By in Submitted to the Graduate Faculty of Texas Tech University In

Home , Hume (soil)

THE USE OF GYPSUM TO AMELIORATE SOIL SODICITY IN IRRIGATED COTTON PRODUCTION ON THE SOUTHERN HIGH PLAINS OF TEXAS

QUINT CHEMNITZ, B.S.

A THESIS

SOIL SCIENCE

Submitted to the Graduate Faculty

of Texas Tech University in

Partial Fulfillment of

the Requirements for

the Degree of

MASTER OF SCIENCE

Approved

Dr. Wayne Hudnall Committee Chairperson

Dr. Richard Zartman

Dr. Stephen Maas

Dr. David Wester

Fred Hartmeister Dean of the Graduate School

December, 2007 Texas Tech University, Quint Chemnitz, December 2007

Acknowledgments

This project was made possible by the United States Department of Agriculture, Texas Natural Resources Conservation Services (Project number: 68‐7442‐5‐464/TXCIG‐ 05‐10 CFDA 10.912). I would also like to thank Texas Tech University for the opportunity and facilities to carry out this project, along with the professors and staff who helped with all aspects of the work. I would like to thank Dr. Wayne Hundnall in particular for his guidance and support for the duration of the project. Without his help and the help of several of my fellow graduate students and research assistants, I would not have been able to complete this project. I would also like to thank my parents for their support and guidance throughout the years. Lastly, I would like to thank my wife, Betty for her patience, understanding, and love.

Texas Tech University, Quint Chemnitz, December 2007 Table of Contents

ACKNOWLEDGMENTS ...... ii ABSTRACT ...... v LIST OF TABLES ...... vi LIST OF FIGURES ...... vii I. INTRODUCTION ...... 1 II. BACKGROUND ...... 3

GYPSUM SOLUBILITY AND MOVEMENT ...... 5 OVER‐ABUNDANCE OF GYPSUM ...... 6 THE CHEMICAL PROPERTIES OF GYPSUM ...... 7 HUMIC ACID ...... 7 SITE LOCATION ...... 9 III. OBJECTIVES ...... 12 IV. EXPERIMENTAL DESIGN ...... 14 V. METHODS ...... 16

FIELD METHODS...... 16 Field Variability ...... 16 Pre-Planting Methods ...... 17 Post-Planting Methods ...... 18 LABORATORY METHODS ...... 19 Sample Preparation ...... 19 Particle Size Analysis ...... 19 Saturated Paste ...... 21 Electrical Conductivity and pH ...... 21 Atomic Absorption Spectrophotometer ...... 21 VI. RESULTS ...... 23

2006 RESULTS ...... 23 Field Variability ...... 23 Particle Size Analysis ...... 26 Electrical conductivity and pH ...... 26

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Atomic Absorption Spectrophotometer ...... 27 Emergence ...... 29 Yield ...... 30 2007 RESULTS ...... 32 Field Variability ...... 32 Particle Size Analysis ...... 35 Electrical conductivity and pH ...... 35 Atomic Absorption Spectrophotometer ...... 37 Emergence ...... 40 Yield ...... 42 VII. ECONOMIC IMPACT ...... 47 VIII. DISCUSSION ...... 48 IX. CONCLUSION ...... 53 BIBLIOGRAPHY ...... 54

Texas Tech University, Quint Chemnitz, December 2007

Abstract

The purpose of this project is to reduce the exchangeable sodium within the soil by the addition of gypsum. Even though the addition of gypsum is the standard reclamation technique for sodic soils, its effectiveness has not been shown for cotton production on the Southern High Plains of Texas. Exchangeable sodium disperses the soil, which increases the potential for wind erosion as well as the formation of a salt‐ based crust. The addition of gypsum to sodic soils improves aggregation of soil particles. The addition of calcium improves particle‐to‐particle association which increases water infiltration and percolation. A flocculated soil allows water to move more easily through the profile which increases the probability of leaching sodium out of the rooting zone and decreases crusting. The accepted rate of gypsum for this study to reduce the sodium adsorption ratio and soil electrical conductivity is approximately 4.5 mt ha‐1 (2 tons acre‐1). Rates half and twice the recommended rate were applied in a completely randomized design. The application of gypsum to the soil was broadcast and in‐row. Plant emergence was counted for 15 consecutive days after planting and the yield was used to measure the effectiveness of the gypsum application.

Results indicate that there is no significant difference in yield between the treated and untreated plots. With no increase in yield, there is no reason to spend money on either a gypsum or humic acid treatment. The cost of gypsum is currently $31.81 for one metric ton. The farmer could potentially spend anywhere between $144,167.25 and $572,842.75 on gypsum to cover a field of 2023 hectares (5,000 acres) depending on the rate. Without sufficient water to move the Na from the rooting zone once it is in solution, the gypsum applied will be of no benefit.

Texas Tech University, Quint Chemnitz, December 2007 List of Tables

1. AVERAGE PARTICLE SIZE DISTRIBUTION OF THE 36 SAMPLES TAKEN FROM 17 SITES IN 2006...... 26 2. AVERAGE PH AND EC COMPARISON OF PRE‐GYPSUM APPLICATION AND POST‐GYPSUM APPLICATION. POST‐APPLICATION AVERAGE IS TAKEN FROM ALL THE PLOTS COMBINED, WHEREAS THE PRE‐APPLICATION AVERAGE IS TAKEN FROM THE ENTIRE HALF PIVOT...... 27 3. STATISTICAL ANALYSIS OF 2006 LINT YIELD SHOWING NO SIGNIFICANT DIFFERENCE BETWEEN RATES AND METHODS OF APPLICATION. MEAN COMPARISON OF LINT YIELD ‐1 ‐1 REPORTED IN KG HA . TREATMENT RATES REPORTED IN MT HA ...... 31 4. AVERAGE PARTICLE SIZE DISTRIBUTION PERCENTAGE FOR STUDY SITE IN 2007...... 35 5. COMPARISON OF AVERAGE PH BY TREATMENT FOR 2007. TREATMENT APPLICATIONS ‐1 ‐1 REPORTED AS MT HA FOR GYPSUM AND KG HA FOR HUMIC ACID...... 36 6. COMPARISON OF PRE VS. POST APPLICATION FOR GYPSUM AND HUMIC ACID AA RESULTS. ‐1 RESULTS REPORTED ARE IN MEQ L . GYPSUM AND HUMIC ACID APPLICATION RATES ARE ‐1 ‐1 REPORTED IN MT HA AND KG HA , RESPECTIVELY...... 39 7. AVERAGE PLANT EMERGENCE FOR 2007 REGARDLESS OF APPLICATION RATE OR METHOD..... 40 8. STATISTICAL ANALYSIS OF 2007 AVERAGE LINT YIELD FOR GYPSUM APPLICATION. TESTED AT A 5 % SIGNIFICANCE LEVEL...... 44 9. COMPARISON OF MEANS BY TREATMENT FOR GYPSUM...... 44 10. COMPARISON OF MEANS BY TREATMENT FOR HUMIC ACID...... 46 11. COMPARISON OF PH BY DEPTH FOR 2007...... 50 12. COMPARISON OF AVERAGE YIELD, EMERGENCE, PRE‐APPLICATION NA, AND POST‐ APPLICATION NA BY TREATMENT OF GYPSUM...... 51 13. COMPARISON OF AVERAGE LINT YIELD OF BOTH YEARS OF THE STUDY...... 52

Texas Tech University, Quint Chemnitz, December 2007

List of Figures

1. THE EXTENT OF THE EARTH’S OCEANS DURING THE CRETACEOUS PERIOD (SCHLEE, 2000). .... 5 2. STRUCTURE OF HUMIC ACID. DIAGRAM COURTESY OF HUMIFULVATE SCIENCE AND ENEREX BOTANICALS...... 9 3. MAP FROM LUBBOCK TO RESEARCH SITE (WILLIAMS FARM), APPROXIMATELY 52 MILES. MAP DRAWN USING GOOGLE EARTH...... 10 4. NRCS SOIL SURVEY OF STUDY SITE. LIGHT BLUE RECTANGLE REPRESENTS THE 2006 STUDY SITE AND ORANGE RECTANGLE REPRESENTS THE 2007 STUDY SITE...... 10 5. PICTURE ILLUSTRATING THE ADDITION OF CALCIUM TO A SODIC SOIL. THE CALCIUM SUPPLIED DISPLACES THE SODIUM IN THE SOIL ALLOWING WATER TO MOVE THE SODIUM OUT OF THE ROOTING ZONE. PICTURE COURTESY OF THE COOPERATIVE RESEARCH CENTRE FOR SOIL & LAND MANAGEMENT, AUSTRALIA...... 13 ‐1 6. YEAR ONE – 2006 GYPSUM PLOT LAYOUT. PLOTS LISTED ON POUNDS ACRE BASIS...... 14 7. YEAR TWO – 2007 GYPSUM AND HUMIC ACID PLOT LAYOUT. * INDICATES A DUPLICATE PLOT. GYPSUM WAS APPLIED INCORRECTLY TO THE PLOT MARKED BY *. DUPLICATE PLOT CAN BE FOUND IN CENTER ROW, LAST PLOT BACK. ALL DATA USED CAME FROM ‐1 UNMARKED PLOT. GYPSUM PLOTS REPORTED IN MT HA AND HUMIC ACID PLOTS ‐1 REPORTED IN KG HA ...... 15 8. SAMPLING SITES OF THE HALF PIVOT, 17 TOTAL. APPROXIMATELY 8‐10 SAMPLES TAKEN FROM EACH SITE, SEPARATED BY DEPTH. MAP GENERATED USING ARCMAP...... 17

9. MAP OF SAMPLE ECA POINTS OBTAINED FOR THE 2006 PLOTS. MAP GENERATED USING ARCMAP...... 24

10. MAP OF INTERPOLATED SURFACE USING ECA DATA POINTS FOR THE 2006 RESEARCH SITE. MAP GENERATED USING ARCMAP...... 25 11. PRE VERSUS POST APPLICATION COMPARISON OF ATOMIC ABSORPTION ‐1 SPECTROPHOTOMETER RESULTS. CA, NA, K, AND MG VALUES REPORTED IN MEQ L . POST‐APPLICATION AVERAGE IS TAKEN FROM ALL THE PLOTS COMBINED, WHEREAS THE PRE‐APPLICATION AVERAGE IS TAKEN FROM THE ENTIRE HALF PIVOT...... 28 ‐1 12. 2006 AVERAGE PLANT EMERGENCES FOR GYPSUM APPLICATION (PLANTS HA ). STANDARD ERROR SHOWN ON GRAPH...... 29 13. AVERAGE LINT YIELDS FOR GYPSUM RATE APPLICATION IN 2006. GYPSUM APPLICATION ‐1 RATE REPORTED IN MT HA . STANDARD ERROR INDICATED ON GRAPH...... 30

14. SAMPLE ECA POINTS TAKEN IN 2007 USING THE DUALEM‐42S. MAP GENERATED USING ARCMAP...... 33

15. IDW SURFACE CREATED USING ARCMAP USING ECA POINTS TAKEN FROM THE DUALEM‐ 42S. BLACK POINTS INDICATE EXTENT OF PLOTS. MAP GENERATED USING ARCMAP...... 34 vii

Texas Tech University, Quint Chemnitz, December 2007

16. COMPARISON OF AVERAGE EC FOR PRE VERSUS POST TREATMENT APPLICATION IN 2007. PRE‐APPLICATION IN BLUE AND POST‐APPLICATION IN RED. TREATMENT RATES REPORTED ‐1 ‐1 IN MT HA AND KG HA FOR GYPSUM AND HUMIC ACID RESPECTIVELY...... 37 17. AVERAGE AMOUNTS OF CA, MG, NA, AND K FOUND IN THE SOIL SAMPLES COLLECTED. ‐1 GYPSUM APPLICATION REPORTED IN MT HA ...... 38 18. AVERAGE AMOUNTS OF CA, MG, NA, AND K FOUND IN THE SOIL SAMPLES COLLECTED. ‐1 HUMIC ACID APPLICATION REPORTED IN KG HA ...... 38 19. 2007 AVERAGE PLANT EMERGENCES FOR GYPSUM IN PLANTS PER HECTARE. GYPSUM ‐1 RATES REPORTED IN MT HA . STANDARD ERROR SHOWN ON GRAPH...... 41 20. 2007 AVERAGE PLANT EMERGENCES FOR HUMIC ACID APPLICATION IN PLANTS PER ‐1 HECTARE. HUMIC ACID RATES REPORTED IN KG HA . STANDARD ERROR SHOWN ON GRAPH...... 41 21. COMPARISON OF AVERAGE PLANT EMERGENCE FOR 2006 AND 2007. PLANT ‐1 ‐1 EMERGENCE REPORTED IN PLANTS HA AND GYPSUM APPLICATION REPORTED IN MT HA . STANDARD ERROR SHOWN ON GRAPH...... 42 ‐1 22. GRAPH OF LINT YIELD FOR 2007. GYPSUM APPLICATION REPORTED IN MT HA . STANDARD ERROR SHOWN ON GRAPH...... 43 23. AVERAGE LINT YIELD FOR HUMIC ACID APPLICATION IN 2007. APPLICATION RATES OF ‐1 ACID ARE IN KG HA . STANDARD ERROR SHOWN ON GRAPH...... 45

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Texas Tech University, Quint Chemnitz, December 2007

Chapter I

Introduction

Gypsum is the most common calcium sulfate mineral found in soils. It is found on all continents and is the result of evaporation of seas and salt lakes. The uses of gypsum date back to the 1800s (Georgia‐Pacific, 2007) and it is still widely used today. The addition of gypsum is the standard reclamation technique used on sodic soils, but its effects have not been shown for cotton production on the Southern High Plains (SHP). The addition of gypsum to sodic soils is intended to improve the aggregation of soil particles. The addition of Ca+2 improves particle‐to‐particle association, which increases water infiltration and percolation. The rate most commonly used to reduce the sodium adsorption ratio (SAR) and soil electrical conductivity (EC) is approximately 4.5 to 6.7 metric tons ha‐1 (2 to 3 tons per acre) (Oster, 2007). Salt and sodium often produce no clear negative symptoms in plants. The main symptom, poor growth, might not be evident unless healthy plants are nearby for comparison (Singer and Munns, 2002).

Most of the farms on the SHP use a center pivot method of irrigation, which applies water that is being pumped from the Ogallala formation. The SHP of Texas is one of the major cotton producing regions in the United States, and with declining water quality in some areas; soil crusting may become a major soil management problem. Surface sealing leads to low water infiltration, producing runoff and erosion even during low intensity rainfall events (Norton and Dontsova, 1998). Cotton yield may not be at a maximum, but it still provides an economical return.

Gypsum (CaSO4∙2H2O) is a white mineral that occurs extensively in natural deposits. It is the most common calcium sulfate mineral found in soils. It is deposited in soils per ascenium (capillary rise from a water table) or per descensium (downward 1

Texas Tech University, Quint Chemnitz, December 2007

movement from an incomplete wetting front) (Doner and Grossl, 2002). It is a major rock‐forming mineral that produces massive beds, usually from precipitation out of highly saline waters. It is usually colorless, white or gray but can also have shades of red, yellow and brown depending on the impurities. The crystal system of gypsum is monoclinic, which means that it has three unequal crystal axes, two of which intersect obliquely and are perpendicular to the third. Gypsum belongs to a group of minerals called the sulfates, and is the most common of the approximately 150 sulfate minerals (Doner and Grossl, 2002). Sulfates are compounds of one or more metals with oxygen ‐2 and sulfur. The oxygen and sulfur join together to form the sulfate ion, SO4 . Technically, gypsum is hydrous calcium sulfate because it has water in its crystal structure, CaSO4∙2H2O (Powell, 1999). Gypsum can be found in several patterns, such as spots, powdery, crusty, crust, and polygonal. Gypsum is also referred to by several other names such as alabaster, satin spar and selenite. Gypsum is by far the most important sulfate mineral in soils and is the least soluble of the more abundant sulfate salts (Donner and Grossl, 2002). Gypsum is the most common ameliorant used for sodic soils, but there is no method to accurately assess the amount of gypsum needed for amelioration (Rengasamy and Churchman, 2000).

Texas Tech University, Quint Chemnitz, December 2007

Chapter II

Background

For cotton production, a sodic soil has an exchangeable sodium percentage (ESP) of more than 6 or an SAR of 12 or greater. Sodium comprises more than six percent of the total exchangeable cations in the soil. Sodic soils are likely to disperse, that is, break down into individual clay particles that block pore spaces. This dispersion causes poor water infiltration, slow internal drainage, surface crusting and it decreases germination and emergence. Dispersion increases the erodibility of the soil. If dispersion occurs in the subsoil, the soil may become impermeable and be a poor environment for growing plants. In a sodic soil, exchangeable sodium cannot be leached unless something displaces it into solution and keeps the soil flocculated. Both of these objectives may be met by the addition of calcium. Gypsum is soluble enough to maintain calcium at a useful concentration (Singer and Munns, 2006).

According to the Natural Resources Conservation Service (NRCS), cotton yields on the SHP have declined as much as thirty percent because of salinity and sodicity. While salinity affects plants directly, sodicity indirectly influences plants by affecting the soil. Texas produces every variety of cotton grown in the U.S. and is the leading producer among states (Metcalf, 2006). On the SHP, 27 counties produce sixty‐four percent of Texas’s cotton crops (Smith et al., 1999). Fifty percent of the cotton is irrigated in this region. Average rainfall is 40 ‐55 cm (16‐22 inches), with wide year to year variations in both total and seasonal rainfall (Smith et al., 1999).

Another property related to sodium hazard of irrigation waters is the bicarbonate concentration. Bicarbonate toxicities associated with some water sources generally arise from deficiencies of iron or other micronutrients caused by the resultant high pH. Precipitation of calcium carbonate from such waters lowers the concentration

Texas Tech University, Quint Chemnitz, December 2007

of dissolved calcium, increases the SAR, and increases the exchangeable‐sodium levels of the soil (Bohn et al., 1985). Gypsum is usually applied to replace the exchangeable sodium with calcium. If sufficient water is available, the soluble sodium is leached out of the rooting zone. Our situation is such that water is not available, therefore a cover crop (ryegrass, Lolium perenne) was planted to remove the sodium and thus reduce both the bicarbonate hazard and sodicity and to reduce wind erosion between growing seasons.

Irrigation water at the study site comes from Cretaceous limestone below the Ogallala Formation that was deposited during the Cretaceous period (65 to 144 million years ago) and is dispersed by a center pivot. The water is pumped from five wells that pump at a rate of approximately 300 liters (80 gallons) per minute. Locally, these wells are referred to as “punch” wells because they have been drilled into the karst topography of the Cretaceous limestone. Karst cavities are recharged from the Ogallala aquifer following precipitation. Water moves slowly through cracks in the caprock thus entering into pockets of Cretaceous limestone. The limestone is composed largely of the mineral calcite, which came from marine organisms. During the Cretaceous Period, the Earth’s oceans extended well into the SHP (Figure 1). The warm shallow seas that covered the area produced beds of limestone, which are evident today. The term Cretaceous, which comes from the Latin word creta, means chalk, and it refers to the calcium carbonate deposited by the shells of marine organisms. The calcite in the ground acts as a cement, holding the existing rock grains together. Subsequently, caves develop in the carbonate rocks due to their solubility, and because of the porous nature of the limestone, water passes very quickly through them and collects in caves, or pockets, until it is tapped by a well for a water source. Because the limestone was deposited within seas, Na in the form of either Na2CO3 or NaCl is a common precipitate in the limestone. Because Na‐salts are more soluble than CaCO3, the water that collects in the karst topography contains varying quantities of Na.

Texas Tech University, Quint Chemnitz, December 2007

Figure 1. The extent of the Earth’s oceans during the Cretaceous Period (Schlee, 2000).

Gypsum Solubility and Movement

Low water intake rates cause runoff even with low intensity rainfall and results in poor water use efficiency. This can occur under both rain‐fed and irrigated farming. The low electrolyte content of rainwater enhances dispersion and surface sealing, which can be eliminated by a surface application of gypsum or gypsum‐like materials (Reichert and Norton, 1994). On the SHP, evapotranspiration is higher than the rainfall, thus salts accumulate via capillary rise and create a crust on the soil surface, decreasing germination and having a negative osmotic effect on the seeds that do germinate.

‐1 In pure water at 40°C, the solubility of gypsum is approximately 2.0 (CaSO4) g L . ‐1 ‐1 In water containing 200 g (NaCl) L , the solubility increases to 8.0 (CaSO4) g L . Thus, water moving upward in a soil profile by evapotranspiration can carry relatively large

Texas Tech University, Quint Chemnitz, December 2007

amounts of CaSO4 compared with downward moving water with the excess, upward moving CaSO4 precipitates on the soil surface (Donner and Grossl, 2002). Gypsum reduces runoff volume and may increase surface roughness and the tortuosity of the flow paths to reduce soil loss (Shainberg et al., 1989). Gypsum can usually be found with calcite, but due to gypsums higher solubility, it usually occurs deeper in the soil profile. Calcium associated with sulphate in gypsum moves down the profile faster than calcium associated with bicarbonate (Lang, 1994).

Over‐Abundance of Gypsum

Gypsum in arid land soils is usually pedogenic. Because most surface waters are under saturated with respect to gypsum, it dissolves relatively easily and is rarely transported in fluvial processes. Gypsum may occur as white surface crusts in arid environments, but also readily produces silt‐sized crystals to several cm in length (Loeppert and Suarez, 1996). If too much gypsum accumulates in soil, it may begin to cause surface damage by way of structural collapse of the soil. In some areas, soils may contain in excess of 50% gypsum on a mass basis, which may pose special environmental problems in their management. For example, finely dispersed gypsum throughout a loam or silt loam soil causes a soil to become highly susceptible to flow erosion (Donner and Grossl, 2002). This is usually not a problem because the rainfall in such areas is not high enough to cause the dissolution of the gypsum. An irrigation leak or any event that may supply enough water to cause the dissolution of gypsum can result in structural collapse of the soil due to the reduction in soil volume. The same properties that make gypsum attractive as a soil amendment can also be problematic. Soil flocculation due to gypsum application may reduce soil crusting, but if unchecked for a period of time, it may begin to do more harm than good.

Texas Tech University, Quint Chemnitz, December 2007

The Chemical Properties of Gypsum

When CaCO3 minerals are present in a soil material that is sulfuricized, gypsum is

formed (CaCO3 + H2SO4 + H2O Æ CaSO4∙2 H2O + CO2). Calcium ions, and to a lesser extent, sulfate ions enhance the plant nutrient status of the soil and replace nonessential, potentially hazardous ions, like Al3+ or OH‐, bound to soil particle surfaces (Sposito, 1989). The soil pH is not affected by the addition of gypsum, but as stated above, it has a beneficial effect on Al in the soil solution.

The reclamation of a sodic soil involves the replacement of exchangeable Na+ 2+ with Ca which can be supplied by the presence or addition of gypsum (CaSO4∙2H2O), calcite (CaCO3), or both (Oster and Frenkel, 1980). Gypsum, being an evaporite mineral, is rare to find at the Earth’s surface because of its solubility. In the sub‐surface, massive gypsum and halite (NaCl) beds are common, as are the salt domes found in Texas and other Gulf Coast areas of the U.S. (Perkins, 2002). Gypsum is the most common sulfate mineral and is a rock forming mineral of many evaporite deposits where it may be associated with other bedded salts. In gypsum, layers of H2O alternate with layers that 2+ contain Ca and SO4 (Perkins, 2002).

Humic Acid

In the second year of this study (2007), we were asked by the land‐owner to apply a product called Hydra Hume. The application of this product is meant to add humic acid to the soil to improve the overall health and increase nutrient uptake. The particular type/brand of humic acid used in this study is marketed by the Helena Chemical Company and is called Hydra Hume DG Coated. It comes in pellet form and is intended to be distributed to the soil via a “timic box.” The Hydra Hume contains 70%

Texas Tech University, Quint Chemnitz, December 2007

humic acids (derived from leonardite) and 30% inert ingredients. Leonardite is a soft, brown, coal‐like deposit, which is often defined as naturally occurring oxidized lignite. Because it is so highly oxidized, leonardite can contain up to 85% humic acids, which makes it a good source for humic acid. Humic acids in general have a high cation exchange capacity which serves to chelate (collect) cations and release them as the plant requires. The chelation process holds nutrients in the soil solution and prevents their leaching and runoff. Humic acids improve humus content in the soil for better tilth, water and nutrient retention, and soil aeration. Seed germination and plant root and top growth are enhanced (Acosta, 2007). Humic acid can be extracted from any material containing well‐decomposed organic matter ‐ soil, coal, composts, etc. Extraction of these materials is accomplished by the treatment of a sodium hydroxide solution. This dissolves much of the organic matter present. If we then take this solution and add enough acid to drop its pH to about 2, organic material will begin to flocculate and can be separated from the liquid portion. The flocculated material is humic acid. What remains in solution is fulvic acid (Kussow, 2002). One chemical structure of humic acid is shown in Figure 2.

Texas Tech University, Quint Chemnitz, December 2007

Figure 2. Structure of Humic Acid. Diagram courtesy of Humifulvate Science and Enerex Botanicals.

Site Location

The study site is located in Borden County, south of the community of Draw, Texas (Figure 3). Cotton is grown during the summer months and ryegrass is grown during winter and fall to help control wind erosion. According to the NRCS soil survey, the area in question is mapped as an Olton clay loam (Fine, mixed, superactive, thermic Aridic Paleustoll) (Figure 4). Approximately 48.5 hectares (120 acres) were available for the project with the second year’s study being located on the same field but in a different location. The farm is irrigated by a center pivot irrigation system.

Texas Tech University, Quint Chemnitz, December 2007

Figure 3. Map from Lubbock to research site (Williams Farm), approximately 52 miles. Map drawn using Google Earth.

Figure 4. NRCS soil Survey of study site. Light blue rectangle represents the 2006 study site and orange rectangle represents the 2007 study site.

Texas Tech University, Quint Chemnitz, December 2007

The soil that this study is being conducted on is mapped as an Olton clay loam, which covers nearly 36,422 hectares (90,000 acres) of Borden County. The Olton series consists of very deep, well drained, moderately slowly permeable soils that formed in loamy, calcareous eolian sediments in the Blackwater Draw Formation of Pleistocene age (National Cooperative Soil Survey, 2004). According to the Natural Resources Conservation Service (NRCS), the Olton series is quite extensive, spanning across the Southern High Plains of western Texas and eastern New Mexico. This series is mainly cultivated to cotton, sorghum (sorghum bicolor), and winter wheat (Triticum aestivum). The climax vegetation is dominantly short grasses with a few midgrasses and includes blue grama (Tripsacum dactyloides) and buffalograss (Buchloe dactyloides), with lesser amounts of vine‐mesquite (Panicum obtusum), western wheatgrass (Poscopyrum smithii), sideoats grama (Bouteloua curtipendula), galleta (Hilaria jamesii), or tobosa (Hilaria mutica), silver bluestem (Bothriochloa laguroides), wild alfalfa (Psoralidium tenuiflorum), and prairieclover (Dalea searlsiae) (National Cooperative Soil Survey, 2004). Because the field has been irrigated using poor quality water via a sprinkler irrigation system, the soil is calcareous to the surface and would not qualify as an Olton clay loam. There is currently not a soil series that matches the current soil characteristics.

Texas Tech University, Quint Chemnitz, December 2007

Chapter III

Objectives

The objectives of this study are to reduce the exchangeable sodium (Na) within the soil, increase cotton seed germination rates, and increase the cotton yield by the addition of gypsum and humic acid. Gypsum is the most economical soil amendment that can be added for a soil sodicity problem. Calcium chloride and calcium nitrate work at least as well as gypsum but they are costly and can be harmful in large amounts and calcium carbonate is too insoluble at high pH’s to be a worthwhile amendment for most sodic soils (Singer and Munns, 2006). The idea behind the addition of gypsum is that the calcium will cause the soil to flocculate, thus creating more space for water, whether it is from irrigation or rain, to move the sodium in the soil out of the rooting zone. We do not intend to remove the sodium from the soil completely; we just want to move it farther down the profile so when evaporation takes place, the sodium will not precipitate and cause a problem. The application of gypsum is relatively easy; the main question is the rate and method, which this study intends to establish. Figure 5 is a representation of the process that this project intends to simulate.

The application of humic acid is intended to increase the overall “soil health” by increasing the water holding capacity and the CEC, stimulating seed germination and root formation, and promoting increases in soil aeration and the break‐up of heavy clay soils.

Texas Tech University, Quint Chemnitz, December 2007

Figure 5. Picture illustrating the addition of calcium to a sodic soil. The calcium supplied displaces the sodium in the soil allowing water to move the sodium out of the rooting zone. Picture courtesy of the Cooperative Research Centre for Soil & Land Management, Australia.

Texas Tech University, Quint Chemnitz, December 2007

Chapter IV

Experimental Design

The experimental design for both years of the study was a completely randomized design. In 2006 there were twenty one plots in total, nine plots treated with an in‐row application of gypsum, nine plots treated with a broadcast application of gypsum, and three control plots with no gypsum application. The rates of gypsum applied were 2.25, 4.5, and 9 metric tons (mt) of gypsum ha‐1 (1, 2, and 4 tons acre‐1). In Figure 6, the letters represent the method of application (GB = gpsum broadcast, GR = gypsum in‐row, and C = control) while the first number is the amount of gypsum applied in metric tons and the second number is the replication number (e.g., GB 9‐1 = gypsum, broadcast application of 9 mt ha‐1, replication 1).

Figure 6. Year One – 2006 Gypsum Plot Layout. Plots listed on pounds acre‐1 basis.

Texas Tech University, Quint Chemnitz, December 2007

The experimental design for the second year of the study (2007) involved forty plots. The design was a completely randomized design as shown in Figure 7. The only difference between the two years is that in 2007 we added nineteen plots that were treated with the product Hydra Hume. The application of the Hydra Hume was similar to that of the gypsum; we applied it in‐row and broadcast at three different rates (17, 34, and 67 kilograms ha‐1). The plots were marked with an “A” for the acid or a “G” for the gypsum. As before, the “B” and the “R” refer to the method of application, either broadcast or in‐row. We did not test the combination of the two soil amendments, gypsum and Hydra Hume combined, because that was not the aim of this study. The addition of the Hydra Hume was at the request of the farmer.

Figure 7. Year Two – 2007 Gypsum and Humic Acid Plot Layout. * indicates a duplicate plot. Gypsum was applied incorrectly to the plot marked by *. Duplicate plot can be found in center row, last plot back. All data used came from unmarked plot. Gypsum plots reported in mt ha‐1 and humic acid plots reported in kg ha‐1.

Texas Tech University, Quint Chemnitz, December 2007

Chapter V

Methods

Field Methods

Field Variability

To determine the variability of the field according to the apparent electrical conductivity (ECa), we used the DUALEM‐42S sensor (DUALEM Inc., Milton, ON). The DUALEM‐42S sensor has dual‐geometry receivers at separations of 2‐ and 4‐m from the transmitter, which provides four simultaneous depths of conductivity sounding, four simultaneous depths of susceptibility sounding, and detection of metal

(www.dualem.com/products.html). Several factors influence ECa measurements, including soil salinity, water content, porosity, structure, temperature, clay content, mineralogy, cation exchange capacity, and bulk density (Rhoades et al., 1999; Friedman, 2005). The electromagnetic induction (EMI) based ECa measurements can be used in conjunction with soil sampling, directed from ECa surface response (Lesch et al., 1995a, 1995b). For field‐scale studies, these measurements are used to determine soil spatial variability and to identify heterogeneities in the field (Corwin and Lesch, 2003). The DUALEM was pulled across the field on a sled using a 4‐wheel drive off‐road vehicle. The driver was wearing a Trimble GPS Pathfinder Pro XRS Backpack System that was attached to a handheld Compaq iPac device to record the location of the ECa values.

Texas Tech University, Quint Chemnitz, December 2007

Pre-Planting Methods

Prior to planting, soil samples were taken from the field (half a circle pivot, approximately 24.3 hectares (60 acres)) using hand‐held soil augers at depths of 0 to 15 cm and 15 to 30 cm (0‐6 and 6‐12 inches respectively) from 17 different locations that represent a fair coverage of the field (Figure 8) so the physical and chemical condition of the soil could be determined.

Figure 8. Sampling sites of the half pivot, 17 total. Approximately 8‐10 samples taken from each site, separated by depth. Map generated using ArcMap.

Texas Tech University, Quint Chemnitz, December 2007

Post-Planting Methods

After the farmer planted the cotton, 21 plots were laid out using a completely randomized design. Each plot was 6 rows wide (each row is 76.2 cm (30 inches)) and approximately 9meters long (4.6 meters x 9.1meters (15 ft. x30 ft.)). Application rates of 2.25, 4.5, and 9 mt of gypsum per hectare were applied in a split plot design using broadcast and in‐row application methods. Emergence was counted for fourteen days after planting in 2006 (May 12, 2006 – May 25, 2006) and sixteen days after planting in 2007 (May 23, 2007 – June 8, 2007). The two center rows of each plot (with two ‘buffer’ rows on each side) were marked with 1.5 meter (5 foot) sections where the emergence counts were taken. Six weeks after planting, soil samples were again taken from each plot. Soil samples were taken from the rows or from the entire plots depending on the application. The use of gypsum was compared to a control where there was no treatment. Prior to the farmer stripping the cotton, each plot of cotton was hand stripped and collected. The cotton was hand ginned and the weight of the lint yield was recorded.

The second year’s study included the addition of humic acid as well as gypsum. The humic acid product used came in a granular form and is marketed by the Helena Chemical Company. The product is Hydra Hume DG coated and is derived from decayed organic matter (humates). The addition of this product was at the request of the farmer and since this project is funded by the United States Department of Agriculture innovative conservation grant, we felt compelled to comply with the request. The application of Hydra Hume was treated the same as the gypsum by applying three different rates (17, 34, and 67 kg ha‐1) using two application methods (in‐row and broadcast). Gypsum and Hydra Hume treated plots were kept separate, meaning we did not test the effect of mixing gypsum and the Hydra Hume in one plot.

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Laboratory Methods

Sample Preparation

Once the soil samples were brought to the lab, they were laid out to air dry prior to being crushed to a size of 2 mm. Each sample was opened and laid out on heavy construction paper for a period no shorter than twenty four hours. Once the samples were determined to be dry enough to crush, they were crushed using a Humbolt H‐4199 soil grinder. We used a mortar and pestle to break the particles that did not pass through the soil grinder. All samples were passed through a 2mm sieve prior to being re‐bagged and processed further.

Particle Size Analysis

The method used for the particle size analysis was the Hydrometer Method, which depends fundamentally on Stokes’ Law. The following is an 11 step procedure that was used to determine the sand, silt, and clay percentages; a detailed review can be found in Methods of Soil Analysis Part 1—Physical and Mineralogical Methods Second Edition (Gee & Bauder, 1986).

1. Weigh 20.0 g soil of 2 mm size into a shaker bottle. 2. Add 25 ml of Sodium Hexametaphosphate (SHMP) solution (100g/liter). If sample is sandy, use 50 g of sample and 10 ml of SHMP. 3. Add approximately 250 ml of deionized water to the shaker bottle. Shake overnight. 4. Weigh 20.0 g soil into a drying pan and place in oven overnight (for corrections to calculations). Weigh after placing in dessicator.

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5. After shaking sample overnight, pour soil solution through 270 mesh sieve into a 1000 ml graduated cylinder. Wash out sands into a weighed beaker and place in oven to dry. 6. Bring volume of 1000 ml graduated cylinder up to 1000 ml with deionized water. 7. Make a blank sample by pouring 25 ml SHMP solution into a 1000 ml graduated cylinder and bringing to volume with deionized water. 8. Stir samples with plunger before starting times. 9. At approximately 6 hours, place hydrometer into graduated cylinder and take reading. 10. When the beakers containing the sands are completely dry, cool in dessicator and weigh. This is the total weight of the sand. 11. If sand fractions are needed, carefully empty beaker into sieves and shake for approximately 3 minutes. Weigh each sand fraction.

Calculations:

% Clay = (Hyd. Reading of Samples – Hyd. Reading of Blank) * 100 / (Weight of Oven dry Sample)

% Sand = (Weight of Sand) * 100 / (Weight of Oven Dry Soil)

% Silt = 100% ‐ (% Clay + % Sand)

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Saturated Paste

A saturated paste was made for each sample by adding de‐ionized water to the crushed soil samples and mixing. The amount of water added to each sample varied, but the consistencies of the pastes were the same. The samples were allowed to equilibrate for 24 hours. The saturated pastes were made according to the procedures outlined in Methods of Soil Analysis (Rhoades, 1996). The soil solution was extracted from the saturated pastes using a vacuum filtration process. The pastes were placed into a funnel with size 2 filter paper and a vacuum was applied to extract the soil solution.

Electrical Conductivity and pH

The soil solutions from each sample were tested to determine their pH and electrical conductivity (EC). The instrument used to determine the EC was a YSI 30 Salinity, Conductivity, and Temperature probe. A minimum of 15 ml of extract was required to use the probe due to the use of a 50 ml tube for storage of the soil solution extract. The pH of each sample was taken using an Orion, model 720A pH probe.

Atomic Absorption Spectrophotometer

The saturated paste extracts were all analyzed for Ca, Mg, Na, and K using a BUCK Scientific Atomic Absorption (AA) Spectrophotometer ACUSYS 211 instrument. Prior to using the AA, the samples were diluted due to their high concentrations. A 1:200 dilution was used for the analysis of Na and a 1:10 dilution was used for the analysis for Ca and Mg, and a 1:20 dilution was used for K. Once the dilutions were made, we added 1 ml of lanthanum chloride (LaCl3) to the dilute samples prepared for

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Ca and Mg. The addition of the LaCl3 was intended to reduce the amount of interference or “tie‐up” the amount of interference chemically that may be caused by the formation of a compound that cannot be decomposed in the flame. Such interferences will cause the results to be low. The samples were read by inserting the tubing directly into the sample, which was aspirated into the air‐acetylene flame. The AA produces a printout of the readings once the operation is cancelled. While reading the samples, standards were placed after every 10 samples to make sure that the instruments readings were not drifting.

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Chapter VI

Results

2006 Results

Field Variability

The EMI data collected with the DUALEM‐42S were used to verify any field variability encountered by taking soil samples using a hand auger. Because of the availability of the equipment, the DUALEM was used on the 2006 site after the completion of that year’s study. The data was “cleaned” by removing outliers using a program that was developed by the USDA called ESAP‐RSSD. This program organizes and cleans raw data files and removes outlier observations. The program removes any outliers that are 4.5 standard deviations away from the mean (The 4.5 value is set by the program and said to be adequate for most survey applications). Figure 9 is a map of the

ECa points taken of the 2006 plots, and Figure 10 shows an inverse distance weighted

(IDW) interpolated surface of the ECa points divided into three categories. The reason we used IDW is because we had dense and relatively evenly‐spaced data points. IDW interpolation considers the values of the sample points and the distance separating them from the estimated cell. Sample points closer to the cell have a greater influence on the cell's estimated value than sample points that are further away. IDW cannot make estimates above the maximum or below the minimum sample values (ESRI GIS and Mapping Software, 2007).

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Figure 9. Map of sample ECa points obtained for the 2006 plots. Map generated using ArcMap.

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Figure 10. Map of interpolated surface using ECa data points for the 2006 research site. Map generated using ArcMap.

Texas Tech University, Quint Chemnitz, December 2007

Particle Size Analysis

The particle size analysis for the 2006 plots was completed using the samples that were taken from the initial field sampling of seventeen sites (Figure 8). The samples were separated by depth and classified. Table 1 shows the averages of the 36 samples that were classified. The entire field is predominantly a clay loam, with 22 of the 36 samples meeting this criterion. Eight samples were classified as sandy clay loams, four were classified as clays, one as a loam, and one as a clay loam parting to a sandy clay loam. Our findings are consistent with the classification done by the NRCS as they have it on the Web Soil Survey. By knowing the particle size distribution, we can better understand the soil hydraulic properties. By having an idea of how water moves on the field, we have a better understanding of why the cotton is growing in the way that it does.

Table 1. Average particle size distribution of the 36 samples taken from 17 sites in 2006.

% Clay % Silt % Sand 6 Hour Hydrometer Reading

Average 33.7 25.4 40.9 8.9

Electrical conductivity and pH

The pre‐application pH and EC data for 2006 are taken from the sampling points in Figure 8. The average pH was 7.54 and the average EC (dS m‐1) was 2.48. With the addition of gypsum, we found that the average pH dropped while the average EC rose (Table 2). The post‐application EC values were taken approximately three weeks after the application of gypsum. The post‐application values include the addition of gypsum as well as the salts added by the irrigation water.

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Table 2. Average pH and EC comparison of pre‐gypsum application and post‐gypsum application. Post‐application average is taken from all the plots combined, whereas the pre‐ application average is taken from the entire half pivot.

2006 Average EC (dS m‐1) Average pH

Pre‐Application 2.5 7.54

Post‐Application 5.1 7.02

Atomic Absorption Spectrophotometer

The pre‐application AA results from 2006 (Figure 11) indicate that the field contains an average of 15.03 meq L‐1 of Na, 9.86 meq L‐1 of Ca, 0.84 meq L‐1 of K, and 5.01 meq L‐1 of Mg. The irrigation water applied to the field contained 7.50 meq L‐1 of Ca, 16.79 meq L‐1 of Na, 2.71 meq L‐1 of K, and 8.39 meq L‐1 of Mg. The irrigation water is higher than all the average values of the soil with the exception of the Ca. After the application of gypsum, the averages, in meq L‐1, nearly doubled the pre‐application results (Figure 11). The average sodium absorption ratio (SAR) (1) rose from 5.90 to 7.04 after the application of gypsum. The 7.04 SAR value is sufficiently high for the soil to be considered sodic.

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AA Results Comparison for 2006

31 25 ‐ 1 L 15 13 meq 10 5 0.8 1.5

Ca Na K Mg

Pre‐Application Post‐Application

Figure 11. Pre versus Post Application comparison of atomic absorption spectrophotometer results. Ca, Na, K, and Mg values reported in meq L‐1. Post‐application average is taken from all the plots combined, whereas the pre‐application average is taken from the entire half pivot.

ሾே௔శሿ ܵܣܴ ൌ (1) భ ටቀ ቁሺሾ஼௔మశሿାሾெ௚మశሿሻ మ

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Emergence

Cotton emergence was counted for 14 days following planting. The cotton emergence was counted as any plant that broke the soil surface, the survival of the plant was not taken into account except for the yield. Figure 12 shows the averages of the cotton emergence for each application rate as well as the control. As the graph shows, the control performed better than any of the treated plots with the in‐row application of 2.25 mt ha‐1 producing the second best plant emergence. Statistically, there is no difference between the treated and the control plots at the 5% level. It was noted in the field that the higher rate of gypsum formed a crust that decreased the emergence.

2006 Average Plant Emergence

26,472 25,435 27,588 23,282 21,688 24,319 1

‐ 17,940 ha Plants

GR‐2.25GR‐4.5 GR‐9 GB‐2.25 GB‐4.5 GB‐9 C

Figure 12. 2006 Average plant emergences for gypsum application (plants ha‐1). Standard error shown on graph.

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Yield

The lint yield in 2006 (Figure 13) was calculated by hand harvesting the cotton and manually ginning each plot. The control plots produced a higher yield than any treated plot.

2006 Average Lint Yield

1,265 1,259 1,038 1,077 1,064 1,109 953 ‐ 1 ha kg

GR‐2.25GR‐4.5 GR‐9 GB‐2.25 GB‐4.5 GB‐9 C

Figure 13. Average lint yields for gypsum rate application in 2006. Gypsum application rate reported in mt ha‐1. Standard error indicated on graph.

Statistically, there was no significant difference between the application of gypsum in any amount or in any method. If gypsum is applied to a field under the same conditions as those tested, it will not improve or lower the lint yield. None of the contrasts tested proved to be significant, and by looking at the mean comparisons we can see that all of the plots performed equally well (Table 3).

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Table 3. Statistical analysis of 2006 lint yield showing no significant difference between rates and methods of application. Mean comparison of lint yield reported in kg ha‐1. Treatment rates reported in mt ha‐1.

Contrast F Value Pr > F Treatment Mean of Standard Lint Error Yield (kg ha‐1)

Method 1.57 0.2307 Broadcast 2.25 mt ha‐1 1,259 152

Rate 0.90 0.3586 Broadcast 4.5 mt ha‐1 1,064 153

Method x Rate 0.52 0.6063 Broadcast 9 mt ha‐1 1,109 35 interaction

Control vs. 2.02 0.1772 In‐Row 2.25 mt ha‐1 1,038 62 Treatment

Control vs. 0.79 0.3905 In‐Row 4.5 mt ha‐1 1,077 131 Broadcast

Control vs. In‐ 3.14 0.0981 In‐Row 9 mt ha‐1 953 73 Row

Control 1,265 115

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2007 Results

Field Variability

The field variability for ECa in 2007 was taken prior to any treatment application using the DUALEM‐42S. Figure 14 shows the points where the ECa readings were taken by the DUALEM‐42S. The data (ECa readings) was “cleaned” using the ESAP program mentioned earlier. The ESAP‐RSSD program uses a statistical technique known as a response surface sampling design (RSSD), which allows for the decorrelation of data. As a result of the decorrelation, outlier readings become easy to detect and subsequently remove from the data. In the ESAP‐RSSD program, validation is the name given to the detection and removal of outliers (Lesch et al., 2000). Figure 15 is a surface created using ArcMap Version 9.2 by ESRI that demonstrates the ECa variability of the field. The surface was created using an IDW interpolation method. This method is good for data that are relatively evenly spaced and that are dense. It takes the weighted average of the data points and interpolates a value for the locations where a point was not taken. The IDW method does not interpolate values above or below the data set. The field shows some variability but not enough in our plots to cause concern. The high variability shown near the east end of the plots can be attributed to the irrigation system being present at the time of sampling. The DUALEM‐42S is sensitive to metal and may have picked up some interference from the center pivot nearby. The locations of our plots are marked on Figure 15.

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Figure 14. Sample ECa points taken in 2007 using the DUALEM‐42S. Map generated using ArcMap.

Texas Tech University, Quint Chemnitz, December 2007

Figure 15. IDW surface created using ArcMap using ECa points taken from the DUALEM‐42S. Black points indicate extent of plots. Map generated using ArcMap.

Texas Tech University, Quint Chemnitz, December 2007

Particle Size Analysis

The particle size analysis data for 2007 indicate that the field consists primarily of a sandy clay loam. Of the 40 samples taken from within the experimental design (Figure 7), 35 were determined to be a sandy clay loam, three were classified as a clay loam, and two were classified as a loam. Table 4 shows the averages for the particle size distribution of the 40 locations where soil samples were taken.

Table 4. Average particle size distribution percentage for study site in 2007.

% Clay % Silt % Sand Textural 6 Hour Class Hydrometer Reading

Average 28.3 21.7 49.9 Sandy Clay 9.5 Loam

Electrical conductivity and pH

The electrical conductivity and pH data for 2007 were taken from the soil samples that were collected from the plots shown in Figure 7. We would expect the pH to rise with the application of gypsum and for the pH to fall with the application of humic acid. As Table 5 shows, in both instances the pH rose.

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Table 5. Comparison of average pH by treatment for 2007. Treatment applications reported as mt ha‐1 for gypsum and kg ha‐1 for humic acid.

Treatment 2007 Pre‐Application pH 2007 Post‐Application pH

GR‐2.25 7.63 7.66

GR‐4.5 7.68 7.67

GR‐9 7.57 7.81

GB‐2.25 7.75 7.70

GB‐4.5 7.52 7.67

GB‐9 7.82 7.72

AR‐17 7.65 8.02

AR‐34 7.68 7.83

AR‐67 7.69 7.88

AB‐17 7.75 7.74

AB‐34 7.63 7.74

AB‐67 7.62 7.68

Control 7.47 7.81

The EC for 2007 reacted similarly to the pH. After the application of gypsum and humic acid, the EC increased in each plot. Figure 16 is a graphical comparison of the pre‐versus post‐application of the treatments.

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Average EC Comparison for 2007 3.5 3.0 2.5

‐ 1 2.0 m 1.5 dS 1.0 0.5 0.0

Pre‐Application Post‐Application

Figure 16. Comparison of average EC for pre versus post treatment application in 2007. Pre‐ application in blue and post‐application in red. Treatment rates reported in mt ha‐1 and kg ha‐1 for gypsum and humic acid respectively.

Atomic Absorption Spectrophotometer

The 2007 AA results were divided into the gypsum application and the humic acid application. Figures 17 and 18 show the average amounts of Ca, Mg, Na, and K in meq L‐1 that were determined from the soil samples taken from the plots. These figures represent data that were taken prior to any application of gypsum or humic acid.

Texas Tech University, Quint Chemnitz, December 2007

Average AA Analysis for Pre‐Gypsum Application in 2007 14.0 12.0 10.0 ‐ 1 L

8.0 6.0 meq 4.0 2.0 0.0 GR‐2.25 GR‐4.5 GR‐9GB‐2.25 GB‐4.5 GB‐9 C

Ca Mg K Na

Figure 17. Average amounts of Ca, Mg, Na, and K found in the soil samples collected. Gypsum application reported in mt ha‐1.

Average AA Analysis for Pre‐Humic Acid Application in 2007 12.0 10.0 8.0 ‐ 1 L 6.0

meq 4.0 2.0 0.0 AR‐17 AR‐34 AR‐67 AB‐17 AB‐34 AB‐67 C

Ca Mg K Na

Figure 18. Average amounts of Ca, Mg, Na, and K found in the soil samples collected. Humic acid application reported in kg ha‐1.

After the application of gypsum and humic acid to the plots, soil samples were taken and the soluble cation concentrations were obtained to determine the change in

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the soil properties. A comparison of the pre versus post‐application data is shown in Table 6.

Table 6. Comparison of pre vs. post application for gypsum and humic acid AA results. Results reported are in meq L‐1. Gypsum and humic acid application rates are reported in mt ha‐1 and kg ha‐1, respectively.

Pre‐Application 2007 (meq L‐1) Post‐Application 2007 (meq L‐1)

Site ID Ca Mg K Na Ca Mg K Na

GR‐2.25 5.85 2.59 0.45 8.90 5.84 4.02 0.50 12.91

GR‐4.5 3.33 1.86 0.42 6.57 3.56 2.47 0.61 8.16

GR‐9 5.05 3.36 0.49 9.96 7.83 5.58 0.51 13.00

GB‐2.25 5.18 2.40 0.49 7.76 5.59 4.08 0.63 12.35

GB‐4.5 4.18 2.60 0.44 8.23 8.57 5.22 0.64 11.78

GB‐9 4.33 2.87 0.52 12.22 13.42 6.50 0.78 13.52

AR‐17 2.55 1.76 0.39 6.56 5.09 3.72 0.52 11.29

AR‐34 4.52 1.66 0.41 6.00 3.45 2.82 0.53 9.74

AR‐67 3.65 2.48 0.65 7.77 3.45 2.82 0.54 9.07

AB‐17 5.20 2.95 0.47 9.40 4.00 3.14 0.54 11.08

AB‐34 5.13 3.22 0.45 9.55 6.60 4.62 0.51 15.77

AB‐67 4.05 1.98 0.37 7.10 5.44 3.65 0.54 11.80

Control 4.39 2.81 0.49 7.97 7.40 4.74 0.55 13.72

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Emergence

Emergence data in 2007 were split into two groups, those treated with gypsum, and those treated with humic acid. Table 7 shows the comparison of the humic acid treatments versus the gypsum treatments versus the control regardless of application rate and method.

Table 7. Average plant emergence for 2007 regardless of application rate or method.

Treatment Average Plant Emergence per hectare

Gypsum Application 45,623

Humic Acid Application 47,681

Control 53,299

As Table 7 shows, the gypsum application performed the worst while the control group had the highest plant emergence. Figures 19 and 20 show how each plot performed within the two applications (gypsum and humic acid). Contrary to the 2006 results, the lower rates of gypsum performed the worst out of the three application rates while the control, similarly to 2006, had the best results. In 2007, when applying gypsum in‐row, the highest emergence came from the application of 4.5 mt ha‐1.

When looking at the humic acid application, 67 kg ha‐1 in‐row and 17 kg ha‐1 broadcast performed equally well and had the highest emergence of the humic acid treated plots. Figure 21 compares the average plant emergence for the gypsum application in 2006 and 2007. It is easy to see that the plant emergence in 2007 is nearly double that of 2006 in most instances.

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2007 Average Plant Emergence for Gypsum

53,298 50,037 46,970 48,993 48,210 43,774

1 35,750 ‐ ha Plants

GR‐2.25GR‐4.5 GR‐9 GB‐2.25 GB‐4.5 GB‐9 C

Figure 19. 2007 average plant emergences for gypsum in plants per hectare. Gypsum rates reported in mt ha‐1. Standard error shown on graph.

2007 Average Plant Emergence for Humic Acid

50,885 50,819 53,298 46,775 48,145 44,035 41,556 1 ‐ ha Plants

AR‐17 AR‐34 AR‐67 AB‐17 AB‐34 AB‐67 C

Figure 20. 2007 average plant emergences for humic acid application in plants per hectare. Humic acid rates reported in kg ha‐1. Standard error shown on graph.

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Average Plant Emergence for 2006 and 2007 Gypsum Application 70,000 60,000

‐ 1 50,000

ha 40,000 30,000

Plants 20,000 10,000 0 GR‐2.25 GR‐4.5 GR‐9GB‐2.25 GB‐4.5 GB‐9 C

2006 2007

Figure 21. Comparison of average plant emergence for 2006 and 2007. Plant emergence reported in plants ha‐1 and gypsum application reported in mt ha‐1. Standard error shown on graph.

Yield

In 2007, the lint yield was calculated from the hand harvested and ginned cotton from the plots. Figure 22 indicates that the control outperformed the treated plots. Filed notes were made that indicated that the 2007 cotton plants looked denser and taller than the cotton plants from 2006.

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2007 Average Lint Yield for Gypsum

2,515 2,363 2,358 2,335 2,249 2,312 2,255 1 ‐ ha kg

GR‐2.25GR‐4.5 GR‐9 GB‐2.25 GB‐4.5 GB‐9 C

Figure 22. Graph of Lint Yield for 2007. Gypsum application reported in mt ha‐1. Standard error shown on graph.

A statistical analysis showed that there was not a significant difference between the control plots and the treated plots at the 5 % significance level. The control plots produced a slightly higher yield than the treated plots. There was not a significant difference when comparing the two application methods, the rates, or the interaction between the application and the rates (Table 8).

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Table 8. Statistical analysis of 2007 average lint yield for gypsum application. Tested at a 5 % significance level.

Contrast F Value Pr > F

Method 0.01 0.9218

Rate 0.00 0.9585

Method x Rate 0.63 0.5481 Interaction

Control vs. Treatment 3.73 0.0741

Control vs. Broadcast 3.13 0.0985

Control vs. In‐Row 3.39 0.0869

When comparing the means of each treatment, it is easy to see that the in‐row treatment of gypsum, at all three rates, outperformed the broadcast treatments, but was less than the control plots (Table 9).

Table 9. Comparison of means by treatment for gypsum.

Treatment Mean of Lint Yield Standard Error

Broadcast 2.25 mt ha‐1 2,255 153

Broadcast 4.5 mt ha‐1 2,358 74

Broadcast 9 mt ha‐1 2,335 118

In‐Row 2.25 mt ha‐1 2,363 101

In‐Row 4.5 mt ha‐1 2,249 8

In‐Row 9 mt ha‐1 2,312 59

Control 2,515 99

Texas Tech University, Quint Chemnitz, December 2007

The lint yield for the humic acid treatments performed similar to that of the gypsum. The humic acid at a rate of 67 kg ha‐1 came closest to the control producing 2460 kg ha‐1 of lint, while the other applications produced nearly the same yield (Figure 23). The statistical analysis showed that the control plots versus any treatment, a broadcast application, and an in‐row application, were all significant. The control plots again outperformed any treated plot. By examining the mean comparisons, we can conclude that no treatment is the best treatment (Table10).

2007 Average Lint Yiled for Humic Acid

2,515 2,367 2,460 2,245 2,396 2,298 2,354 1 ‐ ha kg

AR‐17 AR‐34 AR‐67 AB‐17 AB‐34 AB‐67 C

Figure 23. Average lint yield for humic acid application in 2007. Application rates of acid are in kg ha‐1. Standard error shown on graph.

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Table 10. Comparison of means by treatment for humic acid.

Treatment Mean of Lint Yield (kg ha‐1) Standard Error

Broadcast 17 kg ha‐1 2,298 74

Broadcast 34 kg ha‐1 2,245 240

Broadcast 67 kg ha‐1 2,354 111

In‐Row 17 kg ha‐1 2,367 175

In‐Row 34 kg ha‐1 2,396 60

In‐Row 67 kg ha‐1 2,460 111

Control 2,515 99

Texas Tech University, Quint Chemnitz, December 2007

Chapter VII

Economic Impact

According to the National Cotton Council of America (NCCA), in 2006 the State of Texas planted 2,602,533 hectares (6,431,000 acres) and produced 1,272,599 metric tons (5,845,000 bales) of cotton (including upland and Pima cotton). The average price was 48.42 cents per pound, which equates into a crop value of $1.36 billion dollars. For Borden County, where the study site was located, in 2006, 12,343 hectares (30,500 acres) were planted and produced 2,634 metric tons (12,100 bales) of cotton. With the average cost of cotton at 48.42 cents per pound, this equates to a crop value of $2.81 million dollars. For the study site, in 2006, the average yield was 1109.3 kg ha‐1 (2.06 bales acre‐1), which means that the farmer earned approximately $ 1183.08 dollars per hectare ($478.77 acre‐1). With one circle pivot being approximately 48.56 hectares (120 acres), the farmer will have earned roughly $57,450 dollars per circle pivot. The current price of gypsum is $31.81 for 1 metric ton ($35 per ton); it could cost the farmer anywhere between $144,167.25 (for a rate of 2.25 mt ha‐1 (1 ton acre‐1)) and $572,842.75 (for a rate of 8.9 mt ha‐1 (4 tons acre‐1)) to cover a field of about 2023 hectares (5000 acres), which is the size of land farmed by the owner of the study site. With the cost of gypsum added to the other costs of farming (seed, water, fertilizer etc.), the farmer would have to significantly increase his yield to make a profit. The gypsum will not provide enough of a return to make it worthwhile for the farmer.

The price of the Hydra Hume humic acid application used in the study was $2.20 kg‐1 ($1.00 lb‐1). The suggested application rate is 5.6 kg ha‐1 (5 lbs acre‐1), which would cost the farmer $12.23 ha‐1 or $24,746.18 to cover the farmers entire operation of 2023 hectares (5000 acres).

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Chapter VIII

Discussion

As the data indicate, there is no significant advantage to adding either the gypsum or the humic acid to a field that is in similar condition to the research site. It must be noted that in the first year’s study, the rainfall was somewhat below average accumulating only 53.16 cm (20.93 inches) for the year, while in 2007 we have already received 57.89 cm (22.79 inches) as of September 27, 2007 (West Texas Mesonet, 2007). With increased rainfall, the salts that are present in the soil will be leached out of the rooting zone without the addition of salts from the irrigation water. In 2006, the farmer had to irrigate more often, which increased the Na concentrations in the soil due to high amounts in the irrigation water.

Another issue related to the weather is the planting date. In 2007, the farmer had a difficult time getting into the field to plant the cotton. In 2006, the cotton was planted on May 12th, and there was only 2.51 cm (0.99 inches) of rain that month, which made it easy to get the equipment on the field. In 2007, the cotton was planted on May 23rd, on a day when there was a break in the 20.43 cm (7.97 inches) of rain that fell that month. The soil was too wet for most of the month to get any type of heavy equipment out there. Along with the heavy rains in 2007, part of the field sustained hail damage.

With high amounts of Na in the irrigation water, it is beneficial to have a higher sand content in the soil. Due to their larger particle size, sandy soils can withstand more salts in the soil because they are able to pass the salts through their larger pore size. The 2006 study sites’ particle size analysis indicated it was primarily a clay loam with an average of 33.7 % clay, 25.4 % silt, and 40.9 % sand. In 2007, the percent sand was 9 % higher than that of 2006 and was classified primarily as a sandy clay loam. The higher

Texas Tech University, Quint Chemnitz, December 2007

sand percentage may have contributed to the increase in yield due to the ability of the soil to move larger quantities of water through it.

The average pH in 2006 dropped from 7.54 to 7.02 after the application of gypsum. The post‐application averages in 2006 were taken from the study plots (Figure 6), while the pre‐application averages came from the entire field sampling (Figure 8). The pH was taken from both depths, 0‐15 cm and 15‐30 cm (0‐6 inches and 6‐12 inches) from each sample. The pre‐application in 2006 had an average pH of 7.54 for the 0‐15 and the 15‐30 cm samples while the post‐application pH varied from 6.99 for the 0‐15 cm to 7.05 for the 15‐30 cm depth. This slight change in pH indicates that the Ca supplied by the gypsum application had moved farther down the soil profile, which caused a higher pH in the lower depths. This trend is the same that is seen for the 2007 pH data. Table 11 shows that the pH for the 0‐15 cm depths for both the humic acid and gypsum pre‐application were lower than they were for the post‐application. The humic acid actually has a greater increase in pH with depth than does the gypsum, which is contrary to what was expected. The application of humic acid was thought to lower the pH after application, but our results indicate the contrary. One explanation may be that with the high amounts of rainfall after planting and application, some of the gypsum ran off into plots that were treated with humic acid, which subsequently raised the pH.

Texas Tech University, Quint Chemnitz, December 2007

Table 11. Comparison of pH by depth for 2007.

Depth of Samples Pre‐Application pH in Post‐Application pH in 2007 2007

0‐15 cm Gypsum 7.72 7.76

15‐30 cm Gypsum 7.61 7.65

0‐15 cm Humic Acid 7.69 7.84

15‐30 cm Humic Acid 7.65 7.79

The AA results for 2006 indicate an increase in the meq L‐1 of Na from 15.0 to 30.6 after the application of gypsum. The Ca also increased from 9.9 meq L‐1 to 24.7 meq L‐1 after the application of gypsum. This means that the Ca in the gypsum applied replaced the Na on the exchange sites and the Na went into solution. This may account for some of the increase, but the rest can be attributed to the irrigation water. The irrigation water was adding 16.79 meq L‐1 of Na and 7.50 meq L‐1 of Ca. In 2006, the farmer irrigated six times and applied 1.27 cm (½ inch) of water with each application. That means that the farmer was adding 128 x106 meq L‐1 of Na ha‐1. With the post‐ application amount of Na for the control being 13.73 meq L‐1, it is safe to say that the amount of Na lost in the field, whether it be by plant uptake, percolation, or run‐off was 23x106 meq L‐1.

Table 6 shows the increase from pre‐application to post‐application for nearly all of the values. If we just look at the Na values in the soil pre‐application we can see that the control is the third lowest. When we look at the control value for Na in the post‐ application results, we see that it is the highest, which means it had a large increase, yet the control plots produced the highest yield. Now if we look at the emergence for the control (Figure 14), we can see that it had the highest emergence value as well. This indicates that post‐application Na values do not play a role in the lint yield, which means that if the cotton emerges, the Na in the soil is not enough to affect the lint yield. Table

Texas Tech University, Quint Chemnitz, December 2007

12 shows a comparison of gypsum treatments, their pre and post‐application Na values, emergence, and yield for 2007.

Table 12. Comparison of average yield, emergence, pre‐application Na, and post‐application Na by treatment of gypsum.

Treatments Pre‐Application Emergence Post‐Application Yield Na (meq L‐1) (plants ha‐1) Na (meq L‐1) (kg ha‐1)

GR‐2.25 8.90 46,970 12.91 2,363

GR‐4.5 6.57 50,037 8.16 2,249

GR‐9 9.96 48,993 13.00 2,312

GB‐2.25 7.76 43,774 12.35 2,255

GB‐4.5 8.23 35,750 11.78 2,358

GB‐9 12.22 48,210 13.52 2,335

Control 7.97 53,298 13.72 2,515

Lint yield for the gypsum application of both years of study is shown in Table 13. It shows that for 2007 the projected yield in every plot increased from the prior year. This increase may be attributed to the sandier soils or the increase in rainfall. The increase in plant emergence from 2006 to 2007 is also a key contributing factor for the increase in yield (Figure 21). The applied gypsum may have displaced enough Na to increase the yield, but it is hard to establish given that the two years were not completed on the same field or under the same weather conditions. Thus, multiple years are needed to compare data that are affected by the interaction of environmental conditions and site variability.

Texas Tech University, Quint Chemnitz, December 2007

Table 13. Comparison of average lint yield of both years of the study.

Treatment 2006 Lint Yield 2007 Lint Yield (kg ha‐1) (kg ha‐1)

GR‐2.25 1,038 2,363

GR‐4.5 1,077 2,249

GR‐9 953 2,312

GB‐2.25 1,259 2,255

GB‐4.5 1,064 2,358

GB‐9 1,109 2,335

Control 1,266 2,515

Texas Tech University, Quint Chemnitz, December 2007

Chapter IX

Conclusion

The application of gypsum or humic acid in this study proved not to be of any benefit. In both cases, control plots outperformed treated plots when yields were compared. In our case, Ca supplied by the gypsum did exchange with Na on the exchange sites in the soil, but the water quantity was not sufficient to move the Na in solution out of the rooting zone. With the Na now in solution in the rooting zone, the possibility of a salinity problem is greatly increased. The salinity of the soil will now affect the plant, not the soil, but the outcome will be similar to the sodicity problem, a reduction in emergence and yield will occur. The farmer is better off to plant ryegrass and bale it prior to planting cotton on the field. It is our thoughts that the ryegrass will take up enough Na from the soil to maintain soil aggregation, which decreases the conditions for soil crusting. The key to this method is that the farmer must bale the grass and not leave it on the field to be grazed. Another option for this situation is to flood the field with irrigation water as opposed to using the center pivot. At 1.27 cm (½ inch) per application of water, there is not a sufficient amount of water to move the Na far enough down the soil profile to reduce the risk of a Na problem. Even with a high quantity of poor quality water, Na would be removed from the rooting zone. The problem with this method is the economics. As most of the farmers on the SHP, they cannot afford to supply enough water to the field to keep the Na problem at bay. The application of gypsum was one of the hopes to ameliorate this problem, but as this study showed, it is better not to apply any type of treatment when water is in short supply. The yield return does not justify the economic input for any treatment application.

Texas Tech University, Quint Chemnitz, December 2007

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