area; they represent whole county data. Where data for individual counties are combined, such as Fig. 3b, a weighted average was calculated to account for differences in the proportion of each county falling within the basin.

180

FLaGb

0

20

40

60

80

100

120

140

160

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004Year

Meh

lich-

3 P,

mg/

kg

ig. 3a. Trends in mean STP of samples submitted to NCDA&CS Plant/Waste/Solution aboratory from counties in the Tar-Pamlico River Basin. Vertical bars represent counties nd, from left to right in each year cluster, are: Beaufort, Hyde, Washington, Franklin, ranville, Person, Vance, Warren, Edgecombe, Halifax, Martin, Nash, Pitt, and Wilson. The lue line represents the weighted average of all samples for each year.

25

Based on visual examination of average STP levels from individual counties, no year-to-year trends are apparent except that average values tend to be highest in Pitt County compared to other counties (Figure 3a). (More detailed data on STP can be found in the appendix). Average STP values in all counties, as well as the weighted average of all counties combined, were at their highest level in fiscal year 1996. The reason for this increase is not known for certain. However, 1996 was the year new animal waste regulations went into effect, requiring that animal operations submit nutrient management plans. Because these plans require a measure of STP from waste-application fields, it is likely that more soil samples from animal operations were submitted to the soil test laboratory beginning around this time. Soil samples from operations on which animal waste is field-applied would be expected to have higher STP values relative to samples submitted from fields not receiving animal waste.

0

20

40

60

80

100

120

140

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Fiscal Year

Meh

lich-

3 So

il Te

st P

, mg/

kg

PiedmontTidewaterCoastal Plain

Fig. 3b. Weighted average STP values of soil samples submitted to NCDA for Tar-Pamlico counties in three physiographic regions. Regression lines are shown for the entire period 1990-2004 and for two separated periods, 1990-1995 and 1996-2004. Error bars represent 95% confidence intervals.

The extremely large dataset of soil samples tested for STP allowed for some statistical analysis of trends in the data, but it is important to remember that these samples were not taken in a random manner and, therefore, we cannot account for any sampling bias or error that may be inherent in these numbers. Confidence intervals shown in Fig. 3b correspond only to the variability in STP values within each year and do not account for biases due to sampling. Therefore, again, any trends must be noted with caution. County soil test results were grouped by physiographic region for the period of record and subjected to linear regression analysis (Figure 3b and Table 5a). The weighted average STP reached a maximum in 1996, after which time STP levels tended to remain higher than during the earlier period from 1990 to 1995. Therefore a separate regression

26

analysis was performed for the period from 1990 to 1995 and from 1996 to 2004 in order to examine whether the latter period is significant higher than the earlier period. Results are shown in Figure 3b and Table 5b. Note that error bars are shown in Figure 3b representing 95% confidence intervals. However, these confidence intervals only correspond to variability in STP data for each year, and do not account for any biases that may have occurred in sampling.

Linear regression lines for the entire 1990-2004 period for each physiographic region are shown as dotted lines in Figure 3b. Although, STP values tended to reach a maximum around 1996/1997 and then subsequently decrease, the overall trends show a slight increase from 1990 to 2004. Again, the reason for the occurrence of maximum STP levels in 1996 is possibly due to animal waste regulations that were enacted at that time. As a possible corroboration of this explanation, both broiler and swine populations reached their maximums around this time period (Figures 2a and 2b). The change in annual STP is very slight for samples submitted from both the Piedmont and Tidewater regions, while the increase in the Coastal Plain is more significant. The reason for this result is not known but can be speculated as relating to a more intense growth of animal operations in the Coastal Plain.

The year 1996 demarcates the time at which the NCDA Agronomic Division adopted the presumably more rigorous ICP method of P detection. Because ICP is believed to better detect organic P compounds than the colorimeteric method, samples from the Tidewater region analyzed after 1995 might be expected to have slightly higher STP values. Because the Tidewater region has a high proportion of organic soils, counties from this region should have been the most likely to show an increase in average STP after the change in methodology occurred as compared to the years prior to the change. However, samples from all regions increased after 1995, but it is suspected that this phenomenon is a result of animal waste regulations coming into effect rather than a change in STP methodology.

Table 5a. Trends in weighted averages of STP for different physiographic regions between fiscal years 1990 and 2004.

Region Trend R2 Regression Slope

Piedmont Increase 0.06 +0.5 Coastal Plain Increase 0.54 +1.5 Tidewater Increase 0.16 +0.7

Table 5b. Trends in weighted averages of STP for different physiographic regions between fiscal years 1990-1995 and 1996-2004.

Region Time Period (Fiscal years) Trend R2 Regression

Slope Piedmont 1990 – 1995 Increase 0.20 +1.2 Piedmont 1996 – 2004 Decrease 0.31 -2.2 Coastal Plain 1990 – 1995 Increase 0.09 +0.5 Coastal Plain 1996 – 2004 Decrease 0.04 -0.4 Tidewater 1990 – 1995 Increase 0.31 +2.2 Tidewater 1996 - 2004 Decrease 0.38 -1.6

27

Focusing on linear trends within each of the two periods, Figure 3b shows an increasing tendency, in the weighted mean of STP in soil samples from all three regions for the first period (Table 5b). The Piedmont region is the exception to the occurrence of maximum STP levels in 1996. The Piedmont region had by far the fewest number of samples submitted to the soil testing laboratory from 1990 to 2004 at just below 27,000 versus over 232,000 samples submitted for the Tidewater region and greater than 530,000 for the Coastal Plain region. Soil test P values from counties in the Piedmont region, therefore, had much higher standard errors. After 1996, soil samples from all regions showed decreasing trends in mean STP. The reason for this decrease is not known. Soil samples submitted from Coastal Plain counties in the Tar-Pamlico River Basin are higher in STP, on average, than the other two regions. This result is to be expected as the majority of agriculture activity, especially animal agriculture, in the Tar-Pamlico River Basin occurs in this region.

Despite the large number of samples in this dataset, very few conclusions as to changes in land use and agricultural management can be made. Soil test P represents perhaps the most important component of P dynamics in the environment. Without unbiased data, however, it is hard to fit this piece of information into the whole story. Nor will information on soil P be better in the future as long as routine soil test information is the only source for this data. Other data may become available with time as more survey-type studies are done in the basin, in which STP is collected randomly. It is possible that statistical methods could be used in which a small sample of the entire population of fields in the basin is sampled. At the present time, however, this large data set is not entirely helpful. Fertilizer P

We estimated pounds of fertilizer P in the basin in 1991 and 2003 using harvested crop acres and the maximum P application rates recommended by the NC Ag. Chem. Manual (North Carolina State University, 2004). We used the highest recommended rate for each crop because we wanted to represent a worst-case scenario in terms of P fertilizer applied within the basin. Harvested acreage of individual crops as reported in AgStats was used instead of total harvested cropland. Total harvested cropland may underestimate the amount of fertilizer used because it does not account for more than one crop being grown in a year. No true indication of fertilizer usage exists at present. Data on county fertilizer sales were not used because of the questionable accuracy associated with correlating county fertilizer sales data with fertilizer use in a particular county. McMahon and Woodside (1997) found that P fertilizer usage in eight river basins in North Carolina and Virginia estimated using 1990 county fertilizer sales was between 180 and 990 percent higher than estimates based on recommended application rates. Using recommend application rates instead of county sales and using acreage of individual crops rather than harvested acres allowed us to be very conservative in our estimates of fertilizer P inputs (Table 6a).

28

Table 6a. Estimated fertilizer application rates and total amounts applied for major agricultural crops in the Tar-Pamlico River Basin.

Harvested Acres lbs P applied Crop

Rate, lbs P/ac†

(lbs P2O5/ac) 1991 2003 1991 2003 Barley (for grain)

22 (50) 3,581 0 78,188 0

Corn (for grain) 22 (50) 156,110 89,164 3,408,515 1,946,812

Corn (for silage)

26 (60) 4,860 1,041 127,336 27,275

Cotton 22 (50) 84,620 162,901 1,847,598 3,556,790

Hay, all 79 (180) 22,714 36,772 1,785,380 2,890,376

Oats (for grain) 22 (50) 4,110 2,763 89,738 60,328

Peanuts 9 (20) 42,149 28,164 368,114 245,974

Potatoes 87 (200) 752 1,569 65,677 137,031

Sorghum (for grain)

22 (50) 3,423 1,952 74,738 42,620

Soybeans (for beans)

22 (50) 198,604 213,231 4,336,332 5,007,031

Sweet potatoes 87 (200) 6,104 9,801 533,100 855,983

Tobacco‡ 18 (40) 46,596 24,219 813,904 423,039

Wheat (for grain)

22 (50) 86,782 60,713 1,894,803 1,325,611

Total 660,405 632,289 15,423,424 16,167,537 †Used highest value from NC Ag. Chem. Manual (2004). ‡Flue-cured tobacco.

Despite the decrease in harvested cropland in the basin, there was a small increase in commercial fertilizer input. Sweet potatoes and Irish potatoes have significantly higher P fertilization rates and experienced increases in production, but the increase in acreage of both crops was relatively small. The estimated P fertilization rate used for hay is most likely an overestimation as wild hay is included with other hays in acreage estimates but would not be fertilized. If we assumed a hay fertilization rate of half the amount reported in Table 6a, the increase in P fertilization from 1991 to 2003 would be reduced by approximately 0.5 million pounds. However, this alone would not account for the 1.1 million pound increase in estimated P fertilizer usage from 1991 to 2003. Although there was a sharp decrease in harvested grain corn acreage during this period, a greater increase in harvested cotton acreage occurred. This, along with an increase in harvested acreage of sweet potatoes, Irish potatoes, soybeans, and hay most likely account for the increase in P fertilization in this theoretical scenario.

29

ACCOUNTING FOR P Despite the complexities associated with P movement/dynamics, we can

conceptualize the issue as one of changes in P compartments. The compartments in this scenario are: • Inputs: P added through commercial fertilizer and animal manure from livestock and

poultry; atmospheric deposition of P is assumed to be negligible. • Outputs: P removal through crop uptake and P loadings to stream system. • Storage: residual P.

We are attempting to establish an accounting system that will allow us to draw conclusions periodically in the future as to whether agriculture has avoided increased P loading to surface waters or achieved decreased P loading. In this section, we examine our ability to draw qualitative conclusions for the period of interest, 1991-2003, based on our information for the three compartments discussed above. From this we hope to identify available indicators that will allow for a similar qualitative statement of agriculture’s contribution at points in the future. Compartment Definitions

Phosphorus loading is assumed to account for all potential losses of P, such as surface runoff and soil erosion. Residual P is that P not accounted for after outputs are subtracted from inputs. McMahon and Woodside (1997) suggested that residual P accounts for P stored in the soil and streambed, uptake by biota, retention in forest ecosystems, and losses to groundwater. For our purposes, we assume P stored in the streambed and in non-crop biota, including forest biota, to be relatively constant. This assumption may not be entirely valid because changes in land use are most likely occurring over time, thereby affecting the P storage capacity of the system. However, two indications of changes in forestland show that an overall decrease in woodland has occurred, although the levels are relatively small. It is unclear at this time how much P previously tied up in plants and soil would be expected from the conversion of forestland to other land uses. In addition, these two estimates of changes in land use for the river basin are questionable due to problems described earlier. Information on river basin land use provided by FSA indicates that approximately 3,500 acres of cropland have been converted to forestland since 1991 (Fig. 1c), which would tend to conserve P. However, estimates of overall increases or decreases in woodland cannot be made with certainty. Changes in streambed P are also unknown. Therefore, residual P is assumed to represent simply that P that is retained in the soil, as measured by STP. Qualitative P Budget

In preceding sections, we attempted to quantify two of the three compartments, P inputs and residual P, and we recognized significant uncertainties in those values. We will qualitatively summarize net change in P inputs below. In terms of the residual P compartment (STP), we determined that our only available source of historical soil P data is significantly biased. While we are relatively confident that no significant trends in STP have occurred since 1991, the soil test data will not allow us to quantify the soil P compartment. Overall, we will assume no change to the residual P compartment.

30

Outputs We have not yet evaluated the output compartment, which has two elements, crop

removal and loading to streams. If the amount of P being lost from agriculture in stream transport was known, this quantity plus the amount of P removed from the basin by crop harvest could be subtracted from the estimated P input (fertilizer and manure) to determine a P residual, thereby allowing us to estimate the amount of P being stored in the soil. We could then conclude that if the residual P is increasing in the basin, there may be an increased risk of P loss via erosion or runoff. However, although we have historical data on P concentrations in surface water and we know that the general basin trend for dissolved P in surface waters is decreasing (NCDENR, 2003), we have no way of knowing what proportion of the dissolved P in the stream network can be attributed to agriculture. Other sources of P, such as point source inputs, residential fertilizer use, or stormwater runoff, are not accounted for.

While we cannot quantify P loading to streams from agriculture for the period of record, we can draw qualified conclusions about the change in agriculture’s contribution to stream loading for the period based on the changes in BMPs and P-conserving land uses as quantified in previous sections. The acreage of agricultural land treated by BMPs, which act to reduce losses of P from agricultural land, has increased over the period of interest. Acres of CRP land have also increased. Therefore, we would expect that agriculture’s contribution to P loading has been a decreasing one if all other agricultural variables have remained constant. To be conservative, we can say that P additions to the stream network due to agricultural activities at least have not increased.

The assumption that P loss from erosion and runoff remained relatively static over the time period is perceived to be a good one considering that NRI data for the Tar-Pamlico River Basin indicates no significant increase of soil erosion between 1992 and the most recent year for which data is available, 1997. In addition, Bundy (1997) reported that annual P losses from runoff and erosion represented only a small fraction of the annual inputs of manure and fertilizer. In their study, P losses via erosion and runoff accounted for approximately 1.4% of the added P to cropland.

Along with P loading to streams, the other element of the output compartment is crop removal. Phosphorus removal through crop uptake is indicated in Table 6b. Individual crop removal values from Table 6b are totaled in Table 6c for 1991 and 2003. The mass of P harvested decreased by almost 13%, a result that is consistent with the prevailing trend seen in cropland acres.

Also included in Table 6c is a summary of P inputs from fertilizer and waste based on preceding sections, and a resulting P balance. Overall, P inputs to the basin increased slightly over the period of interest. However, due to the uncertainty in all of our data sources, if we were able to place confidence limits on these results, they would undoubtedly contain both the starting and ending values shown, indicating no significant difference between them. It is also important to note that we assumed that P fertilization rates for individual crops have not changed over the 12-year period. These estimations are very crude and are only used for the purposes of this theoretical exercise we have performed in order to estimate relative magnitudes of P surplus to the basin (table 6c).

31

Table 6b. Total crop production and plant uptake. Yield† Total lbs P removed

Crop 1991 2003

Yield unit lbs P

uptake/ yield unit‡ 1991 2003

Barley (for grain) 196,299 0 Bushels 0.18 35,334 0

Corn (for grain) 14,103,850 9,283,180 Bushels 0.15 2,115,578 1,392,477

Corn (for silage) 74,666 16,394 Tons 1.05 78,399 17,214

Cotton 118,606 227,118 Bales 1.89 224,165 429,253 Hay, all 43,505 83,383 Tons 9.95§ 432,875 829,661 Oats (for grain) 248,160 212,086 Bushels 0.11 27,298 23,330

Peanuts 118,141,270 83,317,290 Pounds 0.003 354,424 249,952 Potatoes 46,475 259,306 Cwt 0.06 2,789 15,558 Sorghum (for grain) 168,969 103,652 Bushels 0.18 30,414 18,657

Soybeans (for beans) 6,743,100 6,415,560 Bushels 0.36 2,427,516 2,309,602

Sweet potatoes 856,220 1,705,272 Cwt 0.04 34,249 68,211

Tobacco 112,298,170 55,526,770 Pounds 0.0020 224,596 111,054 Wheat (for grain) 3,881,470 2,208,100 bushels 0.20 776,294 441,620

† Data on yield are from AgStats. ‡ Data are from Lander, Moffitt and Alt (1998). §Average of alfalfa hay, small grain hay, other tame hay and wild hay. Table 6c. Magnitude of P budget components.

1991 2003 P input/output ------------------ lbs P -----------------

% change

Commercial fertilizer 15,423,424 16,167,537 +4.8 Animal waste 13,597,734 14,971,251 +10.1 Crop Harvest 6,763,931 5,906,589 -12.7 Balance +22,257,227 +25,232,199 +13.4

Overall, the P surplus in the basin that Table 6c depicts has increased slightly.

However, with the error associated with the estimates used, there is most likely no significant change of the P balance occurring in the basin. Additions of manure P showed an increase. McMahon and Woodside (1997) reported that among eight study basins, 50% of agricultural P inputs were from animal waste. This agrees with the results shown in Table 6c, if, and only if the commercial fertilizer values reported in this reflect actual fertilizer additions; and it is doubtful that they do.

The magnitude of these components appears consistent with results from others (Correll et al., 1992; Jaworski et al., 1992; and McMahon and Woodside, 1997). Crop harvest removed approximately 20% of the P added by commercial fertilizer and animal waste. Estimates of P loss to streams from other river basins are between 7 and 10% of the inputs (Correll et al., 1992; Jaworski et al, 1992; and McMahon and Woodside (1997). If we assume, in this theoretical exercise, that 10% of the P inputs are lost in our river basin, we are left with between 19 and 22 million pounds of unaccounted P, which

32

we can assume is the relative size of the residual component. This represents about two thirds of the total amount of P added to the basin. Jaworski et al. (1992) found that residual P accounted for a similar percentage of P inputs, while McMahon and Woodside (1997) and Correll et al. (1992) found slightly less proportions of input P in the residual component, although none of them used STP as a measure of residual P. Many soils in North Carolina have a substantial capacity to adsorb and retain P in stable forms. Whether the soils in the Tar-Pamlico River Basin can store this amount of P for a long period of time is not known .

SUMMARY AND CONCLUSIONS

We offer the following simplified, qualitative P budget for the period of interest, 1991-2003, based on the best available data. There appears to be a 10% increase in P inputs to the basin from animal waste. Changes in fertilizer P inputs cannot be calculated. While crop removal of P appears to have decreased slightly, P-retaining BMPs and P-conserving land uses have substantially increased. Benefits of the latter have not been quantified, although estimates from the North Carolina Cost Share Program would suggest that soil-reducing practices have reduced P by 1M lbs/yr. Without quantifying reductions in P loss from BMPs and P-conserving land uses, the overall P balance seems to be essentially unchanged. Soil test results were used to evaluate the residual P compartment. Statistical trends for the period 1990-1995 showed either no significant change or a slight increase, while in the 1996-2004 period a small decrease was seen in some physiographic regions. However, the nature of the soil test data does not allow for well-founded conclusions. The downward trend in P concentrations in surface waters in the basin may suggest that, for now, the soil is retaining excess P. It is important to note that soils are known to reach a saturation limit, beyond which they hold relatively small amounts of P and only weakly. High accumulation of P in the soil can increase the risk of P loss to the drainage system and loss by runoff and erosion. However, the increased use of BMPs should decrease the risk of P loss to the environment. Another possibility is that P is being retained in stream sediments and grasses and trees not accounted for in cropland acreage, although we have no way to measure this.

All of the conclusions above must be qualified by the many sources of error and uncertainty associated with the data, which may call into question their reliability. The assumption that data items, (number of animal units, harvested crop acreages) are distributed uniformly throughout the basin is a potentially serious source of error. However, the use of remote sensing and aerial photography maps showing land cover can help rectify this issue by revealing the distribution of agricultural and forested lands within the basin. The distribution of animal operations is more problematic. The location of permitted operations can be determined but this does not give information on inventories of animals. In addition, locations of poultry operations using dry litter systems are not currently disclosed. The concentrated nature of animal operations within certain areas can potentially call into question the assumption of uniform distribution.

Another source of error involves the prevalence of missing data values in some datasets. This occurs in AgCensus data and results in values being counted as zero where

33

there is a known population. The biased nature of the STP data is perhaps the biggest dilemma and has already been discussed.

The main increase in P source was hog inventory, estimated to be between 18% and 48%, a potentially large increase in manure P additions to the basin. However, when viewed annually for the past 12 years, the upward trend appears to have peaked around 1999 and is now declining slightly. This is most likely due to the moratorium on expansion of new and existing swine operations. The addition of a new 4.75 million bird layer facility in Hyde County would cause a significant increase in waste P inputs to the basin, that could potentially contribute to an increased localized risk of P loss to surface waters. At this point, the relative approach appears to be adequate as changes of P inputs are not increasing or increasing only slightly, while the use of conservation practices to prevent P loss is increasing rapidly. Although we cannot know with absolute certainty, the relative change approach appears to demonstrate that no major increases in P loading from agricultural activities have occurred to the present day in the Tar-Pamlico River Basin in relation to 1991 levels. This conclusion is indirectly corroborated by water quality data showing a net decrease in P loading in the basin (NCDENR, 2003). In addition, data from a field-scale, randomly sampled agricultural survey in the Tar-Pamlico River Basin, which is currently being completed, has been used in runs of the field-scale model NC PLAT. The average PLAT Rating for the approximately 1,500 fields is 13, which falls in the “Low” range (low ranges from 0-25). The Low PLAT Rating suggests that the potential offsite P loss from these fields is low. Because of the random, statistically defensible way in which the fields were selected, we could not select for nutrient source and all of the fields sampled received commercial fertilizer.

Because an accounting of relative changes in P loss from agricultural lands in the basin will be required each year, data that are available on a year-to-year basis will provide the best indicators of changes in land use patterns. Given this annual accounting need, we evaluated trends in annually available indicators from the baseline year through 2004. Linear regression analysis was performed on each of these data sets. Table 7a shows the resulting trends.

Overall, there are few concrete conclusions that can be drawn from the relative changes seen in these data. Increases are readily apparent in acreage of harvested hay and conservation tillage as are decreases in harvested acreage of small grains and animal units of layers and cattle are readily apparent (Table 7a). The presence of changes in other parameters is less clear. The amount of animal units of both broilers and swine appear to decrease more recently (since about 1997), but because the trends are not linear it is difficult to draw conclusions based on regression lines. Trend lines for many parameters are non-linear, meaning they are both increasing and decreasing throughout the time period of interest. Acreage of cropland, STP, broilers and swine, all increase initially but more recently show a period of decline.

34

Table 7a. Relative change in annually available parameters between 1991 and 2004.

Parameter Trend between

1991-2003 R2

Average increase/decrease

per year Harvested cropland, harvested acres Decrease 0.003 -270

Summation of crops, harvested acres Decrease 0.34 -11,330

Row crop, harvested acres Decrease 0.13 -4,933

Small grain, harvested acres Decrease 0.51 -4,678

Hay, harvested acres Increase 0.91 +1,480 Conservation tillage, acres Increase 0.97 +22,745

Broilers, animal units Decrease 0.13 -1,076 Layers, animal units Decrease 0.82 -775 Swine, animal units Increase 0.32 +1,444 Cattle, animal units Decrease 0.64 -1,191 STP weighted average, mg/kg Increase 0.33 +1.04

STP median, mg/kg Increase 0.0003 +0.021

A more comprehensive summary of available indicators of changes in P loss is proved in Table 7b. Many of these indicators did not have data available for portions of the period of record, precluding the ability to conduct regression analyses. Instead, the table depicts the relative changes that have occurred in the parameters of interest for the entire period since 1991 and how these affect the risk of P loss from agricultural lands in the basin. Since 1991, the majority of land use parameters have experienced a change that helps to reduce the risk of P loss, either by conserving P that is already in the soils or by reducing the amount of P being added to the basin. Animal waste application of manure P has increased since 1991 by 10%, but we have no way of knowing whether this additional P was off-set by a decline in fertilizer P. Given the assumptions that went into these calculations, uncertainty is probably high. It is important to note that an estimate of uncertainty has not been made for any of the annual estimates presented in Table 7b. Changes in both weighted averages and medians of STP describe an increase over the study period. However, because of the bias inherent in the data set, these changes are questionable. The reason for the increase can be one of many factors but is most likely related to the inclusion of soil samples from large animal operations starting around 1996. Therefore, even though STP is included in Table 7b, it is impossible to know with any certainty whether levels of P are increasing in the river basin.

For future years, the percent change from 1991 can be determined and the P loss risk in Table 7b can be revised as necessary. In addition, the continued use of annualized data will allow tracking of trends in land use patterns and increase the reliability of the data set each subsequent year. The potential for improving the annualized data also exists in the future; for example, using land classification data obtained from satellite imagery can allow for the allocation of parameter data to specific locations within the county; i.e. what percentage of broilers for a given county fall within the boundaries of the river basin. This would eliminate one of the major weaknesses of using county data.

35

Table 7b. Relative changes in land use and management parameters and their relative effect on P loss risk.

†Data are from 2002; after 1998, CTIC only reports data in even years.

Parameter Data Source Baseline

1991 2003 % change 1991-2003

P loss risk +/-

Ag. Land, acres AgStats 660,405 632,289 -4 - Cropland conversion, acres

NC Dept. Environ. & Natural Resources 651 7,109 +992 -

Conservation Reserve Program / Wetlands Reserve Program, acres

Conservation Tech. & Information

Center† 19,254 23,107† +20 -

Conservation Tillage, acres

Conservation Tech. & Information

Center† 70,783 317,489† +349 -

All Buffers, acres affected

Farm Service Agency/Local

Advisory Committees

97,760 169,706 +74 -

Water control structure, acres

Farm Service Agency/Local

Advisory Committees

52,865 85,072 +61 -

Scavenger crop, acres

Farm Service Agency/Local

Advisory Committees

21,522 103,057 +379 -

Animal waste P produced, lbs P yr-1 AgChem/AgStats 13,597,734 14,971,251 +10 +

Soil test P, weighted average, mg kg-1

NC Dept. Ag. & Consumer Services 92 109 +19 +

Soil test P, median, mg kg-1

NC Dept. Ag. & Consumer Services 83 92 +11 +

36

REFERENCES American Society of Agricultural Engineers. 2005. ASAE Standard D384.2, Manure Production and Characteristics. ASAE, http://www.asabe.org/redirect.cfm. Bundy, L.G. 1997. A phosphorus budget for Wisconsin cropland. Wisconsin Dept. Agric, Trade & Consumer Protection Report. Dept. Soil Science, Univ. Wisconsin. Correll, D.L, T.E. Jordan, and D.E. Weller. 1992. Nutrient flux in a landscape: effects of coastal land use and terrestrial community mosaic on nutrient transport to coastal waters. Estuaries. 15:431-442. Jaworski, N.A., P.M. Groffman, A.A. Keller, and J.C. Prager. 1992. A watershed nitrogen and phosphorus balance: the Upper Potomac River Basin. Estuaries. 15:93-95. Lander, C.H., D. Moffitt, and K. Alt. 1998. Nutrients available from livestock manure relative to crop growth requirements. Resource Assessment and Strategic Planning Working Pap. 98-1, Natural Resource Conservation Service, US Dept. Agric. (Available on the internet at: www.nrcs.usda.gov/technical/land/pubs/nlweb.html) Mallarino, A.P. 2003. Field calibration for corn of the Mehlich-3 soil phosphorus test with colorimetric and inductively coupled plasma emission spectroscopy determination methods. Soil Sci. Soc. Am. J. 68:1928-1934. McMahon, G. and M.D. Woodside, 1997. Nutrient mass balance for the Albermarle-Pamlico Drainage Basin, North Carolina and Virginia, 1990. Journal of the American Water Resources Association. 33:573-589. North Carolina Dept. Environment and Natural Resources, Division of Water Quality Planning Branch. 2003. Trend Analysis of Nutrient Loading in the Tar-Pamlico Basin. [Online]. Available at http://h2o.enr.state.nc.us/nps/TrendGrimesland91-02prn.pdf (verified 15 July 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. North Carolina State University. 2004. North Carolina Agricultural Chemicals Manual. College of Agriculture and Life Sciences, North Carolina State University. Raleigh, NC. Sikora, F.J., P.S. Howe, L.E. Hill, D.C. Reid, and D.E. Harover. 2005. Comparison of colorimeteric and ICP determination of phosphorus in Mehlich3 soil extracts. Commun. Soil Sci. Plant Anlaysis. 36:875-887.

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United State Dept. Agriculture. National Agriculture Statistics Service. 1992 Census of Agriculture. North Carolina State and County Data. Volume 1, Geographic Area Series. Part 33. United State Dept. Agriculture. National Agriculture Statistics Service. 1997 Census of Agriculture. North Carolina State and County Data. Volume 1, Geographic Area Series. Part 33. United State Dept. Agriculture. National Agriculture Statistics Service. 2002 Census of Agriculture. North Carolina State and County Data. Volume 1, Geographic Area Series. Part 33.

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APPENDIX Figure A1. Poultry operations throughout the state of North Carolina. Data obtained from NC Department of Agriculture and Consumer Services, Veterinary Division, 2004. Includes all poultry operations, regardless of waste type or management.

Swine FacilityCattle FacilityPoultry Facility

Swine FacilityCattle FacilityPoultry Facility

Swine FacilityCattle FacilityPoultry Facility

Figure A2. Confined animal feedlots registered with North Carolina Department of Environment and Natural Resources (NCDENR) as required by NC .0200 Rules for Waste Not Discharged to Surface Waters. Animal operations with fewer than threshold populations and poultry operations with dry litter waste systems are not included. Table A1. Changes in livestock inventories in the Tar-Pamlico River Basin estimated from AgCensus data. AgCensus 1992 1997 2002 % changeAll cattle 44,377 45,178 38,021 -14.3 Hogs and pigs 393,203 649,485 494,711 +18.2

Broilers 5,512,392 5,378,015 4,365,238 -20.8 All chickens 3,879,426 3,304,322 1,650,439 -57.5

39

Table A2. Changes in acreage of forest land in the Tar-Pamlico River Basin. 1992 1997 2002

Land Use Data

Source -------------------- Acres ---------------------- % change Woodland, total†

Census of Agric. 332,491 312,428 293,234 -12

Forest land† NRI 1,679,200 (±66,400)

1,652,500 (±66,100) -- -2

†See text for definition. Table A3. Number of samples submitted to NCDA&CS Agronomic Division, Soil Testing from each county during fiscal years 1990-2004.

Fiscal Year County 1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Beaufort 437 4815 6487 7971 8065 4429 8265 10824 10070 9231 10029 12159 12804 10439 11221

Hyde 680 2694 2939 2505 3235 1925 3167 5247 4648 5375 5124 6079 6683 5243 4312

Washington 874 3430 2414 2220 2419 1949 2924 4073 3925 4122 3387 3412 3729 2609 3635

Edgecombe 2862 5940 5033 4978 6871 3983 6338 9610 10594 11123 9028 11331 10989 4706 6612

Halifax 2105 6603 4518 5330 5053 3573 5734 7892 7210 8325 6429 7217 5737 5527 6618

Martin 2708 4017 2815 2559 3424 2589 4442 4868 5745 7202 6228 7005 5991 5282 4974

Nash 1216 4677 4162 3385 3229 2234 4305 6444 7932 6437 5737 7351 7093 5481 6730

Pitt 2312 5975 6135 3436 4891 3345 4845 7013 8396 8135 6991 11026 9078 8275 8447

Wilson 2419 5769 4179 5136 5481 3062 5879 6246 7830 7279 8353 11007 6203 3817 6250

Franklin 368 551 749 397 434 384 803 638 767 682 743 553 1251 672 566

Granville 161 404 364 274 288 157 386 322 382 458 610 764 579 715 414

Person 97 376 236 228 112 429 372 274 239 407 344 516 382 347 499

Vance 87 155 121 136 175 88 138 89 159 101 273 487 326 103 298

Warren 203 186 221 85 180 126 103 129 218 205 394 553 280 310 200

0

2000

4000

6000

8000

10000

12000

14000

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Fiscal Year

Num

ber o

f sam

ples

BeaufortHydeWashingtonEdgecombeHalifaxMartinNashPittWilsonFranklinGranvillePersonVanceWarren

Fig. A3. Number of samples submitted to NCDA&CS Agronomic Division, Soil Testing from each county during fiscal years 1990-2004. Samples from each physiographic region are separated in the figure’s legend.

40

Table A4. Mean soil test P (Mehlich-3 P) of samples submitted to NCDA&CS Agronomic Division, Soil Testing from each county during fiscal years 1990-2004.

Fiscal Year

County

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Beaufort 96.3 81.8 85.1 78.0 83.8 99.9 113.8 89.7 98.4 94.5 92.5 91.6 92.9 95.7 87.1

Hyde 69.0 80.7 75.1 75.2 73.3 91.0 96.6 86.0 93.3 90.3 89.2 93.2 86.0 93.9 77.5

Washington 83.1 84.8 89.4 85.3 85.0 105.3 108.2 91.8 95.1 95.2 95.9 89.8 107.9 91.2 84.5

Edgecombe 96.4 96.7 95.9 94.8 95.9 101.7 116.1 104.4 108.9 103.2 103.7 105.7 102.0 109.2 100.8

Halifax 66.6 64.9 62.8 62.8 67.7 66.3 75.2 68.2 72.9 74.5 72.9 71.2 75.2 85.8 75.0

Martin 109.5 99.9 107.6 97.7 103.5 111.7 124.9 113.4 114.8 117.8 114.7 115.8 120.8 122.9 112.4

Nash 102.6 101.3 102.6 100.1 104.7 104.9 129.3 113.7 123.2 120.1 118.6 112.8 116.3 112.1 107.7

Pitt 120.3 121.7 122.1 122.9 123.3 129.3 170.2 150.0 156.3 149.2 146.3 146.3 144.3 162.7 142.9

Wilson 102.8 104.5 103.5 98.3 107.5 110.9 137.5 123.4 123.5 128.2 113.1 118.3 116.8 124.1 109.3

Franklin 90.9 79.5 89.6 84.1 83.5 91.7 92.1 91.6 108.7 93.7 100.6 81.1 73.4 94.8 72.4

Granville 63.2 71.6 73.8 64.3 75.5 100.3 116.2 92.5 94.6 90.7 98.5 70.3 67.0 70.4 69.0

Person 82.7 51.6 66.5 54.8 51.4 65.7 60.1 67.9 76.6 89.7 93.5 75.0 81.2 86.3 80.7

Vance 76.4 84.5 65.1 91.1 74.3 88.0 101.5 60.7 82.7 82.6 131.7 96.2 72.9 83.8 76.1

Warren 80.8 72.7 73.7 65.6 90.2 85.0 140.4 86.5 89.6 100.9 122.7 140.2 93.3 120.9 102.7

0

20

40

60

80

100

120

140

160

180

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Fiscal Year

Mea

n ST

P, m

g/kg

BeaufortHydeWashingtonEdgecombeHalifaxMartinNashPittWilsonFranklinGranvillePersonVanceWarren

Fig. A4. Mean soil test P (Mehlich-3 P) of samples submitted to NCDA&CS Agronomic Division, Soil Testing from each county during fiscal years 1990-2004. Samples from each physiographic region are separated in the figure’s legend.

41

Table A5. Median soil test P (Mehlich-3 P) of samples submitted to NCDA&CS Agronomic Division, Soil Testing from each county during fiscal years 1990-2004.

Fiscal Year

County

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Beaufort 88.4 70.7 72.6 66.8 68.7 89.7 86.7 69.0 75.9 72.9 71.8 72.2 72.9 77.8 68.8

Hyde 63.0 72.0 65.5 65.6 64.9 80.0 87.6 74.4 80.9 81.4 80.6 82.3 76.2 84.4 67.8

Washington 72.9 76.4 78.6 75.3 76.2 92.9 94.7 75.2 77.9 78.3 77.4 74.0 77.0 78.3 68.4

Edgecombe 93.1 93.3 92.1 89.5 90.8 100.5 104.6 92.7 96.0 90.9 93.4 93.0 89.8 95.7 86.1

Halifax 56.8 55.4 54.9 54.0 59.0 59.0 62.9 57.9 62.4 62.9 62.2 60.0 61.6 67.5 61.8

Martin 114.5 97.7 109.6 94.5 103.0 115.4 113.1 104.6 104.8 106.6 106.1 105.7 110.4 113.1 103.9

Nash 103.6 104.3 106.7 101.6 107.1 109.1 120.9 104.5 111.2 110.2 107.0 102.9 106.6 100.2 97.5

Pitt 140.8 142.3 139.2 142.2 143.0 149.3 156.2 140.4 143.8 138.7 136.6 131.8 126.7 143.7 127.2

Wilson 105.4 107.1 105.9 98.4 110.9 115.5 127.4 114.9 115.4 119.1 103.9 107.2 108.0 110.1 99.8

Franklin 90.7 74.1 86.9 75.0 79.3 90.7 84.5 87.9 90.4 79.2 84.5 67.9 55.4 81.1 62.3

Granville 52.0 62.9 66.1 62.2 67.6 103.0 109.2 78.6 88.5 84.2 86.6 40.3 37.2 31.6 35.9

Person 75.9 40.7 58.3 39.1 39.0 51.1 49.9 34.5 51.6 49.4 55.3 54.8 56.5 59.4 57.0

Vance 69.4 86.2 56.1 88.0 65.6 98.3 91.9 48.4 69.3 64.4 127.0 79.7 66.8 77.1 68.0

Warren 80.7 76.2 71.5 52.7 84.6 78.9 91.7 78.0 68.5 81.5 91.8 93.6 71.4 95.7 85.9

0

20

40

60

80

100

120

140

160

180

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

Fiscal Year

Med

ian

STP,

mg/

kg

BeaufortHydeWashingtonEdgecombeHalifaxMartinNashPittWilsonFranklinGranvillePersonVanceWarren

Fig. A3. Median soil test P (Mehlich-3 P) of samples submitted to NCDA&CS Agronomic Division, Soil Testing from each county during fiscal years 1990-2004. Samples from each physiographic region are separated in the figure’s legend.

42