Using Soil Conditioners to Improve Soil Physiochemical Properties and Agricultural Productivity in Libya Gandura Abagandura Clemson University, Gabagan@Clemson.Edu

Clemson University TigerPrints

All Dissertations Dissertations

December 2016 Using Soil Conditioners to Improve Soil Physiochemical Properties and Agricultural Productivity in Libya Gandura Abagandura Clemson University, [email protected]

Follow this and additional works at: https://tigerprints.clemson.edu/all_dissertations

Recommended Citation Abagandura, Gandura, "Using Soil Conditioners to Improve Soil Physiochemical Properties and Agricultural Productivity in Libya" (2016). All Dissertations. 2319. https://tigerprints.clemson.edu/all_dissertations/2319

This Dissertation is brought to you for free and open access by the Dissertations at TigerPrints. It has been accepted for inclusion in All Dissertations by an authorized administrator of TigerPrints. For more information, please contact [email protected]. USING SOIL CONDITIONERS TO IMPROVE SOIL PHYSIOCHEMICAL PROPERTIES AND AGRICULTURAL PRODUCTIVITY IN LIBYA

______

A Dissertation

Presented to

the Graduate School of

Clemson University

______

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

Plant and Environmental Sciences

______

Gandura Omar Abagandura

December 2016

______

Accepted by:

Dr. Dara Park, Committee Co-Chair

Dr. William C. Bridges Jr.

Dr. David White

Dr. Alex Chow ABSTRACT

The results of Libyan agricultural review confirmed that soil degradation and limited freshwater resources are the primary causes of the country’s dependence on food imports. The intrusion of the seawater into the groundwater is causing soils to become salt-affected. In addition, many crops in the country are susceptible to pests. The spatial distribution of soil degradation occurrence and type were predicted using regression models, estimating that

666 882 km2 (53.5 %) of Libyan soils are degraded with 46.4 % due to salinization, 6.4 % water erosion and 0.66 % wind erosion.

The majority of the soils in Libya are sandy and have low water holding capacity.

This situation is made more critical because of the low rainfall arid climate.

Conducting research in Libya and obtaining Libyan soils is currently not possible.

Soils in South Carolina are somewhat similar to those of Libya, having a sand dominated surface horizon and root restriction subsurface horizon. In addition,

SC has been experiencing intense mini-droughts during the primary growing season (summer). Thus, while not a perfect match, the information obtained from field and greenhouse experiments conducted in SC could be applied to

Libya. Overall, minimal effect of soil conditioners on volumetric water content and plant growth and yield from the two field experimental research due to the

ii excessive rainfall that that the fields received. Although little effect occurred due to soil conditioners, the pattern of positive response during two very wet seasons suggest that soil conditioners application during dry periods may have beneficial effects. The results from the two greenhouse experiments documented that soil conditioners can be as a soil management for increasing agriculture production in the field or greenhouse. The results from the third experiment documented that soil surfactant application with split water applications can reduce leachate resulting in conserving natural water resources.

The results of nitrogen balance also suggested that the application of surfactants with fertilizer can be a management strategy for improving greenhouse substrates efficiency increasing uptake of N and reducing N leaching, thus improving the horticulture production. Most data suggested that soil conditioners can be used in droughty soils under limited water conditions as found in Libya as a management tool to improve the agriculture production in the country.

iii DEDICATION

I dedicate this dissertation to the soul of my father, may Allah have mercy on him. He passed away when I was 5 years, but I still remember his wonderful smile. I love you father.

iv ACKNOWLEDGMENTS

I would like to acknowledge Allah for giving me opportunity, determination and strength to finish my study. My deepest thanks go to Dr. Dara

Park for being such a wonderful advisor. I appreciate the advice she has given me throughout this entire process. Special thanks also goes to my dissertation committee members, Dr. William Bridges, Dr. David White and Dr. Alex Chow, for their support and encouraging words. I would also thank Dr. Nick Menchyk for his help in the field experiment.

I would like to thank my supportive husband Ezaldeen. I would not be the person I am today without your patience and love. I am so grateful to have you in my life. Special thanks also go to my family in Libya, especially my mother for their support and love. I would also like to express my sincere thanks to my children for allowing me to grow into the person I have become today.

This research would not have possible without the financial support of the Libyan Government, the South Carolina Cotton Board, and AgStone, LLC, SC.

v TABLE OF CONTENTS

Page

TITLE PAGE ...... i ABSTRACT ...... ii DEDICATION ...... iv ACKNOWLEDGMENTS ...... v LIST OF TABLES ...... ix LIST OF FIGURES ...... xii CHAPTER ONE ...... 1 INTRODUCTION ...... 1 PROBLEM DESCRIPTION ...... 1 SPECIFIC OBJECTIVES ...... 5 REFERENCES ...... 8 CHAPTER TWO ...... 11 LIBYAN AGRICULTURE: A REVIEW OF PAST EFFORTS, CURRENT CHALLENGES AND FUTURE PROSPECTS ...... 11 CLIMATE AND GEOGRAPHY OF LIBYA ...... 12 WATER RESOURCES ...... 14 LIBYAN SOILS ...... 17 CROP AGRICULTURE ...... 18 OVERCOMING CURRENT CHALLENGES ...... 28 FUTURE OF LIBYAN AGRICULTURE...... 30 CONCLUSIONS ...... 32 REFERENCES ...... 33 CHAPTER THREE ...... 46 AN ASSESSMENT OF THE SOIL RESOURCES AND DEGRADATION IN LIBYA ...... 46

vi Table of Contents (Continued) Page ABSTRACT ...... 46 INTRODUCTION ...... 47 MARTIAL AND METHODS ...... 50 RESULTS AND DISCUSSIONS ...... 59 CONCLUSIONS ...... 74 REFERENCES ...... 76 CHAPTER FOUR ...... 83 POTENTIAL OF USING AN ALKYLPOLYGLYCOSIDE SOIL SURFACTANT FOR AFFECTING ROOTZONE CHARACTERISTICS AND COTTON GROWTH ...... 83 ABSTRACT ...... 83 INTRODUCTION ...... 84 MATERIALS AND METHODS ...... 88 RESULTS AND DISCUSSION ...... 92 CONCLUSIONS ...... 100 REFERENCES ...... 101 CHAPTER FIVE ...... 107 SOIL CONDITIONERS INCREASE NUTRIENT AVAILABILITY FOR COTTON IN SOILS OF SOUTHERN COASTAL PLAIN DURING TWO GROWING SEASONS OF HIGH PRECIPITATION ...... 107 ABSTRACT ...... 107 INTRODUCTION ...... 108 MATERIALS AND METHODS ...... 112 RESULTS AND DISCUSSION ...... 116 CONCLUSIONS ...... 122 REFERENCES ...... 123 CHAPTER SIX ...... 134

vii Table of Contents (Continued) Page COMPARISON OF SOIL SURFACTANT CHEMISTRIES ON VOLUMETRIC WATER CONTENT AND LEACHATE OF GREENHOUSE SUBSTRATES UNDER TWO IRRIGATION REGIMES ...... 134 ABSTRACT ...... 134 INTRODUCTION ...... 135 MATERIALS AND METHODS ...... 138 RESULTS AND DISCUSSION ...... 143 CONCLUSIONS ...... 153 REFERENCES ...... 154 CHAPTER SEVEN ...... 162 AN INVESTIGATION ON THE APPLICATION OF SOIL SURFACTANT WITH UREA FERTILIZER ON PLANT UPTAKE OF NITROGEN AND NITROGEN LEACHING...... 162 ABSTRACT ...... 162 INTRODUCTION ...... 163 MATERIALS AND METHODS ...... 165 RESULTS AND DISCUSSION ...... 172 CONCLUSIONS ...... 183 REFERENCES ...... 184 CHAPTER EIGH T………………………………………………………………………………………………199 CONCLUSIONS………………………………………………………………………………………………..199

viii LIST OF TABLES

Table Page

Table 2.1. The characteristics of the shallow and fossil basins in Libya...... 16

Table 3.1. Independent and dependent variables used in logistic regression model for the primary agricultural regions in the Libya...... 56

Table 3.2. Area (km2) of different soil textures and land features for the primary agriculture regions in Libya...... 62

Table 3.3. Areas (km2) of the degraded type for each soil texture in the primary agriculture regions in Libya based off of existing data...... 63

Table 3.4. The P-value, Chi-square and AICs for the significant logistic regression models used to identify the degradation occurrence in the primary agriculture regions in Libya...... 67

Table 3.5. Regression coefficients and significance values of variables of the logistic regression Model 2 that best identified degradation occurrence in the primary agriculture regions of Libya...... 68

Table 3.6. Actual degradation, frequency of correctly determining degradation occurrence, and percent of degradation observations that were correctly classified by model predictions from the leave-one-field-out validation analyses.

...... 69

ix List of Tables (Continued) Page Table 3.7. Parameter estimates of probabilities of soil degradation due to (a)

salinization and (b) water erosion, relative to wind erosion...... 73

Table 4.1. Monthly mean soil moisture content (m3 m-3) under the two cotton

variety during the 2013 and 2014 experiments...... 95

Table 4.2. Least squares means and statistical significance of pH and soil

nutrients (kg ha-1) during 2013 and 2014 experiments...... 97

Table 4.3. Least squares means and statistical significance of cotton yield and

fiber quality properties during 2013 and 2014 experiments...... 99

Table 5.1. Soil conditioner names, manufacturers, ingredient, and application

rate used in this study...... 113

Table 5.2. Significance values of variety, irrigation, and COND main effects and

main effect interactions for soil properties, cotton leaf nutrient concentrations

and boll density, lint yield, seed cotton, and gin turnout in 2014 trial...... 117

Table 5.3. The soil nutrients (kg ha-1) content means for the interactions

between conditioner and variety and conditioner and irrigation...... 118

Table 5.4. Cotton yield and fiber quality means for irrigation, variety and

conditioner main effect in this study...... 121

Table 6.1. Chemical properties of the root-zone substrates used in the study. 140

Table 6.2. Soil surfactant chemistries, trade names, manufacturers, and rate

used in this study...... 142

x List of Tables (Continued) Page Table 6.3. Significance values of main effects and main effect interactions for

total leachate volume (ml) for the three substrates...... 144

Table 6.4. The total leachate volume (ml) for surfactant and irrigation for the

three artificial soils...... 145

Table 6.5. Significance values of main effects and main effect interactions for soil

volumetric water content for the three substrates...... 146

Table 6.6. The volumetric water content (cm m-3) for interactions between day

by surfactant and day by irrigation for the three soils...... 147

Table 7.1. The general soil characteristics used in this study...... 167

Table 7.2. Soil surfactant chemistries, trade names, manufacturers, and rate

used in this study...... 168

Table 7.3. The total volume leachate (ml) and dry clipping (kg ha -1) from

different soils obtained from different surfactant treatments...... 177

xi LIST OF FIGURES

Figure Page

Figure 2.1. Location of Libya within the African continent, and primarily agriculture regions. Adapted from Saad et al., 2011...... 12

Figure 2.2. The main crops produced in Libya in metric tons with the international prices in $1,000 USD. Source: FAOSTAT, 2012...... 20

Figure 3.1. The Location of (a) the primary agriculture regions, Kufrah, Murzuq,

Jabal Nafusah, Jabal al Akhdar, and Jifarah; and (b) groundwater aquifers in

Libya...... 51

Figure 3.2. Soil and land features of the primary agriculture regions in Libya, (a)

Jifara, (b) Jabal al Akhdar, (c) Jabal Nafusah, (d) Murzuq, and (e) Kufrah as created by FAO, 2005...... 60

Figure 3.3. Soil degradation type’s proportions across texture levels in the primary agriculture regions in Libya. The thickness of each texture represents the percentage of observations for each texture compared to the total data...... 66

Figure 3.4. Soil degradation occurrence in the primary agriculture regions in

Libya as (a) previously determined (existing), and (b) predicted by LR model using the combination of slope and texture...... 71

Figure 3.5. The probability degradation soil map for all the country developed in this study using MLR model...... 74

xii List of Figures (Continued) Page Figure 4.1. Cumulative rainfall (2010 to 2012) compared to 2013 and 2014 over

the experimental period...... 92

Figure 4.2. Comparison of volumetric soil water content throughout 2013 and

2014 experiments at 10, 20, 30 and 40 cm depths. Horizontal lines denote field

capacity (FC) and permanent wilting point (PWP), and the plant available water

(PAW) for the sandy loam (2013 experiment) and loamy sand (2014 experiment)

soils used. Within months, columns sharing a same letter are not significantly

different based on Fisher LSD Test (p ≥ 0.05)...... 94

Figure 6.1. Leaching columns used in the study were made of polyvinyl chloride

pipe with a length of 37 cm and an inner diameter of 7.6 cm showing the cap

with the hole to collect the leachate water. The four slits were covered with duct

tape...... 140

Figure 6.2. Comparison of the means volumetric water content (cm m-3) for the

interaction between surfactant and depth for (A) washed sand, (B) 80 % sand: 20

% peat and (C) fafard 3B – surfactant. Within each column, means followed by

the same letter are not significantly different at significant level= 0.05...... 149

Figure 6.3. Comparison of the means volumetric water content (cm m-3) for the

interaction between depth by day for (A) washed sand, (B) 80 % sand: 20 % peat,

and (C) fafard 3B – surfactant. Within each column, means followed by the same

letter are not significantly different at significant level= 0.05...... 150

xiii List of Figures (Continued) Page Figure 7.1. Comparison of the mean volumetric water content for: (A) sand, (B)

sandy loam, (C) sandy clay loam on the four weeks...... 174

Figure 7.2. Visual turfgrass quality and density (1–9 scale: 1 = worst, 6 =

acceptable, 9 = best) for: (A and B) sand, (C and D) sandy loam, (E and F) sandy

clay loam during the four weeks of the experiment...... 176

Figure 7.3. Concentration of total N in leachate as affected by soil texture and

surfactant treatments...... 179

Figure 7.4. Recovery of applied N fertilizer of applied N in a bermudagrass, soil

and leachate for the three soils at the end of the experiment. Note difference in

y axis scales...... 180

xiv CHAPTER ONE

INTRODUCTION

PROBLEM DESCRIPTION

Approximately 795 million (one in nine) people of the world does not have enough food for a healthy active life (World Food Programme (WFP), 2015).

Poor nutrition causes the death of approximately 3.1 million children under the age of five each year (Lancet’s Series on Maternal and Child Nutrition, 2013). The situation is projected to become worse. Agricultural production has to increase

70% globally and by 100% in developing countries to feed the growing population that is expected to reach 9 billion in 2050 (Fischer et al., 2011). As these statistics suggest, one of the most important challenges for the coming century is to feed a growing world population.

One of the areas most affected by this situation is Africa, which has the highest percentage of population (1.1 billion), without enough food. One in four people on this continent are undernourished (World Food Programme, 2016). By

2050, Africa is projected to increase to 2.4 billion people, and an estimated 25% of its citizens will be undernourished. Population growth and climate change are the main causes of hunger in Africa. The major risk for long-term food security is climate change. In addition, the projected changes in weather patterns (too much or not enough rainfall) will cause damaging effects to African agriculture

1 (Leal Filho et al., 2010). As a result, African reliance on food stuff imports will increase, despite its vast agricultural potential (Rakotoarisoa et al., 2011).

One of the African countries that depend significantly on imports for food is Libya, which produced only 25% of its food in 2008 (FAOSTAT. 2012). More significantly between 2005 and 2007, Libya imported 98% of its wheat, a staple in the average diet of the population accounting for 40% of the average Libyan caloric intake (Larson et al., 2013). Although there are many issues that impact

Libyan agriculture, causing the country to import the majority of its food, little research has been conducted documenting the various reasons for this situation or proposing solutions to address this need. Reviewing the country’s agricultural history, identifying issues, and prioritizing needs for its agricultural development will increase the agricultural production and decrease the current dependency on imports. Furthermore, improved productivity may lead an increase in food exports, thus stimulating the economy.

Similar to the situation of Africa as a whole, it is estimated that this dilemma in Libya is expected to worsen as its population increases. The Libyan population increased from less than one million in 1955 to 6 million in 2005, and it is expected to reach more than 12 million by the year 2025 (Alghariani, 2013).

Coupled with the population growth, degraded soil, lack of water, and difficulty controlling pests and diseases all affect the food supply in Libya.

Salinization, erosion, and desertification are the primary causes of land degradation in the country (Ben-Mahmoud et al., 2000). According to

Abagandura and Park (2016), approximately 46% of the country is degraded due to salinization, and 6.4% and 0.66% of soil degradation due to water and wind erosion. The desertification has been accelerated by human activities coupled with the climatic conditions of the country (Emgaili, 2003; Saad et al., 2011).

Soils are losing their productivity, and subsequently hindering agricultural development in Libya, although little has been done to address it. Although much data regarding soil degradation was collected in scattered areas (Khaled,

2001), their use is limited because the levels of information technology were in its very early stages in the country (Nwer et al., 2010). For example, according to

Dubovyk et al. (2012), modeling soil and climate factors using technology, in particular GIS, has the potential to explain the spatial distribution of degraded areas, and thus this modeling can be used as a tool for developing indices to assist in the management of soil degradation; however, Libya does not yet have this capability to use this technology as a strategy to enhance the country’s ability to address the land degradation issue.

The second reason for the country’s dependency on food imports is the lack of water. Water resources in Libya are likely to become more stressed as climate change intensifies and the population increases. Similar to many arid and semiarid regions of the world, less than 5% of the country receives more than

100 mm of rain each year (BIndra et al., 2014). As a result, irrigation in Libya is expanding and becoming a common practice for supplementing rainfall, accounting for nearly 80% of Libyan water usage (Alghariani. 2013). Libya relies on groundwater reserves to meet these irrigation needs. Total water pumped from the aquifers exceeds the annual replenishment by approximately four times (Aqeil et al., 2011). This results in a decline in the water level, causing many coastal groundwater aquifers to have high salinity due to seawater intrusion (Shaki and Adeloye, 2006), making the remaining groundwater resources almost unusable (BIndra et al., 2014).

An additional threat to water quality is nutrient leaching. Nitrogen is of the most concern because of its loss to receiving ground water from fertilizers applied to agricultural land. Most of the agricultural production in the country occurs in sandy soils, which are characterized by limited water retention, rapid infiltration, and low nutrient holding capacity (Brady and Well, 2007). Combined with the excessive use of irrigation, this results in excessive leaching of nitrogen,

potentially contaminating the groundwater (International Atomic Energy Agency,

2005). While limited studies have been conducted on N leaching, Salem and Al- ethawi (2013) and Dietzel et al. (2014) have documented that the groundwater of the country is nitrogen-contaminated, potentially due to the large amount of nitrogen fertilizers being used. There exists, therefore, an urgent need to improve both the water and the nitrogen fertilizer use efficiency.

These water challenges require cautious management, one that can support an increasing agricultural sector while at the same time conserve natural water resources. One potential way to meet the increasing food demand and subsequently water in the coming decades may be by enhancing soil properties to increase its available water-holding capacity. This approach reduces agricultural water demand and potential nutrient loss via leaching by maximizing water and nutrient uptake efficiency by the plant (FAO, 2007).

SPECIFIC OBJECTIVES

The overall goal of this study was to identify soil management strategies that have the potential to increase the productivity of Libyan agriculture. Since little research has been conducted on Libyan agricultural issues, the first objective (Chapter 2) was to examine possible issues and propose solutions for addressing them. The results of this investigation confirmed that soil degradation

5 and the lack and deterioration of water are the primary causes of the country’s dependence on food imports.

This led to the second objective (Chapter 3) of identifying priority locations to concentrate agricultural development efforts in Libya. Modeling soil characteristics identify degraded areas and type of degradation, which allows for making informed decisions on where agriculture should be focused and challenges to address in order to make agricultural production a success.

Next, the third objective was to focus on soil treatments, specifically to determine how different soil conditioners influence soil moisture, nutrient dynamics, nitrogen leaching and plant yield and quality. To do this, a series of four experiments were conducted in South Carolina in the field and within a greenhouse. Although these experiments were conducted in SC, the information obtained could be applied to Libya since soils and water quality and availability in these two places are, to certain extent, similar. Both are experiencing climate change resulting in more frequent and intense droughts, increasing water usage due to an increasing population, and either have water quality degradation due to nutrient leaching or limited water sources (United States Department of

Agriculture (USDA), 2014; Shaki, and Adeloye. 2006). One of the strategies is to preserve water resources while maintaining or increasing plant productivity

through the use of soil conditioners. These soil amendments contain a variety of compounds that influence soil water holding content, nutrient dynamics, nitrogen leaching and plant yield and quality. This objective includes four experiments (each a separate chapter) investigating primarily soil surfactants, but also other potentially agriculturally significant soil conditioners:

 Chapter 4 investigates how soil conditioners impact soil water-holding

capacity;

 Chapter 5 explores if soil conditioners affect nutrient availability and

cotton, (Gossypium hirsutum L.) (Phytogen 339 WRF and PHY 499 WRF)

fiber yield and quality;

 Chapter 6 studies the influence of soil surfactants on increasing water

holding content of greenhouse substrates;

 Chapter 7 investigates the effect of the soil surfactants on N fluxes in a

modeled soil-water-plant nexus.

REFERENCES

Abagandura, G., and D. Park. 2016. Libyan agriculture: a review of past efforts,

current challenges and future prospects. JNSR. 6 (18): 57-67.

Alghariani, S.A. 2013. Managing water resources in Libya through reducing

irrigation water demand: more crop production with less water use. Libyan

Studies. 44: 95-102.

Bindra, S.P., S. Abulifa, A. Hamid, H.S. Al Reiani, and H.K. Abdalla. 2013.

Assessment of impacts on ground water resources in Libya and

vulnerability to climate.

Dietzel, M., A. Leis, R. Abdalla, J. Savarino, S. Morin, M.E. Böttcher and S. Köhler.

2014. 17O excess traces atmospheric nitrate in paleo-groundwater of the

Saharan desert. Biogeosciences. 11: 3149-3161.

Fischer, G., E. Hizsnyik, S. Prieler, and D. Wiberg. 2011. Scarcity and abundance

of land resources: competing uses and the shrinking land resource base.

FAO Solaw Background Thematic Report-TR02, 34.

FAOSTAT. 2012. FAO statistical database. Libya.

Food and Agriculture organization. 2015. The State of Food Insecurity in the

World 2015. Available at http://www.fao.org/3/a-i4646e.pdf (verified

21 August. 2016).

8 International Atomic Energy Agency, 2005. Improving Agricultural Water and

Fertilizer Management in Libya. available at http://www-

naweb.iaea.org/nafa/swmn/water-docs/Libya-Fertigation.pdf (verified 21

February. 2016).

Khaled. B. 2001. Land resources information systems in the Near East. Report on

World soil resources. FAO, Report 99, Rome, Italy.

Leal Filho, W., A.O. Esilaba, K.P. Rao, and G. Sridhar (Eds.). 2014. Adapting

African agriculture to climate change: transforming rural livelihoods.

Springer.Change. Scientific Bulletin of the" Petru Maior" University of Targu

Mures. 10(2): 63.

The Lancet. 2013. Series on Maternal and Child Nutrition. Available at

http://www.thelancet.com/series/maternal-and-child-nutrition (verified 21

September. 2016).

Saad, A.M.A., N.M. Shariff, and S. Gairola. 2013. Nature and causes of land

degradation and desertification in Libya: Need for sustainable land

management. Afr. J. Biotechnol. 10(63): 13680-13687.

Salem, M.A., and L.A. Al-ethawi. 2013. A study of the presence of residual of

nitrogenous fertilizer nitrate (NO3-) in some soils of Brack–Ashkada

Agriculture Project. JOLST. 1(1): 84-88.

9 Shaki, A.A., and A.J. Adeloye. 2006. Evaluation of quantity and quality of

irrigation water at Gadowa irrigation project in Murzuq basin, southwest

Libya. Agric. Manage. Water. 84 (1): 193-201.

World Food Programme. 2016. Hunger Statistics. Available at

https://www.wfp.org/hunger/stats (verified 21 August. 2016).

10 CHAPTER TWO

LIBYAN AGRICULTURE: A REVIEW OF PAST EFFORTS, CURRENT CHALLENGES AND FUTURE PROSPECTS

This work has been published:

Abagandura, G., and D. Park. 2016. Libyan agriculture: a review of past efforts, current challenges and future prospects. Journal of Natural Resources. 6(18): 57-

67.

ABSTRACT

By increasing the agricultural sector productivity, Libya will decrease their current dependency on other countries for food and potentially could stimulate the economy by increasing food exports. Like many other countries in the region, agriculture in Libya is constrained by its limited arable land and low soil fertility.

Desertification and limited freshwater resources are the two greatest challenges for future agricultural development. In addition, the intrusion of the seawater into the groundwater is causing soils to become salt-affected. Many important crops in the country are susceptible to pests. These challenges have not been addressed because of the lack of specialists and institutions engaged in the

National Plant Protection Program. Increasing crop productivity depends on identifying proper management strategies of natural resources and pests as well as seed development.

CLIMATE AND GEOGRAPHY OF LIBYA

Libya is located in the Maghreb Region of North Africa (Figure 2.1) encompassing approximately 1.6 million km2 (Saad et al., 2011). It is bordered by the Mediterranean Sea to the north, Egypt to the east, Sudan to the southeast,

Chad and Niger to the south, and Algeria and Tunisia to the west (Lariel, 2015)

(Figure 2.1).

Figure 2.1. Location of Libya within the African continent, and primarily agriculture regions. Adapted from Saad et al., 2011.

Historically, Libya was comprised of three main provinces: Tripolitania,

Cyrenaica and Fezzan (Sunderland and Rosa, 1976). Tripolitania is the northwestern corner of the country including the Nafusah Plateau. Cyrenaica, the largest geographic region, represents the entire eastern half of the country including the Jabal al Akhdar, and Fezzan is home to the desert lands, including the Sahara Desert (Hegazy et al., 2011).

Geographically, Libya has traditionally been divided into four regions, each with a different climate: (1) the Coastal Plains run along the Mediterranean

Sea and experience dry summers and relatively wet winters; (2) the Northern

Mountains, which border the Coastal Plains, include Jabal Nafusah in the west and Jabal al Akhdar in the east. The Northern Mountains have the benefit of a plateau type climate with greater rainfall (approximately 500 mm in Jabal al

Akhdar and 400 mm in the Northern Mountains, annually) and lower temperatures (approximately 20°C); (3) the Internal Depressions, located in the center of Libya, is where pre-desert and desert climatic conditions prevail; and

(4) the Southern and Western Mountain Range characterized as an area with little annual rainfall, from 50 to 150 mm (Alldrissi et al., 1996; Laytimi, 2005).

The remainder of this work is an exhaustive review of the publications related to agriculture in Libya. In general, the cited literature documents a positive interest assisting agriculture development in the country. However,

some information may already be outdated. The state of current natural resources, land under agriculture and related economics may be very different since the recent unrests in the country. It is the authors’ hope that this paper assists Libya in becoming a more self-sustaining nation.

WATER RESOURCES

Rainfall

Depending on the geographic region, the Libyan climate is characterized as either semi-arid (Coastal Plains and the Northern Mountains) or arid (Internal

Depressions and Southern and Western Mountain) (Shaki and Adeloye, 2006).

Rainfall is limited, and its volume and distribution varies from year to year (El-

Asswad, 1995), occurring primarily from October to March in Jabal Nafusah in the west, and Jabal al Akhdar in the east (Saad et al., 2011). The amount of rainfall decreases the further inland from the Mediterranean Sea. Because of these dry conditions, agriculture is primary dependent on irrigation.

Water Availability

Water resources in the country originate from seawater desalination, wastewater, surface water, and groundwater. The existing desalination plant produced 70 million m3 in 2012, exclusively for municipal and industrial purposes

(AGUASAT, 2016). The present capacity of wastewater treatment is estimated at

74 million m3 per year, with treated effluent primary targeted towards agricultural purposes. Surface water, originating mainly from rainfall, represents approximately 170 million m3 per year, contributing less than 3% of the total water use (Aqeil et al., 2011). Sixteen dams and several reservoirs have been constructed to manage surface water resources (Salem, 2007; Wheida and

Verhoeven, 2004).

Groundwater, both shallow and fossil aquifers, represent the main source of water supply in the country (Salem, 2007). Both shallow (Jabal Nafusah,

Jifarah, Jabal al Akhdar, and Murzuq) and fossil groundwater (Kufrah) are recharged by rainfall at a rate of approximately 650 million m3 per year (Salem,

2007).

In comparison, 2,400 million m3 per year is withdrawn, thus exceeding the annual replenishment by approximately four times (Aqeil et al., 2011). The decline in water level and resulting seawater intrusion make the remaining groundwater resources almost unusable because of their high salinity. From

1950 to 1990, the seawater interface advanced 1 to 2 km inland, with salinity increasing significantly from 150 ppm to approximately 1,000 ppm in the coastal aquifers (El-Asswad, 1995). For example, fifteen years ago, the seawater intrusion within the Kufrah basin had resulted in groundwater having a salt

content of 400 -1,000 ppm (Mcloughlin, 1991). It is expected that the seawater intrusion is still increasing. The characteristics of the main aquifers in Libya are summarized in Table 2.1.

Table 2.1. The characteristics of the shallow and fossil basins in Libya.

Basins Area Total dissolved Present and probable Effect of exploitation (Km2) salts (mg l-1) pollution Sources Jabal ala 145,000 1,000–5,000 Sea water intrusion Water level decline and Akhdar and waste disposal sea water intrusion

Kufrahb 760,000 200–2,000 Humans and Water level decline and Fertilizers local contaminations Jifaraha 20,000 500 – 4,500 Sea water intrusion, Water level decline and fertilizers and waste sea water intrusion disposal Jabal 215,000 1,000–5,000 No pollution Water level decline Nafusaha Murzuqa 350,000 500–1,500 Humans and Water level decline and fertilizers local contaminations

Source: Adopted from Wheida and Verhoeven, 2006. a Shallow basin. b Fossil basin.

To make the country self-sufficient in food production, the Libyan Water

Authority implemented the Great Man Made River Project (GMMRP) (Wheida and Verhoeven, 2007). The GMMRP, begun in the 1980's and was transferring

7.1 million m3 of water per day from the Kufrah and Sarir aquifers to the coastal areas through a system of pipes traversing 4,500 km. The GMMRP offered water for irrigation and other uses to approximately 87,981.8 km2 from the Internal

Depressions region to Benghazi on the coast (Aqeil et al., 2011). The water pumping stations are no longer operating because major parts of the GMMRP have been damaged during the recent unrest in the country.

LIBYAN SOILS

Generally, the soils in Libya are very shallow and coarse, thus they are low in organic matter content and water holding capacity (Laytimi, 2005). Soil studies have been conducted by the Russian and American governments over the last four decades, using various classification systems and methods of soil analysis (Newr, 2006). The Food and Agriculture Organization (FAO) generated a

Libyan soils map using their own classification system based on both previously mentioned surveys. Soil orders include Yermosols, Lithosols, Xerosols, Fluvisols,

Regosols, and Solonchaks. The remaining 22.90% of Libya’s land mass is covered by salt flats, rock and dune (Abagandura et al., 201X). Approximately 53.50% of all Libyan soils are estimated to be degraded (Abagandura et al., 201X) to varying degrees, losing their quality and productivity primarily due to salinization, water erosion, and wind erosion. Ben-Mahmoud et al. (2000) documented that

approximately 1,900 km2 is affected by salinity, probably due to using seawater for irrigation, poor drainage, and increasing concentrations of salts in the irrigated water from seawater intrusion.

Salinization is also the primary type of soil degradation (46.40%), with water erosion and wind erosion causing 6.40% and 0.66% of soil degradation, respectively (Abagandura et al., 201X).

CROP AGRICULTURE

Libya has several water and soil issues not conducive to agriculture, with approximately 34,700 km2, (slightly over 2% of the total area of the country and

50% of the arable land) being farmed each year (Secretariat of Economy

Planning, Libya, 1993). Approximately 95% of total land area in the country is desert, while 4% is grassland, suitable for grazing animals, and 1% is forest (FAO,

2012). The most important agricultural zones include Jabal al Akhdar and Jabal

Nafusah, Jifara plain, Kufrah and the desert mountains to the south

(Environment General Authority (EGA), 2008). Croplands cover 355,000 ha, pasture and rangeland comprise approximately 13,300,000 ha, and forestlands encompass about 547,000 ha (Alldrissi et al. 1996). Approximately half of all crops are grown in Jabal al Akhdar with the other half grown in Jabal Nafusah,

Kufrah and the desert mountains to the south (EGA, 2008).

There are 163,714 Libyan farms (FAO, 2005), with 90% being less than 20 ha, 9% between 20-100 ha, and 1% greater than 100 ha (Lariel, 2015). Farms greater than 100 ha are mostly owned by the government (Laytimi, 2005). Due to the arid conditions in the country, irrigation is becoming a common practice to supply water to crops. Approximately 470,000 ha are equipped for irrigation

(FAO, 2005), and are mainly irrigated from groundwater (Laytimi, 2005). The use of greenhouses for agriculture is increasing with 1,000 ha in use in 1991 (Jensen and Alan, 1995) to 2,500 ha in 2009 (Leonardi and De Pascale, 2009). No information is available about the greenhouse cultivation, but from the main author’s experience in Libya, greenhouse operations are both government and privately owned, with the majority of them in Jabal al Akhdar to grow vegetable crops such as tomatoes, cucumbers and potatoes. Hydroponics is also utilized.

Libya's cereal production is limited to wheat and barley, and the main non-grain agricultural crops include potatoes, onions, tomatoes, watermelons, oranges, dates, and olives (United States Department of Agriculture, 2014). Production of the main crops in 2012 are shown in Figure 2.2 (FAOSTAT, 2012).

Figure 2.2. The main crops produced in Libya in metric tons with the international prices in $1,000 USD. Source: FAOSTAT, 2012.

Due to the recent unrest in the country, the 2015 cereal crop was almost

10% below average (254,000 tons). As a result, Libya imported up to 90% cereal for its consumption requirements in 2015. Currently the country depends significantly on imported foodstuffs. Imports in 2015 were estimated to be 3.7 million tons, an increase of about 7% compared to the previous year (FAO,

2016).

Seed Sources

Historically, agricultural seeds in Libya are sourced primarily from a farmer's crops (Alidrissi, 1996). In 1975, the Libyan–Romanian Company for seeds and seedlings was established to promote agriculture in both countries.

This company continued until 1991 (FAO, 2006). In 2005, the Elkhams Center was established to facilitate seed production for cereal crops and vegetables and the amount of seed that the Center produced and distributes annually is unknown. Similar to other factories, the Center was destroyed during recent unrest in the country. On an annual basis, Libya imports 20 tons of both wheat and barley, 8 tons of forages, 346 tons of legumes, and 150 tons of vegetable seeds (Laytimi, 2005). Because of the uprising in the county, seeds have become scarce and expensive. As a result, FAO has provided vegetable seeds to farmers throughout the country (FAO, 2012).

Pests Affecting Significant Agronomic Crops

The primary pests affecting Libyan agriculture (insects, nematodes, fungus, viruses, snails and slugs) are discussed below. Although important plants in the country are susceptible to being infected by these pests (Edongali and

Dabaj, 1982), the number of Libyan specialists and institutions engaged in the

National Plant Protection Program is not sufficient to address these issues.

Insects

Lal and Naji, who documented Scale insects as one of the most problematic pests occurring in many regions of Libya, conducted the earliest published pest survey in 1979. Scale poses a threat to cereal, vegetable, ornamental, fruit and forest tree crops. Since 1980, entomologists have recorded additional harmful insects found in Libya. For example, Mohamed et al. (1994) identified that the cotton leafworm Spodoptera littoralis pest insect in an alfalfa project in Benghazi. In 1999, the chafer insect was reported for the first time in the southwest region of Libya as destroying the leaves and flowers of alfalfa fields and other crops such as sesame and Jew’s mallow, and attacking the new leaves of peach (Dodo et al., 2003). At the same time, Maghrabi and Mahfuod

(2003) recorded red flour beetle destroying wheat.

Since 2002, the use of insecticides to protect vegetable crops grown in greenhouses (particularly in Benghazi) has increased in an effort to control insect pests and improve vegetable quality and yield (Elbagermi and Alaib, 2002).

Limited availability of insecticides has resulted in the following insecticides being mainly used: actellic (2-diethylamino-6-methyl-4-pyrimidinyl-0- dimethylphosphorothioate), and malathion (0,0-dimethyl phosphorodithioate of diethyl mercapto-succinate (56)) (Omar 2013 pers. comm.). Using theses pesticides can contaminate soil, water, air and other vegetation, and may decline

the populations of beneficial soil microorganisms (Aktar et al., 2009). The two aforementioned insecticides are broad spectrum and may pose more harm than good. For example, the same authors reported that malathion insecticide was detected in the air after application to the soil. In addition, the use of the same pesticides for long time may lead to resistance among the target pests (Bourguet et al., 2000).

Nematodes

Nematodes are recognized as one of the main threats to various agricultural crops but has especially been documented to be a major pest to tomatoes, potatoes and cucumbers (Edongali and El-Majberi, 1988). Root-knot

(species not disclosed), root-lesion (species not disclosed) and citrus nematodes

(species not disclosed) are common among all the cultivated crops in the country, causing damage to their respective host plants (Ehwaeti, 2003). The cucumber root-knot nematode (Meloidogyne spp.) is found throughout the country and the most destructive (Fourgani and Edongali, 1989). The four species of root-lesion nematode attack herbaceous and woody plants, being especially problematic for date palms. The citrus nematode has been found in the Coastal Plains and Internal Depression regions country (Edongali and El-

Majberi, 1988). Other plant-parasitic nematodes are known to infect various

crops. For example, Xiphinema indexis is problematic in grapes and fig orchards

(Adam and Amer, 2014). A survey conducted by Edongali and Dabaj (1982) found

Heterodera crucifera presence and damage on cabbage, cauliflower, and other cruciferous plants in Tripolitania. Siddiqi et al. (1987) documented a new species of Telotylenchus siddiqi infecting and causing damage on peach trees in the same region.

Various ways to control nematodes are used in Libya, with chemical treatment being the most common (Ehwaeti, 2003). However, these chemicals are extremely toxic to humans and other non-target organisms, and therefore they were abandoned internationally (FAO, 1987). Currently, Temik and Vyadate

(chemistry not disclosed) as well as other indigenous nematicides are most commonly used in agricultural fields (Ehwaeti, 2003). In addition to synthetic nematicides, cultural practices, organic amendments, and plant extracts have also proven to be effective in nematode control. Moreover, several fungal isolates are also used to inhibit the hatching of root-knot nematode eggs

(Ghazala et al., 2003).

Viruses

While crop-damaging viruses infect many plants in the country, it is unlikely that all have been recorded (Zidan et al., 2002). Several studies have

documented viruses infecting broad bean, citrus, and ornamental plants such as pittosporum. Several broad bean viruses in the western region of Libya have been identified, including the bean yellow mosaic virus, faba bean necrotic yellow virus (Fadel et al., 2003; Zidan et al., 2002), the pea seed borne mosaic virus, and alfalfa mosaic virus (Fadel et al., 2003). Viruses is one of the main problems affecting citrus production in Libya (Joseph, 1995). Citrus orchards have been infected by Citrus tristeza virus for almost 35 years (Abukraa, 2009).

Limited information is available on how producers are controlling viruses.

According to Abukraa (2009), the use of cachexia-free budwood is the recommended method for preventing the introduction of citrus tristeza virus into orchards. No other information on virus control strategies was found.

Fungus

A number of studies have reported several plant diseases caused by fungus including powdery mildew (Khan and Faraj, 1982), leaf spot (Farag et al.,

1982), leaf blight (El-Maleh, 2003), inflorescence rot (Alwani and El-Ammari,

2000), and rust (Mohamed, 1975). In 2002, Czembor reported symptoms of powdery mildew (Blumeria graminis f.sp.) on barley. Leaf spot was observed on fig tree leaves in Tripolitania (Farag et al., 1982). Maize is highly susceptible to the leaf blight disease caused by Cochliobolus heterostrophus (El-Maleh, 2003).

El-Alwani and El-Ammari (2000) reported leaf spot and leaf blight diseases on date palms in various regions of Libya, and documented a serious disease of the date palm caused by Mauginiella scaettae. Rust diseases attack a number of plants in the country. The stem rust fungus Puccinia graminis is damaging to wheat and barley (Mohamed, 1975). Limited information exists on fungicides used to control diseases in Libya, however, Baraka et al. (2003) reported Galben,

Previcur–N, Sandofan, and Ridomil MZ (chemistry not disclosed) are used.

Slugs and Snails

Slugs and snails are the only two animal pests known to Libyan agriculture. The northeast region of Libya is inhabited by four species of plant damaging terrestrial slugs (Tandonia rustica, Tandonia sowerbii, Milax gagates, and Malacolimax tenellus) (Nair et al., 1996). Slugicides are commonly used to control this pest (Omar, 2015 pers. comm.). The land snails Helix aspersa and

Helix pomatia were reported for the first time in 1991, attacking and causing extensive injury to ornamental plants in Benghazi (Kamel et al., 1992). Bisheya et al. (2003) documented that snails are also found along the coastal area from

Misurata to Elkhams in the eastern part of the country with white snails (Theba pisna) being the most common one.

Chemicals Used on Crops

Because of the population growth over the last century, pesticides and fertilizers play an important role in increasing land productivity in Libya.

Crop Pesticides

Libya imports a variety of pesticides including disinfectants, fungicides, and insecticides. There is public concern about their extensive use because of their potential harmful effects on humans, animals, and the environment

(Laytimi, 2005). Soil pollution resulting from heavy pesticide usage has been documented in some areas of Jabel al Alkder (El-Barasi et al., 2010). Many countries, including Libya, have taken special measure to control the entry and the use of these substances. According to Elfallah and Boargob (2005), the control of pesticides has improved in recent years due to the collaboration between the Customs Authority and the EGA with regard to importation and local use. To reduce risks from such chemicals, soil solarization has been implemented in the western region of Libya as a means of pest control for crops under greenhouse production (Dabaj, 2003).

Crop Fertilizers

Between 1995–2002, the average annual total fertilizer use was 67,500 tons per year, or an average of 32 Kg per hectare of arable land (Laytimi, 2005).

This amount fluctuated yearly due to climatic conditions, the amount of fallow

land, and the country’s reaction to United Nations sanctions (Laytimi, 2005).

Phosphate fertilizer is the most common fertilizer applied, making up approximately 55% of all fertilizers applied. Nitrogenous fertilizers, (primarily urea), is the second most commonly applied fertilizer followed by potash fertilizer (Laytimi, 2005). The establishment of the Libyan Fertilizer Company in

2009 introduced Libya to the global fertilizer industry and market, opening the way for production growth and market expansion. Currently there is one factory located in approximately 700 km west of Tripoli, with a combined daily production capacity of 2,200 tons of liquid ammonia and 2,750 tons of prilled urea (Hovland, 2013), however, it is currently not operating due to recent unrest in the country.

OVERCOMING CURRENT CHALLENGES

Agriculture in the country is primarily dependent on underground aquifers for its irrigation needs (Alghariani, 2002). Since groundwater withdrawal exceeds natural aquifer replenishment, Libya initiated a cooperation program with neighboring countries aimed at the adoption of a long-term strategy for managing shared water resources (Salem, 2007). This includes exchange of information related to the present and future withdrawals, along with water level and water quality monitoring data, and plant species selection. For

example, by identifying crops that require less water, the country was able to reduce the irrigation demand (El-Asswad, 1995).

Libya is facing serious problems of soil degradation (Abagandura et al.,

201X). Human activities including grazing and pastoral over-use has caused a significant soil desertification (Gebril and Saeid, 2012). One of the reasons causing desertification in Libya is low vegetation cover resulting from warming of air temperatures and decreases in precipitation (Saad et al., 2011). In the past, several measures were taken to address soil erosion including, storm water capture and retention on sloping agricultural land, establishing windbreaks, and the use of crop rotations (Saad and Shariff, 2011). Other on-farm strategies to increase soil health need to be investigated.

To date, few studies exist on monitoring desertification in Libya using

Remote Sensing and Geographic Information System (GIS) (El-Tantawi, 2005;

Oune, 2006; Saad et al., 2011). It is expected that use of GIS technology will enhance the country’s ability to identify occurrence and type of soil degradation issues (Abagandura et al., 201X), and thus begin to make informed decisions on where to focus efforts and what types of management strategies will be needed to increase agricultural productivity.

In addition to the water and soil challenges, Libya’s dry climate provides favorable conditions for plant pests. Public and government awareness of the danger of serious pests in agriculture is limited.

FUTURE OF LIBYAN AGRICULTURE

The repercussions of soil and water issues (such as irrigation with high salinity) on agricultural development and food security may not be fully understood by many. Libya is seeking solutions for limited water resources by implementing modern, high-efficiency irrigation systems. Drip irrigation similar to those found in Egypt (El-Habbasha et al., 2015), Turkey (Acar et al., 2014),

Tunisia (Thabet, 2013), and Syria (Hussein et al., 2011) is being promoted to allow for deficit irrigation and conservation of water sources.

Another way to face the increasing demand for water may be by using soil surfactants which can enhance the properties of soil that increase its water- holding capacity, thereby reducing agricultural water demand (Cisar et al., 2000).

For example, the addition of a surfactant to a sand soil in Egypt improved its physical properties and barley plant growth (Mohamed and Magdi, 2005).

Soil degradation is an important issue in the country because of its adverse impact on crop yields. Incorporating residue or retaining them on the soil surface in Libya can be an important management to prevent soil

degradation by wind and water erosion (Turmel et al., 2015). Ruiz-Colmenero et al. (2013) reported that using cover crops prevented from soil erosion in Spain

(Mediterranean country) which has a semiarid climate similar to Libya.

Although a few number of African farmers including Libya use remote sensing information (Lowenberg-DeBoer and Erickson, 2010), the adoption of precision agriculture (knowledge-based technical management system) can be another future soil and crop management tool in Libya (Bora et al., 2012; Geipel et al., 2015). For example, it is estimated that the United States could save 2,000 tons of insecticide and approximately 1,893 m3 of herbicide if 10% of its farmers adopts the precision farming when they plant their seeds (Natural Resource

Conservation Service of the United States Department of Agriculture, 2006).

Recent activities showed a renewed a number of research projects promoting agricultural development in the country. In 2012, an agreement between Libyan Government and International Center for Agricultural Research in the Dry Areas (ICARDA) was established to identify several priorities for the future in Libya, particularly on irrigation management and cereals production

(ICARDA, 2012), but has since been postponed. Libya was also provided 71 million dollars in funding from the FAO in 2012 to develop different important areas for agronomic advancement, such as plant production, pesticide

management, seed development, and natural resource management to increase food production (Libya Herald, 2012). Due to unsafe circumstances in the country (Combaz, 2014) (mainly in Tripoli and Benghazi), these FAO programs have been minimized and are managed primarily from regional offices in Egypt and Tunisia (Omar 2015 pers. comm.)

While these efforts will have a direct effect on governmental operated farms, a strong outreach program will be needed to assist smaller private and family farms.

CONCLUSIONS

This review was commissioned to examine Libya’s agricultural history, current challenges and the current and potential solutions. The prominent challenges in Libya include soil degradation and the scarcity of quality irrigation water sources. Secondary challenges are availability to seed sources that are salt and drought tolerant, options for plant chemicals (fertilizers and pesticides), and lack of agricultural development policies and outreach. Cooperation with international organizations to address these challenges has occurred, but has been ineffective since 2011. This has resulted in minimal advancement and the country’s agricultural sector (Lariel, 2015). Urgent actions needed to address these challenges in the future.

REFERENCES

Abagandura, G.O., D. Park, W. David, and W. Bridges. 201X. An assessment of soil

resources and soil degradation in Libya. In review.

Abukraa, H., K. Djelouah, and A. Kafu. A. 2009. Historical review of citrus tristeza

virus (CTV) in Libya. Available at

http://om.ciheam.org/om/pdf/b65/00801397.pdf (verified 6 June. 2016).

Alghariani, S.A. 2002. Future perspectives of irrigation in southern

Mediterranean region: policies and management issues, In: Al-Rasheed, M.,

V.P. Singh, M.M. Sheriff, M.M. (Eds.), Proceedings of the International

Conference on Water Resources Management in Arid Regions 23–27 March,

vol. 4, Kuwait, pp. 313–320.

Acar, B., R. Topak, D. Yavuz, and M.A. Kalender. 2014. Is drip irrigation technique

sustainable solution in agriculture for Semi-Arid Regions. A Case Study of

Middle Anatolian Region, Turkey. IJAED. 2(2): 1-8.

Adam, M.A.M. and A, Amer. 2014. Evaluation of plant extracts from northeast

Libya for their nematicidal activity against the dagger nematode, Xiphinema

index. J. Exp. Biol. Agric. Sci. 2(5): 278-483.

Aktar, W., D. Sengupta, and A. Chowdhury. 2009. Impact of pesticides use in

agriculture: their benefits and hazard. Interdiscip. Toxicol. 2(1): 1-12.

Alldrissi, M., A. Sbeita, A. Jebriel, A. Zintani, A. Shreidi and H. Ghawawi. 1996.

Libya: country report to the fao international technical conference on plant

genetic resources. Available at

http://www.fao.org/fileadmin/templates/agphome/documents/PGR/SoW1/

east/LIBYA.pdf (verified 6 June. 2016).

AGUASAT. 2016, FAO's information system on water and agriculture, Libya.

Available at

http://www.fao.org/nr/water/aquastat/countries_regions/lby/index.stm

(verified 10 June. 2016).

Aqeil, H., J. Tandall, E. Moran. 2011. Water security and interconnected

challenges in Libya. Available at

http://tinmore.com/pdf/WS121027_WaterSecurityLibya.pdf (verified 21

May. 2016).

Baraka, M.M., F.A. Bisheya, S.M. Garew, and A.N. Fahema. 2003. Sensitivity to

some potato cultivars to late blight disease, effect of nitrogen fertilization

and its chemical control. Eighth Arab Congress of Plant Protection, El-Beida,

Libya. 2003. Faculty of Agriculture, Al-Fetah University, Tripoli, Libya.

Ben-Mahmoud, K.R., S. Mansur, and A. L-Gomati. 2000. Desertification in the

third millennium. Desertification Conference Toward Better Management of

Arid and Semi-Arid Lands in the 21st Century, Dubai, United Arab Emirates.

2000. Zayed Prize Fdn, United Arab Emirates.

Bisheya, F.A., A.B. Kafu, M.M. Zantuti, and A.M. Daas. 2003. Mechanical control

of snails at the eastern coastal area of Libya. Eighth Arab Congress of Plant

Protection, El-Beida, Libya. 2003. Agricultural Research Center, Tripoli, Libya.

Bora, G.C., J.F. Nowatzki, and D.C. Roberts. 2012. Energy savings by adopting

precision agriculture in rural USA. Energy Sustain Soc. 2(1): 1-5.

Bourguet, D., A. Genissel, and M. Raymond, 2000. Insecticide resistance and

dominance levels. J. Econ. Entomol. 93(6): 1588-1595.

Cisar, J.L., K.E. Williams, H.E. Vivas, and J.J. Haydu. 2000. The occurrence and

alleviation by surfactants of soil water repellency on sand-based turfgrass

systems. J. Hydrol. 352–358.

Combaz, E. 2014. Political economy of Libya after the Qadhafi regime. GSDRC:

Applied knowledge services. Available at

http://www.gsdrc.org/docs/open/hdq1084.pdf (verified 10 August. 2016).

Czembor, J. 2002. Selections from barley landrace collected in Libya as new

sources of effective resistance to powdery mildew (Blumeria graminis f.sp.).

Rostl. Vyroba. 48: 217-223.

Dabaj, K.H. 2003. Applications of soil solarization under greenhouse cultivation in

Libya. Eighth Arab Congress of Plant Protection, El-Beida, Libya. 2003. Plant

Protection Department Faculty of Agriculture, El-Fateh University, Tripoli,

Libya.

Dodo, M.M.A., A.R.A. Al Deeb, and O.M.A. Bawa. 2003. A new record of chafer

cnsect Euserica murzka (sub family: Melolonthinae, family: Scarabaeidae,

order: Coleoptera). Eighth Arab Congress of Plant Protection, El-Beida, Libya.

2003. Arab Center for Research and Development of Saharan Communities,

Libya.

Edongali, E.A., and K.H. Dabaj. 1982. Preliminary survey of nematodes associated

with vegetable crops in western Libya. J. Nematol. 13: 434-435.

Edongali, E.A. and S.H. El-Majberi. 1988. Plant-parasitic nematodes associated

with citrus plantations in Libya. Pak. j. nematol. 6: 23-24.

Ehwaeti, M.E.M. 2003. An overview of nematode problems and research in Libya.

Eighth Arab Congress of Plant Protection, El-Beida, Libya. 2003. Omar-Al-

Mokhtar University, Agriculture College, Department of Plant Protection, El-

Beida, Libya.

El-Alwani, A.M., and S.S. El-Ammari. 2000. Date palm fungal diseases in three

Libyan oases. Available at

http://www.pubhort.org/datepalm/datepalm2/datepalm2_42.pdf (verified

16 June. 2016).

El-Asswad, R.M. 1995. Agriculture prospects and water resources in Libya.

Ambio. 24: 324-327.

EL-Barasi, Y.M., N.A. Ahmaida, M.W. Barrani, A.O. EL-amrouni, and O. Abdelnaser.

2010. Pollution of agricultural lands by fertilizers and pesticides on El-gubba

and El-abraq area in Libya. Int. J. Eng. 97-102.

El-Habbasha, S.F., E.M. Okasha, R.E. Abdelraouf, and A.S.H. Mohammed. 2015.

Effect of pressured irrigation systems, deficit irrigation and fertigation rates

on yield, quality and water use efficiency of groundnut. Int. J. ChemTech Res.

7(1): 475-487.

Elbagermi, T.M., and M. Alaib. 2002. Phytotoxicity of some insecticides. Eighth

Arab Congress of Plant Protection, El-Beida, Libya. 2003. Environment

Department, Faculty of Public Health, Garyounis University, Benghazi, Libya.

Elfallah, M., and M. Boargob. 2005. The national action plan for the libyan coastal

area. Available at

http://www.Themedpartnership.org/med/documents/library/background-

documents/naps/en/attachments%7Cattachments%3A011%7Cfile (verified

7 June. 2016).

El-Maleh, A. 2003. Transgenic tobacco containing the maize T-URF13 gene as a

novel host for the maize pathogen Cochliobolus heterostrophus race T.

Eighth Arab Congress of Plant Protection, El-Beida, Libya. 2003. Omar Al-

Mukhtar University, El-Beida, Libya.

El-Tantawi, A.M.M. 2005. Climate change in Libya and desertification of Jifara

Plain: using geographical information system and remote sensing

techniques. Ph.D. diss., University of Mainz, Germany.

Environment General Authority. 2008. National biosafety framework of the

Libyan Arab Jamahiriya. Available at http://www.unep.org/

biosafety/Documents/NBFs/Libya%20_Final_NBF_En.pdf (verified 15 June.

2016).

Fadel, S., J. Khalil, and M. Shagrun. 2003. Identification of some broad bean

viruses in the western region of Libya. Eighth Arab Congress of Plant

Protection, El-Beida, Libya. 2003. University of Sabha, Faculty of Agriculture,

Plant Production Department, Libya.

FAOSTAT. 2012. FAO Statistical Satabase, Libya.

Farag, I.S., J.A. Khalil, and N.K. El-Gariani. 1982. Alternaria leaf spot of fig in the

Libyan Arab Jamahiriya. Libyan Journal of Agriculture. 11: 215-217.

Food and Agriculture Organization. 1987. plant nematode problems and their

control in the near east region. FAO. Libya.

Food and Agriculture Organization. 2005. Compendium of food and agriculture

indicators. Statistics Division, Rome Italy. Available at

http://www.fao.org/es/ess/compendium_2004/pdf/ESS_LIB.pdf (verified 10

June. 2016).

Food and Agriculture Organization. 2006. New partnership for Africa’s

development. food security scheme (wheat, dates & olives, seed

production). Available at

http://www.fao.org/docrep/009/ag390e/ag390e00.HTM / (verified 11

August. 2016).

Food and Agriculture Organization. 2012. Libya: FAO in emergency. Available at

http://www.fao.org/emergencies/countries/detail/en/c/161512/ (verified

10 August. 2016).

Food and Agriculture Organization. 2016. FAO GIEWS country brief on Libya.

Available at http://www.fao.org/giews/countrybrief/country.jsp?code=LBY

(verified 10 August. 2016).

Fourgani, G.M., and E.A. Edongali. 1989. Speciation of root-knot nematodes

(Meloidogyne spp.) associated with crop plants in Libya. Int. Nem. Net. New.

6: 38-39.

Geipel, J., M. Jackenkroll, M. Weis, and W. Claupein. 2015. A sensor web-enabled

infrastructure for precision farming. ISPRS Int. J. Geo-Inf. 4(1): 385-399.

Gebril, A.O., and A.G. Saeid. 2012. Importance of pastoral human factor

overloading in land desertification: case studies in northeastern Libya. World

Acad Sci Eng Technol. 70: 1107-1112.

Ghazala, S.M., B.A. Ghashira, and K.H. Dabaj. 2003. Effects of some fungal

isolates on egg hatching inhibition of root knot nematodes Meloidogyne

spp. under laboratory conditions. Eighth Arab Congress of Plant Protection,

El-Beida, Libya. 2003. Plant Protection Department Faculty of Agriculture, El-

Fateh University, Tripoli, Libya.

Hegazy, A.K., L. Boulos, H.F. Kabiel, and O.S. Sharashy. 2011. Vegetation and

species altitudinal distribution in Al-Jabal Al-Akhdar landscape. Pak. J. Bot.

43:1885-1898.

Hovland, K.M. 2013. Yara CEO: Libyan plant runs at 70%, gas supply still unstable.

Available at http://online.wsj.com/article/BT-CO-20130422-703366.html

(verified 15 June. 2016).

Hussein, F., M. Janat, and A. Yakoub. 2011. Assessment of yield and water use

efficiency of drip-irrigated cotton (Gossypium hirsutum L.) as affected by

deficit irrigation. Turk. J. Agric. For. 35(6): 611-621.

International Center for Agricultural Research in the Dry Areas. 2012. Libya-

ICARDA Partnerships renewed. Available at

http://www.icarda.org/blog/libya-icarda-partnerships-renewed (verified 15

June. 2016).

Jensen, M.H., and J.M. Alan. 1995. Protected agriculture: a global review. Vol.

253. World Bank Publications.

Joseph, M.B. 1995. Virus and virus-like diseases of citrus in the near east region.

Food and Agriculture Organization of the United Nations. France. 1995.

Kamel, E.G., H.L. Abbas, G.A. Nair, and F.F. El-Toumi. 1992. Nutritional biology of

the land snails Helix aspersa and Helix pomatia (Mollusca: Pulmonata) in

Libya. J. Basic Appl. Zool. 8: 195-204.

Khan, M.W., I.S. Faraj. 1982. Powdery mildews in The Libyan Jamahiriya. III.

identity of certain powdery mildew fungi. Libyan Agriculture Research

Center Journal International. 11, 121-126.

Lariel, N. 2015. Challenges in the sustainability of Libyan agriculture:

opportunities for the Libyan seed system. Ph.D. diss., Colorado State

University., US.

Lal, O.P., and A.H. Naji. 1979. Observations on some new insect pests and

parasites from the Socialist People Libyan Arab Jamahiriya. Riv. Agr. Subtrop.

Trop. 219-232.

Laytimi, A. 2005. Market and trade policies for Mediterranean agriculture: the

case of fruit/ vegetable and olive oil. Available at

http://medfrol.maich.gr/documentation /view/reports/wp1-asr/Libya.pdf

(verified 22 June. 2016).

Leonardi, C and S. De Pascale. 2009. Greenhouse production systems in

Mediterranean area. Available at

http://www.agrospaceconference.net/sperlonga2010/4_Leonardi_DePascal

e.pdf (verified 15 August. 2016).

Libya Herald. 2012. Libya to invest $71 million in joint projects with FAO.

Available at http://www.libyaherald.com /2012/05/21/libya-to-invest-71-

million-in-joint-projects-with-fao/ (verified 15 February. 2016).

Lowenberg-DeBoer, J., and B. Erickson. 2010. The search for the killer app:

precision farming in Africa. GJIA. 11(2): 107-116.

Maghrabi, H., and N. Mahfuod. 2003. The cannibalistic phenomenon of adults

and larvae of the confused flour beetle, Tribolium confusum (Coleoptera:

Tenebrionidae). Eighth Arab Congress of Plant Protection, El-Beida, Libya.

2003. Zoology Department, Faculty of Science, Al-Fatah University, Tripoli,

Libya.

Mcloughlin, P.F.M. 2011. Libya's great manmade river project: prospects and

problems. Nat. Resour. Forum.15: 220-227.

Mohamed, H.A. 1975. Plant disease survey in Libya. Libyan Agriculture Research

Center Journal International. 4:105-107.

Mohamed, A., K. Bhuyan, and M. Elmesalty. 1994. Laboratory and field

evaluations of selected chemical insecticides on the third larval stage of the

cotton leaf worm spodoptera littoralis in Benghazi, Libya. Zool. Anz. 233: 69-

73.

Mohamed, M.M and S.M. Magdi. 2005. An experimental test of anionic

surfactant (DLBA). effect on some growth parameters. J. Agric. Sci. 30 (1):

723-727.

Nair, G.A., F.F. El-Toumi, K.M.A. Eltayeb, A., Bosnaina, and K.C. Bhuyan. 1996.

Habitat occurrence and density of some pulmonate slugs of north-east Libya

(mollusca, milacidae and limacidae). Afr. Zool. 110: 251-256.

Natural Resource Conservation Service of the United States Department of

Agriculture. 2006. Conservation practices that save: precision agriculture.

Available at http://www.usada.gov.(verified 11 June. 2016).

Nwer, B.A.B. 2006. The application of land evaluation technique in the north-east

of Libya. Ph.D. diss., Cranfield Univ., UK.

Oune, O. 2006. Monitoring desertification in south west Tripoli using multi-

temporal remotely sensing data and GIS. Ph.D. diss., University of Dundee,

Scotland.

Ruiz-Colmenero, M., R. Bienes, D.J. Eldridge, and M.J. Marques. 2013. Vegetation

cover reduces erosion and enhances soil organic carbon in a vineyard in the

central Spain. Catena. 104: 153-160.

Saad, A.M.A., and M. Shariff. 2011. Estimating desertification in the arab world

using GIS approach. MEJSR. 8: 1046-1053.

Saad, A.M.A., N.M. Shariff, and S. Gairola. 2011. Nature and causes of land

degradation and desertification in Libya: need for sustainable land

management. Afr. J. Biotechnol. 10: 13680-13687.

Salem, O.M. 2007. Management of shared groundwater basins in Libya. Afri.

Wat. J. 1 (1): 109-120.

Secretary of Planning. 1993. Economic and social development achievements in

28 years. The Secretary of Planning. Libya.

Shaki, A.A., and A.J. Adeloye. 2006. Evaluation of quantity and quality of

irrigation water at Gadowa irrigation project in Murzuq basin, southwest

Libya. Agric. Manage. Water. 84: 193-201.

Siddiqi, Z.A., A. Rashid, N. Farooqi, and F.A. Bisheya. 1987. A survey of plant-

parasitic nematodes associated with citrus in Libya and trials on chemical

control. Indian J. Nematol. 17: 76-80.

Sunderland, E., and P. Rosa. 1976. The incidence of red-green color blindness in

the populations of Tripolitania, Cyrenaica and Fezzan in Libya, and of the

Kikuyu, Kamba, Taita, Taveta and Luo Tribes of Kenya. Am. J. Phys. Anthropol.

44: 151-156.

Thabet, M. 2013. Drip irrigation systems and water saving in arid climate: a case

study from south Tunisia. IJWRAE. 2(4): 226-230.

Turmel, M.S., A. Speratti, F. Baudron, N. Verhulst, and B. Govaerts. 2015. Crop

residue management and soil health: a systems analysis. Agric. Sys. 134: 6-

16.

United States Department of Agriculture. 2014. Libya: cereal production.

Production estimates and crop assessment division of the foreign

agricultural service. Available at

http://www.fas.usda.gov/pecad/highlights/2004/05/libya/ (verified 11 June.

2016).

Wheida, E., and R. Verhoeven. 2007. An alternative solution of the water

shortage problem in Libya. Wat. Res. Mang. 21: 961–982.

Wheida, E., and R. Verhoeven. 2004. Desalination as a water supply technique in

Libya. Desalination. 165: 89-97.

Zidan, F., J. Khalil, and M. Shagrun. 2002. Survey and identification of pea viruses

in Libya. Arab. J. Pl. Prot. 20: 154-156.

CHAPTER THREE

AN ASSESSMENT OF THE SOIL RESOURCES AND DEGRADATION IN LIBYA

This work is under consideration for publication:

Abagandura, G.O., D. Park, W. David, and W. Bridges. 201X. An assessment of soil resources and soil degradation in Libya. AMBIO.

ABSTRACT

Soil degradation is considered one of the most important factors limiting agricultural development in Libya, however little effort has been taken to identify the distribution of soil degradation occurrence and type for the country.

Existing soil texture, landscape feature and soil degradation data from Libya’s primary agricultural regions was integrated into one map. Then two models were developed to determine the remainder of the country’s degradation occurrence and type. Thirty-three percent (33%) of the total primary agriculture areas show degradation ranging from 20-99%. A logistic regression model that included soil texture and slope best predicted soil degradation occurrence (P = 0.0003, χ2 =

8.432, and Akaike Information Criterion = 34.02). This model predicts that 53.5% of Libyan soils have some degradation. A multinomial logistic regression model using the same variables from the logistic model determined soil degradation type. The model predicts that salinization was the primary type of soil degradation (46.40%), with water erosion and wind erosion causing 6.40% and

0.66% of soil degradation, respectively. The intention is for these models to assist stakeholders in identifying areas where agriculture is most likely to be successful, while also applicable to countries with similar climate and soils in

North Africa.

INTRODUCTION

The primary issue hindering agricultural development in Libya is soil degradation, a condition caused by salinization, water erosion and wind erosion due to the geology and climate (Nwer et al., 2013; Saad et al., 2013), and improper use of natural resources (Gebril and Saeid 2012). The low rainfall and high evaporation promotes soil salinity and subsequently leads to soil instability

(Habel, 2013). Approximately 700,797 ha of some parts of the Primary

Agriculture Regions (PAR) (identified as the Kufrah, Murzuq, Jabal Nafusah, Jabal al Akhdar, and Jifarah regions of Libya, regardless of irrigation, are degraded due to salinity (Hachicha and Abdegawed, 2003), with 12% of the northwestern areas and 23% of the northeastern areas considered salt-affected (Nwer, 2013).

Increased use of fresh groundwater as a potable water source for a growing population and extensive agriculture activities increased seawater intrusion into groundwater wells (Nwer et al., 2013) further increasing the soil salinity problem. Extensive irrigation with this contaminated water source, coupled with

poor drainage, resulted in the proliferation of salt affected soils in the western part of Jifarah (Atman and Lateh, 2013) and in Jabal Nafusah (Laytimi, 2005).

Rainfall in Libya is unevenly distributed with deleterious effects. The occasional heavy showers (Nwer, 2013) accelerates soil erosion by detaching and transporting vulnerable soil either by rain splash or rill and gully erosion (Pang et al., 2006). Water erosion has degraded 797 ha of the Jabal Nafusah soils and

Jabal al Akhdar soils, collectively (Mahmud, 1995; Ben-Mahmoud et al., 2000).

Loss of vegetation covers from over-grazing and over-cultivation of Libya’s two primarily rain-fed agriculture areas (Jabal Nafusah and Jabal al Akhdar) resulted in bare soil further exasperating erosion by stormwater (Gebril and Saeid, 2012).

Soils of the Jabal al Akhdar region has been degraded the most by wind erosion (Aburas, 2014) since there is minimal plant vegetative cover protecting the soil (Laytimi, 2005). The soils of Jifarah, one of the most cultivable areas of the country due to the availability of groundwater experiences both water and wind erosion due to aridity, poor vegetation cover, and poor landuse decisions during the last half of the 20th century (El-Tantawi, 2005). According to Oldeman et al. (1990), removal of the natural vegetation, deficient agricultural practices like overgrazing, and overexploitation of vegetation for domestic use caused further soil damage.

While new strategies may address soil degradation issues (Ben-Mahmoud et al., 2000), more complete baseline data and information infrastructure to determine the best management strategy are lacking (Khaled, 2001). Over the past 30 years, numerous soil surveys have been conducted in Libya (primarily in

Jabal Nafusah, Jifarah Plain and Jabal al Akhdar in addition to scattered areas in the south) (Nwer et al, 2013). Different agencies and their interests resulted in varied parameters and geographic extent (Khaled, 2001), thereby limiting its practical use (Nwer et al., 2013) beyond the conclusions made in the previous paragraphs. An alternative, remote approach to classify soil and type degradation across the country must be considered since field determinations are resource intensive (Mueller et al., 2005). Since soil and climate characteristics are linked to the development of the soil degradation, empirical models can be developed to identify areas most susceptible to soil degradation.

These empirical models can help identify areas that are either already degraded or most prone to soil degradation, and protect Libya’s most viable areas for sustainable agriculture development.

The objectives of this study were to:

 Integrate and normalize existing soil data across Libya, creating a baseline

data set of soil coverage and degraded areas using ArcGIS 10.3 software

(ESRI, Redlands, California). This step will result in a more efficient and

economical method for updating soil information and making it available to a

wide variety of stakeholders.

 Assess the vulnerability of the primary agricultural regions (PAR) to

degradation based on existing soil information and study the nature of the

PAR soil degradation type effect.

 Develop prediction models for degradation occurrence and type by using

fundamental soil and climate characteristics.

MARTIAL AND METHODS

ASSESSMENT OF THE PRIMARILY AGRICULTURE REGIONS

Study Area Description

This study was conducted for the PAR in the country of Libya: Kufrah,

Murzuq, Jabal Nafusah, Jabal al Akhdar, and Jifarah covering 846,135.1 km2

(Figure 3.1 a), or 52.4% of the total area of the country. Yields from rainfed agriculture in Libya are generally low due to the climate, thus the PAR were primarily selected due to the availability of groundwater aquifers for irrigation

(Figure 3.1 b). In 2005, approximately 22% of the country’s PAR depends on groundwater fed irrigation (Food and Agriculture Organization (FAO), 2005).

Figure 3.1. The Location of (a) the primary agriculture regions, Kufrah, Murzuq, Jabal Nafusah, Jabal al Akhdar, and Jifarah; and (b) groundwater aquifers in Libya.

Creation of Baseline Map (Objective 1)

To complete objective one, ArcGIS was used to integrate data from three existing maps: the African Soil Classification Map (ASCM) created by the FAO in

2005, the Libyan Primary Agricultural Regions Degradation Map (LPARDM) created by Libyan Government (LG) in 2010, and the Libyan Regional Map (LRM) created by Environmental Systems Research Institute (ESRI) in 2012.

 African soil classification map

The FAO generated the Libyan GIS portion of the ASCM from Russian and

American topsoil (1 to 5 cm) surveys (Nwer, 2006). However, the governments used different classification systems and methods of soil analyses: the American surveys were conducted in the northwestern, central, northeastern, and southern zones over an area of 9 to 4,000 km2, and used the hydrometer method at a scale of 1:200,000 and 1:500,000, while the Russian surveys of the northwest and west zone, determined texture by the feel test at a scale of

1:300,000 and 1:500,000. The clip and export data tools in ArcGIS were used to export data from ASCM to create the Libyan Soil Classification Map (LSCM).

 Libyan primary agricultural regions degradation map

Soil degradation data generated in 2010 by LG for salinization as well as wind and water erosion for PAR. Some of the surveys that identified soil texture also measured soil salinity for PAR. Electrical conductivity was determined by laboratory analysis from 1:2.5 soil–water suspensions. Wind and water erosion deterioration were determined from Landsat satellite imagery between 1960 and 1980 at 1:25,000 to 1:60,000 scale.

 Libyan region map

In 2012, ESRI created a map of education infrastructure in the 24 Libyan regions including the five within the PAR. The layer of the PAR was clipped creating the Libyan Regions Data Map (LRDM).

To achieve objective two, The LSDM and LSCM with the LRDM were projected in the same reference system (Africa_Albers_Equal_Area Conic), then intersected in ArcGIS to relate the soil degradation of the PAR to soil texture.

New polygons were created by the intersection of the input polygons. The total areas of each feature were calculated. To study the relationship between soil texture and soil degradation type, Pearson’s Chi-squared test was used to determine how proportions of the degradation types change across texture levels. The distribution of soil degradation and each texture is illustrated using a

mosaic plot. The vertical length of each rectangle is proportional to the proportions of the soil degradation in each texture level.

Modeling Climatic and Soil Characteristics to the Occurrence and Type of Soil

Degradation Within PAR (Objective 2)

 Modeling PAR soil degradation occurrence

Logistic regression (LR) is commonly used in environmental and ecological studies to predict the probability of an event occurring (Dai et al., 2001; Lee and

Min, 2001; Lee et al., 2013). In the present study, the event is the occurrence of soil degradation. Several factors must be considered when developing a LR model including topography (Jenny, 1941; Liu et al., 1994; Liu et al., 2015), soil temperature and moisture (Wei et al., 2014), and soil texture (Fecan et al., 1998;

Li et al., 2011). Rainfall and air temperature also affects degradation. For example, the greater the intensity and duration of a rainfall, the higher the soil degradation potential (Wang et al., 2013). Although wind intensity is one of the factors inducing movement of soil (Borrelli et al., 2014), wind data were not available to use as a variable.

The relationship between soil degradation (the distribution of the dependent variable, (Y)) and the independent variables (soil texture, soil

moisture, soil temperature, slope, rainfall and air temperature) were evaluated using a LR model.

Since LR calculations cannot be done in ArcMap, all the previously mentioned variables in the ArcMap attribute table were converted to point shapefile and then exported to Microsoft Excel to be used in the LR calculations in JMP. The LR The degradation results were imported into ArcMap as a table with respective latitude and longitude. Convert a point shapefile to a polygon shapefile tool in ArcMap was used to display the results as polygons in all maps created in this study.

All possible combinations of the independent variables (2K, where K is the number of the independent variables) were evaluated for inclusion in the model.

The form of the model was

푛 푃(푌) = 1/1 + exp −(훽표 + ∑ 훽푖푋 푖) 푖=1

Where, P(y) is probability of soil degradation being 1, β0 is an intercept of the model,

βi (with 1<=i <=14) are the model parameters to be estimated.

The variables (denoted independent variables, Xi) related to the probability of soil degradation are listed in Table 3.1.

Table 3.1. Independent and dependent variables used in logistic regression model for the primary agricultural regions in the Libya.

Variable Description I Dependent Soil degradationa Degraded soil (1—degraded, 0—not)e Salinization Water erosion Wind erosion II Independent Soil moistureb I Class I “dry” (1—class I, 0—other classes)d Soil moistureb II Class II “xeric” (1—class II, 0—other classes)d Soil temperatureb I Class I “aridic” (1—class I, 0—other classes)d Soil temperatureb II Class II “thermic” (1—class II, 0—other classes)d Soil temp*eratureb III Class III “moderate” (1—class III, 0—other classes)d Soil temperatureb IV Class IV “warm” (1—class IV, 0—other classes)d Soil textureb I Class I “coarse loamy” (1—class I, 0—other classes)d Soil textureb II Class II “loamy” (1—class II, 0—other classes)d Soil tex---tureb III Class III “silty loam” (1—class III, 0—other classes)d Soil textureb IV Class IV “sand” (1—class IV, 0—other classes)d Soil textureb V Class V “ loamy very fine sand” (1—class V, 0—other classes)d Slopec Slopee (%) Climate (temperature)c Temperaturee (c°) Climate (rainfall)c Rainfalle (cm) a LG (2010). b FAO (2005). c ESRI Landscape (2014). d Categorical variable. e Continuous variable.

Model development included first examining the overall significance of each LR model using the overall Chi-square test and p-values. Then the LR model’s goodness-of-fit was determined using the Akaike Information Criterion

(AIC), where the optimal fitted model is identified by the minimum AIC value.

Next, F-tests (with associated p-values) were used to compare the two models with similar low AIC values and Fisher’s F-tests (with associated p-values) tested each independent variable considered in the LR. The significant variables are kept to build the LR model.

To validate the LR model developed in this study, the Leave-One-Field-

Out Validation approach was used (Pike et al., 2009). A series of analyses were performed in which one PAR was left out and used as a test case. The coefficients for the LR model were used to estimate the probability of a degradation occurrence in the test case (Bishop, 2002). If the probability of degradation for a test case was < 0.5, it assumed to have no degradation and if the probability was ≥ 0.5 it was assumed to have degradation. (Mueller et al.,

2005; Pike et al., 2009). The assumed degradation was compared to the actual degradation. This was repeated with each PAR being a test case.

Misclassification (disagreement between actual and predicted degradation) totals were used to examine prediction errors.

Modeling PAR Soil Degradation Type

The LR model determined the presence of soil degradation. The next logical step was to determine the type of the degradation. To do this, a

Multinomial Logistic Regression (MLR) model was developed. MLR models are useful because they allow for more than two response variables (Lin et al., 2014).

The response data included three types of soil degradation (salinization, water erosion and wind erosion).

Only significant variables in LR became part of the MLR models. One soil degradation type served as the reference category. Because soil texture is categorical, one category of texture was the reference. Wald’s chi-square tests were used to determine if the independent variables used in MLR were related to the soil degradation type. The coefficients in the MLR were interpreted based on odds ratio (OR). The OR indicated the amount by which the odds of the soil degradation types (with respect to the reference type) changed as the independent variables changed by one unit (with respect to the reference category) (Institute for Digital Research and Education, 2014). Although the magnitude of odds ratio of a category changes with the reference category, its relative trend and the fit of the overall model is not affected. An OR ≥ 1 indicated a positive correlation between the independent variable and the probability of

soil degradation type existence, while < 1 indicated negative correlation

(Debella-Gilo and Etzelmüller, 2009). With wind erosion as the selected reference, a model was developed for soil salinization and water erosion.

Determining Soil Degradation Occurrence and Type for Libya (Objective 3)

After developing LR and MLR, the next step was to model areas outside the boundaries of the PAR for to predict degradation occurrence and type for the reminder of Libya. Dunes, salt flats and rocks were omitted from the models.

Although dunes play a very important role in preventing and delaying intrusion of waters into inland areas (Gómez-Pina et al., 2002), and rocks increase hydraulic roughness and friction, decreasing the overland flow speed and thus decreasing soil erosion (Jomaa et al., 2012), they would not be considered for agriculture purposes. All statistical calculations were performed using JMP® Pro

12.0.1 software (product of SAS Institute Inc., Cary, NC).

RESULTS AND DISCUSSIONS

Baseline Soil Data Map

Intersecting the LSDM, LPARDM, and LRM in ArcGIS created existing data baseline maps for soil texture and landscape feature distribution (Figure 3.2).

Figure 3.2. Soil and land features of the primary agriculture regions in Libya, (a) Jifara, (b) Jabal al Akhdar, (c) Jabal Nafusah, (d) Murzuq, and (e) Kufrah as created by FAO, 2005.

Calculations included areas (km2) of each soil texture and land feature present in each PAR (Table 3.2), and the area (km2) of the degradation type for each texture within each PAR in Libya (Table 3.3).

Silty loam soils dominated in all the regions except for Jifarah, which is dominated by coarse loamy soils (Figure 3.2 and Table 3.2). Dunes and rocks covered 25.6% of Kufrah and 24.6% of Murzuq. Salt flats, which developed from groundwater evaporating and developing a salt pan (Schulz et al. 2015), covers

0.55% of Jabal Nafusah.

Table 3.2. Area (km2) of different soil textures and land features for the primary agriculture regions in Libya.

Region Soil Land feature Silty loam Loamy Coarse loamy Loamy very Sandy clay Sand Rock Salt flats Dunes fine sand

Kufrah 296,893 0 0 18,289 0 0 825 0 108, 923

Murzuq 205,031 877 0 37,566 0 186 5,305 0 81,376

Jabal Nafusah 59,451 1,128 15,499 461 0 0 0 336 0

Jabal al Akhdar 6,568 666 0 4,082 0 0 0 0 0

Jifarah 0 0 2,664 0 0 0 0 0 0

Total 567,943 2,671 18,163 60,398 0 186 6,130 336 190,299 Libya 1,012,990 22,427 81,134 109,602 8,434 10,312 38,502 676 330,366

The quantity of degradation measured at 215,370 km2 (33.16%) of the total area of the PAR (Table 3.3). Jabal Nafusah, Jabal al Akhdar, and Jifarah are the most degraded regions (98.2%, 68.5% and 99.4%). Salinization is the greatest type of degradation in all the PAR with exception of Jifarah which has soils degraded primarily from water erosion (Table 3.3). Laytimi (2005) reported that irrigation with saline groundwater led to soil salinization in some areas of the

PAR. Water erosion is also a significant source of degradation in the Jabal

Nafusah region, probably because the terrain includes steep slopes coupled with a very high content of very fine sand particles and very low clay content. Sandy soils lack the ability to aggregate leading to weaker physical resistance to water erosion (Khaled, 2001; Aboufayed, 2013). The only PAR affected by wind erosion is Murzuq.

Table 3.3. Areas (km2) of the degraded type for each soil texture in the primary agriculture regions in Libya based off of existing data.

Region Silty loam Loamy Loamy very Coarse loamy Sand fine sand SA WA WI SA WA WI SA WA WI SA WA WI SA WA WI Kufrah 63,964 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Murzuq 49,627 0 205,7 0 0 0 7,851 0 6,064 0 0 0 0 0 187 Jabal 55,061 3,028 0 671 457 0 254 206 0 381 15, 118 0 0 0 0 Nafusah Jabal al 6,173 0 0 0 0 0 1,607 0 0 0 0 0 0 0 0 Akhdar Jifarah 0 0 0 0 0 0 0 0 0 562 2,102 0 0 0 0 Total 174, 825 3,028 2,057 671 457 0 9,712 206 6,064 943 17, 220 0 0 0 187 a SA refers to salinization, WA refers to water erosion and WI refers to wind erosion.

Relationship of Climatic and Soil Characteristics to Soil Degradation Occurrence and Type

The proportions of degraded type (salinization, water, and wind erosion) changes across soil texture (p < 0.001) (Figure 3.3) and reflected what is found in each region. For example, salinization occurs in all soil textures except sand.

Water drains freely from sand soils with very little to no capillary rise to be expected (Li et al., 2013). In comparison, the other finer textured soils have micropores in which capillarity resulted in evaporation of the water from the soil surface and concentrated dissolved salts precipitated at the soil surface (Osman,

2014). Soil degradation from wind erosion occurs most frequently in the sandy texture, most likely because sandy soils have poor structure, and as a result, are highly susceptible to wind erosion (Morgan, 2009). No degradation existed from water erosion and salinity in the sandy texture, most likely because sands contain macropores that allow water to drain freely producing little runoff

(Adekalu et al., 2007).

Logistic Regression Development for The Distribution of Soil Degradation

Occurrence

From the sixty-four (64) models with all possible combinations of the independent variables, only five possible models appeared useful (Table 3.4) for predicting soil degradation. Models 1 and 2 had the lowest AIC values. A Fisher’s

F-test indicated no significant difference between Model 1 and Model 2 (F value

= 9.344 and p = 0.4312). The F-tests with associated p-values of the Model 1 variables suggested that only slope and soil texture had significant relationships with soil+ degradation (data not shown). Model 2 has only significant variables.

(Table 3.5). Model 2 is easier and less cost prohibitive than Model 1 since it uses slope and texture variables compared to soil temperature and moisture.

Table 3.4. The P-value, Chi-square and AICs for the significant logistic regression models used to identify the degradation occurrence in the primary agriculture regions in Libya.

Model Variables P value Chi-square AIC 1 Slope, Texture, Soil moisture, Soil temperature 0.0001 7.32 33.32 2 Slope, Texture 0.0003 8.43 34.02 3 Slope, Texture, Soil moisture 0.0041 8.22 46.33 4 Slope, Texture, Soil temperature 0.0382 9.00 42.11 5 Slope, Soil moisture, Soil temperature 0.0422 9.11 50.11

Table 3.5. Regression coefficients and significance values of variables of the logistic regression Model 2 that best identified degradation occurrence in the primary agriculture regions of Libya.

Variable LR Model Coefficient p-value Intercept 7.04 0.6643 Slope 0.11 0.0022 Texture[Silty loam] -10.10 <.0001 Texture[Loamy] -12.14 <.0001 Texture[Loamy very fine sand] -14.14 <.0001 Texture[Sand] -17.15 <.0001 Coarse loamy -11.14 <.0001

The influence of slope on soil degradation, especially from water erosion is well documented (Liu et al., 1994; Liu et al., 2015; Sensoy and Kara, 2014). In the present study, slope exhibited a positive relationship with the extent of soil degradation. One-degree increase in slope results in a 0.11 increase in the logit of soil degradation. This relationship is best seen in the degradation map of the

Jabal Nafusah region with slopes ranging from 4 to 28% and having the largest degraded area due to water erosion (30%). This result supports Liu et al. (2014) who documented that variations in slope can affect soil erosion.

Salako (2004), reported that soil susceptibility to degradation is influenced by small differences in texture. The chance of soil degradation increases most when a silty loam soil is present compared to other textures

(Table 3.5). The present regression model agreed with the existing degradation data of the PAR, showing that silty texture soil had the highest degradation occurrence compared to other textures (Table 3.3).

LR Models Validation

Model performance tests included the leave-one-field-out validation analyses and completed by utilizing the existing data in the PARs (Table 3.6,

Figure 3.4). Jifara has no previously determined non-degraded areas so it was not included in the validation. The validation identified that 68% to 82% (Table

3.6 and Figure 3.4) of Model 2 degradation occurrence predictions as correct and thus this model was used to predict degradation occurrence in the remaining areas of Libya.

Region Predicteda Actual Degradation Degraded Not degraded Percentage correctb (# of Observations) (%) Kufrah Degraded 77 23 77 Not degraded 23 79 77.4 Murzuq Degraded 41 12 77.3 Not degraded 21 80 79 Jabal Degraded 50 11 82 Nafusah Not degraded 9 20 68 Jabal al Degraded 54 12 81 Akhdar Not degraded 9 32 82 a Degraded if probabilities ≤ 0.5 and non-degraded if probabilities were > 0.5.

Figure 3.4. Soil degradation occurrence in the primary agriculture regions in Libya as (a) previously determined (existing), and (b) predicted by LR model using the combination of slope and texture.

Prediction Distribution of the Soil Degradation Occurrence and Type Throughout

Libya

To develop the models of predicting soil degradation type, wind erosion was the reference dependent variable and sand texture was the reference category for the independent variable. The reference category for variables was last the category which JMP software automatically selected them. Slope and soil texture, significant variables in predicting the soil degradation distribution, are again significant in predicting the soil degradation type (Table 3.6). Interpreting the OR identified that silty loam texture influences salinization most and slope is the leading factor in soil degradation from water erosion (Table 3.7). The resulting map (Figure 3.5) created by these models show the distribution of degradation occurrence and type for the entire country, identifying that 53.5% of

Libya’s soils are degraded with the greatest type of degradation being salinization (46.4%) and the least being wind erosion (0.66%).

Table 3.7. Parameter estimates of probabilities of soil degradation due to (a) salinization and (b) water erosion, relative to wind erosion.

Variable (a) Salinization (b) Water erosion Coefficient p-value Wald Exp (B) Coefficient p-value Wald Exp (B) test test Intercept 6.09 0.6213 12.34 7.44 0.1521 10.14 Slope 0.11 0.0022 10 10 4.45 0.0043 16.22 16.18 Texture [Coarse 17.44a <.0001 17.23 14 1.22a <.0001 11.34 9.34 loamy] Texture [Loamy 3.65a <.0001 11.55 10.33 2.65a <.0001 12.45 10.33 very fine sand] Texture [Loamy] -3.11a <.0001 10.11 12.55 -3.11a <.0001 14.55 2.33 Texture [Silty loam] 15.10a <.0001 15.23 16.17 3.55a <.0001 15.44 11.22 a The difference of each texture to the reference.

Figure 3.5. The probability degradation soil map for all the country developed in this study using MLR model.

CONCLUSIONS

In Libya, the PAR degradation is a result of salinization, water erosion and wind erosion, with impacts to agricultural development in the country. The combination of slope and soil texture (using LR and MLR models) predicted the spatial distribution of soil degradation and the type of degradation. Overall,

666,882 km2 (53.50%) of Libyan soils are degraded, 46.4% due to salinization,

6.40% due to water erosion, and 0.66 % due to wind erosion.

Additional parameters, which were difficult to obtain for this work, may enhance the model performance. For example, wind intensity may also influence the occurrence of soil degradation since it induces soil movement. Different management practices at the individual farm scale may also affect the occurrence of soil degradation (Garen et al., 1999). Future research is needed to collect data to assist in determining soil erodibility factors, the cropping and land-cover factor, and the support practice factor to integrate the Revised

Universal Soil Loss Equation (RUSLE) to estimate soil loss. Beyond assisting stakeholders in management of Libyan soils, these soil degradation prediction models may be applied to neighboring countries that have similar geography and climate conditions.

REFERENCES

Aboufayed, A.F. 2013. Measuring the amount of eroded soil and surface runoff

water in the field. World Acad Sci Eng Technol. 7(12): 825-828.

Aburas, M.M. 2014. Soil erosion, crusting and degradation in the South of Al-

Jabal al Akhdar, Libya. International Conference of Agricultural Engineering.

Available at

http://www.geyseco.es/geystiona/adjs/comunicaciones/304/C01300001.pdf

(verified 10 October. 2016).

Adekalu, K.O., I.A. Olorunfemi, and J.A. Osunbitan. 2007. Grass mulching effect

on infiltration, surface runoff and soil loss of three agricultural soils in

Nigeria. Bioresour. Technol. 98: 912–917.

Atman, S.A., and L. Habibah. 2013. A study on soil salinity in Al-Jafarah, Libya

using remote sensing technology. World Appl. Sci. J. 23: 430-434.

Ben-Mahmoud, R., S. Mansur, and A. Al-Gomati. 2000. Land degradation and

desertification in Libya, Land Degradation and Desertification Research Unit,

Libyan Center for Remote Sensing and Space Science, Tripoli, Libya.

Bishop, C.M. 2002. Neural networks for pattern recognition. Oxford Univ. Press,

Oxford, England. Available at

http://cs.du.edu/~mitchell/mario_books/Neural_Networks_for_Pattern_Re

cognition_-_Christopher_Bishop.pdf (verified 9 October. 2016).

76 Borrelli, P., C. Ballabio, P. Panagos, and L. Montanarella. 2014. Wind erosion

susceptibility of European soils. Geoderma. 232-234: 471-478.

Dai, F.C., C.F. Lee, and Z.W. Xu. 2001. Assessment of landslide susceptibility on

the natural terrain of Lantau Island, Hong Kong. Environ. Geol. 40: 381–391.

Debella-Gilo, M., and B. Etzelmüller. 2009. Spatial prediction of soil classes using

digital terrain analysis and multinomial logistic regression modeling

integrated in GIS: Examples from Vestfold county, Norway. Catena. 77(1): 8-

18.

El-Tantawi, A.M.M.T. 2005. Climate change in Libya and desertification of Jifara

Plain using geographical information system and remote sensing

techniques. Dissertation. PhD Thesis. Mainz, Germany: Johannes Gutenberg-

Universität Mainz.

Food and Agriculture Organization. 2005. Libyan Arab Jamahiriya Nutrition

Profile – Food and Nutrition Division. Available at

ftp://ftp.fao.org/es/esn/nutrition/ncp/lby.pdf (verified 10 October. 2016)

Garen, D., D. Woodward, and F. Geter. 1999. A user agency's view of hydrologic,

soil erosion and water quality modelling. Catena. 37: 277–289.

Gebril, A.O., and A.G. Saeid. 2012. Importance of pastoral human factor

overloading in land desertification: case studies in northeastern Libya.

World Acad Sci Eng Technol. 6: 10-26.

77 Gómez-Pina, G., J.J. Muñoz-Pérez, J.L. Ramírez, and C. Ley. 2002. Sand dune

management problems and techniques, Spain. J. Coastal Res. 36(36): 325-

332.

Habel, A.Y. 2013. The role of climate on the aggregate stability and soil

erodibility of selected El- Jabal Al-Akhdar soils-Libya. Alexandria J. Agric. Res.

58: 261-271.

Hachicha, M., and G. Abdegawed. 2003. Aspects of salt-affected soils in the Arab

world. Available at http://www.nerc-wallingford.ac.uk/research/cairoworkshop/cairoworkshop.pdf.

(verified 10 October. 2016)

Institute for Digital Research and Education. 2014. Annotated SPSS output,

multinomial logistic regression. Available at

http://www.ats.ucla.edu/stat/spss/output/mlogit.htm. (verified 10 October.

2016).

Jomaa, S., D.A. Barry, B.C.P. Heng, A. Brovelli, G.C. Sander, and J.Y. Parlange.

2012. Influence of rock fragment coverage on soil erosion and hydrological

response: Laboratory flume experiments and modeling. Water Resour. Res.

48(5).

Khaled, B. 2001. Land resources information systems in the Near East. Report on

World soil resources. FAO, Report 99, Rome, Italy.

78 Laytimi, A. 2005. Market and trade policies for Mediterranean agriculture: the

case of fruit/vegetable and olive oil. Available at http://medfrol.maich.gr/documentation/view/reports/wp1-asr/Libya.pdf.

(verified 13 October. 2016).

Lee, K., H. Ahn, H. Moon, R.L. Kodell and J.J. Chen. 2013. Multinomial logistic

regression ensembles. J. Biopharm. Stat. 23(3): 681.

Lee, S., and K. Min. 2001. Statistical analysis of landslide susceptibility at Yongin,

Korea. Environ. Geol. 40: 1095–1113.

Li, X., S.X. Chang, and K.F. Salifu. 2013. Soil texture and layering effects on water

and salt dynamics in the presence of a water table: a review. Environ. Rev.

22(1): 41-50.

Lin, Y., X. Deng, X. Li, and E. Ma. 2014. Comparison of multinomial logistic

regression and logistic regression: which is more efficient in allocating land

use. Front. Earth Sci. 8(4): 512-523.

Liu, B. Y., M.A. Nearing, and L.M. Risse. 1994. Slope gradient effects on soil loss

for steep slopes. Trans. ASAE. 37(6): 1835-1840.

Liu, Q. J., J. An, L.Z. Wang, Y.Z. Wu, and H.Y. Zhang. 2015. Influence of ridge

height, row grade, and field slope on soil erosion in contour ridging systems

under seepage conditions. Soil Tillage Res. 147: 50-59.

79 Liu, Q.J., Z.H. Shi, X.X. Yu, and H.Y. Zhang. 2014. Influence of microtopography,

ridge geometry and rainfall intensity on soil erosion induced by contouring

failure. Soil Tillage Res. 136: 1–8.

Mahmoud, K.B. 1995. Libyan Soils. First Edition ed. Tripoli: National Research

Scientific Organization.

Mueller, T.G., H. Cetin, R.A. Fleming, C.R. Dillon, A.C. Karathanasis, and S.A.

Shearer. 2005. Erosion probability maps: Calibrating precision agriculture

data with soil surveys using logistic regression. J. Soil Water Conserv. 60(6):

462-468.

Nwer, B.A.B. 2013. Soil resources in Libya. In Soil Resources of Mediterranean

and Caucasus Countries, ed. Y. Yigini, P. Panagos, and L. Montanarella, 175-

188. Office for Official Publications of the European Communities:

Luxembourg.

Nwer, B., A. Ali, and H. Zurqani. 2013. Soil salinity mapping model developed

using RS and GIS in Libya. Global Science & Technology Forum.

Nwer, B.A.B. 2006. The application of land evaluation technique in the north-

east of Libya. PhD Thesis. Bedford, UK: Cranfield University.

Osman, K.T. 2013. Wind Erosion. In Soil degradation, conservation and

remediation, ed. Osman, K.T, 103. Dordrecht: Springer.

80 Pang, Y., L. Liang, Y. Tang, and Z. Zhu. 2015. Numerical analysis and experimental

investigation of combined erosion of cavitation and sandy water

erosion. Proc. Inst. Mech. Eng. 230(1): 31.

Pike, A.C., T.G. Mueller, A. Schörgendorfer, S.A., Shearer, S.A., and A.D.

Karathanasis. 2009. Erosion index derived from terrain attributes using

logistic regression and neural networks. Agron. J. 101(5): 1068.

Saad, A.M.A., N.M. Shariff, and S. Gairola. 2013. Nature and causes of land

degradation and desertification in Libya: Need for sustainable land

management. Afr. J. Biotechnol. 10(63): 13680-13687.

Salako, F.K.2003. Susceptibility of coarse-textured soils to soil erosion by water in

the tropics. Available at

http://www.iaea.org/inis/collection/NCLCollectionStore/_Public/38/100/38

100125.pdf. (verified 10 October. 2016).

Schulz, S., M. Horovitz, R. Rausch, N. Michelsen, U. Mallast, M. Köhne, and R.

Merz. 2015. Groundwater evaporation from salt pans: Examples from the

eastern Arabian Peninsula. J. Hydrol. 531: 792-801.

Sensoy, H., and O. Kara. 2014. Slope shape effect on runoff and soil erosion

under natural rainfall conditions. IFOREST. 7.2: 110.

81 Wang, L., J. Huang, Y. Du, Y. Hu, and P. Han. 2013. Dynamic assessment of soil

erosion risk using Landsat TM and HJ satellite data in Danjiangkou Reservoir

area, China. Remote Sens. 5(8): 3826-3848.

Wei, W., F. Jia, L. Yang, L. Chen, H. Zhang and Y. Yang. 2014. Effects of surficial

condition and rainfall intensity on runoff in a loess hilly area, China. J.

Hydrol. 513: 115–126.

82 CHAPTER FOUR

POTENTIAL OF USING AN ALKYLPOLYGLYCOSIDE SOIL SURFACTANT FOR AFFECTING ROOTZONE CHARACTERISTICS AND COTTON GROWTH

This work is under consideration for publication:

Abagandura, O.G., D. Park, M.A. Jones, and W. Bridges. 201X. Potential of using an alkylpolyglycoside soil surfactant for affecting rootzone characteristics and cotton growth. Journal of Extension.

ABSTRACT

Soil surfactants (SURF) have the potential to improve water infiltration and distribution uniformity within the soil. The objective of this study was to evaluate a commonly used surfactant to increase water and nutrients available to cotton roots and for cotton growth. Two repeated field experiments were conducted at the Pee Dee Research and Education Center in Florence, South Carolina. Two cotton varieties (PHY 499 WRF and PHY 339 WRF) were grown as dryland or with irrigation and received either no SURF or two applications of alkyl polyglycoside

SURF. Soil volumetric water content (VWC) was measured during each experiment. Soil nutrient status and cotton quality were determined in both years. In addition, tissue analysis was estimated in 2014. The fields received excessive rainfall in 2013 and 2014 experimental periods compared to the 3-year average. Soil treated with SURF generally had higher VWC than the control, but

83 the differences were not statistically significant. VWC under the PHY 499 WRF was generally greater than PHY 339 WRF, but was only significant in July, August and October in 2013 and in August in 2014. Soil treated with SURF had lower Mn and higher pH, Ca, Mg, and P in 2013 and lower Ca, Mg and P in 2014 compared to the UNT. Soil under PHY 339 WRF had higher pH, B, Ca, K, Mg, and P compared to 499 WRF. While in 2013 irrigated soils had higher pH, B and K and lower Ca and P, it had higher Ca and K in 2014 compared to dryland. Cotton grown in SURF generally had higher nutrient concentrations in 2014; however, the effect was not significant. Boll density, lint yield, seed cotton and gin turnout in both years were greater from PHY 499 WRF compared to PHY 339 WRF. PHY

499 WRF had higher strength, elongation and micronaire compared to PHY 339

WRF in 2013. Although many patterns were not significant, the fact that there was a pattern of positive response to SURF even during two very wet seasons suggest that applying a SURF during dry periods (or perhaps even under normal seasons) may prove to be beneficial.

INTRODUCTION

Over the past fifty years, droughts occurring in the southeastern United

States have increased in frequency and intensity (South Carolina State

Climatology Office- South Carolina Department of Natural Resources (SCO-DNR),

84 2012). For eight of the past ten years (from 2004-2014) South Carolina has experienced drought like conditions (United States Department of Agriculture

(USDA), 2014). The USDA designated thirty-five counties in South Carolina as natural disaster areas because of agricultural losses caused by drought and extreme heat (Melvin, 2015). The impact of drought has resulted in the decline of many South Carolina major reservoirs and lower crop yields of dryland corn, peanuts and soybeans (SCO-DNR, 2012). In 2008, supplemental irrigation in agriculture accounted for nearly a quarter of South Carolina’s water usage

(Farahani et al., 2008). From 2007 to 2012, the area of irrigated farmland in

South Carolina increased 20% from 132,439 to 159,239 acres (USDA-National

Agricultural Statistics Service, 2012).

Cotton is a significant cash crop in the southeastern United States. South

Carolina Cotton generated $195 million in 2014, representing approximately 10% of the total agricultural revenue in the state (United Stated Department of

Agriculture (USDA), 2015). The combination of the economic importance of cotton with the limited availability of water during drought years in the region has promoted research in the development of water conservation strategies that reduce water use while at the same time maintaining plant growth. Baigorria et al. (2008) analyzed cotton yields and climate data in the Southeastern United

States from 1970 to 2004 and reported that cotton yields can be decreased both

85 by drought conditions and by diseases associated with wet and humid conditions.

Most of the cotton production in South Carolina occurs in sandy soils which are characterized as having low water holding capacity, rapid infiltration, and low fertility (Brady and Well, 2008). In these soils, irrigation has been shown to nearly double the non-irrigated cotton yield from approximately 750 to near

1,200 to 1,500 lbs. of lint per acre (Farahani et al., 2008).

One potential way to maintain cotton under drought climate conditions as well as normal weather patterns in droughty soils may be the use of soil surfactant (SURF). Surfactants reduce surface tension resulting in liquids to infiltrate soils and distribute water uniformly through a soil profile (Cisar et al.,

2000; Hallett, 2008; Karnok and Tucker 2001; Müller and Deurer, 2011; Oostindie et al., 2008). Surfactants were first used in agriculture as “spreaders” and

“stickers” to increase the uniformity of pesticide applications (Ishiguro and Fujii,

2008). Since the 1960s, researchers have expanded the potential use of SURFs by investigating their use to address common problems of water penetration and distribution in the soil matrix (Mobbs et al., 2012). For example, Revolution (a modified alkylated polyol, Aquatrols, Paulsboro, NJ) SURF increased soil volumetric water content (VWC) of sand rootzones of golf greens (Aamlid et al.,

2009). Numerous studies have demonstrated that different SURF chemistries can

86 improve water penetration and distribution in the soil (Dekker and Ritsema,

1994; Park et al., 2005).

Results from investigating the effects of SURF on plant quality and growth are conflicting. An increase in bentgrass (Agrostis palustris Huds.) root length was attributed to an increased VWC in sand soil from the application of a polyethylene and polyproplylene glycols SURF (Karnok and Tucker, 2001). The application of a SURF (IrrigAid Gold, alkylpolyglycoside) caused an increase in early fruit yield of tygress tomato, mainly because of the increase in the soil water around roots, which increased the availability of the nutrients in the soil solution (Emmanuel and Santos, 2012).

Conversely, SURF may cause problems in plant nutrition. Application of

SURF (chemistries not disclosed) had a toxic effect on barley seedlings, marked by yellowing of leaves and decreased yield (Cairns, 1972), and decreased tea

(Camellia sinensis var. sinensis) transpiration rate (Ichimura et al., 2005). The application of SURF in some studies had no effect on plant quality. For example, applying polyoxyethylene esters of cyclic acid (47%) and polyoxyethylene ethers of alkylated phenols (47%) and silicone antifoam emulsion (6%) (Aquatrols Corp. of America, Pennsauken, NJ) to greenhouse substrate did not influence potted floral crops (Bhat et al., 1992). Walworth and Kopec, (2004) also reported that applying a SURF (chemistries not disclosed) did not affect the color, density, and

87 quality of Tifway 419 bermudagrass (Cyanodon dactylon x transvaalensis), either positive or negative.

No research has been conducted to investigate how SURF influence water movement and cotton response grown on native sandy loam and loamy sand soils. The objective of this research was to investigate how an industry

SURF affects root zone moisture distribution and response of two cotton varieties under dryland and irrigated field conditions.

MATERIALS AND METHODS

Study Site

A field experiment was conducted on a Goldsboro Series sandy loam from May 13th to October 20th 2013 and repeated in an adjacent Noboco Series loamy sand field from April 19th to September 23th 2014 at the Pee Dee

Research and Education Center (PDREC) in Florence, South Carolina (34°29′217″

N, 79°73′476″ W). For both soils, a sandy clay loam texture is below the soil surface horizon beginning at 38 to 64 cm for the Goldsboro field and 33 to 64 cm for the Noboco field.

A split-split plot experimental design was used to examine irrigation regime as the main plot factor, cotton variety as the sub-plot factor, and SURF treatments as the sub-sub plot factor randomized within cotton variety.

88 Treatment combinations were replicated four times. Each plot (4 m X 12 m) consisted of four, 1 m by 12 m long rows of cotton. Phytogen 339 WRF and PHY

499 WRF, (Phytogen, Dow AgroSciences, Indianapolis, IN) were planted and maintained as either dryland or with irrigation (2.5 cm per irrigation event) by a lateral irrigation system. The varieties were selected to compare the regional standard (PHY 499 WRF) to a new variety that has been bred for higher yields and increased maturity (PHY 339 WRF).

The two SURF treatments were an untreated water control (UNT) and a non-ionic alkyl polyglycoside EO/PO block polymer SURF (IrrigAid Gold, Aquatrols

Corporation, Paulsboro, NJ) at a rate of 5.6 L ha-1 at the experiment initiation and at 2.8 L ha-1 one month later. The SURF could have been applied monthly, but resources (time and equipment clearance that can clear taller cotton) limit farmers to apply it monthly. Climate data were collected from a weather station located approximately 500 m from both fields.

Measurements

Soil Volumetric Water Content

The VWC was measured every two weeks using a Time Domain

Reflectometry (TDR) probe (Profile Probe PR2/4, Dynamax, Houston, TX) with one access tube being installed per plot. A collar and a black cap were placed on top of the tube to protect it from farming operations and weather. The Profile

89 Probe PR2/4 was inserted into the access tubes to take three VWC measurements at depths of 10, 20, 30 and 40 cm by rotating the probe at 120° increments. The average was determined. Because the PR2/4 probe was damaged in 2014, VWC was determined using a POGO2-W (Stevens Water

Monitoring Systems, Portland, OR) that measures the upper 5.6 cm of soil depth for the first two months of the 2014 experiment.

Mineral Concentration in Soil and Cotton Leaves

Soil samples (0 to 15 cm) were collected at the beginning and the end of each experiment using a 4.5 cm diameter soil auger and sent to the Clemson

University Agricultural Services Laboratory for pH and the plant-available contents of calcium (Ca), magnesium (Mg), phosphorus (P), potassium (K), boron

(B), and manganese (Mn) (Jones et al., 2011).

Since leaf tissue analysis is less effective in predicting the nutrient needs of the crop after cotton begins to bloom, (Rochester et al., 2012) leaves were collected before bloom and prepared following the procedure outlined by Zhao and Oosterhuis (1998) for subsequent analysis of N, P, K, Ca, Mg, S, Fe, Mn, Zn, and Cu (Jones et al., 2011).

Cotton Yield and Fiber Quality

At the time of harvest, boll numbers (the number of bolls per plant were counted for six plants per plot), lint yield, seed cotton and gin turnout were

90 determined. Samples from each plot were sent to Cotton Fiber Testing

Laboratory (Louisiana State University, Baton Rouge, LA) to determine fiber quality (fiber length, uniformity, short fiber, strength, elongation and micronaire)

(Davis et al., 2014).

Statistical Analysis

year was included as a factor (found significant for all the measurements). A statistical analyses based on a three-way analysis of variance

(ANOVA). The significance level for the variables and their interactions was set at

α= 0.05. Prior to the analysis, assumptions of ANOVA were tested by Levene’s test for homogeneity of variance across trials and the Shapiro-Wilk test for normality. Data within each year were normal and variance homogeneous for all variables.

The differences of treatment means were considered significant when the level of probability equal or greater than 0.05. Fisher’s Protected LSD test was used for pairwise means comparison when factor or factor interactions were found significant. All analyses were performed using JMP® Pro 12.0.1 software

(product of SAS Institute Inc., Cary, NC). No interaction effects were found to be significant within each experiment, and thus main effects are only discussed.

91 RESULTS AND DISCUSSION

The fields received 113 cm of rainfall during the 2013 growing season, and 74 cm in the 2014 growing season compared to the 29 cm average received during the 2010 to 2012 growing seasons (Figure 4.1).

Rainfall over the two experimental seasons did not reflect the current trend of short, intense periods of droughts that otherwise have been occurring over the past ten years in the region (USDA, 2014). Due to the excessive rainfall, there was only one irrigation event in 2013 (2.5 cm) and two irrigation events in

2014 (5 cm).

Figure 4.1. Cumulative rainfall (2010 to 2012) compared to 2013 and 2014 over the experimental period.

Soil Volumetric Water Content

Although VWC increased with depth for both years, only at 40 cm was it significantly greater than at the other depths (Figure 4.2). Plant available water

(PAW) is between VWC 0.08 m3 m-3 at permanent wilting point (PWP) to 0.18 m3 m-3 at field capacity (FC) for the sandy loam soil used in 2013 and between VWC

0.05 m3 m-3 PWP to 0.12 m3 m-3 FC for the loamy sand used in 2014 (Brady and

Weil, 2008; USDA Natural Resources Conservation Service, 2008).

During the majority of the 2013 experiment (except for September), soils were at, or close to saturation (Figure 4.2 a). In 2014 experiment, soils were saturated in August but dried down considerably in September where PAW was at the 20 and 30 cm depths but below PWP at the shallowest depth (10 cm)

(Figure 4.2 b). The higher VWC at the 40 cm depth is most likely due to the increase in clay content. At this depth, the sensor was located at the top of the sandy clay loam Bt horizon which has plant available water from a VWC 0.17 m3 m-3 PWP to 0.27 m3 m-3 FC (USDA Natural Resources Conservation Service, 2008).

Therefore, for each year the SWC at the 40 cm depth was at the upper end of FC, and sometimes considered saturated (Figure 4.2 a-b).

10 20 30 40

0.35 a 0.30 a a

) a a 3 - m 0.25 3 b b b b b b 0.20 b b b b b FC b b b

VWC (m VWC 0.15 b 0.10 PWP

0.05

- Jun Jul Aug Sep Oct

0.30 a

0.25

b ) 0.20

3 b - b

m a 3 0.15

FC VWC (m VWC 0.10 b b b 0.05 PWP

- Aug Sep

Figure 4.2. Comparison of volumetric soil water content throughout 2013 and 2014 experiments at 10, 20, 30 and 40 cm depths. Horizontal lines denote field capacity (FC) and permanent wilting point (PWP), and the plant available water (PAW) for the sandy loam (2013 experiment) and loamy sand (2014 experiment) soils used. Within months, columns sharing a same letter are not significantly different based on Fisher LSD Test (p ≥ 0.05).

94 Although the SURF treated soil generally had higher VWC than the UNT, the differences were not statistically significant (all p > 0.05, data not shown).

Mitra (2003) reported that application of the same SURF as used in this experiment to a clay loam soil reduced the surface tension of water and increased infiltration and percolation rates, helping water penetration into the soil and decreasing GN-1 bermudagrass (Cynodon dactylon) moisture stress.

Madsen et al. (2012) reported that applying SURF (a blend of alkyl polyglycoside and ethylene oxide/propylene oxide block copolymers) to a sandy loam increased the VWC compared to the control under greenhouse conditions.

Excessive rainfall that the fields received may have leached the SURF out of the monitored root zone. The VWC under PHY 499 WRF was generally greater than PHY 339 WRF, but only significant in July, August and October in 2013 and in August in 2014 (Table 4.1).

Table 4.1. Monthly mean soil moisture content (m3 m-3) under the two cotton variety during the 2013 and 2014 experiments.

Year Variety June July August September October 2013 PHY 339 WRF 0.20 0.21 0.19 0.16 0.19 PHY 499 WRF 0.22 0.23 0.21 0.17 0.20 p-value 0.0600 0.0240 0.0030 0.3540 0.0060 2014 PHY 339 WRF 0.05a 0.11a 0.08 0.08 PHY 499 WRF 0.06a 0.13a 0.09 0.08 p-value 0.4312 0.9140 0.0050 0.8019 a Means in June and July 2014 are for the surface soil moisture only.

95 Mineral Concentrations in the Rootzone and Cotton Leaves

At experiment initiation, mineral concentrations within the rootzone were 494 to 802 kg ha-1 for Ca, 73 to 139 kg ha-1 for Mg, 18 to 83 kg ha-1 for P, 78 to 383 kg ha-1 for K, 0.3 to 0.7 kg ha-1 for B, and 4 to 9 kg ha-1 for Mn in 2013 and were 484 to 754 kg ha-1 for Ca, 61 to 123 kg ha-1 for Mg, 70 to 99 kg ha-1 for P, 52 to 191 kg ha-1 for K, 0.3 to 0.7 kg ha-1 for B, and 6 to 9 kg ha-1 for Mn in 2014.

At the end of the 2013 experiment, soil treated with SURF had higher pH,

Ca, Mg, and P and lower Mn compared to the UNT. In comparison soil treated with the SURF had lower Ca, Mg and P than the UNT at the end of the 2014 experiment (Table 4.2).

At the end of the 2013 experiment, soil under PHY 499 WRF had lower pH, B, Ca, K, Mg, and P compared to PHY 339 WRF. Soil under PHY 499 WRF also had higher VWC in this study, so perhaps there were more minerals in solution available for plants to take up leaving less in the soil compared to soil under PHY

339 WRF. Irrigated soils had higher pH, B and K and lower Ca and P in 2013 and higher Ca and K in 2014 compared to dryland (Table 4.2).

Nutrient uptake was measured only in 2014 as samples were not collected in 2013 because the field was inaccessible due to flooding.

96 Table 4.2. Least squares means and statistical significance of pH and soil nutrients (kg ha-1) during 2013 and 2014 experiments.

2013 Experiment pH Ca Mg P K B Mn Surfactant SURF 6.3 664.8 113.0 48.5 124.2 0.4 5.1 Control 6.0 578.8 90.8 39.2 143.4 0.4 6.2 p-value 0.0006 0.0002 <.0001 <.0001 0.1299 1.0000 0.0003 Variety PHY 339 WRF 6.2 657.1 107.3 49.7 166.6 0.5 6.0 PHY 499 WRF 6.0 586.6 96.6 38.0 101.6 0.3 5.0 p-value 0.0012 0.0057 0.0295 <.0001 0.0007 0.0001 0.0758 Irrigation Dryland 6.0 648.5 106.4 60.0 121.6 0.4 5.9 Irrigated 6.3 595.2 97.5 27.6 146.6 0.4 5.5 p-value 0.0035 0.0207 0.0447 <.0001 0.0379 0.0025 0.2508 2014 Experiment Surfactant SURF 6.3 588.3 87.9 82.5 108.6 0.4 7.9 Control 6.3 629.8 100.0 88.4 108.8 0.4 7.8 p-value 0.9285 0.0423 0.0096 0.0340 0.9857 0.4863 0.8601 Variety PHY 339 WRF 6.3 608.5 92.8 87.0 109.3 0.4 8.2 PHY 499 WRF 6.4 606.0 99.0 83.8 108.0 0.4 7.5 p-value 0.3286 0.9397 0.1891 0.1941 0.8998 1.0000 0.2957 Irrigation Dryland 6.3 541.4 94.5 86.2 93.9 0.4 8.4 Irrigated 6.3 668.2 97.3 84.5 120.6 0.4 7.3 p-value 0.9285 0.0027 0.5318 0.4863 0.0123 0.4863 0.1219

97 Regardless of treatment, leaf mineral concentrations (data not shown) were all similar and within the recommended range for cotton (Jones et al.,

2011). Walworth and Kopec (2004) also reported that applying a SURF did not affect N, P, K, Ca, Mg, S, Na, B, Cu, Fe, and Mn uptake by Tifway 419 bermudagrass (Cyanodon dactylon x transvaalensis) roots.

Cotton Yield and Fiber Quality

With the exception of micronaire in 2013 and fiber strength in 2014, cotton yield and fiber quality were similar between the SURF and the UNT in both experiments (Table 4.3). Micronaire from the 2013 experiment was of premium quality from SURF compared to the UNT which was of base quality

(Table 4.3). 2014 cotton fibers were also stronger from SURF compared to the

UNT (p< 0.05) (Table 4.3). Very little study has focused on cotton fiber quality and SURF.

Tangentially, however, applying alkyl phenol ethoxylate, sodium salts of soya fatty acids, isopropyl alcohol SURF did not affect Russet Burbank potato

(Solanum tuberosum) yield grown on loamy sand soil under field conditions

(Arriaga et al., 2009).

In both years, boll numbers, lint yield, seed cotton and gin turnout were greater from PHY 499 WRF compared to PHY 339 WRF (Table 4.3). Although in

98 both years PHY 339 WRF cotton fibers were slightly longer compared to PHY 499

WRF, PHY 499 WRF had better strength and elongation in

2013, and was of premium quality (micronaire) compared to the base quality of

PHY 339 WRF in both years (Table 4.3).

Cotton yield and fiber quality was similar among irrigation treatments in both years with the exception of greater lint yield, seed cotton and gin turnout from irrigated cotton compared to non-irrigated cotton in 2014 (Table 4.3).

Table 4.3. Least squares means and statistical significance of cotton yield and fiber quality properties during 2013 and 2014 experiments.

Property Surfactant Variety Irrigation

SURF UNT PHY 339 PHY 499 Dryland Irrigated WRF WRF

2013 Experiment

Boll number (boll 15.4 13.3 14.1 b 16.6 a 14.3 14.4 plant-1) Lint yield (kg ha-1) 1,149 1,225 1,201 b 1,220 a 1,232 1,248 Seed cotton (kg ha-1) 2,834 2,682 2,789 b 2,801 a 1,232 1,233 Gin turnout (%) 43.7 43.1 41.4 b 45.2 a 43.1 43.8 Fiber length (cm) 3.1 3.1 3.1 a 2.54 b 3.1 3.1 Uniformity (%) 83.3 83.2 83.2 83.3 83.3 83.2 Short fiber 7.9 8.0 7.9 7.9 8.0 7.9 Strength (g tex-1) 32.8 32.4 31.8 b 33.4 a 32.6 32.7 Elongation (%) 7.8 7.7 7.5 b 7.9 a 7.8 7.7 Micronaire 4.2 b 4.4 a 4.1 b 4.5 a 4.3 4.3 2014 Experiment Boll number (boll 14.8 15.8 13.4 b 15.2 a 14.5 16.0 plant-1) Lint yield (kg ha-1) 1,150 1,230 1,184 b 1,196 a 1,132 b 1,248 a Seed cotton (kg ha-1) 2,824 2,674 2,705 b 2,793 a 1,132 b 1,248 a Gin turnout (%) 43.5 43.5 42.4 b 44.2 a 42.8 b 43.7 a Fiber length (cm) 2.8 2.8 3.1 a 2.8 b 2.8 2.8

99 Uniformity (%) 83.9 84.5 84.0 84.4 84.2 84.2 Short fiber 8.1 7.9 8.1 7.9 8.0 8.0 Strength (g tex-1) 31.1 a 30.4 b 30.6 30.9 30.7 30.8 Elongation (%) 8.2 8.3 8.1 8.4 8.4 8.2 Micronaire 4.1 4.1 3.8 b 4.3 a 4.1 4.1

CONCLUSIONS

The research fields received excessive rainfall, four times that amount in

2013 and two and a half times in 2014 compared to the previous three-year average. Surfactants are used to increase water infiltration uniformity throughout the soil profile. The VWC at the four depths documents that the uncommonly wet season resulted in enough water to keep droughty soils at field capacity or saturated for most of the growing seasons. Instead of cotton being drought stressed as seen in previous years, cotton during this two-year study was water stressed from saturated soils. Moreover, it is probable that the rains flushed the SURF out of the soil profile early on. Results suggest that SURF may influence VWC and mineral uptake and should be further examined for use in more drought like conditions.

100 REFERENCES

Aamlid, T.S., M. Larsbo, and N. Jarvis. 2009. Effects of surfactant use and peat

amendment on leaching of fungicides and nitrate from golf greens. Biologia.

64(3):419-423.

Arriaga, F.J., B. Lowery, and K.A. Kelling. 2009. Surfactant impact on nitrogen

utilization and leaching in potatoes. Am. J. Potato Res. 86(5): 383-390.

Baigorria, G.A., J.W. Hansen, N. Ward, J.W. Jones, and J.J. O'Brien. 2008.

Assessing predictability of cotton yields in the southeastern United States

based on regional atmospheric circulation and surface temperatures. J.

Appl. Meteor. Climatol. 47(1): 76-91.

Bhat, N.R., T.L. Prince, H.K. Tayama, and S.A. Carver. 1992. Rooted cutting

establishment in media containing wetting agent. HortScience. 27(1): 78-78.

Brady, N.C., and R.R. Weil. 2008. Soil Water: Characteristics and Behavior. In The

Nature and Properties of Soils (No. Ed. 11), pp. 86-100. Prentice-Hall Inc.

Cairns, R.R. 1972. Effects of surfactants applied to samples of solonetz soil on

water penetration and plant growth. Can. J. Soil Sci. 52(2): 267-269.

Cisar, J.L., K.E. Williams, H.E. Vivas, and J.J. Haydu. 2000.The occurrence and

alleviation by surfactants of soil water repellency on sand-based turfgrass

systems. J. Hydrol. 231–232: 352–358.

101 Davis, R.F., H.J. Earl, and T. Timper. 2014): Effect of simultaneous water deficit

stress and Meloidogyne incognita infection on cotton yield and fiber quality.

J. Nematol. 46(2): 108-118.

Dekker L.W., and C.J. Ritsema. 1994. How water moves in a water repellent

sandy soil: 1. Potential and actual water repellency. Wat. Resour. Res. 30:

2507–2517.

Emmanuel, A.T., and M.B. Santos. 2012. Effects of soil surfactant IrrigAid Gold on

water management for tomato production in Florida Spodosols. Fla. State

Hort. Soc. 125: 153–154.

Farahani, H., A. Khalilian, and B. Smith. 2008. Irrigation water management in

South Carolina– Trends and needs. Proceedings of the 2008 South Carolina

Water Resources Conf, Charleston, North Carolina. 14-15 Oct. 2008.

Charleston Area Event Center, North Carolina.

Hallett, P.D. 2008. A brief overview of the causes, impacts and amelioration of

soil water repellency–a review. Soil Water Res. 3(1): 521-528.

Ishiguro, M., and T. Fujii. 2008. Upward infiltration into porous media as affected

by wettability and anionic surfactants. Soil Sci. Soc. Am. J. 72(3): 741-749.

Ichimura, K., T.Y. Fujiwara, H. Yamauchi, H., Horie, and K. Kohata. 2005. Effects of

tea seed saponins on the vase life. JARQ. 39: 115–119.

102 Jones, M.A., J.K. Greene, M.W. Marshall, and J.D. Mueller. 2011. South Carolina

cotton grower’s guide. Clemson Univ. Coop. Ext. Publ. EC 589. Revised April

2011. Clemson Univ., Clemson, SC. Available at

www.clemson.edu/psapublishing/pages/AGRO/ EC589.pdf (verified 29 May.

2016).

Karnok, K.J., and K.A. Tucker. 2001. Wetting agent treated hydrophobic soil and

its effect on color, quality and root growth of creeping bentgrass. Int.

Turfgrass Soc. Res. J. 9: 537-541.

Madsen, M.D., S.L. Petersen, B.L. Roundy, A.G. Taylor, and A.G. Hopkins. 2012.

Comparison of postfire soil water repellency amelioration strategies on blue

bunch wheatgrass and cheatgrass survival. Rangeland Ecol Manag. 65: 182–

188.

Melvin, J. 2015. 35 S.C. counties declared primary disaster areas due to drought.

Available at http://newsstand.clemson.edu/mediarelations/35-s-c-counties-

declared-primary-disaster-areas-due-to-drought/(verified 1 August 2016).

Mitra, S. 2003. Using surfactants in optimizing water usage on turfgrasses.

California State Polytechnic University, Center for Turf Irrigation and

Landscape Technology.

103 Mobbs, T.L., R.T. Peters, J.R. Davenport, M.A. Evans, and J.Q. Wu. 2012. Effects

of four soil surfactants on four soil-water properties in sand and silt loam. J.

Soil Water Conserv. 67(4): 275-283.

Müller, K., and M. Deurer. 2011. Review of the remediation strategies for soil

water repellency. Agric Ecosyst Environ. 144(1): 208-221.

Oostindie, K., L.W. Dekker, J.Q. Wesseling, and C.J. Ritsema. 2008. Soil surfactant

stops water repellency and preferential flow paths. Soil Use Manage. 24(4):

409-415.

Park, D.M., J.L. CIsar, D.K. Mcdermitt, S.K.E. William, J.J. Haydu, and W.P. Miller.

2005. Using red and infrared reflectance and visual observation to monitor

turf quality and water stress in surfactant-treated bermudagrass under

reduced irrigation. Int. Turfgrass Soc. Res. J. 10: 115–120.

Rochester, I.J., G.A. Constable, D.M. Oosterhuis, M.E. Derrick. 2012. Nutritional

requirements of cotton during flowering and fruiting. Nutritional

requirements of cotton during flowering and fruiting. In Flowering and

Fruiting in Cotton, eds M. Oosterhuis and J.T. Cothren (Cordova, TN: The

Cotton Foundation Cordova). 35–45.

South Carolina State Climatology Office- South Carolina Department of Natural

Resources (SCO-DNR). 2012. The South Carolina drought response program.

Available at

104 http://www.dnr.sc.gov/climate/sco/Drought/drought_response_program.p

hp (verified 15 January. 2016).

United States Department of Agriculture and Natural Resources Conservation

Service. 2012. 294 South Carolina 2012 census of agriculture - County Data.

Available at

https://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,

_Chapter_2_County_Level/South_Carolina/st45_2_010_010.pdf (verified 15

January. 2016).

United States Department of Agriculture- National Agricultural Statistics Service,

South Carolina Field Office (USDA-NASS). 2013. South Carolina agricultural

facts. Available at

http://www.nass.usda.gov/Statistics_by_State/South_Carolina/Publications

/SC_QuickFacts2013.pdf (verified 15 January. 2016).

United States Department of Agriculture. 2014. South Carolina drought update.

Available at http://www.clemson.edu/extension/hgic/drought.html (verified

15 January. 2016).

United Stated Department of Agriculture. 2015. South Carolina agricultural facts.

Available at

https://www.nass.usda.gov/Statistics_by_State/South_Carolina/Publication

s/More_Features/SC%20BragSheet2014_final.pdf (verified 25 July 2016).

105 United States Department of Agriculture and Natural Resources Conservation

Service. 2008. Soil Quality Indicators. Available Water Capacity. Available at

http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_053288.p

df (verified 1 August. 2016).

Zhao, D., and D.M. Oosterhuis. 1998. Influence of shade on mineral nutrient

status of field-grown cotton. J. Plant Nutr. 21(8): 1681-1695.

Walworth, J., and D.M. Kopec. 2004. Aquatrols surfactant study on turfgrass

nutrient uptake. Turfgrass, Landscape and Urban IPM Research Summary.

106 CHAPTER FIVE

SOIL CONDITIONERS EFFECT ON NUTRIENT AVAILABILITY FOR COTTON IN SOILS OF SOUTHERN COASTAL PLAIN DURING TWO GROWING SEASONS OF HIGH PRECIPITATION

This work is under consideration for publication:

Abagandura, O.G., D. Park, M.A. Jones, and W. Bridges. 201X. Soil conditioners increase nutrient availability for cotton in soils of southern coastal plain during two growing seasons of high precipitation. Journal of Cotton Science.

ABSTRACT

Claims of soil conditioners assisting in nutrient availability, crop protection and crop growth and quality are being promoted to farmers with minimal scientific support. The objective of this study was to evaluate the effect of three soil conditioners commonly marketed to South Carolina cotton growers to promote soil and plant nutrient levels and fiber yield and quality. Two field experiments were conducted on sandy loam in 2013 and repeated on a loamy sand in 2014 at the Pee Dee Research and Education Center in Florence, South Carolina. Two irrigation levels (dryland and irrigated), two cotton varieties (PHY 499 WRF and

PHY 339 WRF), and four conditioners (control, surfactant, humic acids, and hormones) were arranged in a split-split plot design with a factorial arrangement of 16 treatments. At the end of the two experiments, soil samples were collected and analyzed. Cotton yield and fiber quality were determined at harvest in both

107

years. In addition, tissue analysis was determined in 2014. The experimental fields received excessive rainfall in 2013 and 2014 compared to the 3 -year average. Soil pH was not affected by the four conditioners. Although HORM treated soil under PHY 339 WRF had higher Ca, K and B concentrations than all other COND treatments, COND did not influence Ca, Mg and K under PHY

499WRF. Humic acids caused an increase in soil B under dryland conditions and hormones caused an increase in soil B, K and Mg levels under irrigated conditions. PHY 499 WRF had lower Mg concentrations compared to PHY 339

WRF. Higher Ca was found in cotton treated with the hormone compared to all other conditioners. In general, minimal response to soil conditioners existed due to the greater than normal rainfall which likely caused soil conditioners to be leached from the soil profile.

INTRODUCTION

Rising temperatures, progressively variable precipitation coupled with the lack of freshwater will possibly affect the cotton production sustainability

(Timothy et al., 2014). In addition, any nutrient deficiencies during cotton growth will restrict seed cotton yield and also fiber and seed quality (Ejaz et al., 2011).

To increase cotton yield while maintaining acceptable lint quality, best

108 management practices including proper soil moisture management is required

(Nuti et al., 2006).

Cotton requires varying amounts of water during its growth to maximize yield, with water use gradually increasing from the initial stage and peaking at mid-season (Fisher, 2012). Cotton production accounted for 7.6% of the total

U.S. irrigated acreage that was harvested in 2012 (USDA, 2015). The rainfall extremes (too much or not enough) causes damaging effect on yield in

Southwestern USA, so many producers’ grown cotton under full irrigation

(Farahani et al., 2009).

From 2004 to 2012, South Carolina has been experiencing more frequent and more intense droughts of shorter duration (USDA, 2014). Most of the cotton production in South Carolina occurs on sandy soils, characterized by its poor water holding capacity and poor soil fertility (Osman, 2013). Both drought and droughty soils have raised concerns about the sustainability of cotton production in the state as well as in the southeastern USA (Farahani et al., 2008). Enhancing soil properties such as soil structure and nutritional and water retention capacities can result in better yield and fiber quality (Johnson et al., 2002; Read et al., 2006).

109

Soil conditioners are materials that make the soil more suitable for plant growth (Tsado et al., 2011) and have been documented to improve plant nutrition (Havlin et al., 1999; Parvathy et al., 2014; Six et al., 2000). The use of humic acids (HA) as a nutrient resource and as an amendment is common in arid or semi-arid zones (Taraniuk et al., 2007). Humic acids are derived from the decomposition of plant and animal tissues (Gaffney et al., 1996) and account for approximately one third of soil humus (Kuwatsuka et al., 1978). They are characterized as being dark-colored, alkali-soluble, acid-insoluble compounds

(Schnitzer, 1991) that are more resistant to decomposition than compounds directly synthesized by plants (Stevenson, 1994). Furthermore, humic acids control acidity and cation exchange in soils due to their functional groups (Qualls et al., 2003), reduce nutrient leaching potential (Mackowiak et al., 2001), increase nutrient availability for plant uptake (Canellas et al., 2002; Khaled and

Fawy, 2011; Mackowiak et al., 2001), and increase crop yield (Chen et al., 2004;

Spark et al., 1997).

A second type of soil conditioner includes surfactant (SURF), which are synthetic chemicals (Banks et al., 2014) used to reduce the surface tension of water (Cisar et al., 2000; Leinauer, 2002), increase infiltration and percolation

(Dekker et al., 2005; Karagunduz et al., 2001; Kostka and Bially, 2005), decrease moisture stress on plant (Emmanuel and Santos, 2012; Karnok and Tucker,

110

2001), increase nutrient content within plants (Haller and Stocker, 2003;

Mohamed, 2004), and increase plant yield (Karnok and Tucker, 2001). For example, the addition of ethanediyl-1,2-bis (dimethyldodecyl ammonium chloride; CS12) to a sand soil improved its physical properties, Giza123 barley plant growth, and nutrient content and uptake (Mohamed and Magdi, 2005).

Although considerable efforts have examined how SURF affects soil water characteristics, none have focused on SURF effects on cotton nutrient uptake and cotton yield and fiber quality.

Third type of soil conditioners are hormones (HORM). These chemicals are naturally occurring in plants (Gaspar et al., 1996) or synthetic compounds that are plant growth regulators. They may mimic natural plant HORM (Davies,

1995) by encouraging or interfering with biosynthesis, metabolism, translocation of plant hormones (Abdelgadir et al., 2009). Replacing or supplementing a plant

HORM may change the course of plant development (Gianfagna, 1995; Lovatt and García 2006). Hormones are applied with fertilizers to ensure elements continue to be available to the plant as needed to improve plant growth. For example, the application of N fertilizers with glycinebetaine increased wheat yield (Triticum aestivum L.) (Díaz-Zorita et al., 2001), and gibberellic acid improved dry biomass yield of ryegrass (Lolium perenne L.) (Zaman et al., 2014;

Zaman et al., 2016). Research on using hormones in cotton production is limited

111 and has focused on mepiquat chloride. It has proved to reduce the high proportions of vegetative growth relative to reproductive growth, thus avoids yield decreases (Hodges et al., 1991; Jones et al., 2011; Ren et al., 2013; Rosolem et al., 2013), and as a result decreases the required effort to terminate excessive growth (Mao et al., 2014). Cotton vegetative growth decreased when applying a mepiquat chloride HORM (n,n-dimethylpiperidinium chloride) (Gwathmey and

Clement, 2010 and Reddy et al., 1996). Cotton leaf area, dry matter, number of fruiting branches, photosynthesis and chlorophyll were significantly reduced by mepiquat chloride (Rosolem et al., 2013).

Little is known about how soil conditioners influence nutrient availability for cotton uptake, and cotton yield and fiber quality. The objective of this study was to evaluate the effect of three soil conditioners on soil and cotton nutrient levels, and on cotton yield and fiber quality.

MATERIALS AND METHODS

Site Description and Experimental Design

A two-year field study was conducted at the Pee Dee Research and

Education Center in Florence, South Carolina (34°29′217″ N, 79°73′476″ W) on a

Goldsboro Series sandy loam from May to October 2013 and repeated on a

Noboco Series loamy sand from April to September 2014. Two irrigation (IRR)

112 levels (dryland and irrigated), two cotton varieties (VAR) (PHY 499 WRF and PHY

339 WRF), and four conditioner (COND) levels (HA, SURF, HROM, and untreated

(UNT)) were arranged in a split-split plot design with a factorial arrangement of

16 treatments. IRR was the main plot factor, VAR was the sub-plot factor, and

COND applications was the sub-sub plot factor randomly assigned within each

variety. Treatments were replicated four times.

Treatments

Cotton cultivar varieties PHY 499 WRF and PHY 339 WRF (Phytogen, Dow

AgroSciences, Indianapolis, IN) were planted May 13th 2013, and in April 19th

2014, respectively in plots consisting of four, 1 m X 12 m rows. All plots received

the same management (planting, fertilizer, weed control, and defoliation). The

name, manufacturers, ingredient, rates of the four conditioner treatments used

in this study are listed in Table 5.1. CONDs could have been applied monthly as

labeled but was not practical for farmers not having time to apply and not having

the equipment that can clear taller cotton. In addition, being able to get the

sprayer out in the wet fields was an issue for both years.

Table 5.1. Soil conditioner names, manufacturers, ingredient, and application rate used in this study.

COND Name Manufacturer Chemistry Ratea

113 A Experimental PRB Environmental Group, Inc, Humic acids 25.72 L ha-1b Peachtree , GA, USA 12.86 L ha-1c

SURF IrrigAid Gold Aquatrols Corporation, Paulsboro, non-ionic ethylene oxide and an 4.67 L ha-1b NJ, USA alkyl polyglycoside 2.33 L ha-1c HORM Duo Maxx Timac Agro, Reading, PA, USA polyphenolic acids with ICNe and 3.1 mL kg-1d Rhizovitf complex UNT a Based on manufacturer’s recommended rates. b At initiation of 2013 and 2014 experiments. c In August 2013 and 2014. d Applied with the fertilizer at initiation. e A complex of a urease inhibitor plus nitrification inhibitors. f Organic acids and plant growth regulators to stimulates soil microbes.

Sampling and Analyses

Soil samples collected (0 to 15 cm depth) before and after the

experiment in 2013 and 2014 were sent to the Clemson Agricultural Service

Laboratory for pH and calcium (Ca), magnesium (Mg), phosphorus (P), potassium

(K), boron (B), and manganese (Mn).

Boll number (bolls plant-1), lint yield (kg ha-1), seed cotton (kg ha-1), and

gin turnout (%) were determined at harvest and six cotton samples from each

plot were sent to the Cotton Fiber Testing Laboratory (Louisiana State University,

Baton Rouge, LA) to determine fiber quality (fiber length, uniformity, short fiber,

strength, elongation and micronaire).

114 Tissue analysis before bloom (Rochester et al., 2012) was determined.

Nutrient uptake was measured only in 2014 because the field was inaccessible in

2013 due to flooding. The leaf from the third node from the uppermost fully expanded main stem from ten plants in each plot were sampled and dried at

70°C for 72 hr in an oven following the procedure found in Zhao and Oosterhuis

(1998). These samples were sent to the Clemson Agriculture Laboratory for subsequent analysis of N, P, K, Ca, Mg, S, Fe, Mn, and Cu (Jones et al., 2011).

Monthly precipitation totals for April through October of 2012 and 2013 were recorded at a weather station located approximately 500 m from the fields.

Statistical Analysis

All statistical assumptions were checked throughout the analyses.

Residuals were homogeneous across trails (Levene’s test) and normal distributed

(Shapiro-Wilk test). All statistical analyses for the study were based on a factorial analysis of variance (ANOVA). Year was included as random factor (found to be not significant alone, or as part of a higher order interaction). Fisher LSD test was used for pairwise mean comparison when a factor or a factor interactions were found significant. Significant higher order interactions are first discussed, followed by main effects after consideration of the interaction. All analyses were conducted using JMP® Pro 12.0.1 software (SAS Institute Inc., Cary, NC) at a significant level (α) of 0.05.

115 RESULTS AND DISCUSSION

Climate

The PDREC region had experienced drought for the previous 8 years

(2004 to 2012). That trend, however, was not repeated during the two years this study was conducted (2013 and 2014). In 2013 the field received four times (113 cm) and in 2014 received two and half times (74 cm), as much as the 2010 to

2012 growing season mean (29 cm). Since the fields received an excessive amount of rainfall, the cotton in both years was not water stressed. Water was available in the soil (between the permanent wilting point and field capacity) at

10 cm, 20 cm and 30 cm and was at saturation at 40 cm soil depth (these results were obtained from adjacent study conducted in the same fields and during the same years (Abagandura et al., 201X). Irrigation was applied by a lateral irrigation system once in 2013 (13 September) and twice in 2014 (14 May and 23

June) for a total of 2.5 cm in 2013 and 5 cm in 2014.

Soil pH and Mineral Content

At experiment termination, soil pH ranged from 6.0 to 6.3 with no factor influence. Mineral concentrations within the soil were 354 to 1088 kg ha-1 for Ca,

42 to 159 kg ha-1 for Mg, 68 to 122 kg ha-1 for P, 82 to 238 kg ha-1 for K, 0.3 to 0.9 kg ha-1 for B, and 4 to 9 kg ha-1 for Mn.

116 A VAR by COND interaction influenced Ca, K and B (Table 5.2), with

HORM treated soil under PHY 339 WRF having greater Ca, K and B concentrations than all other COND treatments (which were statistically similar)

(Table 5.3). In comparison, COND did not influence Ca, Mg and K under PHY

499WRF (Tables 5.2 and 5.3). The IRR by COND interaction influenced Mg, Ca and B (Table 5.2). Applying a HA resulted in less B under irrigated cotton compared to dryland cotton. However, under dryland conditions, similar soil B was found from all COND except HA being greater than SURF (Table 5.3).

Applying HORM significantly increased B, Mg and Ca soil concentrations under the irrigated cotton compared to all other COND (Table 5.3). Under dryland cotton, soil Mg and Ca were lowest under the UNT and highest under HA (Table

5.3).

Table 5.2. Significance values of variety, irrigation, and COND main effects and main effect interactions for soil properties, cotton leaf nutrient concentrations and boll density, lint yield, seed cotton, and gin turnout in 2014 trial.

Property VAR IRR COND VAR x VAR x IRR x IRR x IRR COND COND COND x VAR Soil pH and minerals pH 0.1834 0.6167 0.1055 0.8674 0.0574 0.3049 0.3211 Ca 0.0524 0.1293 <.0001 0.5268 0.0117 0.0352 0.7534 Mg 0.6891 0.8638 0.0022 0.746 0.8854 0.0056 0.2211 P 0.5952 0.2816 0.1947 0.3939 0.4056 0.3283 0.2112 K 0.0455 0.6254 0.0074 0.5719 0.0417 0.6527 0.5321 B 0.0005 0.8332 0.3976 0.8332 0.0017 0.0134 0.4112 Mn 0.1414 0.0595 0.0945 0.2924 0.2329 0.2992 0.9321 Cotton leaf nutrient concentrations

117 N 0.0907 0.3006 0.6851 0.0936 0.5494 0.3399 0.4423 P 0.7188 0.9282 0.4541 0.5895 0.7084 0.9183 0.7534 K 0.7287 0.4822 0.5665 0.5779 0.7399 0.6860 0.7721 Ca 0.2055 0.3221 0.0248 0.6795 0.1918 0.2953 0.5311 Mg 0.0105 0.8517 0.9558 0.3064 0.4351 0.6182 0.9376 S 0.7124 0.4008 0.767 0.0698 0.2920 0.6467 0.5671 Fe 0.0513 0.5946 0.0126 0.2714 0.3971 0.0625 0.6533 Mn 0.8412 0.2267 0.4839 0.7495 0.2811 0.3863 0.8211 Cu 0.8913 0.6821 0.9482 0.6821 0.7742 0.195 0.3211 Cotton yield Bolls 0.467 0.1556 0.6753 0.1567 0.4321 0.5674 0.7213 Lint yield 0.3452 0.0943 0.1109 0.6543 0.2341 0.769 0.3967 Seed cotton 0.0543 0.4321 0.5643 0.5432 0.6543 0.7765 0.921 Gin turnout 0.5463 0.5467 0.345 0.3456 0.6543 0.5674 0.5822

Table 5.3. The soil nutrients (kg ha-1) content means for the interactions between conditioner and variety and conditioner and irrigation.

Conditioner Variety Irrigation PHY 339 WRF PHY 499 WRF Dryland Irrigated Ca UNT 472.00a b A 458.25 a A 457.25 b A 473.00 b B SURF 481.62 b A 505.50 a A 518.75 ab A 468.37 b B HA 528.25 b A 514.00 a A 564.37 a A 477.87 b B HORM 627.65 a A 509.37 a B 555.62 a A 581.37 a A Mg UNT 72.00 a A 71.00 a A 70.75 c A 72.25 b B SURF 73.50 a A 79.12 a A 80.37 bc A 72.25 b B HA 79.62 a A 80.87 a A 87.00 ab A 73.50 b B HORM 89.37 a A 88.75 a A 80.12 bc B 98.00 a A K UNT 139.87 b A 131.75 a A 139.37 a A 132.25 a A SURF 112.62 b A 122.37 a A 113.25 a A 121.75 a A HA 124.25 b A 113.50 a A 115.25 a A 122.50 a A HORM 150.00 a A 119.87 a B 134.50 a A 135.37 a A B UNT 0.47 b A 0.44 a A 0.45 abc A 0.46 b A

118 SURF 0.44 b A 0.45 a A 0.42 bc A 0.46 b A HA 0.46 b A 0.43 a A 0.49 a A 0.40 c B HORM 0.55 a A 0.40 a B 0.46 ab A 0.49 a A a Within each column, means followed by the same lower case letter are not significantly different, within each row means followed by the same upper case letter are not significantly different at significant α= 0.05.

Mineral Concentrations in cotton Leaves

Mineral concentrations within the cotton leaves were 30.6 to 44.6 g kg-1 for N, 2.3 to 3.7 g kg-1 for P, 20 to 29.3 g kg-1 for K, 19.9 to 34.2 g kg-1 for Ca, 3 to

6.7 g kg-1 for Mg, 3.1 to 7.9 g kg-1 for S, 0.06 to 0.21 g kg-1 for Fe, 0.027 to 0.30 g kg-1 for Mn, and 0.006 to 0.009 g kg-1 for Cu. These concentrations were within recommended ranges (Mitchell and Baker, 2000; Jones et al., 2011).

Minimal factor influence on cotton leaf nutrient concentrations were determined (Table 5.2). Phytogen 499 WRF had lower leaf Mg concentrations

(4.2 g Mg kg-1) compared to PHY 339 WRF (4.8 g Mg kg-1). Higher Ca was found in cotton treated with the HORM (34 g kg-1) compared to all other COND treatments (19.03, 31.56 and 21.13 g Ca kg-1 for UNT, SURF, and HA, respectively). Iron in cotton leaves was similar among the conditioners (86.87,

119 85.55, and 86.87 g Fe kg-1, for SURF, HA, and HORM, respectively), which were greater than Fe found in the UNT cotton (76.72 g Fe kg-1) (Table 5.2).

Banks (2011) reported that plant tissue N from corn (Zea mays L.) grown on a silt loam soil were not significantly different between alkylphenol ethoxylate with alcohol ethoxylate and an untreated control. However, a study conducted by the same author and others in 2014 documented that the application of the same surfactant caused an increase in N in corn grown on silty clay loam soil. Nikbakht et al. (2008) reported that HAs (prepared from leonardite) were reported to increase the uptake of N, P, K, Ca, Mg, Fe and Zn in gerbera (Gerbera jamesonii L.) cv. ‘Malibu grown in perlite and peat moss in the greenhouse. The application of HA (well-rotted cow manure) to a sandy loam soil increased Ca, Fe, and S in garlic (Allium sativum L.) under field condition (Denre et al., 2014).

Cotton Yield and Fiber Quality

Similar cotton fiber quality and gin turnout were determined from PHY

499 WRF grown in 2010 (Jones et al., 2011) and from this study in the same location. However, lint yields in the present study (Table 5.4) were 33% less than the average lint yields obtained in 2010 (data not shown). No factor and factor interaction influenced cotton yields and fiber quality (Table 5.2). However, there

120 was a pattern of HORM applied cotton having greater plant yield metrics in

comparison to the other COND treatments, although these patterns were not

statistically significant (Tables 5.4). Mohamed (2014) documented that applying

ethanediyl-1,2-bis surfactant (didecyl dimethyl ammonium chloride; CS12) to a

sandy soil improved barley (Giza123) growth. Atiyeh et al. (2002) reported HA

(vermicompost) increased tomato (Rutgers) and cucumber (Long Green) growth.

The application of HA increased wheat yield (Triticum aestivum L.) under arid

conditions (Khan et al., 2010). Although not significant, the irrigated cotton had

higher growth metric values compared to dryland (Table 5.4).

All cotton fiber properties in this study (Table 5.4) fall inside the optimal

fiber quality ranges (Jones et al., 2011; Luo et al., 2016). No factor interactions

influenced fiber quality properties. Irrigation had no influence on all the cotton

fiber properties, possibly because the excessive rainfall that the field received

during 2013 and 2014. Fiber strength and elongation were greater from PHY 499

WRF compared to PHY339WRF (Table 5.4), however PHY 499 WRF micronaire

was of base quality and had shorter fiber length compared to the higher quality

and longer lengths of PHY 339 WRF cotton fibers.

Table 5.4. Cotton yield and fiber quality means for irrigation, variety and conditioner main effect in this study.

Property Irrigation Variety Conditioner

121 DRY IRR PHY 339 PHY 499 UNT SURF HA HORM WRF WRF Boll number (bolls plant- 16a 16 17 17 15 16 17 17 1) Lint yield (kg ha-1) 1483 1544 1517 1522 1508 1517 1517 1522 Seed cotton (kg ha-1) 3413 3536 3565 3384 3394 3440 3468 3483 Gin turnout (%) 43.5 43.7 42.5 44.7 43.4 43.7 43.8 44.5 Length (mm) 29.0 29.0 29.7 a 28.5 b 29.2 29.0 29.0 29.0 Uniformity (%) 84 84 84 84 84 84 84 84 Short fiber 7.74 7.74 7.62 7.87 7.61 7.83 7.66 7.79 Strength (g tex-1) 32.1 31.8 31.4 b 32.6 a 31.0 31.8 32.1 32.0 Elogngation (%) 7.7 7.8 7.5 b 8.1 a 7.7 7.8 7.8 7.8 Micronaire 4.21 4.26 4.06 b 4.41 a 4.21 4.29 4.23 4.23 a Within each row means followed by the same letter are not significantly different at significant α= 0.05.

CONCLUSIONS

The uncommonly wet seasons resulted in the need of only one irrigation

in 2013 and two in 2014. It is likely that soil conditioners were quickly lost, and

may be the reason why there were minimal treatment differences determined.

However, the increase in the concentration of soil Ca, B and Mg treated with

CONDs warrants more studies of COND during normal and drought conditions.

Additional studies on different COND rates and frequencies are merited for

understanding how they influence cotton yield and fiber quality.

122 REFERENCES

Abdelgadir, H.A., D. Steven, and J. Johnson. 2009. Promoting branching of a potential biofuel crop Jatropha curcas L. by foliar application of plant growth regulators. Plant Growth Regul. 58: 287-295.

Abagandura, O.G., D. Park, M.A. Jones, and W. Bridges. 201X. Potential of using an alkylpolyglycoside soil surfactant for affecting rootzone characteristics and cotton growth. Manuscript submitted for publication.

Atiyeh, R.M., S. Lee, C.A. Edwards, N.Q. Arancon, and J.D. Metzger. 2002.

The influence of humic acids derived from earthworm-processed organic wastes on plant growth. Bioresource technol. 84(1): 7-14.

Banks, M.L. 2011. Effects of selected surfactants on nutrient uptake and soil microbial community. Doctoral dissertation. Ph.D. diss., University of

Missouri, Columbia., USA.

Banks, M.L.L., R.J. Kremer, F. Eivazi, P.P. Motavalli, and K.A. Nelson. 2014.

Effects of selected surfactants on nutrient uptake in corn (Zea mays L.). J. Plant

Nutr. 38: 1036–1049.

Canellas, L.P., F.L. Olivares, A.L. Okorokova-Façanha, and A.R. Façanha.

2002. Humic acids isolated from earthworm compost enhance root elongation,

123 lateral root emergence, and plasma membrane H+-ATPase activity in maize roots. Plant Physiolo. 130(4): 1951-1957.

Chen Y., C.E. Clapp, and H. Magen. 2004. Mechanisms of plant growth stimulation by humic substances: The role of organic-iron complexes. Soil Sci.

Plant Nutr. 50: 1089–1095.

Cisar, J.L., K.E. Williams, H.E. Vivas, and J.J. Haydu. 2000. The occurrence and alleviation by surfactants of soil water repellency on sand-based turfgrass systems. J. Hydrol. 231–232: 352–358.

Davies, P.J. 1995. The plant hormones: their nature, occurrence and functions. in Plant Hormones Physiology, Biochemistry and Molecular

Biology.ed Davies PJ (Kluwer Academic Publishers, Dordrecht, The Netherlands), pp1–12.

Dekker, L.W., K. Oostindie, S.J. Kostka, and C.J. Ritsema. 2005. Effects of surfactant treatments on the wettability of a water repellent grass-covered dune sand. Austral. J. Soil Res. 43: 383–395.

Denre, M., S. Ghanti, and K. Sarkar. 2014. Effect of humic acid application on accumulation of mineral nutrition and pungency in garlic (Allium sativum

L.). Int. J. Biotechnol. Mol. Biol. Res. 5(2): 7-12.

124 Díaz-Zorita, M., M.V. Fernández-Canigia, and G.A. Grosso. 2001.

Applications of foliar fertilizers containing glycinebetaine improve wheat yields. J. Agron. Crop Sci. 186(3): 209-215.

Emmanuel, A.T., and M.B. Santos. 2012. Effects of soil surfactant IrrigAid

Gold on water management for tomato production in Florida Spodosols. Fla.

State Hort. Soc. 125: 153–154.

Ejaz, A.W., R. Ahmad, G. Raja, M.H. Ehsanullah, A. Ahmad, and M. Nasir.

2011. Response of foliar application of KNO3 on yield, yield components and lint quality of cotton (Gossypium hirsutum L). Afr. J. Agric. Res. 24: 5457-5463.

Farahani, H. J., G. Izzi, and T.Y. Oweis. 2009. Parameterization and evaluation of the AquaCrop model for full and deficit irrigated cotton. Agron. J.

101(3): 469-476.

Farahani, H., A. Khalilian, and B. Smith. 2008. Irrigation water management in South Carolina– Trends and needs. Proceedings of the 2008

South Carolina Water Resources Conf, Charleston, North Carolina. 14-15 Oct.

2008. Charleston Area Event Center, North Carolina.

Fisher, D.K. 2012. Cotton water requirements in humid areas. Available at https://www.plantmanagementnetwork.org/edcenter/seminars/CottonIncCrop

125 ManagementSeminar/SessionSummary/FisherIrrigationWorkshop.pdf (verified

29 May. 2016).

Gaffney, J. S., N.A. Marley, and S.B. Clark. 1996. Humic and fluvic acids and organic colloidal materials in the environment (No. ANL/ER/CP--89942;

CONF-9608110--1). Argonne National Lab., IL (United States).

Gaspar, T., C. Kevers, C. Penel, H. Greppin, D.M. Reid, and T.A. Thorpe.

1996. Plant hormones and plant growth regulators in plant tissue culture. In

Vitro Cell Dev Biol Plant. 32(4): 272-289.

Gianfngna, T. 1995. Natural and synthetic growth regulators and their use in horticultural and agronomic crops. Plant Hormones. Ed. P.J. Davies. Norwell,

MA: Kluwer Academic Publishers.

Gwathmey, C.O., and J.D. Clement. 2010. Alteration of cotton source–sink relations with plant population density and mepiquat chloride. Field Crops Res.

116: 101–107.

Haller, W.T, and R.K. Stocker. 2003. Toxicity of 19 adjuvants to juvenile

Lepomis macrochirus (bluegill sunfish). Environ. Toxicol. Chem. 22: 615–619.

Havlin, J., J. Beaton, S. Tisdale, and W. Nelson. 1999. Soil Fertility and

Fertilizers, Sixth Edition. Prentice-Hall Inc.

126 Hodges, H.F., V.R. Reddy, and K.R. Reddy. 1991. Mepiquat chloride and temperature effects on photosynthesis and respiration of fruiting cotton. Crop

Sci. 31: 1301–1308.

Johnson, R.M., R.G. Downer, J.M. Bradow, P.J. Bauer, and E.J. Sadler.

2002. Cotton: Variability in cotton fiber yield, fiber quality, and soil properties in the southeastern coastal plain. Agron. J. 94(6): 1305.

Jones, M.A., J.K. Greene, M.W. Marshall, and J.D. Mueller. 2011. South

Carolina cotton grower’s guide. Clemson Univ. Coop. Ext. Publ. EC 589. Revised

April 2011. Clemson Univ., Clemson, SC. Available at www.clemson.edu/psapublishing/pages/AGRO/ EC589.pdf (verified 29 May.

2016).

Karnok, K.J., and K.A. Tucker. 2001. Wetting agent treated hydrophobic soil and its effect on color, quality and root growth of creeping bentgrass. Int.

Turfgrass Soc. Res. J. 9: 537-541.

Karagunduz, A., K.D. Pennell, and M.H. Young. 2001. Influence of a nonionic surfactant on the water retention properties of unsaturated soils. Soil

Sci. Soc. Am. J. 65: 1392-1399.

127 Khaled, H., and H.A. Fawy. 2011. Effect of different levels of humic acids on the nutrient content, plant growth, and soil properties under conditions of salinity. Soil Water Res. 6(1): 21-29.

Khan, R. U., A. Rashid, M.S. Khan, and E. Ozturk. 2010. Impact of humic acid and chemical fertilizer application on growth and grain yield of rainfed wheat (triticum aestivum l.). Pakistan J. Agric. Res. 23:3-4.

Kostka, S.J., and P.T. Bially. 2005. Synergistic surfactant interactions for enhancement of hydrophilicity in water repellent soils. Int. Turfgrass Soc. Res. J.

10: 108–114.

Kuwatsuka, S., K. Tsutsuki, and K. Kumada. 1978. Chemical studies on soil humic acids: 1. Elementary composition of humic acids. Soil Sci. Plant Nutr.

24(3): 337-347.

Leinauer, B. 2002. Wetting agents and their impact on water retention of turfgrass root zones. Aust. Turf Mangt. 4(1): 17–19.

Lovatt, C. J., and S. Salazar-García. 2006. Plant growth regulators for avocado production. Plant Growth Regul Soc Am Q. 34: 34-35.

Luo, Q., M. Bange, and D. Johnston. 2016. Environment and cotton fibre quality. Clim. Change. 1-15.

128 Mackowiak, C.L., P.R. Grossl, and B.G. Bugbee. 2001. Beneficial effects of humic acid on micronutrient availability to wheat. Soil Sci. Soc. Am. J. 56: 1744–

1750.

Mao, L., L. Zhang, X. Zhao, S. Liu, W. Van der Werf, S. Zhang, and Z. Li.

2014. Crop growth, light utilization and yield of relay intercropped cotton as affected by plant density and a plant growth regulator. Field Crop Res. 155: 67-

76.

Mitchell, C.C., and W.H. Baker. 2000. Reference sufficiency ranges —

Field crops. Available at http://www.clemson.edu/sera6/scsb394notoc.pdf.

(verified 29 May. 2016).

Mohamed, M.M. 2004. An experimental test of anionic surfactant (DLBA) as a soil conditioner. J. of Agric. Sci. 29(12): 7617-7625.

Mohamed, M.M. 2014. Effect of cationic surfactant on some growth parameters and nutrients uptake of barley plant. JNSCI. 12(4): 84-87.

Mohamed, M.M and S.M. Magdi. 2005. An experimental test of anionic surfactant (DLBA). Effect on some growth parameters. J. of Agric. Sci. 30 (1): 723-

727.

129 Nikbakht, A., M. Kafi, M. Babalar, Y.P. Xia, A. Luo, and N.A. Etemadi. 2008.

Effect of humic acid on plant growth, nutrient uptake, and postharvest life of gerbera. J. Plant Nutr. 31(12): 2155-2167.

Nuti, R.C., Casteel, S.N., R.P. Viator, J.E.Lanier, K.L. Edmisten, D.L. Jordan, and R. Wells. 2006. Management of cotton grown under overhead sprinkle and sub-surface drip irrigation. J. Cotton Sci. 10: 76-88.

Osman, K.T. 2013. Wind Erosion. In Soil degradation, conservation and remediation, ed. Osman, K.T, 103. Dordrecht: Springer.

Parvathy, P.C., A.N. Jyothi, K.S. John, and J. Sreekumar. 2014. Cassava starch based superabsorbent polymer as soil conditioner: Impact on soil Physico-

Chemical and biological properties and plant growth. Clean (Weinh). 42(11):

1610-1617.

Qualls, R.G., A. Takiyama, and R.L. Wershaw. 2003. Formation and loss of humic substances during decomposition in a pine forest floor. Soil Sci. Soc. Am.

J. 67(3): 899-909.

Read, J.J., K.R. Reddy, and J.N. Jenkins. 2006. Yield and fiber quality of upland cotton as influenced by nitrogen and potassium nutrition. Eur. J. Agron.

24(3): 282-290.

130 Reddy, A.R., K.R. Reddy, and H.F. Hodges. 1996. Mepiquat chloride (PIX) induced changes in photosynthesis and growth of cotton. Plant Growth Regul.

20: 179–183.

Ren, X., L. Zhang, M. Du, J.B. Evers, W. van der Werf, X. Tian, and Z. Li.

2013. Managing mepiquat chloride and plant density for optimal yield and quality of cotton. Field Crops Res. 149: 1–10.

Rochester, I.J., G.A. Constable, D.M. Oosterhuis, and M.E. Derrick. 2012.

Nutritional requirements of cotton during flowering and fruiting. Nutritional requirements of cotton during flowering and fruiting. In Flowering and Fruiting in

Cotton, eds M. Oosterhuis and J.T. Cothren (Cordova, TN: The Cotton Foundation

Cordova). 35–45.

Rosolem, C.A., D.M. Oosterhuis, and F.S.D. Souza. 2013. Cotton response to mepiquat chloride and temperature. Scientia Agricola. 70(2): 82-87.

Schnitzer, M. 1991. Soil organic matter: the next 75 years. Soil Sci. 151:

41–58.

Spark, K.M., J.D. Wells, and B.B. Johnson. 1997. Characteristics the sorption of humic acid by soil minerals. Aust. J. Soil Res. 35: 103–112.

131 Six, J., K. Paustian, E.T. Elliott, and C. Combrink. 2000. Soil structure and

Organic Matter: I. Distribution of aggregate-size classes and aggregate- associated carbon. Soil Sci. Soc. Am. J. 64: 681-689.

Stevenson, F.J. 1994. Humus Chemistry—Genesis, Composition,

Reactions. 2nd ed., Wiley, New York.

Taraniuk, I., E.R. Graber, A. Kostinski, and Y. Rudich. 2007. Surfactant properties of atmospheric and model humic-like substances (HULIS). Geophys.

Res. Lett. 34 (16).

Timothy, A., Dabbert, and A. Michael. 2014. Challenges and perspectives on improving heat and drought stress resilience in cotton. J Cotton Sci. 18: 393–

409.

Tsado, J. H., M.A. Ojo, U.S. Mohammed, and D.T. Gana. 2011. On farm evaluation of the effect of crystallizer and soil conditioners on maize growth and yield in Niger State. JASR. 11(1): 91-96.

United Stated Department of Agriculture. 2015. South Carolina agricultural facts. Available at https://www.nass.usda.gov/Statistics_by_State/South_Carolina/Publications/Mo re_Features/SC%20BragSheet2014_final.pdf (verified 25 July 2016).

132 United States Department of Agriculture. 2014. South Carolina drought update. Available at http://www.clemson.edu/extension/hgic/drought.html

(verified 15 June 2016).

United State Department of Agriculture. 2015. How Important is

Irrigation to U.S. Agriculture. Available at http://www.ers.usda.gov/topics/farm- practices-management/irrigation-water-use/background.aspx (verified 15 July

2016).

Zaman, M., A. Ghani, L.V. Kurepin, R.P. Pharis, S. Khan, and T.J. Smith.

2014. Improving ryegrass-clover pasture dry matter yield and urea efficiency with gibberellic acid. J. Sci. Food Agric. 94(12): 2521-2528.

Zaman, M., L.V. Kurepin, W. Catto, and R.P. Pharis. 2016. Evaluating the use of plant hormones and biostimulators in forage pastures to enhance shoot dry biomass production by perennial ryegrass (Lolium perenne L.). J. Sci. Food

Agric. 96(3): 715-726.

Zhao, D., and D.M. Oosterhuis. 1998. Influence of shade on mineral nutrient status of field-grown cotton. J. Plant Nutr. 21(8): 1681-1695.

133 CHAPTER SIX

COMPARISON OF SOIL SURFACTANT CHEMISTRIES ON VOLUMETRIC WATER CONTENT AND LEACHATE OF GREENHOUSE SUBSTRATES UNDER TWO IRRIGATION REGIMES

This work is under consideration for publication:

Abagandura, O.G., D. Park, and W. Bridges. 201X. Comparison of soil surfactant chemistries on volumetric water content and leachate of greenhouse substrates under two irrigation regimes. HortScience.

ABSTRACT

Water retention is considered an important characteristic for selecting greenhouse substrates. Soil surfactants have the potential to improve water infiltration and soil distribution uniformity throughout the substrates. The objective of this study was to determine how soil surfactants influence volumetric water content (VWC) and leachate pH and volume in three horticulturally significant substrates. Two ten-week experiments were conducted at the Clemson University Greenhouse Facility with a Fafard 3B –surfactant, 80% sand: 20% peat, and washed sand. Four surfactant treatments were investigated

(a) 20% of modified block co-polymer, (b) 30% of alkoxylated polyols and 21% of glucoethers and (c) 50% of nonionic polyols and 5% 1,2-propanediol and (d) water control. The same volume of water was applied either once a week (ONCE) or as two applications a week (SPLIT) to a total of 96 leaching columns laid out in

134 a split-plot design, with four replicates. The VWC was measured at 5, 10, 15 and

25 cm depths three times a week. Percolate volume and pH were measured after soil surfactants and irrigation was applied. Leachate pH between the soil surfactant treatments and control and irrigation levels for the three soils were similar. Applying irrigation volume as two events in conjunction with using a surfactant reduced leachate up to 75%. Regardless of the soil substrate, more water was retained when soil surfactants (especially AgStone15) were applied.

Incorporating split irrigation applications and surfactants provides a more optimal root zone environment for plant growth and reduces the potential of water loss (and water constituents) to the surrounding environment.

INTRODUCTION

Growing food crops in shade and greenhouses has increased during the past decade (Case et al., 2005; United States Department of Agriculture, 2014).

This is a response to population growth and increases in the quality of living

(Michael and Lieth, 2007) to better control soil-born pests and diseases

(Anderson et al, 2011; De Jonghe et al., 2007), as well as lack of native fertile soil

(Nafiye and Gubbuk, 2015). For best productivity, artificial and soilless growing substrates are used (Asaduzzaman et al., 2015). Various components are used to formulate these substrates, including peat moss, bark, compost, vermiculite,

135

perlite, and sand (Gizas and Savvas, 2007; Shaw et al., 2004). Soilless substrates are considered to be essentially free of diseases, insects, and weed seeds

(Anderson et al, 2011), and therefore result in increased yields per unit area

(Linardakis and Manios,1991; Lopez-Medina et al., 2004), increasing the productivity in containerized horticulture systems relative to productivity of field grown crops (Caron et al., 2015).

Although using substrates has its advantages (Michael and Lieth, 2007), water management can be problematic (Caron et al., 2015), since substrates are primarily composed of organic matter (Fields, 2013; Horowitz and Elmore, 1991;

Taylor et al., 2015). When organic materials (such as waxes (Hallett et al., 2006), alkanes (Mainwaring et al., 2004), and fatty acids (Graber et al., 2009)) in these substrates dry out, a large number of nonpolar sites on the soil particle surfaces will form (Ellerbrock et al., 2005; Wallis and Horne, 1992) resulting in a weak attraction between soil and the applied water (Valat et al. 1991; Zheng et al.,

2016), leading the substrate to become hydrophobic and causing a reduction in substrate wettability (Michel et al., 2001). Subsequently, preferential flow patterns occur and water retention decreases (Blackwell, 2000; Dekker and

Ritsema, 1994; Greco, 2002).

136

Water repellency is a common phenomenon in horticultural substrates

(Abad et al., 2002). To prevent water repellency from occurring, some substrates

(such as different Fafard mixes, Sun Gro Horticulture; Agawam, MA, US) contain soil surfactants (Bilderback and Lotsheider, 1997; Blodgett et al., 1993; Taylor et al., 2012). The incorporation of soil surfactants to soilless substrates is not only to overcome any water repellency that may develop but also to increase the penetration of water through a substrate and improve its VWC in general

(Bilderback and Lorscheider, 1997; Czarnota and Thomas, 2006; Fields et al.,

2014). However, storage conditions (hot, dry) of bulk soilless substrates may be conducive to promoting water repellency (Hanisco, 2015) and the degradation of a surfactant added (Feitkenhauer et al., 2002; Mezzanotte et al. 2003).

Perhaps applying the surfactant at time of substrate use would increase its hydrological properties. Improving the wettability of the substrates by the application of soil surfactants has been documented by a number of researchers.

For example, the application of alkylphenol ethoxylate surfactant with irrigation to bark: peat: perlite (equal volume) substrate increased its VWC (Blodgett et al.,

1993). Alkyl phenol ethoxylate (APE) surfactant decreased substrate leaching fraction and increased wettability rate for bark substrate (Michael et al., 2008).

Similar results were obtained when a non-ionic ether poly-ethylene-glycol none-

137 phenol soil surfactant was applied to coconut coir substrate (Urrestarazu et al.,

2008).

The ability to manage water more efficiently continues to receive much research attention, in part because new soil surfactant chemistries are being developed to not only overcome hydrophobicity but also reduce leaching and enhance plant health and productivity (Curtis and Thomas, 2016; Zontek and

Kostka, 2012). The objective of this study was to determine the influence of surfactant chemistries on the leachate pH and volume, and VWC of three horticulturally significant substrates.

MATERIALS AND METHODS

Site Location and Experimental Design

Two experiments were conducted at Clemson University’s Research

Greenhouse Complex (Clemson, SC, USA; latitude 34°40′8″; longitude 82°50′40″) from 2 September to 13 November 2014, and from 3 February to 16 April 2015.

The temperature ranged from 28˚C to 27˚C and the humidity ranged from 52 to

55 HR. The experiment was a split plot experimental design with four replicates per treatment, with irrigation regime as the main plot factor, soil as the sub-plot factor, and surfactant treatments randomized within soils.

138 Column Preparation

Leaching columns 7.6 cm dia. by 30 cm length were polyvinyl chloride pipe. A screen was laid over the bottom of each column to retain soil followed by a slip cap with a 1.9 cm hole bored in the bottom to allow columns to drain. Four slits were cut horizontally into each column at 5, 10, 15 and 25 cm from the top lip (Figure 6.1) to insert a Theta probe (Dynamax, Inc., Houston, TX) to measure

VWC and covered with duct tape when not in use.

Substrate Preparation

Three substrates were used in this study: (a) washed sand that was dried and screened through a 2 mm sieve (WSAND) (b) 80:20 sand:peat rootzone mix

(SD:PT), (c) a Fafard 3B without the standard surfactant (SunGro Horticulture,

Anderson, SC) (FAFRD-S). Substrate chemical properties are listed in Table 6.1.

Each 32 columns were packed with one substrate to a height of 28.5 cm by uniformly tapping using a wooden rod (Jalali and Ostovarzadeh, 2009) to achieve uniform packing and bulk density of (1.5 g cm-3 for WSAND, 1.57 g cm-3 for SD:PT and 1.05 for g cm-3 FAFARD-S. Six shelving units approximately 7.6 cm above the floor were used to vertically hold the columns. Each shelf held 21 columns, seven randomly selected of each substrate.

139 a b

Figure 6.1. Picture of leaching columns used in the study (a) and diagram of construction (b). The columns were made of polyvinyl chloride pipe with a length of 37 cm and an inner diameter of 7.6 cm showing the cap with the hole to collect the leachate water. The four slits were covered with duct tape.

Table 6.1. Chemical properties of the root-zone substrates used in the study.

Property WSAND SD:PT FAFRD-S pH 7.30 7.80 5.60 P (kg ha–1) 6.00 3.00 2.00 K (kg ha–1) 8.00 212.00 110.00 Mg (kg ha–1) 16.00 109.00 71.00 Ca (kg ha–1) 1536.00 299.00 56.00

140 Soil Surfactant Application and Measurements

The three soil surfactant chemistries investigated are listed in Table 6.2.

For ease of discussion they will be referred to by their trade name. Distilled water was applied to the 96 columns to thoroughly wet the profiles. The next day the experiments began with the application of the soil surfactants with one of two irrigation treatments: (a) irrigating with 2.54 cm on Tuesday (ONCE) or (b) irrigating with 1.27 cm of water on Tuesday and again, on Thursday (SPLIT).

Irrigation was performed by hand using a calibrated syringe. After a month, the irrigation amount was reduced to half of the initial rates to study the effect of surfactants under limited irrigation.

After each irrigation, leachate was collected into cups that had been previously placed under each column. Leachate volume was measured using a graduated cylinder, and pH determined (VWR International Model SB70P,

Radnor, PA). One hour after irrigation, VWC at the 5, 10, 15 and 25 cm depths of the column were measured. To investigate the effect of the surfactants on a longer dry period, VWC was also measured on Mondays. This experiment continued for 10 weeks. At the end of the experiment the soils in the leaching columns were removed, and the leaching columns were thoroughly cleaned to

141 remove any surfactant residue before being repacked with new soils to repeat the experiment.

Table 6.2. Soil surfactant chemistries, trade names, manufacturers, and rate used in this study.

Active ingredient Surfactants Manufacturer Rate (L ha-1) 20% of modified block co- Agstone15X Agstone, LLC, 19 polymer Greenville, SC

30% of alkoxylated polyols, 21% Dispatch Aquatrols 0.58 of glucoethers Sprayable Corporation of America, Paulsboro, NJ 50% of nonionic polyols, 5% 1,2- Aqueduct Aquatrols 25.5 propanediol Corporation of America, Paulsboro, NJ Water control

Statistical Analysis

Year was included as a random factor (found not significant for all metrics). Data were analyzed using analysis of variance (ANOVA) to test the effects of soil surfactants on VWC, leachate pH and volume. Assumptions of

ANOVA were tested by Levene’s test for homogeneity of variance across trials and the Shapiro-Wilk test for normality. Data within each year were normal and variance homogeneous for all variables. Since substrate as a main effect and

142 interaction was significant for all metric, data are presented for each separately.

Significant higher order interactions are first discussed, followed by main effects after consideration of the interaction. Treatment means were separated using

Fisher’s LSD test. All significance tests were performed with a significant level (α) equal to 0.05, and all calculations were performed using JMP® Pro 12.0.1 software (SAS Institute Inc., Cary, NC).

RESULTS AND DISCUSSION

Leachate pH and Total Volume

Measuring leachate pH was terminated after three weeks since the pH values did not change nor were influenced from surfactant or irrigation regime.

Leachate pH ranged from 7.20 to 7.50 for WSAND, 7.30 to 7.70 for SD:PT, and

5.20 to 5.70 for FAFRD-S. Guillén et al. (2005) reported similar results in which applying a none phenol poly-ethylene glycol surfactant to a new coco fiber substrate growing a tomato crop (Lycopersicon esculentum mill. cv) did not affect leachate pH.

The total leachate collected were 490.00 ml for WSAND, 300.12 ml for

SD:PT, and 285.32 ml for FAFRD-S. The higher volume of water leached from

WSAND was expected due to sands high macroporosity (Osman, 2012).

Bouyoucos (1939) and Moskal et al. (2001) also reported that organic sources

143 increase VWC, and subsequently resulted in the lowest leachate volume. For the three substrates, the interaction between surfactant and irrigation for total volume was significant (Table 6.3).

Table 6.3. Significance values of main effects and main effect interactions for total leachate volume (ml) for the three substrates.

WSAND SD:PT FAFRD-S Prob > F Irrigation <.0001 <.0001 <.0001 Surfactant 0.7306 0.6998 0.0176 Irrigation*Surfactant 0.0088 0.0155 0.0032

The significant surfactant by irrigation interaction identified that applying

AgStone15 resulted in less leachate compared to other surfactants for SD:PT and

FAFRD-S ONCE irrigated (Table 6.4). AgStone15 and Aqueduct reduced leachate in ONCE SD:PT, and SPLIT applied to SD:PT and FAFRD-S. No surfactant influenced total leachate from SPLIT irrigated to WSAND (Table 6.4). Similar results were found by Abagandura et al., 201X who reported that applying

AgStone15 (similar chemistry) to sandy loam and washed sand grown by bermudagrass (Tifway 419) resulted in less volume leached compared to a control. An increase in leachate volume from application of a surfactant

(nonylphenol polyethylene glycol) to a coco fiber substrate (irrigated daily for 6

144 min with flow rate of 3 L h-1 using drip irrigation) was postulated due to the lower surface tension between the water and the substrate particles (Guillén et al., 2005). Simply applying water over two irrigations reduced leachate by 42–

75% (Table 6.4).

Table 6.4. The total leachate volume (ml) for surfactant and irrigation for the three artificial soils.

Surfactant Irrigation Dispatch AgStone15 Aqueduct Control Sprayable WSAND ONCE 451.90aa A 343.60 a B 352.80 a B 454.40 a A SPLIT 165.00 b A 114.17 b A 112.25 b A 153.34 b A SD:PT ONCE 344.90 a A 269.20 a B 356.70 a A 361.30 a A SPLIT 144.67 b A 64.50 b B 89.00 b AB 106.00 b A FAFRD-S ONCE 173.20 a A 100.32 a B 222.80 a A 222.20 a A SPLIT 100.30 b A 72.04 b B 82.11 b B 104.10 b A a Mean separations within each substrate by Fisher LSD test at P ≤ 0.05. Means followed by the same lower case letter within a column are not significantly different, and means followed by the same upper case letter are not significantly different within each row.

Soil Volumetric Water Content

Only three, second order interactions were significant for VWC (Table

6.5). The lowest VWC was recorded from the WSAND (5.00 to 9.50 cm m-3), and the highest from the FAFRD-S (15.00 to 20.00 cm m-3), which agree with the

145 higher total volume leached from WSAND and lowest total leachate volume from the FAFRD-S (Table 6.4). The VWC recorded on Tuesdays were the highest (7.00 to 16.01 cm m-3) compared to Thursday (6.53- 14.04 cm m-3) and Monday (5.34-

11.75 cm m-3).

Table 6.5. Significance values of main effects and main effect interactions for soil volumetric water content for the three substrates.

WSAND SD:PT FAFRD-S Prob > F Irrigation 0.0002 0.0077 <.0001 Surfactant 0.0084 <.0001 <.0001 Depth <.0001 <.0001 <.0001 Day <.0001 0.0005 <.0001 Irrigation*Surfactant 0.4284 0.8434 0.1265 Irrigation*Depth 0.3411 0.0728 0.0732 Irrigation*Day 0.0073 0.0413 0.0821 Surfactant*Depth <.0001 <.0001 0.0015 Surfactant*Day 0.4652 0.9478 0.0921 Depth*Day <.0001 <.0001 <.0001 Irrigation* Surfactant*Depth 0.9987 0.3311 0.5921 Irrigation*Surfactant*Day 0.9561 0.5498 0.9211 Irrigation*Depth*Day 0.3411 0.1157 0.8295 Surfactant*Depth*Day 0.3387 0.1486 0.4223 Irrigation*Surfactant*Depth*Day 0.9950 0.9956 0.6722

Irrigation X Day: There were only significant trends for WSAND AND

SD:PT, and duration of dry down as well as irrigation application did not influence VWC in the FAFRD-S substrate (Table 6.6). VWC decline over time was

146 most evident from Tuesday to Monday for the SPLIT for WSAND and ONCE and

SPLIT for SD:PT (Table 6.6). Similar VWC were determined on Thursday.

Table 6.6. The volumetric water content (cm m-3) for interactions between day by surfactant and day by irrigation for the three soils.

Irrigation Day ONCE SPLIT WSAND Tuesday 9.93aa A 10.30 a A Thursday 8.92 ab A 8.77 b B Monday 6.32 b B 6.42 c B SD:PT Tuesday 11.47 a A 10.05 a B Thursday 9.56 b A 9.30 b A Monday 6.20 c B 6.50 c A FAFRD-S Tuesday 17.58 a A 17.21 a A Thursday 17.34 a A 16.80 a A Monday 16.50 a A 16.47 a A a Mean separations within each substrate by Fisher LSD test at P ≤ 0.05. Means followed by the same lower case letter within a column are not significantly different, and means followed by the same upper case letter are not significantly different within each row.

Surfactant x Depth: In the WSAND, the, highest VWCs were documented in the shallowest (5cm) and the deepest (25 cm) depths (Figure 6.2 A). The VWCs from AgStone15 at the 5 cm depth and from Aqueduct at the 10 cm depth were significantly higher compared to all other treatments (Figure 6.2 A). At 25 cm, all surfactants resulted in greater VWC than the Control (Figure 6.2 A). In the SD:PT

147 substrate, AgStone15 and Aqueduct resulted in the highest VWC at each depth, with the exception of 15 cm in which Aqueduct had greater VWC (Figure 6.2 B).

The VWC from FAFRD-S treated with AgStone15 was significantly higher than the

Control and Aqueduct at the deeper depths (15 and 25 cm) (Figure 6.2 C). The

VWC from AgStone15 was similar to Aqueduct and Dispatch Sprayable at the 5 and 25 cm depths respectively (Figure 6.2 C).

Reinikainen and Herranen (1997), reported an increased VWC of peat after the application of an ethoxylated alcohol surfactant. Abagandura et al.,

201X also found that AgStone15 (same chemistry) increased the VWC of a sandy loam soil compared to control.

148 A (WSAND) Dispatch Sprayable AgStone15 Aqueduct Control 25 ) 3 - 20 m 3 15 b a b 10 b a b ab a c VWC (cm VWC b b b 5

0 5 cm 10 cm 15 cm 25 cm

B (SD:PT)

25 ) 3 -

m 20 3 15 a a a a 10 b b a b b a VWC (cm VWC ab ab b ab ab b 5

0 5 cm 10 cm 15 cm 25 cm C (FAFRD-S) 25 a ) ab b 3 b

- a 20 b b b m a a 3 b 15 b

10 VWC (cm VWC 5

0 5 cm 10 cm 15 cm 25 cm

Figure 6.2. Comparison of the means volumetric water content (cm m-3) for the interaction between surfactant and depth for (A) washed sand, (B) 80 % sand: 20 % peat and (C) Fafard 3B – surfactant. Within each column, means followed by the same letter are not significantly different at significant level= 0.05.

149 Soil Depth X Day: For WSAND, the VWC at 5 cm (Figure 6.3 A) was higher than all other depths on Tuesday and Thursday. By Monday, the trend reversed and VWC was significantly greater only at the 25 cm depth. The WSAND substrate has primarily large particles with macropores allowing the water to move downward quickly.

Overall, the same trend found on Tuesdays and Mondays in WSAND was apparent in the SD:PT substrate but was more enhanced on Tuesdays in which

VWC was significantly lower with each incremental depth (Figure 6.3 B).

The VWC were only significantly different on Thursdays and Mondays in the FAFRD-S substrate (Figure 6.3 C). Although there was a pattern of VWC increase with depth on Tuesday, only was the VWC significantly higher at the 25

Soil Depth X Day: For WSAND, the VWC at 5 cm (Figure 6.3 A) was higher than all other depths on Tuesday and Thursday. By Monday, the trend reversed and VWC was significantly greater only at the 25 cm depth. The WSAND substrate has primarily large particles with macropores allowing the water to move downward quickly.

Overall, the same trend found on Tuesdays and Mondays in WSAND was apparent in the SD:PT substrate but was more enhanced on Tuesdays in which

VWC was significantly lower with each incremental depth (Figure 6.3 B). The

150 VWC were only significantly different on Thursdays and Mondays in the FAFRD-S substrate (Figure 6.3 C). Although there was a pattern of VWC increase with depth on Tuesday, only was the VWC significantly higher at the 25 cm depth, and by Monday the difference in VWC with depth were more apparent (Figure 6.3 C).

151 A (WSAND) 5 cm 10 cm 15 cm 25 cm 25

20 ) 3 - a a 15 a 10 b b b b

vwc vwc (cm m b b b 5 b b

0 Tuesdays Thursdays Mondays

B (SD:PT)

) 20 3 - 15 a b a a 10 c b b d b b b VWC (cm m b 5

0 Tuesdays Thursdays Mondays

C (FAFRD-S) 25 a a 20 b b b ) bc 3

- b 15 c

10 VWC (cm m 5

0 Tuesdays Thursdays Mondays Figure 6.3. Comparison of the means volumetric water content (cm m-3) for the interaction between depth by day for (A) washed sand, (B) 80 % sand: 20 % peat, and (C) fafard 3B – surfactant. Within each column, means followed by the same letter are not significantly different at significant level= 0.05.

152 CONCLUSIONS

Surfactants did not influence pH of leachate. Applying a surfactant alone did not reduce leachate, but when used in conjunction with irrigation volume applied over two events resulted in 42 to 75% reduction in leachate. Overall, applying the AgStone15 with irrigation volume applied over two events resulted in the least leachate. A natural progression of dry down (as evident of soil VWC) was followed from the Tuesday to following Monday, but were less apparent in the FAFRD-S in which there is a greater capacity to hold water (perhaps due to the nature of the substrate). In general, in all substrates, surfactants (especially the AgStone15) increased VWC at the different depths. This work documents that surfactants can increase root zone soil water content for more ideal plant growing conditions. In addition, split water applications and integrating a surfactant can have conserving natural water resources by reducing leachate

(and perhaps constituents within) that may potentially be lost to surrounding natural environments.

153 REFERENCES

Abad, M., P. Noguera, R. Puchades, A. Maquieria, and V. Noguera. 2002.

Physicochemical and chemical properties of some coconut coir dusts for use as peat substitute for containerised ornamental plants. Biores. Tech. 82: 241-245.

Abagandura, O.G., D.M. Park, and W. Bridges. 201X. An Investigation on the Application of Soil Wetting Agents with Urea Fertilizer on Plant Uptake of

Nitrogen and Nitrogen Leaching. Manuscript submitted for publication.

Anderson, N.O., J. Annis, M. Buchholz, J. Cutting, E. Heuring, E. Jankila, and V. Price. 2011. Undergraduate sustainable learning: Effects of sustainable soilless media on production and sensory evaluation of cucumbers, basil, parsley, and lettuce. Sustainability. 3(9): 1381-1398.

Asaduzzaman, M., M. Saifullah, A.S.R. Mollick, M.M. Hossain, G.M.A.

Halim, and T. Asao. 2015. Influence of soilless culture substrate on improvement of yield and produce quality of horticultural crops. Available at http://cdn.intechopen.com/pdfs-wm/48098.pdf (verified 10 September. 2016).

Bilderback, T.E. and M.R. Lotsheider. 1997. Wetting agents used in container substrates: Are they BMPs. Acta Hort. 450:313–320.

154 Blackwell, P.S. 2000. Management of water repellency in Australia, and risks associated with preferential flow, pesticide concentration and leaching. J.

Hydrol. 231: 384-395.

Blodgett, A.M., D.J. Beattie, J.W. White, and G.C. Elliott. 1993. Hydrophilic polymers and wetting agents affect absorption and evaporative water loss.

HortScience. 28(6): 633–635.

Bouyoucos, G.J. 1939. Effect of organic matter on water-holding capacity and the wilting point of mineral soils. Soil Sci. 47: 377–383.

Caron, J., R. Heinse, and S. Charpentier. 2015. Organic materials used in agriculture, horticulture, reconstructed soils, and filtering applications. Vadose

Zone J. 14(6).

Case, L.T., H.M. Mathers, and A F. Senesac. 2005. A review of weed control practices in container nurseries. HortTechnology. 15(3): 535-545.

Curtis, M.A., and J.G. Thomas. U.S. Patent 13,156,670, 2016.

Czarnota, M. and P. Thomas. 2006. Using surfactants, wetting agents, and adjuvants in the greenhouse. Available at http://extension.uga.edu/publications/detail.cfm?number=B1319 (verified 10

September. 2016).

155 De Jonghe, K., D. Hermans, and M. Höfte. 2007. Efficacy of alcohol alkoxylate surfactants differing in the molecular structure of the hydrophilic portion to control Phytophthora nicotianae in tomato substrate culture. Crop

Prot. 26(10): 1524-1531.

Dekker, L.W. and C.J. Ritsema. 1994. How water moves in a water repellent sandy soil—1. Potential and actual water repellency. Water Resour.

Res. 30: 2507–2517.

Ellerbrock, R.H., H.H. Gerke, J. Bachmann, and M.O. Goebel. 2005.

Composition of organic matter fractions for explaining wettability of three forest soils. Soil Sci. Soc. Amer. J. 69: 57– 66.

Feitkenhauer, H. and U. Meyer. 2002. Anaerobic digestion of alcohol sulfate (anionic surfactant) rich wastewater – batch experiments. Part II:

Influence of the hydrophobic chain length. Biores. Technol. 82: 123–129.

Fields, J.S. 2013. Hydrophysical properties and hydration efficiency of traditional and alternative Greenhouse substrate components. Theses, North

Carolina State University, SC, US.

156 Fields, J.S., W.C. Fonteno, and B.E. Jackson. 2014. Hydration efficiency of traditional and alternative greenhouse substrate components. HortScience.

49(3): 336-342.

Gizas, G., D. Savvas. 2007. Particle size and hydraulic properties of pumice affect growth and yield of greenhouse crops in soilless culture.

HortScience. 42: 1274-1280.

Graber, E.R., S. Tagger, and R. Wallach. 2009. Role of divalent fatty acid salts in soil water repellency. Soil Sci. Soc. Am. J. 73(2): 541-549.

Greco, R. 2002. Preferential flow in macroporous swelling soil with internal catchment: model development and applications. J. Hydrol. 269(3): 150-

168.

Guillén, C., F. Sánchez, M. Urrestarazu, and P. Mazuela. 2005. Effect of

Wetting Agent on Fertigation Parameters in Tomato on New and Reused Coco

Fiber. Acta Hort. 697: 165–170.

Hallett, P.D., N.A. White, and K. Ritz. 2006. Impact of basidiomycete fungi on the wettability of soil contaminated with a hydrophobic polycyclic aromatic hydrocarbon. Biologia. 61(19): S334-S338.

157 Hanisco, M. 2015. Reduce waste with a quality surfactant program.

Available at http://gpnmag.com/wp- content/uploads/08_Grower1011_GPN%20FINAL.pdf (verified 10 October.

2016).

Horowitz, M., and C.L. Elmore. 1991. Leaching of oxyfluorfen in container media. Weed Technol. 5(1): 175-180.

Jalali, M., and H. Ostovarzadeh. 2009. Evaluation of phosphorus leaching from contaminated calcareous soils due to the application of sheep manure and ethylenediamine tetraacetic acid. Environ. Earth Sci. 59(2): 441-448.

Linardakis, D.K., and B.I. Manios. 1991. Hydroponic culture of strawberries in plastic greenhouse in a vertical system. Acta Hort. 287: 317-326.

Lopez-Medina, J., A. Peralbo and F. Flores. 2004. Closed soilless growing system: a sustainable solution for strawberry crop in Huelva (Spain). Acta Hort.

213-21 5.

Mainwaring, K.A., C.P. Morley, S.H. Doerr, P. Douglas, C.T. Llewellyn, G.

Llewellyn, G, I. Matthews, and B.K. Stein. 2004. Role of heavy polar organic compounds for water repellency of sandy soils. Environ. Chem. Lett. 2(1): 35-39.

158 Mezzanotte, V., F. Castiglioni, R. Todeschini, and M. Pavan.2003. Study on anaerobic and aerobic degradation of different non-ionic surfactants. Biores.

Technol. 87: 87–91.

Michel, J.C., L.M. Riviere, and M.N. Bellon-Fontaine. 2001. Measurement of wettability of organic materials in relation to water content by the capillary rise method. European J. Soil. Sci. 52: 459–467.

Michael, R., and Lieth, H. 2007. Significance of soilless culture in

Agriculture. In Soilless Culture: Theory and Practice: Theory and Practice. PP (6).

Elsevier Science.

Michael, W.O., J.D. Samara, and J.B. Thomas. 2008. Effects of enhanced

APG surfactant on leaching and wettability of six bark substrates.

HortScince.18(2): 295-300.

Moskal, T.D., L. Leskiw, M.A. Naeth, and D.S. Chanasyk. 2001. Effect of organic carbon (peat) on moisture retention of peat: mineral mixes. Can. J. Soil

Sci. 81(2): 205-211.

Nafiye, A.D.A.K., and H. Gubbuk. 2015. Effect of planting systems and growing media on earliness, yield and quality of strawberry cultivation under soilless culture. Not. Bot. Horti Agrobot. Cluj-Napoca. 43(1): 204.

159 Osman, K.T., 2012. Soils: principles, properties and management. In Soil

Water, Irrigation, and Drainage. PP (72). Springer Science & Business Media.

Reinikainen, O., and M. Herranen. 1997. The influence of surfactant on physical properties of peat. Acta Hortic. 450: 375-379.

Shaw, N.L., D.J. Cantliffe, J. Funes, C. Shine III. 2004. Successful Beit

Alpha cucumber production in the greenhouse using pine bark as an alternative soilless media. HortTechnology. 12: 289-294.

Taylor, K.O., M.R. Reddy, C.E. Niedziela Jr, M.M. Peet, M. M., and G.

Gayle. 2012. Effect of root substrates and seed cover materials on the germination and growth of organic tomato transplants. J. Appl. Hortic. 14(2): 83-

87.

Taylor, L.L., A.X. Niemiera, R.D. Wright, G.K. Evanylo, and W.E. Thomason.

2013. Nitrification in pine tree substrate is influenced by storage time and amendments. HortScience 48(1): 115-122.

United States Department of Agriculture. National Agricultural Statistics

Service. 2014. 2012 Census of Agriculture (Publication No. AC-12-A-51). Available at

160 http://www.agcensus.usda.gov/Publications/2012/Full_Report/Volume_1,_Chap ter_1_US/usv1.pdf. (verified 15 September. 2016).

Urrestarazu, M., C, Guillén, P.C. Mazuela, and G. Carrasco. 2008.

Surfactant effect on physical properties of new and reused rockwool and coconut coir waste. Sci. Hortic. 116(1): 104-108.

Valat, B., C. Jouany, and L.M. Riviere. 1991. Characterization of the wetting properties of air dried peats and composts. Soil Sci. 152: 100-107.

Wallis, M. G., and D.J. Horne. 1992. Soil water repellency. In Advances in soil science (pp. 91-146). Springer New York.

Wilson, G. 1985. Effects of additives to peat on the air and water capacity. Acta Hortic. 172: 207-209.

Zheng, W., E.K. Morris, A. Lehmann, and M.C. Rillig. 2016. Interplay of soil water repellency, soil aggregation and organic carbon. A meta-analysis.

Geoderma. 283: 39-47.

Zontek, S.J., and S.J. Kostka. 2012. Understanding the different surfactant chemistries. USGA Green Sec. Rec. 50(15): 1-6.

161 CHAPTER SEVEN

AN INVESTIGATION ON THE APPLICATION OF SOIL SURFACTANT WITH UREA FERTILIZER ON PLANT UPTAKE OF NITROGEN AND NITROGEN LEACHING

This work is under consideration for publication:

Abagandura, G., D. Park, W. Bridges, and K. Brown. 201X. An investigation on the application of soil surfactant with urea fertilizer on plant uptake of nitrogen and nitrogen leaching. Journal of Environmental Quality.

ABSTRACT

Increasing nitrogen (N) plant uptake efficiency may result in better plant quality and growth, less N susceptible to leaching and potential contamination to surrounding environments. Soil surfactants have been previously documented to increase water infiltration and enhance water uniformity throughout the soil profile. Thus applying a surfactant should also increase N uptake and use efficiency. To investigate this theory, 15N labeled urea was applied with one of three surfactant treatments (no surfactant, Agstone 15X, and IrrigAid Gold) to bermudagrass grown in leaching columns filled with one of three soils (sand, sandy loam and a sandy clay loam). Control (no surfactant no urea) was used to determine ambient 15N. Each treatment combination was replicated five times and the greenhouse experiment was repeated. Bermudagrass quality and density, leachate volume, and volumetric water content were determined over a

162 28d period following application. At experiment termination 15N in plant, soil, and leachate were determined and used to develop a 15N mass balance. Applying surfactants with urea resulted in higher soil volumetric water content (in all soils), less total N leached from sand, and higher bermudagrass color and density than urea and control (in all soils). Surfactants applied with urea decreased 15N in leachate from sand by 36-44 %, increased 15N retained in the sandy loam by 28 –

49 %, and increased 15N in bermudagrass grown in the sandy clay loam by 60-66

% compared to urea applied alone. Applying surfactants with urea can increase bermudagrass N uptake efficiency and reduce potential N leaching.

INTRODUCTION

Nitrogen (N) is an important plant nutrient, second only to water availability (Mengel et al., 2006). Therefore, N fertilizers are used extensively all over the world (Halitligil et al., 2002). Only 40 to 60% of applied N is used by a crop (Sebilo et al. 2013) and the remaining N either remains in the soil profile or is lost through volatilization, denitrification, or leached past the root-zone to potentially contaminate natural water resources (Halitligil et al., 2002; Sebilo et al., 2013; Sieling and Hanus, 2006). Twenty percent of shallow wells in the

Delmarva Peninsula, USA exceeded the N maximum contaminant level for

163 drinking water (10 mg L–1, United States Environmental Protection Agency

(USEPA), 2011) (Kasper et al., 2015).

Nitrogen leaching into surface and groundwater is a particularly important issue in turfgrass cultivation, with an estimated 30% of applied N to turfgrass leached annually (Barton and Colmer, 2006). Studies conducted by

Brown et al. (1982) and Guertal and Howe (2012) reported leachate N concentrations exceeded the maximum contaminant level for drinking water under bermudagrass, one of the most common turfgrass species in the southwest and southeast US (Bowman et al., 2006; Fagerness et al., 2004;

Snyder et al., 1984).

Management practices that reduce N leaching are applying N at the rate the plant can utilize (Barton and Colmer, 2006; Carpenter et al, 1998), recycling clippings (Harivandi et al., 2001; Qian et al., 2003) and minimizing irrigation rates and frequencies (Barton et al., 2006; Espevig and Aamlid 2012). Another potential way to reduce N leaching may be the use of soil surfactants (SURF), chemicals that are often applied to turfgrass systems to reduce surface tension, and thus, water enters the soil and is distributed throughout the soil profile uniformly, and subsequently makes water (and constituents) more available for

164 plant uptake, increases turfgrass quality, and can reduce leaching (Cisar et al.,

2000; Hallett, 2008).

While an increase in turf quality may be due to more N uptake (Johnson et al., 1987), and more N has been found in turfgrass grown on SURF treated soils (Aamlid et al., 2009a), no scientific research has been completed to trace applied N throughout the soil-plant-water nexus when a SURF is applied to support previous findings. Adding 15N-enriched material to a system is considered the most reliable way to trace exactly where the N moves through a system (Bedard-Haughn et al., 2003; Peterson and Fry, 1987; Robinson, 2001).

Since SURFs increase the soil retention of water and applied nutrients, it is suspected it would increase nutrient plant uptake and decrease N leaching.

Thus, the objective of this study was to directly identify if surfactants reduce N leaching in turfgrass while maintaining acceptable growth by using 15N enriched urea fertilizer.

MATERIALS AND METHODS

Site Location

The experiment was conducted at Clemson University’s Greenhouse

Complex (Clemson, SC, USA; latitude 34°40′8″; longitude 82°50′40″) from 12

165 August to 13 September 2015 and repeated from 2 February to 16 April 2016.

Leaching columns were constructed from polyvinyl chloride (PVC) pipes with a diameter of 10 cm and a height of 46 cm. On the bottom of each column, a screen was laid over the PVS lip, followed by a slip cap to retain the soil. A hole was drilled in the bottom of each slip cap to allow for percolate to flow out of the column.

Packing the Soil

Columns were filled with one of three soils (air-dried and screened through a 2 mm sieve to remove any unwanted waste) (Table 7.1). The three soils will be referred to as: S for sand, SL for sandy loam and SCL for sandy clay loam sand for the remainder of the paper. Four shelves were used to hold the columns vertically. Each shelf held 15 columns. Columns within SURF were romumly assign to one of the 12 treatments. Commercially grown bermudagrass sod (Tifway 419, Cynodon dactylon L.× C. transvaalensis Burtt-Davy) (a deep grass with a fine texture, excellent weed and disease resistance, that remains dormant during winter months (Polomski and Shaughnessy, 2015), (Carolina

Fresh Farms and Garden, Anderson, SC)) was washed until the rootzone was free of soil and then cut to the diameter of the columns. Sodded columns were irrigated twice daily (~0.5 cm per application) until established (Bowman et al.,

166 2002). During the experiment, the plants were grown in a greenhouse with a temperature range of 27–28◦ C, a relative humidity range of 50–55 % in both years. Supplementary lighting was used for 12 hr (from 6 am to 5 pm) in both years when the light intensity outside the greenhouse goes below 300 watts per square meter (W m-2) for a period of 30 minutes.

Table 7.1. The general soil characteristics used in this study.

Series Texture Horizon Location Soil Bulk Organic pH Density (g matter cm-3) (%) ---- Sanda (S) --- Play sand (Quikrete, 7.30 1.60b 0 Atlanta GA) Toccoa Sandy Ap Calhoun Field Station, 6.80 1.35c 4 loam Clemson, SC, US (SL) (34°67′265″ N, 82°84′664″ W) Lynchburg Sandy Bt Pee Dee Rec, Florence, 7.00 1.50c 2 clay loam SC, US (SCL) (34°29′217″ N, 79°73′476″ W) a Sand was purchased in bags, washed with water, and dried before screening through a 2 mm sieve. b Source: Guertal and Howe, 2012. c Source: Soil Web Survey.

The experiment began once the bermudagrass was established. Columns were irrigated with 1.3 cm deionized water on Mondays, Thursdays and Fridays

167 for four weeks. Four SURF treatments were hand applied at experiment initiation with irrigation water and 5.16% enriched 15N labeled urea (ICON, New Jersey,

US) at a rate of 24 kg ha-1 (Table 7.2). Urea was selected because it is a common source of N fertilizer used in agriculture due to its high N content and solubility

(Saggar et al., 2013; Soares et al., 2012).

Table 7.2. Soil surfactant chemistries, trade names, manufacturers, and rate used in this study.

Active ingredient Trade name / Manufacturer Rate (L

Abbreviation ha-1)

5-10% of D-glucopyranose, IrrigAid Gold Aquatrols Corporation of 407 oligomeric, deyloctyl glycosides / IRG+U America, Paulsboro, NJ

10% of Oleic acid esters of Agstone 15X Agstone, Greenville, SC 815 copolymer / AGS+U

Water+urea Na / WTR+U ------

Water control† Na / CNTRL ------a CNTRL used to account for natural 15N abundance.

Measurements

Soil Volumetric Water Content and Leachate

168 Soil volumetric water content (VWC) was determined in the upper 5 cm of the soil before each irrigation event using a hand-held time domain reflectrometry device (Dynamax, Inc., Houston, TX). Values were averaged for each week. Leachate was collected after each irrigation in cups placed under each column. Leachate volume was measured using a graduated cylinder, collected and stored in 100 ml Nalgene bottles (Plastic Corp., Ohio, US) at -4˚C

(Dubourg et al., 2015). At the end of experiment, the samples were thawed at room temperature (24˚C) and subsamples were filtered (25 mm quartz filters), dried at 60˚C for 48 hr (Dubourg et al., 2015), and packed into 8 x 11 mm tin capsules and analyzed for total N and 15N analysis (Stable Isotope Facilities,

University of Saskatchewan, Saskatchewan, Canada). A preliminary study comparing this method to the diffusion method (Li et al., 2010) found that there was no significant difference in 15N and Total-N recovery between the two methods (P = 0.5353).

Turfgrass Quality and Growth

169 Each week, color and density ratings (scale of 1-9) were observed visually and recorded on a rating sale of 1 to 9 (9 = dark green turf, 1 = dead/ brown turf, and 6 = minimally acceptable turf) (Aamlid et al., 2009b; Trenholm and Unruh,

2009). The bermudagrass was maintained at a 2.54 cm height weekly and individual column clippings were collected throughout the experiment. The clippings were oven-dried at 60°C and weighed.

At the end of the experiment, aboveground biomass was removed and any remaining soil gently washed away. Then the columns were emptied, roots were removed and also gently washed to remove as much soil as possible. Both aboveground biomass and roots were oven-dried at 60°C for 48 h (Engelsjord et al., 2004; Erickson et al., 2010) and weighed. To obtain accurate root weights, subsamples were ashed in a muffle furnace (Thermolyne, Thermo Fisher

Scientific Inc., Hudson, NH, US) and reweighed (Smit et al., 2013; Rowell, 2014).

Preparation of Samples for 15N and Total-N Analysis

At the end of each experiment, soil samples were extracted from each column using a 2.5 cm diameter soil core taken from the edge of each column for the full column depth. Soil samples were placed in brown bags and air dried at room temperature.

170 For each column, clippings, roots, and aboveground biomass were subsampled in the same proportion as their weights and ground to a fine powder

(Hamilton Beach Fresh-Grind Coffee, Wayfair LLC, Boston, MA), combined, subsampled and packed into 12.5 x 5 mm tin capsules. To prevent contamination, the grinder and all equipment were completely cleaned between samples using compressed air and a brush. Soil was also prepared in the same manner as bermudagrass samples.

N Budget

15N budget was calculated by measuring 15N in the soil, leachate and bermudagrass by subtracting out 15N values from columns receiving the CNTRL

(natural abundance) from the other treatments (Cannavo et al., 2013). The following fluxes were determined and summed to calculate the N balance: UFN

=Percent utilization of fertilizer N by bermudagrass (a measure of fertilizer use

15 efficiency), RFNs= percent recovery of N fertilizer in soil, and RFNl = percent recovery of 15N fertilizer in leachate (Allen et al., 2004; Bedard-Haughn et al.,

2003; Jose et al., 2000). In addition, total-N was determined in leachate to identify potential of environmental concern.

Experimental Design and Data Analysis

171 A completely randomized experimental design was used with five replicates per treatment, with soil as the main plot factor, and SURF treatments randomized within soils. The analysis of variance (ANOVA) was assessed (data within each year were normal (Shapiro-Wilk test) and variance homogeneous for all variables (Levene’s test) and then used to test the effects of soil SURF. The

Fisher LSD test was used for multiple means comparison. All significance tests were performed with α=0.05 and all calculations were performed using the JMP®

Pro 12.0.1 software (product of SAS Institute Inc., Cary, NC). Experiment was first tested as a factor and was found not significant for all measurements. Therefore, data were pooled and re-analyzed. Soil was a significant factor (all p-value <

0.05) for all measurements, therefore, results are discussed for each soil separately.

RESULTS AND DISCUSSION

Soil Volumetric Water Content

Water in all three soils was between the permanent wilting point (1, 5, and 6 cm m-3 for S, SL, SCL respectively) and field capacity (7, 18, and 22 cm m-3 for S, SL, SCL respectively) and plant available (USDA Natural Resources

Conservation Service, 2008; Brady and Weil, 2008) (Figure 7.1). Significant differences were observed in VWC each week for each soil and SURF treatment

172 (Figure 7.1). The VWC measured in the S was less than half of VWC measured in the SL and SCL (Figure 7.1). In the S, during the first week directly after SURF application, IRG+U and AGS+U increased the VWC compared to WTR+U and

CNTRL (Figure 1 A). For the remaining three weeks, VWC in S was similar regardless being treated with IRG+U, AGS+U and WTR+U (Figure 7.1 A). In the first, second and fourth week in SL, and the first through third weeks in the SCL, applying the IRG+U and AGS+U resulted in similar and significantly higher VWC compared to WTR+U and CNTRL (which were significantly similar) (Figure 7.1 B and C).

173 (A) Sand Figure 7.1. Comparison of IRG+U AGS+U WTR+U CNTRL the mean volumetric 20 water content for: (A) sand, (B) sandy loam, (C) )

3 15 - sandy clay loam on the four weeks. 10 a a ab a ab ab a a b ab VWC (cm m (cm VWC 5 a a b b c b

- First week Second week Third week Fourth week

(B) Sandy loam

20 a a a a b a bc a b b c b ) b

3 15 a - b b 10

VWC (cm (cm m VWC 5

- First week Second week Third week Fourth week (C) Sandy clay loam

20 a a a a a a ) a 3

- 15 b b b b b b b b b 10

vwc (cm m (cm vwc 5

- First week Second Third week Fourth week week

174 Generally, all surfactant treatments helped retain more moisture for all soils. This increase in VWC using SURF is well documented (Abagandure et al.,

201X; Dekker et al., 2005; Madsen et al., 2012), possibly due to the reduce of the water surface tension caused by SURF allowing water to infiltrate into the soil pore spaces and distribute uniformly (Leinauer, 2002).

Bermudagrass Quality and Growth

Regardless of soil, bermudagrass color and density were similar at the beginning of the experiment (Figure 7.2). Application of AGS+U was the only treatment that resulted in significantly high rating bermudagrass color in S, all other treatments were close to or below the acceptable criteria (rating < 6)

(Figure 7.2 A). Applying the AGS-U on S resulted in similar density to IRG-U at

8DAI and 22DAI (Figure 7.2 A and B). All SURF treatments resulted in acceptable

(rating > 6), bermudagrass color and density when grown on the two finer textured soils (SL and SCL) (Figure 7.2 C-F). Bermudagrass color and density grown on these two soils was highest when treated with AGS+U and similar to

IRG+U for all dates with the exception on 22DAI in the SL soil, in which AGS+U resulted in significantly greater density than IRG+U (Figure 7.2 D). With the exception of color

175 IRG+U AGS+U WTR+U CNTRL B IRG+U AGS+U WTR+U CNTRL A 9 9 8 8 a a a a a a 7 7 a b a ab b b b b b ab b b b b b 6 b b COLOR 6 c DENSITY 5 5 4 4 1DAI 8DAI 15DAI 22DAI 1DAI 8DAI 15DAI 22DAI

C 9 a a D 9 a a ab a a a a b a a ab ab b 8 8 b b c b b 7 7 c c c d

COLOR 6 6 DENSITY 5 5

4 4 1DAI 8DAI 15DAI 22DAI 1DAI 8DAI 15DAI 22DAI

9 a a E 9 a a a F a a a a a a a a a ab a a 8 8 b c b b 7 7 b b b 6 COLOR 6 DENSITY

5 5

4 4 1DAI 8DAI 15DAI 22DAI 1DAI 8DAI 15DAI 22DAI Figure 7.2. Visual turfgrass quality and density (1–9 scale: 1 = worst, 6 = acceptable, 9 = best) for: (A and B) sand, (C and D) sandy loam, (E and F) sandy clay loam during the four weeks of the experiment.

at 8DAI, all bermudgrass grown in the SCL treated with urea performed similar, and was better than the CNTRL (Figure 7.2 E and F). Although a 0.5 increase in

176 color and density may not always be statistically difference, it is commonly accepted among practitioners that it is enough of a difference to consider adopting a management strategy.

The positive effect of SURF on turfgrass quality has been documented in other studies. The application of a proprietary blend of polyethylene and polyproplylene glycols SURF to 85/15 sand/peat increased Penncross bentgrass

(Agrostis palustris Huds.) color and density compared to control (Karnok and

Tucker, 2001). Higher bentgrass (Agrostis stolonifera) quality was observed after the application of alkyl ether of methyl oxirane-oxirane copolymer SURF compared to control in a sand soil (Kostka et al., 2008).

In all soils, SURF influenced total clipping weights (Table 7.3).

Bermudagrass total clipping weights in S from the IRG+U was higher than the

AGS+U and WTR+U (which were similar). The total clipping weights in SL and SCL from the AGS+U and IRG+U were significantly higher than the WTR+U and CNTRL

(which were statically similar) (Table 7.3). The ashed root weights were not affected by SURF (p = 0.7351, data not shown).

Table 7.3. The total volume leachate (ml) and dry clipping (kg ha -1) from different soils obtained from different surfactant treatments.

S SL SCL Total leachate (ml)

177 IRG+U 214 b 134 112 AGS+U 209 b 151 102 WTR+U 268 a 168 140 CNTRL 276 a 167 115 Clipping (kg ha -1) IRG+U 252 a 264 a 318 a AGS+U 207 b 244 a 304 a WTR+U 197 b 204 b 220 b CNTRL 173 c 183 b 209 b a Means in a column followed by same letter are not significantly different based on Fisher LSD Test (p ≥ 0.0).

Leachate Volume and Total N leached

As expected, total volume leachate from the S was higher compared to SL and SCL (Table 7.3). This result was expected because S was washed of all the fine particles that could hinder the drainage process, while both SL and SCL include clay which increases their water holding capacity and reducing the leaching, data that agree with the lower VWC from this soil (Figure 7.1 A) compared to SL (Figure 7.1 B) and SCL (Figure 7.1 C). The total volume leached from CNTRL was higher in the three soil compared to other treatments (Table

7.3). In general, IRG+U and AGS+U decreased the total volume leachate compared to the WTR+U and CNTRL, with the volume leached from AGS+U being the lowest (Table 7.3).

178 Mean total N in the leachate did not exceed the safe threshold in water

(> 10 mg L–1, USEPA, 2011) in all soils (nor any individual leaching column) (Figure

7.3). The application of IRG+U and AGS+U decreased total N concentrations in the leachate by half compared to WTR+U in the S soil (Figure 7.3). Although not significant, bermudagrass grown in SL and SCL leached less N from IRG+U and

AGS+U compared to WTR+U (Figure 7.3). Higher concentrations of N in was leached from bermudagrass grown in S compared to other two soils, possibly due to the low water holding capacity of S resulting in an increase in water and N leached.

IRG+U AGS+U WTR+U

3 a ) 1 - 2

Total N (mg L N (mg Total 1 b

0 S SL SCL Figure 7.3. Concentration of total N in leachate as affected by soil texture and surfactant treatments.

179 IRG+U AGS+U A B IRG+U AGS+U WTR+U 100 60 ab a a a b b b a 80 c c (%) L 40 60 b b RFN 40 20 20 Total recovery (%) recovery Total 0 0 S SL SCL S SL SCL 60 60 C D a a 50 50

40 a 40 b (%)

S 30 30 a b

c (%) UFN RFN 20 20 b b 10 10

0 0 S SL SCL S SL SCL Figure 7.4. Recovery of applied N fertilizer of applied N in a bermudagrass, soil and leachate for the three soils at the end of the experiment. Note difference in y axis scales.

15N Fluxes and Budget

The CNTRL was used to integrate natural 15N abundance into fluxes and the budget, and thus is not discussed alone. Soil texture influenced results the most (p = 0.0034) for total recovery, RFNL, RFNs, and UFN respectively) (Figure

180 7.4). The highest total recovery was from S (Figure 7.4 A) with the largest contributing flux being RFNl (Figure 7.4 B). Again, this is most likely due to the low water holding capacity of S which would subsequently result in an increase in water and dissolved N leaching through the profile. In comparison, RFNs was the greatest flux in the SL, and UFN for the SCL (Figure 7.4 C and D). Perhaps as

VWC increases with texture fineness (increase in micropores), more N was available for plants to utilize.

For each soil, the fluxes where the highest 15N was recovered, was the only time in which SURF influenced recoveries (with the exception of S in which both RFNs and RFNL was significant). However similar SURF pattern were evident in each soil. In the S soil, where it would be expected to be most difficult to retain N, both IRG+U and AGS+U retained 11% and 18% more 15N recovery than the WTR+U, respectively. Using a SURF reduced RFNL by 19% (IRG+U) and 23%

(AGS+U) (Figure 7.4 B) in the S. As fines increased (SL and SCL) AGS+U resulted in greater RFNS compared to the IRG+U and WTR+U (Figure 7.4 C). Finally, both

SURF treatments increased UFN by 18% (IRG+U) and 20% (AGS+U) in the SCL

(Figure 7.4 D).

Wherley et al. (2011) reported the total N recovery from Tifway 419 bermudagrass grown on S soil was approximately 58% and 68% of applied N,

181 which is lower than found in this study for S. Conversely, Fagerness et al. (2004) reported that Tifway 419 bermudagrass N uptake grown in S was approximately

65% of N applied, higher than the UFN percentage obtained by this study. Total recovery of N by kentucky bluegrass (Poa pratensis L.) grown on SL was 64% to

81% of applied N (Miltner et al., 1996), similar to the range that was obtained in the current study for SL.

Nitrogen loss from denitriﬁcation and volatilization were not measured in this study which may explain why the total N recovery for the three soils were not equal to inputs (Engelsjord et al., 2004). However, N lost through denitriﬁcation was unlikely to since the soil saturation condition did not persist

(D'haene et al., 2003; Hergoualc’h et al., 2009; Naeth et al., 1990). N loss through volatilization can range from zero to greater than 60% (Knight et al., 2007). It is suspected that urea volatilization was a primary N loss because urea hydrolyzes quickly in the soil especially under warm humid conditions (Soares et al., 2012).

Warm temperatures in the greenhouse may have increased the soil temperature, and high humidity directly after irrigation may also have occurred

(McGarry et al., 1987). In comparison, the volatilization was 0.35% from creeping bentgrass (Agrostis stolonifera L. var. palustris (Huds.) Farw. cv.) grown on sand

(Stiegler et al., 2011), 20.1% from bermudagrass (Cynodon dactylon (L.) Pers. ×

182 Cynodon transvaalensis Burtt Davy) grown on loamy sand (Huckaby et al., 2012),

36% from Kentucky blue-grass grown on loam (Bowman et al., 1987), and 39% from Kentucky bluegrass (Poa pratensis L.) grown on silt loam (Nelson et al.,

1980).

CONCLUSIONS

This study investigated if bermudagrass quality and N leaching were affected by the application of surfactants with fertilizer to three soils. Surfactants increased the soil retention of water compared to just water alone.

Subsequently, dissolved N within the water was available for bermudagrass roots to take up, leading to increase the fertilizer efficiency and reduce the N loss through leaching. The total N leached did not excess the health safety threshold for drinking water and was reduced by half in the sand soil by applying the urea with a surfactant. Perhaps more differences would have been statistically identified for 15N fluxes and total recovery if the experiment was conducted over a longer period of time to include multiple surfactant applications. The results from this study suggest that application of surfactants with fertilizer can be a management strategy that can increase bermudagrass uptake of N and reduce N leaching from turfgrass systems.

183 REFERENCES

Aamlid, T.S., M. Larsbo, and N. Jarvis. 2009a. Effects of surfactant use and peat amendment on leaching of fungicides and nitrate from golf greens. Biologia.

64(3): 419-423.

Aamlid, T.S., T. Espevig, and A. Kvalbein. 2009b. The potential of a surfactant to restore turfgrass quality on a severely water-repellent golf green. Biologia. 64(3): 620-623.

Abagandura, O.G., D.M. Park, W. Bridges, and K. Brown. 201X.

Comparison of soil surfactant chemistries on volumetric water content and leachate of two greenhouse substrates under two irrigation regimes. Manuscript submitted for publication.

Allen, S.C., S. Jose, P.K.R. Nair, B.J. Brecke, and C.L. Ramsey. 2004.

Competition for 15N-labeled fertilizer in a pecan (Carya illinoensis K. Koch)-cotton

(Gossypium hirsutum L.) alley cropping system in the southern United States.

Plant Soil. 263(1): 151-164.

Bowman, D.C., J.L. Paul, W.B. Davis, and S.H. Nelson. 1987. Reducing ammonia volatilization from Kentucky bluegrass turf by irrigation. HortScience.

22: 84-87.

184 Brown, K.W., J.C. Thomas, and R.L. Duble. 1982. Nitrogen source effect on nitrate and ammonium leaching and runoff losses from greens. Agron. J. 74:

947–950.

Barton., L., and T.D. Colmer. 2006. Irrigation and fertilizer strategies for minimizing nitrogen leaching from turfgrass. Agr. Water Manage. 80(1): 160-175.

Bedard-Haughn, A., J.W. Van Groenigen, and C. Van Kessel. 2003. Tracing

15N through landscapes: potential uses and precautions. J. Hydrol. 272(1): 175-

190.

Brady, N.C., and R.R. Weil. 2008. Soil Water: Characteristics and Behavior.

In Soil Water: Characteristics and Behavior, pp. 210-212. Prentice-Hall Inc.

Bowman, D.C., D.A. Devitt, and W.W. Miller. 2006. The effect of moderate salinity on nitrate leaching from bermudagrass turf: a lysimeter study. Water, Air, Soil Pollut. 175(1-4): 49-60.

Bowman, D.C., C.T. Cherney and T.W. Rufty. 2002. Fate and transport of nitrogen applied to six warm-season turfgrasses. Crop Sci. 42(3): 833-841.

Carpenter, S.R., N.F. Caraco, D.L. Correll, R.W. Howarth, A.N. Sharpley, and V.H. Smith. 1998. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol Appl. 8: 559–568.

185 Cannavo, P., J.M. Harmand, B. Zeller, P. Vaast, J.E. Ramírez, and E.

Dambrine. 2013. Low nitrogen use efficiency and high nitrate leaching in a highly fertilized Coffea arabica–Inga densiflora agroforestry system: a 15N labeled fertilizer study. Nutr. Cycling Agroecosyst. 95(3): 377-394.

Cisar, J.L., K.E. Williams, H.E. Vivas, and J.J. Haydu. 2000. The occurrence and alleviation by surfactants of soil water repellency on sand-based turfgrass systems. J. Hydrol. 231–232: 352–358.

Dekker, L.W., K. Oostindie, S.J. Kostka, and C.J. Ritsema. 2005. Effects of surfactant treatments on the wettability of a water repellent grass covered dune sand. Aust. J. Soil Res. 43: 383–395.

D'haene, K., E. Moreels, S. De Neve, B.C. Daguilar, P. Boeckx, G. Hofman, and O. Van Cleemput. 2003. Soil properties influencing the denitrification potential of Flemish agricultural soils. Biol. Fertil. Soils. 38(6): 358-366.

Dubourg, P., R.L. North, K. Hunter, D.M. Vandergucht, O. Abirhire, G.M.

Silsbe, G.M., and J.J. Hudson. 2015. Light and nutrient co-limitation of phytoplankton communities in a large reservoir: Lake Diefenbaker,

Saskatchewan, Canada. J. Great Lakes Res. 41: 129-143.

186 Engelsjord, M.E., B.E. Branham, and B.P. Horgan. 2004. The fate of nitrogen-15 ammonium sulfate applied to Kentucky bluegrass and perennial ryegrass turfs. Crop Sci. 44(4): 1341-1347.

Erickson, J. E., D.M. Park, J.L. Cisar, G.H. Snyder, and A.L. Wright. 2010.

Effects of sod type, irrigation, and fertilization on nitrate-nitrogen and orthophosphate-phosphorus leaching from newly established St. Augustinegrass sod. Crop Sci. 50(3): 1030-1036.

Espevig, T., and T.S. Aamlid. 2012. Effects of rootzone composition and irrigation regime on performance of velvet bentgrass putting greens. I. Turf quality, soil water repellency and nutrient leaching. Acta Agric Scand Sect B Soil

Plant Sci. 62(1): 96-105.

Fagerness, M.J., D.C. Bowman, F.H. Yelverton, and T.W. Rufty. 2004.

Nitrogen use in Tifway bermudagrass, as affected by trinexapac-ethyl. Crop Sci.

44(2): 595-599.

Guertal, E.A., and J.A. Howe. 2012. Nitrate, ammonium, and urea leaching in hybrid bermudagrass as affected by nitrogen source. Agron. J. 104(2):

344-352.

187 Halitligil, M., A. Akin, and A. Ýlbeyi. 2002. Nitrogen balance of nitrogen-15 applied as ammonium sulphate to irrigated potatoes in sandy textured soils. Biol.

Fertil. Soils. 35.5: 369-378.

Hallett, P.D. 2008. A brief overview of the causes, impacts and amelioration of soil water repellency–a review. Soil Water Res. 3(1): 521-528.

Harivandi, M.A., W.L. Hagan, and C.L. Elmore. 2001. Recycling mower effects on biomass, nitrogen recycling, weed invasion, turf quality, and thatch.

ITS. 9: 882-885.

Hergoualc'h, K., J.M. Harmand, P. Cannavo, U. Skiba, R. Oliver, and C.

Hénault. 2009. The utility of process-based models for simulating N2O emissions from soils: a case study based on Costa Rican coffee plantations. Soil Biol.

Biochem. 41(11): 2343-2355.

Huckaby, E.C., C.W. Wood, and E.A. Guertal. 2012. Nitrogen source effects on ammonia volatilization from warm-season sod. Crop Sci. 52(3): 1379-

1384.

Johnson, B.J., R.N. Carrow, and R.E. Burns. 1987. Bermudagrass turf response to mowing practices and fertilizer. Agron. J. 79: 677-680.

188 Jose, S., A.R. Gillespie, J.R. Seifert, and D.J. Biehle. 2000. Defining competition vectors in a temperate alley cropping system in the midwestern

USA: 2. Competition for water. Agrofor Syst. 48(1): 41-59.

Karnok, K.J., and K.A. Tucker. 2001. Wetting agent treated hydrophobic soil and its effect on color, quality and root growth of creeping bentgrass. ITS. 9:

537-541.

Kasper, J.W., J.M. Denver, and J.K. York. 2015. Suburban groundwater quality as influenced by turfgrass and septic sources, Delmarva Peninsula, USA. J.

Environ. Qual. 44(2): 642-654.

Knight, E.C., E.A. Guertal, and C. Wood. 2007. Mowing and nitrogen source effects on ammonia volatilization from turfgrass. Crop Sci. 47(4): 1628-

1634.

Kostka, S., G. Schuermann, and M. Franklin. 2008. Alkyl-capped block copolymer surfactants for remediation of soil water repellency and heterogeneous rootzone moisture. J. ASTM Int. 4(7): 1-8.

Leinauer, B. 2002. Wetting agents and their impact on water retention of turfgrass root zones. Australian Turfgrass Management. Available at

189 http://old.agcsa.com.au/static/atm_articles/html/4_1c.html (verified 6 August.

2016).

Li, S.L., C.Q. Liu, Y.C. Lang, Z.Q. Zhao, and Z.H. Zhou. 2010. Tracing the sources of nitrate in karstic groundwater in Zunyi, Southwest China: a combined nitrogen isotope and water chemistry approach. Environ. Earth Sci. 60(7): 1415-

1423.

Madsen, M.D., S.L. Petersen, B.A. Roundy, A.G. Taylor, and A.G. Hopkins.

2012. Comparison of postfire soil water repellency amelioration strategies on blue bunch wheatgrass and cheatgrass survival. Rangeland Ecol. Manag. 65: 182–

188.

McGarry, S.J., P.O'Toole and M.A. Morgan. 1987. Effects of soil temperature and moisture content on ammonia volatilization from urea-treated pasture and tillage soils. Ir. J. Agric. Res. 26(2/3): 173-182.

Mengal, K., E.A. Kirkby, T. Appel, and H. Kosegarton. 2006. Principles of plant nutrition. Springer.

Miltner, E.D., B.E. Branham, E.A. Paul, and P.E. Rieke. 1996. Leaching and mass balance of 15N-labeled urea applied to a Kentucky bluegrass turf. Crop Sci.

36(6): 1427-1433.

190 Naeth, M.A., D.J. Pluth, D.S. Chanasyk, A.W. Bailey, A.W., Fedkenheuer.

1990. Soil compaction impacts of grazing in mixed prairie and fescue grassland ecosystems of Alberta. Can. J. Soil Sci. 70: 157-167.

Nelson, K., A.J. Turgeon, and J.R. Street. 1980. Thatch influence on mobility and transformation of nitrogen carriers applied to turf. Agron. J. 72(3):

487-492.

Peterson, B.J., and B. Fry. 1987. Stable isotopes in ecosystem studies.

Annu. Rev. Ecol. Syst. 293-320.

Polomski, B., and D. Shaughnessy. 2015. Bermudagrass. Available at http://www.clemson.edu/extension/hgic/plants/landscape/lawns/hgic1208.html

(verified 6 August. 2016).

Qian, Y.L., W. Bandaranayake, W.J. Parton, B. Mecham, M.A. Harivandi, and A.R. Mosier. 2003. Long-term effects of clipping and nitrogen management in turfgrass on soil organic carbon and nitrogen dynamics. J. Environ. Qual. 32(5):

1694-1700.

Robinson, D. 2001. &15N as an integrator of the nitrogen cycle. Trends

Ecol Evol. 16: 153–162.

191 Rowell, D.L. 2014. Soil science: Methods and applications. Harlow, Essex;

New York; Longman Scientific & Technical.

Saggar, S., J. Singh, D.L. Giltrap, M. Zaman, J. Luo, M. Rollo, and T.J. Van der Weerden. 2013. Quantification of reductions in ammonia emissions from fertilizer urea and animal urine in grazed pastures with urease inhibitors for agriculture inventory: New Zealand as a case study. Sci. Total Environ. 465: 136-

146.

Sebilo, M., B. Mayer, B. Nicolardot, G. Pinay, and A. Mariotti. 2013. Long- term fate of nitrate fertilizer in agricultural soils. Proc. Natl. Acad. Sci. 110(45):

18185-18189.

Sieling K., and H. Hanus. 2006. N balance as an indicator of N leaching in an oilseed rape – winter wheat – winter barley rotation. Agric., Ecosyst. Environ.

115: 261–269.

Smit, A.L., A.G. Benghough, C. Engels, M. Van Noordwijk, and S. Pellerin.

2013. Root methods: a handbook. Springer, Berlin.

Snyder, G.H., B.J. Augustin, and J.M. Davidson. 1984. Moisture sensor- controlled irrigation for reducing N leaching in bermudagrass turf. Agron. J.

76(6): 964-969.

192 Soares, J.R., H. Cantarella, and M.L. de Campos Menegale. 2012.

Ammonia volatilization losses from surface-applied urea with urease and nitrification inhibitors. Soil Biol. Biochem. 52: 82-89.

Stiegler, J.C., M.D. Richardson, D.E. Karcher, T.L. Roberts, and R.J.

Norman. 2011. Field-based measurement of ammonia volatilization following foliar applications of urea to putting green turf. Crop Sci. 51(4): 1767-1773.

Trenholm, L.E., and J.B. Unruh. 2009. Central Florida fertilizer trials on

Empire zoysiagrass and Pensacola bahiagrass. Proc. Fla. State Hortic. Soc. 122:

386-389.

United States Department of Agriculture and Natural Resources

Conservation Service. 2008. Soil Quality Indicators. Available Water Capacity.

Available at http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_053288.pdf

(verified 21 October. 2016).

United States Environmental Protection Agency. 2011. edition of the drinking water standards and health advisories. USEPA. Rep. 820-R-11-002.

USEPA, Washington, DC.

193 Wherley, B., D. Bowman, W. Shi, and T. Rufty. 2011. Effect of soil saturation on development and 15N-nitrate uptake efficiency of two warm season grasses emerging from dormancy. J. Plant Nutr. 34(13): 2039-2054.

194 CHAPTER EIGHT

CONCLUSIONS

The purpose of this project was to increasing productivity of Libyan agriculture. After reviewing past Libyan agriculture (Chapter 2), soil degradation, limited freshwater resources, and the arid climate are the primary causes for the country’s dependence on food and ware imports. Organization of soil data and determination of soil degradation, and identifying strategies to combat limited freshwater resources and droughty soils (high in sand) is needed.

Organized soil data and information on soil degradation for the whole country is needed in order for informed decisions to be made about existing agricultural areas and to identify new areas for agriculture development. After organizing existing soil and climatic data, two logistic regression models were developed and predicted (Chapter 3) that 53.5 % of Libyan soils are degraded, with 46.4 % due to salinization, 6.4 % due to water erosion and 0.6 % due to wind erosion. Beyond the government owned PARs, minimal irrigation systems exist in Libya. Thus strategies to increase the soil water content and plant growth and quality was the focus of the remainder of this project.

One potential strategy to overcome these challenges may be the use of soil conditioners, substances that enhance physical and chemical properties of

195 soil. Four experiments were conducted in South Carolina. The soil and the weather in Libya and South Carolina to some extent are similar. Both have sand dominated surface horizons and root restricting subsurface horizons. South

Carolina commonly experiences intense mini-droughts during the primary growing season (summer). Thus the information obtained from these experiments can be applied to Libya.

The objectives of the two field experiments were to investigate how soil conditioners affect rootzone water content, rootzone minerals and plant yield and quality (Chapter 4 and Chapter 5). There was minimal effect of soil conditioners on all metrics measured. The uncommonly wet growing season, was most likely the reason for the lack of response from soil conditioner applications.

More work is needed under more “normal” dry climatic conditions.

To further investigate how soil surfactants influence soil water content and plant growth, two greenhouse experiments were conducted. The first experiment (Chapter 6) investigated the influence of various surfactant chemistries on percolate and soil water content at different depths within contrasting soils at two different irrigation regimes. Percolate was significantly reduced when surfactants were applied and when irrigation water volume was applied over two irrigation events. Irrigation accounts for 50 % of water usage in

196 Libya., thus conserving water is important. This research documented that scheduling irrigation needs over two events a week and applying soil surfactants are two promising strategies for water conservation.

Nitrogen leaching from agricultural land to ground water is considered one of the primary causes of water pollution in Libya. The last greenhouse experiment utilized isotope labeled urea to examine the influence of surfactant on N movement and bermudagrass growth and quality (Chapter 7). Results varied among soils investigated, but indicated that applying a surfactant with urea increased N uptake and reduced N leaching. Since soil surfactants increased

N uptake and minimized N leaching, the application of these surfactants show promise to increase crop growth and quality and reduce N contamination to

Libyan water resources.

This project reviewed agricultural efforts in Libya and identified needs and strategies in order to increase the agricultural productivity. Development of a soil and climate database and soil degradation models will assist stakeholders in strategically determining areas in which agriculture is most likely to succeed and how to manage areas currently under production. Applying irrigation water over two events and integrating soil surfactants can increase volumetric water content, reduce leachate, and increase plant growth and quality. The tools and

197 strategies identified by this work can assist in increasing plant productivity, conserve water resources, and potentially reduce N contamination to surrounding water resources.

198