Predicting Phosphatic Soil Distribution in Alachua County, Florida

PREDICTING PHOSPHATIC SOIL DISTRIBUTION IN ALACHUA COUNTY, FLORIDA

RAVINDRA RAMNARINE

A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIRE MENTS FOR THE DEGREE OF MASTER OF SCIENCE

UNIVERSITY OF FLORIDA

2003

Ravindra Ramnarine

To my brothers, Rishi and Shiva

ACKNOWLEDGMENTS

I would like to express my deepest gratitude to my advisor, Dr. Willie G. Harris, who provided guidance and support both in my academic and personal life. Dr. Harris was always enthusiastic in providing advice and assistance throughout the project; and was instrumental in its timely completion. I also thank my supervisory committee members for their expertise in different aspects of the project: Dr. Sabine Grunwald

(GIS), Dr. Vimala Nair (total P analysis), and Dr. Kenneth Portier (statistical analysis).

I thank Dr. Stanley Latimer for assistance in GIS operations; and transferring of

GIS data to the GPS unit. A major part of the project involved GIS and I thank Larry

“Rex” Ellis, Tait Chirenje, and Mike Tischler, who helped me overcome various GIS hurdles. The field sampling would not have been possible without the assistance of Mr.

Keith Hollien, whose knowledge of the roads and tolerance of some “bad weather” were deeply appreciated. Also, I thank Keith Hollien and Natalie Rodriguez for assistance with the mineralogical analyses. Many of my professors and colleagues assisted in the project; some providing advice and others, a helping hand. They include Dr. Randall

Brown, Dr. Mary Collins, Dr. Michael Binford, and Ms. Myrlène Chrysostome.

I would also like to thank the landowners and park rangers who gave us permission for field sampling. Many thanks also go to my friends and family for their encouragement and support throughout my study. Finally, this research was supported in part by a grant from the USDA – Initiative for Future Agriculture and Food Systems (IFAFS).

TABLE OF CONTENTS Page

ACKNOWLEDGMENTS ...... iv

LIST OF TABLES...... vii

LIST OF FIGURES ...... viii

ABSTRACT...... x

CHAPTER

1 INTRODUCTION ...... 1

2 LITERATURE REVIEW ...... 6

Description of the Study Region...... 6 Geology ...... 6 Topography and Physiography...... 12 Hydrology...... 14 Soils ...... 17 Previous Studies on Phosphatic Soils...... 19 Overview of Soil-Landscape Modeling...... 21

3 MATERIALS AND METHODS ...... 24

Geospatial Data Collection and Analysis ...... 24 Selection of Sampling Sites...... 39 Field Soil Sampling ...... 42 Laboratory Analysis for Total Phosphorus and Nodule Mineralogy...... 44 Statistical Analysis of Data Obtained from Sampling Regions...... 46

4 RESULTS AND DISCUSSION...... 47

Site and Soil Observations...... 47 Comparison of Total Phosphorus in the Scarp, Plain and Nonphosphatic Regions...49 Phosphate Distribution as Related to Soil, Geologic and Topographic Attributes.....57 Soils ...... 57 Geology ...... 59 Topography...... 60 Final Phosphatic Soils Map ...... 62

v 5 CONCLUSIONS ...... 65

APPENDIX

A PROCEDURES USED FOR OBTAINING GEOGRAPHIC DATA...... 67

B CREATING A DEM FOR ALACHUA COUNTY ...... 69

LIST OF REFERENCES...... 70

BIOGRAPHICAL SKETCH ...... 75

LIST OF TABLES

Table page

2-1. Geologic units found in Alachua County ...... 13

2-2. Extent of soil orders in Alachua County...... 18

3-1. Data layers, sources and extent...... 26

3-2. Classification of soil map units into obligate and facultative...... 35

4-1. Field observations at sites in the scarp region...... 48

4-2. Field observations at sites in the plain region...... 48

4-3. Field observations at sites in the nonphosphatic region...... 49

4-4. Comparison of total P values with soil and geologic map units for the upper and lower sampling depths for the scarp region...... 51

4-5. Comparison of total P values with soil and geologic map units for the upper and lower sampling depths for the plain region...... 52

4-6. Comparison of total P values with soil and geologic map units for the upper and lower sampling depths for the nonphosphatic region...... 53

4-7. Topographic attributes of sample sites in the scarp, plain and nonphosphatic regions...... 61

4-8. Estimated percentage and acreage of “phosphatic soils” based on data from this study...... 63

vii

LIST OF FIGURES

Figure page

1-1. Soil/landscape/geologic model used to predict phosphatic soil distribution in Alachua County, Florida...... 5

2-1. Location of Alachua County, Florida...... 7

2-2. Major roads and cities in Alachua County ...... 8

2-3. Geology and hydrology of Alachua County...... 13

2-4. Map showing elevations in Alachua County...... 14

2-5. Map showing slope gradients in Alachua County...... 15

2-6. Physiographic divisions in Alachua County...... 16

2-7. Distribution of soil orders in Alachua County...... 18

3-1. Steps in delineating a portion of the scarp hypothesized to be an area of high probability of phosphatic soils occurrence...... 30

3-2. Steps in delineating a portion of the plateau, hypothesized to be an area of low probability of phosphatic soils occurrence...... 33

3-3. The general distribution of obligate and facultative map units ...... 38

3-4. Predicted phosphatic soils map for Alachua County...... 40

3-5. Elevation below 30.5 m that was delineated as the plain area...... 41

3-6. Location of sampling sites in Alachua County...... 45

4-1. Total P concentrations for upper and lower depths for the three regions before log transformation ...... 54

4-2. Total P concentrations for upper and lower depths for the three regions after log transformation ...... 55

4-3. Comparison of TP concentrations for the upper and lower sampling depths for the nonphosphatic, plain and scarp regions...... 56

viii 4-4. Line graph showing the odds of finding a phosphatic soil in the nonphosphatic, plain and scarp regions...... 57

4-5. X-ray diffraction patterns of ground nodule material from samples with high TP content ...... 59

4-6. Predicted phosphatic soils map showing areas of high, medium and low probability of finding a phosphatic soil...... 64

Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science

PREDICTING PHOSPHATIC SOIL DISTRIBUTION IN ALACHUA COUNTY, FLORIDA

Ravindra Ramnarine

August, 2003

Chair: Willie G. Harris Major Department: Soil and Water Science

Phosphatic soils occur extensively in Florida, where phosphatic geologic materials are exposed by erosion. It is important to identify areas of phosphatic soil because they influence phosphorus risk and environmental impact assessments. Using phosphatic geology as the sole criterion for determining phosphatic soil distribution is insufficient, since deposits may be mapped at depths that do not influence soil properties. We tested a geographic information systems (GIS) approach to predicting the distribution of phosphatic soils in Alachua County, Florida, based on a landscape/geologic/soil conceptual model that takes into account features associated with dissection and erosion.

The hypothesis was that phosphatic soils occur on erosional landscapes where phosphatic geologic materials are exposed or are near the surface.

Two areas (scarp and plateau) were delineated based on an initial prediction that these would have the highest and lowest probability of phosphatic soils, respectively.

Soil map units were designated as “obligate” if they occurred at 90% or greater area in

the scarp compared to the total area (scarp and plateau); and “facultative” if their occurrence on the scarp was between 60 and 90%. An initial predictive phosphatic soils map was produced by including all “obligate” map units and only “facultative” map units sharing a boundary with an “obligate”. Phosphatic soils on the plain and scarp regions were distinguished because of suspicion (from geologic information) that the plain would have a lower proportion of phosphatic soils than the scarp. Soils predicted to be nonphosphatic made up the “nonphosphatic” region.

Fifteen sample sites for field validation were randomly selected within the scarp, plain, and nonphosphatic regions. Samples were taken at two depths for each site: upper

(0 to 25 cm) and lower (100 to 125 cm). Total phosphorus (TP) and nodule mineralogy were determined. Mean TP concentration for the lower depth was 6195 mg kg-1 for the scarp phosphatic region, 2485 mg kg-1 for the plain phosphatic region, and 193 mg kg-1 for the nonphosphatic region (p < 0.0001). The aluminum phosphate mineral, wavellite, was found in nodules of samples with TP concentrations of 902 mg kg-1 or higher. Based on the latter and corroborative TP data for the three regions, a phosphatic soil was operationally defined as one containing ≥ 1000 mg kg-1 TP at the 100 to 125 cm depth, with higher TP concentrations at the lower sampling depth. The initial predictive map was revised to show that the probability of finding a phosphatic soil was highest for the scarp phosphatic region (73%), medium for the plain phosphatic region (33%), and low for the nonphosphatic region (7%) (p < 0.001). The final map corresponded reasonably well with a radon protection map, but some discrepancies suggest that radon could still be a risk even when the source is below the 2-m soil zone.

xi CHAPTER 1 INTRODUCTION

The presence of phosphorus at undesirable concentrations in water resources is of increasing importance due to its negative and sometimes irreversible effects on the environment. Phosphorus (P) is regarded as the nutrient that limits eutrophication in most surface freshwaters and in some estuaries (Sharpley et al., 1994). Expanding agriculture and sandy soils have aroused concerns about transport of phosphorus to surface water in the Suwannee River Basin (Katz and DeHan, 1996), where phosphate-rich geologic deposits can naturally result in elevated phosphorus levels in soils. Increase in phosphorus concentrations is mainly attributed to agricultural operations (manure applications, fertilizer inputs); but soil phosphorus can also be inherited from naturally occurring phosphatic parent materials. When the latter is the case, high extractable phosphorus can be mistakenly attributed to agricultural activity. Extractable phosphorus concentrations are considered in some areas when assessing risk of phosphorus transport from soils (Sims, 1993; Sharpley et al., 1996). For example, the current Florida P Index uses soil-test phosphorus concentrations (Mehlich 1) to index the level to which the soil has already being “loaded” with phosphorus. However, Mehlich 1 extractable phosphorus would be elevated in phosphatic soils, even if no agricultural P loading had occurred.

The very high Mehlich 1-P (> 60 mg kg-1) for phosphatic soils would not necessarily indicate that the P sorption sites have been exhausted, because P is generally in the form of minerals. Also, phosphatic soils commonly are relatively high in

1 2 components (e.g., metal oxides and other clay-sized minerals) that have a strong affinity for phosphorus (Wang et al., 1991a). Hence, the ability to recognize and predict the occurrence of phosphatic soils is important since it will help in identifying areas where naturally occurring P could

• Confound environmental risk assessments based on extractable P. • Naturally elevate phosphorus in streams, aquifers and lakes.

Phosphatic soil distribution is also important because of its relevance to natural phosphorus fertility and fertilizer requirements. Phosphorus uptake by plants in naturally phosphatic soils is influenced by the plant’s ability to extract the nutrient, which can depend on the species of phosphate minerals. The distribution of phosphatic soils could closely relate to radon risk assessment, since they contain varying amounts of uranium which radioactively decays to form radon gas.

Two physiographic regions in peninsular Florida, Western Valley and Northern

Highlands Marginal Zone (Williams et al., 1977), share a boundary that also corresponds to a geologic boundary between Miocene phosphatic formations and Eocene limestone formations. These two regions have been significantly affected by erosional processes and could potentially have phosphatic soils. The Western Valley is predominantly a karst plain characterized by subsurface drainage; and corresponds to what this thesis refers to as the “plain region.” Pockets of phosphatic materials can be found on the plain

(personal communication, W.G. Harris, 2001); but are only mapped geologically if extensive enough. Their origin is uncertain; they could be due to incomplete removal of the Hawthorn Group by marine or fluvial erosion (Cooke, 1945); or to more recent depositional processes (Vernon, 1951). The Northern Highlands Marginal Zone is a diffuse scarp (Cody Scarp; referred to as the “scarp region” in this thesis) corresponding

3 to an area where thick phosphatic deposits are undergoing erosion. A third physiographic region, the Northern Highlands Plateau, is less affected by erosion, and younger quaternary sediments overlie the phosphatic formations (Hawthorn Group). This plateau region should be less likely to have phosphatic soils due to the prevalent influence of the nonphosphatic quaternary deposits.

Phosphatic soils will most likely develop in areas where phosphatic geologic formations are exposed or are near the surface. However, these areas do not necessarily correspond to areas of phosphatic formations on a geologic map. Geologic maps alone do not provide sufficient information for predicting phosphatic soils, because geologic formations (including those rich in phosphatic sediments) are mapped as deep as 6.1 m, which is too deep to influence soils. In effect, some phosphatic geologic map units may not contain phosphatic soils, and nonphosphatic geologic units may in some cases contain them because of pockets of near-surface phosphatic materials that are too small to be mapped at a geologic scale.

Soil maps alone do not convey phosphatic soil distribution because phosphatic soils have not generally being distinguished in soil surveys. Soil features that might indicate phosphate occurrence were not captured well in the legend development and soil mapping at the time of the soil survey of Alachua County (Brown et al. 1993). The soil correlator(s) in charge of the survey discouraged setting up soil map units based on this attribute (probably due to shifts in Soil Taxonomy and lack of corroborative laboratory data). However, soil maps may still have potential to be used in refining prediction of phosphatic soil occurrence. Therefore, soils information has been used in this thesis (as integrated with geologic and topographic information) in developing a predictive map of

4 phosphatic soil occurrence. For example, soils dictated the final delineation of the

“nonphosphatic" region on the predictive map. This region was not specific to any physiographic division. Also, soils were used to extend the phosphatic soil unit to the plain, where no phosphatic geologic material was mapped. This approach differs from that of radon risk mapping for Alachua County (Brown et al., 1993), for which delineations were based on geologic boundaries modified to more closely correspond to shallow exposure of phosphatic materials. The plain and scarp regions were separated for testing purposes because of the suspicion that the plain would have less probability of phosphatic soil occurrence than the scarp. This separation was also made by Brown et al.,

1993.

Alachua County, partially within the Suwannee River Basin, was selected as a study area for this research because of accessibility, the presence of phosphatic geologic formations, and the presence of physiographic features (described above) that have potential to aid in predicting naturally phosphatic soils. A geographic information system

(GIS) was used as a tool to integrate geographic information and generate a predictive map of phosphatic soil occurrence. GIS and global positioning system (GPS) were used for site selection and field validation. The prediction of phosphatic soil distribution in

Alachua County was based on a soil/landscape/geologic conceptual model (Figure1-1).

The model concept is that highest probability of phosphatic soil occurrence would coincide with areas of dissected terrain, where topography (slope and elevation) determines the extent by which soils are influenced by the underlying geology. Also, the model specifies an intermediate probability in the plain region, where some pockets of phosphatic material occur (though unmapped).

Figure 1-1. Soil/landscape/geologic model used to predict phosphatic soil distribution in Alachua County, Florida.

The hypothesis of this research is that phosphatic soils occur on erosional landscapes where phosphatic geological material is exposed near the surface.

Specifically, the prediction for this research is that probability of phosphatic soil occurrence increases from the nonphosphatic region (as predicted by the model), to the plain phosphatic region, to the scarp phosphatic region.

In summary, this project seeks to identify the distribution of naturally occurring phosphatic soils using Alachua County, Florida as a region to test a conceptual model based on soils, landscape, and geology. The overall objective of the project is to predict the distribution of phosphatic soils in Alachua County. Specific objectives include

• To develop a phosphatic soils map of Alachua County using available geographical data.

• To field test the map by comparing total P (TP) concentrations of surface and subsurface horizons of soils within and outside of the delineated area of phosphatic soils.

CHAPTER 2 LITERATURE REVIEW

Description of the Study Region

The study region is Alachua County, which is located in north-central Florida

(Figure 2-1), and covers an area approximately 250982 hectares or 2512 square kilometers. The county is about 53 km long and 58 km wide (Thomas et al., 1985).

Gainesville, the county seat, is located at 29°39' N latitude and 82°21' W longitude. The major highway is Interstate 75 (I-75), which runs approximately center of the county in a northwest to southeast direction (Figure 2-2). The Santa Fe River forms the northern boundary line of the county and is connected to many tributaries/streams, which flow through the county. The Santa Fe River’s sink is the more prominent Suwannee River.

The county of Alachua forms part of the Suwannee River Basin and St. Johns River

Basin.

Geology

Alachua County has four major geologic units that have influenced soil development. They are in chronological order, the Ocala formation, the Hawthorn Group

(comprised of the Coosawhatchie formation and undifferentiated Hawthorn Group), the

Cypresshead formation, and the undifferentiated Tertiary/Quaternary Sediments (Puri and

Vernon, 1964; Scott, 1988). In the 1980s, the Florida Geological Survey mapped the geology of the state as part of a statewide radon investigation. The geologists decided to map the first lithostratigraphic unit occurring within 6.1 m of the land surface (Scott,

2001). For example, if the Hawthorn Group occurred 6.1 m or less below land surface,

6 7

Figure 2-1. Location of Alachua County, Florida. USCB: United States Census Bureau. Albers Equal Area Conic (Albers). the Hawthorn lithostratigraphic unit was mapped; if the Hawthorn Group lies more than

6.1m below the land surface the undifferentiated Tertiary/Quaternary sediments were mapped.

The Ocala formation consists of carbonate rocks and underlies the entire county but exposures occur mainly in the southern and western parts. Here a limestone plain is formed and in many places is covered by layer(s) of loose sand (Williams et al., 1977).

The carbonate rocks are primarily limestone and occasionally dolostone (Lane, 1994).

Hoenstine and Lane (1991) indicated that the carbonates comprising the Ocala limestone were deposited 38 to 40 million years ago (mya). The Ocala Limestone was first mentioned by Dall and Harris (1892), who referred to the limestones exposed near Ocala,

Figure 2-2. Major roads and cities in Alachua County. FDEP: Florida Department of Environmental Protection. Albers Equal Area Conic (Albers).

Marion County, in central Florida as the Ocala Limestone. It was later named by Puri

(1957) as the Ocala Group but was downgraded to the Ocala formation by Scott (1991).

The thickness of the Ocala Limestone (To) is highly variable because of erosion during times of exposures. The thickness ranges between 15.2 and 58 m (Hoenstine and Lane,

1991). Where the Ocala limestone is at or near the surface, extensive karst features are exhibited such as sinkholes, disappearing streams and springs. Underlying the Ocala

Limestone is the older Avon Park Formation (limestone) also of Eocene age (Randazzo and Jones, 1997). The term karst refers to the type of terrain created by the dissolution of carbonate sediments (White, 1988). Some features that are indicative of karst include

9 caves, collapse and subsidence sinkholes, and solution depressions. These features can increase the surface water to ground water interactions by providing a pathway for the two systems to interact. The karst nature of limestones comprising the foundation of

Florida influenced the development of Pleistocene landforms. For millions of years, naturally acidic rain and ground water flowed through these limestones, dissolving a myriad of conduits and caverns out of the rock. In some cases, the caverns collapsed, forming new sinkholes and modifying the existing landforms through collapse and lowering of the limestone bedrock (Lane, 1994).

The Hawthorn Group (Coosawhatchie) is exposed along a terrace scarp (Cody scarp), that trends generally northwest to southeast in the central portion of the county.

Much of the outcrop area (of the Hawthorn) is relatively rugged hill and valley terrain and is covered by a thin layer of the older Plio-Pleistocene Terrace Deposits. The

Hawthorn Group is the phosphate-enriched parent material. The Hawthorn Group consists of varying amounts of clay, quartz sand, carbonate and phosphate. Phosphate grains are disseminated throughout and are diagnostic of the unit (Scott, 1983 and 1988).

The deposition of marine phosphatic sediments in Florida occurred during the

Miocene epoch. This was due to the upwelling of cold, nutrient-rich, phosphorus-laden water from the deep ocean basins (Riggs, 1979). The increased phosphorus supply was due to population explosion of marine organisms such as plankton. When these organisms died, they produced a storehouse of organic material, which mixed with sediments and were eventually buried. The development of phosphate deposits resulted from the reworking of the phosphatic sediments and the concentration of the phosphate by current and wave action (Lane, 1994).

The Hawthorn Group in Florida is composed of a number of different formations and members (Scott, 1988). The Coosawhatchie (Thc) Formation is exposed or lies beneath Plio-Pleistocence sediments of varying thickness. Within the outcrop region, the

Coosawhatchie Formation varies from a “light gray to olive gray, poorly consolidated, variably clayey and phosphatic sand with few fossils, to an olive gray, poorly to moderately consolidated, slightly sandy, silty clay with few to no fossils” (Scott, 1988).

Silicified nodules are often present in the Coosawhatchie Formation sediments in the outcrop region. The sediment may contain 20 percent or more phosphate (Scott, 1988).

Permeability of the Coosawhatchie sediments is generally low, forming part of the intermediate confining unit/aquifer system. Pirkle (1956) indicated that Hawthorn beds usually range in thickness from 0.5 to 9 m but can be up to 36.6 m.

The undifferentiated Hawthorn Group (Th) occurs to a small extent in the south- west of the county. The correlation of these sediments to the formations of the Hawthorn

Group exposed to the center of the county is uncertain (Lane, 1994). There is little to no phosphate present in these sediments and fossils are rare. Ages have not been documented but stratigraphic position suggests inclusion in the Hawthorn Group.

These sediments may be residual from the weathering and erosion of the Hawthorn

Group.

The Cypresshead Formation (Tc) was named by Huddlestun (1988), and is composed of siliciclastics (clayey sands). It occurs only in the eastern part of the county.

In general, the Cypresshead Formation in exposure occurs 30.5 m (or higher) above mean sea level (msl). The permeable sands of the Cypresshead Formation form part of the surficial aquifer system. Many of Florida’s modern topographic features and surficial

11 sediments were created or deposited during the various Pleistocene sea level high stands.

The waves and currents eroded the exposed formations of previous epochs, reshaping the earlier landforms and redistributing the eroded sediments over a wide area. At the same time, rivers and longshore currents transported large quantities of sediments into Florida from the coastal plain surrounding the Appalachian Mountains (Lane, 1994).

The Pleistocene seas are responsible for the layers of sand over the limestones underlying Florida’s Gulf coast, infilling the irregular rock surface. During the sea-level high stands, and as the seas retreated, shore waves and near-shore currents eroded a series of relict, coast-parallel scarps and constructed sand ridges spanning the state. Many of these features are formed on or carved out of older geologic landforms. This process is believed to be the primary factor in forming the Cody scarp (Lane, 1994). Scott (2001) indicated that the undifferentiated sediments range in thickness from less than 30 cm to greater than 30.5 m.

The undifferentiated Tertiary-Quaternary Sediments (TQu) are siliciclastics that are separated from undifferentiated Quaternary Sediments solely on the basis of elevation.

Sediments that occur 30.5 m above mean sea level, are predominantly older than

Pleistocene but contain some sediments reworked during the Pleistocene (Scott, 2001).

The undifferentiated Tertiary-Quaternary sediments occur in a band extending from the

Georgia-Florida state line in Baker and Columbia Counties southward to Alachua

County. These sediments consist mainly of sands, sandy clays and clays. The undifferentiated Tertiary-Quaternary sediments are part of the surficial aquifer system

(Scott, 2001). The undifferentiated Quaternary Sediments (Qu) covers much of Florida’s surface but occurs only to a small extent in the southeast of Alachua County. The

12 sediments can be of varying thickness and consist of siliciclastics, organics and freshwater carbonates. Where these sediments exceed 6.1 m thick, they were mapped as discrete units. The siliciclastics consist of variably organic-bearing sands and silty clays.

The features of the geologic units are summarized in Table 2-1. The location of the geologic units in Alachua County is shown in Figure 2-3.

Topography and Physiography

Elevations in Alachua County range from approximately 7.6 m above sea level in the northwestern corner along the Santa Fe River to about 61 m in the area northwest of

Gainesville (Figure 2-4) (Williams et al., 1977). The western plain of the county is nearly flat and bordered on the east by the Cody Scarp, a dissected area that can have slopes up to 30%, and an upland plateau area (Figure 2-5).

The entire county is in the Coastal Plain Province (Fenneman, 1938) and is delineated as part of the Central Highlands Region (Pirkle, 1956). Data sourced from the

St.Johns River Water Management District (SJRWMD) describe the physiography of the county in great detail (Figure 2-6). The major physiographic provinces or sub-provinces in the county are

• High Flatwoods • San Felasco Hammock • Haile Limestone Plain

The High Flatwoods correspond to the Northern Highlands Plateau (Williams et al.,

1977). This upland plateau is nearly level, sloping gently to the west, north, and east with elevations ranging from 42.7 to 61 m above sea level. The San Felasco Hammock corresponds with the Northern Highlands Marginal Zone (Williams et al., 1977). This is the dissected zone and ranges in elevation from 24.4 to 57.9 m. The Haile Limestone

Table 2-1. Geologic units found in Alachua County

Unit Lithologies Epoch Period Undifferentiated Sand, clay, organics Pleistocene (1.8 Quaternary quaternary sediments to 0.011 myaa) Undifferentiated TQ Sand, clay Pliocene (5.3 to Tertiary/ sediments 1.8 mya) Quaternary Cypresshead formation Sand, clay Pliocene Tertiary Hawthorn group, Dolostone, limestone, Miocene (23.8 Tertiary undifferentiated sand, clay, phosphate to 5.3 mya) Hawthorn group, Sand, clay, limestone, Miocene Tertiary coosawhatchie formation. dolostone, phosphate Ocala limestone Limestone, dolostone Eocene (54.8 to Tertiary 33.7 mya) amillion years ago.

Figure 2-3. Geology and hydrology of Alachua County. FDEP: Florida Department of Environmental Protection. Albers Equal Area Conic (Albers).

Figure 2-4. Map showing elevations in Alachua County. USGS-NED: United States Geological Survey-National Elevation Dataset. Albers Equal Area Conic (Albers).

Plain corresponds to the major portion of the Western Valley described by Williams et al., 1977. The western plains region has low relief with elevation ranging from about 25 to 30.5 m above sea level with most of the area been between 21.3 and 24.4 m above sea level. The plain is devoid of stream channels and the Ocala limestone is near the surface.

Hydrology

Three primary aquifer systems are found in Alachua County and they are closely correlated to the geologic groups. They are the water table aquifer (or surficial aquifer), the secondary artesian aquifer (or intermediate aquifer), and the Floridan aquifer

(Hoenstine and Lane, 1991; Tihansky, 1999).

Figure 2-5. Map showing slope gradients in Alachua County. USGS-NED: United States Geological Survey-National Elevation Dataset. Albers Equal Area Conic (Albers).

The water table aquifer is primarily near the surface and consists of a few meters of

Plio-Pleistocene sediments overlying the Hawthorn formation (Hoenstine and Lane,

1991). Flow through this system is topographically controlled and the water table fluctuates in response to rainfall. Perched ponds may exist sporadically in surface depressions and the aquifer can serve as storage for recharge of lakes and underlying aquifers. This aquifer is absent in western Alachua County.

The secondary artesian aquifer is comprised of the highly variable sediments of the

Hawthorn Group (Hoenstine and Lane, 1991). Most of the Hawthorn sediments are clays and sandy clays with very low hydraulic conductivity. Groundwater resides in lenses and

Figure 2-6. Physiographic divisions in Alachua County. SJRWMD: St. Johns Water River Management District. Albers Equal Area Conic (Albers) laterally discontinous beds and clay units are the principal barrier to the underlying

Floridan aquifer.

The Floridan Aquifer is within the upper several hundred meters of limestone and underlies the entire county. The Upper Floridan Aquifer is where most of the interaction between surface water and ground water is occurring at present. This occurs since the carbonates are very susceptible to dissolution by the slightly acidic surface water that provides recharge to the aquifer. The aquifer is confined where it is overlain by the

Hawthorn formation and unconfined where the Ocala limestone is near the surface

(Thomas et al., 1985). Crane (1986) discusses the importance of water quality since the

Floridan aquifer is the main source of potable water.

The Cross County Fractured Zone described by Williams et al. (1977) is approximately 72 km long and extends from Orange Lake in the southeast to the Santa Fe

River sink in the northwest. The zone has greatly affected the surface water drainage patterns as all of the streams that flow across this are swallowed by sinkholes.

Soils

Soils are complex natural bodies resulting from the combination of five factors – climate, organisms, topography, parent material and time (Jenny, 1941; Brady and Weil,

1996). The processes of soil formation are influenced by geomorphic changes and takes place at the surface of the earth. The rate of development is mainly dependent on the resistance of existing features to the nature and intensity of environmental factors involved (Buol et al., 2002). Alachua County is comprised of 73 soil map units, representative of 50 different soil series (Thomas et al., 1985). Most of the soils are acidic, ranging from pH 4.2 to 6.5. The topography, slope and depth to water table vary from soil to soil. The major mapping units in the county are Pomona sand (21419 hectares, 9.3%), Arredondo fine sand, 0 to 5% slopes (20535 hectares), Millhopper sand,

0 to 5% slopes (20399 hectares), Candler fine sand, 0 to 5% slopes (11002 hectares),

Sparr fine sand (8578 hectares), Newnan sand (8433 hectares) and Tavares sand, 0 to 5 % slopes (8431 hectares). The major soil series are classified below:

• Pomona: sandy, siliceous, hyperthermic Ultic Alaquods • Arredondo: loamy, siliceous, hyperthermic Grossarenic Paleudults • Millhopper: loamy, siliceous, hyperthermic Grossarenic Paleudults • Candler: hyperthermic, uncoated Typic Quartzipsamments • Sparr: loamy, siliceous, hyperthermic Aquic Arenic Paleudults • Tavares: hyperthermic, uncoated Typic Quartzipsamments

Seven soil orders are represented in the county, with Ultisols and Spodosols being dominant (Table 2-2). The distribution of the soil orders is shown in Figure 2-7.

Table 2-2. Extent of soil orders in Alachua County. Order Acreage (hectares) Percent Ultisols 103980 41.4 Spodosols 55300 22.0 Entisols 39152 15.6 Alfisols 16189 6.5 Histosols 7673 3.1 Inceptisols 4507 1.8 Mollisols 282 0.1 Othera 7298 2.9 Water 16602 6.6 Total 250982 100.0 aClassified as urban land; and pits and dumps, by the United States Department of Agriculture – Natural Resources and Conservation Service.

Figure 2-7. Distribution of soil orders in Alachua County. USDA-NRCS: United States Department of Agriculture – Natural Resources and Conservation Service. Dataset: Soil Survey Geographic (SSURGO). Albers Equal Area Conic (Albers).

Previous Studies on Phosphatic Soils

The most important natural sources of phosphate in Florida soils are the phosphate- bearing sediments of the Hawthorn Group (described above) and Bone Valley Formation

(outcropping in areas south of Alachua County). Marine phosphate deposits are extensive in Florida and are comprised of land pebble deposits and hard rock deposits (Cathcart,

1980). Two apatite group minerals predominate in the phosphatic sediments: carbonate- fluorapatite (or francolite) and carbonate-hydroxylapatite (or dahlite). Weathering of both minerals introduces phosphate into ground and surface waters (Lawrence and

Upchurch, 1982).

There is evidence of natural phosphatic soil occurrence in Alachua County

(Thomas et al., 1985) but there is little documentation on their distribution and abundance. Studies on phosphatic soils have centered around their properties with little emphasis on their distribution. Carlisle et al. (1981) found phosphatic pebbles in some soils within the two-meter depth in Alachua County. The phosphatic pebbles ranged in size from 2 to 75 mm in diameter and occupied about 15% of the soil volume in certain horizons of the soil.

Total phosphorus (TP) content in the lower horizons of some of these soils was as high as 34.8 g kg-1. In soils, P can be retained by clay minerals and aluminum and iron oxides and hydroxides in addition to phosphate minerals (Tisdale et al., 1999). Harris et al. (1996) examined the relationship between phosphorus retention and the morphology of sandy coastal plain soils. Results showed that phosphorus adsorption was closely related to clay content but not to silt content. Wang et al. (1989) investigated the occurrence of phosphate minerals in some of Florida’s phosphatic soils. They found that wavellite [Al3(PO4)2(OH)3.5H2O] was the most frequent aluminum hydroxyl phosphate

in highly weathered soils, including seven Ultisols and one Alfisol. Flourapatite

[Ca5(PO4)3F] and crandallite [Ca Al3(PO4)2] were found to a lesser extent and were

restricted to horizons of minimal leaching. Phosphatic soils formed over phosphoritic

deposits have been analyzed to verify the presence of noncrystalline P and associated P

forms (Wang et al., 1991b). The total P ranged from 93 to 2,294 mg kg-1 in the top 15 to

20cm. Maximum TP was 42,842 mg kg-1 found in the Bt horizon. Total P was

determined by the Na2CO3 fusion method. Results of a study by Wang et al. (1991a) suggested that the phosphate interfered with the crystallization of Fe in phosphatic soils.

The total phosphorus content of the soil is made up of the inorganic and organic fraction. The inorganic fraction is represented by nonoccluded and occluded P forms.

The total phosphorus content is sometimes determined using the alkaline oxidation method described by Dick and Tabatabai (1977). However TP can also be measured using the ignition method (Anderson, 1976). Phosphates that are held by physical adsorption on the edges of clay minerals are considered non-occluded and those incorporated into a mineral’s surface structure are termed occluded (Walker and Syers,

1976). The effects of horizontal redistribution of soil constituents within a landscape utilizing TP data was demonstrated by Smeck and Runge (1971).

The phosphates in Florida contain varying amounts of uranium incorporated in the mineral francolite (Lane, 1994). The percentages of uranium present range from hundredths to tenths of a percent of the total mineral. The uranium isotope U238 is the

most abundant form of uranium present in Florida’s phosphates. As U238 decays

radioactively, radon (Rn222) eventually forms as one part of the decay series (Lane, 1994).

Radon, a short-lived radioactive isotope, occurs as a colorless, odorless gas that may

21 accumulate in buildings, causing potential health problems (lung cancer). Wherever the

Hawthorn Group phosphatic sediments are present near the surface, the possibility of radon problems exist (Brown et al., 1993; Lane, 1994). Therefore radon exposure risk should relate to some extent to the distribution of phosphatic soils.

Overview of Soil-Landscape Modeling

Soil-landscape modeling segregates the soil continuum into meaningful soil units and explains the spatial distribution of soil properties from local hill slopes to continental scales (Wysocki et al., 2000). Gerrard (1981) indicated that soil patterns and landscape features often coincide, and knowledge of one can be used as basis for prediction of the other. The spatial relation between soils and landforms is normally the reflection of the interaction between geomorphological and pedological processes. In landscape evolution, landforms are individually transformed and have implications on soil genesis.

Simonson (1959) described soil formation as the interaction of four processes: additions, removals, translocations, and transformations. This he called the “process model” and may be more useful than Jenny’s (1941) for comprehending the spatial relationships and dynamics within soil landscapes.

Geomorphic surfaces are the result of activities such as erosion, deposition, and tectonic activity at or near the earth surface (Hall, 1983). Beckett and Webster (1971) indicated that soils formed on transported parent materials tend to be more variable than those weathered from bedrock in situ.

Soil-landscape models can be used to describe the spatial distribution of soil properties at the landscape scale (Grunwald, 2001). Grunwald describes three types of soil-landscape models as follows

• Empiric, factorial models – These use factors such as climate, organism, topography, geology, and time to predict the spatial distribution of soils.

• Spatial models – These incorporate geostatistical methods to predict soil and landscape properties at unsampled locations within a specified area.

• Deterministic, pedo-dynamic (processs-based) - These integrate algorithms to describe soil forming processes.

A Geographic Information System (GIS) is a valuable tool for organizing, storing, manipulating, retrieving and displaying spatially related information regarding the prediction of non-point source pollution (Loague et al., 1998). GIS is increasingly being used for inventory, analysis, modeling and management of the natural environment

(Burrough, 1986). The major aims of environmental modeling are to understand the physical world and to provide a predictive tool for management.

A number of examples of GIS application to environmental modeling have been published. Rogowski (1996) used GIS analysis (geostatistics) and published data to quantify soil variability. Gessler et al. (1995) integrated terrain and soil attributes to predict and understand soil-landscape processes. The use of landform attributes derived from a digital elevation model for spatial prediction of soil properties was demonstrated by Odeh et al. (1994). Gobin et al. (2001) developed soil-landscape models to predict the spatial distribution of soil texture at the surface horizon across a catchment in Nigeria.

Moore et al. (1993), used terrain analysis to predict soil attributes based on the hypothesis that catenary soil development occurs in many landscapes in response to water movement.

In Wisconsin, GIS was used for wetland restoration and improving water quality

(Richardson and Gatti, 1999). The Universal Soil Loss Equation was used with GIS data layers of soils, cropping management, topography, hydrography, and land cover to

23 estimate the potential eroded sediment delivered to streams within the watershed.

Similarly, Baban and Yusof (2001) used remote sensing and GIS to map the land use/cover distribution on Langkawi Island, Malaysia. Field data together with available secondary data consisting of topography, land use and soil maps were used.

In Colorado, Stark et al. (1999) used GIS to identify the impact of septic-system nitrates on water quality in the upper Blue River watershed in Summit County. The authors explored relationships among soil types, geology and the results of water quality sampling. Cooper and Bottcher (1993) used GIS as a tool for watershed modeling and water resource planning. Their model allowed for the determination of areas under stress and estimated future impacts of land use management decisions. Denizman (1998) applied GIS to examine the spatial distribution of karstic depressions in the Lower

Suwannee River Basin.

Geologic, soil, and radiometric information have been used to develop radon protection maps for thirty Florida counties (Brown et al., 1993; http://www.fgdl.org/metadata_v2_v3/fgdl_2000_v3/fgdl_custom_format/radprt.htm).

Soils data were used to model radon flux, and the map predictions were compared with radon entry values. Predictions based on soils data were not significantly better than those based on geology alone.

CHAPTER 3 MATERIALS AND METHODS

The methodology used during this research can be divided into five components:

• Geospatial data collection and analysis • Selection of sampling sites • Field soil sampling • Laboratory analysis for total phosphorus and nodule mineralogy • Statistical analysis of data obtained from sampling regions

Geospatial Data Collection and Analysis

Geographic data for the project was assembled and processed using Geographic

Information System (GIS) software. The GIS component was accomplished using mainly the ArcView GIS 3.2 software but some operations were carried out using the ArcGIS 8.2 software. Both ArcView and ArcGIS were developed by the Environmental Systems

Research Institute (ESRI) at Redlands, California.

A project database was created where datasets of available geographic information were stored and organized. The project database (directory) was created with subdirectories to store metadata and shapefiles, and included a workspace folder. Other folders were created as the project developed to allow for efficient organization of new datasets.

Digital geographic data (shapefiles) for Alachua County were obtained from the

Florida Geographic Data Library (FGDL) website (http://www.fgdl.org/). FGDL is located at the GeoPlan Center, College of Design Construction and Planning, University of Florida. FGDL compiles data from a number of different sources such as the United

States Geological Service (USGS), Natural Resources Conservation Service (NRCS),

24 25

Florida Department of Transportation (FDOT) and Environmental Protection Agency

(EPA). Approximately 240 layers (themes) can be accessed at both the statewide and countywide level.

Data layers together with their metadata were downloaded from the FGDL website.

The descriptive data layer name, data source and geodataset extent is shown in Table 3-1.

A detailed description of how the datasets were downloaded and unzipped for the county and statewide themes is given in Appendix A. To accomplish some of the GIS tasks, the following ArcScripts or extensions were obtained from the ESRI website at http://www.esri.com/index.html

• Grid Converter ver.2.1. by Johannes Weigel. Grid Converter is a tool used to convert grid themes to shapes and vice versa using the legend of the grids (shapes) as classification.

• Grid Transformation Tools by ESRI. This extension is dependent on the Spatial Analyst version 1.1. The menu features include: flip, mirror, rotate, shift, merge, mosaic, combine, aggregate, and resample.

• XTools created by Mike Delaune. XTools is a package of tools used in vector spatial analysis including various overlay, shape conversion and table tools.

All FGDL data are assigned the same projection, Albers Equal Area Conic (Albers), and coordinate system. The USGS-NED DEM was originally in geographic coordinate system and reference coordinates were given in latitude/longitude. It was necessary to merge the DEM for North and Central Florida to obtain a DEM of Alachua County and reproject it into Albers. This dataset was only used for creation of preliminary maps and not for analyses. The method of creating a DEM for Alachua County is detailed in

Appendix B. The project file was stored in the workspace folder. The ArcView GIS software was initiated and a new project was opened with a new view. All shapefiles were added to the view window.

Table 3-1. Data layers, sources and extent.

Data layer Data source Geodataset extent City and county seat Florida Division of Emergency State locations Management (FDEM) City limits US Geological Survey (USGS) State FDOT major roads 1998 Florida Department of County Transportation (FDOT) Florida county boundaries US Census Bureau (USCB) County, State Specific soils – soil survey USDA - Natural Resources County geographic (SSURGO) Conservation Service (NRCS) Streams US Geological Survey and Florida County Department of Environmental Protection (FDEP) Surficial geology Florida Department of State Environmental Protection Topographic five-foot US Geological Survey County contour lines USGS 1:24,000 US Geological Survey County hydrography - polygons USGS 1:24,000 roads US Geological Survey County Land parcels Alachua County Property County Appraiser Elevation – digital elevation US Geological Survey – National State model (DEM) Elevation Dataset (NED)a aDataset not obtained from FGDL.

The map projection parameter for all shapefiles is given below

• Projection Albers Equal Area Conic • Units Meters • Datum HPGN • Spheroid GRS1980 • 1st standard parallel 24 0 0.000 • 2nd standard parallel 31 30 0.000 • Central meridian -84 0 0.000 • Latitude of projection's origin 24 0 0.000 • False easting (meters) 400000.00000 • False northing (meters) 0.00000

After adding all the themes, Geoprocessing operations were carried out on the statewide themes to obtain geospatial data specific for Alachua County. To obtain the geology of Alachua County, the Geoprocessing wizard was used to perform a clip

27 operation. A new theme showing the geology only in Alachua County was produced and added to the view window. The clip operation was also used to obtain city and city limits for Alachua County.

The attribute table that comes with the ssoils.shp file does not have the name of the soil map units. To obtain the names of the soil map unit a join operation was carried out.

The ssoil.dbf and mapunit.dbf tables (obtained with soils theme) were added to the project window and joined using Muid (map unit identification) as the common field.

In the view properties window the map units was set to meters and the distance units to kilometers. The Legend Editor/Symbol window was used to change the symbology and color of the themes. The metadata that came with the topography shapefile stated that the Contz field should be used for elevation and that non-elevation values such as 0, 777 and 900 must be excluded. The topographic attribute table was thus edited to reflect these changes.

With the corrected topography, geology and soils data, analyses were carried out to initially predict the areas with the highest probability of phosphatic soils. The sloping area along the Cody Scarp where the phosphatic Hawthorn Group is exposed by erosion was delineated. This was done by creating a new polygon theme (box) and performing a clip operation on the topography shapefile. A TIN (Triangular Irregular Network) was created from the new shapefile using “create TIN from features” in the Surface menu.

The field “Contz” was used as the height source. The TIN file was converted to a grid using an output grid cell size of 30 m. This grid file was then converted to a shapefile using the “convert grid to shape” function in the Grid Converter menu. This new shapefile allowed the selection of the desired elevation ranges. The Query Builder function was used to determine all elevations between 30.5 and 48.8 m. These elevations

28 were used to define the boundaries of the scarp. A slope map was also created from the topography grid file using “derive slope” from the Surface menu. The legend editor was used to develop slope ranges and the slope map was converted from raster to vector format to allow for GIS operations.

It was noticed that the elevation range used to delineate the scarp contained areas with little or no slope (plateaus) to the north of the county. To use a different elevation range for the northern area a new polygon theme was created. The initial range in elevation (30.5 to 48.8 m) was reduced to 30.5 to 45.7 m to better represent dissection along the scarp. This was done by selecting the elevations above 45.7 m and using the erase function from the XTools menu. However, there still remained a small percentage of non-sloping areas that prevented proper scarp delineation to the East. Thus, another polygon theme was created to use slope to define the eastern boundary of the scarp. A slope map was created from the elevation data using “Derive slope” from the Surface menu. All slopes between 0 and 2% were selected and the Erase tool was used to erase flat areas in the eastern boundary. A join operation was used to create the final scarp area using the 30.5 to 45.7 m and 30.5 to 48.8 m elevation themes. Thus, the elevation ranges were set to 30.5 to 45.7 m in the northern part of the scarp delineation and 30.5 to 48.8 m in the southern part. These elevation ranges were used because they best describe the dissected area of the scarp that is hypothesized to have the highest probability of phosphatic soils.

A slope of 0 to 2% was used to define the boundary of the scarp with the plateau, but all slopes within the delineated area were included (i.e. if there was a plateau or basin in the delineation it was included). An area to the north of the county, near the Santa Fe

River, was included to account for alluvial phosphatic soils that may have formed along

29 the river plain. This area was less than 30.5 m in elevation. This area along the river bank was joined to the scarp area. A new polygon theme was created and a clip operation carried out to define the final scarp area that was used for phosphatic soil map unit determination and calculations. The final scarp area was clipped with the soil theme to determine map units that have the highest probability of being phosphatic. The procedure for determining the scarp area that defines the highest probability of phosphatic soil occurrence is summarized in Figure 3-1.

Another area to the east of the scarp was chosen to represent an area with the highest probability of nonphosphatic soils. This was the plateau area in northeast of

Alachua County. This area was chosen because it occurred at a high elevation, was relatively flat and showed little dissection. The depth to the Hawthorn also increases as you move from the scarp to the eastern county boundary. A polygon theme was created to include the area to be used to delineate the nonphosphatic soils. Again, the topography map was clipped onto the polygon and a TIN created from the topolines. The TIN was converted into a grid and then to a shapefile. All elevations above 42.7 m were initially selected using the Query Builder. It was noticed that the elevation decreased from the middle of the county (near the scarp) to the eastern and northern boundaries. Thus, a new polygon theme was created to divide the “plateau” area, to allow for selection of elevations above 45.7 m near the scarp and above 42.7 m towards the eastern boundary.

The boundary of the scarp area was buffered to 1.6 km and used to clip the plateau area shapefile. The buffer was used to reduce the influence of phosphatic soils that may be present in the transitional area between the scarp and plateau. A slope map was created for the area and all slopes greater than 5% were erased. The latter was done to

B C

Figure 3-1. Steps in delineating a portion of the scarp hypothesized to be an area of high probability of phosphatic soils occurrence. A) Map showing slope and dissection in selected area. B) All elevations between 30.5 to 48.8 m selected. C) Flat areas (> 45.7m) removed using clip and erase. D) Area to North, near Santa Fe River is included. E) Final Scarp area selected after slope and elevation adjustments. F) Clip operation used to determine soil map units on scarp.

E D

Figure 3-1. Continued

remove dissected areas on the plateau since it is hypothesized that nonphosphatic soils will most likely be found in areas of high elevations with little or no slope. Thus, the final plateau area contained elevations greater than 42.7 and 45.7 m, however, slope was the major criterion in determining the nonphosphatic area. This final plateau area was clipped with the soils layer to determine the soil map units that are most likely nonphosphatic. The procedure for obtaining the highest probable nonphosphatic area is summarized in Figure 3-2.

The name and acreage of soil map units on the delineated scarp (phosphatic) and plateau (nonphosphatic) were obtained by exporting .dbf files for the two new soils theme into Microsoft Excel. The soil map units were then determined to be “obligate” or

“facultative” depending on their percentage occurrence within the area initially designated as having the highest probability of being phosphatic, that is, the scarp area.

The total acreage of each map unit was determined and the percentage of the map unit occurring on the scarp was calculated. If the area percentages of the map unit delineations that occur on the scarp were between 90 and 100% of the total area (scarp and plateau), the map unit was designated as obligate. Obligate map units were also required to have a total area of 20 hectares or more (Table 3-2).

If the percentage of a map unit occurring on the scarp was between 60 and 90% of its total area (scarp and plateau) or the unit met “obligate” criteria except land area, the map unit was deemed as facultative. Thus, the facultative phosphatic soils occurred in the scarp area but were also present in other areas of the county. Figure 3-3 shows the general distribution of all obligate phosphatic and all facultative phosphatic map units in the county.

Figure 3-2. Steps in delineating a portion of the plateau, hypothesized to be an area of low probability of phosphatic soils occurrence. A) Slope map showing area selected. B) Elevations above 42.7 m selected. C) Elevations above 45.7m selected (blue). D) Scarp area buffered to 1.6 km. E) Slopes > 5% removed in final plateau area. F) Soil map units determined on plateau area.

Figure 3-2. Continued

Table 3-2. Classification of soil map units into obligate (O) and facultative (F). Soil map unit (Scarp) Hectares Soil map unit (Plateau) Hectares % Unita Class Apopka sand, 0 to 5% slopes 35.7 Apopka sand, 0 to 5% slopes 70.8 33.5 Apopka sand, 5 to 8% slopes 8.0 Apopka sand, 5 to 8% slopes 0.8 90.9 F Arents, 0 to 5% slopes 8.0 Arents, 0 to 5% slopes 34.1 19.0 Arredondo fine sand, 0 to 5% slopes 2089.1 Arredondo fine sand, 0 to 5% slopes 7.6 99.6 O Arredondo fine sand, 5 to 8% slopes 308.9 100.0 O Arredondo-Urban land complex, 0 to 5% slopes 278.9 Arredondo-Urban land complex, 0 to 5% slopes 21.4 92.9 O Bivans sand, 2 to 5% slopes 267.8 100.0 O Bivans sand, 5 to 8% slopes 460.1 100.0 O Bivans sand, 8 to 12% slopes 136.5 100.0 O Blichton sand, 0 to 2% slopes 81.9 Blichton sand, 0 to 2% slopes 12.4 86.9 F Blichton sand, 2 to 5% slopes 608.7 Blichton sand, 2 to 5% slopes 4.0 99.4 O 35 Blichton sand, 5 to 8% slopes 765.4 Blichton sand, 5 to 8% slopes 2.3 99.7 O Blichton-Urban land complex, 0 to 5% slopes 34.3 Blichton-Urban land complex, 0 to 5% slopes 1.6 95.4 O Boardman loamy fine sand, 5 to 8% slopes 13.9 100.0 F Bonneau fine sand, 2 to 5% slopes 275.8 Bonneau fine sand, 2 to 5% slopes 143.5 65.8 F Candler fine sand, 0 to 5% slopes 54.3 Candler fine sand, 0 to 5% slopes 145.3 27.2 Candler fine sand, 5 to 8% slopes 11.4 100.0 F Chipley sand 404.7 Chipley sand 724.8 35.8 Floridana sand, depressional 0.2 Floridana sand, depressional 69.3 0.3 Fort Meade fine sand, 0 to 5% slopes 1072.4 100.0 O Gainesville sand, 0 to 5% slopes 1284.4 100.0 O Gainesville sand, 5 to 8% slopes 122.3 100.0 O Kanapaha sand, 0 to 5% slopes 499.5 Kanapaha sand, 0 to 5% slopes 6.3 98.8 O Kendrick sand, 2 to 5% slopes 1912.8 Kendrick sand, 2 to 5% slopes 3.1 99.8 O Kendrick sand, 5 to 8% slopes 450.0 100.0 O

Table 3-2. Continued

Soil map unit (Scarp) Hectares Soil map unit (Plateau) Hectares % Unita Class Lake fine sand, 0 to 5% slopes 101.8 100.0 O Lake sand, 0 to 5% slopes 139.7 Lake sand, 0 to 5% slopes 56.6 71.2 F Ledwith muck 10.9 0.0 Lochloosa fine sand, 0 to 2% slopes 2907.0 0.0 Lochloosa fine sand, 2 to 5% slopes 1309.6 Lochloosa fine sand, 2 to 5% slopes 172.5 88.4 F Lochloosa fine sand, 5 to 8% slopes 737.0 Lochloosa fine sand, 5 to 8% slopes 3.1 99.6 O Mascotte, Wesconnett and Surrency soils, flood 81.4 0.0 Micanopy loamy fine sand, 2 to 5% slopes 20.4 Micanopy loamy fine sand, 2 to 5% slopes 13.3 60.6 F Millhopper sand, 0 to 5% slopes 4025.0 Millhopper sand, 0 to 5% slopes 1035.5 79.5 F Millhopper sand, 5 to 8% slopes 528.1 100.0 O Millhopper-Urban land complex, 0 to 5% slopes 589.8 Millhopper-Urban land complex, 0 to 5% slopes 316.6 65.1 F Monteocha loamy sand 130.2 Monteocha loamy sand 2534.0 4.9

Mulat sand 49.2 Mulat sand 434.4 10.2 36 Myakka sand 5.9 Myakka sand 270.1 2.1 Newnan sand 205.0 Newnan sand 2201.3 8.5 Norfolk loamy fine sand, 2 to 5% slopes 486.1 Norfolk loamy fine sand, 2 to 5% slopes 7.6 98.5 O Norfolk loamy fine sand, 5 to 8% slopes 259.8 100.0 O Ocilla, Alapaha and Mandarin soils, occ. flood 15.5 100.0 F Oleno clay 240.4 100.0 O Pedro-Jonesville complex, 0 to 5% slopes 10.1 100.0 F Pelham sand 230.7 Pelham sand 1719.9 11.8 Pelham, Plummer and Mascotte soils, occ. flood 211.5 Pelham, Plummer and Mascotte soils, occ. flood 11.9 94.7 O Pickney sand, frequently flooded 98.8 100.0 O Pits and Dumps 38.0 Pits and Dumps 104.5 Placid sand, depressional 2.8 Placid sand, depressional 168.1 1.6 Plummer fine sand 40.2 Plummer fine sand 442.2 8.3

Table 3-2. Continued

Soil map unit (Scarp) Hectares Soil map unit (Plateau) Hectares % Unita Class Pomona sand 214.7 Pomona sand 7775.8 2.7 Pomona sand, depressional 14.2 Pomona sand, depressional 1119.6 1.3 Pompano sand 39.2 Pompano sand 363.6 9.7 Pottsburg sand 4.7 Pottsburg sand 178.4 2.6 Riviera sand 14.7 Riviera sand 8.8 62.5 F Samsula muck 4.1 Samsula muck 454.4 0.9 Shenks muck 86.7 0.0 Sparr fine sand 288.0 Sparr fine sand 1049.5 21.5 Surrency sand 159.9 Surrency sand 812.6 16.4 Tavares sand, 0 to 5% slopes 591.0 Tavares sand, 0 to 5% slopes 484.8 54.9 Terra Ceia muck 61.8 0.0 37 Udorthents, 0 to 2% slopes 6.6 100.0 F Urban land 53.6 Urban land 31.1 Urban land-Millhopper complex 42.4 Urban land-Millhopper complex 33.8 55.7 Water 115.6 Water 2541.9 Wauberg sand 4.7 Wauberg sand 0.9 83.7 F Wauchula sand 42.1 Wauchula sand 3380.3 1.2 Wauchula-Urban land complex 29.2 Wauchula-Urban land complex 576.8 4.8 Zolfo sand 90.9 0.0 Total area 22283.4 Total area 32790.1 Water 115.6 Water 2541.9 Land area 22167.8 Land area 30248.2 a % Unit – The acreage of an individual soil map unit found on the scarp as a percent of its total acreage (scarp and plateau).

Figure 3-3. The general distribution of obligate (A) and facultative map units (B).

The final phosphatic soils delineation was created by including all obligate

phosphatic map unit delineations and only facultative map unit delineations that share a

boundary with an obligate map unit. The latter criterion was used because the probability

of finding a phosphatic soil will increase in a facultative map unit adjacent to an obligate

map unit. Shapefiles of the obligate and facultative soil map units were created and the

"Select by theme” feature used to determine adjacency of polygons. The initial predicted

phosphatic soils map is shown in Figure 3-4.

Since we hypothesized that the concentrations of total phosphorus will be higher in

soils in the scarp area, it was decided to delineate the scarp from the limestone plain area

using elevation. Thus, all elevations less than 30.5 m west of the scarp was selected and

clipped to phosphatic soils theme to determine the phosphatic soils on the plain. The

remaining phosphatic soils on the scarp and to the east were called phosphatic soils on

the scarp. The scarp and plain area delineation is shown in Figure 3-5.

To summarize, there were three sampling unit delineations for the county:

phosphatic soils on the plain, phosphatic soils on the scarp and nonphosphatic soils.

Selection of Sampling Sites

An Avenue Script was downloaded from ESRI (http://www.esri.com/index.html) to

generate randomly distributed points in ArcView GIS. The file name of the script is

known as random_sites.zip and was produced by Stephen Lead. The script was used to

generate 970 randomly distributed points in Alachua County, which, approximated to 1

point per 1.6 km2. The X and Y locations in the point shapefile were returned in Easting and Northing (meters). The points were converted into a shapefile. Each point had a unique number (1 to 970) and reference coordinates. The acreage of the 3 sampling regions was calculated to be 250982 hectares, of which, 62032 hectares represented

Figure 3-4. Predicted phosphatic soils map (red) for Alachua County. phosphatic soils on the plain, 31003 hectares represented phosphatic soils on the scarp and 157947 hectares represented nonphosphatic soils.

The points were clipped to the three sampling regions: scarp-phosphatic (S), plain- phosphatic (P) and nonphosphatic (N). Due to differences in size of the sampling regions, the 970 random points were distributed as follows: 224 points on the plain, 111 points on the scarp and 635 points on the nonphosphatic region. The points were imported in an

Excel worksheet and 30 points were randomly chosen by a random number generate function (Randbetween) in Microsoft Excel. The intent was to sample 15 sites per region, but more points (30) were selected in anticipation that they would be required due to restricted accessibility in some areas. The decision to initially sample 15 sites in each

Figure 3-5. Elevation below 30.5 m (blue) that was delineated as the plain area. region, despite unequal size, was based on the suspicion that the largest region

(nonphosphatic) would have the least variability. It was planned that more samples be taken if variability indicated the need to do so.

The letter designation S, P, N was placed in front of each number for each unit

(category). Therefore, a shapefile of random points was produced for each sampling unit.

The next step involved importing the 3 random points shapefile into the Global

Positioning System (GPS) unit. The GPS unit used for the entire exercise was a handheld

Garmin eTrex Vista personal navigator (Garmin International Inc.; Kansas, USA). The

GPS accuracy of the Garmin is less than 15 m, and less than 3 m with Wide Area

Augmentation System (WAAS) enabled.

The 3 shapefiles was defined to match the Albers projection using ArcToolbox.

The shapefiles were then reprojected to Geographic Coordinate System

(latitude/longitude). The reprojected shapefiles were imported into ArcView. The

attribute tables for the shapefile were edited to add two new fields (latitude and

longitude). A numeric width of 16 and a decimal place of 8 were used to define the new

item (field). To calculate values, the field name was highlighted and the calculate

function in the Field menu was used. Avenue scripts, “[shape].getX” and “[shape].getY”

were used to obtain the latitude and longitude of all the points. The .dbf was then

imported into Microsoft Excel. The .dbf file was set up as a comma separate variable

(.csv) file. A new field was created in the 3 shapefiles called “study area”. The field was

edited and given a unique area name- scarp, plain and nonphosphatic. Using the

Geoprocessing wizard, the 3 shapefiles were merged to return the random sample points

for the entire county. The file was saved in .csv format and opened in notepad and saved

as a text (.txt) file.

A waypoint file was uploaded into the Garmap2 ver.1.15 software. This software

was obtained from the web at http://www.catnet.ne.jp/fukuda/garmap/e_garmap.html.

The .txt file was imported into GarMap2 as waypoints and the waypoint file was used to set up the data in a format that can be imported into the GPS. A new ID was created to allow for sample points to be in alphabetical order to allow for easier manipulation on the

GPS. The waypoints were then uploaded to the GPS unit using the datum WGS (World

Geodetic Survey) 84.

Field Soil Sampling

The field soil sampling was conducted over a two-month period (February and

March, 2003). Before going out to the field, maps (layouts) of different scales were

43 created to show the location of the sites to be sampled. The maps showed the roads and land parcels, and sampling unit boundaries. These maps were used to get to the vicinity of the site after which, the GPS unit was used to get to the exact location. If the precise location could not be accessed, the sample was taken as close as possible to the site once it was within the same sampling unit delineation. Sites that were apparently disturbed or under agricultural use were not sampled, and the next randomly selected site on the list was considered. It was preferable to obtain samples from minimally disturbed sites so as to reduce the risk of anthropogenic influence. Some of the reasons why the exact location of sites was not obtained are: proximity to highway or railroad, ranch or pasture, buildings or parking lot, high water table (temporary flooding) and fenced private property.

A mud auger was used to obtain samples at two depths: 0 to 25 cm and 100 to 125 cm. Two depths were taken to distinguish anthropogenic from natural phosphate, since

TP concentration will increase with depth in naturally phosphatic soils. The sample profiles were not described but relevant information such as land use, depth to the Bt horizon and presence/absence of nodules was recorded. Fifteen sites each were sampled in the phosphatic plain, scarp and nonphosphatic regions. The sites matched the randomly selected sites except where accessibility was a factor. The sites were georeferenced and were recorded as a waypoint with the letter designation “A” to differentiate it from the randomly selected points (e.g. N765 and N765A).

The 45 points were downloaded from the GPS unit to the Garmin software. The points were exported as a .txt file to Excel and saved as a table in .dbf format. The .dbf table was added to ArcView GIS as an Event theme in the View menu. Longitude was

44 used in the X-field and Latitude was used in the Y-field. This points theme was converted into a shapefile and reprojected into Albers projection. This is because the coordinates obtained from the GPS unit are in latitude/longitude (decimal degrees) and had to be converted to Albers projection (Easting/Northing) to overlay correctly. The Arctoolbox application in ArcGIS 8.2 was used to define a projection system for all three shapefiles and then to project to Albers. The location of the sampling sites is shown in Figure 3-6.

Laboratory Analysis for Total Phosphorus and Nodule Mineralogy

Total Phosphorus (TP) was analyzed using the ignition method by Anderson, 1976.

The soil samples were air-dried at 37.80C and hand sieved using a 2-mm mesh sieve.

One gram of soil was weighed and placed in a 50 mL beaker. It was then placed in a muffle furnace at 3500C and ignited for 1 hour and then at 5500C for 2 hours. The samples were allowed to cool overnight. 20 mL of 6M HCl was added to the sample and allowed to slowly evaporate on a hot plate. When the residue was dry the temperature was raised to dehydrate the Si. The beaker and its contents were allowed to cool and

2.25mL of 6 M HCl was added. The samples were then filtered using Whatman #42 filter paper and filtrate collected in 50 mL volumetric flasks. The contents of the beaker and filter paper were rinsed into the flask and the volume made up to 50mL mark.

Preparation of the sample for TP measurement was carried out using the method described by Murphy and Riley, (1962). The samples were developed for color for 30 minutes and the absorbance read with a photometer set at a wavelength of 720 nm.

Absorbance of standards at 0, 5, 10, 15, 20, and 25µgP/mL were recorded and regression analysis for concentration versus absorbance was used. A scatter plot graph was created and a regression line/equation obtained. The regression equation was used to calculate

Figure 3-6. Location of sampling sites in Alachua County (scarp, plain and nonphosphatic regions)

TP if the r2 value was 0.985 or higher. If the reading on a sample went higher than the top standard, a dilution was carried out. Following QA/QC principles, every 8th sample was duplicated i.e. one sample per batch.

The abundance of nodules was determined for samples collected at each site.

Mineralogical analysis was performed on selected nodules from soils with high TP content by the University of Florida Soil Mineralogy Laboratory. Nodules were ground in a ball mill. The powder was transferred to a small beaker, suspended in deionized water, and subjected to approximately 15 seconds of ultrasonic vibration. The suspension was allowed to settle for approximately 30 seconds, after which, an aliquot of the

supernatant was transferred to a low-background quartz crystal x-ray diffraction mount.

Samples were scanned with Cu Kα radiation from 2 - 400 2θ on a computer controlled x- ray diffractometer equipped with stepping motor and graphite crystal monochromator.

Statistical Analysis of Data Obtained from Sampling Regions

Basic descriptive statistics was carried out using Microsoft Excel 2000. Minimum, maximum, range, mean and standard deviation of TP readings were calculated for each sampling unit. Statistical analysis of data was executed using Minitab statistical software, release 13.32. Histograms and normal probability plots (Anderson-Darling Normality

Test) for TP values for the upper and lower sampling depth were created for the three regions to assess the normality of the distribution. Box plots were used to describe TP distribution within each region using untransformed and log transformed data.

Bartlett’s test and Levine’s test for homogeneity of variance was carried out for the untransformed and log transformed data using a 95% confidence level (Ott and

Longnecker, 2001). One-way Analysis of Variance (ANOVA) was carried out on log transformed data for upper and lower sampling depths, to observe if the hypothesis of equality of means holds. The Chi-square test was used to assess differences in proportions of phosphatic soils in the three regions. Jonckheere’s test (Lehmann and

D’Abrera, 1975) was used to test the odds of encountering a phosphatic soil in each region and looking at the odds ratio. This tested the research hypothesis that the mean TP content increases in the order “nonphosphatic” region (as predicted from the model)

CHAPTER 4 RESULTS AND DISCUSSION

Site and Soil Observations

All sites in the scarp region were forested and appeared minimally disturbed (Table

4-1). On the scarp region the vegetation at the sites included mainly pines (Pinus spp.), oaks (Quercus spp.) and other hardwoods. Most of the sites in the plain (Table 4-2) and nonphosphatic (Table 4-3) regions were also forested. The vegetation at the sites in the plain region consisted of a mix of oak and pine trees, and shrubs. The sites predicted to be nonphosphatic (then designated as “nonphosphatic”) were dominated by pine plantations and typical flatwood understory (saw palmetto – Serenoa repens, gallberry –

Illex glabra etc.).

The presence or absence of a Bt horizon was determined within the top 125cm of the soil profile. A Bt horizon was present in forty percent of sites sampled in each of the scarp, plain and nonphosphatic regions. The depth to the Bt for the scarp sites ranged from 75 to 110 cm (Table 4-1), for the plain sites from 30 to 110cm (Table 4-2), and for the nonphosphatic sites 50 to 110cm (Table 4-3). Thus, the presence of a Bt horizon is not an indicator of phosphatic soils. However, Bt horizons had higher TP concentrations than overlying sandy horizons for soils of the scarp and plain regions.

Nodules were common in the lower sampling depths of soils with high TP concentrations. Eighty percent of sites in the scarp region contained nodules in the lower depth (Table 4-1). The percentage of nodules (by weight) in each sample varied from

47 48

Table 4-1. Field observations at sites in the scarp (S) region.

Site ID Land usea Bt horizonb Nodulesc S 17A Forest - hardwoods 100cm Yes S 26A Forest - pine trees Yes S 129A Forest - hardwoods Yes S 257A Forest - mixed (pines and oaks) No S 304A Forest - hardwoods 110cm Yes S 334A Forest - hardwoods Yes S 366A Forest - pine tress Yes S 380A Forest - hardwoods Yes S 430A Forest - pine trees 80cm Yes S 628A Forest - flatwoods and shrubs 75cm Yes S 688A Shrubs and pine bromeliads 100cm Yes S 765A Forest - hardwoods 75cm Yes S 840A Forest - flatwoods No S 893A Forest - hardwoods No S 928A Forest - mixed (pines and oaks) Yes aLand use as observed on the date of soil sampling. bDepth to upper Bt horizon boundary, if it occurred within 125cm. cOccurrence of nodules.

Table 4-2. Field observations at sites in the plain (P) region.

Site ID Land usea Bt horizonb Nodulesc P 130A Forest - mix of hardwoods and few pine trees 30cm No P 133A Forest - oak with sparse undergrowth No P 171A Forest - mix of hardwoods and pines No P 188A Forest - oak No P 252A Forest - scattered hardwoods and pine No P 256A Young planted pine with few scattered cedar trees 40cm No P 290A Shrubs 80cm No P 332A Forest - hardwoods 110cm No P 412A Grass with few oak trees 110cm No P 482A Planted pine No P 749A Forest - oak and shrubs No P 764A Forest - very large oaks and shrubs Yes P 815A Forest - oak and shrubs No P 875A Forest - oak with very few pine trees 60cm No P 882A Forest - hardwoods (mainly oak) No aLand use as observed on the date of soil sampling. bDepth to upper Bt horizon boundary, if it occurred within 125cm. cOccurrence of nodules.

Table 4-3. Field observations at sites in the nonphosphatic region.

Site ID Land usea Bt horizonb Nodulesc N 157A Few oak and palmetto (partly cleared) No N 220A Forest - pine No N 275A Planted pine with sparse understory 80cm No N 320A Forest - pine No N 438A Forest - pine No N 453A Forest - mixed (pine and oak) No N 516A Bedded pine plantation with flatwood understory 110cm No N 548A Forest - mixed and shrubs 50cm No N 584A Forest - pine, saw palmetto, gallberry Yes N 620A Forest - pine plantation 110cm Yes N 700A Pine plantation with flatwood understory Yes N 741A Forest - pine 90cm No N 775A Forest - pine, saw palmetto, gallberry 70 cm No N 804A Grass (near pasture) No N 958A Forest - pine plantation No aLand use as observed on the date of soil sampling. bDepth to upper Bt horizon boundary, if it occurred within 125cm. cOccurrence of nodules.

0.7% to 47.4%. Generally, as percent nodules increase the total phosphorus concentrations increase but the relationship is not strong (R2 of 0.38). The number of sites having nodules in the lower depth was much less for the plain and nonphosphatic regions

(Tables 4-2 and 4-3).

Comparison of Total Phosphorus in the Scarp, Plain and Nonphosphatic Regions

The mean TP concentrations for the lower sampling depth (100 to 125cm) were highest for the scarp (6195 mg kg-1), intermediate for the plain (2485 mg kg-1), and lowest for the nonphosphatic region (193 mg kg-1) (Tables 4-4, 4-5, and 4-6; Figures 4-1,

4-2 and 4-3) (ANOVA; p<0.0001). This same trend was observed for the upper sampling depths. The mean TP concentration was greater in the lower than the upper sampling depth for combined data from all three regions (Figure 4-1) (ANOVA; p<0.0001).

However, the depth difference was small for the nonphosphatic region. The fact that TP

50 concentrations were not greater in the upper sampling depth compared to the lower sampling depth, even in the nonphosphatic region, is evidence that the sampling sites were minimally impacted by anthropogenic influences. The site in the nonphosphatic region closest to the scarp (N320A) had a TP value of 1395 mg kg-1 (Table 4-6), highest of the soils sampled within the region. This high concentration could be due to the proximity of the site to the scarp where the phosphatic geologic material is still influencing the overlying soils.

The hypothesis that the average phosphorus concentration would increase in the order “nonphosphatic” region < plain phosphatic region < scarp phosphatic region was supported by the Jonckheere’s test for ordered alternatives (p = 0.0023).

For the purposes of this discussion, soils with ≥ 1000 mg kg-1 in the lower sampling zone will be referred to as “phosphatic soils”. Based on this criterion, 73 percent of the soils sampled in the scarp region were phosphatic, whereas 33 percent and 7 percent of soils sampled on the plain and “nonphosphatic” region were phosphatic, respectively.

These differences in abundance of phosphatic soils between regions were statistically significant (Chi-square test; p < 0.001). The odds of finding a phosphatic soil were highest for the scarp region and lowest for the nonphosphatic region (Figure 4-4).

The high TP concentrations on the scarp indicate the influence of the phosphate- rich geologic material as exposed at or near the land surface by erosion and hence within the zone of soil formation. Phosphatic geologic material is not mapped on the plain region, yet high levels of natural phosphate exist locally. This can be explained by factors such as sea level fluctuations that eroded phosphatic geological material but left pockets of residual or re-worked phosphate-rich materials on the karst plain.

Table 4-4. Comparison of total P values with soil and geologic map units for the upper and lower sampling depths for the scarp region.

Site ID Soil map unit Soil orderc Geologic Total P (mg kg-1) map unit Upper Lower S17A Millhopper sand; 0 to 5% Ultisols Thca 274 26216 slopes S26A Kendrick sand; 2 to 5% Ultisols Thc 2993 3241 slopes S129A Blichton sand; 5 to 8% slopes Ultisols Thc 760 902

S257A Lake sand; 2 to 5% slopes Entisols Tob 84 106 S304A Millhopper sand; 0 to 5% Ultisols Thc 4924 7175 slopes S334A Millhopper sand; 0 to 5% Ultisols Thc 783 1308 slopes S366A Blichton sand; 5 to 8% slopes Ultisols Thc 3442 4707 S380A Millhopper sand; 0 to 5% Ultisols Thc 2308 3124 slopes S430A Lochloosa fine sand; 2 to 5% Ultisols Thc 2406 11366 slopes S628A Gainesville sand; 0 to 5% Entisols Thc 605 6835 slopes S688A Blichton sand; 2 to 5% slopes Ultisols Thc 553 10882 S765A Lochloosa fine sand; 2 to 5% Ultisols Thc 461 1963 slopes S840A Millhopper sand; 0 to 5% Ultisols Thc 521 456 slopes S893A Millhopper sand; 0 to 5% Ultisols Thc 258 149 slopes S928A Lochloosa fine sand; 5 to 8% Ultisols Thc 2037 14503 slopes Mean 1494 6195 Standard deviation 1448 7155 Maximum 4924 26216 Minimum 84 106 Number of sites with TP > 1000 mg kg-1 6 11 Number of sites with TP > 2000 mg kg-1 6 9 aCoosawhatchie Formation bOcala Limestone cThe soil order is given for the series in the map unit name, since sites were not sampled to a depth sufficient for definitive site-specific classification.

Table 4-5. Comparison of total P values with soil and geologic map units for the upper and lower sampling depths for the plain region.

Site ID Soil map unit Soil orderb Geologic Total P (mg kg-1) map unit Upper Lower P130A Bonneau fine sand; 2 to 5% Ultisols Toa 1519 23492 slopes P133A Arredondo fine sand; 0 to 5% Ultisols To 133 43 slopes P171A Arredondo fine sand; 0 to 5% Ultisols To 230 62 slopes P188A Lake fine sand; 0 to 5 % Entisols To 174 53 slopes P252A Arredondo fine sand; 0 to 5% Ultisols To 103 96 slopes P256A Arredondo fine sand; 0 to 5% Ultisols To 395 2220 slopes P290A Arredondo fine sand; 0 to 5% Ultisols To 627 2696 slopes P332A Arredondo fine sand; 0 to 5% Ultisols To 252 953 slopes P412A Bonneau fine sand; 2 to 5% Ultisols To 423 3226 slopes P482A Arredondo fine sand; 0 to 5% Ultisols To 112 25 slopes P749A Millhopper sand; 0 to 5% Ultisols To 1611 791 slopes P764A Kendrick sand; 2 to 5% Ultisols To 3336 2503 slopes P815A Lake fine sand; 0 to 5 % Entisols To 475 266 slopes P875A Bonneau fine sand; 2 to 5% Ultisols To 119 795 slopes P882A Arredondo fine sand; 0 to 5% Ultisols To 105 57 slopes Mean 641 2485 Standard deviation 888 5918 Maximum 3336 23492 Minimum 103 25 Number of sites with TP > 1000 mg kg-1 3 5 Number of sites with TP > 2000 mg kg-1 1 5 aOcala Limestone bThe soil order is given for the series in the map unit name, since sites were not sampled to a depth sufficient for definitive site-specific classification.

Table 4-6. Comparison of total P values with soil and geologic map units for the upper and lower sampling depths for the nonphosphatic region.

Site ID Soil map unit Soil ordere Geologic Total P (mg kg-1) map unit Upper Lower N157A Sparr fine sand Ultisols Thca 82 208 N220A Candler fine sand, 0 to 5 % Entisols Tob 443 163 slopes N275A Lochloosa fine sand, 0 to 2 Ultisols Thc 185 214 % slopes N320A Chipley sand Entisols Thc 280 1395 N438A Newnan sand Spodosols Thc 23 18 N453A Tavares sand, 0 to 5% Entisols Thc 370 208 slopes N516A Newnan sand Spodosols TQuc 41 99 N548A Pelham sand Ultisols Thc 59 48 N584A Plummer fine sand Ultisols Thc 58 103 N620A Lochloosa fine sand, 0 to 2 Ultisols Tcd 70 36 % slopes N700A Pomona sand Spodosols TQu 43 162 N741A Lochloosa fine sand, 0 to 2 Ultisols Tc 344 101 % slopes N775A Pomona sand Spodosols Thc 58 82 N804A Newnan sand Spodosols TQu 121 33 N958A Pomona sand Spodosols TQu 46 26 Mean 148 193 Standard deviation 141 340 Maximum 443 1395 Minimum 23 18 Number of sites with TP > 1000 mg kg-1 0 1 Number of sites with TP > 2000 mg kg-1 0 0 aCoosawhatchie Formation bOcala Limestone cUndifferentiated Tertiary/Quaternary Sediments dCypresshead Formation eThe soil order is given for the series in the map unit name, since sites were not sampled to a depth sufficient for definitive site-specific classification.

A * Outlier Upper depth (0-25cm) • Mean 5000

4000

TP 3000 mg/kg 2000

1000

Nonphosphatic Plain Scarp

B * Outlier Lower depth (100-125cm) • Mean

20000 TP mg/kg

10000

Nonphosphatic Plain Scarp

Figure 4-1. Total P concentrations for upper (A) and lower depths (B) for the three regions before log transformation. The upper and lower boundaries define the interquartile range and the line within the box represents the median.

A • Mean Upper depth (0-25cm) 9

7 Ln TP mg/kg 6

3 Nonphosphatic Plain Scarp

B • Mean Lower depth (100-125cm)

Ln TP 7 mg/kg 6

Nonphosphatic Scarp Plain

Figure 4-2. Total P concentrations for upper (A) and lower depths (B) for the three regions after log transformation. The upper and lower boundaries define the interquartile range and the line within the box represents the median.

Ln TP 7 mg/kg 6 56 5

Upper Lower Upper Lower Upper Lower Nonphosphatic Plain Scarp

Figure 4-3. Comparison of TP concentrations for the upper (0 to 25 cm) and lower (100 to125 cm) sampling depths for the nonphosphatic, plain and scarp regions. The mean is represented by a dot and the median by a line in the box.

2.75

2.5

2 s d 1.5 Od

Odds ratio = 5.5 Odds ratio = 7.1

0.5 0.50

0.07 0 Non-Phosphatic Plain Scarp

Figure 4-4. Line graph showing the odds of finding a phosphatic soil in the nonphosphatic, plain and scarp regions.

Phosphate Distribution as Related to Soil, Geologic and Topographic Attributes

Soils

Forty percent of the soil map units sampled in the scarp phosphatic region was

Millhopper sand, 0 to 5% slopes (Table 4-4). Next in abundance were the Blichton sand,

5 to 8% slopes and the Lochloosa fine sand, 2 to 5% slopes (Table 4-4). Ultisols dominated this region (13 out of 15 sites) with Entisols occurring at two sites. There was a wide range in TP values for some soil map units. For example, Millhopper sand, 0 to

5% slope had a TP value of 149 mg kg-1 at one site (S893A) and 26216 mg kg-1 at another (S17A). This is consistent with the fact that phosphatic parent material was not used as an explicit criterion for the mapping of soils in Alachua County (Brown et al,

1993).

Sites in the plain phosphatic region were dominated by the map unit Arredondo

fine sand, 0 to 5% slopes (Table 4-5), with Bonneau fine sand, 2 to 5% slopes, being the

next most frequently sampled unit. Again, no consistent relations between map unit and

TP concentrations were found. Ultisols were also the prevalent soils in the plain region

(13 out of 15 sites) with Entisols occurring at the other two sites.

The soil map units Lochloosa fine sand, 0 to 2% slopes, Newnan sand, and Pomona

sand occurred at 20% each for sites sampled in the “nonphosphatic” region (Table 4-6).

The “nonphosphatic” region had the widest variety of soil map units and this can be due

to the large acreage distributed throughout the county. Forty percent of the sites in the

nonphosphatic region were Spodosols. This soil order was absent at sites in the scarp and

plain regions, which, leads to the inference that most Spodosols in Alachua County are

nonphosphatic soils. Forty percent of the sites were Ultisols and twenty percent were

Entisols. Thus the latter two soil orders are found distributed throughout the county.

Mineralogical analysis on selected nodules from soils with TP concentrations of

902 to 10882 mg kg-1 revealed the presence of the phosphate mineral wavellite

[Al3(PO4)2(OH)3.5H2O] (Figure 4-5). The presence of an aluminum phosphate mineral

and high and increasing phosphate concentrations with depth indicate that the soils are

naturally phosphatic. Wavellite is a common weathering product of calcium phosphate

minerals in weathering zones of phosphatic geologic materials throughout the world

(Flicoteau and Lucas, 1984), but is not commonly found in soils where P is elevated from

anthropogenic influences (Pierzynski et al., 1990). Detection of wavellite in samples

with a TP content as low as 902 mg kg-1 substantiates reasoning for using 1000 mg kg-1

TP as the baseline for defining a phosphatic soil.

Figure 4-5. X-ray diffraction patterns of ground nodule material from samples with high TP content. The phosphate mineral wavellite is indicated by “W” and other minerals include quartz (Q) and kaolinite (K). Samples represented (top to bottom) are S129A, S26A, S304A, S366A, S380A, S688A, P764A and S765A.

Geology

All of the scarp phosphatic sites except one were located over mapped phosphatic geologic material (Coosawhatchie Formation) (Table 4-4). The potential for phosphatic geologic units to affect overlying soils depends on its depth from the surface and susceptibility to erosion. Erosion would be expected to be significant along the scarp.

All sites in the plain phosphatic region were in a nonphosphatic geologic map unit, the Ocala Limestone (Table 4-5). However, high TP levels were found at five of the

sites. This confirms the hypothesis that phosphatic soils can be found in areas where the

underlying geology is mapped as nonphosphatic. It also confirms the findings of other

authors (Simonson, 1959; Beckett and Webster, 1971; Gerard 1981) that landscape

features and evolutions have implications on soil genesis and spatial properties.

The sites in the nonphosphatic region contained the widest variety of underlying

geologic units. Of much interest is the fact that more than half of the sites sampled (8 out

of 15) was within the phosphate-rich geologic map unit (Coosawhatchie Fm, Hawthorn

Group) (Table 4-6). All of these sites except one had low TP values (<250 mg kg-1). This confirms that the depth to the geologic material must be taken into account in predicting its influence on soils.

Generally, as one moves from the scarp towards the eastern boundary of the county there is an increase in depth to the Hawthorn Group. Thus, even if the area is mapped geologically as phosphatic material, the latter is too deep to influence the overlying soils.

The clay enriched geologic material also acts as a barrier for water movement and is responsible for high water tables and prevalence of Spodosols on the “flatwoods” of the

Northern Highlands Plateau, (which, this study confirms to have low probability of phosphatic soil occurrence). This Spodosol-landscape relation explains why Spodosols in

Alachua County tend to be nonphosphatic.

Topography

The scarp region is an erosional landscape with a mean elevation of 41.3 m and a mean slope of 7.6% (Table 4-7). Therefore, near surface exposure of the phosphatic geologic material is expected, and consistent with high TP values in soils. The mean elevation for the scarp sites was much greater than the plain and comparable to the

Table 4-7. Topographic attributes of sample sites in the scarp (S), plain (P) and nonphosphatic (N) regions.

Scarp Plain “Nonphosphatic” Site Elevation Slope Site Elevation Slope Site Elevation Slope (m) (%) (m) (%) (m) (%) S17A 42.9 7.4 P130A 28.0 1.4 N 157A 27.4 1.7 S26A 46.1 6.3 P133A 24.1 0.4 N 220A 28.7 2.2 S129A 56.1 4.7 P171A 22.0 2.2 N 275A 54.3 1.0 S257A 18.3 4.9 P188A 18.2 8.0 N 320A 53.1 0.6 S304A 38.6 4.5 P252A 20.8 3.1 N 438A 36.9 1.5 S334A 44.8 7.2 P256A 24.2 0.6 N 453A 24.7 0.0 S366A 52.1 2.0 P290A 23.8 3.3 N 516A 50.0 0.1 S380A 45.2 17.0 P332A 21.5 3.1 N 548A 48.2 0.6 S430A 47.5 1.5 P412A 28.5 1.3 N 584A 29.0 0.3 S628A 41.6 9.6 P482A 19.1 4.1 N 620A 41.8 2.5 S688A 43.9 14.9 P749A 28.8 0.5 N 700A 26.7 1.0 S765A 41.3 6.7 P764A 28.6 3.5 N 741A 39.5 5.0 S840A 48.7 1.9 P815A 26.2 1.7 N 775A 46.7 0.7 S893A 17.0 13.3 P875A 20.5 1.2 N 804A 47.4 1.4 S928A 35.3 11.7 P882A 25.4 2.2 N 958A 45.8 0.7 Mean 41.3 7.6 Mean 24.0 2.4 Mean 40.0 1.3

“nonphosphatic” sites. Slopes were also much greater for the scarp sites than for the plain

and “nonphosphatic” sites.

The mean elevation for sites on the plain was 24 m above sea level (Table 4-7).

This confirms findings by Williams et al. (1977) that most of the karst plain was between

21.3 to 24.4 m above sea level. The mean slope was 2.4% for sites, which, is much lower

than the scarp sites but higher than the “nonphosphatic” sites. Due to the lower elevation

of the plain region there is the possibility for translocation of phosphatic materials from

the scarp region.

The mean elevation for “nonphosphatic” sites was 40 m above sea level (Table 4-7).

This elevation is representative of sites that were taken both on the plateau and plain.

The mean slope was 1.3%, indicating that sites were fairly flat and less prone to erosion.

Final Phosphatic Soils Map

Field validation of the predicted soils map showed that the number of sites having phosphatic soils decreased from the scarp, to the plain, to the nonphosphatic region

(Table 4-8). The preliminary phosphatic soil map was replotted to distinguish between the plain phosphatic region and scarp phosphatic region, which, differed significantly in phosphatic soil occurrence. This final map reflects the differences in probability of finding a phosphatic soil among the three regions in the county (Figure 4-6).

Examination of the sites for the scarp reveals that the sites with low TP concentrations were not found in dissected terrain and either at elevations less than 22.9 m (S257A and S893A) or elevations greater than 54.9 m (S129A). Also, all of the low phosphorus sites on the scarp were sampled on facultative soil map units. Sites S257A and S893A were located nearest to the Santa Fe River but not on the alluvial flood plains.

Since none of the random sampling sites occurred on the flood plains near the Santa Fe river, we missed the opportunity to determine their TP concentrations. The distribution of phosphatic soils on the plain followed a random pattern with pockets of phosphatic soils scattered throughout. In the nonphosphatic region, only the site closest to the scarp

(N320A) had elevated phosphate level.

The final map resembles an Alachua County radon protection map

(http://www.doh.state.fl.us/environment/facility/radon/maps/resbalac.htm), but some significant deviations are evident. Radon potential may in some areas be influenced by sources deeper than the standard lower boundary of soils (2 m). Discrepancies could be investigated to determine whether distinctions are real, since mapping phosphatic soils and radon potential are separate objectives.

Table 4-8. Estimated percentage and acreage of “phosphatic soils”a based on data from this study.

Region Acreage Observed probability Confidence Predicted acreage of (hectares) of phosphatic soils limits “phosphatic soils”b Scarp 31003 73.3 ± 22.3 22735 Plain 62032 33.3 ± 23.9 20677 Nonphosphatic 141345 6.7 ± 12.5 9423 aDefined here as soils with ≥ 1000 mg kg-1 TP at the 100 to125cm depth. b(Observed probability) multiplied by (total acreage).

Figure 4-6. Predicted phosphatic soils map showing areas of high, medium and low probability of finding a phosphatic soil.

CHAPTER 5 CONCLUSIONS

Geologic, topographic and soils data were successfully integrated using GIS to predict phosphatic soil distribution. The delineation of the scarp and plateau area together with the classification of phosphatic soil map units into obligate and facultative enabled the delineation of three regions for Alachua County that differ significantly in TP and phosphatic soil occurrence. All phosphatic soils analyzed showed an increase in TP with depth, but most nonphosphatic soils had comparable TP concentration with depth. The lack of a tendency of TP to decrease with depth is evidence (in addition to field observations) that high phosphorus concentrations are not attributable to anthropogenic influences. Since sites were mostly forested and minimally disturbed, up-to-date data on

TP concentrations were obtain for nonphosphatic and phosphatic soils under natural conditions.

This study suggests that a phosphatic soil can be defined as a soil containing ≥

1000 mg kg-1 TP at the 100 to 125cm sampling depth, with an increasing TP trend with depth. The presence of the highly weathered aluminum phosphate mineral wavellite, in soils with TP concentrations as low as 902 mg kg-1 provides further justification for using 1000 mg kg-1 TP as the basal level for a phosphatic soil.

Geologic maps alone do not enable accurate prediction of phosphatic soil occurrence. Depth to the geologic material and landscape features such as dissection and erosion must be considered. Knowledge of historical events responsible for the formation and litholigies of the landform is also important. This study documented that

65 66 nonphosphatic soils can occur over areas mapped geologically as phosphatic while phosphatic soils can occur in localized phosphatic areas that are too small to be geologically mapped. Also, phosphatic soils are not specific to any physiographic division.

This study provides a conceptual approach and protocol that might be used for further studies in predicting probability of phosphatic soil occurrence in other counties.

The model may not be applicable verbatim but the components and principles can probably be transferred and applied successfully to other areas. This study did not focus on sampling of individual map units, and data for individual map units are not conclusive.

In hindsight, the precision of the map and probabilities of finding a phosphatic soil in the three areas might be improved by not including the alluvial plain near the Santa Fe River as part of the scarp delineation. Alluvial phosphatic soils might better be represented by studying these areas independently. Also, since one nonphosphatic soil was obtained east of the scarp phosphatic region, further studies may be required to define the eastern boundary of phosphatic soils along the scarp.

There is reasonably close correspondence between the final phosphatic soil map

(Figure 4-6) and a radon protection map for Alachua County

(http://www.doh.state.fl.us/environment/facility/radon/maps/resbalac.htm). However, disparities occur, particularly in the plateau area where radon risk is shown as moderate but probability of phosphatic soil occurrence is predicted to be low. Possibly, radon risks could be elevated in areas where radon sources are below the 2-m depth used for soil assessment.

APPENDIX A PROCEDURES USED FOR OBTAINING GEOGRAPHIC DATA

How to obtain geospatial data for statewide themes

• Access the FGDL website at http://www.fgdl.org/.

• Click on “Download data for free”

• Read the “Legal disclaimer for FGDL data”. Click O.K.

• Select Data from “Statewide themes”.

• Select a dataset from the “select state map themes” e.g. Surficial Geology.

• Click on “submit selections for download”.

• The geospatial data and metadata appear on the right of the window for the theme.

• Double click on the geospatial data icon. A file download window appears. Choose “save this file to disk”. Click O.K. Browse to the folder where you want to save the data.

• Save geospatial data in shapefiles folder for ease of reference. Click Save.

• Double click on the metadata icon. Repeat step (h) and save metadata in the metadata folder. Click Save.

How to obtain geospatial data for county themes

• Access the FGDL website at http://www.fgdl.org/.

• Click on “Download data for free”

• Read the “Legal disclaimer for FGDL data”. Click O.K.

• Select Data from “Countywide themes”.

• A page with 4 steps appear:

Step 1: Select Map Themes (highlight) e.g. County Boundaries.

Step 2: Select Counties from List (highlight) e.g. Alachua

67 68

Step 3: Add Selections to List (click). The county and map theme appear in a window on the right side of the page.

Step 4: Submit Selections for Download (click). The geospatial data and metadata for “Alachua County Boundaries” appear on page.

• Double click on the geospatial data icon. A file download window appears. Choose “save this file to disk”. Click O.K. Browse to the folder where you want to save the data.

• Save geospatial data in shapefiles folder for ease of reference. Click Save.

• Double click on the metadata icon. Repeat step (f) and save metadata in the metadata folder. Click Save.

How to unzip files. This was done using the Power Archiver ver. 6.1.1 software.

• Metadata – Open the folder containing zipped files. Select a file and double click. A window opens with .htm file e.g. surgeo.htm. Select the file and click Extract. The Extract window opens. Extract to the desired folder in the project database. e.g. H:\Users\Alachua\metadata. Click the Extract button. Close the window. Repeat for all zipped metadata files

• Shapefiles – Open the folder containing zipped files. Select a file and double click. A window opens with geospatial files e.g. .shp, .shx, .dbf. Select all the files using the Shift key. Click Extract. The Extract window opens. Extract to the desired folder in the project database. e.g. H:\Users\Alachua\shapefiles. Click the Extract button. Close the window. Repeat for all zipped geospatial data files.

APPENDIX B CREATING A DEM FOR ALACHUA COUNTY

How to create a DEM for Alachua County

• The USGS-NED DEM for the state of Florida can be sourced from the website http://gisdata.usgs.net/NED/default.asp

• The DEM is divided into North, Central and South Florida. Portions of Alachua County exist in the North and Central DEMs.

• Use the software ArcGIS 8.2. Open ArcMap and add the North and Central DEM to an empty view. The DEM is displayed in geographic coordinate system.

• Use ArcToolbox and reproject the Alachua County shapefile from Albers Equal Area Conic (Albers) Projection to Geographic.

• Convert the Alachua shapefile to a grid (raster format) using the Grid Converter tool. Use a cell size of 30 m.

• Use the Raster Calculator in Spatial Analyst to “add” each DEM independently to the Alachua County Grid.

• Open ArcView 3.2. Add the Alachua north and central DEMs. Use Merge in the Grid Transform tools to obtain a DEM showing full coverage of Alachua County.

• Use ArcToolbox to reproject the Alachua County DEM into Albers projection.

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BIOGRAPHICAL SKETCH

Ravindra Ramnarine was born in Trinidad, a small island in the Caribbean. He developed a keen interest in science at his high school, St. Stephen’s College, where he was awarded a National Scholarship to pursue a 2-year diploma course in agriculture at the Eastern Caribbean Institute of Agriculture and Forestry (ECIAF). He graduated from

ECIAF with honors and received many awards including the student with the highest academic standing. The latter earned him the prestigious President’s Medal and another scholarship to the University of the West Indies (UWI), Trinidad. He graduated from

UWI with a Bachelor of Science degree in agriculture, earning Upper Second Class

Honors. From 1993 to 1995 and from 1998 to 2001, Ravindra was employed as an

Agricultural Assistant I in the Soil and Land Capability Section, Research Division,

Ministry of Agriculture. In 2001, he was granted a USDA assistantship to pursue a

Master of Science degree in the Soil and Water Science Department at the University of

Florida (UF).

At UF, Ravindra concentrated on routine academic studies and also became a member in a number of professional societies and clubs (Soil Science Society of

America, Soil and Water Conservation Society, Florida Association of Environmental

Soil Scientists, Agronomy-Soils club, Alpha Zeta, Gamma Sigma Delta, and Gator Table

Tennis club). His thrust area was Soil-Landscape Analysis with his concentration areas being Pedology and Geographic Information Systems. He was also awarded the Victor

W. Carlisle Fellowship for outstanding research in Environmental Pedology.