Contemporary Advancements in Soil Characterization: Geochemical, Morphological, and Spectroscopic Approaches

Autumn Acree, M.S.

A Dissertation

Plant and Soil Science

Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of

DOCTOR OF PHILOSOPHY

Approved

David Weindorf Chair of Committee

Matthew Siebecker

Katie Lewis

John Galbraith

Nic Jelinski

Mark Sheridan Dean of the Graduate School

May, 2020

ACKNOWLEDGMENTS I would like to thank those who have contributed to my Ph.D. and to achieving this stage in my life. First, I would like to thank the Ed and Linda Whitacre

Presidential Graduate Fellowship for providing resources and financial support to be able to attend Texas Tech University and conduct my research. I would like to thank my adviser, committee chair, and friend, Dr. David Weindorf. Dr. Weindorf’s mentorship, drive, and passion is unparalleled. I would also like to thank my committee member, Drs. Lewis, Siebecker, Galbraith, and Jelinski for their support and guidance throughout my research. Drs. Lewis and Siebecker were amazing mentors at Texas Tech. Drs. Galbraith and Jelinski provided support through fieldwork in northern Alaska and external mentorship. I am also thankful for Dr. Erica

Irlbeck for agreeing to serve as my graduate dean’s representative.

I would also like to extend my dearest gratitude to my fellow graduate students, undergraduate student workers, department head Dr. Ritchie, and all faculty and staff in Plant and Soil Science for their support. I am also very thankful for the collaborators throughout my research. Dr. Laura Paulette was a tremendous collaborator in Romania and is a great friend. I enjoyed working with Dr. Paulette in the Transylvanian Plain, Romania and in northern Alaska. Dr. Titus Man was also an amazing collaborator in Romania with his help in remotely sensed imagery. Drs. Ping and Clark were vital collaborators for my research in Alaska with their extensive knowledge of arctic pedology. I would also like to thank Dr. Natasja van Gestel for her overwhelming support throughout my research.

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Last but not least, I would like to thank my family for their unwavering support throughout my life. I would like to thank Brad for being the best fiancé and support system. I would like to thank my late grandmother Nellie Curington (MeMe) for being my best friend; I love and miss you dearly.

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TABLE OF CONTENTS ACKNOWLEDGMENTS ...... IIii

ABSTRACT ...... VIiv

LIST OF TABLES ...... VIIIviii

LIST OF FIGURES ...... IXix I. INTRODUCTION ...... 1

History of Soil Classification ...... 1 Advancements in Soil Characterization ...... 6 References ...... 9 II. COMPARATIVE GEOCHEMISTRY OF URBAN AND RURAL PLAYAS IN THE SOUTHERN HIGH PLAINS ...... 12

Abstract ...... 12 Introduction ...... 13 Materials and Methods ...... 16 General Occurrence and Features ...... 16 Field Sampling and Characterization ...... 17 Statistical Analysis ...... 20 Results and Discussion ...... 20 Particle Size Analysis and Mineralogy ...... 20 Soil Organic Matter ...... 26 Soil pH ...... 27 Soil Electrical Conductivity ...... 29 Soil Elemental Data ...... 30 Soil Property Interactions and Statistical Classification ...... 34 Conclusions ...... 42 Acknowledgments ...... 43 References ...... 44 III. CHARACTERIZATION OF GELOLLS IN NORTHERN ALASKA, USA ……………………………………………………………………..… 49

Abstract ...... 49 Introduction ...... 51 Materials and Methods ...... 52

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General Occurrence and Features ...... 52 Field Characterization and Sampling ...... 57 Laboratory Characterization ...... 58 Results and Discussion ...... 59 Cumulic Haplogelolls (Established) ...... 62 Typic Haplogelolls (Established) ...... 64 Fluventic Haplogelolls (Proposed) ...... 67 Pachic Haplogelolls (Proposed) ...... 70 Genetic Model for Gelolls in the Brooks Range and Arctic Foothills ...... 73 C Stock Modeling Implications ...... 74 Conclusions ...... 75 Acknowledgments ...... 76 References ...... 77 IV. SOIL CLASSIFICATION IN ROMANIAN CATENAS VIA ADVANCED PROXIMAL SENSORS ...... 80

Abstract ...... 80 Introduction ...... 82 Materials and Methods ...... 85 General Occurrence and Features ...... 85 Field Characterization and Sampling ...... 87 Laboratory Characterization ...... 90 Statistical Analysis ...... 91 Results and Discussion ...... 93 General Soil Physicochemical Properties ...... 93 Proximal Sensor Model Performance ...... 93 Practical Applications ...... 100 Future Considerations ...... 104 Conclusions ...... 105 Acknowledgments ...... 106 References ...... 107 CONCLUSIONS ...... 112

APPENDIX ...... 114

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ABSTRACT In recent decades, Pedology has advanced from a mostly qualitative method to a more quantitative approach of describing and classifying soil. Complimenting traditional morphological description, the integration of proximal and remote sensors allow modern Pedologists to make laboratory quality readings directly in seconds.

However, U.S Soil Taxonomy (USST) was last updated in 1999. Thus, contemporary technological advancements are not currently established methods of classifying soils.

Proximal and remote sensors provide voluminous data at minimal cost, which has driven a shift towards quantitative pedology. With the new edition of USST expected in 2022, new approaches are needed to insert contemporary technological advancements into the quantitative USST environment. This research encompasses three different examples of updates needed for the new edition of USST with deference to integration of proximal sensors and novel taxa previously never proposed. Study #1 compared the soil properties of urban and rural playas in the Southern High Plains

(SHP) using portable X-ray fluorescence (PXRF) spectroscopy. Playas in the SHP collect runoff and provide habitat for migratory waterfowl. Evaluating playa chemical composition provided information to optimize management for ecological functionality. Rural playas featured higher concentrations of Pb, Cr, As, Zn, Cu, and

Ni than urban playas, necessitating different management. Further, clay content of urban playas fell below Vertisol requirements, justifying taxadjuncts or alternate classification with implications for differential playa hydrology. Study #2 established new taxa in northern Alaska. The vast majority of Alaska has not been mapped due to rugged terrain and harsh environment. This study morphologically described and

vi Texas Tech University, Autumn Acree, May 2020 classified ten Gelolls (cold, dark, carbon-rich, fertile soils) previously mapped on only

12.5 ha in Alaska before my research project. Results indicated that more subgroups need to be added to future editions of USST to more appropriately describe Gelolls. A novel concept of Geloll formation was developed inclusive of limestone, calcium rich geology, sufficient drainage, and high coarse fragments (rock) content. Using remote sensing technology and this concept, an estimated 3,100,000 ha of Gelolls in Alaska were identified; therefore, most Gelolls in Alaska have not yet been mapped. Based on this research, the USST working group has expressed interest in using this research to advocate for new additions to the forthcoming version of USST. Study #3 used PXRF and visible near infrared (VisNIR) spectrometry to detect and quantify calcic horizon depth from the mineral soil surface to make soil taxonomic determinations in rural

Romania without traditional laboratory methods. PXRF in isolation was adept at identifying calcic horizons directly; combining PXRF + VisNIR data, model stability was improved, but predictive accuracy remained the same. The U.S. Army Engineer

Research and Development Center (ERDC) and US Department of Agriculture Soil

Survey Staff have both expressed interest in the findings of this study, especially as related to soil taxonomy combined with proximal sensors. These advancements are revolutionizing soil science and classification.

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LIST OF TABLES 2.1. Physicochemical properties of rural and urban playa surface soils (0-5 cm) in Lubbock County, Texas...... 23

2.2. Comparative soil pH and electrical conductivity (EC) data for surface soil horizons of playas on the Southern High Plains of Texas (Soil Survey Staff, 2018b)...... 28

2.3. Elemental data produced by portable X-ray fluorescence spectroscopy for rural and urban playa surface soils (0-5 cm) in Lubbock County, Texas. Unless otherwise noted, all values are in mg kg-1...... 31

2.4. Pearson correlation matrix for physicochemical properties of surface (0- 5 cm) soils in rural playas of Lubbock County, Texas...... 36

2.5. Pearson correlation matrix for physicochemical properties of surface (0- 5 cm) soils in urban playas of Lubbock County, Texas...... 37

2.6. Confusion matrix showing random forest-based classification of playas of Lubbock County, Texas. The shaded cells represent the number of correctly classified samples...... 41

3.1. Soil profile characteristics and physicochemical data for ten pedons in northern Alaska, USA. Bold horizon nomenclature indicates the presence of coarse fragment pendants...... 60

3.2. Soil profile characteristics and physicochemical data for ten pedons in northern Alaska, USA. Bold horizon nomenclature indicates the presence of coarse fragment pendants...... 61

4.1. Fullval model performance statistics relating proximal sensor data to laboratory derived CaCO3 percentage for soils of Cluj County, Romania. ... 97

4.2. Coreval model performance statistics relating proximal sensor data to laboratory derived CaCO3 percentage for soils of Cluj County, Romania. ... 98

A1. Physicochemical properties of 10 cm soil layers from nineteen chernozem catena cores in Cluj County, Romania...... 114

A2. Physicochemical properties of 10 cm soil layers from six phaeozem catena cores in Cluj County, Romania...... 120

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LIST OF FIGURES 2.1. Location of the study sites in Lubbock County, TX, USA. Sites 1-5 represent rural playas. Sites 6-10 represent urban playas. Site 11 represents a hybrid playa...... 18

2.2. Particle size analysis of 225 surface soil (0-5 cm) from playa and urban lakes in Lubbock County, TX, USA...... 22

2.3. X-ray diffraction (XRD) analysis revealing a) bulk mineralogy of a rural playa, b) clay separation of same playa, and c) bulk mineralogy of an urban lake in Lubbock County, TX, USA...... 25

2.4. Principal components analysis screeplot indicating the relative importance of each principal component for distinguishing rural versus urban playas in Lubbock County, TX, USA...... 38

2.5. Principal component (PC) analysis scoreplot visualizing playa separation (urban vs. rural) in PC space for soils from Lubbock County, TX, USA...... 39

2.6. Principal component analysis variable correlation plot illustrating clustering of variables for urban and rural soils of Lubbock County, TX, USA...... 40

2.7. Relative importance of variables in random forest models in distinguishing rural versus urban playas in Lubbock County, TX, USA...... 41

3.1. Location of the study area in northern Alaska, USA...... 54

3.2. Upland sideslope bench site in northern Alaska showing strongly contrasting soil classification types. Soils to the left are Turbels, poorly drained and display permafrost within 0.5 m of the soil surface. Soils to the right are Gelolls, well drained, with no evidence of permafrost in the upper 2 m...... 56

3.3. Horizonation of Pedon A described as a Cumulic Haplogeloll in northern Alaska, USA. Pedon A had a mollic epipedon over 40 cm thick (48 cm) and an irregular decrease in organic carbon content was observed in the Ab horizon. There was no permafrost within 2 m. Therefore, Pedon A met the criteria for a Cumulic Haplogeloll...... 63

3.4. Horizonation of Pedon E described as a Typic Haplogeloll in northern Alaska, USA. Pedon E had a mollic epipedon 28 cm thick. No permafrost was present within 2 m. Pedon E did not meet the criteria for other haplogeloll subgroups; therefore, Pedon E was classified as a Typic Haplogeloll...... 66

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3.5. Secondary calcium carbonate pendants found on rock fragments in various pedons adjacent to drainages near Galbraith Lake, Alaska, USA. Pendant development in a coarse fragment matrix supports stage I calcic horizon designation (Schoeneberger et al., 2012)...... 67

3.6. Horizonation of Pedon F currently classified as a Typic Haplogeloll in northern Alaska, USA. The mollic epipedon was 27 cm thick. The organic carbon content in the C horizon was 1.47%. There was no permafrost present within 2 m. Fluventic subgroup would better described this pedon; however, Fluventic is not currently a subgroup for Haplogelolls in the 12th edition of Keys to Soil Taxonomy...... 69

3.7. Horizonation of Pedon I currently classified as a Typic Haplogeloll in northern Alaska, USA. The mollic epipedon was 53 cm thick. No permafrost was present within 2 m. Pedon I is better described by the Pachic subgroup; however, Pachic is not currently a subgroup for Haplogelolls in the 12th edition of Keys to Soil Taxonomy...... 72

4.1. Location of sampling sites in the Transylvanian Plain, Cluj County, Romania...... 88

4.2. Proximal sensor scanning of freshly extracted soil cores from soils in the Cluj County, Romania...... 89

4.3. Visible near infrared spectra (350-2,500 nm) for low CaCO3 subsoil (6.2%; 110-120 cm) and high CaCO3 subsoil (12.4%; 70-80 cm) from two soil cores in Cluj County, Romania. Carbonate percentages reported herein were determined via bulk soil pressed powder X-ray diffraction...... 96

4.4. Relative influence of portable X-ray fluorescence (PXRF) and visible near infrared spectral wavelengths in predicting CaCO3 content in soils of Cluj County, Romania...... 99

4.5. Plots showing partial least square regression predicted CaCO3 (%) vs. laboratory determined CaCO3 (%) while using a) portable X-ray fluorescence (PXRF) variables b) visible near infrared (VisNIR) spectral variables, and c) PXRF + VisNIR variables for 300 soil samples from Cluj County, Romania. The solid line represents the 1:1 line. Data partitioning was on whole cores...... 100

4.6. CaCO3 percentages converted from the Ca percentages via PXRF of chernozems and phaeozems by depth from the mineral soil surface on summit, shoulder, backslope, footslope, and toeslope positions. The blue lines represent chernozems, and the green line represents phaeozems...... 102

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4.7. CaCO3 percentages via traditional laboratory pressure calcimetry of chernozems and phaeozems by depth from the mineral soil surface on summit, shoulder, backslope, footslope, and toeslope positions. The blue lines represent chernozems, and the green line represents phaeozems...... 103

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CHAPTER I

INTRODUCTION

History of Soil Classification

Soil classification is the process of categorizing soils into groups based on certain criteria or objectives and according to a system of set principles (Buol et al.,

2003). Soil classification is important for soil series comparison and organization.

There are currently more than 23,000 soil series in the United States. Soil classification has changed drastically over time dating back more than a century and a half. Before U.S. soil classification began, Russian scientists were pioneers in soil classification and genesis. In 1883, Russian geologist and geographer Vasily

Dokuchaev studied Russian chernozems (equivalent to Mollisols in the US and

Cernisoluri in Romania) (Simonson, 1989). Dokuchaev described chernozems as a deep, dark, rich soil profile with a well-developed CaCO3 horizon (Simonson, 1989).

Dokuchaev suggested soil is a dynamic natural body that forms as a result of external factors such as water, air, and vegetation (Buol et al., 2003). Dokuchaev proposed a practical way to classify soils based on soil taxa and described horizons based on an

A-B-C notation from the surface downward (Nikiforoff, 1931; Simonson, 1989). K.D.

Glinka, a student of Dokuchaev, authored The Great Soil Groups of the World and

Their Development and proposed soils be classified by their profiles including the A-

B-C notation (Glinka, 1914). In 1922, the A-B-C notation horizonation system was used for the first time (Nikiforoff, 1931). In 1927, Glinka’s book was translated to

German and English (Glinka, 1927).

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In 1906, Eugene W. Hilgard, published Soils. Hilgard was a professor of agriculture at the University of California and director of the agricultural experiment station in Berkley, California (Scheuring, 1995). Hilgard proposed vegetation and climate were the major indicators for soil physical and chemical properties

(Montgomery, 2007).

Milton Whitney, a soil chemist from Johns Hopkins University, was the first chief of the USDA Division of Agricultural Soils where he managed the early stages of soil survey such as mapping and describing soils (Helms et al., 2002). Whitney developed a classification system for soils based on three categories: topographic location or region, geologic origin of the parent material, and soil texture (ratio of sand, silt, and clay) (Helms et al., 2002). Whitney concluded there was a strong relationship between soil texture and soil fertility (Helms et al., 2002). George N.

Coffey went to work for the federal soil survey in North Carolina under Whitney’s leadership (Helms et al., 2002). Coffey had extensive background in Russian and

French soil classification systems and integrated their concepts into soils in the U.S.

(Helms et al., 2002). Coffey believed Hilgard overemphasized vegetation as an index for soil characteristics (Coffey, 1912). Coffey proposed that soils were independent natural bodies and should be classified on the basis of their own properties not on genetic processes, which was largely rejected by the Bureau of Soils (Helms et al.,

2002).

Hugh H. Bennett is considered the father of soil conservation (Helms et al.,

2002). Bennett graduated from University of North Carolina in Chapel Hill in 1903 and led the soil conservation movement in the U.S. in the 1920s and 1930s (Helms et

2 Texas Tech University, Autumn Acree, May 2020 al., 2002). Bennett created and headed a new federal agency (USDA’s Soil

Conservation Service) where he emphasized soil erosion as a major factor in land capability classification (Bennett, 1921; Helms et al., 2002).

In 1928, Marbut, influenced by Glinka’s book, developed a Normal Soil

Concept classification system (Simonson, 1989; Buol et al., 2003). A “normal soil” within this concept was considered well drained on a gently sloping hillslope profile with well-developed horizons (Buol et al., 2003). Soils that were poorly drained on low lying areas with high water tables were considered “abnormal” (Buol et al., 2003).

Climate was the dominant factor, while other factors such as parent material became secondary (Buol et al., 2003). In Marbut’s classification system, there were two major classes of soils, Pedalfers and Pedocals, based on solum composition (Marbut, 1928).

Marbut divided these classes by running a line south west of northern Minnesota to the

Texas-Mexico border (Marbut, 1928). Pedalfers consisted of aluminum and iron and were east of the line, while Pedocals had accumulations of CaCO3 and were west of the line (Marbut, 1928).

Charles E. Kellogg led Soil Survey Division starting in 1935 and had a long and storied career with the USDA Soil Survey (Helms et al., 2002). Kellogg believed

Marbut’s classification system was too broad, so Kellogg focused on improving soil survey (Kellogg, 1963). Kellogg emphasized the use and interpretation of soil survey data to direct activities and aid people (Helms et al., 2002). Kellogg was the author or contributor of numerous well known soil books such as Soil Survey Manual, Soil

Taxonomy, Soils and Men (1938 Yearbook in Agriculture), and The Soils That Support

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Us (Helms et al., 2002). Kellogg believed no system of classification should be so sacrosanct that it cannot be changed, altered, or updated as needed (Kellogg, 1963).

In Soils and Men (1938 Yearbook in Agriculture), Baldwin, Kellogg, and

Thorpe developed a more highly differentiated system of classification to include great groups (Baldwin et al., 1938). This system was refined further in later years, but it represented a major advance in soil morphology and classification. However, there were several problems with the 1938 system such as too much emphasis on parent material with a lack of importance on external factors, too much importance on color at the great group level, and classification was based on uncultivated (virgin) soils

(Soil Survey Staff, 1938). In 1941, Hans Jenny wrote Factors of Soil Formation: A

System of Quantitative Pedology where Jenny discussed what are now termed the five key factors of soil formation: climate, parent material, topography, time, and organisms (Jenny, 1994). In 1946, there was an effort to form a more well developed definition of great groups (Thorp and Smith, 1949; Simonson and Steele 1960).

In 1951, Guy Smith from Bureau of Soils was commissioned to study a new way of classifying soils. Smith proposed soils could be described in a disturbed or cultivated state. Smith emphasized the properties of the soil are the basis for classification. Smith believed processes of soil formation are important, but not necessarily a basis for classification. Smith sent the first draft of a new system for review (Cline, 1979). In 1952, Smith completed revisions to the originally proposed system, the 2nd Approximation, and sent it for peer review across the world (Cline,

1979). In 1953-54, the 3rd Approximation was sent to every state’s soil survey division and provided Smith with further input for future revisions (Cline, 1979). In 1955, the

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4th Approximation was completed (Cline, 1979). Most of the soil orders that are currently in United States Soil Taxonomy (USST) were beginning to form (Soil Survey

Staff, 1999). Smith was developing a numerical system as well as a verbal system for description (Cline, 1979). In 1956, the 5th Approximation included revisions that contained the taxa for soils (Cline, 1979). In 1957, the 6th Approximation was completed (Cline, 1979). In 1960, the 7th Approximation was sent to the field level soil survey staff to classify soils (Soil Survey Staff, 1964). In 1967, a supplement to the 7th Approximation was completed that included temperature classes (Soil Survey

Staff, 1964). In 1975, Soil Taxonomy was published (Soil Survey Staff, 1975). In

1983, the 1st edition of Keys to Soil Taxonomy was published (Soil Survey Staff,

1983). In 1999, a 2nd edition of USST was published and is the current standard for soil classification in the United States (Soil Survey Staff, 1999).

Soil Taxonomy is a hierarchical classification system with order as the highest level followed by sub-order, great group, subgroup, family, and series. Currently,

USST recognizes twelve orders of soil (Soil Survey Staff, 1999). Through the categories of order, suborder, and great group, emphasis has been placed on features or processes that appear to dominate the course or degree of soil development. Great groups are further divided into subgroups. There are more than 2,400 subgroups currently recognized. At the family level, soils are grouped based on physical and chemical properties that carry important interpretive information, including aeration and the movement and retention of water, that affect the growth of plants and engineering uses. Soil temperature is also recognized at the family level. Series is the lowest category in the system. The differentiae used for series generally are the same

5 Texas Tech University, Autumn Acree, May 2020 as those used for classes in other categories, but the range permitted for one or more properties is narrower than the range permitted in higher categories.

Soil Taxonomy is more quantitative than other taxonomic systems around the world. World Reference Base (WRB) for Soil Resources, a widely used system developed by the International Union of Soil Science, is more conceptual and qualitative with limited influence of soil temperature and moisture (FAO, 2006). Soil

Taxonomy has caused a great amount of laboratory, chemical, and morphological analysis to be conducted on soils. Diagnostic criteria are used to identify features within the soil. For example, an argillic horizon (accumulation of illuviated clay) requires specific clay percentage increases and thickness to be described as argillic

(Soil Survey Staff, 2014). A calcic horizon requires specific CaCO3 percentages and thickness to be considered calcic (Soil Survey Staff, 2014). Measuring these percentages are based on laboratory methods established by the Soil Survey Staff and other organizations such as the Soil Science Society of America.

Advancements in Soil Characterization

In the last 20 years, there have been changes in the way pedology is undertaken. Along with traditional morphological description, there are new tools that allow modern Pedologists to make laboratory quality readings in-situ in seconds. The development of proximal sensors (PXRF, VisNIR, NixPro, DualEM, LIBS, etc.) and remote sensors (drones, satellite imagery, GPS, google earth, etc.) have provided voluminous data collection with minimal cost which has driven a shift towards quantitative pedology or pedometrics (McBratney, 2019). While powerful, sensor data

6 Texas Tech University, Autumn Acree, May 2020 is meaningless without the interpretation and context provided by a qualified

Pedologist.

Since the current USST was last updated in 1999, contemporary technological advancements are not currently established methods of classifying soils. Traditional soil evaluation is time consuming and costly, often requiring weeks for laboratory processing. The development of field portable, proximal sensing systems has revolutionized testing protocols, allowing for rapid analysis of soils, vegetation, and water in-situ, in seconds, and at a fraction of the cost of traditional analysis

(McBratney, 2019). With the 3rd edition of USST being developed for 2022 with a new soil order (Artesols; Galbraith, 2019), new approaches are needed to properly insert contemporary technological advancements into the quantitative USST architecture.

Three examples of updates form the three chapters of this dissertation. Two concern the use of proximal sensors for soil characterization. One concerns the recognition of new taxonomy in Alaska.

The two key technologies used as part of this dissertation are portable X-ray fluorescence (PXRF) and visible near infrared diffuse reflectance spectrometry

(VisNIR DRS). PXRF provides elemental quantification on ~25 different elements in

60 sec (Weindorf and Chakraborty, 2016). Soil parameters such as pH, salinity, cation exchange capacity (fertility), clay content, gypsum content, and heavy metals can be directly read by PXRF (Sharma et al., 2014; Swanhart et al., 2014; Sharma et al.,

2015; Zhu et al., 2011; Weindorf et al., 2013; Zhu and Weindorf, 2009; Chakraborty et al., 2017). VisNIR DRS captures a continuous reflectance spectrum from 350 to 2,500 nm. Using specific wavelengths of reflected light, soil organic carbon, clay content,

7 Texas Tech University, Autumn Acree, May 2020 organic chemicals (e.g., oil pollution), and certain metals can be determined by multivariate analysis of various reflectance spectra (Horta et al., 2015). Not every sensor is applicable to every pedology query; one must understand which tool to use under which circumstances. As a contemporary Pedologist with expertise in the latest sensor technologies, the studies contained herein represent examples of how to use modern tools to advance pedology geochemically, morphologically, and spectroscopically.

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Weindorf, D.C., Herrero, J., Castañeda, C., Bakr, N., Swanhart, S., 2013. Direct soil gypsum quantification via portable x-ray fluorescence spectrometry. Soil Sci. Soc. Am. J. 77(6):2071-2077.

Weindorf, D.C., Chakraborty, S., 2016. Portable X-ray fluorescence spectrometry analysis of soils. Methods Soil Anal Available online at. https://dl.sciencesocieties.org/ publications/msa/abstracts/1/1/methods- soil.2015.0033 (verified 17 Aug. 2018).

Zhu, Y., Weindorf, D.C., 2009. Determination of soil calcium using field portable X- ray fluorescence. Soil Sci. 174(3), 151-155.

Zhu, Y., Weindorf, D.C., Zhang, W., 2011. Characterizing soils using a portable x-ray fluorescence spectrometer: 1. Soil texture. Geoderma 167–168, 167–177.

11 Texas Tech University, Autumn Acree, May 2020

CHAPTER II

COMPARATIVE GEOCHEMISTRY OF URBAN AND RURAL PLAYAS IN THE SOUTHERN HIGH PLAINS1 Autumn Acree, David C. Weindorf, Somsubhra Chakraborty, and Maria Godoy

Abstract

Playas are common natural occurrences in the Southern High Plains of Texas and act as sources of runoff in rural and urban settings and provide habitat for migratory waterfowl. Rural and urban playas in Lubbock County, TX were geochemically compared in order to optimize management for ecological functionality. Soil texture, specifically sand and clay, and organic matter concentration influenced geochemical properties such as trace element concentration, pH, and electrical conductivity. Rural playas contained more clay, organic matter, trace elements, and electrical conductivity than urban playas. Urban playas had a higher pH and roughly twice the sand content of rural playas. The prevalence of sand in urban playas likely influences groundwater recharge dynamics and potentially disqualifies these lakes as Vertisols owing to their sandy nature. The soil mineralogy between urban and rural playas were also different.

Quartz and muscovite were the dominant minerals in urban and rural playas, respectively. The differing soil properties between rural and urban playas signify the necessity of unique management strategies.

______

1 Published in Geoderma in March 2019; doi: 10.1016/j.geoderma.2018.11.010

Abbreviations: SHP – Southern High Plains; SOM – soil organic matter; PXRF – portable X-ray fluorescence; XRD – X-ray diffraction; PCA – principal components analysis; RF – random forest; EC – electrical conductivity; NIST – National Institute of Standards and Technology. 12 Texas Tech University, Autumn Acree, May 2020

Introduction

The Southern High Plains (SHP) of Texas encompasses an extensive area (126,470 km2) of farmland and rangeland of critical importance for cotton, corn, grain sorghum, peanut, and beef cattle production (Brejda et al., 2000). While the area does show a gentle inclination toward the north, the general surface of the SHP is exceedingly flat with many areas having topographic relief of no more than 1-2% (Soil Survey Staff,

2006); most surface soils are more than 30,000 years old (Holliday, 1990). Heavy convective thunderstorm events lead to infrequent, but substantive runoff of surface water into naturally occurring, low-lying areas on the SHP where playas form. Playas are shallow, somewhat circular lakes which receive hydrologic inputs from highly localized watersheds and average 6.3 ha in size (Bolen et al., 1989). Given their unique geomorphology, playas are oft characterized by Vertisols featuring slickensides and gilgai microrelief (Soil Survey Staff, 2006). The depth of water contained in playas varies widely from a few centimeters to one to two meters. Some dry out completely between runoff events, while others feature surface water year- round. In some areas, subsurface geology disproportionately affects a given playa.

Hypersaline conditions can form through a combination subsidence from dissolution of calcium and gypsum (cretaceous) (Wood and Jones, 1990); yet such conditions are quite rare relative to the number of non-saline, freshwater playas. Playas also serve as important overwintering habitat for migratory waterfowl, especially Canadian Geese

(Haukos and Smith, 1992). Playas occur in both urban and rural settings and may be considered jurisdictional wetlands subject to federal protection (Haukos and Smith,

2003). Osterkamp and Wood (1987) estimates that 25,000 playas occur on the SHP.

13 Texas Tech University, Autumn Acree, May 2020

Playas are the major surface drainages for the SHP with only a few sporadic shallow draws (e.g., Yellowhouse, Blackwater) otherwise providing drainage across the area. Playas are closed systems; therefore, no outflow occurs. The sources and amount of surface water runoff into playas depend on the location, precipitation, watershed size and slope, soil type, and type and quantity of vegetation. Urban areas

(such as the City of Lubbock) contain both naturally occurring playas as well as artificial drainage lakes constructed for localized drainage; in some instances, naturally occurring urban playas have been substantively modified, expanded, or excavated to increase their water storage capacity. Estimated average precipitation runoff into West Texas playas is 2.2-7.0 billion m3 y-1 (Templer and Urban, 1996).

Guthery and Bryant (1982) estimated that 33% of all playas were modified and 69% of playas >4 ha were modified from their natural condition. In Lubbock County, water volumes of playas peak in June and are mostly dry from December to March.

The sources of runoff into the playas are location dependent. Urban playas and lakes take in city storm water, potentially containing a litany of petroleum hydrocarbons, polycyclic aromatic hydrocarbons (PAHs) and a variety of trace elements (Hoffman et al., 1985). Within the city of Lubbock, TX, ~85 natural playas exist along with 9 playa variants and 9 urban lakes. By comparison, rural playas are frequently surrounded by farmland; therefore, runoff could contain fertilizers, pesticides, herbicides, and other chemicals. For example, 2-4 D, glyphosate, and various defoliants are commonly employed by SHP farmers for weed control and cotton defoliation prior to harvest (Anderson et al., 2013). Also, irrigation with well water from the Ogallala, Dockum, and Trinity/Edwards aquifers provides unique

14 Texas Tech University, Autumn Acree, May 2020 hydrological inputs (with associated dissolved solids, elements) to irrigated lands which differ from non-irrigated lands which receive no such inputs. For example, the

High Plains Underground Water District (2018) notes that irrigation water from the

Ogallala, Edwards Trinity, and Dockum aquifers generally provide good, fair, and poor quality water, respectively, owing to salts dissolved in the groundwater.

However, the former is heavily used and has dramatically decreased in saturated thickness across the SHP. In some instances, wells may pass through certain geologic strata which impart trace elements (e.g., arsenic) to the irrigation waters (Hudak,

2000). The annuli of playas have also been studied as potential aquifer recharge zones as clay content is lower along the perimeter and may allow for additional water infiltration/percolation throughout the soil (Wood and Osterkamp, 1987; Gurdak and

Roe, 2009). As playas represent unique geomorphological features across the SHP and play an important ecological role in everything from hydrological balances to avian migration patterns, understanding the differential geochemical composition of urban and rural playas is essential for proper land management.

Given the fact that urban vs. rural playas of the SHP display substantive differences in their physicochemical characteristics and hydrologic inputs, a better understanding of their geochemical differences is needed. As such, the objective of this study was to determine and compare the geochemical composition of urban and rural playas on the SHP. We hypothesize that urban playas will feature different geochemical signatures relative to their rural counterparts possibly necessitating unique management strategies for each to optimize their ecological functionality.

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Materials and Methods

General Occurrence and Features

Sampling was conducted in playas within and near Lubbock, Texas, USA in Fall,

2017 in Major Land Resource Area 77C: Southern High Plains – Southern Part (Soil

Survey Staff, 2006). The area has a semi-arid climate influenced by its location in a transition between desert conditions to the west and humid climates to the east and southeast. The greatest rainfall occurs from May through September when warm, moist tropical air from the Gulf of Mexico produces afternoon and evening thunderstorms, sometimes accompanied by large hail pellets. Snow may occur from late October until April with light accumulation. Lubbock averages ~49 cm of precipitation per year and annual sunshine of 71%. The average annual snowfall is 22 cm. The annual high temperature is 23.5oC and annual low is 8.3oC and the average temperature is 15.9oC. In late winter and spring, winds in excess of 11 m s-1 occasionally occur for periods of 12 h or more and can bring widespread dust for several hours; haboobs are possible.

Most playas of the area are comprised of Randall clay (Very-fine, smectitic, thermic Ustic Epiaquert) (Soil Survey Staff, 2018a), while soils surrounding the playas can range from Alfisols and Mollisols to Inceptisols. Randall clays are very deep, poorly drained, very slow permeable soils that are derived from the Blackwater Draw

Formation of Pleistocene age. Another common playa soil is the Ranco clay (Fine, smectitic, thermic Ustic Epiaquert) (Soil Survey Staff, 2018a). Ranco clays are also derived from the Blackwater Draw Formation of Pleistocene age and are very deep,

16 Texas Tech University, Autumn Acree, May 2020 poorly drained, very slowly permeable, and are frequently ponded for long periods throughout the year. The smectitic clays of the Randall and Ranco series swell when moist and limit infiltration into the underlying Ogallala aquifer; the latter has <50% clay in the particle size control section. Given the low relative humidity and high summer temperatures of the area, water in playas can evaporate quickly; therefore, playas often retain only a portion of their total storage capacity. Even though playas are common in West Texas, little research has been done on the ecological functions of the playas. Presumably, intermittent periods of inundation enhance nutrient cycling and biological productivity.

Field Sampling and Characterization

For this study, eleven playas and urban lakes were chosen for comparative study; five in urban environments, five in rural environments, and one in a transitional urban/rural environment. For purposes of discussion moving forward, these will collectively be termed ‘playas’, though some were altered or anthropogenic in nature.

The general location of the sampled playas is given as Fig. 2.1. At each playa, 20 surface soil samples (0-5 cm) were collected; in total, n=220. In playas featuring deep surface water, samples were collected at the waterline around the perimeter of the playa, spaced roughly equidistally. Dry or lightly saturated playas (~5-10 cm surface water) were sampled via transecting with localized randomization. Efforts were made to stay within the clay (vertic) zone avoiding transitional upland annuli as much as possible. Each sampling site was geolocated with an E-trex handheld global positioning system receiver with an accuracy of ~±3 m. Sample collection was per

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Schoeneberger et al. (2012); the sampling trowel was cleaned between sample collection and all samples were placed in sealed plastic bags for transport to the laboratory.

Fig. 2.1. Location of the study sites in Lubbock County, TX, USA. Sites 1-5 represent rural playas. Sites 6-10 represent urban playas. Site 11 represents a hybrid playa.

All analyses were performed in the pedology and geochemistry laboratories of

Texas Tech University. Upon arrival, all samples were air dried and disaggregated to pass a 2 mm sieve. Samples were subjected to particle size analysis per Gee and

Bauder (1986) using a model 152-H soil hydrometer with clay readings made at 1440 min. Soil reaction (pH) was made in 1:1 soil/water solution using a model Orion

8157BNUMD ROSS Ultra pH/ATC Triode. Similarly, soil salinity was assessed via electrical conductance using a model 4063CC digital salinity bridge. (US Salinity

Laboratory Staff, 1954). For both salinity and pH, the 1:1 soil/water solution was

18 Texas Tech University, Autumn Acree, May 2020 made with deionized water and allowed to equilibrate 30 minutes prior to assessment

(Thomas, 1996; Dahnke and Whitney, 1988). Loss on ignition (LOI) organic matter was performed at 400°C for 16 h per Nelson and Sommers (1996). For all analyses,

10% of the total sample set was randomly selected for validation studies. Elemental characterization was per Weindorf and Chakraborty (2016) using a DP-6000

(Olympus, Waltham, MA, USA) portable X-ray fluorescence (PXRF) spectrometer operated on line power (110 VAC) at 10-40 KeV in Soil Mode at 30 s per beam (90 s for one total scan). The PXRF was calibrated with a standard 316 metallic alloy, and instrument performance was validated via scanning National Institute of Standards and

Technology (NIST) certified reference soil 2711a (Koch et al., 2017). Recovery percentages (PXRF/NIST) were as follows: 0.99 Fe, 0.95 Ca, 1.04 Al, 1.36 As, 1.07

Cu, 1.09 Pb, 0.88 Si, 1.05 Zn, 0.88 Cr, 0.93 K, and 1.29 Ni. Select bulk soil samples from each playa were subjected to X-ray diffraction analysis (XRD) for mineralogical determination. Oriented clay separations were made for select profiles, the interlayer spacing stabilized with ethylene glycol. XRD data of selected soil samples was performed on a Rigaku Ultima III X-ray diffractometer (Rigaku Corporation, Tokyo,

Japan) equipped with Cu Kα radiation (λ =1.54059 Å) and a scintillation detector. The data were collected in parallel beam geometry utilizing continuous mode from 3 to 80°

2ϴ, step width of 0.02°, and collection time of 3.87 s per step. Data analysis was performed using MDI Jade v9.1.1 software.

19 Texas Tech University, Autumn Acree, May 2020

Statistical Analysis

Mean separation was used to compare across rural, urban, and hybrid playas using

R 3.5.1 (R Core Development Team, 2017). Pearson correlation matrix in Excel was used to determine correlations between different soil parameters. The particle size of the urban, rural, and hybrid playas were plotted using DPLOT. Principal component analysis (PCA) which is a popular dimension-reduction technique was executed to separate samples coming from rural, urban and hybrid playas based on their geochemical composition via XLstat software version 2017 (Addinsoft, Paris, France).

“Screeplots” were produced to examine the cumulative variability explained by principal components (PCs). Furthermore, random forest (RF) model was executed to classify samples coming from different playas and to identify the influential factors for their separation. RF is an ensemble method and comprised of randomly generated regression trees via recursive partitioning of data (Breiman, 2001). Notably, RF can indicate the relative importance of each explanatory variable used in the prediction via total reduction in node impurities. In this study “RandomForest” (Liaw and Wiener,

2002) packages was used in R. In RF, a total of 1000 trees were grown (ntree) in the forest, keeping five minimum observations in the terminal nodes. All statistical analyses were conducted at a significance level of α = 0.05.

Results and Discussion

Particle Size Analysis and Mineralogy

Particle size analysis revealed that rural playas generally feature elevated clay content with urban playas shifting to sandier variants (Fig 2). Urban playas had a

20 Texas Tech University, Autumn Acree, May 2020 higher mean sand content (60.92%) than rural playas (28.84%; Table 2.1). Rural playas had a higher amount of clay (36.29%) and silt (34.87%) than urban playas

(20.96% and 18.12% respectively; Fig. 2.2). Soil texture can influence elemental transport into groundwater. Sandy soils have high infiltration rates which promote elemental movement with water through the soil profile and into groundwater

(Angulo-Jaramillo et al., 2016). Groundwater mobilizes and transports trace elements which can negatively impact soil, plants, and wildlife habitats (Tanji and Valoppi,

1989). Considering samples were collected at the surface (0-5cm), soil texture below 5 cm is unknown. Further research needs to be done at depths >5 cm to determine soil texture and porosity deeper into the profile and evaluate elemental movement into groundwater. Importantly, of the 225 surface soils sampled, 114 revealed clay contents

<30%, potentially disqualifying such soils as Vertisols. Technically, the 30% clay requirement applies to a weighted average of the fine-earth fraction between the mineral soil surface and a depth of 18 cm or in an Ap horizon (whichever is thickest)

(Soil Survey Staff, 2014). Yet many of the urban playas and lakes sampled in this study are substantively less than 30% in clay, are quite sandy in the upper part of the profile, and have sandy layers extending down 10-20 cm or more. The sandy nature of urban playas is likely due to runoff from road management in winter months, specifically road sanding operations. In the SHP, such results would give a stronger indication of a playa variant or urban lake as opposed to a naturally occurring playa.

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Fig. 2.2. Particle size analysis of 225 surface soil (0-5 cm) from playa and urban lakes in Lubbock County, TX, USA.

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Table 2.1. Physicochemical properties of rural and urban playa surface soils (0-5 cm) in Lubbock County, Texas. Playas n Organic Matter pH EC Sand Silt Clay Mean Range Median Range Mean Range Mean Range Mean Range Mean Range % loss-on-ignition ------dS m-1------%------Rural 1 20 8.94 7.01-11.26 6.67 5.77-7.80 1.03 0.52-1.64 22.2 12.7-40.6 39.3 26.1-47.4 38.6 26.0-52.0 2 20 7.81 5.68-10.27 6.57 5.90-8.08 0.80 0.42-1.17 25.7 5.0-60.6 35.7 9.4-54.0 38.6 22.0-51.8 3 20 4.36 2.75-6.10 6.50 5.74-7.78 0.51 0.21-0.87 47.6 15.9-65.4 23.8 14.1-41.2 28.6 16.0-43.0 4 20 8.41 3.65-11.24 6.44 5.52-7.98 0.80 0.52-1.13 20.8 5.6-65.4 43.3 15.4-68.4 35.9 19.0-48.8 5 20 7.25 4.09-11.45 6.36 6.11-7.25 1.00 0.70-1.62 27.9 8.7-66.6 32.3 9.4-45.0 39.8 24.0-53.8 Mean 7.35a 6.46b* 0.83a 28.8b 34.9a 36.3a Urban 6 25 3.85 1.92-13.50 6.43 5.48-8.09 0.70 0.40-1.16 63.1 34.6-90.6 17.1 3.4-35.4 19.8 6.0-36.0 7 20 3.85 1.12-6.36 8.04 6.82-8.35 0.58 0.21-1.02 56.1 27.1-93.3 17.2 3.4-35.4 26.6 3.3-49.3 8 20 3.74 1.71-13.60 7.35 6.69-8.00 0.70 0.39-1.41 58.5 40.1-74.7 24.1 13.4-37.7 17.4 8.0-25.7 9 20 2.97 1.06-6.03 7.67 7.70-8.43 0.50 0.24-0.74 61.7 29.9-87.9 17.3 2.2-30.1 21.0 8.0-40.0 10 20 3.62 0.18-6.04 6.75 5.67-7.62 0.65 0.21-1.02 64.7 48.9-78.6 15.1 6.2-24.0 20.2 9.3-30.8 Mean 3.62b 7.26a** 0.63b 60.9a 18.1b 21.0b a,b Different lowercase letters indicate significant differences within a parameter at α = 0.05. * Value represents the median pH for rural playas. ** Value represents the median pH for urban playas.

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Somewhat surprisingly, muscovite (K(Al1.88Fe0.12)(AlSi3O10)(OH)2) comprised

25.5% of bulk soil and 76.7% of the clay fraction in a select rural playa (Figs 2.3a and

2.3b). By comparison, the Bw2 horizon (43-76 cm) of a Ranco pedon in Lubbock

County (Pedon ID: S2000TX303001) revealed a “medium” peak size for mica, and

“small” peaks for montmorillonite and kaolinite (Soil Survey Staff, 2018). Similarly, mica peaks were characterized as “medium” for a Bss1 horizon (28-56 cm) for a

Ranco soil in Lynn County, TX (Pedon ID: S2000TX305001); kaolinite was “small” and montmorillonite was “very small” (Soil Survey Staff, 2018). While montmorillonite and associated smectites were not identified strongly in the playa samples we tested, it is plausible that their peaks (generally below 10° 2ϴ) are being masked by a broad reflection signature in that region which may be partly due to crystalline lattice degradation (Miller et al., 1972). Clay separation was performed to allay the masking effect, but muscovite remained the dominant signature as it tends to resist weathering especially with substantive elemental substitutions in the crystal structure. XRD results identified quartz (SiO2) as the dominant mineral of an urban lake bulk soil at Maxey Park (Fig. 2.3c). Interestingly, magnesium calcite was also found in substantial quantities; results confirmed by PXRF analysis which revealed

Mg and Ca concentrations of 15,334 and 121,174 mg kg-1, respectively.

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Fig. 2.3. X-ray diffraction (XRD) analysis revealing a) bulk mineralogy of a rural playa, b) clay separation of same playa, and c) bulk mineralogy of an urban lake in Lubbock County, TX, USA.

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Soil Organic Matter

Rural playas had a higher amount of soil organic matter (7.35% LOI) than urban playas (3.62% LOI; Table 2.1). Soil organic matter generally increases as clay content increases. Clay slows the soil organic matter decomposition process and has the potential for aggregate formation that physically protect organic matter from mineralization by microorganisms (Körschens et al., 1998). Since rural playas have more clay than urban playas, clay content could explain the higher organic matter.

Rural playas could also contain more organic matter from runoff than urban playas.

Rural playas have more sources of runoff than urban playas in West Texas. Urban playas rely on precipitation for a water source, which is ~49 cm per year. However high intensity, convective thunderstorms are common and could potentially transport organic matter, pesticides/pesticides (e.g. cotton defoliants/desiccants), or fertilizers into associated drainage basins. Rural playas containing more organic matter than urban playas could explain the higher concentration of trace elements because they are sorbed on the organic soil fraction. Several studies have further established that organic matter concentration increases at irrigated sites relative to local background concentrations (Siebe, 1994; Siebe and Fischer, 1996). Crawford et al. (2001) and

Texas Agrilife Research (2018) provide an overview of chemicals commonly used in cotton production. While certain chemical products are no longer used in cotton defoliation and/or desiccation, residues within the soil can have long residence times.

26 Texas Tech University, Autumn Acree, May 2020

Soil pH

Urban playas had a higher pH (median pH 7.26) than rural playas (median pH

6.46; Table 2.1). Urban playas also had a higher concentration of Ca (15,266 mg kg-1;

Table 2.2) than rural playas (3,079 mg kg-1) which could explain the higher pH

(Loeppert and Suarez, 1996). Dissolved calcium carbonate or bicarbonates can increase the pH of surface water (Tucker and D’Abramo, 2008). A high pH in surface water can affect early life stages of aquatic organisms such as fish and crustaceans

(Tucker and D’Abramo, 2008). The level of pH also has an effect on the absorbability and solubility of plant essential nutrients (Osman, 2012). Most nutrients can dissolve easily in a lower pH, which could result in excess amounts of Al, Fe, and Mn. At a lower pH, deficiencies can occur in P, K, and Mg. Nutrients do not dissolve easily at higher pH levels (pH 8 or greater), which could cause Ca, Fe, and phosphate to precipitate. Collectively, Mn, phosphate, Fe, Cu, Zn, and B absorptions are reduced at a higher pH which will cause nutrient deficiencies (Jensen, 2010). Urban playas contained more sand, had a higher pH, and less organic matter than rural playas; degradation of organics generally produces acidity, which could have also influence rural soil pH, especially since soil pH was only slightly acidic. According to the

NRCS, Ranco soil series are slightly alkaline to moderately alkaline at the surface, and

Randall soil series are slightly acidic to slightly alkaline at the surface (Table 2.2).

Both the urban and rural playas had neutral pH aligning with the Randall soil series description.

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Table 2.2. Comparative soil pH and electrical conductivity (EC) data for surface soil horizons of playas on the Southern High Plains of Texas (Soil Survey Staff, 2018b). Ranco Pedons Pedon ID: S2000TX303001 Pedon ID: S2000TX305001 Lubbock County, TX Lynn County, TX 6.4 (Ap1 horizon; 0-8 cm) pH (1:1 7.5 (A horizon; 0-23 cm) H2O) 6.7 (Ap2 horizon; 8-18 cm)

-1 0.21 (EC1:2; Ap1 horizon; 0-8 cm) EC (dS m ) 1.25 (ECe; A horizon; 0-23 cm) 0.13 (EC1:2; Ap2 horizon 8-18 cm)

Randall Pedons Pedon ID: S2000TX445001 Pedon ID: 01TX189001 Terry County, TX Hale County, TX 7.0 (A1 horizon; 0-11 cm) pH (1:1 6.7 (A and Bw horizons; 0-8 cm and 8-18 cm respectively) H2O) 7.3 (A2 horizon; 11-29 cm) 0.17 (EC ; A horizon; 0-8 cm) 0.45 (EC ; A1 horizon; 0-11 cm) EC (dS m-1) 1:2 e 0.14 (EC1:2; Bw horizon; 8-18 cm) 0.40 (ECe; A2 horizon; 11-29 cm)

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Soil Electrical Conductivity

Rural playas had a higher EC (0.83 dS m-1) than urban playas (0.63 dS m-1; Table

2.1). Higher EC in the rural playas could possibly be due to storm water runoff whereby fertilizer may run off-site. Most irrigation water contains some level of dissolved salts. Once the water evaporated, the dissolved salts remain in the soil. West

Texas has a high evapotranspiration rate; therefore, more salts will remain in the soil because there is not enough rainfall to leach the salts out of the profile (Fipps, 2018).

It is estimated that 20 billion m3 of agricultural irrigation water is used in Texas per year with West and South Texas making up the majority of irrigation water used

(Texas Water Development Board, 2012). However, neither urban nor rural playas were considered saline as EC was < 4.0 dS m-1. There are several saline and non- saline playas on the SHP. Saline playas are generally found along rivers and occur on shallow water tables where underground water (containing dissolved salts) is naturally pushed upward via capillary action. Non-saline playas generally are found in agricultural fields and urban settings as a source of runoff and typically do not occur in shallow water tables. Table 2.2 provides four additional pedons for comparison, albeit with different dilutions ECe and 1:2 (v/v) than that used in the present study (1:1 v/v).

With deference to established relationships between various salinity dilution methods

(Hogg and Henry, 1984), comparisons of data collected in the present study with that of the Soil Survey Staff revealed slightly higher salinity in the former. However, levels were still well below that required to induce any level of vegetative stress (Weil and

Brady, 2017).

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Soil Elemental Data

Rural playas contained a higher concentration of trace elements such as Pb (25 mg kg-1), Cr (64 mg kg-1), As (12 mg kg-1), Zn (100 mg kg-1), Cu (28 mg kg-1), and Ni (43 mg kg-1) than urban playas (Table 2.3) possibly due to higher clay content holding onto the elements in the rural playas. Clay has a high cation exchange capacity (CEC) that allows for more elements to be retained on the exchange sites than sand which has no CEC. Gomes et al. (2001) found a correlation between amount of clay and trace element sorption in agricultural soil. Even so, the aforementioned levels in rural playas largely still fall below the residential screening limits established by the US

Environmental Protection Agency for soil (Brevik and Burgess, 2016). The one exception is As, where US residential screening limits are 0.39 mg kg-1. Importantly, the presence of Pb has been known to interfere with As determination. Furthermore, the reported concentrations of As are close to the instrument detection limit for As.

Considering the recovery percentage for As was 1.36, the instrument could have overestimated the As levels.

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Table 2.3. Elemental data produced by portable X-ray fluorescence spectroscopy for rural and urban playa surface soils (0-5 cm) in Lubbock County, Texas. Unless otherwise noted, all values are in mg kg-1.

Playa n Pb Cr As Zn Cu Ni Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Rural 1 20 29 22-33 66 52-77 13 10-16 111 83-131 31 24-38 46 33-55 2 20 26 20-31 67 48-91 10 7-14 104 71-136 29 21-35 44 36-55 3 20 17 10-24 52 33-70 8 5-11 61 30-91 19 10-29 29 16-43 4 20 27 11-32 63 40-73 13 5-16 108 52-133 30 14-37 47 11-58 5 20 27 16-31 71 54-86 15 10-17 118 67-142 29 22-35 52 38-60 Mean 25a 64a 12a 100a 28a 43a Urban 6 25 16 5-50 45 23-68 4 2-13 50 20-153 15 6-32 24 8-40 7 20 15 4-41 47 23-77 6 2-11 55 13-109 16 4-43 30 21-47 8 20 15 9-35 39 23-55 3 2-7 59 28-238 16 6-43 17 10-32 9 20 9 2-16 43 22-70 4 1-7 36 9-74 13 4-22 22 9-33 10 20 25 6-184 47 31-77 5 2-9 59 22-164 16 7-39 27 15-38 Mean 16b 44b 4b 52b 15b 24b

Fe K Ca Si (%) Al (%) Mean Range Mean Range Mean Range Mean Range Mean Range Rural 1 20 35813 26033-41887 16327 14707-18531 4031 1164-9811 23.4 20.9-26.0 9.0 7.9-10.1 2 20 33462 22833-42160 15281 12811-17711 4592 121-18474 24.0 21.6-29.5 8.4 7.0-9.5 3 20 22350 12228-31088 13093 8734-16227 766 428-1473 28.5 23.7-34.8 7.4 6.2-8.8 4 20 36227 14917-43152 17174 12948-19010 3279 501-9892 25.1 22.4-32.0 9.4 6.6-10.6

31 Texas Tech University, Autumn Acree, May 2020

Table 2.3. Continued

5 20 38601 23199-44339 17212 15135-18953 1338 258-2405 24.9 21.8-29.0 10.1 8.5-11.1 Mean 33291a 15817a 3079b 25.2b 8.8a Urban 6 25 14561 5829-27938 9761 4165-13435 10713 208-107007 29.5 17.1-36.5 5.8 3.6-7.6 7 20 15928 2400-31093 10093 469-15798 34326 227-121174 27.2 18.6-41.5 5.2 1.9-7.5 8 20 12491 7291-23528 9259 5488-12492 6940 525-28516 30.8 18.1-36.9 4.8 3.8-6.4 9 20 13614 3721-25037 8736 1710-14273 13908 652-85484 30.7 20.5-38.7 5.7 2.7-7.6 10 20 15109 6688-26765 8568 5034-12432 7896 53-57974 30.4 14.9-37.0 5.7 3.6-7.9 Mean 14351b 9306b 15266a 29.7a 5.5b a,b Different lowercase letters indicate significant differences within a parameter at α = 0.05.

32 Texas Tech University, Autumn Acree, May 2020

To evaluate the influence of clay content on elemental results, clay contents were standardized between urban and rural playas with elemental data adjusted proportionally. Following on, no difference was observed in Pb, Zn, Cu, and Ni concentrations between urban and rural playas indicating that clay content was the main factor in the increase in these trace elements in the rural playas. Chromium concentration became higher in urban playas after the clay correction indicating clay was not the only factor in chromium concentration. Reedy et al. (2007) found that high

As concentrations correlate with phosphate in cotton cropland in SHP, Texas that can be attributed to the phosphate fertilizer applications. However, groundwater of the

Southern High Plains (especially the southern part) is known to contain high As concentrations where oxidizing systems facilitate As sorption onto hydrous metal oxides which are mobilized by high pH (Scanlon et al., 2009). Hudak (2000) compiled data from 173 wells of the area and found As ranging from 1.1 to 171.9 µg L-1. Hudak

(2000) further stated “several lines of evidence suggest the arsenic came from pesticides instead of local rock formations.”

Collectively, Al, Fe, and K were present at higher concentrations in rural playas

(88,456 mg kg-1, 33,291 mg kg-1, 15,817 mg kg-1 respectively) than urban playas

(Table 2.3). Both Fe and Al oxides are a major part a clay soil colloid; therefore, more clay in rural playas could explain higher amounts of Al and Fe. Potassium is a component of muscovite which made up the majority of the clay in the rural playas which could explain the higher K concentration in rural playas. Silica was higher in urban playas than rural playas (Table 2.3). Silica dioxide is the most common

33 Texas Tech University, Autumn Acree, May 2020 constituent of sand which could explain the higher concentration of silica in the urban playas.

Soil Property Interactions and Statistical Classification

A Pearson’s correlation matrix was established between pH, EC, organic matter, particle size, and elemental concentration for rural and urban playas (Table 2.4 and

Table 2.5, respectively). The Pearson’s correlation matrix indicated that trace elements increased with clay content and organic matter and decreased as sand content increased, which aligns with Gomes et al. (2001) correlation showing the increase in trace element sorption with clay percentage. Organic matter showed to increase with clay content and decrease in sandier soils. Körschens et al. (1998) also found an increase in organic matter as clay content increases. Soil pH showed to decrease with increasing EC values and clay content. These results were also revealed with mean separation as rural playas had more clay, more organic matter, higher trace element concentrations, higher EC, and lower pH than urban playas. Indeed, results from both

PCA and RF supported the geochemical differences of rural and urban playas.

Notably, PC screeplot exhibited that the first six PCs cumulatively explained almost

90% of the total variability while ~80% of the total variance was contained in the first two PCs (Fig. 4). The first and second PCs contained ~72% and ~6% of the total variability. Furthermore, PC scoreplot was produced to provide a visual assessment of how playa samples were separated in the PC space (Fig. 2.5). Clearly, the samples from rural and urban playas were almost separated along PC1 while hybrid samples cannot be separated from the rural samples. No obvious extreme outliers were

34 Texas Tech University, Autumn Acree, May 2020 observed in PC scoreplot. Additionally, the PCA variable correlation plot (Fig. 2.6) exhibited the positive correlations between trace elements, clay content and organic matter, supporting the observations of Pearson’s correlation analysis (Tables 2.4 and

2.5). As expected, a positive correlation was observed between sand and Si.

35 Texas Tech University, Autumn Acree, May 2020

Table 2.4. Pearson correlation matrix for physicochemical properties of surface (0-5 cm) soils in rural playas of Lubbock County, Texas. pH EC LOI Sand Clay Silt Al Si K Cr Fe Ni Cu Zn As Pb pH 1

EC -0.282* 1

LOI 0.022 0.380 1

Sand 0.145 -0.457 -0.749 1

Clay -0.345 0.309 0.626 -0.770 1

Silt 0.072 0.423 0.592 -0.847 0.313 1

Al -0.152 0.537 0.562 -0.658 0.589 0.489 1

Si 0.112 -0.387 -0.745 0.589 -0.583 -0.391 -0.370 1

K -0.045 0.579 0.654 -0.708 0.530 0.612 0.848 -0.416 1

Cr -0.048 0.490 0.409 -0.458 0.457 0.301 0.632 -0.442 0.480 1

Fe -0.120 0.568 0.769 -0.809 0.727 0.598 0.855 -0.736 0.827 0.650 1

Ni -0.031 0.556 0.575 -0.646 0.482 0.560 0.882 -0.435 0.738 0.715 0.823 1

Cu -0.070 0.536 0.814 -0.795 0.677 0.620 0.679 -0.793 0.767 0.570 0.910 0.720 1

Zn -0.123 0.587 0.774 -0.799 0.732 0.579 0.825 -0.765 0.814 0.649 0.989 0.807 0.918 1

As -0.132 0.590 0.622 -0.678 0.609 0.502 0.860 -0.574 0.793 0.607 0.906 0.778 0.764 0.888 1

Pb -0.133 0.570 0.817 -0.810 0.713 0.612 0.795 -0.753 0.810 0.630 0.960 0.791 0.931 0.961 0.826 1 *Bold numbers are significant at α = 0.01 (two-tailed test)

36 Texas Tech University, Autumn Acree, May 2020

Table 2.5. Pearson correlation matrix for physicochemical properties of surface (0-5 cm) soils in urban playas of Lubbock County, Texas. pH EC LOI Sand Clay Silt Al Si K Cr Fe Ni Cu Zn As Pb pH 1

EC -0.201* 1

LOI -0.059 0.355 1

Sand -0.249 -0.184 -0.441 1

Clay 0.302** 0.087 0.418 -0.822 1

Silt 0.095 0.215 0.292 -0.795 0.309 1

Al -0.168 0.158 0.262 -0.470 0.497 0.256 1

Si -0.249 -0.230 -0.469 0.553 -0.601 -0.284 -0.152 1

K 0.084 0.191 0.430 -0.808 0.730 0.573 0.765 -0.456 1

Cr -0.159 0.135 0.176 -0.368 0.383 0.207 0.655 -0.132 0.521 1

Fe 0.034 0.315 0.511 -0.759 0.740 0.479 0.732 -0.579 0.855 0.599 1

Ni 0.145 0.076 0.170 -0.454 0.583 0.138 0.596 -0.248 0.476 0.661 0.617 1

Cu 0.027 0.347 0.733 -0.628 0.539 0.476 0.402 -0.619 0.623 0.383 0.763 0.348 1

Zn -0.004 0.447 0.863 -0.524 0.424 0.424 0.307 -0.563 0.507 0.293 0.665 0.275 0.912 1

As 0.174 0.114 0.346 -0.522 0.595 0.238 0.504 -0.554 0.595 0.486 0.802 0.584 0.621 0.504 1

Pb -0.124 0.286 0.623 -0.399 0.389 0.252 0.432 -0.440 0.519 0.457 0.637 0.330 0.790 0.773 0.613 1

*Italicized numbers are significant at α = 0.05 (two-tailed test). **Bold numbers are significant at α = 0.01 (two-tailed test).

37 Texas Tech University, Autumn Acree, May 2020

Scree plot 14 100

12 80

60 8

Eigenvalue 6 40 Cumulative variability (%)

20 2

0 0 PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 PC11 PC12 PC13 PC14 PC15 Principal Component

Fig. 2.4. Principal components analysis screeplot indicating the relative importance of each principal component for distinguishing rural versus urban playas in Lubbock County, TX, USA.

38 Texas Tech University, Autumn Acree, May 2020

Rural

3 Urban

Hybrid 2

PC2(6.59%) 0 -10 -8 -6 -4 -2 0 2 4 6

-1

-2

-3 PC1 (72.48%)

Fig. 2.5. Principal component (PC) analysis scoreplot visualizing playa separation (urban vs. rural) in PC space for soils from Lubbock County, TX, USA.

39 Texas Tech University, Autumn Acree, May 2020

Fig. 2.6. Principal component analysis variable correlation plot illustrating clustering of variables for urban and rural soils of Lubbock County, TX, USA.

Contrariwise, RF classification algorithm produced ~92% classification accuracy while separating samples from different playas using the PXRF elements, soil pH, EC,

LOI, clay, sand and silt content (Table 2.6). The number of correctly classified samples in rural, urban and hybrid playas were 90, 96 and 15, respectively. Based on the mean decrease in accuracy (MDA), the variable importance plot indicated the influential variables which were responsible for producing high classification accuracy

(Fig. 2.7). Notably, the MDA in RF algorithm is calculated in the out of bag error calculation stage and the variables with high MDA values are considered more

40 Texas Tech University, Autumn Acree, May 2020 influential for classification purpose. In this study, Al followed by As, Pb, K, Fe, organic matter and Cu appeared as the influential variables for grouping samples coming from different playas. This observation was further supported by the noticeably higher concentrations of Al, As, Pb, K, organic matter and Cu in rural playas as compared to urban playas, as discussed earlier. Overall, the PCA and RF classification indicated the clear differences in rural and urban playa samples due to variable geochemistry.

Table 2.6. Confusion matrix showing random forest-based classification of playas of Lubbock County, Texas. The shaded cells represent the number of correctly classified samples. from/to Rural Urban Hybrid Total % correct Rural 90 2 5 97 92.78 Urban 9 96 0 105 91.42 Hybrid 1 1 15 17 88.23 Total 100 99 20 219 91.78

RF variable importance Pb Cu Cr Al

VariablesSand pH -0.05 0 0.05 0.1 0.15 0.2 0.25 Mean decrease accuracy

Fig. 2.7. Relative importance of variables in random forest models in distinguishing rural versus urban playas in Lubbock County, TX, USA.

41 Texas Tech University, Autumn Acree, May 2020

Playa characteristics in the Southern High Plains are influenced by the arid/semi- arid environment. Playas occur in many different parts of the world. In hyperarid

North Africa, playa soils have accumulations of soluble salts and gypsum (Hamdi-

Aissa et al., 2004). Hyperarid environments would accumulate more soluble salts and gypsum than semi-arid/arid climates due to a lack of sufficient leaching. Hyperarid environments are nearly rainless with an average precipitation of <25 mm per year and at least 12 consecutive rainless months (Meigs, 1953). A lack of precipitation results in gypsum and soluble salt accumulation at the soil surface because they will not be leached out of the profile (Blaylock et al., 1994). If soluble salts and gypsum are present in high concentrations, soil pH will increase which poses major problems for nutrient availability and plant growth (Osman, 2012). Groundwater can mobilize and transport soluble salts, which can result in soil structure deterioration and plant loss

(Blaylock et al., 1994). Playas in Australia are in an arid environment with intense evaporation. The Australian playas have concentrated groundwater to more than 200 g

L-1 total dissolved solids (Jacobson, 1988). Since surface samples in rural playas of the

SHP contained accumulations of trace elements and urban playas had a high percentage of sand, more research needs to be done at depths >5 cm to further evaluate texture, elemental movement, and total dissolved solids to assess potential effects on transport to groundwater.

Conclusions

Geochemical properties of rural and urban playas in the SHP were compared.

Rural playas featured more clay, promulgating increased organic matter, trace

42 Texas Tech University, Autumn Acree, May 2020 elements, and higher EC. Urban playas had more sand, higher pH, less organic matter, lower trace element concentration, and lower EC. Soil texture and pH can influence elemental movement into groundwater impacting plant growth, wildlife habitats, and soil structure; therefore, further research needs to be done to evaluate texture, pH, and elemental movement through the soil profile to groundwater. Urban playa mineralogy was different than rural playas. Quartz was the dominant mineral in an urban playa while muscovite dominated a rural playa. The differences in texture, mineralogy, and chemical properties indicate that rural and urban playas need to be managed uniquely for optimized ecological functionality.

Acknowledgments

The authors gratefully acknowledge support from the BL Allen Endowment in

Pedology and the Texas Tech University Presidential Fellowship Program in conducting this research.

43 Texas Tech University, Autumn Acree, May 2020

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48 Texas Tech University, Autumn Acree, May 2020

CHAPTER III

CHARACTERIZATION OF GELOLLS IN NORTHERN ALASKA, USA1 Autumn Acree, David C. Weindorf, John Galbraith, Nic Jelinski, and Laura Paulette

Abstract

Mollisols are dark colored, carbon-rich mineral soils occupying a large proportion (836 soil series) of the soils of the Central Plains of the United States. By contrast, only eight official soil series of Mollisols have been mapped in Alaska (six Haplocryolls, one

Calcicryoll, one Haplogeloll). Little information exists about Geloll pedogenesis, taxonomic variability, and extent. In this study, 39 horizons were morphologically described across ten Geloll pedons in northern Alaska. Based on analogous taxonomic structure in Cryolls, two pedons would meet the criteria for a Fluventic subgroup as the organic carbon content was >0.3% (1.47% and 0.88%) at a depth of 125 cm below the mineral soil surface. Three pedons would meet the criteria for a Pachic subgroup because the mollic epipedon was thicker than 40 cm (52 cm, 53 cm, and 54 cm) and the texture class was finer than loamy fine sand (sandy loam). However, no Fluventic or Pachic subgroups currently exist for Haplogelolls. Two pedons were classified as Cumulic

Haplogelolls, and three pedons were Typic Haplogelolls. Field and laboratory characterization allowed for the development of a Geloll pedogenic concept generally represented by well-drained soils with limestone/dolomitic parent material. Such soils generally feature thin or no surficial organic horizons and large quantities of coarse fragments and coarse textured materials. Soil organic carbon calculations for

______

1 Accepted for publication in Soil Science Society of America Journal 03.03.2020 49

Texas Tech University, Autumn Acree, May 2020 suspected areas of Alaska Gelolls total 1.18 Pg; closely aligning with estimates of previous studies. Future work should expand the explanatory taxonomy based on new morphological expressions observed throughout gelic temperature regime regions.

Texas Tech University, Autumn Acree, May 2020

Introduction

Mollisols are dark colored, carbon-rich mineral soils that constitute a large portion of the Central Plains of the United States. Bockheim (2014) summarizes the key requirements of the mollic epipedon set forth by the Soil Survey Staff (2014) as follows: the moist Munsell color value and chroma must be ≤3, (ii) the soil organic carbon (SOC) must be >0.6 %, (iii) the base saturation must be >50 %, and (iv) the minimum thickness of mollic colors must be at least 10, 18, or 25 cm, depending on the depth and texture of the solum. The Soil Survey Staff (1999) estimate that 21.49% of US soils and 6.89 % of soils worldwide are Mollisols. Naturally fertile, Mollisols are widely used for grain (e.g., wheat, oats, corn) production and are among the most productive cropland soils in the world. In other systems of soil classification, Mollisols roughly equate to Chernozems,

Kastanozems, and Phaeozems in the World Reference Base (WRB) for Soil Resources

(UN-FAO, 2014), Isohumosols or Black soils (China), or Dermosols (Australia).

To date, only eight official soil series of Mollisols have been mapped in Alaska. Of those, six are Haplocryolls, one is a Calcicryoll, and one is a Haplogeloll (Soil Survey

Staff, 2018). Concerning the latter, the Kanauguk series (Loamy-skeletal, mixed, superactive, subgelic Lithic Haplogeloll) is the only Geloll mapped in Alaska, constituting 12.5 ha northeast of Nome. However, the actual spatial extent of Gelolls in

Alaska is likely between 0.4 and 3.1 million ha (M. Clark, personal communication; Soil

Survey Staff, 2012). As only one series has been mapped, there is little information about the taxonomic variability and extent of Gelolls.

Gelolls must meet all the criteria of a Mollisol in a gelic temperature regime, yet without meeting permafrost and/or gelic material requirements of Gelisols, which key out

Texas Tech University, Autumn Acree, May 2020 before Mollisols. The 12th (most recent) version of U.S. Keys to Soil Taxonomy (Soil

Survey Staff, 2014) specifies that Gelolls key out after the Alboll, Aquoll, and Rendoll suborders. Presently, only one great group exists: Haplogelolls. Taxonomic subgroups available for Haplogelolls include: Lithic, Andic, Aquic, Oxyaquic, Turbic, Cumulic, or

Typic (Soil Survey Staff, 2014). To date, only the lithic subgroup has been used to establish a soil series.

Given the largely unknown extent and scarce taxonomic classification of Gelolls, the objectives of this study were to further document the concept behind their pedogenesis, taxonomic variability, and likely extent in northern Alaska, USA. Given the large amount of organic carbon sequestered in Mollisols (Lal, 2004), a more precise delineation of

Gelolls could have large impacts on the modeling of national carbon stocks with implications on global climate change.

Materials and Methods

General Occurrence and Features

Soils studied as part of this project occur along the boundary of two major land resource areas (MLRAs) in Alaska: 244 – northern Brooks Range Mountains, and 245 –

Arctic Foothills (Fig. 3.1). Bedrock geology in the area is dominated by sedimentary carbonates, primarily calcium carbonate (Jorgenson and Grunblatt, 2013). When calcium carbonate is present in the soil, base saturation is assumed to be 100% (Soil Survey Staff,

2014; Rawal et al., 2019). Therefore, carbonatic parent material is vital for Mollisols as this material induces a high base saturation to meet the requirement for Mollisols.

Average annual precipitation of the study area approximates 214 mm (Galbraith Lake) to

Texas Tech University, Autumn Acree, May 2020

234 mm (Chandalar Lake) (Western Regional Climate Center, 2019; US Climate Data,

2019). These amounts would form aridic/ustic moisture regimes in warmer areas or at lower latitudes. However, low evapotranspiration rates at high elevations and high latitudes leave much of the landscape with an excess of moisture, resulting in aquic or udic soil moisture regimes. Ustic soil moisture is suspected only in areas of excessive drainage. At Galbraith Lake, the mean annual soil temperature (MAST) at ~50 cm from two years of temperature data (2016 and 2017) was -5.22oC, satisfying the requirements for a gelic soil temperature regime (UAF Geophysical Laboratory, 2020). Two soil temperature regimes (STRs) are commonly found in Alaska: gelic and cryic (Soil Survey

Staff, 2006). Gelic STR requirements are as follows: (i) MAST ≤ 0oC in Gelic suborders or Gelic great groups, or (ii) 1oC or lower in Gelisols either at a depth of 50 cm below the soil surface or at a densic, lithic, or paralithic contact, whichever is shallower (Soil

Survey Staff, 2014). For mineral soil, the mean summer soil temperature (MSST) is also considered. If the soil is not saturated with water during some part of the summer, the

MSST is required to be 0°C to 15°C devoid an O horizon; if there is an O horizon, the

MSST is required to be 0°C to 8°C (Soil Survey Staff, 2014). If the soil is saturated with water during some part of the summer, the MSST is required to be 0°C to 13°C without an O horizon and is 0°C to 6°C with an O horizon or a histic epipedon (Soil Survey Staff,

2014). For organic soil, the requirement is 0°C to 6°C (Soil Survey Staff, 2014). The control section for soil temperature is 50 cm below the soil surface or the upper boundary of a root limiting layer, whichever is shallower (Soil Survey Staff, 2014).

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MLRA 245

MLRA 244

Fig. 3.1. Location of the study area in northern Alaska, USA.

All sites are located in Arctic Bioclimate Zone E (Walker et al., 2005). The vegetation of these sites can be broadly divided into three categories. Category 1, sites A,

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B, E, F, G, and H, were located on stabilized riparian sites with >75% vegetation cover, near active streambeds and floodplains. Shrub cover (predominantly Salix glauca and

Salix reticulata, and other Salix spp.) ranged from 20%-80%, with shrub height varying from approximately 50 cm to 1.5 m. The dominant herbaceous species at these sites included Dryas spp. and Oxytropis campestris. Ground cover was dominated by

Hylocomium splendens, Datylina arctica, Masonhalea richardsonii, and Cetraria cucullata. Category 2, sites I and J, were located on stabilized, island-like isolated terraces on an active, braided gravelly floodplain. Vegetation cover was >90%, with a minor shrub component (Salix spp., <5% cover, typically <0.2 m in height. Dryas integrifolia, Dryas octopetala, Oxytropis campestris, Astragalus spp., Carex spp., and

Poa spp. dominated the herbaceous species at these sites and comprised a significant proportion of the total vegetative cover (>80%). Ground cover of non-vascular plants and lichens was less prevalent than in Group 1 (<25% cover) and was dominated by

Hylocomium splendens and Datylina arctica. Category 3, sites C and D, were located on a colluvial bench or kame terrace above the Atigun Valley and characterized by non- acidic prostrate dwarf-shrub, sedge, forb, fruticose-lichen tundra (Walker and Maier,

2008). Vegetation cover was >90%, with a minor low shrub component (<15% Salix spp., 0.2-0.5 m). Dominant herbaceous species included Dryas integrifolia, Astragalus spp., and Oxytropis spp. Ground cover of non-vascular plants at these sites was dominated by Hylocomium splendens, Datylina arctica, Masonhalea richardsonii, and

Cetraria cucullata. The vegetation across all investigated Geloll sites was distinct from adjacent vegetation on sites characterized by Gelisols with near-surface permafrost.

These adjacent sites were typically characterized by Moist to Wet Graminoid Tussock

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Sedge and Sedge-Moss Graminoid tundra (Walker and Maier, 2008), and the boundary between these vegetation and soil types was often very abrupt (Fig. 3.2).

Turbels Gelolls

Fig. 3.2. Upland sideslope bench site in northern Alaska showing strongly contrasting soil classification types. Soils to the left are Turbels, poorly drained and display permafrost within 0.5 m of the soil surface. Soils to the right are Gelolls, well drained, with no evidence of permafrost in the upper 2 m.

Climate has the greatest influence on permafrost formation in northern Alaska as temperatures stay below 0°C with a MAST of -4 to -10°C, yielding a Gelic soil temperature regime (Soil Survey Staff, 2006); therefore, slope and aspect have little influence (Ping et al., 2004; Shur and Jorgenson, 2007). Most soils of the area are

Gelisols of mixed mineralogy, specifically Fibristels in poorly drained basins and depressions where organic materials accumulate, and Aquiturbels, Histoturbels,

Molliturbels, and Haploturbels in loamy/stony colluvium, slope alluvium, and over

Texas Tech University, Autumn Acree, May 2020 residuum (Ping et al., 2004). Minor areas of Entisols (e.g., dune sands) and Inceptisols also occur (Ping et al., 2004). In mountains or active floodplains dominated by coarse materials with high thermal conductivity, permafrost may not be present within 2 m of the surface or at all (Ping et al., 2004).

Field Characterization and Sampling

Morphological descriptions and field sampling of ten Geloll soil pedons occurred in early July, 2018. At each sampling site, soils were observed either in a cut-bank which was excavated headwards and cleaned to reveal the natural soil profile, or in a soil pit

(~60 cm x 60 cm) excavated to a depth of at least 1 m. Active layer morphology was described in the field, and morphological properties including soil color, soil structure, soil texture, visible secondary carbonates, redoximorphic features, and visual differences in volume estimates of coarse fragments were utilized to define genetic horizons

(Schoeneberger et al., 2012). Due to the large volume of coarse fragments in the sampled soils, the compliant cavity method (Grossman and Reinsch, 2002) was utilized to determine bulk densities for mineral soil materials in the sampled profiles. A cavity was hand-excavated in each described horizon, and all materials from the cavity were carefully removed, placed into a bin, and weighed in the field using a digital balance. A thin sheet of pliable polyvinylidene chloride (PVDC) was molded to the cavity. Dry, well-graded fine sand of known density was subsequently poured into the cavity and hand packed until the sand was level with the top of the cavity. The PVDC containing the sand was subsequently removed from the cavity and the sand was weighed. Thus, bulk density estimates were calculated from volume estimates for the cavity (derived from the mass and known density of the graded sand), while mass estimates of the cumulative

Texas Tech University, Autumn Acree, May 2020 volume of material (fine earth and coarse fragments) removed from the cavity were obtained in the field. As it is inappropriate to use the compliant cavity, clod, or ring methods on organic materials, surface organic horizon bulk densities were collected using a serrated knife to cut blocks of ~262 cm3 (2in x 2in x 4in) from the pit face, then weighed with the digital balance. Across all pedons, a total of 43 horizons were described, with a total of 39 physical samples collected in the field.

Samples were then shipped to Texas Tech University where they were dried at 60°C, hand pulverized, and sieved to separate the fine earth (<2 mm) from coarse fragments (>2 mm). The fine earth fraction (<2 mm) was utilized for characterization of soil pH, SOC, soil inorganic carbon (SIC), particle size distribution, and loss on ignition organic matter.

Laboratory Characterization

Particle size analysis was performed on 50 g samples per Gee and Bauder (1986) using a model 152-H soil hydrometer with temperature corrected clay readings made at

1440 min and sand readings at 40 sec. Soil dispersion was ensured via physical agitation and the use of 10% sodium hexametaphosphate. Soil pH and soil salinity were analyzed via a 1:1 v/v suspension using a model Orion 8157BNUMD ROSS Ultra pH/ATC Triode for pH and using a model 4063CC digital salinity bridge (Thomas, 1996; Dahke and

Whitney, 1988). Soil organic matter was determined as percent loss-on-ignition (LOI) performed on 10 g samples at 400oC for 16 h per Nelson and Sommers (1996). Total soil carbon (TC, g 100 g-1) and nitrogen (TN, g 100 g-1) were determined on a subset of samples by dry combustion at 800 ºC using a LECO 2000 CN analyzer (Dumas, 1831;

Wright and Bailey, 2001). Calcium carbonate percentage was determined on 0.5 g samples with a Hohner Calcimeter (Hohner, Wrexham, Wales, U.K.) via pressure

Texas Tech University, Autumn Acree, May 2020 calcimetry (Loeppert and Suarez, 1996) and inorganic C determined by the ratio of C in

CaCO3. Organic carbon percentage was calculated by subtracting inorganic C from total

Results and Discussion

The results of all morphological descriptions, field, and laboratory testing are presented in Tables 3.1 and 3.2.

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Table 3.1. Soil profile characteristics and physicochemical data for ten pedons in northern Alaska, USA. Bold horizon nomenclature indicates the presence of coarse fragment pendants. 1 Pedon Horizon Depth Clay Sand Bulk Density LOI pH EC Efferv. Fam. Part. Size Class CaCO3 Total C Total N Org. C --cm-- --%-- --%-- ---g cm-3--- --%-- --dS m-1------%------Cumulic Haplogelolls Pedon A Oe 0-4 -- -- 0.21 ------Loamy-skeletal ------A1 4-30 11 49 0.79 7.85 6.76 1.29 SL 29.61 8.39 0.31 4.84 A2 30-52 11 53 1.03 4.21 7.85 0.32 SL 34.44 9.45 0.15 5.31 AC 52-85 9 53 1.29 6.21 7.70 0.42 SL 35.05 8.90 0.22 4.70 C 85-120 7 89 0.76 1.22 7.93 0.11 SL 35.64 6.11 0.05 1.83 Ab 120-180+ 9 75 0.76 3.11 7.83 0.24 ST 33.84 7.78 0.13 3.72 Pedon B Oi 0-5 ------Coarse-loamy ------A1 5-28 9 54 -- 5.79 7.33 0.61 SL 30.82 7.74 0.20 4.04 A2 28-43 11 57 -- 5.72 7.06 0.74 SL 34.45 9.15 0.21 5.02 Bw1 43-69 11 53 -- 6.60 7.07 0.85 SL 30.82 8.06 0.26 4.36 Bw2 69-111 10 50 -- 7.03 7.25 0.82 SL 29.61 8.51 0.26 4.96 Bgb/Oe 111-134 8 33 -- 8.28 7.46 0.73 SL 33.85 9.08 0.33 5.02 Cb 134-158 6 86 -- 1.57 7.63 0.19 SL 48.95 7.93 0.06 2.05 Ab 158+ 8 26 -- 9.27 6.88 1.48 SL 16.92 9.35 0.40 7.31 Typic Haplogelolls Pedon C Oe 0-9 -- -- 0.21 ------Loamy-skeletal ------A1 9-28 7 33 0.54 24.16 5.54 1.81 NE 0.00 11.11 0.84 11.11 A2 28-41 9 39 -- 17.50 5.96 1.23 NE 0.00 9.58 0.71 9.58 2Bw 41-62+ 9 55 -- 7.24 6.08 1.19 NE 0.00 2.69 0.30 2.69 Pedon D Oe 0-9 -- -- 0.21 ------Loamy-skeletal ------A 9-35 7 52 0.47 38.39 5.54 0.35 NE 0.00 14.37 1.29 14.37 C 35-74+ 11 66 -- 7.00 6.01 1.03 NE 0.00 2.50 0.26 2.50 Pedon E Ak 0-28 8 74 1.37 2.43 6.79 1.02 VE Sandy-skeletal 33.85 5.92 0.12 1.86 CAk 28-45 6 92 1.35 1.58 7.82 0.22 VE 35.67 5.40 0.06 1.12 Ck 45-100+ 6 88 1.46 2.12 7.99 0.16 VE 36.87 4.60 0.08 0.18 Proposed Fluventic Haplogeloll Pedon F A1 0-8 9 49 0.74 4.56 7.74 0.48 SL Sandy-skeletal 32.03 7.83 0.17 3.99 A2 8-27 7 83 1.32 1.95 6.83 0.61 SL 42.92 7.30 0.08 2.15 C 27-150+ 5 87 1.90 1.55 8.09 0.11 SL 45.91 6.98 0.05 1.47 Pedon G Ak 0-19 8 68 1.34 6.03 7.1 0.76 SL Sandy-skeletal 21.15 4.56 0.17 2.02 CAk 19-52 8 86 1.87 2.47 7.78 0.20 SL 29.00 3.98 0.08 0.50 Ck 52-101+ 8 83 1.87 2.07 8.06 0.18 SL 27.20 4.14 0.07 0.88 Proposed Pachic Haplogeloll Pedon H A1 0-8 9 33 0.34 9.78 6.72 1.41 SL Loamy-skeletal 27.19 7.95 0.37 4.69 A2 8-40 13 59 1.09 7.49 6.14 2.14 SL 29.00 9.13 0.38 5.65 AC 40-52 7 38 0.71 3.40 8.94 1.51 SL 34.44 7.20 0.25 3.07 C1 52-71 13 71 1.46 2.67 7.16 0.68 SL 22.12 5.29 0.10 2.64 C2 71-150+ 7 87 1.78 1.62 7.66 0.27 ST 33.23 5.81 0.06 1.82 Pedon I Ak 0-22 5 72 0.52 13.47 7.57 0.68 VS Sandy-skeletal 18.73 6.31 0.39 4.06 Bk1 22-34 5 67 1.28 10.92 7.89 0.54 VS 16.92 6.98 0.42 4.95 Bk2 34-53 7 77 1.39 3.93 7.83 0.38 SL 21.75 3.60 0.12 0.99 Ck 53-95+ 7 87 1.18 2.12 8.12 0.15 ST 19.94 3.35 0.06 0.96 Pedon J Ak1 0-12 9 72 1.07 5.73 6.47 1.43 ST Loamy-skeletal 16.92 4.20 0.21 2.17 Ak2 12-32 8 56 0.74 3.85 8.91 1.85 ST 13.29 3.38 0.34 1.79 Bk 32-54 12 63 1.18 5.92 6.51 2.33 ST 15.71 4.48 0.32 2.60 Ck 54-85+ 8 81 1.24 2.30 7.02 0.80 ST 18.13 4.54 0.12 2.36 1NE=non-effervescent, SL=slightly, ST=strongly; VS=very strongly; VE=violently.

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Table 3.2. Soil profile characteristics and physicochemical data for ten pedons in northern Alaska, USA. Bold horizon nomenclature indicates the presence of coarse fragment pendants. 1 2 3 4 5 6 Pedon Horizon Depth (cm) Hor. Bound. Soil Texture Coarse Fragment % Est. Moist Color Structure Consistency Roots Redox Cumulic Haplogelolls Pedon A Oe 0-4 A,S ------A1 4-30 G,W Loam 20 GR 10YR 2/2 2,F,SBK FR 3,F/VF -- A2 30-52 G,W Sandy Loam 30 GR 10YR 3/2 2,F,SBK FR 3,F/VF -- AC 52-85 C,W Sandy Loam 45 GR 10YR 4/2 2,M,SBK FR 2,F/VF -- C 85-120 G,W Fine Sand 80 GR 10YR 3/2 0,SG L 2,F/VF -- Ab 120-180+ -- Sandy Loam 80 GR 10YR 2/2 1,SBK L 2,F/VF -- Pedon B Oi 0-5 C,S ------A1 5-28 C,W Sandy Loam 10 GR 10YR 3/1 1,F,SBK VF 2,VF/F -- A2 28-43 G,W Sandy Loam 11 GR 10YR 2/2 1,M,SBK VF 2,VF/F -- Bw1 43-69 C,W Sandy Loam 10 GR 10YR 3/1 2,M,SBK F 3,VF/F F,C Bw2 69-111 C,W Loam 14 GR 10YR 3/1 2,M,SBK VF 2,VF/F F,D&C Bgb/Oe 111-134 C,W Silt Loam 7 GR 10YR 4/1, 10YR 2/1 2,M,SBK F 2,C/VF/F F,D&C Cb 134-158 A,W Loamy Fine Sand 60 GR, 10 CB 2.5Y 3/2 0,SG L 3,VF/F -- Ab 158+ -- Silt Loam -- 2.5Y 3/1 2,M,SBK F 1,VF/F M,D Typic Haplogelolls Pedon C Oe 0-9 C,W ------A1 9-28 C,W Silt Loam 10 GR 10YR 2/2 1,M,SBK VFR 3,VF/F; 1,M -- A2 28-41 A,W Silt Loam 10 GR 10YR 2/1 1,M,SBK FR 2,VF/F -- 2Bw 41-62+ -- Sandy Loam 20 CB, 10 BY, 50 GR 7.5YR 2/1 1,F,SBK L 1,VF/F -- Pedon D Oe 0-9 C,W ------A 9-35 C,W Sandy Loam 30 GR 10YR 3/2 2,F,SBK FR 3,VF/F; 1,M -- C 35-74+ -- Sandy Loam 10 ST, 10 CB, 60 GR 10YR 3/2 0,SG L 3,VF/F; 1,M -- Pedon E Ak 0-28 C,W Sandy Loam 60 GR, 15 CB 10YR 3/2 2,F,SBK FR 3,M/VF/F -- CAk 28-45 C,W Fine Sand 80 GR, 10 CB 10YR 4/2 0,SG L 3,VF/F -- Ck 45-100+ -- Loamy Fine Sand 50 ST, 45 GR 10YR 4/2 0,SG L 1,M/VF/F -- Proposed Fluventic Haplogeloll Pedon F A1 0-8 C,S Loam 15 GR 10YR 3/2 2,F,GR F 3,VF/F; 2,M/VC -- A2 8-27 G,W Loamy Sand 80 GR 10YR 3/2 1,F,SBK VF 3,F/VF; 1,C -- C 27-150+ -- Loamy Sand 10 ST, 50 GR, 30 CB 10YR 4/2 0,SG L 2,F/VF -- Pedon G Ak 0-19 A,W Sandy Loam 15 CB, 10 GR 10YR 3/2 1,M,GR VFR 3,V/VF; 2,M -- CAk 19-52 C,W Loamy Fine Sand 30 CB, 45 GR 10YR 3/2 0,SG L 2,VF/F; 1,M -- Ck 52-101+ -- Loamy Fine Sand 10 ST, 40 CB, 40 GR 10YR 3/2 0,SG L 2,VF/F; 1,M -- Proposed Pachic Haplogeloll Pedon H A1 0-8 C,S Silt Loam -- 10YR 3/2 2,F,GR VFR 3,VF/F -- A2 8-40 C,W Sandy Loam 30 GR 10YR 2/2 2,M,SBK FR 3,VF/F -- AC 40-52 A,W Silt Loam 10 GR 10YR 3/2 2,M,SBK FR 2,M/C/F -- C1 52-71 C,S Sandy Loam 61 GR 10YR 4/2 0,SG VFR 2,F -- C2 71-150+ -- Loamy Sand 85 GR 10YR 4/2 0,SG L 2,F -- Pedon I Ak 0-22 C,S Sandy Loam -- 10YR 2/2 1,F,GR VFR 3,VF/F/M -- Bk1 22-34 C,W Sandy Loam 5 GR, 5 ST, 5 CB 10YR 2/2 1,M,SBK VFR 2,VF/F/M -- Bk2 34-53 C,W Sandy Loam 10 B, 10 CB, 10 ST, 30 GR 10YR 3/2 1,M,SBK VFR 2,VF/F/M -- Ck 53-95+ -- Loamy Fine Sand 10 B, 10 CB, 10 ST, 45 GR 10YR 4/2 0,SG L 1,VF/F/M -- Pedon J Ak1 0-12 C,W Sandy Loam 20 GR 10YR 3/2 2,F,GR VFR 1,M; 3,VF/F -- Ak2 12-32 C,W Sandy Loam 5 GR 10YR 3/2 1,C,SBK FR 1,M; 2,VF/F -- Bk 32-54 C,W Sandy Loam 10 CB, 30 GR 10YR 3/2 2,M,SBK FR 2,VF/F -- Ck 54-85+ -- Loamy Fine Sand 5 ST, 20 CB, 65 GR 10YR 4/2 0,SG L 1,VF/F -- 1A=abrupt, C=clear, G=gradual; S=smooth, W=wavy. 2GR=gravel, CB=cobbles, ST=stones, B=boulders. 30=structureless, 1=weak, 2=moderate; F=fine, M=medium; SBK=subangular blocky, GR=granular, SG=single grain. 4FR=friable, L=loose, VFR=very friable. 51=few, 2=common, 3=many; C=coarse, F=fine, M=medium, VC=very coarse, VF=very fine. 6F=few (<2%), M=many (>20%); C=concentrations, D=depletions.

Texas Tech University, Autumn Acree, May 2020

Cumulic Haplogelolls (Established)

Cumulic is the penultimate subgroup to key out for Haplogelolls in Keys to Soil

Taxonomy. The criteria in Keys to Soil Taxonomy for Cumulic Haplogelolls are as follows: (i) mollic epipedon ≥ 40 cm thick with a textural class finer than loamy fine sand, and (ii) an irregular decrease in organic-carbon content between 25 cm and 125 cm depth below the mineral soil surface (Soil Survey Staff, 2014). Two pedons (A and B) met the criteria for classification as Cumulic Haplogelolls following field description and laboratory analysis. These pedons were found alongside deeply dissected creek beds west of Galbraith Lake (Fig. 3.1) derived from alluvial parent material. Rocks in the area were identified as conglomerate, limestone, sandstone, and chert. The soil was generally sandy in nature and contained large amounts of coarse fragments.

Pedon A (Fig. 3.3) was on a 1% slope and had six horizons described to a depth of

180 cm. The surface featured an Oe horizon 4 cm thick followed by A1 (4-30 cm), A2

(30-52 cm), AC (52-85 cm), C (85-120 cm), and Ab (120-180+ cm) horizons. The mollic epipedon was 48 cm thick. The family particle size class was loamy-skeletal. An irregular decrease in organic carbon content was found throughout: A1 (4.84%), A2 (5.31%), AC

(4.70%), C (1.83%) and Ab (3.72%). Soil textures included gravelly loam, gravelly sandy loam, very gravelly sandy loam, extremely gravelly fine sand, and extremely gravelly sandy loam, respectively. Due to the mollic thickness and the irregular decrease in organic carbon, Pedon A was classified as a Cumulic Haplogeloll.

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Fig. 3.3. Horizonation of Pedon A described as a Cumulic Haplogeloll in northern Alaska, USA. Pedon A had a mollic epipedon over 40 cm thick (48 cm) and an irregular decrease in organic carbon content was observed in the Ab horizon. There was no permafrost within 2 m. Therefore, Pedon A met the criteria for a Cumulic Haplogeloll.

Pedon B featured eight horizons morphologically described on a 1% slope. The horizons and depths from the soil surface were as follows: Oi 0-5 cm, A1 5-28 cm, A2

28-43 cm, Bw1 43-69 cm, Bw2 69-111 cm, Bgb/Oe 111-134 cm, Cb 134-158 cm, and Ab

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158+ cm. The mollic epipedon was 106 cm thick and was slightly effervescent. There was an irregular decrease in organic carbon content throughout: Al (4.04%), A2 (5.02%),

Bw1 (4.36%), Bw2 (4.96%), Bgb/Oe (5.02%), Cb (2.05%), and Ab (7.31%). Based on the mollic thickness and the irregular decrease in organic carbon, Pedon B was classified as a Cumulic Haplogeloll. The family particle size class was coarse-loamy. Few (<2%) redoximorphic concentrations were observed in the Bw1, few redoximorphic concentrations and depletions were in the Bw2 and Bgb/Oe, and many redoximorphic depletions were in the Ab. Soil texture was sandy loam down to 69 cm. The soil texture shifted to loam from 69-111 cm, silt loam at 111-134 cm, extremely gravelly loamy fine sand at 134-158 cm, and silt loam at 158+ cm.

Typic Haplogelolls (Established)

Typic is the last subgroup to key out for Haplogelolls in Keys to Soil Taxonomy; it captures the central concept of Haplogelolls that do not meet the criteria for any other

Haplogeloll subgroup. Three pedons (Pedon C, Pedon D, and Pedon E) were classified as

Typic Haplogelolls. Pedons C and D were located on a well-drained, moderately-sloping upland bench. The parent material for Pedon C was loess over colluvium or lateral moraine ablation till deposits. Pedon D had no detectable loess deposit. Gelisols were found in the swale adjacent to the bench due to water accumulation and poor drainage

(Fig. 3.2). Ping et al. (2015) reported physicochemical data at the same sampling sites of

Pedons C and D whereby BSP clearly exceeded mollic requirements, ranging from 92 to

100%. Furthermore, pH data reported by Ping at al. (2015) was slightly acidic in the upper part of the solum (pH 6.4 to 6.6), further substantiating the data presented herein

Texas Tech University, Autumn Acree, May 2020

(Table 3.1). Pedon E was located on a dissected cutbank alongside the creek bed flowing into Galbraith Lake and adjacent to Pedon F.

Pedon C was on a 9% slope and had four horizons described to a depth of 62 cm. The horizons were as follows: Oe (0-9 cm), A1 (9-28 cm), A2 (28-41 cm), and 2Bw (41-62+ cm). The mollic epipedon was 41 cm thick. The family particle size class was loamy- skeletal. Soil texture changed from silt loam with 10% coarse fragments in the A1 and A2 to sandy loam with 80% coarse fragments in the 2Bw, indicating a lithologic discontinuity.

Pedon D was on a 6% slope with three horizons morphologically described. An Oe horizon was observed from the surface down to 9 cm. An A horizon followed from 9 to

35 cm; below that, a C horizon was described to a depth of 74 cm. The mollic epipedon was 26 cm thick. The family particle size class was loamy-skeletal with 30% gravels in the A horizon and 80% coarse fragments in the C horizon. No lithologic discontinuity was described in this pedon because the pedon was sandy in nature throughout the profile. No pendants were found in this pedon. Soil texture was gravelly sandy loam for the A horizon and extremely gravelly sandy loam for the C horizon.

Pedon E (Fig. 3.4) featured an Ak (0-28 cm), CAk (28-45 cm), and Ck horizon (45-

100+ cm) with soil textures extremely gravelly sandy loam, extremely gravelly fine sand, and very stony loamy fine sand, respectively. This pedon was on a 1.5% slope. The mollic epipedon was 28 cm thick. Even at the soil surface, the gravels contained weak pendants justifying Ak horizonation. These pendants could be the result of pedogenesis or could be influenced by the abundant organic matter. Organic matter has a low pH making Ca soluble at pH 6.5 (Goss et al., 2007). Weakly developed pendants of CaCO3

Texas Tech University, Autumn Acree, May 2020 were found in the CAk horizon (Fig. 3.5). No B horizon was observed due to lack of structure and no CaCO3 in the fine earth fraction. There were 75% coarse fragments observed in the Ak, 90% in the CAk, and 95% in the Ck. The majority of coarse fragments in the Ak and CAk were gravels followed by cobbles. The Ck horizon had mostly stones and gravels. Approximately 10% of the cobbles had water worn pendants.

Fig. 3.4. Horizonation of Pedon E described as a Typic Haplogeloll in northern Alaska, USA. Pedon E had a mollic epipedon 28 cm thick. No permafrost was present within 2 m. Pedon E did not meet the criteria for other Haplogeloll subgroups; therefore, Pedon E was classified as a Typic Haplogeloll.

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Fig 3.5. Secondary calcium carbonate pendants found on rock fragments in various pedons adjacent to drainages near Galbraith Lake, Alaska, USA. Pendant development in a coarse fragment matrix supports stage I calcic horizon designation (Schoeneberger et al., 2012).

Fluventic Haplogelolls (Proposed)

Two pedons (Pedon F and Pedon G) met the criteria for a Fluventic subgroup; yet,

Fluventic is not recognized as a subgroup for Haplogelolls in Keys to Soil Taxonomy

(Soil Survey Staff, 2014). Currently, these pedons would classify as Typic Haplogelolls; however, the Fluventic subgroup provides more information about these pedons than the

Typic subgroup. Considering the Typic subgroup in Keys to Soil Taxonomy is only described as “other Haplogelolls,” additional subgroups for Haplogelolls need to be added in future editions of Keys to Soil Taxonomy to better describe these pedons and for convenience in naming soil intergrades (Smith, 1986). The Fluventic subgroup exists in

Keys to Soil Taxonomy for other Mollisols such as Haplocryolls, Haploxerolls,

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Haplustolls, Calciudolls, and Hapludolls. The criteria for the Fluventic subgroup for the aforementioned Mollisols are as follows: (i) slope <25% and (ii) human-transported material <50 cm thick in the surface horizons and either (iii) organic-carbon content

≥0.3% at 125 cm below the mineral soil surface or (iv) an irregular decrease in organic- carbon content between 25 cm and 125 cm of the mineral soil surface or a densic, lithic, or paralithic contact, whichever is shallower (Soil Survey Staff, 2014). Both pedons had slopes <25% (1% and 4%, respectively) with no human-transported material and organic- carbon contents >0.3% at 125 cm below the mineral soil surface (1.47% and 0.88%, respectively). The family particle size class for all three pedons was sandy-skeletal.

Pedon F was located alongside deeply-dissected creek beds west of Galbraith Lake.

Pedon G was located alongside deeply-dissected creek beds south east of Pedon F.

Pedon F (Fig. 3.6) was on a deeply-dissected active flood plain with alluvial parent material on a 1% slope. Three morphological horizons were described: A1 (0-8 cm), A2

(8-27 cm), and C (27-150+ cm). The texture of the horizons was determined in the field as gravelly loam for A1, extremely gravelly loamy coarse sand for A2, and very gravelly coarse sand for the C horizon. The organic carbon content was 1.47% from 27-150 cm (C horizon); therefore, Pedon F would be better described with the Fluventic subgroup rather than Typic. Coarse fragment percentage was estimated to be 15% gravel in the A1, 80% gravel in the A2, and 10% stones, 50% gravels, and 30% cobbles in the C horizon. The mollic epipedon was 27 cm thick.

Texas Tech University, Autumn Acree, May 2020

Fig. 3.6. Horizonation of Pedon F currently classified as a Typic Haplogeloll in northern Alaska, USA. The mollic epipedon was 27 cm thick. The organic carbon content in the C horizon was 1.47%. There was no permafrost present within 2 m. Fluventic subgroup would better described this pedon; however, Fluventic is not currently a subgroup for Haplogelolls in the 12th edition of Keys to Soil Taxonomy.

Texas Tech University, Autumn Acree, May 2020

Pedon G had oriented CaCO3 pendants on coarse fragments in all horizons (Fig. 3.5).

This pedon was on a 4% slope. An Ak horizon was observed from 0-19 cm with a soil texture of cobbly sandy loam with 15% cobbles and 10% gravels. Under the Ak was a

CAk horizon from 19-52 cm with a texture of very gravelly loamy fine sand and 30% cobbles and 45% gravels. Following, was a Ck horizon from 52 -101+ cm that was a very cobbly loamy fine sand with 10% stones, 40% cobbles, and 40% gravels. The mollic epipedon was 52 cm thick. There was an irregular decrease in organic carbon content throughout: Ak (2.02%), CAk (0.50%), and Ck (0.88%). Since the Ck had 0.88% organic

C, the Fluventic subgroup is the better suited subgroup for Pedon G.

Pachic Haplogelolls (Proposed)

A Pachic subgroup is not currently recognized for Haplogelolls in Keys to Soil

Taxonomy; however, three Haplogeloll pedons (Pedon H, Pedon I, and Pedon J) met the criteria for a Pachic subgroup. The criteria for a Pachic subgroup in Cryolls are as follows: (i) mollic epipedon ≥ 40 cm thick and (ii) a texture class finer than loamy fine sand (Soil Survey Staff, 2014). Currently, these pedons would classify as Typic

Haplogelolls; however, the Pachic subgroup would give more information about these pedons than the Typic subgroup. These pedons were located on an alluvial fan near

Galbraith Lake. The deep dissection by the adjacent stream, high gravel content, and low clay content allowed the soil to be well drained. All three pedons were in deeply- dissected alluvium with low clay content and large quantities of coarse fragments. Two of the pedons (Pedons I and J) had CaCO3 pendants throughout the profile (Fig. 3.5). While the pedons did have over 15% by weight CaCO3 in every horizon, there was either <5%

Texas Tech University, Autumn Acree, May 2020

identifiable secondary carbonates or the underlying horizon did not have 5% less CaCO3 than an overlying horizon. Therefore, a Calcic subgroup was ruled out.

For Pedon H, five horizons were described to a depth of 150 cm on a 1% slope.

Horizons were described as A1 (0-8 cm), A2 (8-40 cm), AC (40-52 cm), C1 (52-71 cm), and C2 (71-150+ cm). The mollic epipedon was 52 cm thick classifying this pedon as a

Pachic Haplogeloll. While the profile did contain some contain calcium carbonate, such carbonates were attributed to depositional alluvium from upstream not pedogenesis within the profile due to the random orientation of distribution of carbonates observed therein. The family particle size class was loamy-skeletal. Soil textures were as follows: silt loam (A1), gravelly sandy loam (A2), silt loam (AC), extremely gravelly sandy loam

(C1), and extremely gravelly loamy sand (C2).

Pedon I (Fig. 3.7) was located on a deeply-dissected remnant of an alluvial fan with a

4% slope near Galbraith Lake with four horizons morphologically described to a depth of

95 cm below the mineral soil surface. The horizonation was as follows: Ak (0-22 cm),

Bk1 (22-34 cm), Bk2 (34-53 cm), and Ck (53-95+ cm). The mollic epipedon was 53 cm thick classifying Pedon I as a Pachic Haplogeloll. Secondary carbonates were found in the fine earth in the Ak and Bk1 horizons. Calcium carbonate pendants were found on the coarse fragments in the Bk1, Bk2, and Ck horizons. There were no coarse fragments in the Ak horizon, 15% coarse fragments (5% gravels, 5% stones, and 5% cobbles) in the

Bkl horizon, 60% coarse fragments (10% boulders, 10% cobbles, 10% stones, and 30% gravels) in the Bk2 horizon, and 75% coarse fragments (10% boulders, 10% cobbles,

10% stones, and 45% gravels) in the Ck horizon. The majority of coarse fragments were limestone with a small amount as sandstone and conglomerate. The soil texture was

Texas Tech University, Autumn Acree, May 2020 sandy loam to 53 cm, and then shifted the loamy fine sand from 53-95+ cm. Coarse fragment modifiers were placed on horizons Bk1, Bk2, and Ck making the soil textures stony sandy loam, gravelly sandy loam, and very gravelly loamy fine sand, respectively.

The family particle size class was sandy-skeletal.

Fig. 3.7. Horizonation of Pedon I currently classified as a Typic Haplogeloll in northern Alaska, USA. The mollic epipedon was 53 cm thick. No permafrost was present within 2 m. Pedon I is better described by the Pachic subgroup; however, Pachic is not currently a subgroup for Haplogelolls in the 12th edition of Keys to Soil Taxonomy.

Texas Tech University, Autumn Acree, May 2020

Pedon J was on a 10% slope and had four horizons morphologically described to 85 cm below the mineral soil surface. The first horizon was determined to be an Ak1 horizon to a depth of 12 cm with a soil texture of gravelly sandy loam with 20% gravels. The second horizon was described as Ak2 from 12-32 cm with a texture of sandy loam with secondary CaCO3 and 5% gravels. The third horizon was described as Bk from 32-54 cm with secondary CaCO3 and a texture of gravelly sandy loam with 40% coarse fragments.

The coarse fragments in the Bk included 10% cobbles and 30% gravels. The fourth and final horizon described was a Ck horizon to a depth of 85+ cm with a texture of extremely gravelly loamy fine sand. There were 90% coarse fragments (5% stones, 20% cobbles, and 65% gravels) in the Ck. The mollic epipedon was 54 cm thick. The family particle size class was loamy-skeletal.

Genetic Model for Gelolls in the Brooks Range and Arctic Foothills

All Gelolls were found on parent material containing a source of CaCO3 to support the base saturation requirements of Mollisols. The Gelolls investigated here were found along creek beds in the low foothills of Galbraith Lake with large amounts of alluvial coarse fragments and coarse textured materials that have high thermal conductivity allowing for warmer soil temperatures and a drier, well-drained soil that is unfavorable for shallow permafrost. The area where Gelolls were found transitioned into an area with thick organic matter accumulations and low inputs of colluvial or alluvial materials resulting in low thermal conductivity and supporting shallow permafrost; thus, classifying the soils in this area as Gelisols. Gelolls (Pedons C and D) were also found on an upland sideslope bench site with colluvial coarse fragments on a slight to moderate slope allowing for high thermal conductivity and precipitation runoff. The precipitation

Texas Tech University, Autumn Acree, May 2020 accumulated in a nearby footslope area with thick vegetation that was dominated by

Gelisols (Fig. 3.2). Along the Artic Foothills or the Artic Coastal Plain, where the slope is nearly level, the cold climate often supports thick accumulations of organic matter that predispose the soil toward Histels or Histoturbels depending on the occurrence of cryoturbation (Ping et al., 2004). Upon studying, comparing, and contrasting the microclimate of Gelolls and Gelisols, a Geloll concept was formed. The general landscape concept where Gelolls are thought to occur is in well-drained areas with limestone in the parent material. Generally, thin or no organic horizon and large quantities of coarse fragments and coarse textured materials are common. Since limestone dominates the Brooks Range and the Arctic Foothills, a larger scale survey needs to be undertaken in these areas to further determine the extent of Gelolls in northern Alaska. Financial constraints limited the present study to sampling sites that were within reasonable hiking distance from the Dalton Highway. A more extensive study throughout the Brooks Range will undoubtedly require helicopter use for remote access to elucidate the full extent of Gelolls in this part of northern Alaska.

C Stock Modeling Implications

Understanding the full extent of Gelolls in Alaska can impact estimations of soil organic C stocks with implications for global climate change. Temperatures in arctic regions, such as Alaska, are increasing at a rate almost double the average rate of global warming during the past 100 years (Intergovernmental Panel on Climate Change, 2007).

Precipitation in Alaska is estimated to increase by 20% in the next 100 years

(Intergovernmental Panel on Climate Change, 2007). Since respiration rates are dependent on soil moisture and temperature (Borken et al., 2003; Kirschbaum, 2000;

Texas Tech University, Autumn Acree, May 2020

Pumpanen et al., 2003), soil organic C sequestration in Alaska can have major implications for global climate change. Bliss and Maursetter (2010) recommended soil taxonomy be the basis for estimation of soil organic C stocks since soil orders sequester carbon at different intensities. Bliss and Maursetter (2010) determined soil organic C content to 1 m in Mollisols in Alaska to be 29.7 kg C m-2; however, the measurements were based on only five pedons. Further, their estimates of total soil organic C in Alaska were 32 to 53 Pg. However, they advocated an estimation of 48 Pg C based on the use of soil taxonomic subgroups. Bliss and Maursetter (2010) considered a total soil area of

Alaska of 1,188,846 km2. Based upon 3.1 million ha of suspected Gelolls, they occupy

2.6% of the total soil area of Alaska. Taking 2.6% of Bliss and Maursetter’s soil organic

C estimate of Alaska of 48 Pg C would place Gelolls constituting 1.25 Pg C. Based upon bulk density and organic carbon data across the 10 Geloll profiles evaluated for the present study, soil organic C within Gelolls is estimated to be 1.18 Pg. Further, four thin

O horizons (4 to 10 cm in thickness) were excluded from the present analysis, which would conceivably increase the soil organic C slightly to even more closely match the predictions of Bliss and Maursetter (2010). Evaluation of Geloll extent will provide a more detailed analysis of soil organic C sequestration in Alaska with potential implications for global climate change.

Conclusions

Multiple Gelolls were identified and characterized in the Brooks Range and Artic

Foothills in northern Alaska. The major factors affecting Geloll formation were parent material, drainage, and coarse fragment content. Sedimentary, carbonatic parent material was crucial to meet the base saturation requirement for Mollisols. A well-drained soil is

Texas Tech University, Autumn Acree, May 2020 an unfavorable environment for shallow permafrost formation making it optimal for

Geloll formation. A large quantity of coarse fragments can effectively elevate soil temperatures, which is also unfavorable for shallow permafrost formation. Five of the ten

Gelolls classified in this study met the criteria for Fluventic or Pachic subgroups, which are not currently recognized as subgroups for Gelolls in U.S. Keys to Soil Taxonomy.

Given there is currently only one Geloll series mapped on 12.5 ha in northeast Alaska, it is now evident that more Gelolls exist in northern Alaska than currently mapped. A more extensive study needs to be conducted to further delineate the full extent of Gelolls and determine whether Fluventic and Pachic subgroups need to be added to U.S. Keys to Soil

Taxonomy to most appropriately classify the Gelolls. Subgroup classification will also aid in refining soil organic carbon stock predictions. Soil organic carbon stocks derived from the ten profiles in this study and applied to the total suspected area of Gelolls in Alaska

(1.18 Pg C) support previous estimates (1.25 Pg C) as extrapolated from Bliss and

Maursetter (2010). Considering Mollisols sequester a large amount of C, understanding the full extent of Gelolls in northern Alaska will better determine the global impact on climate change.

Acknowledgments

The authors gratefully acknowledge the BL Allen Endowment in Pedology and the Ed and Linda Whitacre Presidential Graduate Fellowship at Texas Tech University in conducting this research. The authors are grateful to Karen Vaughan, Walker C.

Weindorf, Chelsea Duball, Amanda Pennino, Mark Clark, Alexander Kholodov, and the

Toolik Lake Field Research Station for Assistance in field sampling.

Texas Tech University, Autumn Acree, May 2020

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Loeppert, R.H., Suarez, D.L., 1996. Carbonate and gypsum. In: Sparks, D.L. (Ed.), Methods of soil analysis. Part 3. Chemical methods. Soil Science Society of America, Madison, WI. pp. 437-474. Nelson, D.W., Sommers, L.E., 1996. Total carbon, organic carbon and organic matter. In: Sparks, D.L. (Ed.), Methods of soil Analysis: Chemical methods, Part 3. Soil Science Society of America, Madison, WI. pp. 961–1010.

Ping, C.L., Clark, M.H., Swanson, D.K., 2004. Cryosols in Alaska. In: Cryosols: Permafrost-Affected Soils. 1st ed. Springer-Verlag Berlin Heidelberg, New York, New York. pp. 71–94.

Ping, C.L., Clark, M.H., Michaelson, G.J., 2015. Guidebook-Artic Soils Geography Field Study-Permafrost Affected Soils, NRM-F489/F689, AFES Bulletin 118, University of Alaska, Fairbanks.

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CHAPTER IV

SOIL CLASSIFICATION IN ROMANIAN CATENAS VIA ADVANCED PROXIMAL SENSORS1 Autumn Acree, David C. Weindorf, Laura Paulette, Natasja van Gestel, Somsubhra Chakraborty, Titus Man, Cynthia Jordan, José Luis Prieto

Abstract

The Transylvanian Plain (TP), Romania is widely used for agronomic production. The

Chernisol soil class covers a vast majority of the TP as defined by the Sistemul Roman

De Taxonomie A Solurilor. Chernisols are fertile, dark soils similar to Mollisols in the

United States. Chernisols have four key soil types, two of them occur in the TP: chernozems and phaeozems. Chernozems require a chroma of ≤ 2 in the Am horizon when moist and a calcic horizon or secondary CaCO3 within 125 cm. Phaeozems require chroma of ≤ 3.5 when wet and a calcic horizon or secondary CaCO3 deeper than 125 cm. Traditionally, morphological assessment in combination with laboratory data has been used to assess the depth of secondary CaCO3, thus establishing the taxonomic classification. Herein, the efficacy of portable X-ray fluorescence (PXRF) and visible near infrared spectroscopy (VisNIR) were evaluated to make such taxonomic determinations directly on 25 soil cores collected across five toposequences. Cores were scanned on-site with both sensors at 10 cm increments to determine depth to CaCO3 accumulation. Comparing Ca percentages from PXRF in isolation with traditional laboratory pressure calcimetry via simple linear regression

______

1 Submitted for publication to Geoderma

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(SLR), the following model validation data (based on whole core splitting, Coreval) were obtained: R2 = 0.92; root mean squared error (RMSE) = 0.81%; residual prediction deviation (RPD) = 3.27: ratio of performance to interquartile range (RPIQ):

5.34. Thus, most of the Ca in soils of the TP is associated with secondary CaCO3.

Using the three most prominent latent variables, VisNIR spectra (smoothed to 10 nm bands) were combined with PXRF data via partial least squares regression (PLSR) to determine if any improvements could be achieved by the combined approach.

2 Combined Coreval models produced the following: R = 0.89; RMSE = 0.98%; RPD =

2.73; RPIQ = 4.46. Boosted regression tree Coreval modeling produced similar results

(R2 = 0.90; RMSE = 0.89%; RPD = 2.99; RPIQ = 4.87). With deference to the law of parsimony, use of PXRF data in isolation for calcic horizon identification and quantification appears preferable for most Pedological applications given its robust, strong performance. Minimal differences were observed using two different sample splitting schemes (whole core vs. full sample set) relative to PXRF data predictive models for CaCO3 prediction, especially for PXRF in isolation. Of the sites investigated, PXRF identified six phaeozems (P) and nineteen chernozems (C).

Specifically, the following were identified on different slope profiles: summits

(1P/4C), shoulders (1P/2C), backslopes (3P/7C), footslopes (0P/4C), and toeslopes

(1P/2C). Localized landslides and erosion precluded the identification of a common landscape model differentiating P/C on landscapes. Nonetheless, proximal sensors were adept at directly differentiating soil properties needed for definitive taxonomic classification on-site, with no need for traditional pressure calcimetry.

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Introduction

The Transylvanian Plain (TP), Romania is widely used for agricultural production including wheat, corn, sugar beet, sunflower, and tobacco. Located in north central

Romania and somewhat counterintuitive to its name, the TP consists of rolling hills bounded by the Carpathian Mountains, Târnavei Tableland, and Apuseni Mountains

(Posea and Velcea, 1975). The Chernisol soil class covers a vast majority of the TP

(FAO, 2006). Chernisols in Sistemul Român de Taxonomie a Solurilor (Florea and

Munteanu, 2012) are fertile, dark carbon-rich soils similar to Mollisols in the United

States’ Keys to Soil Taxonomy (Soil Survey Staff, 2014). Chernisols are soils with clear organic matter accumulation defined by the presence of a diagnostic mollic (Am) or A forestalic (Amf) horizon followed by an intermediate horizon (AC, AR, Bv, and

Bt) with mollic horizon colors (e.g., chroma and value < 3.5 moist) on at least 10-15 cm and at least on the faces of the aggregates. These horizons are usually followed by a Cca horizon in the upper 125 cm (200 cm for sandy soils), C horizon, or R horizon

(for Rendzina).

The requirements for an Am horizon are as follows: color value and chroma of 3.5 or less moist and 5.5 or less dry, (ii) organic matter content 1% or greater, (iii) base saturation percentage 53% or greater, and (iv) a minimum thickness of 25 cm (Florea and Munteanu, 2012). The structure for Chernisols are usually granular or blocky and friable. Chernisols feature four soil types (kastanozem, rendzina, chernozem and phaeozem), but only the latter two occur in the TP. The difference between a chernozem and a phaeozem depends on chroma of the Am and depth of secondary

Texas Tech University, Autumn Acree, May 2020 calcium carbonate accumulation (Cca calcic horizon). A chernozem requires a chroma of 2 or less when moist and CaCO3 accumulating within 125 cm. A phaeozem requires chroma of 3.5 or less when wet and secondary CaCO3 deeper than 125 cm (Florea and

Munteanu, 2012). Requirements for a calcic horizon are as follows: carbonates >12% and (ii) at least 5% more carbonates than the underlying horizon or (iii) at least 3-5% limestone as soft powdery lime, and (iv) minimum thickness of 20 cm (Florea and

Munteanu, 2012).

Currently, to determine whether a soil is a chernozem or phaeozem, soil samples need to be collected and transported to a laboratory in order to measure organic carbon and CaCO3 percentage. These methods can take weeks or months to produce results.

By contrast, advanced proximal sensors produce rapid results in seconds and can be done in-situ. Portable X-ray fluorescence (PXRF) spectrometry provides elemental concentration within 60 seconds (Weindorf and Chakraborty, 2016); elements which have been used as a predictive proxy for a wide number soil physicochemical properties (Weindorf et al., 2013; Sharma et al., 2014; Sharma et al., 2015; Swanhart et al., 2014; Sun et al., 2020). Visible near infrared (VisNIR) spectroscopy uses reflected light in the range of 350 to 2500 nm to infer soil moisture, organic carbon, and other properties (Horta et al., 2015). To date, there are no known soil classification systems worldwide which integrate proximal sensors as an approved methodology for taxonomic differentiation. Nonetheless, Benedet et al. (2020) used PXRF and VisNIR data in a random forest algorithm to predict soil subgroups in Brazil; results produced a classification accuracy of 89%. Furthermore, dozens of studies have shown the

Texas Tech University, Autumn Acree, May 2020 application of proximal sensors for measuring soil properties. As a precursor for gypsum and calcic horizon quantification, Zhu and Weindorf (2009) used PXRF to determine soil Ca, reporting an R2 of 0.98 relative to traditional laboratory methods.

Chakraborty et al. (2017) used PXRF spectrometry to establish the developmental stage of calcic horizons. Using simple “IF and THEN” rules applied to PXRF- determined Ca content in field intact and ground samples, they were able to classify carbonate developmental stage with considerable accuracy. Other work by Weindorf et al. (2013) quantified gypsum via PXRF yielding an R2 of 0.91 relative to thermogravimetry. Yet taxonomic differentiation has remained reliant upon traditional laboratory techniques. For example, as related to the present study, titration or pressure calcimetry (Chaney et al., 1982; Loeppert and Suarez, 1996) remain the standard laboratory methods applied to determine if a soil meets or fails certain taxonomic criteria. With a new version of US Soil Taxonomy forthcoming (J.

Galbraith, personal communication) within the next five years, an argument is presented herein that direct use of proximal sensors is appropriate for the taxonomic differentiation and classification of certain diagnostic horizons and features in soils with limited or no need for traditional laboratory analysis. As a simple, straightforward example of such, soils of the Transylvanian Plain, Romania were investigated whereby the depth of calcic horizon formation directly impacts their taxonomic classification. This study builds upon previous investigations of Romanian catenas (Duda et al., 2017; Chakraborty et al., 2019) whereby proximal sensors were used to establish spatial variability in soil properties and develop an external

Texas Tech University, Autumn Acree, May 2020 parameter orthogonalization (EPO) approach for removing the moisture signature in

VisNIR soil spectra.

Therefore, the objectives of this study were to: 1) sample soils from multiple toposequences/catenas where depth to calcic horizon formation substantively affects taxonomic classification, 2) characterize soils at those locations via on-site morphological, traditional laboratory, and proximal sensor approaches, and 3) compare the results of such to establish the applicability proximal sensors for direct use in taxonomic classification of soils. We hypothesize that proximal sensors will be able to aptly identify CaCO3, the depth at which it accumulates, and whether such soils are properly classified as chernozems or phaeozems.

Materials and Methods

General Occurrence and Features

The TP is approximately 395,000 ha and is a portion of the Transylvanian Basin

(2,000,000 ha; Haggard et al., 2012). The southern portion of the TP is dominantly characterized by Chernisols (equivalent to Mollisols in U.S. Keys to Soil Taxonomy

(USST)), with an average elevation of 389 m. The northern portion is dominantly

Luvisols (equivalent to Alfisols in USST), with an average elevation of 417 m

(Haggard et al., 2012). Protisols, Antrisols, and Hidrisols, all equivalent to Entisols in

USST, are also found in the TP (Haggard et al., 2012). The TP generally features a carbonate rich parent material (Haggard et al., 2012), with geologic salt formations found in some areas. Soil temperature and moisture regimes are mesic (~9.2°C mean annual temperature) and udic (~576 mm annual precipitation), respectively (Climate-

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Data.org, 2020). Köppen climate classification of the TP is generally Dfb (temperate continental) with some areas bordering on Dfc (cool continental) in the foothills of the

Apuseni Mountains (Administrația Națională de Meteorologie, 2008). Land use is mostly farmland and pastureland for sheep; isolated small areas of hardwood forest are found across the landscape. Phaeozems typically form in mezohidrophile herbaceous vegetation (Agrostis tenuis, Festuca sulcata, F. pseudovina, F. valesiaca, Botriochloa iscaemum, Koeleria gracilis, Poa pratensis var. angustifolia, Lolium perenne,

Medicago lupulina, M. falcata, Trifolium repens, Lotus corniculatus, Astragalus onobrychis, Fragaria viridis, and Origanum vulgare) and deciduous forests (Quercus petraea mixed with Tilia tomentosa, Carpinus betulus, Acer campestre, Sorbus torminalis, Fraxinus excelsior) and shrubs (Corylus avellana, Crataegus monogyna,

Evonimus europaea, E. verrucosa, Cornus mas, C. sanguinea, Ligustrum vulgare,

Sambucus nigra; Paulette, 2008). Chernozems typically form in natural steppe vegetation of herbaceous mezoxerophiles, predominantly high grasses with well- developed roots such as Festuca valesiaca, Sitpa lessingiana, Stipa Capilati,

Andropogon ischaemum, Agropyrum cristatum, Agropyrum repens, Poa bulbosa, and

Poa angustifolia, forest steppe, wetter valleys, and depressions of the steppe zone under herbaceous species (e.g., Carex precox, Poa pratensis, etc.) and woody species

(e.g., Quercus pubescens, Qercus pudunculiflora, Quercus cerris, Quercus frainetto) or shrubs of the genus Acer tataricum, Crataegus monogyna, Rosa sp. (Paulette,

2008).

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The toeslopes and colluvial/alluvial plains feed into a saline seep that eventually forms Salina Turda. The sediments of the TP are of marine origin consisting of marl, clay marl, sand, and sandy clay complexes (Jakab, 2007). Landslides and erosion are common across the TP given the topographic relief, dominant grassland/agronomic production, and unsustainable farming and tillage practices up and down slope (Iurian et al., 2013).

Field Characterization and Sampling

Field sampling occurred in Cluj County, RO generally between the communities of Turda and Ploscoș in June, 2018. A total of 25 pedons were sampled across various toposequences and catenas (Fig. 4.1). A percussion hammer was used to insert a stainless steel, slotted core to a depth of 120 cm. Each core was manually extracted with a fulcrum and dual levers (Eijkelkamp, The Netherlands). A tape measure was used to divide each core into 10 cm increments (e.g., 0-10 cm, 10-20 cm, etc.). As such, 25 cores divided into 10 cm increments to a depth of 120 cm yielded a total of

300 samples which formed the complete dataset.

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Fig. 4.1. Location of sampling sites in the Transylvanian Plain, Cluj County, Romania.

Elemental characterization was determined on the field-moist core on-site using a

Vanta M series (Olympus, Waltham, MA, USA) PXRF spectrometer operated on line power (110 VAC) at 10–40 keV in GeoChem Mode at 45 s per beam (90 s for one total scan) per Weindorf and Chakraborty (2016). Line power was provided using a

DC power inverter attached to sampling vehicle on-site. Prior to scanning, the PXRF was calibrated with a 316 alloy coin. In the core slot, a knife was used to gently scrape the surface of the core to expose a fresh surface for scanning. Then, the core was scanned by placing the aperture of the PXRF in direct contact with the exposed core surface (Fig. 4.2).

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Fig. 4.2. Proximal sensor scanning of freshly extracted soil cores from soils in the Cluj County, Romania.

VisNIR spectroscopy was facilitated using a model PSR-3500 spectroradiometer

(Spectral Evolution, Haverhill, MA, USA) with a spectral range of 350 to 2,500 nm.

Specifically, a handheld contact probe with integrated 5W light source was used to scan each 10 cm increment of the freshly extracted (field moist) soil core. The contact probe was pressed slightly into the core to ensure full contact. Each fixed depth increment was scanned in triplicate, rotating the contact probe 90° between scans. 89

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Prior to scan initiation at each soil core, the spectroradiometer was calibrated using an

NIST traceable radiance calibration panel to ensure that fluctuating downwelling irradiance could not saturate the detector. The instrument was operated on line power

(110 VAC), again using a DC power inverter to supply line level power from the sampling vehicle.

Remotely sensed imagery was acquired using a Phantom Advanced 4 (DJI,

Shenzhen, China) unmanned aerial vehicle (UAV). Custom missions were created in

Pix4Dmapper (MicroSurvey Software Inc., Canada) to photograph each toposequence/catena at regular intervals (80% overlapping scenes) using the integrated

4K color camera system. Aerial images were acquired at an altitude of 30 m above the surface. Agisoft PhotoScan 1.4.5 (Agisoft LLC, St. Petersburg, Russia) software was used to orthorectify each image and create an orthomosiac digital elevation model of each toposequence/catena whereby site position in tandem and topographic position could be used to further elucidate the pedogenesis within the sampled cores. In this process, the photos were aligned using high accuracy and a high-quality dense cloud of points was generated. These points were the input data for orthophoto imagery and

DEM interpolation (Pineux et al., 2017). Upon completion of all field scanning with proximal sensors, samples were carefully removed from the core and placed in sealed plastic bags for shipment to the laboratory.

Laboratory Characterization

Standard soil physicochemical characterization was performed in the pedology laboratory of Texas Tech University (Lubbock, TX, USA). Prior to laboratory

Texas Tech University, Autumn Acree, May 2020 analyses, all samples were oven dried (105°C) and disaggregated to pass a 2 mm sieve. Particle size analysis was performed using a model 152-H soil hydrometer with sand and clay readings taken at 40 sec and 1440 min, respectively (Gee and Bauder,

1986). Loss on ignition soil organic matter was performed in a muffle furnace at

400°C for 16 h per Nelson and Sommers (1996). Saturated paste electrical conductivity (ECp) and soil reaction (pH) were determined with a model 4063CC digital salinity bridge (Dahke and Whitney, 1988) and electrometric pH meter (Orion

8157BNUMD ROSS Ultra pH/ATC Triode) per Thomas (1996). Calcium carbonate percentage was determined via pressure calcimetry per Loeppert and Suarez (1996) using a Hohner Calcimeter (Hohner, Wrexham, Wales, U.K.). Haggard et al. (2012) established that parent materials of the TP are rife with CaCO3, commonly precipitated as secondary CaCO3 in masses, threads, or throughout the soil matrix. Effervescence testing precluded the likelihood of gypsum in TP soils. Finally, select samples were subject to pressed powder X-ray diffraction analysis for confirmation of mineralogy

(data not shown); results clearly indicated that CaCO3 was the dominant Ca-bearing mineral. Thus, PXRF Ca percentages were converted to CaCO3 percentage for statistical comparison to laboratory determined CaCO3 (pressure calcimeter).

Statistical Analysis

Prior to statistical analysis, the 25 cores were randomly divided a priori into calibration (18 cores x 12 depths yield n=216) and validation (7 cores x 12 depths yield n=84) datasets (hereafter termed Corecal and Coreval). In this manner, validation samples were from different cores than the calibration samples. However, for a

Texas Tech University, Autumn Acree, May 2020 comparison on the impact of sample splitting, a separate analysis was conducted by randomly splitting all samples of the entire dataset as 70% for calibration (n=210) and

30% for validation (n=90)(hereafter termed Fullcal and Fullval). In this approach, some validation samples may be from the same sample core as calibration samples, a common concern for this type of modeling potentially overestimating the performance of the model. For some analyses, raw spectra were used. In other instances, data transformation was performed such that first or second derivative data were obtained.

Savitzky-Golay smoothing and binning (an averaging technique across wavebands of

10 nm) were also adopted in various model algorithms (Savitzky and Golay, 1964).

All spectral data represent means of the three individual scans collected under field conditions (Chakraborty et al., 2013). All statistical analyses were performed in R version 3.6.0 (R Core Team, 2020). Simple linear regression was applied to PXRF data in isolation using Ca as a proxy to predict CaCO3, while partial least squares regression (PLSR) and boosted regression tree (BRT) were used to establish the utility of VisNIR data in isolation and VisNIR + PXRF data in predicting CaCO3 relative to laboratory derived data. The ‘pls’ package in R was used to run PLSR (Mevik et al.,

2019), and the ‘gbm’ package was used to run BRT (Greenwell et al., 2019). In the present study, two approaches were attempted: 1) using the entire spectrum (350-2,500 nm) at 1 nm intervals, and 2) using 10 nm bands across the reflectance spectrum (e.g.,

350 to 360 nm...). For each condition, six models were tested: 1) raw spectra, 2) first derivative (1D) spectra, 3) second derivative (2D) spectra, 4) Savitzky-Golay smoothed spectra (Savitzky and Golay, 1964), 5) 1D smoothed spectra, and 6) 2D

Texas Tech University, Autumn Acree, May 2020 smoothed spectra. Model performance was determined via consideration of coefficient of determination (R2), root mean squared error (RMSE), residual prediction deviation

(RPD), and ratio of performance to interquartile range (RPIQ). In defining RPD as standard deviation (SD) divided by RMSE, Chang et al. (2001) notes that RPDs < 1.4 are non-reliable models, RPDs between 1.4 and 2.0 reflect fair models, and RPDs > 2 represent satisfactory models. By comparison, RPIQ is the interquartile distance

(IQ=Q3-Q1) divided by the standard error of prediction (SEP) (Bellon-Maurel et al.,

2010).

Results and Discussion

General Soil Physicochemical Properties

Average soil texture across chernozems was determined as a silty clay loam

(14.91% sand, 32.05% clay, 53.05% silt; Appendix Table A1). For phaeozems, average soil texture average was clay loam (21.19% sand, 30.18% clay, 48.63% silt;

Appendix Table A2). Chernozems and phaeozems had a mean pH of 7.86 (Appendix

-1 Table A1) and 7.51 (Appendix Table A2), respectively, and a mean ECp of 1.22 ds m

(Appendix Table A1) and 2.18 ds m-1 (Appendix Table A2), respectively. Chernozems featured lower mean soil organic matter (7.79%) than phaeozems (9.96%) (Appendix

Tables A1 and A2). Across all horizons, traditional laboratory CaCO3 determination for chernozems produced a mean of 13.41%, with phaeozems less than half that at a mean of 5.47% (Appendix Tables A1 and A2).

Proximal Sensor Model Performance

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Initially, PXRF Ca was tested in isolation to determine its efficacy in predicting

CaCO3 content of the sampled soils. After conversion of PXRF Ca content to CaCO3, data was compared to CaCO3 content obtained via traditional laboratory based

2 pressure calcimetry where the following Fullval statistics were achieved: R 0.90,

RMSE 0.95%, RPD 3.16, and RPIQ 5.60. By comparison, Coreval achieved the following: R2 0.91, RMSE 0.82%, RPD 3.27, and RPIQ 5.34. Thus, it can be inferred that a SLR PXRF based model for predicting CaCO3 content in soils performs satisfactorily. Further, this supports the findings of Chakraborty et al. (2017) who successfully used PXRF to predict CaCO3 development stage.

Next, VisNIR spectral data was used in isolation to predict CaCO3 content.

Previous work by Ben-Dor and Banin (1995) noted that reflectance at 1,482 and 1,647 nm provided significant prediction of CaCO3. However, they remarked that almost all compressed spectra evaluated produced roughly the same standard error of prediction, thus concluding that position within the spectra (albedo) was more important than any specific absorption band. In the present study, examples of VisNIR reflectance are shown as Fig. 4.3; both a low CaCO3 sample (6.2%; 110-120 cm) and high CaCO3 sample (12.4%; 70-80 cm) are shown to illustrate the relative differences in reflectance. Both PLSR and BRT approaches were used to model the spectra, can handle high-dimensional data sets, and have been successfully applied in predicting a variable of interest based on spectral reflectance values (e.g., Chakraborty et al., 2010;

Dangal et al., 2019; Vasques et al., 2009, 2008). Consistently, 1D models featuring

Savitzky-Golay smoothed spectra produced strong results. Using 1 nm resolution with

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2 PLSR and BRT, the following Fullval statistics were obtained: PLSR, R 0.74, RMSE

1.19%, RPD 2.52, RPIQ 4.46; BRT, R2 0.76, RMSE 1.48, RPD 2.03, RPIQ 3.60

2 (Table 4.1). By comparison, simple 1D Coreval achieved the following: BRT, R 0.71,

RMSE 1.45%, RPD 1.84, RPIQ 3.01 (Table 4.2). Such results generally agree with

2 Ben-Dor and Banin (1995) who reported an R of 0.69 when using NIR for CaCO3 prediction. Evaluating 10 nm band data (1D Savitzky-Golay), the following Fullval statistics: PLSR, R2 0.82, RMSE 1.25%, RPD 2.39, RPIQ 4.24; BRT: R2 0.80, RMSE

1.35%, RPD 2.22, RPIQ 3.94 (Table 4.1). By comparison, Coreval (simple 1D) achieved the following: BRT, R2 0.72, RMSE 1.45%, RPD 1.84, RPIQ 3.00 (Table

4.2). Clearly, some of the evaluated models produced satisfactory results. However, they were generally less robust than PXRF in isolation. Furthermore, while all sample characterization was performed ex-situ on dried, disaggregated samples, application of proximal sensor scanning in-situ may require moisture correction. VisNIR is known to be highly sensitive to moisture (Zhu et al., 2010) and external parameter orthogonalization has previously been applied to VisNIR models in soils of Romania

(Chakraborty et al., 2019). While both PXRF and VisNIR data can be affected by moisture, the latter is generally considered more sensitive than the former.

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0.35 0.30 0.25 0.20 0.15

Reflectance 0.10 0.05 0.00 350 450 550 650 750 850 950 1050 1150 1250 1350 1450 1550 1650 1750 1850 1950 2050 2150 2250 2350 2450 Wavelength (nm)

Low carbonate (6.2%) High carbonate (12.4%)

Fig. 4.3. Visible near infrared spectra (350-2,500 nm) for low CaCO3 subsoil (6.2%; 110-120 cm) and high CaCO3 subsoil (12.4%; 70-80 cm) from two soil cores in Cluj County, Romania. Carbonate percentages reported herein were determined via bulk soil pressed powder X-ray diffraction.

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Table 4.1. Fullval model performance statistics relating proximal sensor data to laboratory derived CaCO3 percentage for soils of Cluj County, Romania. Partial Least Squares Regression Principal Components Results with Boosted Regression Trees Final # Model Data type Latent factors R2* RMSE (%) RPD RPIQ R2 RMSE (%) RPD RPIQ of trees VisNIR at 1 nm intervals Model 1 Original VisNIR (2151 spectral values) 4 0.63 1.69 1.78 3.16 0.67 1.73 1.74 3.08 1550 Model 2 1st derivative 5 0.67 1.41 2.13 3.77 0.75 1.52 1.97 3.49 3100 Model 3 2nd derivative 4 0.19 1.95 1.54 2.72 0.66 1.81 1.66 2.95 2850 Model 4 Smoothed (Savitzky-Golay) 4 0.63 1.69 1.78 3.15 0.67 1.73 1.74 3.08 1550 Model 5 1st derivative on smoothed data 11 0.74 1.19 2.52 4.46 0.76 1.48 2.03 3.60 2650 Model 6 2nd derivative on smoothed data 1 0.39 2.18 1.38 2.44 0.71 1.65 1.82 3.22 2350

VisNIR at 10 nm intervals Model 7 VisNIR: 216 spectral values 4 0.63 1.69 1.78 3.16 0.67 1.73 1.74 3.08 1450 Model 8 1st derivative 10 0.80 1.24 2.41 4.28 0.78 1.41 2.13 3.77 2500 Model 9 2nd derivative 2 0.68 1.51 1.99 3.53 0.77 1.49 2.02 3.57 1200 Model 10 Smoothed (Savitzky-Golay) 4 0.63 1.69 1.78 3.16 0.69 1.68 1.79 3.17 1000 Model 11 1st derivative on smoothed data 10 0.82 1.25 2.39 4.24 0.80 1.35 2.22 3.94 2450 Model 12 2nd derivative on smoothed data 3 0.69 1.51 1.99 3.52 0.75 1.58 1.90 3.36 3000

PXRF + VisNIR at 1 nm intervals Model 13 PXRF + original VisNIR (2151 spectral values) 3 0.90 0.92 3.26 5.77 0.88 1.02 2.93 5.20 2550 Model 14 PXRF + 1st derivative 10 0.89 0.74 4.04 7.15 0.88 1.05 2.87 5.08 2350 Model 15 PXRF + 2nd derivative 4 0.88 0.89 3.39 6.00 0.87 1.09 2.77 4.90 2350 Model 16 PXRF + Smoothed (Savitzky-Golay) 3 0.90 0.92 3.26 5.77 0.88 1.02 2.95 5.23 2600 Model 17 PXRF + 1st derivative on smoothed data 7 0.90 0.75 3.99 7.07 0.89 1.02 2.94 5.20 2400 Model 18 PXRF + 2nd derivative on smoothed data 5 0.87 0.85 3.53 6.26 0.88 1.07 2.82 4.99 2400

PXRF + VisNIR at 10 nm intervals Model 19 PXRF + VisNIR: 216 spectral values 5 0.90 0.87 3.47 6.14 0.88 1.02 2.96 5.24 2600 Model 20 PXRF + 1st derivative 11 0.89 0.66 4.52 8.01 0.88 1.05 2.86 5.07 2600 Model 21 PXRF + 2nd derivative 4 0.89 0.84 3.57 6.32 0.88 1.06 2.84 5.04 2750 Model 22 PXRF + Smoothed (Savitzky-Golay) 5 0.90 0.87 3.46 6.13 0.88 1.02 2.95 5.22 2600 Model 23 PXRF + 1st derivative on smoothed data 12 0.89 0.64 4.66 8.26 0.89 1.03 2.92 5.16 2350 Model 24 PXRF + 2nd derivative on smoothed data 4 0.90 0.85 3.55 6.28 0.87 1.09 2.76 4.88 2700

Simple Linear Regression PXRF in isolation 0.90 0.95 3.16 5.60 *R2= Coefficient of determination; RMSE = root mean squared error (%); RPD = residual prediction deviation; RPIQ = ratio of performance to interquartile range.

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Table 4.2. Coreval model performance statistics relating proximal sensor data to laboratory derived CaCO3 percentage for soils of Cluj County, Romania.

*R2= Coefficient of determination; RMSE = root mean squared error (%); RPD = residual prediction deviation; RPIQ = ratio of performance to interquartile range.

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Beyond evaluating each sensor in isolation, previous studies have shown that combining PXRF with VisNIR spectroscopy can potentially improve predictive model performance (Weindorf et al., 2016; Weindorf and Chakraborty, 2014). As indicated in Tables 4.1 and 4.2, several PXRF + VisNIR models produced strong predictive results. Fig. 4.4 shows the relative influence of PXRF relative to spectra in the combined model; PXRF dominates the contribution at >70%. Nonetheless, combined models were generally very close in performance to PXRF in isolation (Fig. 4.5).

Thus, with deference to the law of parsimony, a PXRF predictive model in isolation appears to be preferable for the prediction of CaCO3 for most Pedological applications.

Fig. 4.4. Relative influence of portable X-ray fluorescence (PXRF) and visible near infrared spectral wavelengths in predicting CaCO3 content in soils of Cluj County, Romania.

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A) B) C)

Fig. 4.5. Plots showing partial least square regression predicted CaCO3 (%) vs. laboratory determined CaCO3 (%) while using a) portable X-ray fluorescence (PXRF) variables b) visible near infrared (VisNIR) spectral variables, and c) PXRF + VisNIR variables for 300 soil samples from Cluj County, Romania. The solid line represents the 1:1 line. Data partitioning was on whole cores.

Practical Applications

The Soil Survey Staff (1993) note that soil maps at a scale of 1:24,000 (common mapping resolution in many US soil surveys used for agriculture and urban planning), feature delineations based upon field observations and remotely sensed data with soil map unit boundaries verified at closely spaced intervals. Generally, consociations within a map unit include no more than 15% dissimilar soils if limiting. Within a given soil map polygon, the goal is to be 85% accurate as far as composition is concerned in the map unit design (Soil Survey Staff, personal communication, 2020).

In strongly calcic materials such as those of the TP (Haggard et al., 2012), even an experienced Pedologist would be challenged to morphologically differentiate secondary CaCO3 accumulations in soils with an error of <1%. By comparison, the

PXRF-based predictive models produced an R2 of 0.90 and RMSE of 0.95%, performance concomitant with expected mapping accuracy goals. Since PXRF proved useful in determining calcic horizon depth from the mineral soil surface and the depth at which such secondary carbonates occur in Sistemul Roman de Taxonomie a

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Solurilor is critical in establishing the taxonomic classification of such soils in

Romania, it serves to reason that PXRF data (with proper interpretation by a qualified

Pedologist) was successfully used to distinguish unique taxonomy in Romanian soils.

PXRF and traditional pressure calcimetry both identified six phaeozems (P) and nineteen chernozems (C) (Figs. 4.6 and 4.7). Specifically, the following were identified on different slope profiles: summits (1P/4C), shoulders (1P/2C), backslopes

(3P/7C), footslopes (0P/4C), and toeslopes (1P/2C). A model differentiating P/C on common landscape positions was unable to be determined due to localized landslides, erosion, and agronomic practices (Iurian et al., 2013). Nonetheless, site position was verified both on-site and via remotely sensed orthoimagery from custom UAV missions.

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Fig. 4.6. CaCO3 percentages converted from the Ca percentages via PXRF of chernozems and phaeozems by depth from the mineral soil surface on summit, shoulder, backslope, footslope, and toeslope positions. The blue lines represent chernozems, and the green line represents phaeozems.

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Fig. 4.7. CaCO3 percentages via traditional laboratory pressure calcimetry of chernozems and phaeozems by depth from the mineral soil surface on summit, shoulder, backslope, footslope, and toeslope positions. The blue lines represent chernozems, and the green line represents phaeozems.

Sample partitioning from cored samples is a common concern in predictive data modeling. In essence, to ensure realistic performance of the predictive models, samples from the calibration and validation datasets should not be related. Yet therein lies the problem. What combination of factors establishes such a relationship? Should the samples not be from the same core as processes of eluviation and illuviation move

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Texas Tech University, Autumn Acree, May 2020 clays and dissolved solutes through a soil profile? Should samples not be from the same toposequence or catena owing to topographic relationships between eroding residuum and depositional colluvium? Should samples not be from the same parent material (e.g., lithology)? Soon, we arrive at the Jenny’s (1941) classical factors of soil formation: climate, parent material, topography, time, biota. Where is the line drawn in establishing that one sample is not related to another on a landscape rife with contiguous soil forming variables? A comparison of two different data partitioning schemes in this study found that splitting samples by entire cores modestly decreased model performance in VisNIR data in isolation, but had virtually no effect on PXRF data in isolation. Combined PXRF+VisNIR models generally produced modestly lower model stability in terms of RPD and RPIQ, whereas R2 slightly increased. More detailed study of data partitioning dynamics across continuous landscapes with proximal sensors are needed as pedometric approaches continue to gain in popularity.

Future Considerations

The 3rd edition of Soil Taxonomy is anticipated in 2022. Included in such are new taxa such as the proposed 13th soil order (Artesols) (Galbraith, 2019). New taxa such as that proposed within Artesols will require new methods of soil analysis to investigate human altered human transported (HAHT) materials. For example, radioactive materials, those rife with heavy metals, and other impacted soils will require new methodologies for successful characterization. Future studies may establish where PXRF can be used to determine calcic horizons in-situ without any need for laboratory analyses. Zhu et al. (2011) established that PXRF determined Rb

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Texas Tech University, Autumn Acree, May 2020 was strongly associated with clay content. Then, Sun et al. (2020) found high concentrations of Rb in argillic horizons; notably, their study was conducted in-situ.

Silva et al. (2019) used PXRF elemental data in characterizing generally nondescript

Oxisols and Ultisols concluding “PXRF holds great promise for tropical soil characterization and the development of prediction models.” In summary, we support the statement of Weindorf et al. (2012) who note “PXRF is suggested as a tool for enhancing field morphological horizonation.” As predictive models become increasingly refined, data from PXRF (and other proximal sensors) hold promise for use as direct, quantitative taxonomic differentia, which should be directly written into the next version of US Soil Taxonomy.

Conclusions

In summary, 25 pedons were scanned with PXRF and VisNIR in 10 cm increments to a depth of 120 cm to determine whether proximal sensors can be used to determine calcic horizons to make soil taxonomic classifications in-situ without any need for laboratory analyses. The results showed PXRF can be used to determine calcic horizons in-situ in the Transylvanian Plain, Cluj County, Romania. Based on the

PXRF-determined Ca percentages, phaeozems and chernozems were able to be distinguished. Combined VisNIR - PXRF models modestly increased the predictive model stability. Yet for most field applications, PXRF in isolation appears to be a simple, effective determinant of calcic horizons. Therefore, PXRF was shown to be a useful tool in making soil taxonomic classifications directly with no need for laboratory analyses. This represents a critical precedent with applicability in defining

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Texas Tech University, Autumn Acree, May 2020 future diagnostic subsurface horizons and features with implications for soil taxonomic classification whereby proximal sensor data is used directly as the quantitative differentia in establishing such.

Acknowledgments

Mihai Buta, and Andrei Vranceanu for assistance in field sampling.

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CHAPTER V

CONCLUSIONS

Pedology is advancing with contemporary technologies. With the 3rd edition of

U.S. Soil Taxonomy coming in 2022, two studies (Ch. 1 and Ch. 3) within this dissertation used proximal sensors to determine soil properties (trace elements (Ch. 1) and calcic horizons (Ch. 3)) in order to advocate for proximal sensors to become an established method of characterizing soils. One study (Ch. 2) was on establishing new taxonomy for Gelolls in Alaska, since there is sparse information on Gelolls in the current U.S. Keys to Soil Taxonomy, to advocate for additions in the next edition.

In Ch. 1, PXRF determined rural playas in the SHP had a higher concentration of trace elements, specifically Pb, Cr, As, Zn, Cu, and Ni than urban playas. Rural playas also had more clay and organic matter than urban playas. The differences in texture and chemical properties indicate rural and urban playas need to be managed differently for optimized ecological functionality, given playas are a habitat for migratory waterfowl.

In Ch. 2, ten Geloll pedons were morphologically described and classified

Brooks Range and Artic Foothills in northern Alaska. Sedimentary carbonatic parent material, sufficient drainage, and high coarse fragment content were determined to be the major factors affecting Geloll formation. Five of the ten Gelolls met the criteria for

Fluventic or Pachic subgroups, which are not currently recognized as subgroups for

Gelolls in U.S. Keys to Soil Taxonomy, indicating a need for additions in the next edition. Future research needs to be done in northern Alaska to further delineate extent

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In Ch. 3, PXRF was adept in identifying calcic horizons in-situ in the TP,

Romania. Using PXRF in isolation, chernozems and phaeozems were able to be classified. Combined VisNIR-PXRF models did not substantively increase predictive accuracy. PXRF in isolation was determined to be a useful tool in identifying calcic horizons to make soil taxonomic classifications in-situ with no need for traditional laboratory pressure calcimetry. Further research needs to be done to examine proximal sensors as the direct quantitative differentia to identify other diagnostic horizons (e.g. argillic) and features in order to make soil taxonomic classifications in-situ.

With the evolution of pedometrics and quantitative pedology, there is a need for proximal sensors to be incorporated into Soil Taxonomy, World Reference Base for Soil

Resources, and other systems worldwide. Considering laboratory testing can be costly and requires access to equipment, proximal sensors provide reliable data quickly and at a cheaper cost which has potential for a global impact. Enhancing soil classification systems worldwide with contemporary technologies can bring pedology to a global stage.

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APPENDIX

PHYSICOCHEMICAL PROPERTIES OF CHERNOZEMS AND PHAEOZEMS IN CLUJ COUNTY, ROMANIA

Table A1. Physicochemical properties of 10 cm soil layers from nineteen chernozem catena cores in Cluj County, Romania.

Chernozem Depth pH EC Sand Clay Silt CaCO3 LOI -1 --cm-- dS m ------%------1 0-10 7.73 1.07 7.70 35.94 56.36 10.27 9.60 10-20 7.72 1.06 9.62 37.94 52.43 10.90 9.53 20-30 7.85 0.95 9.41 37.94 52.65 15.10 8.69 30-40 7.91 0.87 15.34 37.87 46.79 14.50 8.05 40-50 7.90 0.87 17.41 35.87 46.72 15.70 8.23 50-60 7.85 0.92 15.34 37.87 46.79 16.92 16.93 60-70 7.81 0.94 9.34 39.94 50.72 17.52 7.25 70-80 7.90 0.75 9.70 35.87 54.43 16.92 6.89 80-90 7.82 0.76 21.45 33.87 44.68 21.16 6.50 90-100 7.97 0.75 11.55 33.73 54.72 19.34 6.43 100-110 7.81 0.75 13.26 35.80 50.94 17.53 6.47 110-120 7.95 0.77 11.26 35.80 52.94 11.48 6.74 2 0-10 7.73 1.02 13.34 31.80 54.86 7.86 9.04 10-20 7.73 0.99 9.41 27.94 62.65 6.65 8.75 20-30 7.84 0.86 9.62 29.94 60.43 6.65 8.44 30-40 7.88 0.80 13.41 31.94 54.65 10.27 7.12 40-50 7.91 0.75 11.05 33.94 55.01 11.48 6.17 50-60 7.96 0.68 8.98 31.94 59.08 12.69 6.03 60-70 7.92 0.71 9.98 33.87 56.14 13.30 6.31 70-80 7.96 0.70 15.98 33.87 50.14 13.30 6.28 80-90 7.92 0.70 23.84 35.87 40.29 14.51 6.28 90-100 8.00 0.66 13.77 35.87 50.36 14.50 6.49 100-110 8.05 0.66 21.70 37.87 40.43 17.52 6.09 110-120 8.04 0.63 15.77 33.87 50.36 16.92 6.14 3 0-10 7.86 0.91 15.84 31.94 52.22 7.25 9.65 10-20 7.82 0.93 16.13 30.02 53.86 6.04 9.93 20-30 7.78 1.01 26.06 32.09 41.86 8.46 9.94 30-40 8.04 0.74 11.91 37.02 51.06 18.13 7.36 40-50 8.09 0.62 9.77 40.59 49.64 15.11 6.61 50-60 8.05 0.59 11.62 42.38 46.00 15.11 6.16 60-70 8.08 0.54 15.84 40.30 43.86 19.34 5.58

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70-80 7.45 2.13 13.91 40.30 45.78 18.13 6.00 80-90 8.06 0.53 13.98 40.30 45.71 17.53 6.19 90-100 8.14 0.51 11.77 42.16 46.07 18.13 5.85 100-110 8.12 0.54 17.41 40.16 42.43 13.90 6.13 110-120 8.13 0.50 13.48 38.16 48.36 14.50 6.97 4 0-10 7.73 0.94 21.98 29.66 48.36 12.09 12.55 10-20 7.96 0.77 21.77 35.66 42.58 12.69 8.03 20-30 8.08 0.77 29.62 37.58 32.79 12.09 7.20 30-40 8.09 0.81 15.48 39.66 44.86 10.88 7.40 40-50 8.20 0.90 19.41 39.66 40.94 11.48 7.06 50-60 8.20 0.92 19.41 39.66 40.94 12.69 6.96 60-70 8.22 1.08 19.34 41.66 39.01 13.30 6.43 70-80 8.12 1.34 19.34 37.66 43.01 16.92 6.36 80-90 7.88 1.87 23.62 37.73 38.65 16.32 6.31 90-100 7.77 2.88 13.41 36.02 50.58 15.11 6.36 100-110 7.75 3.13 15.26 36.02 48.72 14.50 6.71 110-120 7.74 3.09 15.12 36.02 48.86 12.09 6.48 5 0-10 7.84 3.31 25.19 30.02 44.79 9.07 8.99 10-20 7.71 0.95 19.48 33.94 46.58 9.07 9.23 20-30 7.94 0.73 23.55 35.94 40.50 12.69 7.85 30-40 8.01 1.05 23.34 37.87 38.79 16.32 6.30 40-50 8.10 0.49 19.05 37.80 43.15 20.55 10.50 50-60 8.07 0.48 17.05 35.80 47.15 21.15 5.04 60-70 7.93 0.47 24.83 32.43 42.74 17.53 6.76 70-80 8.18 0.44 16.83 30.43 52.74 18.74 3.82 80-90 8.15 0.46 22.54 36.43 41.02 19.94 3.61 90-100 8.24 0.39 14.54 32.43 53.02 19.94 3.59 100-110 8.25 0.44 14.54 34.36 51.10 15.71 3.96 110-120 8.37 0.32 34.40 22.43 43.17 12.09 2.90 6 0-10 7.95 0.66 11.21 37.78 51.01 13.90 8.80 10-20 8.07 0.58 9.21 41.78 49.01 16.92 6.70 20-30 8.17 0.53 10.70 39.78 49.51 18.13 5.66 30-40 8.25 0.47 6.63 35.78 57.58 21.76 14.45 40-50 8.33 0.48 12.70 35.78 51.51 21.15 4.58 50-60 8.35 0.43 6.70 33.78 59.51 21.76 4.14 60-70 8.38 0.51 4.70 35.78 59.51 17.53 5.19 70-80 8.42 0.49 10.99 27.86 61.15 16.92 3.51 80-90 8.37 0.67 14.85 35.93 49.22 19.34 5.20 90-100 8.29 0.73 6.27 37.01 56.72 12.09 5.62 100-110 8.51 0.65 4.34 36.72 58.94 10.88 4.09 110-120 8.61 0.58 4.34 34.43 61.22 20.55 3.55 7 0-10 7.63 1.80 21.78 28.56 49.66 4.23 16.49

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10-20 7.88 0.76 13.93 34.63 51.44 4.23 31.63 20-30 7.96 0.82 12.14 34.63 53.22 4.23 11.30 30-40 7.91 0.80 16.00 32.70 51.30 5.44 11.27 40-50 8.04 0.73 9.64 33.42 56.94 7.86 10.78 50-60 8.10 0.70 9.71 33.14 57.15 10.27 9.79 60-70 8.16 0.62 7.64 34.99 57.37 10.88 8.80 70-80 8.09 0.75 7.71 34.92 57.37 12.69 7.51 80-90 7.32 1.71 7.86 30.85 61.30 14.51 5.37 90-100 7.59 1.56 5.86 30.85 63.30 20.55 5.10 100-110 7.49 1.59 7.71 28.78 63.51 18.13 5.73 110-120 7.69 1.25 17.35 30.78 51.87 23.57 4.01 8 0-10 7.31 1.54 21.42 28.78 49.80 5.44 15.77 10-20 7.62 0.98 13.71 28.07 58.22 7.86 11.90 20-30 7.71 0.97 11.28 30.00 58.72 9.07 9.96 30-40 7.78 0.94 8.70 32.00 59.30 10.88 9.74 40-50 7.79 0.91 11.28 32.00 56.72 13.30 9.39 50-60 7.88 0.83 7.28 30.36 62.36 15.71 6.16 60-70 7.91 0.83 9.50 30.22 60.29 14.51 7.70 70-80 7.84 0.82 11.86 32.22 55.93 15.71 7.81 80-90 8.07 0.59 13.78 30.22 56.00 21.76 6.00 90-100 8.15 0.52 7.71 32.22 60.07 22.36 5.78 100-110 8.11 0.57 8.99 32.29 58.72 19.94 6.41 110-120 8.03 0.61 6.78 32.29 60.94 19.34 6.21 9 0-10 7.60 1.23 20.85 28.29 50.86 6.65 12.68 10-20 7.74 0.96 16.85 28.36 54.79 6.65 10.39 20-30 7.70 0.97 14.92 30.29 54.79 6.65 10.45 30-40 7.72 1.00 10.85 30.29 58.86 6.65 10.39 40-50 7.86 0.92 10.92 34.43 54.65 7.86 10.43 50-60 7.94 1.02 14.92 32.29 52.79 12.69 8.31 60-70 8.00 0.97 10.56 33.58 55.86 16.32 7.33 70-80 8.26 0.94 8.63 35.22 56.14 24.18 5.79 80-90 8.23 1.13 8.34 37.01 54.65 29.01 5.19 90-100 8.36 1.44 4.13 40.94 54.94 26.59 4.99 100-110 8.32 1.61 3.98 46.86 49.15 21.76 4.75 110-120 8.23 1.64 2.06 48.86 49.08 20.55 4.35 10 0-10 7.30 1.79 16.13 30.86 53.01 8.46 13.80 10-20 7.71 1.27 10.20 36.79 53.01 10.88 10.39 20-30 7.71 1.82 10.34 32.65 57.01 13.30 10.42 30-40 7.42 6.22 9.35 32.22 58.43 12.69 11.08 40-50 7.30 9.05 15.28 6.07 78.65 12.69 10.55 50-60 7.27 8.78 15.14 6.07 78.79 7.86 10.65 60-70 6.91 8.69 18.99 8.00 73.01 3.63 11.78

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70-80 6.88 8.44 26.99 6.07 66.94 4.23 9.49 80-90 7.15 7.86 15.06 6.07 78.86 4.23 8.01 90-100 7.26 7.36 19.14 4.07 76.79 4.84 6.80 100-110 7.34 6.68 11.28 36.22 52.50 4.23 6.54 110-120 7.47 5.86 9.14 46.22 44.65 3.63 7.30 11 0-10 6.89 1.35 35.14 22.14 42.72 3.02 20.59 10-20 6.40 0.82 22.00 32.86 45.14 1.81 16.86 20-30 6.46 0.58 17.86 32.65 49.50 2.42 14.63 30-40 6.50 0.50 15.71 34.65 49.64 3.02 13.26 40-50 6.52 0.58 17.50 36.65 45.86 1.81 11.58 50-60 6.71 0.61 17.14 39.01 43.86 3.02 10.29 60-70 7.66 0.67 17.14 36.86 46.00 6.04 8.93 70-80 7.80 0.65 21.28 34.86 43.86 7.86 8.88 80-90 6.89 1.86 17.50 20.79 61.71 9.67 6.77 90-100 7.02 1.70 25.42 22.86 51.71 10.88 6.33 100-110 7.07 1.78 23.50 20.86 55.64 12.69 5.38 110-120 7.23 1.62 25.35 18.86 55.78 12.69 5.27 12 0-10 7.71 0.94 16.92 25.08 58.00 15.11 13.89 10-20 8.00 0.62 14.99 31.15 53.86 17.53 8.01 20-30 7.98 0.65 10.63 32.23 57.14 12.09 8.34 30-40 8.03 0.57 8.49 33.87 57.64 16.92 7.56 40-50 8.18 0.52 ------15.11 8.17 50-60 8.31 0.52 8.34 35.66 56.00 21.76 6.13 60-70 8.58 0.53 4.42 37.58 58.00 28.41 4.19 70-80 8.68 0.57 4.49 35.51 60.00 22.97 4.03 80-90 8.52 0.99 4.56 35.44 60.00 15.71 3.94 90-100 8.42 1.62 2.85 37.30 59.86 21.15 4.33 100-110 8.21 3.18 6.78 39.30 53.93 13.30 5.06 110-120 8.34 3.07 2.78 35.22 62.00 15.71 3.59 13 0-10 7.37 1.35 14.85 27.30 57.86 19.34 9.21 10-20 7.55 1.10 10.49 25.08 64.43 19.34 7.14 20-30 7.72 0.91 10.49 25.01 64.50 22.36 5.85 30-40 7.85 0.79 6.20 28.94 64.86 22.36 5.02 40-50 7.98 0.68 10.20 30.94 58.86 22.36 5.45 50-60 8.03 0.70 6.06 30.94 63.01 25.99 4.91 60-70 8.19 0.54 4.13 28.94 66.94 16.32 4.55 70-80 8.21 0.60 20.20 32.94 46.86 16.92 5.10 80-90 8.21 0.57 10.42 28.94 60.65 14.51 4.02 90-100 8.28 0.61 8.13 31.08 60.79 13.90 3.96 100-110 8.26 0.69 9.91 31.08 59.01 12.09 4.04 110-120 8.30 0.75 7.77 31.01 61.22 10.88 3.76 14 0-10 7.57 0.99 19.41 21.08 59.51 7.25 13.80

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10-20 7.67 0.84 21.41 25.08 53.51 7.86 11.11 20-30 7.77 0.75 15.48 25.08 59.44 9.67 11.22 30-40 7.84 0.70 13.55 27.08 59.37 10.88 9.48 40-50 7.82 0.76 13.19 29.08 57.73 12.09 9.02 50-60 7.99 0.67 10.90 29.22 59.87 13.30 8.14 60-70 7.99 0.71 12.40 29.87 57.73 15.71 6.89 70-80 8.10 0.67 10.47 31.66 57.87 17.53 6.59 80-90 8.20 0.65 10.47 31.58 57.94 16.92 5.79 90-100 8.18 0.78 12.76 31.58 55.66 16.92 5.83 100-110 8.26 0.89 9.19 33.51 57.30 18.13 5.10 110-120 8.31 0.91 9.12 33.44 57.44 18.13 4.77 15 0-10 5.93 1.69 29.12 1.44 69.44 3.02 14.45 10-20 6.43 2.83 23.41 9.44 67.15 4.23 12.16 20-30 6.58 2.42 17.70 29.44 52.86 9.07 10.07 30-40 6.92 2.03 13.05 31.37 55.58 13.30 8.60 40-50 7.10 1.91 12.62 29.30 58.09 19.94 5.32 50-60 7.39 1.36 10.47 29.22 60.30 24.78 4.61 60-70 7.75 1.25 8.47 31.30 60.23 21.15 4.08 70-80 7.64 1.26 12.54 33.22 54.23 21.15 3.84 80-90 7.76 1.30 10.90 27.22 61.87 20.55 4.75 90-100 8.04 1.08 11.12 29.22 59.66 17.53 3.31 100-110 7.98 1.20 5.05 39.22 55.73 10.27 4.28 110-120 8.27 1.11 7.05 35.30 57.66 15.11 3.77 16 0-10 7.07 1.37 26.90 23.30 49.80 1.81 17.59 10-20 7.19 1.47 18.83 25.37 55.80 3.02 12.80 20-30 7.40 1.28 20.69 25.37 53.94 5.44 12.85 30-40 7.42 1.26 14.90 27.37 57.73 7.86 11.99 40-50 7.27 1.49 14.90 25.37 59.73 12.69 9.20 50-60 7.48 1.26 12.76 25.37 61.87 8.46 6.51 60-70 7.72 0.93 12.47 27.37 60.16 24.78 5.09 70-80 7.81 0.96 10.40 29.37 60.23 10.88 4.74 80-90 7.89 0.88 12.40 31.51 56.09 24.18 4.00 90-100 8.03 0.93 16.11 30.16 53.73 19.34 3.55 100-110 7.96 1.04 8.33 40.02 51.66 20.55 3.72 110-120 8.12 1.03 10.69 35.94 53.37 25.38 3.34 17 0-10 7.39 0.92 21.12 23.22 55.66 2.42 15.19 10-20 7.79 0.64 16.98 29.22 53.80 3.02 11.57 20-30 7.70 0.70 14.90 29.22 55.87 2.42 10.77 30-40 7.86 0.71 20.90 29.22 49.87 3.02 11.09 40-50 7.83 0.70 17.12 29.22 53.66 1.81 11.27 50-60 7.23 1.45 15.12 31.30 53.58 1.81 11.55 60-70 7.86 0.63 18.83 31.22 49.94 1.81 11.28

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70-80 8.06 0.71 12.62 33.22 54.16 5.44 9.00 80-90 8.42 0.77 14.47 35.15 50.38 9.67 8.22 90-100 8.40 0.92 14.47 39.30 46.23 12.09 7.50 100-110 8.45 1.16 12.18 40.16 47.66 16.92 6.29 110-120 8.55 1.24 9.55 33.87 56.58 34.45 4.04 18 0-10 7.80 0.94 27.05 27.87 45.08 6.65 11.10 10-20 7.95 0.62 26.98 29.80 43.22 8.46 8.88 20-30 8.16 0.47 28.90 31.80 39.30 14.51 6.99 30-40 8.05 0.48 29.05 31.73 39.22 18.74 5.69 40-50 8.09 0.44 28.56 31.80 39.64 18.13 5.24 50-60 8.11 0.45 30.56 27.58 41.86 18.74 4.57 60-70 8.19 0.40 32.34 27.51 40.14 18.74 4.17 70-80 8.13 0.42 34.27 29.51 36.22 16.92 4.61 80-90 8.26 0.38 32.34 27.51 40.14 16.92 4.14 90-100 8.22 0.39 34.34 27.58 38.07 15.71 4.12 100-110 8.27 0.37 46.42 27.51 26.07 13.90 3.99 110-120 8.34 0.39 30.56 27.51 41.93 16.32 3.92 19 0-10 7.68 1.01 22.49 33.51 44.00 3.63 12.48 10-20 7.77 0.73 22.34 31.58 46.07 3.63 10.92 20-30 7.73 0.74 18.27 31.58 50.14 3.63 10.84 30-40 7.79 0.70 12.20 31.58 56.22 2.42 11.21 40-50 7.86 0.68 18.34 33.66 48.00 3.63 10.55 50-60 7.88 0.72 16.42 33.66 49.93 5.44 9.45 60-70 8.01 0.60 16.56 35.58 47.86 8.46 8.68 70-80 8.13 0.59 12.34 39.66 48.00 11.48 7.39 80-90 8.23 0.64 12.06 39.66 48.29 13.30 6.58 90-100 8.43 0.66 13.98 41.66 44.36 15.71 6.14 100-110 8.50 0.71 18.13 39.66 42.22 18.13 5.44 110-120 8.47 0.80 12.13 39.80 48.07 16.32 5.47

Mean: 7.86 1.22 14.91 32.05 53.03 13.41 7.79

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Table A2. Physicochemical properties of 10 cm soil layers from six phaeozem catena cores in Cluj County, Romania.

Phaeozem Depth pH EC Sand Clay Silt CaCO3 LOI -1 --cm-- dS m ------%------1 0-10 6.28 1.46 24.40 28.36 47.24 1.21 17.46 10-20 6.70 0.55 ------0.00 13.08 20-30 6.91 0.61 16.69 32.36 50.95 0.00 11.95 30-40 7.10 0.54 20.76 34.36 44.88 0.00 11.21 40-50 7.21 0.50 14.11 39.73 46.16 0.00 10.00 50-60 7.35 0.49 11.75 41.44 46.81 0.00 9.36 60-70 7.72 1.33 15.90 37.22 46.88 1.81 8.95 70-80 7.98 0.54 13.82 35.15 51.02 4.84 8.65 80-90 8.00 0.53 18.04 37.08 44.88 4.23 8.25 90-100 8.09 0.52 12.18 39.08 48.74 7.86 7.42 100-110 8.11 0.58 18.04 37.08 44.88 9.07 7.07 110-120 8.10 0.63 13.68 39.01 47.31 8.46 6.77 2 0-10 6.60 6.56 26.42 30.43 43.15 9.07 14.51 10-20 6.95 6.20 ------6.04 12.72 20-30 7.10 6.05 4.56 34.36 61.08 8.46 8.86 30-40 7.40 7.29 4.56 38.29 57.15 8.46 7.23 40-50 7.56 8.72 6.42 40.22 53.37 8.46 7.85 50-60 7.70 8.59 12.13 38.14 49.73 11.48 6.59 60-70 8.02 6.74 10.13 34.14 55.73 10.88 5.21 70-80 8.06 8.12 8.00 32.78 59.22 11.48 5.26 80-90 8.25 8.44 10.00 30.70 59.30 10.88 4.88 90-100 8.22 10.40 41.86 30.63 27.51 11.48 4.53 100-110 8.35 12.42 23.78 38.63 37.58 14.51 5.26 110-120 8.24 17.23 7.71 38.63 53.66 11.48 6.20 3 0-10 6.62 1.01 42.78 23.15 34.07 1.81 16.58 10-20 6.63 0.62 44.85 24.86 30.29 1.81 13.92 20-30 6.82 0.61 42.78 28.86 28.36 2.42 12.00 30-40 6.95 0.60 40.78 30.72 28.50 3.02 11.05 40-50 7.65 0.60 36.92 28.65 34.43 4.23 9.40 50-60 7.96 0.59 33.06 32.65 34.29 7.86 8.46 60-70 7.95 0.57 36.85 30.58 32.58 7.25 8.04 70-80 7.97 0.55 34.78 32.58 32.65 8.46 7.82 80-90 7.96 0.54 34.70 32.58 32.72 8.46 7.47 90-100 7.99 0.56 32.78 32.22 35.01 7.25 7.60 100-110 7.96 0.63 30.85 34.22 34.94 7.25 7.34 110-120 7.95 0.67 35.14 22.14 42.72 6.65 7.21 4 0-10 7.39 1.02 29.35 22.79 47.86 4.23 17.38 10-20 7.77 0.71 25.42 25.66 48.92 3.63 13.86 120

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20-30 7.80 0.69 21.28 24.02 54.70 4.84 12.16 30-40 7.88 0.65 19.35 25.66 54.99 7.25 10.70 40-50 7.94 0.55 22.85 21.44 55.71 9.07 9.58 50-60 8.47 0.21 20.70 7.37 71.93 5.44 6.74 60-70 8.50 0.22 22.70 7.30 70.00 6.65 6.67 70-80 8.83 0.16 18.70 5.22 76.07 3.02 6.56 80-90 8.38 0.51 24.70 17.22 58.07 4.84 6.75 90-100 8.05 1.84 12.78 25.08 62.14 10.27 6.35 100-110 7.98 3.06 14.99 2.86 82.14 7.86 6.66 110-120 7.95 3.31 17.14 2.94 79.93 7.86 7.88 5 0-10 6.27 1.26 21.14 28.94 49.93 1.81 18.49 10-20 6.11 0.86 16.99 32.86 50.14 2.42 16.14 20-30 6.21 0.85 34.92 28.94 36.14 2.42 16.05 30-40 6.25 1.07 14.78 33.08 52.14 1.81 14.72 40-50 7.06 0.97 14.70 29.30 56.00 3.02 13.13 50-60 7.55 0.95 16.85 31.22 51.93 4.84 12.03 60-70 7.54 0.94 17.06 29.15 53.78 5.44 10.83 70-80 7.64 0.92 16.99 29.15 53.86 7.86 10.38 80-90 7.68 0.98 14.99 27.08 57.93 9.07 9.43 90-100 7.80 0.80 12.92 27.08 60.00 9.07 9.34 100-110 7.95 0.80 12.85 25.08 62.07 9.07 9.57 110-120 7.93 0.85 14.85 23.08 62.07 12.09 8.03 6 0-10 6.13 0.78 26.62 17.94 55.44 1.21 18.99 10-20 6.06 0.42 20.54 27.87 51.58 1.21 13.91 20-30 6.15 0.35 20.76 29.80 49.44 0.00 13.08 30-40 6.44 0.34 17.77 33.44 48.79 1.21 11.75 40-50 6.49 0.43 17.19 35.30 47.51 1.81 10.71 50-60 6.83 0.55 18.83 43.30 37.87 0.00 10.70 60-70 7.35 0.70 28.69 43.22 28.09 1.81 10.60 70-80 7.57 0.92 16.69 43.30 40.02 1.21 9.50 80-90 8.12 1.22 16.69 43.22 40.09 1.81 9.15 90-100 7.99 1.47 20.83 43.22 35.94 3.02 8.32 100-110 8.15 1.50 15.26 39.22 45.51 4.23 7.94 110-120 8.19 1.56 23.12 39.22 37.66 6.04 7.04

Mean: 7.51 2.18 21.19 30.18 48.63 5.47 9.96

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